Tag Archives: AWS Lambda

Securing Amazon S3 presigned URLs for serverless applications

Post Syndicated from Raaga N.G original https://aws.amazon.com/blogs/compute/securing-amazon-s3-presigned-urls-for-serverless-applications/

Modern serverless applications must be capable of seamlessly handling large file uploads. This blog demonstrates how to leverage Amazon Simple Storage Service (Amazon S3) presigned URLs to allow your users to securely upload files to S3 without requiring explicit permissions in the AWS Account. This blog post specifically focuses on the security ramifications of using S3 presigned URLs, and explains mitigation steps that serverless developers can take to improve the security of their systems using S3 presigned URLs. Additionally, the blog post also walks through an AWS Lambda function that adheres to the provided recommendations, ensuring a robust and secure approach to handling S3 presigned URLs. For more information on S3 presigned URLs, see Working with presigned URLs.

Presigned URL Workflow for Serverless Applications

The following architecture diagram illustrates a serverless application that generates an S3 presigned URL. By using S3 presigned URLs, serverless applications can offload to S3 the computation required to receive files. The diagram captures a seven-step process between the client, Amazon API Gateway, the Lambda function, and S3.

A typical workflow to upload a file to a serverless application hosted on S3 includes the following steps:

  1. Client submits a request to upload a file.
  2. API Gateway receives the client request and invokes a Lambda function that then generates the S3 presigned URL.
  3. The Lambda function makes a getSignedUrl API call to S3.
  4. S3 returns the presigned URL for the object to be uploaded.
  5. The Lambda function returns a presigned URL to the API.
  6. Client receives the S3 presigned URL to upload the file.
  7. Client uploads the file directly to S3 using the presigned URL.

How to Secure Presigned URLs

When designing a serverless application that utilizes S3 presigned URLs to store data in S3, a developer must consider several primary security aspects. S3 presigned URLs are public resources that do not authenticate users, and anyone in possession of a valid S3 presigned URL can access the associated resource. Consequently, it is important to implement additional security measures to ensure that these URLs are not misused or accessed by unauthorized parties. The following blog post contains techniques you can use to make your presigned URLs more secure.

1. Add a Content-MD5 checksum using the X-Amz-Signed header

When you upload an object to S3, you can include a precalculated checksum of the object as part of your request. S3 will perform an integrity check and verify if the object sent is the same as the object received. S3 supports the use of MD5 checksums to verify the integrity of objects uploaded. You provide the MD5 digest by including a Content-MD5 header in the initial PUT request. Upon receiving the object, S3 will calculate the MD5 digest and compare it with the one you originally provided. The upload operation succeeds only if both MD5 digests match, ensuring end-to-end data integrity. If an unintended party gets their hands on the S3 presigned URL, then they will not be able to use it without possessing the same object. This provides protection against arbitrary file uploads.

The key element for a developer to remember is that when the client uploads the file to the S3 presigned URL, it must supply the correct MD5 in Base64 using the Content-MD5 header. Developers can see a sample serverless application with client-side code to extract the MD5 digest, request a S3 presigned URL, and upload a file in this GitHub repository. This sample application uses NodeJS v20 in the Lambda function.

2. Expire the S3 presigned URLs 

An S3 presigned URL remains valid for the period of time specified when the URL is generated. It is important to ensure that the S3 presigned URL does not remain accessible for longer than required as it can be reused when still valid. You can define the expiration time of the S3 presigned URL by either passing X-Amz-Expires as a query parameter or by setting the expiresIn parameter when using the AWS SDK for JavaScript.

S3 validates the expiration time and date at the time of initial HTTP Request. However, to support situations where the connection drops and the client needs to restart uploading a file, you may want your S3 presigned URL to remain valid for the entire anticipated time needed to upload the file to S3. The challenge is to generate an S3 presigned URL that is valid long enough to accommodate the file’s upload, yet still short enough that you prevent reuse.

A solution we propose to overcome these challenges is to dynamically set the S3 presigned URL’s expiration time by using the browser Network Information API. Using this new API, when the client browser places the initial request for an S3 presigned URL, the client also transmits the file’s size and the network type, so the Lambda function can calculate the anticipated transfer time.

Within the Lambda function, we can now estimate the transfer time for this size of file on this type of network, using sample code as featured in this GitHub repository.

With the estimated transfer time calculated, the Lambda function can now request the S3 presigned URL and set the expiresIn parameter to the transfer time, resulting in an S3 presigned URL that is only available for the time needed to upload that size of file on this type of network.

If you are using the AWS SDK, you may also be using AWS Signature Version 4 (SigV4) to sign your requests. To create a defense in depth approach, which will place a ceiling on total expiration time, you can utilize condition keys in bucket policies. For an example policy, see Limiting presigned URL capabilities.

3. Generating a UUID to replace the uploaded filename

When an application allows a user to upload files, the application exposes itself to various security threats, such as path traversal attacks. Path traversal vulnerabilities allow attackers to access files that are not meant to be accessed or to overwrite files outside the intended directory structure. In order to secure your applications against such vulnerabilities, the most effective approach is to incorporate user input validation and sanitization. You can sanitize the filename by replacing it with a generated UUID (Universally Unique Identifier).

You can see an example function in the server-side code for Lambda in this GitHub repository.

4. Applying the Principle of Least Privilege and using a separate Lambda function to create S3 presigned URLs

The capabilities of an S3 presigned URL are constrained by the permissions of the principal that created it. To offer fine-grained access, the very first step in limiting use of an S3 presigned URL should be building a specific Lambda function that generates these URLs. By having a Lambda function dedicated to this purpose, you do not risk an overly permissive Lambda function. The second step is to limit your specific Lambda function’s access to S3.

Adhering to the Principle of Least Privilege, it’s important to restrict the Lambda function’s permissions to only the required prefixes in the bucket and allow it to perform only the required actions on the bucket, instead of granting full bucket access. This minimizes the potential attack surface and mitigates the risk of unintended data exposure or modification. It is important to limit the permissions to the minimum required set of actions and resources.

This example AWS Identity and Access Management (IAM) policy demonstrates how to grant the Lambda function read access (GET) to objects within the "Example-Prefix" prefix of a specific S3 bucket. The IAM policy is attached to the Lambda function via an execution role, which together establish what actions the Lambda function can perform.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "ReadStatement",
      "Action": [
        "s3:GetObject"
      ],
      "Resource": [
        "arn:aws:s3:::EXAMPLE-BUCKET/Example-Prefix/",
        "arn:aws:s3:::EXAMPLE-BUCKET/Example-Prefix/*"
      ],
      "Effect": "Allow"
    }
  ]
}

This example IAM policy demonstrates how to grant the Lambda function permissions to upload (PUT) objects within the "Example-Prefix" prefix of a specific S3 bucket.

{   
    "Version": "2012-10-17",
    "Statement": [
        {   
            "Sid": "UploadStatement",
            "Action": [
                "s3:PutObject",
                "s3:ListBucket"
            ],
            "Resource": [
                "arn:aws:s3:::EXAMPLE-BUCKET/Example-Prefix/",
                "arn:aws:s3:::EXAMPLE-BUCKET/Example-Prefix/*"
            ],
            "Effect": "Allow"
        }
    ]
}

This approach will ensure that your Lambda function possesses the minimum required permissions to perform its intended tasks and reduces the risk of unintended data access or modification.

If you want to restrict the use of S3 presigned URLs and all S3 access to a particular network path, you can also define a network-path restriction policy on the S3 Bucket. This restriction on the bucket requires that all requests to the bucket originate from a specified network. AWS Prescriptive Guidance says, an extension of least privilege is to maintain a data perimeter that’s consistent with your organization’s needs. The goal of an AWS perimeter is to ensure that the access is allowed only if the request is coming from a trusted entity, for trusted resources from a trusted network. These data perimeters are applicable to S3 presigned URLs as well.

5.Creating one-time use S3 presigned URLs

Serverless applications developers may want each S3 presigned URL to only be used once. Developers can incorporate a token-based mechanism to facilitate secure one-time use of an S3 presigned URL. This involves generating unique tokens for each authorized user or client and associating these tokens with the S3 presigned URLs. When a client attempts to access the resource using the S3 presigned URL, they must provide the corresponding token for validation. This additional layer of security ensures that only authorized entities can access the S3 presigned URLs and the associated resources. Furthermore, you can leverage a database to track the issued tokens and expire them after each use. A solution to implement such a mechanism has been discussed in detail in How to securely transfer files with presigned URLs.

Cleaning up

You may clean up the sample application by deleting the API Gateway, Lambda function, and S3 bucket. In addition, please do not forget to delete any IAM execution roles you created for the Lambda function.

Conclusion

In this blog we have discussed various considerations that a developer must make when designing an application that leverages S3 presigned URLs. By incorporating robust security measures, such as proper access control, input sanitization, expiration handling and integrity checks, developers can mitigate potential risks when using S3 presigned URLs.

Accelerate lightweight analytics using PyIceberg with AWS Lambda and an AWS Glue Iceberg REST endpoint

Post Syndicated from Sotaro Hikita original https://aws.amazon.com/blogs/big-data/accelerate-lightweight-analytics-using-pyiceberg-with-aws-lambda-and-an-aws-glue-iceberg-rest-endpoint/

For modern organizations built on data insights, effective data management is crucial for powering advanced analytics and machine learning (ML) activities. As data use cases become more complex, data engineering teams require sophisticated tooling to handle versioning, increasing data volumes, and schema changes across multiple data sources and applications.

Apache Iceberg has emerged as a popular choice for data lakes, offering ACID (Atomicity, Consistency, Isolation, Durability) transactions, schema evolution, and time travel capabilities. Iceberg tables can be accessed from various distributed data processing frameworks like Apache Spark and Trino, making it a flexible solution for diverse data processing needs. Among the available tools for working with Iceberg, PyIceberg stands out as a Python implementation that enables table access and management without requiring distributed compute resources.

In this post, we demonstrate how PyIceberg, integrated with the AWS Glue Data Catalog and AWS Lambda, provides a lightweight approach to harness Iceberg’s powerful features through intuitive Python interfaces. We show how this integration enables teams to start working with Iceberg tables with minimal setup and infrastructure dependencies.

PyIceberg’s key capabilities and advantages

One of PyIceberg’s primary advantages is its lightweight nature. Without requiring distributed computing frameworks, teams can perform table operations directly from Python applications, making it suitable for small to medium-scale data exploration and analysis with minimal learning curve. In addition, PyIceberg is integrated with Python data analysis libraries like Pandas and Polars, so data users can use their existing skills and workflows.

When using PyIceberg with the Data Catalog and Amazon Simple Storage Service (Amazon S3), data teams can store and manage their tables in a completely serverless environment. This means data teams can focus on analysis and insights rather than infrastructure management.

Furthermore, Iceberg tables managed through PyIceberg are compatible with AWS data analytics services. Although PyIceberg operates on a single node and has performance limitations with large data volumes, the same tables can be efficiently processed at scale using services such as Amazon Athena and AWS Glue. This enables teams to use PyIceberg for rapid development and testing, then transition to production workloads with larger-scale processing engines—while maintaining consistency in their data management approach.

Representative use case

The following are common scenarios where PyIceberg can be particularly useful:

  • Data science experimentation and feature engineering – In data science, experiment reproducibility is crucial for maintaining reliable and efficient analyses and models. However, continuously updating organizational data makes it challenging to manage data snapshots for important business events, model training, and consistent reference. Data scientists can query historical snapshots through time travel capabilities and record important versions using tagging features. With PyIceberg, they can receive these benefits in their Python environment using familiar tools like Pandas. Thanks to Iceberg’s ACID capabilities, they can access consistent data even when tables are being actively updated.
  • Serverless data processing with Lambda – Organizations often need to process data and maintain analytical tables efficiently without managing complex infrastructure. Using PyIceberg with Lambda, teams can build event-driven data processing and scheduled table updates through serverless functions. PyIceberg’s lightweight nature makes it well-suited for serverless environments, enabling simple data processing tasks like data validation, transformation, and ingestion. These tables remain accessible for both updates and analytics through various AWS services, allowing teams to build efficient data pipelines without managing servers or clusters.

Event-driven data ingestion and analysis with PyIceberg

In this section, we explore a practical example of using PyIceberg for data processing and analysis using NYC yellow taxi trip data. To simulate an event-driven data processing scenario, we use Lambda to insert sample data into an Iceberg table, representing how real-time taxi trip records might be processed. This example will demonstrate how PyIceberg can streamline workflows by combining efficient data ingestion with flexible analysis capabilities.

Imagine your team faces several requirements:

  • The data processing solution needs to be cost-effective and maintainable, avoiding the complexity of managing distributed computing clusters for this moderately-sized dataset.
  • Analysts need the ability to perform flexible queries and explorations using familiar Python tools. For example, they might need to compare historical snapshots with current data to analyze trends over time.
  • The solution should have the ability to expand to be more scalable in the future.

To address these requirements, we implement a solution that combines Lambda for data processing with Jupyter notebooks for analysis, both powered by PyIceberg. This approach provides a lightweight yet robust architecture that maintains data consistency while enabling flexible analysis workflows. At the end of the walkthrough, we also query this data using Athena to demonstrate compatibility with multiple Iceberg-supporting tools and show how the architecture can scale.

We walk through the following high-level steps:

  1. Use Lambda to write sample NYC yellow taxi trip data to an Iceberg table on Amazon S3 using PyIceberg with an AWS Glue Iceberg REST endpoint. In a real-world scenario, this Lambda function would be triggered by an event from a queuing component like Amazon Simple Queue Service (Amazon SQS). For more details, see Using Lambda with Amazon SQS.
  2. Analyze table data in a Jupyter notebook using PyIceberg through the AWS Glue Iceberg REST endpoint.
  3. Query the data using Athena to demonstrate Iceberg’s flexibility.

The following diagram illustrates the architecture.

Overall Architecture

When implementing this architecture, it’s important to note that Lambda functions can have multiple concurrent invocations when triggered by events. This concurrent invocation might lead to transaction conflicts when writing to Iceberg tables. To handle this, you should implement an appropriate retry mechanism and carefully manage concurrency levels. If you’re using Amazon SQS as an event source, you can control concurrent invocations through the SQS event source’s maximum concurrency setting.

Prerequisites

The following prerequisites are necessary for this use case:

Set up resources with AWS CloudFormation

You can use the provided CloudFormation template to set up the following resources:

Complete the following steps to deploy the resources:

  1. Choose Launch stack.

  1. For Parameters, pyiceberg_lambda_blog_database is set by default. You can also change the default value. If you change the database name, remember to replace pyiceberg_lambda_blog_database with your chosen name in all subsequent steps. Then, choose Next.
  2. Choose Next.
  3. Select I acknowledge that AWS CloudFormation might create IAM resources with custom names.
  4. Choose Submit.

Build and run a Lambda function

Let’s build a Lambda function to process incoming records using PyIceberg. This function creates an Iceberg table called nyc_yellow_table in the database pyiceberg_lambda_blog_database in the Data Catalog if it doesn’t exist. It then generates sample NYC taxi trip data to simulate incoming records and inserts it into nyc_yellow_table.

Although we invoke this function manually in this example, in real-world scenarios, this Lambda function would be triggered by actual events, such as messages from Amazon SQS. When implementing real-world use cases, the function code must be modified to receive the event data and process it based on the requirements.

We deploy the function using container images as the deployment package. To create a Lambda function from a container image, build your image on CloudShell and push it to an ECR repository. Complete the following steps:

  1. Sign in to the AWS Management Console and launch CloudShell.
  2. Create a working directory.
mkdir pyiceberg_blog
cd pyiceberg_blog
  1. Download the Lambda script lambda_function.py.
aws s3 cp s3://aws-blogs-artifacts-public/artifacts/BDB-5013/lambda_function.py .

This script performs the following tasks:

  • Creates an Iceberg table with the NYC taxi schema in the Data Catalog
  • Generates a random NYC taxi dataset
  • Inserts this data into the table

Let’s break down the essential parts of this Lambda function:

  • Iceberg catalog configuration – The following code defines an Iceberg catalog that connects to the AWS Glue Iceberg REST endpoint:
# Configure the catalog
catalog_properties = {
   "type": "rest",
   "uri": f"https://glue.{region}.amazonaws.com/iceberg",
   "s3.region": region,
   "rest.sigv4-enabled": "true",
   "rest.signing-name": "glue",
   "rest.signing-region": region
}
catalog = load_catalog(**catalog_properties)
  • Table schema definition – The following code defines the Iceberg table schema for the NYC taxi dataset. The table includes:
    • Schema columns defined in the Schema
    • Partitioning by vendorid and tpep_pickup_datetime using PartitionSpec
    • Day transform applied to tpep_pickup_datetime for daily record management
    • Sort ordering by tpep_pickup_datetime and tpep_dropoff_datetime

When applying the day transform to timestamp columns, Iceberg automatically handles date-based partitioning hierarchically. This means a single day transform enables partition pruning at the year, month, and day levels without requiring explicit transforms for each level. For more details about Iceberg partitioning, see Partitioning.

# Table Definition
schema = Schema(
    NestedField(field_id=1, name="vendorid", field_type=LongType(), required=False),
    NestedField(field_id=2, name="tpep_pickup_datetime", field_type=TimestampType(), required=False),
    NestedField(field_id=3, name="tpep_dropoff_datetime", field_type=TimestampType(), required=False),
    NestedField(field_id=4, name="passenger_count", field_type=LongType(), required=False),
    NestedField(field_id=5, name="trip_distance", field_type=DoubleType(), required=False),
    NestedField(field_id=6, name="ratecodeid", field_type=LongType(), required=False),
    NestedField(field_id=7, name="store_and_fwd_flag", field_type=StringType(), required=False),
    NestedField(field_id=8, name="pulocationid", field_type=LongType(), required=False),
    NestedField(field_id=9, name="dolocationid", field_type=LongType(), required=False),
    NestedField(field_id=10, name="payment_type", field_type=LongType(), required=False),
    NestedField(field_id=11, name="fare_amount", field_type=DoubleType(), required=False),
    NestedField(field_id=12, name="extra", field_type=DoubleType(), required=False),
    NestedField(field_id=13, name="mta_tax", field_type=DoubleType(), required=False),
    NestedField(field_id=14, name="tip_amount", field_type=DoubleType(), required=False),
    NestedField(field_id=15, name="tolls_amount", field_type=DoubleType(), required=False),
    NestedField(field_id=16, name="improvement_surcharge", field_type=DoubleType(), required=False),
    NestedField(field_id=17, name="total_amount", field_type=DoubleType(), required=False),
    NestedField(field_id=18, name="congestion_surcharge", field_type=DoubleType(), required=False),
    NestedField(field_id=19, name="airport_fee", field_type=DoubleType(), required=False),
)

# Define partition spec
partition_spec = PartitionSpec(
    PartitionField(source_id=1, field_id=1001, transform=IdentityTransform(), name="vendorid_idenitty"),
    PartitionField(source_id=2, field_id=1002, transform=DayTransform(), name="tpep_pickup_day"),
)

# Define sort order
sort_order = SortOrder(
    SortField(source_id=2, transform=DayTransform()),
    SortField(source_id=3, transform=DayTransform())
)

database_name = os.environ.get('GLUE_DATABASE_NAME')
table_name = os.environ.get('ICEBERG_TABLE_NAME')
identifier = f"{database_name}.{table_name}"

# Create the table if it doesn't exist
location = f"s3://pyiceberg-lambda-blog-{account_id}-{region}/{database_name}/{table_name}"
if not catalog.table_exists(identifier):
    table = catalog.create_table(
        identifier=identifier,
        schema=schema,
        location=location,
        partition_spec=partition_spec,
        sort_order=sort_order
    )
else:
    table = catalog.load_table(identifier=identifier)
  • Data generation and insertion – The following code generates random data and inserts it into the table. This example demonstrates an append-only pattern, where new records are continuously added to track business events and transactions:
# Generate random data
records = generate_random_data()
# Convert to Arrow Table
df = pa.Table.from_pylist(records)
# Write data using PyIceberg
table.append(df)
  1. Download the Dockerfile. It defines the container image for your function code.
aws s3 cp s3://aws-blogs-artifacts-public/artifacts/BDB-5013/Dockerfile .
  1. Download the requirements.txt. It defines the Python packages required for your function code.
aws s3 cp s3://aws-blogs-artifacts-public/artifacts/BDB-5013/requirements.txt .

At this point, your working directory should contain the following three files:

  • Dockerfile
  • lambda_function.py
  • requirements.txt
  1. Set the environment variables. Replace <account_id> with your AWS account ID:
export AWS_ACCOUNT_ID=<account_id>
  1. Build the Docker image:
docker build --provenance=false -t localhost/pyiceberg-lambda .

# Confirm built image
docker images | grep pyiceberg-lambda
  1. Set a tag to the image:
docker tag localhost/pyiceberg-lambda:latest ${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_REGION}.amazonaws.com/pyiceberg-lambda-repository:latest
  1. Log in to the ECR repository created by AWS CloudFormation:
aws ecr get-login-password --region ${AWS_REGION} | docker login --username AWS --password-stdin ${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_REGION}.amazonaws.com
  1. Push the image to the ECR repository:
docker push ${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_REGION}.amazonaws.com/pyiceberg-lambda-repository:latest
  1. Create a Lambda function using the container image you pushed to Amazon ECR:
aws lambda create-function \
--function-name pyiceberg-lambda-function \
--package-type Image \
--code ImageUri=${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_REGION}.amazonaws.com/pyiceberg-lambda-repository:latest \
--role arn:aws:iam::${AWS_ACCOUNT_ID}:role/pyiceberg-lambda-function-role-${AWS_REGION} \
--environment "Variables={ICEBERG_TABLE_NAME=nyc_yellow_table, GLUE_DATABASE_NAME=pyiceberg_lambda_blog_database}" \
--region ${AWS_REGION} \
--timeout 60 \
--memory-size 1024
  1. Invoke the function at least five times to create multiple snapshots, which we will examine in the following sections. Note that we are invoking the function manually to simulate event-driven data ingestion. In real world scenarios, Lambda functions will be automatically invoked with event-driven fashion.
aws lambda invoke \
--function-name arn:aws:lambda:${AWS_REGION}:${AWS_ACCOUNT_ID}:function:pyiceberg-lambda-function \
--log-type Tail \
outputfile.txt \
--query 'LogResult' | tr -d '"' | base64 -d

At this point, you have deployed and run the Lambda function. The function creates the nyc_yellow_table Iceberg table in the pyiceberg_lambda_blog_database database. It also generates and inserts sample data into this table. We will explore the records in the table in later steps.

For more detailed information about building Lambda functions with containers, see Create a Lambda function using a container image.

Explore the data with Jupyter using PyIceberg

In this section, we demonstrate how to access and analyze the data stored in Iceberg tables registered in the Data Catalog. Using a Jupyter notebook with PyIceberg, we access the taxi trip data created by our Lambda function and examine different snapshots as new records arrive. We also tag specific snapshots to retain important ones, and create new tables for further analysis.

Complete the following steps to open the notebook with Jupyter on the SageMaker AI notebook instance:

  1. On the SageMaker AI console, choose Notebooks in the navigation pane.
  2. Choose Open JupyterLab next to the notebook that you created using the CloudFormation template.

notebook list

  1. Download the notebook and open it in a Jupyter environment on your SageMaker AI notebook.

upload notebook

  1. Open uploaded pyiceberg_notebook.ipynb.
  2. In the kernel selection dialog, leave the default option and choose Select.

select kernel

From this point forward, you will work through the notebook by running cells in order.

Connecting Catalog and Scanning Tables

You can access the Iceberg table using PyIceberg. The following code connects to the AWS Glue Iceberg REST endpoint and loads the nyc_yellow_table table on the pyiceberg_lambda_blog_database database:

import pyarrow as pa
from pyiceberg.catalog import load_catalog
import boto3

# Set AWS region
sts = boto3.client('sts')
region = sts._client_config.region_name

# Configure catalog connection properties
catalog_properties = {
    "type": "rest",
    "uri": f"https://glue.{region}.amazonaws.com/iceberg",
    "s3.region": region,
    "rest.sigv4-enabled": "true",
    "rest.signing-name": "glue",
    "rest.signing-region": region
}

# Specify database and table names
database_name = "pyiceberg_lambda_blog_database"
table_name = "nyc_yellow_table"

# Load catalog and get table
catalog = load_catalog(**catalog_properties)
table = catalog.load_table(f"{database_name}.{table_name}")

You can query full data from the Iceberg table as an Apache Arrow table and convert it to a Pandas DataFrame.

scan table

Working with Snapshots

One of the important features of Iceberg is snapshot-based version control. Snapshots are automatically created whenever data changes occur in the table. You can retrieve data from a specific snapshot, as shown in the following example.

working with snapshots

# Get data from a specific snapshot ID
snapshot_id = snapshots.to_pandas()["snapshot_id"][3]
snapshot_pa_table = table.scan(snapshot_id=snapshot_id).to_arrow()
snapshot_df = snapshot_pa_table.to_pandas()

You can compare the current data with historical data from any point in time based on snapshots. In this case, you are comparing the differences in data distribution between the latest table and a snapshot table:

# Compare the distribution of total_amount in the specified snapshot and the latest data.
import matplotlib.pyplot as plt

plt.figure(figsize=(4, 3))
df['total_amount'].hist(bins=30, density=True, label="latest", alpha=0.5)
snapshot_df['total_amount'].hist(bins=30, density=True, label="snapshot", alpha=0.5)
plt.title('Distribution of total_amount')
plt.xlabel('total_amount')
plt.ylabel('relative Frequency')
plt.legend()
plt.show()

matplotlib graph

Tagging snapshots

You can tag specific snapshots with an arbitrary name and query specific snapshots with that name later. This is useful when managing snapshots of important events.

In this example, you query a snapshot specifying the tag checkpointTag. Here, you are using the polars to create a new DataFrame by adding a new column called trip_duration based on existing columns tpep_dropoff_datetime and tpep_pickup_datetime columns:

# retrive tagged snapshot table as polars data frame
import polars as pl

# Get snapshot id from tag name
df = table.inspect.refs().to_pandas()
filtered_df = df[df["name"] == tag_name]
tag_snapshot_id = filtered_df["snapshot_id"].iloc[0]

# Scan Table based on the snapshot id
tag_pa_table = table.scan(snapshot_id=tag_snapshot_id).to_arrow()
tag_df = pl.from_arrow(tag_pa_table)

# Process the data adding a new column "trip_duration" from check point snapshot.
def preprocess_data(df):
    df = df.select(["vendorid", "tpep_pickup_datetime", "tpep_dropoff_datetime", 
                    "passenger_count", "trip_distance", "fare_amount"])
    df = df.with_columns(
        ((pl.col("tpep_dropoff_datetime") - pl.col("tpep_pickup_datetime"))
         .dt.total_seconds() // 60).alias("trip_duration"))
    return df

processed_df = preprocess_data(tag_df)
display(processed_df)
print(processed_df["trip_duration"].describe())

processed-df

Create a new table from the processed DataFrame with the trip_duration column. This step illustrates how to prepare data for potential future analysis. You can explicitly specify the snapshot of the data that the processed data is referring to by using a tag, even if the underlying table has been changed.

# write processed data to new iceberg table
account_id = sts.get_caller_identity()["Account"] 

new_table_name = "processed_" + table_name
location = f"s3://pyiceberg-lambda-blog-{account_id}-{region}/{database_name}/{new_table_name}"

pa_new_table = processed_df.to_arrow()
schema = pa_new_table.schema
identifier = f"{database_name}.{new_table_name}"

new_table = catalog.create_table(
                identifier=identifier,
                schema=schema,
                location=location
            )
            
# show new table's schema
print(new_table.schema())
# insert processed data to new table
new_table.append(pa_new_table)

Let’s query this new table made from processed data with Athena to demonstrate the Iceberg table’s interoperability.

Query the data from Athena

  1. In the Athena query editor, you can query the table pyiceberg_lambda_blog_database.processed_nyc_yellow_table created from the notebook in the previous section:
SELECT * FROM "pyiceberg_lambda_blog_database"."processed_nyc_yellow_table" limit 10;

query with athena

By completing these steps, you’ve built a serverless data processing solution using PyIceberg with Lambda and an AWS Glue Iceberg REST endpoint. You’ve worked with PyIceberg to manage and analyze data using Python, including snapshot management and table operations. In addition, you ran the query using another engine, Athena, which shows the compatibility of the Iceberg table.

Clean up

To clean up the resources used in this post, complete the following steps:

  1. On the Amazon ECR console, navigate to the repository pyiceberg-lambda-repository and delete all images contained in the repository.
  2. On the CloudShell, delete working directory pyiceberg_blog.
  3. On the Amazon S3 console, navigate to the S3 bucket pyiceberg-lambda-blog-<ACCOUNT_ID>-<REGION>, which you created using the CloudFormation template, and empty the bucket.
  4. After you confirm the repository and the bucket are empty, delete the CloudFormation stack pyiceberg-lambda-blog-stack.
  5. Delete the Lambda function pyiceberg-lambda-function that you created using the Docker image.

Conclusion

In this post, we demonstrated how using PyIceberg with the AWS Glue Data Catalog enables efficient, lightweight data workflows while maintaining robust data management capabilities. We showcased how teams can use Iceberg’s powerful features with minimal setup and infrastructure dependencies. This approach allows organizations to start working with Iceberg tables quickly, without the complexity of setting up and managing distributed computing resources.

This is particularly valuable for organizations looking to adopt Iceberg’s capabilities with a low barrier to entry. The lightweight nature of PyIceberg allows teams to begin working with Iceberg tables immediately, using familiar tools and requiring minimal additional learning. As data needs grow, the same Iceberg tables can be seamlessly accessed by AWS analytics services like Athena and AWS Glue, providing a clear path for future scalability.

To learn more about PyIceberg and AWS analytics services, we encourage you to explore the PyIceberg documentation and What is Apache Iceberg?


About the authors

Sotaro Hikita is a Specialist Solutions Architect focused on analytics with AWS, working with big data technologies and open source software. Outside of work, he always seeks out good food and has recently become passionate about pizza.

Shuhei Fukami is a Specialist Solutions Architect focused on Analytics with AWS. He likes cooking in his spare time and has become obsessed with making pizza these days.

Monitoring network traffic in AWS Lambda functions

Post Syndicated from Anton Aleksandrov original https://aws.amazon.com/blogs/compute/monitoring-network-traffic-in-aws-lambda-functions/

Network monitoring provides essential visibility into cloud application traffic patterns across large organizations. It enables security and compliance teams to detect anomalies and maintain compliance, while allowing development teams to troubleshoot issues, optimize performance, and track costs in multi-tenant software as a service (SaaS) environments. Implementing robust network monitoring allows organizations to effectively manage their security, compliance, and operational requirements while continuously enhancing their applications.

In this post, you will learn methods for network monitoring in AWS Lambda functions and how to apply them to your scenarios.

Overview

Lambda is a secure and highly scalable serverless compute service where each function operates in an isolated execution environment with strict security boundaries. This architecture delivers key advantages, such as enhanced security, automatic compute capacity scaling, and minimal operational overhead. Minimizing infrastructure management allows Lambda to enable organizations to redirect their focus from managing servers to other critical aspects, such as performance optimization and network traffic analysis. In turn, these enable organizations to build more secure and efficient applications.

Lambda network monitoring addresses diverse organizational needs, such as compliance requirements for audit logs and anomaly detection, business needs for traffic metering and customer billing, and development needs for troubleshooting network issues. Traditional agent-based or host-based monitoring methods often aren’t compatible with the strongly isolated, ephemeral execution environment of Lambda, which necessitates alternative approaches to meet these critical requirements.

You can use AWS-native, integrated network monitoring solutions, such as Amazon Virtual Private Cloud (Amazon VPC) Flow Logs, or build your own custom solution, as detailed in this post. Each solution offers distinct capabilities with varying levels of granularity and real-time visibility. When choosing an approach, you must evaluate key factors such as the desired data granularity, operational complexity, latency tolerance, and cost implications.

Using VPC Flow Logs

VPC Flow Logs is the AWS-recommended tool for network activity monitoring. If your scenario necessitates monitoring of the network activity of Lambda functions, then you can attach these functions to a VPC and enable Flow Logs. This captures detailed network traffic data, such as source and destination IPs, ports, protocols, and traffic volume for all traffic flowing through the network interfaces used by your functions.

When you attach your functions to a VPC, the Lambda service automatically creates an Elastic Network Interface (ENI) for functions to communicate with VPC-based resources. By default, VPC-attached functions can only access private resources within the VPC. If you need your functions to communicate with other AWS services, then you should use VPC Endpoints. If your function needs to communicate with the public internet, then you should use an NAT Gateway for egress traffic. The following diagram shows how you can use VPC Flow Logs for Lambda functions.

Flow Logs provide detailed insights into the IP traffic flowing to and from the network interfaces within a VPC, offering valuable data for network audits and activity monitoring. This approach promotes a clear separation of concerns between application and networking layers, with VPC constructs typically managed by a dedicated operations or infrastructure team.

VPC Flow Logs provides a robust network monitoring solution. However, when using it with Lambda functions, you should evaluate the following considerations:

  • It captures ENI-level information. ENIs can be reused across multiple functions, thus it won’t provide per-function or per-invoke granularity.
  • It only logs IP addresses, not DNS names (if capturing DNS names is a requirement for you).
  • It introduces infrastructure management into your serverless applications. You must learn VPC constructs or involve your infrastructure team.
  • Potential added data transfer costs. Go to the pricing for NAT Gateway, VPC Endpoints, and Flow Logs for more details.

The following sections explore Lambda network monitoring methods that can either be augmented with VPC Flow Logs for better granularity or used without attaching your functions to a VPC.

Proxying network traffic

You can configure the Lambda runtime to route egress network traffic through a side-car proxy that runs as a Lambda layer within the Lambda execution environment and logs network activity. The proxy layer should be agnostic to the language runtime. AWS recommends that you use compiled languages such as Rust or Golang for maximum reusability and minimal added latency. The following diagram shows emitting logs from a network proxy layer.

Applying proxy configuration differs across language runtimes. In Python you set proxy_http and proxy_https environment variables. Java uses JVM flags. Node.js doesn’t provide a native way to configure proxy using command line flags or environment variables. Therefore, you need to make code changes, such as configuring a proxy for your AWS SDK or using a third-party open source libraries such as global-agent or Interceptors.

The proxy approach is most suitable if you’re okay with making function code or configuration changes that might vary across runtimes. Furthermore, adding a network proxy server process inside the execution environment consumes resources shared with the function code, which can add network latency.

Refer to the post Enhancing runtime security and governance with the AWS Lambda Runtime API proxy extension for ways to intercept inbound requests/responses between the Lambda Runtime API and Runtime process.

Runtime-agnostic techniques

Following techniques use the fact that the Lambda execution environment is a Linux-based micro-VM. Lambda runtimes operate within a restricted user space that prevents the use of traditional OS-level monitoring tools needing elevated privileges, such as tcpdump, iptables, ptrace, or eBPF. The following techniques are specifically designed to work under these user space constraints, allowing their use without needing elevated privileges.

Reading OS networking layer information from procfs

Use this method when you need to obtain the OS-level information, such as metering transferred bytes, or see all open connections. You can use it to implement tenant chargebacks or detect network traffic pattern changes. The method is based on the proc pseudo-filesystem (also known as procfs) available in Linux OS, which provides an interface to kernel data and allows you to read OS-level information. For example, /proc/cpuinfo and /proc/meminfo pseudo-files provide information about current CPU and memory usage, while /proc/net/* provides you with network layer information. Reading /proc/net/tcp and /proc/net/udp gives you a list of active TCP/UDP connections, such as remote IP addresses and ports. Reading /proc/net/dev provides the list of network devices with detailed usage statistics, such as bytes transferred and received.

“The procfs method provides a simple but powerful way to collect critical network telemetry data from Lambda functions, such as up-to-date network statistics and file descriptor counts, which enables us to monitor outbound connections from Lambda functions. Better yet, it enables us to support multiple Lambda runtimes with a single implementation in our Rust-based, next-generation Lambda Extension”—AJ Stuyvenberg, Staff Engineer at Datadog.

The sample project provides the LambdaNetworkMonitor-Procfs stack to show this technique. For every invocation, the function reads /proc/net/dev, and sends network statistics to log and Amazon CloudWatch Metrics, as shown in the following figure.

Reading the /proc/net/dev pseudo-file is a simple and effective way to monitor Lambda functions networking without adding latency. However, it doesn’t capture DNS names and the IP addresses to which they resolve.

Intercepting network-related libc calls

Low-level network operations in Linux, such as DNS lookup and connection creation, are managed by the C Standard Library (libc). You can intercept libc function calls made by Lambda runtimes to monitor network traffic at the OS level. This is a significantly more advanced and complex mechanism, enabling visibility into OS-level activities, as shown in the following figure.

Intercepting libc function calls, such as getaddrinfo (DNS lookup) and connect, allows you to log details such as DNS name, IP addresses, ports, and protocols at a granular level, as shown in the following figure. This method allows you to capture comprehensive information about DNS queries and initiated network connections. It can provide precise per-function and per-invoke network monitoring, such as hostnames and IP addresses.

The following is a simplified flow:

  1. A function sends a request to example.com.
  2. The runtime calls libc getaddrinfo to resolve the DNS name.
  3. You intercept this call, log the DNS name, and forward the call to the original libc getaddrinfo.
  4. The original libc getaddrinfo returns resolved IP addresses. You log them and return to the runtime.
  5. The runtime calls libc connect method to create a new connection.
  6. You intercept this call, log the IP address, forward the call to the original libc connect, and so on.

To implement this technique, you need to use a language that compiles to a shared object (.so) file. To implement libc method signatures you should use a language such as C, C++, or Rust. The following sample code uses Rust for its strong safety guarantees and implements overriding the getaddrinfo libc function (DNS lookup).

pub extern "C" fn getaddrinfo(
    node: *const c_char,
    service: *const c_char,
    hints: *const addrinfo,
    res: *mut *mut addrinfo,
) -> c_int {
    let printable_node = format!("{}", PrintableCString::from(node));
    let printable_service = format!("{}", PrintableCString::from(service));

    log::debug!("> getaddrinfo node={printable_node} service={printable_service}");

    LIBC_GETADDRINFO(node, service, hints, res)
}

The following should be considered:

  • The method signature fully matches the libc function signature of the same name.
  • The node and service arguments would commonly be DNS name and port.
  • At the end, the function invokes the real libc getaddrinfo and returns the result.

When compiled to an .so file, you must package it as a Lambda layer, attach the layer to your functions, and configure the execution environment to use it through the Linux dynamic linker capability called preloading. Set the LD_PRELOAD environment variable to point to your .so file to instruct the OS to preload it before it loads any other library, such as libc. You can configure this either as a function environment variable or through a wrapper script, as shown in the following figure.

#!/bin/sh
echo "running wrapper..."
args=("$@")
export LD_PRELOAD=/opt/liblambda_network_monitor.so
exec "${args[@]}"

This technique allows you to get detailed connection-level monitoring such as DNS lookups, resolved IP addresses, ports, protocols, and count bytes transferred. Depending on your requirements, it can be adapted to track further network-related information as needed.

The sample project provides the LambdaNetworkMonitor-LdPreload stack to show this technique, as shown in the following figure. For every invocation, the function prints intercepted libc functions, DNS names, and connection IP addresses.

Considerations

  • OS-level techniques necessitate strong understanding of Linux fundamentals and careful implementation. AWS recommends that you closely evaluate which methods to use and keep your solution minimally invasive.
  • LD_PRELOAD is an advanced low-level technique that allows you to override libc methods and OS behavior. Incorrectly implemented hooks could lead to undefined behavior and crashes. Make sure your code is robust to recursion and thread-safe. Test it thoroughly in a controlled environment before using it in production.
  • The LD_PRELOAD technique relies on dynamic linking. It works with dynamically linked runtimes such as Node.js, Python, and Java. It doesn’t work with runtimes that use static linking, such as Golang.
  • When using runtime-dependent functionality, consider using Lambda runtime update controls to make sure that runtimes are only updated when the next function update occurs.
  • Always install layers from trusted sources only. Use infrastructure as code (IaC) tools to attach and audit layer configurations, such as AWS Identity and Access Management (IAM) permissions.

Conclusion

Monitoring network traffic in Lambda functions is a common requirement for many organizations. In case you need to audit IP-level network logs, AWS recommends that you to attach your functions to a VPC and use Flow Logs. If you need per-function or per-invoke granularity, then you can augment it with techniques described in this post.

These techniques provide valuable insights for debugging, auditing, and monitoring, but they also necessitate a solid understanding of Linux fundamentals and careful implementation. They offer a practical solution for organizations that need Lambda network monitoring, allowing them to troubleshoot issues and maintain compliance.

To learn more about Serverless architectures and asynchronous Lambda invocation patterns, go to Serverless Land.

AWS Weekly Roundup: Amazon Nova Premier, Amazon Q Developer, Amazon Q CLI, Amazon CloudFront, AWS Outposts, and more (May 5, 2025)

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-nova-premier-amazon-q-developer-amazon-q-cli-amazon-cloudfront-aws-outposts-and-more-may-5-2025/

Last week I went to Thailand to attend the AWS Summit Bangkok. It was an energizing and exciting event. We hosted the Developer Lounge, where developers can meet, discuss ideas, enjoy lightning talks, win SWAGs at AWS Builder ID Prize Wheel, take a challenge at Amazon Q Developer Coding Challenge, or learn Generative AI at Learn Amazon Bedrock booth.

Here’s a quick look:

Thank you to AWS Heroes, AWS Community Builders, AWS User Group leaders and developers for your collaboration.

Coming up next in ASEAN is AWS Summit Singapore—make sure you don’t miss it by registering now.

Last Week’s Launches
Here are some launches last week that caught my attention:

  • Amazon Nova Premier Now Generally Available — Amazon Nova Premier, our most capable model for complex tasks and teacher for model distillation, is now generally available in Amazon Bedrock. It excels at complex tasks requiring deep context understanding and multistep planning, while processing text, images, and videos with a 1M token context length. With Nova Premier and Amazon Bedrock Model Distillation, you can create highly capable, cost-effective, and low-latency versions of Nova Pro, Lite, and Micro, for your specific needs.

  • Amazon Q Developer elevates the IDE experience with new agentic coding experience — This new interactive, agentic coding experience for Visual Studio Code allows Q Developer to intelligently take actions on behalf of the developer. Amazon Q Developer introduces an interactive coding experience in Visual Studio Code, offering real-time collaboration for coding, documentation, and testing. It provides transparent reasoning, and supports automated or step-by-step changes in multiple languages.

  • New Foundation Models in Amazon Bedrock — Amazon Bedrock expands its model offerings with two significant additions:
    • Writer’s Palmyra X5 and X4 models feature extensive context windows (1M and 128K tokens respectively) and excel in complex reasoning for enterprise applications. They support multistep tool-calling and adaptive thinking with high reliability standards.
    • Meta’s Llama 4 Scout 17B and Maverick 17B models offer natively multimodal capabilities using mixture-of-experts architecture for enhanced reasoning and image understanding. They support multiple languages and extended context processing, with simplified integration through the Bedrock Converse API.
  • Second-Generation AWS Outposts Racks Released — AWS announces the general availability of second-generation Outposts racks with significant enhancements including the latest x86 EC2 instances, simplified networking, and accelerated networking options. These improvements deliver doubled vCPU, memory, and network bandwidth, 40% better performance, and support for ultra-low latency workloads, making them ideal for demanding on-premises deployments.

  • Amazon CloudFront SaaS Manager Launches — Amazon CloudFront SaaS Manager helps SaaS providers and web hosting platforms efficiently manage content delivery across multiple customer domains. The service dramatically reduces operational complexity while providing high-performance content delivery and enterprise-grade security for every customer domain.

  • Amazon Aurora Now Supports PostgreSQL 17 — Amazon Aurora now supports PostgreSQL 17.4, offering community improvements and Aurora-specific enhancements like optimized memory management and faster failovers. The release includes new features for Babelfish, security fixes, and updated extensions, available in all AWS Regions.
  • CloudWatch Introduces Tiered Pricing for Lambda Logs — Amazon CloudWatch launches tiered pricing for AWS Lambda logs and new delivery destinations. Pricing in US East starts at $0.50/GB for CloudWatch and $0.25/GB for S3 and Firehose, both tiering down to $0.05/GB. This update enhances flexibility in log management across all supporting Regions.
  • RDS for MySQL Updates Minor Versions — Amazon RDS for MySQL now supports minor versions 8.0.42 and 8.4.5, delivering security fixes, bug fixes, and performance improvements. Users can upgrade automatically during maintenance windows or use Blue/Green deployments for safer updates.
  • Amazon Bedrock Model Distillation Generally Available — Amazon Bedrock Model Distillation is now generally available, supporting new models like Amazon Nova and Claude 3.5. It enables smaller models to accurately predict function calling for Agents, delivering up to 500% faster responses and 75% lower costs with minimal accuracy loss for RAG use cases. The service includes automated workflows for data synthesis and student model training.
  • AI Search Flow Builder for Amazon OpenSearch Service — Amazon OpenSearch Service now offers an AI search flow builder for OpenSearch 2.19+ domains. This low-code designer enables creation of sophisticated AI-enhanced search flows using AWS and third-party services, supporting use cases like RAG, query rewriting, and semantic encoding.

From Community.AWS
Here’s my personal favorites posts from community.aws:

Upcoming AWS events
Check your calendars and sign up for these upcoming AWS events:

  • AWS Summit — Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Poland (6 May), Bengaluru (May 7 – 8), Hong Kong (May 8), Seoul (May 14-15), Singapore (May 29), and Sydney (June 4–5).
  • AWS re:Inforce – Mark your calendars for AWS re:Inforce (June 16–18) in Philadelphia, PA. AWS re:Inforce is a learning conference focused on AWS security solutions, cloud security, compliance, and identity. You can subscribe for event updates now!
  • AWS Partners Events – You’ll find a variety of AWS Partner events that will inspire and educate you, whether you are just getting started on your cloud journey or you are looking to solve new business challenges.
  • AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Yerevan, Armenia (May 24), Zurich, Switzerland (May 25), and Bengaluru, India (May 25).

You can browse all upcoming in-person and virtual events.

That’s all for this week. Check back next Monday for another Weekly Roundup!


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AWS Lambda introduces tiered pricing for Amazon CloudWatch logs and additional logging destinations

Post Syndicated from Shridhar Pandey original https://aws.amazon.com/blogs/compute/aws-lambda-introduces-tiered-pricing-for-amazon-cloudwatch-logs-and-additional-logging-destinations/

Effective logging is an important part of an observability strategy when building serverless applications using AWS Lambda.

Lambda automatically captures and sends logs to Amazon CloudWatch Logs. This allows you to focus on building application logic rather than setting up logging infrastructure and allows operators to troubleshoot failures and performance issues more easily.

On May 1st, 2025, AWS announced changes to Lambda logging, which can reduce Lambda CloudWatch logging costs and make it easier and more cost-effective to use a wider range of monitoring tools. Lambda logs are now available at volume-based tiered pricing when using CloudWatch Logs Standard and Infrequent Access log classes. When generating Lambda logs at scale, you can expect an immediate cost reduction under this new pricing model. Lambda also now supports Amazon S3 and Amazon Data Firehose as additional destinations for Lambda logs, in addition to CloudWatch Logs. Lambda logs sent to S3 and Firehose are also available at volume-based tiered pricing.

This blog post covers some recent Lambda logging enhancements and describes how this change delivers a simpler, more cost-effective logging experience for Lambda.

Overview

Logging provides developers and operators with valuable data for debugging and troubleshooting application behavior, performance issues, and potential failures. It becomes even more important for serverless applications built using Lambda because of the ephemeral and stateless nature of the Lambda execution environment. Lambda’s built-in integration with CloudWatch Logs ensures that logs for every function invocation are readily available for analysis. The captured log data includes application logs generated by your Lambda function code and system logs generated by the Lambda service while running your function code. CloudWatch Logs allows you to search, filter, and analyze log data to troubleshoot issues, track metrics, and set up alerts.

Logging requirements evolve as serverless applications grow in complexity and scale, sometimes spanning hundreds or thousands of Lambda functions which generate substantial log volumes. Organizations need sophisticated logging solutions that can handle this scale while remaining cost-effective. Some scenarios—such as monitoring critical business transactions—demand real-time log analysis, while others focus on after-the-fact forensic analysis. Debug logs from development and staging environments often need high granularity, whereas you may want lower verbosity in production logs to improve the signal-to-noise ratio.

Recent Lambda logging enhancements

In recent years, Lambda and CloudWatch Logs have expanded Lambda’s logging capabilities to meet the evolving needs of serverless applications. These capabilities provide deeper insights, greater control, and more cost-effective solutions to capture, process, and consume logs to enhancing the serverless observability experience. Lambda advanced logging controls gives developers control over log generation and content. These controls allow you to capture Lambda logs in JSON structured format. You don’t have to use logging libraries and customize log levels (INFO, DEBUG, WARN, ERROR) separately for application and system logs. This helps reduce logging costs by ensuring only necessary logs are generated while maintaining appropriate visibility across different environments. For example, you can set verbose DEBUG level logging in development environments while limiting production logging to ERROR level to improve the signal-to-noise ratio and control costs.

The Infrequent Access log class for CloudWatch Logs introduced a cost-effective solution for logs that need retention but are accessed less frequently. Infrequent Access is 50% lower per GB ingestion price than the Standard log class This tailored set of capabilities allows you to reduce your logging costs while maintaining access to historical data for compliance, audit purposes, or forensic analysis.

CloudWatch Logs Live Tail is an interactive, real-time log streaming and analytics capability. Live Tail streamlines debugging and monitoring workflows; it allows you to observe log output as functions execute without navigating away from the Lambda console. This makes it easier to identify and diagnose issues during development and troubleshooting. Logs Live Tail is also available in Visual Code IDE.

Tiered pricing for Lambda logs in CloudWatch Logs

Starting today, Lambda logs sent to CloudWatch Logs are classed as Vended Logs, which are logs from specific AWS services that are available at volume tiered pricing. This replaces the previous flat rate model when using CloudWatch Logs Standard log class. For example, in the US East (N. Virginia) AWS Region, you were charged at $0.50 per GB when using Standard log class for your Lambda logs. Under the new pricing model, you are charged for sending your Lambda logs to CloudWatch Logs starting at $0.50 per GB for initial usage. As log volume increases, the price per GB automatically decreases through multiple tiers, reaching rates as low as $0.05 per GB in the lowest tier. This pricing change applies automatically to all Lambda logs sent to CloudWatch Logs, requiring no code or configuration changes from you.

Data Ingested CloudWatch Logs Standard CloudWatch Logs Infrequent Access
First 10 TB per month $0.50 per GB $0.25 per GB
Next 20 TB per month $0.25 per GB $0.15 per GB
Next 20 TB per month $0.10 per GB $0.075 per GB
Over 50 TB per month $0.05 per GB $0.05 per GB

Table 1: Tiered pricing for Lambda logs in CloudWatch Logs in US East (N. Virginia) Region

When generating Lambda logs at scale, you will see an immediate cost reduction under this new pricing model. For example, if you generate 60 TB of Lambda logs monthly in CloudWatch Logs, costs would decrease by 58% (from $30,000 to $12,500). The pricing tiers scale with your logging volume, ensuring that cost benefits increase as your application grows. This allows you to maintain comprehensive logging practices that previously may have been cost-prohibitive. Vended logs tiered pricing is applied on all vended logs ingested to CloudWatch and not tiered per service.

When ingesting other vended logs, such as Amazon Virtual Private Cloud flow logs and Amazon Route 53 resolver query logs, you will see larger discounts as the tiering is applied at a consolidated log ingestion volume.

New Lambda logging destinations: Amazon S3 and Amazon Data Firehose

Starting today, Lambda also supports Amazon S3 and Amazon Data Firehose as destinations for Lambda logs, in addition to CloudWatch Logs. When using S3 or Firehose as a destination, logging costs start at $0.25 per GB. The tiered pricing also applies, with rates reducing to as low as $0.05 per GB in the lowest tier. This tiering is also applied at a consolidated log ingestion volume.

Data Ingested Delivery Cost to Amazon S3 Delivery Cost to Amazon Data Firehose
First 10TB per month $0.25 per GB $0.25 per GB
Next 20TB per month $0.15 per GB $0.15 per GB
Next 20TB per month $0.075 per GB $0.075 per GB
Over 50TB per month $0.05 per GB $0.05 per GB

Table 2:Tiered pricing for Lambda logs delivery to Amazon S3 and Amazon Data Firehose in US East (N. Virginia) Region

Direct delivery of Lambda logs to S3 provides enhanced flexibility in log management. Support for Firehose streamlines Lambda log delivery to additional destinations such as Amazon OpenSearch Service, HTTP endpoints, and third-party observability providers. This matches the established log delivery pattern used with other AWS compute services such as Amazon Elastic Container Service (Amazon ECS) and Amazon Elastic Compute Cloud (Amazon EC2).

This new capability provides significant cost benefits and streamlines log delivery to additional logging destinations, making it easier to use a wider range of monitoring tools (including CloudWatch) when building serverless applications using Lambda.

New Lambda logging destinations in action

All new and existing Lambda functions have CloudWatch Logs as the default logging destination, with S3 and Firehose as alternative choices. When you select S3 or Firehose as your logging destination, Lambda sends logs to the selected destination via a new CloudWatch Logs Delivery log class. This log class enables efficient routing but doesn’t support CloudWatch Logs Standard log class features, such as Logs Insights and Live Tail.

To set up S3 or Firehose as the destination for your Lambda logs in the Lambda console:

  1. Navigate to the Lambda console, and select or create a function to set up an S3 or Firehose logging destination.
  2. In the Configuration tab, select Monitoring and operations tools on the left pane.
  3. Select Edit in the Logging configuration. This opens the Edit logging configuration page.

    Figure 1. Edit logging configuration in Lambda console

    Figure 1. Edit logging configuration in Lambda console

  4. In the Log destination section, select Amazon S3 or Amazon Data Firehose. Amazon CloudWatch Logs is the default selection.

    Figure 2. Select log destination in the Edit logging configuration page

    Figure 2. Select log destination in the Edit logging configuration page

  5. Under CloudWatch delivery log group, choose Create new log group or Existing log group.
  6. To create a new delivery log group to send logs to S3, enter a log group name and specify the destination S3 bucket. Provide an AWS Identity and Access Management (IAM) role for CloudWatch Logs to deliver logs to S3.
    Follow similar steps to send logs to a Firehose stream.

    Figure 3. Create new CloudWatch delivery log group for S3

    Figure 3. Create new CloudWatch delivery log group for S3

  7. To use an existing delivery log group, select one from the Delivery log group. The selected delivery log group must have a configured destination (S3 or Firehose) and match the destination you selected.

    Figure 4. Select existing CloudWatch delivery log group for Firehose

    Figure 4. Select existing CloudWatch delivery log group for Firehose

Advanced logging controls are also available for S3 and Firehose destinations. These controls include JSON structured format selection and log level filters for both application and system logs. This gives you enhanced log management controls for easier search, filter, and analysis. You can also use AWS Command Line Interface (AWS CLI) and infrastructure as code (IaC) tools such as AWS CloudFormation and AWS Cloud Development Kit (AWS CDK) to set up Lambda logs delivery to S3 and Firehose.

Best practices

To get the most out of the changes announced today, ensure that your logging strategy is closely aligned with the requirements of your workload. For example, consider sending critical production logs to CloudWatch Logs to take advantage of its advanced real-time analytics and alerting features. You now automatically benefit from volume-based discounts through tiered pricing in CloudWatch Logs for high-volume logging scenarios. For logs that need long-term retention for historical analysis, you can use S3’s storage classes to further reduce costs. When using your existing or third-party monitoring tools, direct integration through Firehose eliminates the need for custom forwarding solutions and associated costs.

Logging cost optimization extends beyond destination selection. Monitor log volumes regularly to understand the impact of pricing tiers. Implement appropriate retention policies to prevent unnecessary storage of old logs and log sampling for high-volume debug logs. Consider using different logging strategies across development, staging, and production environments to balance observability needs with cost efficiency.

Conclusion

Tiered pricing for Lambda logs in CloudWatch Logs and support for S3 and Firehose as additional logging destinations improves Lambda application observability. You can now manage logging costs at scale and expand Lambda monitoring solutions through cost-effective, easy-to-configure integrations. Whether you’re building new serverless applications or optimizing existing ones, these enhancements help you implement comprehensive logging strategies that scale cost-effectively with your workload.

The new features announced today are available in all commercial AWS Regions where Lambda and CloudWatch Logs are available. Support for configuring log delivery to S3 and Firehose in the Lambda console is available in US East (Ohio), US East (N. Virginia), US West (Oregon), and Europe (Ireland) Regions, with additional Regions coming soon. Review the Lambda documentation and CloudWatch Logs documentation to learn more about these features and how to use them. Review the CloudWatch pricing page to learn more about how these features are priced.

For more serverless learning resources, visit Serverless Land.

Integrating aggregators and Quick Service Restaurants with AWS serverless architectures

Post Syndicated from Mike Gomez original https://aws.amazon.com/blogs/compute/integrating-aggregators-and-quick-service-restaurants-with-aws-serverless-architectures/

In this post, you learn how to use AWS serverless technologies, such as Amazon EventBridge and AWS Lambda, to build an integration between Quick Service Restaurants (QSRs) and online ordering and food delivery aggregators. These aggregators have taken off as an option to QSRs to expand their consumer base, enabling them with delivery options to help grow their businesses.

QSR overview

QSRs prioritize speedy and convenient service, offering a streamlined menu. To meet evolving consumer expectations, QSRs can use API integrations with third-party aggregators. This technological synergy enables QSRs to expand their capabilities, introducing diverse payment methods and incorporating delivery services. These features have become standard in this restaurant segment.

Behind the scenes, the APIs are used to orchestrate the interaction between the aggregator and the QSR while having a consistent ordering and delivery experience.

QSR business objectives are:

  • Providing consistent ordering and delivery experiences
  • Offering personalized menu items
  • Retaining repeat customers
  • Reducing third-party delivery cancellation due to lack of delivery personalization options

This post starts with a simple architecture and adds components to solve architectural challenges.

Architecture

As a solutions architect, you’ve been approached by a thriving local restaurant business seeking technological solutions to fuel their expansion. Your task is to design an optimal integration architecture that aligns with their technical requirements, streamlines operations, and enhances customer experience.

At the core of this integration is Amazon API Gateway, which accepts the incoming orders from various delivery aggregators. The API Gateway becomes the front door, connecting the QSRs with the end customers for a streamlined and dynamic order processing system.

Driving the backend of this integration are Lambda functions. These functions validate orders and securely communicate with delivery aggregators. Lambda functions can scale dynamically based on-demand, and make sure of optimal resource usage and cost-effectiveness.

Order placement workflow

The following steps outline the serverless integration between API Gateway and Lambda functions, as shown in the following figure:

  • Customers can place orders either through food delivery aggregators or the business’s own ordering system.
  • The order request is sent to API Gateway.

This architecture works for small and simple integrations. To scale this architecture for high traffic, use asynchronous integration to reduce the coupling between API and Lambda function.

Order routing workflow

The following steps outline a serverless integration where API Gateway connects to Lambda functions through Amazon EventBridge as the event routing service, as shown in the following figure:

  1. API Gateway receives the order request.
  2. The API Gateway routes the customer’s order request to an EventBridge bus for processing.

EventBridge routes events (for example order status changes) to Lambda functions, making sure of resiliency during service disruptions. This eliminates manual error handling and keeps QSRs and aggregators synchronized.

EventBridge delivers the following essential capabilities:

  • EventBridge receives events triggered by various actions, such as new orders or menu updates.
  • It routes events to the relevant Lambda functions, initiating the appropriate actions.
  • EventBridge supports event replay, allowing recovery from Lambda deployment issues or function failures. This feature enables business continuity by storing events during service disruptions and automatically resuming processing when the system stabilizes.

To maintain order history and enable fast data retrieval, the system needs a highly performant database. Amazon DynamoDB, a serverless NoSQL database service, meets these requirements by efficiently storing and managing order information and metadata. The order processing Lambda function interacts with DynamoDB to persist order details. This approach enables asynchronous processing of the stored data by other backend processes. The database solution provides the scalability and responsiveness needed to handle growing order volumes while maintaining consistent performance, separating order intake from subsequent processing steps.

Order processing workflow

The following steps outline the order processing workflow, as shown in the following figure:

  • The order processing Lambda function validates the order and updates the DynamoDB database with the new order details.
  • The function publishes error events to EventBridge, enabling downstream processing for error handling and retry logic. These events can trigger more Lambda functions designed to manage specific error scenarios and recovery processes.

EventBridge implementation patterns: single or dual bus approaches

EventBridge offers multiple approaches for event bus topology. Architects can choose to either use a single event bus with distinct event patterns based on order status or implement a multi-bus strategy.

The single-bus approach uses one event bus for all events with routing rule patterns based on order status. For example, rules would match specific statuses (for example “new” or “processed”) to trigger appropriate Lambda functions. Although it is architecturally simple, it needs careful management of the event schema to avoid potential errors. However, a single-bus approach requires careful handling to prevent recursive processing, where messages trigger additional messages in an endless loop.

Alternatively, the multi-bus method, separating order placement and processing across different buses, effectively prevents loops and recursion issues. This approach provides better separation of transactions, albeit with a slightly more complex setup.

EventBridge can directly target external services using the API destination option, eliminating the need for Lambda functions for third party integrations.

Orchestrating order processing

In complex order processing systems for QSRs, managing multiple interdependent Lambda functions can become challenging, potentially leading to intricate code and difficult-to-maintain architectures. To address this, AWS Step Functions can be introduced as an orchestration layer.

Step Functions acts as a central coordinator for the business logic needed in QSR order flows. This service manages the progression of activities in the order processing workflow, thereby efficiently coordinating tasks such as kitchen preparation and delivery logistics. Defining and managing complex workflows allows Step Functions to optimize the overall efficiency of QSR operations, providing a structured and adaptable solution. This orchestration enhances the restaurant’s ability to handle dynamic processing, achieving a smooth and responsive integration with delivery services while streamlining the underlying architecture.

The following steps outline the orchestration of order processing, as shown in the following figure:

  • Order processing trigger respective Lambda function, which updates the order data in the DynamoDB database.
  • The updated order is made available for subsequent Lambda functions that process more business logic being performed by further Lambda functions.

In a multi-bus EventBridge architecture, the process flows are as follows:

  1. The first EventBridge bus receives the initial order event and routes it to a Step Functions workflow.
  2. The Step Functions workflow orchestrates the order processing, coordinating various tasks and checks.
  3. Upon completion, the Step Functions workflow emits an event with the processing results to the second EventBridge bus.
  4. Based on the output from the Step Function workflow, this second bus contains a rule that triggers the Aggregator API as an API destination.

User engagement workflow

When a customer places an order, there must be a way to confirm or notify them when the order is ready. For this purpose, you can use AWS End User Messaging services to push notifications for order completion and new offers to customers.

Analyzing customer data and individual preferences allows Amazon Personalize to be used to present personalized recommendations and promotions.

Amazon Personalize can analyze historical order data to enhance the user experience through personalized recommendations, such as optimal delivery times, preferred menu items, and tailored promotions based on individual ordering patterns.

Conclusion

This post showed how to use AWS serverless services to build a platform for your order processing without worrying about managing underlying infrastructure. The serverless services included were Amazon API Gateway, AWS Lambda, Amazon EventBridge, AWS Step Functions, AWS End User Messaging, and Amazon Personalize.

This post is a brief introduction to event-driven architectures focused on integrations of internal ordering systems with delivery aggregators and third-party ordering platforms. This can help expand the user base, and it has been a key factor in the growth of many QSRs. Making the ordering, take-out, and delivery experience more efficient translates to revenue growth, reduction of order abandonment, as well as increased recurrent customer retention and brand loyalty.

For more serverless learning resources, visit Serverless Land. To find more patterns, go directly to the Serverless Patterns Collection.

How to use AWS Transfer Family and GuardDuty for malware protection

Post Syndicated from James Abbott original https://aws.amazon.com/blogs/security/how-to-use-aws-transfer-family-and-guardduty-for-malware-protection/

Organizations often need to securely share files with external parties over the internet. Allowing public access to a file transfer server exposes the organization to potential threats, such as malware-infected files uploaded by threat actors or inadvertently by genuine users. To mitigate this risk, companies can take steps to help make sure that files received through public channels are scanned for malware before processing.

This post demonstrates how to use AWS Transfer Family and Amazon GuardDuty to scan files uploaded through a secure FTP (SFTP) server for malware as part of an overall transfer workflow. For readers who might have read an earlier blog post on this topic, the key difference is that this solution is fully managed and doesn’t require the deployment of compute resources. GuardDuty automatically updates malware signatures every 15 minutes instead of using a container image for scanning, avoiding the need for manual patching to keep the signatures up to date.

Prerequisites

To deploy the solution in this post, you will need:

  • An AWS account: You need access to AWS to deploy this solution. If you don’t have an account that you can use, see Start building on AWS today.
  • AWS CLI: Install and configure the AWS Command Line Interface (AWS CLI) to be authenticated to your AWS account. Set up the environment variables for your AWS account using the access token and secret access key for your environment.
  • Git: You will use Git to pull down the example code from GitHub.
  • Terraform: You’ll use Terraform to run the automation. Follow the Terraform installation instructions to download and set up Terraform.

Solution overview

This solution uses Transfer Family and GuardDuty. Transfer Family provides a secure file transfer service that you can use to set up an SFTP server, and GuardDuty is an intelligent threat detection service. GuardDuty monitors for malicious activity and anomalous behavior to protect AWS accounts, workloads, and data. At a high level, the solution uses the following steps:

  • A user uploads a file through a Transfer Family SFTP server.
  • A Transfer Family managed workflow invokes AWS Lambda to execute an AWS Step Functions workflow.
    • The workflow begins only after a successful file upload.
    • Partial uploads to the SFTP server will invoke an error handling Lambda function to report a partial upload error.
  • A step function state machine invokes a Lambda function to move uploaded files to an Amazon Simple Storage Service (Amazon S3) bucket for processing and then starts scanning using GuardDuty.
  • The GuardDuty scan result is sent as a callback to the step function.
  • Infected files are moved or cleaned.
  • The workflow sends the user the results through an Amazon Simple Notification Service (Amazon SNS) topic. This can be a notification of an error or malicious upload during the scan or notification of a successful upload and a clean scan for further processing.

Solution architecture and walkthrough

The solution uses GuardDuty Malware Protection for S3 to scan newly uploaded objects to the S3 bucket. You can use this feature of GuardDuty to set up a malware protection plan for an S3 bucket at the bucket level or to watch for specific object prefixes.

Figure 1: Solution architecture

Figure 1: Solution architecture

The following steps (shown in Figure 1) describe the workflow for this solution starting from the point the file is uploaded until it’s scanned and marked as safe or as infected, leading to subsequent steps that can be customized based on your use case.

  1. A file is uploaded using the SFTP protocol through Transfer Family.
  2. If the file is successfully uploaded, Transfer Family uploads the file to the S3 bucket called Unscanned and the Managed Workflow Complete workflow is triggered. This is the workflow used to handle successful uploads and invokes the Step Function Invoker Lambda function.
  3. The Step Function Invoker starts the state machine and kicks off the first step in the process by invoking the GuardDuty – Scan Lambda function.
  4. The GuardDuty – Scan function moves the file to the Processing bucket. This is the bucket from which the files will be scanned.
  5. When an object upload activity is detected, GuardDuty automatically scans the object. In this implementation, a malware protection plan is created for the Processing bucket.
  6. When a scan completes, GuardDuty publishes the scan result to Amazon EventBridge.
  7. An EventBridge rule has been created to invoke a Lambda Callback function whenever a scan event has completed. EventBridge will invoke the function with an event that contains the scan results. See Monitoring S3 object scans with Amazon EventBridge for an example.
  8. The Lambda Callback function notifies the GuardDuty – Scan task using the callback task integration pattern. The results of the GuardDuty scan are returned to the GuardDuty – Scan function and these results are passed to the Move File task.
  9. If the result is a clean scan with no threats detected, the Move File task will place the file in the Clean S3 bucket, indicating that the file is successfully scanned and safe for further processing.
  10. At this point, the Move File function publishes a notification to the Success SNS topic to notify the subscribers.
  11. If the result indicates that the file is malicious, the Move File function will instead move the file to the Quarantine S3 bucket for further investigation. The function will also delete the file from the Processing bucket and publish a notification in the Error topic in SNS to notify the user of a potential malicious file being uploaded.
  12. If the file upload is unsuccessful and the file isn’t fully uploaded, then Transfer Family will trigger the Managed Workflow Partial workflow.
  13. Managed Workflow Partial is an error handling workflow and invokes the Error Publisher function, which is used for reporting errors that occur anywhere in the workflow.
  14. The Error Publisher function identifies the type of error—whether it’s because of the partial upload or an issue elsewhere in the workflow—and sets the error status accordingly. It will then publish an error message to Error Topic in SNS.
  15. The GuardDuty – Scan task has a timeout to make sure that an event is published to Error Topic to prompt a manual intervention to investigate further if the file isn’t successfully scanned. If the GuardDuty – Scan task fails, the Error clean up Lambda function is invoked.

Finally, there’s an S3 Lifecycle policy attached to the Processing bucket. This is to make sure that no file is left in the Processing bucket for more than one day.

Code repository

The GitHub AWS-samples repository has a sample implementation developed using Terraform and Python-based Lambda functions to implement this solution. The same solution can also be implemented using AWS CloudFormation. The code has the components needed to deploy the entire workflow to demonstrate the abilities of Transfer Family and the GuardDuty malware protection plan.

Install the solution

Use the following steps to deploy this solution to your test environment.

  1. Clone the repository to your working directory using Git.
  2. Navigate to the root directory of your cloned project directory.
  3. Update the terraform locals.tf file with the values of your choice for the S3 bucket names, SFTP server names, and other variables.
  4. Run terraform plan.
  5. If everything looks good, run a terraform apply and enter yes to create the resources.

Clean up

After testing and exploring the solution, it’s important to clean up the resources you created to avoid incurring unnecessary costs. To delete the resources created by this solution, navigate to the root directory of your cloned project and run the following command:

terraform destroy

This command will remove the resources created by Terraform, including the SFTP server, S3 buckets, Lambda functions, and other components. Confirm the deletion by entering yes when prompted.

Conclusion

By using the approach outlined in the post, you can make sure that the files received over SFTP and uploaded to your S3 bucket are scanned for threats and are safe for further processing. The solution reduces the exposure surface by making sure that public uploads are scanned in a safe environment before they’re sent to other components of your system.

If you have feedback about this post, submit comments in the Comments section below.

James Abbott

James Abbott

James is a Principal Solutions Architect at AWS, working in Global Financial Services. When not in the office he enjoys mountain biking in North Carolina.

Santhosh Srinivasan

Santhosh Srinivasan

Santhosh is a Sr. Cloud Application Architect with the Professional Services team at AWS. He specializes in building and modernizing large scale enterprise applications in the cloud with a focus on the financial services industry.

Suhas Pasricha

Suhas Pasricha

Suhas is a Cloud Infrastructure Architect in the AWS Professional Services team. He has a background in web development and infrastructure automation. At Amazon, he has been helping customers set up and operate an enterprise-wide landing zone and cloud environment. In his spare time, he likes to read and play video games.

Optimizing cold start performance of AWS Lambda using advanced priming strategies with SnapStart

Post Syndicated from Shan Kandaswamy original https://aws.amazon.com/blogs/compute/optimizing-cold-start-performance-of-aws-lambda-using-advanced-priming-strategies-with-snapstart/

Introduced at re:Invent 2022, SnapStart is a performance optimization that makes it easier to build highly responsive and scalable applications using AWS Lambda. The largest contributor to startup latency (often referred to as cold-start time) is the time spent initializing a function. This includes loading the function’s code and initializing dependencies. For latency-sensitive workloads such as APIs and real-time data processing applications, high startup latency can result in a suboptimal end user experience. Lambda SnapStart can reduce startup duration from several seconds to as low as sub-second, with minimal or no code changes. This post discusses ‘Priming’, a technique to further optimize startup times for AWS Lambda functions built using Java and Spring Boot.

Spring Boot applications typically experience high cold start latency during JVM and framework initialization, where significant time is spent loading classes and performing Just-In-Time (JIT) compilation of Java bytecode. This blog post uses a Spring Boot application as an example that retrieves 10 records from a ‘UnicornEmployee’ table in an Amazon RDS for PostgreSQL database, where each employee record includes employee name, location, and hire date.

The sample application uses Amazon API Gateway which triggers an AWS Lambda function that connects to the database through RDS Proxy to return the employee data. While this sample application uses dummy employee data for demonstration, the patterns and optimization techniques discussed in this post are applicable to real-world scenarios with similar data access patterns. Sample code for this implementation can be found in our GitHub repository at lambda-priming-crac-java-cdk.

Background: How SnapStart works

The post assumes familiarity with SnapStart and provides a short background. For additional details, refer to the SnapStart documentation.

To quickly recap, the INIT phase for a Lambda function involves downloading the function’s code, starting the runtime and any external dependencies, and running the function’s initialization code. For functions that don’t use SnapStart, this phase occurs each time your application scales up to create a new execution environment. When SnapStart is activated, the INIT phase happens when you publish a function version.

The following image shows a comparison of a Lambda request lifecycle with and without SnapStart.

Figure 1 – comparison of a Lambda request lifecycle with and without SnapStart

At the end of the INIT phase, Lambda executes your before-checkpoint runtime hooks. Lambda then snapshots the memory and disk state of the initialized execution environment, persists the encrypted snapshot, and caches it for low-latency access. When the function is subsequently invoked, new execution environments are resumed from the cached snapshot (during the RESTORE phase), speeding up function startup.

Figure 2 – new execution environments are resumed from the cached snapshot.

You can validate this speedup by comparing the RESTORE duration with the INIT duration recorded before SnapStart in your Lambda function’s Amazon CloudWatch Logs. As demonstrated in the following table, enabling SnapStart reduces the startup latency of our sample Spring Boot application by 4.3x from 6.1s to 1.4s. The 6.1s cold start latency for ON_DEMAND is primarily due to the combination of (1) initializing the JVM and Spring Boot framework, (2) JIT compilation of lazy loaded application code during initial invocation and (3) the time needed to establish a database connection with RDS through Amazon RDS Proxy. By enabling SnapStart, Lambda initializes the JVM and Spring Boot prior to the function invocation – resulting in the significantly reduced latency of 1.4s.

Method Cold Start Invocations p50 P90 P99 p99.9
PrimingLogGroup-1_ON_DEMAND 128 5047.94 ms 5386.78 ms 6158.80 ms 6195.84 ms
PrimingLogGroup-2_SnapStart_NO_PRIMING 111 1177.87 ms 1288.73 ms 1419.94 ms 1425.63 ms

You can reduce cold starts even further for your latency-sensitive Spring Boot applications by using priming techniques on Lambda functions. Let’s explore how to implement priming techniques.

Priming explained

Priming is the process of preloading dependencies and initializing resources during the INIT phase, rather than during the INVOKE phase to further optimize startup performance with SnapStart. This is required because Java frameworks that use dependency injection load classes into memory when these classes are explicitly invoked, which typically happens during Lambda’s INVOKE phase. You can proactively load classes using Java runtime hooks, that are part of the open source CRaC (Coordinated Restore at Checkpoint) project. This post demonstrates how to use this hook, called beforeCheckpoint(), to prime SnapStart-enabled Java functions, in two ways:

  1. Invoke Priming: This approach involves directly invoking application endpoints or methods in your pre-snapshotting hook so that they are JIT compiled during the INIT phase and included in the snapshot. This can include operations such as invoking API Gateway endpoints or fetching data from an S3 bucket or RDS database to proactively execute the code paths, ensuring that the underlying classes are included in the snapshot.
  2. Class Priming: This approach involves proactive initialization of classes during the INIT phase, ensuring that they are included in the function’s snapshot without risking unwanted changes to application state or data. This can be achieved by leveraging Java’s forName() method, which loads, links, and initializes the specified class. Initialization refers to the JVM process of loading the class definition into memory, verifying the bytecode, preparing static fields with default values, and executing static initializers. This is different from instantiation, which creates objects of the class using constructors. To generate a list of the classes required for pre-loading, you can use the following VM option, writing the list to a file called classes-loaded.txt:
    -Xlog:class+load=info:classes-loaded.txt

While invoke priming can offer better performance, it requires additional effort to ensure that the actions performed are idempotent and do not have unintended side effects, for instance processing financial transactions in a banking application. For this reason, invoke priming should only be used when code executed during priming is either idempotent or does not modify state. For scenarios where this is not possible, class priming provides a safer alternative by only initializing classes without executing their methods. Note that this assumes your application does not execute state-modifying code during class initialization.

With this context, let’s look at how to implement Invoke and Class priming for a Spring Boot sample application.

Example priming Implementation using CRaC runtime hooks before taking a Lambda snapshot

This post demonstrates both Invoke priming and Class priming using the sample Spring Boot application. The choice between the two approaches depends on the specific requirements and complexities of your application.

Step 1: Set up your Spring Boot Application using the aws-serverless-springboot3-archetype as explained in our Quick Start Spring Boot3 guide, adding database connectivity code, or simply clone the sample project from GitHub repository.

  1. Create a Spring Boot Application.
    // src/main/java/software/amazon/awscdk/examples/unicorn/UnicornApplication.java
    package software.amazon.awscdk.examples.unicorn;
    …
    @Import({ UnicornConfig.class })
    @SpringBootApplication
    public class UnicornApplication {
    
        private static final Logger log = LoggerFactory.getLogger(UnicornApplication.class);
    
        public static void main(String... arguments) {
            SpringApplication.run(UnicornApplication.class, arguments);
        }
    
    }

  2. Add all the necessary Maven dependencies for Spring Boot, AWS Lambda, and Database Connection in your pom.xml file. The following, highlighted, dependency contains the classes required to use the CRaC runtime hooks.
    ...
            <dependency>
                <groupId>org.crac</groupId>
                <artifactId>crac</artifactId>
            </dependency>
    ...

  3. Configure Database Connection – Set up the database connection details in application.properties.
    spring.datasource.password=${SPRING_DATASOURCE_PASSWORD} 
    spring.datasource.url=${SPRING_DATASOURCE_URL} 
    spring.datasource.username=postgres 
    spring.datasource.hikari.maximumPoolSize=1 

Step 2: Implement Lambda Function Handler with CRaC runtime hooks and Invoke Priming Approach:

Create Lambda Function Handler and integrate CRaC runtime hooks to execute beforeCheckpoint() and afterRestore() methods in your application for before taking and after restoring the snapshot.

  1. Implement the RequestHandler<UnicornRequest, UnicornResponse> interface in the Lambda function handler class.
  2. Implement the CRaC resource interface with two methods: beforeCheckpoint() and afterRestore(), which defines actions performed before Lambda creates the snapshot and after the snapshot is restored.
  3. Add invoke priming by creating a UnicornRequest object with a GET request to a specific endpoint (such as, /unicorn) and call the handleRequest(unicornRequest, null) method.

This ensures that the code paths associated with the specified endpoint are JIT compiled and optimized for faster execution during the first invocation after the snapshot is restored.

/src/main/java/software/amazon/awscdk/examples/unicorn/handler/InvokePriming.java
package software.amazon.awscdk.examples.unicorn.handler;

import org.crac.Core;
import org.crac.Resource;
...
public class InvokePriming implements RequestHandler<APIGatewayV2HTTPEvent, APIGatewayV2HTTPResponse>, Resource {
	...

@Override
public APIGatewayV2HTTPResponse handleRequest(APIGatewayV2HTTPEvent event, Context context) {
    var awsLambdaInitializationType = System.getenv("AWS_LAMBDA_INITIALIZATION_TYPE");
    var unicorns = getUnicorns();
    var body = gson.toJson(unicorns);
    return APIGatewayV2HTTPResponse.builder().withStatusCode(200).withBody(body).build();
}

@Override
public void beforeCheckpoint(org.crac.Context<? extends Resource> context)
        throws Exception {
    var event = APIGatewayV2HTTPEvent.builder().build();
    handleRequest(event, null);
}
...
}

Step 3: Implement Class priming Approach:

The class priming approach focuses on pre-loading required classes to achieve optimal performance. To implement class priming, generate the list of classes that are loaded during the application startup and function execution by running the application locally using the following JVM argument: -Xlog:class+load=info:classes-loaded.txt

  1. Ensure that your application classes included in the generated classes-loaded.txt file are not mutating state during static initialization.
    Note: the generated classes-loaded.txt contains class entries in the following format:

    [0.068s][info][class,load] software.amazon.awscdk.examples.unicorn.handler.ClassPriming source: file:/var/task/

  2. Extract only the fully qualified class names from each line and remove the additional logging information. For Example:
    software.amazon.awscdk.examples.unicorn.handler.ClassPriming

  3. Use the ClassLoaderUtil.loadClassesFromFile() utility method to extract the generated class entries.
    	     //src/main/java/software/amazon/awscdk/examples/unicorn/service/ClassLoaderUtil.java
    package software.amazon.awscdk.examples.unicorn;
    	...
    public class ClassLoaderUtil {
    	...
        public static void loadClassesFromFile() {
            log.info("loadClassesFromFile->started");
            Path path = Paths.get("classes-loaded.txt");
    
            try (BufferedReader bufferedReader = Files.newBufferedReader(path)) {
                Stream<String> lines = bufferedReader.lines();
                lines.forEach(line -> {
                    var index1 = line.indexOf("[class,load] ");
                    var index2 = line.indexOf(" source: ");
    
                    if (index1 < 0 || index2 < 0) {
                        return;
                    }
    
                    var className = line.substring(index1 + 13, index2);
                    try {
                        Class.forName(className, true,
                                ClassPriming.class.getClassLoader());
                    } catch (Throwable ignored) {
                    }
                });
    
                log.info("loadClassesFromFile->finished");
            } catch (IOException exception) {
                log.error("Error on newBufferedReader", exception);
            }
        }
    ...
    }

  4. Read a file (such as, /classes-loaded.txt) that contains a list of classes that have been loaded during the application’s execution in the beforeCheckpoint() method.
  5. Use the Class.forName() method to load and initialize the class, ensuring that it is ready during the snapshot.
    Note: by systematically pre-loading these classes, the Class priming approach simplifies the optimization process and reduces the complexities associated with Invoke priming.

    //src/main/java/software/amazon/awscdk/examples/unicorn/handler/ClassPriming.java
    package software.amazon.awscdk.examples.unicorn.handler;
    
    ...
    import org.crac.Core;
    import org.crac.Resource;
    
    public class ClassPriming implements RequestHandler<APIGatewayV2HTTPEvent, APIGatewayV2HTTPResponse>, Resource {
    
    ...
            ConfigurableApplicationContext configurableApplicationContext =
    				SpringApplication.run(UnicornApplication.class);
    
            this.unicornService = configurableApplicationContext.getBean(UnicornService.class);
            this.gson = configurableApplicationContext.getBean(Gson.class);
    
            Core.getGlobalContext().register(this);
        }
    
        @Override
        public APIGatewayV2HTTPResponse handleRequest(APIGatewayV2HTTPEvent event, Context context) {
            var unicorns = getUnicorns();
            var body = gson.toJson(unicorns);
    
            return APIGatewayV2HTTPResponse.builder().withStatusCode(200).withBody(body).build();
        }
    
        @Override
        public void beforeCheckpoint(org.crac.Context<? extends Resource> context)
                throws Exception {
    
            ClassLoaderUtil.loadClassesFromFile();
    
        }
    ...
    }

Step 4: AWS CDK Infrastructure Setup

Before proceeding, review the prerequisites in the project README file.

The CDK stack deploys a serverless application and required infrastructure for testing different Lambda optimization strategies. It creates a VPC with private subnets, an RDS for PostgreSQL instance with a database proxy, and five Lambda functions implementing different optimization approaches (ON_DEMAND without SnapStart, SnapStart without priming, SnapStart with invoke priming, and SnapStart with class priming). Each Lambda function is integrated with API Gateway for HTTP access, configured with Java 21 runtime on ARM64 architecture, and includes CloudWatch log groups for monitoring.

Follow these steps to deploy the infrastructure:

  1. Clone the sample repository:
    git clone https://github.com/aws-samples/lambda-priming-crac-java-cdk.git

  2. Deploy the CDK stack:
    cd lambda-priming-crac-java-cdk/infrastructure
    cdk synth
    cdk deploy --require-approval never --all 2>&1 | tee cdk_output.txt

  3. Save the API Gateway URLs:
    The deployment will output five URLs in this format:

    # ON_DEMAND endpoint (without SnapStart)
    LambdaPrimingCracJavaCdkStack.PrimingJavaRestApi1ONDEMANDEndpoint = https://[id].execute-api.us-east-1.amazonaws.com/prod/
    
    # SnapStart without priming endpoint
    LambdaPrimingCracJavaCdkStack.PrimingJavaRestApi2SnapStartNOPRIMINGEndpoint = https://[id].execute-api.us-east-1.amazonaws.com/prod/
    
    # SnapStart with invoke priming endpoint
    LambdaPrimingCracJavaCdkStack.PrimingJavaRestApi3SnapStartINVOKEPRIMINGEndpoint = https://[id].execute-api.us-east-1.amazonaws.com/prod/
    
    # SnapStart with class priming endpoint
    LambdaPrimingCracJavaCdkStack.PrimingJavaRestApi4SnapStartCLASSPRIMINGEndpoint = https://[id].execute-api.us-east-1.amazonaws.com/prod/
    
    # Database setup endpoint
    LambdaPrimingCracJavaCdkStack.PrimingJavaRestApi5DBLOADEREndpoint = https://[id].execute-api.us-east-1.amazonaws.com/prod/

  4.  Extract the URLs into variables for testing:
    ONDEMAND_URL=$(grep -oE 'https://[a-zA-Z0-9.-]+\.execute-api\.[a-zA-Z0-9-]+\.amazonaws\.com/prod/' "cdk_output.txt" | head -n 1) \
    
    NOPRIMING_URL=$(grep -oE 'https://[a-zA-Z0-9.-]+\.execute-api\.[a-zA-Z0-9-]+\.amazonaws\.com/prod/' "cdk_output.txt" | head -n 2 | tail -n 1) \
    
    INVOKEPRIMING_URL=$(grep -oE 'https://[a-zA-Z0-9.-]+\.execute-api\.[a-zA-Z0-9-]+\.amazonaws\.com/prod/' "cdk_output.txt" | head -n 3 | tail -n 1) \
    
    CLASSPRIMING_URL=$(grep -oE 'https://[a-zA-Z0-9.-]+\.execute-api\.[a-zA-Z0-9-]+\.amazonaws\.com/prod/' "cdk_output.txt" | head -n 4 | tail -n 1) \
    
    SETUP_URL=$(grep -oE 'https://[a-zA-Z0-9.-]+\.execute-api\.[a-zA-Z0-9-]+\.amazonaws\.com/prod/' "cdk_output.txt" | head -n 5 | tail -n 1)

Step 5: Load database and run performance testing using artillery:

  1. Initialize the database with sample data.
    curl -X GET "$SETUP_URL"
    
    #Expected output: {"message":"Database schema initialized and data loaded"}

  2. Run performance tests for all endpoints
    artillery run -t "$ONDEMAND_URL" -v '{ "url": "/unicorn" }' ./loadtest.yaml && \
    artillery run -t "$NOPRIMING_URL" -v '{ "url": "/unicorn" }' ./loadtest.yaml && \
    artillery run -t "$INVOKEPRIMING_URL" -v '{ "url": "/unicorn" }' ./loadtest.yaml && \
    artillery run -t "$CLASSPRIMING_URL" -v '{ "url": "/unicorn" }' ./loadtest.yaml

Step 6: Compare the load test results for On-demand (non-SnapStart), SnapStart, Invoke priming, and Class priming

The performance test results in the table below are sorted from slowest to fastest startup latency. The function without SnapStart performs the slowest due to JVM initialization, class loading and JIT compilation that occurs when the function is invoked. Notice a 4.3x improvement with SnapStart, which resumes invocations from a pre-initialized snapshot thereby avoiding JVM initialization and initial JIT compilation. SnapStart with class priming achieves a 1.4x speed-up over SnapStart, by proactively loading/initializing classes during INIT so that they are included in your function’s snapshot. Finally, SnapStart with invoke priming achieves the fastest performance – with a 781.68ms p99.9 cold-start latency that is 1.8x faster than SnapStart. This is because in addition to initializing classes, it also executes methods on the instances of those classes, resulting in even more components being included in the function’s snapshot.

Note that with invoke priming, any application code you execute must either be idempotent or modify stub data only. For instance, consider application code that triggers a financial transaction. If this code is executed during invoke priming with real user data, it may drive unintended effects with potentially serious consequences. Class priming avoids this, since application classes are initialized rather than being instantiated and their methods executed. This assumes that application code does not execute state modifying logic during class initialization. We recommend that you keep these considerations in mind when using invoke and/or class priming, and choose the appropriate approach for your use case.

Method Cold Start Invocations p50 P90 P99 p99.9
PrimingLogGroup-1_ON_DEMAND 128 5047.94 ms 5386.78 ms 6158.80 ms 6195.84 ms
PrimingLogGroup-2_SnapStart_NO_PRIMING 111 1177.87 ms 1288.73 ms 1419.94 ms 1425.63 ms
PrimingLogGroup-4_SnapStart_CLASS_PRIMING 82 857.81 ms 997.49 ms 1085.94 ms 1085.94 ms
PrimingLogGroup-3_SnapStart_INVOKE_PRIMING 66 608.42 ms 688.88 ms 781.68 ms 781.68 ms

 Conclusion

This post showed how AWS Lambda SnapStart, enhanced by CRaC runtime hooks, unlocks granular control over cold-start optimization for Java applications through two distinct priming strategies:

  • Invoke Priming: improves performance by executing critical endpoints during snapshot creation, ideal for idempotent workflows.
  • Class Priming: preloads classes without triggering business logic, mitigating side-effect risks.

To implement these optimization techniques in your applications evaluate your use case and opt for the optimal priming approach. Track latency reductions and resource utilization of your application via Amazon CloudWatch metrics to quantify performance improvements. By integrating these strategies, developers can achieve sub-second cold starts while maintaining the scalability and cost-efficiency of serverless architecture using Java.

To dive deeper, check out the GitHub repository with the full example code, including setup instructions and reusable patterns you can adapt to your own projects. For more examples of Java applications running on AWS Lambda, visit serverlessland.com and explore a wide range of resources, tutorials, and real-world use cases.

AWS Lambda standardizes billing for INIT Phase

Post Syndicated from Shubham Gupta original https://aws.amazon.com/blogs/compute/aws-lambda-standardizes-billing-for-init-phase/

Effective August 1, 2025, AWS will standardize billing for the initialization (INIT) phase across all AWS Lambda function configurations. This change specifically affects on-demand invocations of Lambda functions packaged as ZIP files that use managed runtimes, for which the INIT phase duration was previously unbilled. This update standardizes billing of the INIT phase across all runtime types, deployment packages, and invocation modes. Most users will see minimal impact on their overall Lambda bill from this change, as the INIT phase typically occurs for a very small fraction of function invocations. In this post, we discuss the Lambda Function Lifecycle and upcoming changes to INIT phase billing. You will learn what happens in the INIT phase and when it occurs, how to monitor your INIT phase duration, and strategies to optimize this phase and minimize costs.

Understanding the Lambda function execution lifecycle

The Lambda function execution lifecycle consists of three distinct phases: INIT, INVOKE, and SHUTDOWN. The INIT phase is triggered during a “cold start” when Lambda creates a new execution environment for a function in response to an invocation. This is followed by the INVOKE phase where the request is processed, and finally, the SHUTDOWN phase where the execution environment is terminated. For a summary of the execution lifecycle, watch AWS Lambda execution environment lifecycle.

During the INIT phase, Lambda performs a series of preparatory steps within a maximum duration of 10 seconds. The service retrieves the function code from an internal Amazon S3 bucket, or from Amazon Elastic Container Registry (Amazon ECR) for functions using container packaging. Then, it configures an environment with the specified memory, runtime, and other settings. When the execution environment is prepared, Lambda executes four key tasks in sequence:

  1. Initiate any extensions configured (Extension INIT)
  2. Bootstrap the runtime (Runtime INIT)
  3. Execute the function’s static code (Function INIT)
  4. Run any before-checkpoint runtime hooks (applicable only for Lambda SnapStart)

Understanding the billing changes

Lambda charges are based on the number of requests and the duration it takes for the code to run. The duration is calculated from the moment the function code begins running until it completes or terminates, rounded up to the nearest millisecond. Duration cost depends on the amount of memory that you allocate to your function.
https://docs.aws.amazon.com/lambda/latest/dg/provisioned-concurrency.html
Previously, the INIT phase duration wasn’t included in the Billed Duration for functions using managed runtimes with ZIP archive packaging, as evidenced in Amazon CloudWatch logs:

REPORT RequestId: xxxxx   Duration: 250.06 ms  Billed Duration: 251 ms  Memory Size: 1024 MB
Max Memory Used: 350 MB   Init Duration: 100.77 ms

However, functions configured with custom runtimes, Provisioned Concurrency (PC), or OCI packaging already included the INIT phase duration in their Billed Duration. Effective August 1, 2025, INIT phase will be billed across all configuration types and the INIT phase duration will be included in the Billed Duration for on-demand invocations of functions using managed runtimes with ZIP archive packaging as well. After this change, the REPORT Request ID log line will show the following:

REPORT RequestId: xxxxx   Duration: 250.06 ms  Billed Duration: 351 ms  Memory Size: 1024 MB
Max Memory Used: 350 MB   Init Duration: 100.77 ms 

The further INIT phase duration charges will follow the standard on-demand duration pricing that is specific to each AWS Region, which can be found on the Lambda pricing page. For AWS Lambda@Edge functions, the INIT phase duration will be billed according to Lambda@Edge duration rates.

Finding the INIT phase duration and impact to Lambda billing

You can already monitor the time spent in the INIT phase of your function invocations using the “init_duration” CloudWatch metric. This metric is also reported as “Init Duration” in the “REPORT RequestId” log line within CloudWatch Logs. These tools offer valuable insights into the INIT time of Lambda functions, which will now be factored into billing calculations.

For a more comprehensive analysis, you can use the following CloudWatch Log Insights query to generate a detailed report estimating the previously unbilled duration of the INIT phase. The query helps you understand the proportion of the unbilled INIT phase time relative to your overall Lambda usage, enabling more accurate cost projections following this billing change.

filter @type = "REPORT" and @billedDuration < (@duration + @initDuration) 
| stats sum((@memorySize/1000000/1024) * (@billedDuration/1000)) as BilledGBs, 
sum((@memorySize/1000000/1024) * ((ceil(@duration + @initDuration) - @billedDuration)/1000)) as UnbilledInitGBs, 
(UnbilledInitGBs/ (UnbilledInitGBs+BilledGBs)) as Ratio

The CloudWatch Log Insights query provides three essential metrics:

  1. BilledGBs: Represents the total GB-s (gigabyte-seconds) currently being billed for the chosen log groups.
  2. UnbilledInitGBs: Shows the total GB-s consumed during INIT phase that was previously not included in billing.
  3. Ratio: Indicates the percentage of total GB-s attributed to previously unbilled INIT phase duration.

Using these existing monitoring capabilities allows you to proactively assess and optimize your Lambda function INIT times, potentially minimizing the impact of the new billing structure on your overall costs.

Understanding and optimizing Lambda INIT phase

The Lambda INIT phase is triggered in two specific scenarios: during the creation of a new execution environment and when a function scales up to meet demand. This INIT code runs only during these “cold starts” and is bypassed during subsequent invocations that use existing warm environments. After the INIT phase, Lambda runs the function handler code to process the invocation.

Following the handler execution, Lambda freezes the execution environment. To improve resource management and performance, the Lambda service retains the execution environment for a non-deterministic period of time. During this time, if another request arrives for the same function, then the service may reuse the environment. This second request typically finishes faster, because the execution environment already exists and it isn’t necessary to download the code and run the INIT code. This is called a “warm start.”

Developers can use the INIT phase to create, initialize, and configure objects expected to be reused across multiple invocations during function INIT instead of doing it in the handler. Initializing the dependencies/shared objects upfront reduces the latency of subsequent invocations. For example:

  • Download more libraries or dependencies
  • Establish client connections to other AWS services such as Amazon S3 or Amazon DynamoDB
  • Create database connections to be shared across invocations
  • Retrieve application parameters or secrets from Amazon Systems Manager Parameter Store or AWS Secrets Manager

When developing Lambda functions, it’s important to strategically decide what code runs during the INIT phase as opposed to the handler phase, because it affects both performance and costs.

Optimizing package/library size

The INIT phase includes creating an execution environment, downloading the function code and initializing it. Three main factors influence its performance:

  1. The size of the function package, in terms of imported libraries and dependencies, and Lambda layers.
  2. The amount of code and INIT work.
  3. The performance of libraries and other services in setting up connections and other resources.

Larger function packages increase code download times. You can decrease INIT phase duration by reducing package size, resulting in faster cold starts and lower INIT costs. Furthermore, optimizing loading of libraries can also significantly impact package size. For example, in Node.js functions, you should use specific path imports (for example import DynamoDB from "aws-sdk/clients/dynamodb") rather than wildcard imports (for example import {* as AWS} from "aws-sdk") to speed up the INIT phase. Tools such as esbuild can further optimize performance by minifying and bundling packages. For details, read Optimizing node.js dependencies in AWS Lambda.

Optimizing INIT phase execution and cost efficiency

The frequency of INIT phase executions (or cold starts) directly impacts both performance and cost efficiency. According to an analysis of production Lambda workloads, INITs (cold starts) typically occur in under 1% of invocations—meaning code in the INIT phase may execute just once per hundred invocations.

You can use the INIT phase to perform one-time operations that benefit subsequent invocations. Common optimization patterns include pre-calculating lookup tables or transforming static datasets. For example, downloading static data from Amazon S3 or DynamoDB during INIT, making it available for all subsequent function invocations without repeated downloads.

Lambda SnapStart

Lambda SnapStart provides an effective solution for reducing cold start latency and INIT phase costs. When it’s enabled, SnapStart creates a snapshot during the first function INIT and reuses it for subsequent cold starts, eliminating the need for repeated INIT phase executions. This approach is particularly valuable for functions with longer INIT times due to loading module dependencies/frameworks, initializing the runtime, or executing one-time INIT code. SnapStart is supported for Java, .NET, and Python runtimes. You can implement SnapStart through the Lambda console or AWS Command Line Interface (AWS CLI), making sure that your code adheres to the AWS serialization guidelines for snapshot restoration compatibility. Using SnapStart allows you to significantly improve function startup times and optimize costs across multiple popular programming languages.

Provisioned Concurrency

Provisioned Concurrency is a Lambda feature that pre-initializes execution environments before any invocations occur. This proactive approach effectively eliminates the performance impact of the INIT phase on individual function calls, because the INIT is completed in advance.

Although all functions using the Provisioned Concurrency benefit from reduced startup times as compared to on-demand execution, the impact is particularly pronounced for certain runtime environments. For example, C# and Java functions—which typically experience slower INIT but faster execution times as compared to Node.js or Python—can achieve significant performance gains through this feature. Implementing Provisioned Concurrency allows you to effectively manage both consistent traffic patterns and expected usage spikes, thereby minimizing cold start latency across your serverless applications. This optimization strategy is particularly valuable for functions with complex INIT requirements or those serving latency-sensitive workloads. From a cost optimization perspective, Provisioned Concurrency is most suitable for workloads with sustained usage patterns above 60% usage, because this typically provides better cost efficiency compared to on-demand execution.

Conclusion

Effective August 1, 2025, AWS is standardizing the INIT phase billing for AWS Lambda. AWS provides multiple ways for you to optimize both the performance and costs of your Lambda functions. Whether you’re using SnapStart, implementing Provisioned Concurrency, or optimizing INIT code, we recommend working closely with AWS support teams to identify the most suitable optimization approach for your specific workload requirements.

For more support and guidance, consider participating in AWS Cost Optimization workshops or consulting the Lambda documentation.

Streamlining trace sampling behavior for AWS Lambda functions with AWS X-Ray

Post Syndicated from Joshua Smith original https://aws.amazon.com/blogs/compute/streamlining-trace-sampling-behavior-for-aws-lambda-functions-with-aws-x-ray/

Effective tracing enables developers and operators to quickly identify performance bottlenecks, troubleshoot issues across service boundaries, and make sure of optimal end-user experiences. This makes it crucial for maintaining and optimizing distributed serverless applications. This post explores the importance of distributed tracing for operating serverless applications and announces an important update to tracing behavior for AWS Lambda, which streamlines how trace context is handled in PassThrough mode. This blog post will demonstrate how this change gives you better control over how your Lambda functions handle tracing with AWS X-Ray through practical examples. Whether you’re building new applications or operating existing ones, this update helps you achieve more predictable and efficient tracing across your serverless applications built using Lambda.

Overview

Distributed serverless applications spanning numerous AWS services require robust monitoring as they scale. Traditional troubleshooting approaches fall short due to Lambda’s ephemeral nature, making it difficult for development teams to track requests across components, understand performance bottlenecks, and optimize costs by eliminating unnecessary function invocations. Without end-to-end visibility, production issues become increasingly time-consuming to resolve.

X-Ray addresses these observability challenges by providing powerful distributed tracing capabilities that help developers understand how their Lambda functions interact with other AWS services and identify performance issues. As serverless architectures grow in complexity, having fine-grained control over tracing behavior becomes crucial for maintaining efficient and cost-effective observability strategies that enable teams to effectively operate production workloads.

Lambda and X-Ray have steadily enhanced tracing capabilities in recent years to improve observability for serverless applications. In November 2022, X-Ray introduced trace linking between Amazon Simple Queue Service (Amazon SQS) and Lambda, enabling end-to-end tracing for event-driven applications. In February 2023, X-Ray added active tracing support for Amazon Simple Notification Service (Amazon SNS), allowing you to trace messages that flow through SNS topics to Lambda functions. In May 2023, X-Ray added tracing support to SnapStart-enabled Lambda functions, helping you troubleshoot and optimize the performance of latency-sensitive Java applications built using SnapStart-enabled functions. In November 2023, Lambda launched a unified experience in the Lambda console that brings together metrics, logs, and traces in a single view, allowing you to more directly troubleshoot and optimize your functions.

Building upon these enhancements, Lambda has now rolled out streamlined trace sampling behavior, which gives you better control over how your functions handle tracing with X-Ray. This launch makes an important change to tracing behavior in Lambda when the tracing configuration is set to PassThrough mode. With this launch, Lambda propagates the tracing context as is without any modifications in PassThrough mode. This means that Lambda won’t create any trace segments or subsegments for functions set to PassThrough mode, even if the incoming invocation contains a decision to sample the request. However, Lambda service does propagate the tracing context as received by the function.

This change to the X-Ray PassThrough mode for Lambda gives you more control and predictability over your tracing configuration. This enables you to optimize your tracing strategy and better understand the performance and behavior of your serverless applications. This post shows three different scenarios to demonstrate the new tracing behavior.

Understanding the Lambda/X-Ray tracing behavior: before and after

Tracing in Lambda with X-Ray is a powerful tool for gaining insights into the performance and behavior of serverless applications. Enabling tracing allows you to identify bottlenecks, troubleshoot issues, and optimize your Lambda functions. Lambda supports two tracing modes for X-Ray: Active and PassThrough. With Active tracing, Lambda automatically creates trace segments for function invocations and sends them to X-Ray. On the other hand, PassThrough mode propagates the tracing context to downstream services.

Previously, if you enabled tracing in an upstream service that invokes your function, Lambda would follow this sampling decision and send traces to X-Ray automatically, even in the case where the Lambda function was configured to use PassThrough mode. The following figure shows this process. This behavior could result in unexpected trace segments, which could become an overhead, particularly in high throughput scenarios.

Figure 1. Previous behavior: Lambda sends traces to X-Ray even when function tracing configuration is set to PassThrough

The updated X-Ray PassThrough mode for Lambda provides a more intuitive and consistent tracing experience. You can now expect Lambda to respect the incoming tracing context (if it exists) and propagate it without any modifications. In turn, downstream services can make their own tracing decisions based on their configuration. The following figure shows this updated behavior.

Figure 2. New behavior: When function tracing configuration is set to PassThrough, Lambda doesn’t send traces to X-Ray or modify sampling decision

PassThrough tracing configuration with upstream sampling

To configure your Lambda function to use PassThrough tracing mode in the console, complete the following steps:

  1. In the Lambda console, navigate to your function.
  2. On the Configuration tab, choose Monitoring and operations tools in the left pane.
  3. Confirm that X-Ray active tracing shows as Not enabled. If it’s enabled, then choose Edit.
  4. Under X-Ray, turn off Active tracing, then choose Save, as shown in the following figure.

    Figure 3. Lambda console showing function with active tracing disabled

You can also make use of the AWS Command Line Interface (AWS CLI) to achieve the aforementioned setting:

aws lambda update-function-configuration --function-name YOUR_FUNCTION_NAME --tracing-config Mode=PassThrough

This configuration allows your Lambda function to propagate the tracing context received from the upstream service without any changes. If you were previously using this configuration, then you no longer see trace segments created by the Lambda function on the X-Ray console. This configuration is useful when you want to propagate the tracing context without generating trace segments, in scenarios that need optimizing for tracing costs or overhead. The following figure shows the workflow.

Figure 4. A tracing map that shows the UpstreamFunction Lambda function isn’t displayed on the trace map, because it’s configured to use PassThrough tracing mode after this change

If you want to see trace segments for your Lambda function, then you need to set the tracing mode to Active.

Active tracing configuration

When you configure your Lambda function to use active tracing mode, and if there is no sampling decision from the upstream request, Lambda samples requests at the rate of one request per second and 5% of further requests. If there is a decision not to sample, then Lambda respects this sampling decision.

To configure your Lambda function to use active tracing mode, complete the following steps:

  1. On the Lambda console, navigate to the AWS X-Ray section on the Lambda function’s configuration page, as described in the previous section.
  2. Turn on Active tracing, then choose Save, as shown in the following figure.

    Figure 5: Lambda console showing active tracing enabled

You can also use the AWS CLI to set this configuration:

aws lambda update-function-configuration --function-name YOUR_FUNCTION_NAME --tracing-config Mode=Active

With active tracing mode, you can always see traces for sampled requests for your Lambda function on the X-Ray console. This mode is particularly useful when you want to have complete visibility into the performance and behavior of your Lambda function. The following figure shows the workflow for upstream and downstream Lambda functions with active tracing enabled.

Figure 6. A trace map showing both the UpstreamFunction and DownstreamFunction Lambda functions. This is because both functions have active tracing enabled.

The following screenshot shows a full trace corresponding to the preceding trace workflow with both upstream and downstream Lambda functions. Detailed insights gained from comprehensive tracing can be invaluable for troubleshooting, performance optimization, and understanding the end-to-end behavior of your serverless application.

Figure 7. A full trace corresponding to the preceding trace map with both upstream and downstream Lambda functions

PassThrough tracing configuration without upstream sampling

When you configure your Lambda function to use PassThrough tracing mode, and the upstream service has sampling turned off, Lambda continues to propagate the tracing context without any modifications, and without generating traces.

To configure your Lambda function to use PassThrough tracing mode, complete the following steps:

  1. On the Lambda console, navigate to the AWS X-Ray section on the Lambda function’s configuration page.
  2. Under X-Ray, turn off Active tracing, then choose Save, as shown in the following figure.

    Figure 8. Lambda console showing active tracing disabled

This configuration remains the same in the updated PassThrough configuration and is particularly useful when you want to allow downstream services to make their own tracing decisions.

Conclusion

The new streamlined trace sampling behavior for AWS Lambda functions provides you with more control and flexibility over insights into your applications. Whether you choose to use PassThrough mode with upstream sampling on or off, or active tracing mode, you can now configure your Lambda functions to handle tracing in a way that best suits your application’s needs.

This update empowers you to optimize your tracing setup, balance tracing costs and benefits, and gain valuable insights into the performance and behavior of your serverless applications.

This change in tracing behavior now applies to all new and existing functions in all AWS Regions where Lambda and AWS X-Ray are available, at no further cost. To learn more about the new tracing sampling behavior for Lambda, see the post Visualize Lambda function invocations using AWS X-Ray.

For more serverless learning resources, visit Serverless Land.

How Smartsheet reduced latency and optimized costs in their serverless architecture

Post Syndicated from Anton Aleksandrov original https://aws.amazon.com/blogs/architecture/how-smartsheet-reduced-latency-and-optimized-costs-in-their-serverless-architecture/

Cloud software as a service (SaaS) companies are often looking for ways to enhance their architectures for performance and cost-efficiency. Serverless technologies offload infrastructure management, allowing development teams to focus on innovation and delivering business value. As application architectures grow and face more demanding requirements, continued optimization helps maximize both the technical and financial advantages of the serverless approach.

In this post, we discuss Smartsheet’s journey optimizing its serverless architecture. We explore the solution, the stringent requirements Smartsheet faced, and how they’ve achieved an over 80% latency reduction. This technical journey offers valuable insights for organizations looking to enhance their serverless architectures with proven enterprise-grade optimization techniques.

Solution overview

Smartsheet is a leading cloud-based enterprise work management platform, enabling millions of users worldwide to plan, manage, track, automate, and report on work at scale. At the core of the platform lies an event-driven architecture that processes real-time user activity across various document types. Given the collaborative nature of the platform, multiple users can work on these documents concurrently. Every document interaction triggers a series of events that must be processed with minimal latency to maintain data consistency and provide immediate feedback. Processing delays can impact user experience and productivity, making consistently low latency a fundamental business requirement.

Smartsheet’s traffic pattern is spiky during business hours and mostly dormant during nights and weekends. Within peak periods, traffic can fluctuate as users collaborate in real time. To efficiently manage dynamic workloads, which can surge from hundreds to tens of thousands of events per second within minutes, Smartsheet implements a serverless event processing architecture using services such as Amazon Simple Queue Service (Amazon SQS) and AWS Lambda. This architecture uses the elasticity of serverless services and the ability to automatically scale dynamically based on the traffic volume. It makes sure Smartsheet can efficiently handle sudden traffic surges while automatically scaling down during off-peak hours, optimizing for both performance and cost-efficiency.

The following diagram illustrates the high-level architecture of the Smartsheet event processing pipeline.

high-level architecture of the Smartsheet event processing pipeline

Optimization opportunity

Smartsheet uses Lambda functions to serve both batch jobs and API requests. The primary runtime used for building those functions is Java. Lambda automatically scales the number of execution environments allocated to your function on demand to accommodate traffic volume. When Lambda receives an incoming request, it attempts to serve it with an existing execution environment first. If no execution environments are available, the service initializes a new one. During initialization, the Smartsheet’s function code commonly sends several requests to external dependencies, such as databases and REST APIs, which might take time to reply.

The following diagram illustrates how Lambda functions reach out to external dependencies during initialization.

Lambda functions reach out to external dependencies during initialization

These tasks introduced execution environment initialization latency, commonly referred to as a cold start. Although cold starts typically affect less than 1% of requests, Smartsheet had stringent low latency requirements for their architecture to further prioritize the best possible end-user experience.

“To reduce customer request latency while keeping costs low, our engineering team utilized Lambda provisioned concurrency with auto scaling and Graviton, which resulted in an 83% reduction in P95 latency while providing a high quality of service as we continue to scale our platform and its limits,” says Abhishek Gurunathan, Sr Director of Engineering at Smartsheet.

Addressing the cold start with provisioned concurrency

To reduce cold start latency, the Smartsheet team adopted provisioned concurrency in their architecture, a capability that allows developers to specify the number of execution environments that Lambda should keep warm to instantly handle invocations. The following diagram illustrates the difference. Without provisioned concurrency, execution environments are created on demand, which means some invocations (typically less than 1%) need to wait for the execution environment to be created and initialization code to be run. With provisioned concurrency, Lambda creates execution environments and runs initialization code preemptively, making sure invocations are served by warm execution environments.

invocations are served by warm execution environments

Provisioned concurrency includes a dynamic spillover mechanism, making your serverless architecture highly resilient to traffic spikes. When incoming traffic exceeds the preconfigured provisioned concurrency, additional requests are automatically served by on-demand concurrency rather than being throttled. This provides seamless scalability and maintains service availability even during traffic surges, while still providing the performance benefits of pre-warmed execution environments for the majority of requests.

The Smartsheet team configured provisioned concurrency to match their historical P95 concurrency needs. This resulted in immediate improvements—the number of cold starts dropped dramatically and P95 invocation latency dropped by 83%. As the team monitored system performance, they quickly identified another architecture optimization opportunity—the Lambda functions were heavily used during work hours but had significantly fewer invocations at night and on weekends, as illustrated in the following graph.

Lambda functions were heavily used during work hours but had significantly fewer invocations at night and on weekends

Setting a static provisioned concurrency configuration worked great for busy periods, but was underutilized during off-times. The Smartsheet team wanted to further fine-tune their architecture and increase provisioned concurrency utilization rates to achieve higher cost-efficiency. This led them to look into provisioned concurrency auto scaling to match traffic patterns as well as adopting an AWS Graviton architecture.

Auto scaling provisioned concurrency and Graviton architecture

Two common approaches to enable provisioned concurrency are setting a static value and using auto scaling. With static configuration, you specify a fixed number of pre-initialized execution environments that remain continuously warm to serve invocations. This approach is highly effective for architectures that handle predictable traffic patterns. Unpredictable traffic patterns, however, can lead to under-provisioning during peak periods (with spillover to on-demand concurrency resulting in more cold starts) or underutilization during low-usage periods. To address that, provisioned concurrency with auto scaling dynamically adjusts the configuration based on utilization metrics, automatically scaling the number of execution environments up or down to match the actual demand. This dynamic approach optimizes for cost-efficiency and is particularly recommended for architectures with fluctuating traffic patterns.

The following figure compares static and dynamic provisioned concurrency.

static and dynamic provisioned concurrency

To further optimize the architecture for cost-efficiency, the Smartsheet team has implemented provisioned concurrency auto scaling based on utilization metrics. Smartsheet used an infrastructure as code (IaC) approach with Terraform to define auto scaling policies for maximum reusability across hundreds of functions. The policies track the LambdaProvisionedConcurrencyUtilization metric and define the scaling threshold according to the function purpose. For functions implementing interactive APIs, the auto scale threshold is 60% utilization to pre-provision execution environments early, keeping latency extra-low, and making functions more resilient towards traffic surges. For functions that implement asynchronous data processing, Smartsheet’s goal was to achieve the highest utilization rate and cost-efficiency, so they’ve defined the auto scale threshold at 90%.

The following diagram illustrates the architecture of auto scaling policies based on provisioned concurrency utilization rate and workload type.auto scaling policies based on provisioned concurrency utilization rate and workload type

Another optimization technique Smartsheet employed was switching the CPU architecture used by their Lambda functions from x86_64 to arm64 Graviton. To achieve this, Smartsheet adopted the ARM versions of Lambda layers they’ve used, such as Datadog and Lambda Insights extensions. This was required because binaries built using one architecture might be incompatible with a different one. Because Smartsheet functions were implemented with Java and packaged as JAR files, they didn’t have any compatibility issues when moving to Graviton. With Terraform used for codifying the infrastructure, this architecture switch was a simple property change in aws_lambda_function resources, as illustrated in the following code:

property change in aws_lambda_function resources

By switching to a Graviton architecture, Smartsheet saved 20% on function GB-second costs. See AWS Lambda pricing for details.

Best practices

Use the following techniques and best practices to optimize your serverless architectures, reduce cold starts, and increase cost-efficiency:

  • Fine-tune your Lambda functions to find the optimal balance between cost and performance. Increasing memory allocation also adds CPU capacity, which often means faster execution and can lead to reduced overall costs.
  • Use a Graviton2 architecture for compatible workloads to benefit from a better price-performance ratio. Depending on the workload type, switching to Graviton can yield up to 34% improvement.
  • Use provisioned concurrency and Lambda SnapStart to reduce cold starts in your serverless architectures. Start with static provisioned concurrency based on your historical concurrency requirements, monitor utilization, and introduce auto scaling into your architecture to achieve the optimal cost-performance profile.

Conclusion

Serverless architectures using services like Lambda and Amazon SQS offload the infrastructure management and scaling concerns to AWS, allowing teams to focus on innovation and delivering business value. As Smartsheet’s journey demonstrates, using provisioned concurrency and Graviton in your architectures can help significantly improve user experience by reducing latencies while also achieving better cost-efficiency, providing a practical blueprint for optimization across the organization. Whether you’re running large-scale enterprise applications or building new cloud solutions, these proven techniques can help you unlock similar performance gains and cost-efficiencies in your serverless architectures.

To learn more about serverless architectures, see Serverless Land.


About the authors

 

Enriching and customizing notifications with Amazon EventBridge Pipes

Post Syndicated from Chris McPeek original https://aws.amazon.com/blogs/compute/enriching-and-customizing-notifications-with-amazon-eventbridge-pipes/

This blog post authored by Elie Elmalem, Associate Scale Solutions Architect

When implementing event-driven architectures, customers frequently need to enrich their incoming events with additional information to make them more valuable for downstream consumers. Traditionally, customers using Amazon EventBridge would accomplish this by writing AWS Lambda functions to augment their events with supplementary data. However, this approach requires writing and maintaining custom code, adding complexity to their event processing pipeline.

Amazon EventBridge Pipes simplifies this process by providing a streamlined, managed service for event enrichment without the need to write and manage custom Lambda functions. This blog post demonstrates how you can use EventBridge Pipes’ built-in data enrichment capabilities to dynamically enhance your events with additional context and customer-specific details, making event processing more efficient and easier to maintain.

Amazon EventBridge Pipes

Amazon EventBridge creates a direct connection between sources and targets. Using an EventBridge bus helps you route and fan-out events to services in a pub/sub pattern. EventBridge Pipes on the other hand help you with point-to-point service integrations patterns. What sets it apart from the traditional event bus/rule pattern is its data transformation and enrichment support.

When defining an EventBridge Pipes, you specify the source and the target of the pipe. Pipes support a variety of sources and targets. Between the source and target, EventBridge Pipes supports filtering and enrichment. The filtering enables you to select and process a targeted subset of events. Enrichment allows you to enhance data by adding missing information before sending it to a target. For instance, if an event lacks necessary information, it ensures the target can properly consume the event. Enriching data can be very powerful, as it makes it possible to enhance a generic event and transform it. EventBridge Pipes support enrichment using Lambda functions, AWS Step Functions, Amazon API Gateway and EventBridge API destinations. More details about these concepts can be found in the Amazon EventBridge Pipes concepts documentation.

Representation of EventBridge Pipes showing filter and enrichment steps.

Figure 1: Representation of EventBridge Pipes showing filter and enrichment steps

This blog post will use the enrichment step of the pipe to create custom notifications.

Overview

To illustrate the functionality, this post uses a use case from a clothing retailer. Businesses such as this retailer want to keep their loyal customers engaged. Often, they rely on bulk promotional emails which lack personalization. In this use case, the retailer wants to send targeted promotion codes. As soon as the 10th order is placed, the code is sent via email or SMS to their customer.

Without EventBridge Pipes, this would be implemented using EventBridge to respond to the order event. All the events are sent to a custom Lambda function to process it. If the order meets the right conditions, the Lambda function sends a notification with the discount code to the customer using Amazon Simple Notification Service (Amazon SNS).

Traditional approach using EventBridge.

Figure 2: Traditional approach using EventBridge

While this architecture works, it requires you to maintain the integration code as well as the data enriching logic within the Lambda function as the function needs to extract the necessary information from the events and manage routing to SNS. As more microservices follow the same pattern, the code becomes more complex. This can lead to longer execution times along with higher cost and greater maintenance effort.

Simplifying using Amazon EventBridge Pipes

Amazon EventBridge Pipes can be used to simplify the previous implementation by handling the enrichment and integration between services. Amazon EventBridge Pipes take care of sending the event to your configured enrichment step and then routes the enriched event to the target. If the chosen method is a Lambda function, it leaves the function code to only focus on enrichment logic. It eliminates the need for code to extract the necessary fields from the event and to send notifications.

Solution architecture using EventBridge Pipes

Figure 3: Solution architecture using EventBridge Pipes

As the event comes into the pipe, the enrichment step triggers a Lambda function, which will check eligibility and returns the message to route to SNS. If the customer is not eligible for a discount code, it returns an order confirmed message with the data retrieved from the original order event. If the customer is eligible for a discount, the message also contains the discount code.

This is the architecture for the updated flow:

  1. A customer orders a new item. The order is sent to a Simple Queue Service (SQS) Orders queue.
  2. The new message on the Orders queue triggers the EventBridge Pipes.
  3. The pipe triggers an AWS Lambda function to enrich the data.
  4. The functions checks if the customer is eligible for a discount code against an Amazon DynamoDB table. The table contains the number of times each customer has ordered.
  5. The Lambda function returns the custom message that will be sent to the customer, either with or without the discount code.
  6. The message is routed to an SNS topic by the EventBridge Pipe
  7. Customer receives the notification via its preferred subscription method.

Building the updated flow

To build the updated flow, I have chosen to use the AWS Cloud Development Kit (CDK) in Python. You can use the code given here to deploy it into your account. The code can also be found on GitHub.

Note: This sample code is for testing purposes only and is not intended to be used in a production account.

For this solution, you need the following prerequisites:

  1. The AWS Command Line Interface (CLI) installed and configured for use.
  2. An Identity and Access Management (IAM) role or user with enough permissions to create an IAM policy, DynamoDB table, SQS queue, SNS topic, Lambda Function and EventBridge Pipes.
  3. AWS CDK
  4. Python version 3.9 or above, with pip and virtual virtualenv.

Once the prerequisites are met, set up a new Python CDK project in an empty directory:

mkdir blog_code
cd blog_code
cdk init app –-language python

Then, activate the virtual environment and install the CDK’s dependencies:

source .venv/bin/activate
python -m pip install -r requirements.txt

The cdk init command creates a blog_code folder. The GitHub repository contains the code for the blog_code_stack.py file inside the blog_code folder.

Then, within the blog_code folder, create a new folder called lambda. Inside this new folder, create a file called index.py. This file will contain the code for the enrichment lambda function. Once again, this code can be found in the GitHub repository. Here is a section of the Lambda code:

def lambda_handler(event, context):

    message = json.loads(event[0]['body'])

    id = message['id']
    order_content = message['order_content']
    
    nmb_orders = get_number_of_orders(id)
    
    # Calculate orders left
    orders_left = MAX_ORDERS - nmb_orders
    
    # Update the DynamoDB table with the new number of orders
    if nmb_orders == MAX_ORDERS:
        update_table(id, 0)
    else:
        update_table(id, nmb_orders)
    
    if orders_left == 0:
        return [f"Thank you for your order of {order_content}. You have earned a 10% discount code on your next order: XA5GT2SF"]
    else:  
        # Return the confirmation message
        return [f"Thank you for your order of {order_content}. This is your confirmation message! Only {orders_left} orders left until a 10% discount!"]

The Lambda function works in the following way:

  1. It receives an event from the EventBridge Pipe which consists of the order and the ID of the user who made the order
  2. It gets the number of orders that the user has already placed by calling a GetItem command on the DynamoDB table.
  3. It calculates how many orders are left before the user gets the discount code.
  4. It updates the DynamoDB table with the new number of orders to account for the one that was just placed.
  5. If the user has placed the right number of orders, it returns a confirmation message with the discount code. Otherwise, it notifies the user of the number of orders that still need to be placed to get the discount.

Now, deploy the CDK stack into your account. Make sure that you are in the root directory of your project:

cdk bootstrap
cdk deploy

Once the stack has finished deploying, you will find an EventBridge pipe visible on the console by going to the EventBridge service page and clicking on Pipes in the left panel.

Testing the solution

To test the solution, you must first set up a subscription to the SNS topic to receive notifications. It is recommended to set up email notifications for simplicity and testing purposes. To do so, follow the instructions on the Amazon SNS documentation for the topic with name TargetTopic. When the subscription is set up, don’t forget to check your email inbox and confirm the subscription.

Once notifications are set up, visit the DynamoDB console page. You need to manually add an entry to the eligibility table to mimic a real environment:

  1. Click on Tables in the left panel
  2. Select the EligibilityTable table.
  3. Click Explore table items then Create item
  4. Enter an id with a value of 01.
  5. Click Add new attribute and select String.
  6. Under attribute name, enter orders, and under value enter 8.
  7. Click Create item.

The Items returned table should look like the following. This assumes that the customer has already place 8 orders.

Items returned table after adding a new item.

Figure 4: Items returned table after adding a new item

Now, visit the SQS console page. You will need to send a message to the queue to mimic new orders being placed.

  1. Click on the queue called SourceQueue.
  2. Click Send and receive messages.
  3. Under message body, paste in the following message and click Send message:
{
    "order_content": "large shirt",
    "id": "01",
    "username": "johndoe01",
    "transaction_time": "10:04:00"
  }

After a few minutes, you should receive an email confirming your order, as your order message is considered to be the 9th order. Send the message again to place a 10th order and you should receive your discount code!

Email received with a discount code.

Figure 5: Email received with a discount code

Cleanup

To delete the resources in your account, run the following command in the root directory of your project:

cdk destroy

Conclusion

This blog post showed how Amazon EventBridge Pipes and its enrichment feature can help you create tailored notifications. First, it discussed how it could be implemented using EventBridge and then presented a simplified implementation using EventBridge Pipes.

For more information on common patterns for EventBridge Pipes, you can check out Implementing architectural patterns with Amazon EventBridge Pipes.

For more serverless learning resources, visit Serverless Land. To find more patterns, go directly to the Serverless Patterns Collection.

Announcing the General Availability of the Amazon EventBridge Scheduler L2 Construct

Post Syndicated from Svenja Raether original https://aws.amazon.com/blogs/devops/announcing-the-general-availability-of-the-amazon-eventbridge-scheduler-l2-construct/

Today we’re announcing the general availability (GA) of the Amazon EventBridge Scheduler and Targets Level 2 (L2) constructs in the AWS Cloud Development Kit (AWS CDK) construct library. EventBridge Scheduler is a serverless scheduler that enables users to schedule tasks and events at scale. Prior to the launch of these L2 constructs, developers had to define all relevant properties (via L1 constructs) across schedules and provide the glue logic between resources when defining their AWS CDK applications. The graduated constructs make it easier for users to configure EventBridge schedules, groups, and targets for AWS service integrations. They follow the AWS CDK L2 higher-level API design simplifications and provide a backwards-compatible guarantee across minor versions. Developers can use those alongside other existing stable AWS CDK constructs ready for production use.

Background

The AWS Cloud Development Kit (CDK) is an open-source software development framework for defining cloud infrastructure in code and provisioning it through AWS CloudFormation. It contains pre-written modular and reusable cloud components known as constructs. Constructs are the basic building blocks representing one or more AWS CloudFormation resources and their configuration. They are available in different abstraction levels. L1 constructs are the lowest-level constructs which map directly to AWS CloudFormation resources without abstractions. L2 constructs are thoughtfully developed and provide a higher-level abstraction through an intuitive intent-based API. They leverage default property configurations, best practice security policies, and convenience methods that make it simpler and quicker to define and deploy resources.

Amazon EventBridge Scheduler is a serverless scheduler that allows users to create, run, and manage tasks from one central, managed service. With EventBridge Scheduler, users can create schedules using cron and rate expressions for recurring patterns, or configure one-time invocations. EventBridge supports templated and universal targets. Templated targets include common API operations across a group of core AWS services, such as publishing a message to an Amazon Simple Notification Service (Amazon SNS) topic or invoking an AWS Lambda function. Universal targets are customized triggers supporting more than 270 AWS services and over 6,000 API operations on a schedule. Users can use schedule groups to organize their schedules.

With the L2 constructs for Amazon EventBridge Scheduler and Targets, it becomes even simpler for users to configure and integrate those resources into their CDK applications. Let’s explore the benefits by looking at some examples.

Using the L2 EventBridge Scheduler construct

We introduce two use cases for the EventBridge Scheduler and Targets L2 constructs to demonstrate their usage within common scenarios. Each example is equipped with sample code, emphasizing the simplifications achieved by the L2 constructs.

Example 1 – One time reminder through Amazon SNS

In the first use case, users want to configure one-time notifications to receive reminders of their favorite conferences at a specific time, for example a user may want to set a reminder one month before the start of AWS re:Invent to be reminded of their participation.

The example below uses the EventBridge Scheduler construct with a templated Amazon SNS target. The target applies an on-time schedule configuration and is configured with an Amazon Simple Queue Service (Amazon SQS) dead-letter queue to capture and retry failed events. The schedule payload is encrypted using a customer-managed AWS Key Management Service (AWS KMS) key.

const snsTarget = new targets.SnsPublish(topic, {
   input: ScheduleTargetInput.fromObject({
     message: "Reminder: AWS re:Invent starts in one month.",
   }),
   deadLetterQueue: deadLetterQueue,
});
 
const schedule = new Schedule(this, "ReminderSchedule", {
  description:
     "This schedule publishes a one-time notification to an Amazon SNS topic.",
   schedule: ScheduleExpression.at(
     new Date(2025, 10, 1), // Nov 01, 2025
     cdk.TimeZone.AMERICA_LOS_ANGELES
   ),
   target: snsTarget,
   key: key,
});

From the code example, we can see that well-defined interfaces for ScheduleTargetInput, and ScheduleExpression make it easy to select matching configuration values.

The SnsPublish target and Schedule constructs seamlessly integrate with the existing L2 constructs for Amazon SNS, Amazon SQS, and Amazon KMS. They abstract away the gluing logic used to configure the target API operation, dead-letter queue, and encryption settings with correct references. Instead of manually crafting permissions, the construct generates an AWS Identity and Access Management (IAM) execution role with the minimum necessary permissions to interact with the templated target, as shown in the policy below.

{
 "Version": "2012-10-17",
 "Statement": [
 {
 "Action": "sns:Publish",
 "Resource": "arn:aws:sns:us-east-1:123456789012:<TOPIC_NAME>",
 "Effect": "Allow"
 },
 {
 "Action": "kms:Decrypt",
 "Resource": "arn:aws:kms:us-east-1:123456789012:key/<UUID>",
 "Effect": "Allow"
 },
 {
 "Action": "sqs:SendMessage",
 "Resource": "arn:aws:sqs:us-east-1:123456789012:<QUEUE_NAME>",
 "Effect": "Allow"
 }
 ]
 }

The construct sets default properties. For example, it applies default configurations for the retry policy if not explicitly stated. As shown in Figure 1, the above defined schedule has been defined with a 1-day maximum event retention time and 185 maximum retries.

Default configurations for the Retry Policy

Example 2 – Start / Stop EC2 instance during business hours

In the second scenario, a recurring cron schedule is used to automatically stop Amazon EC2 instances during the business hours of a specific time zone.

The example below uses the EventBridge Scheduler construct with a universal target to perform the Amazon EC2 stopInstance API operation. It creates a custom schedule group to organize the schedules by time zone and allows an Amazon Lambda function to read all schedules in it for administrative purposes.

const group = new ScheduleGroup(this, "ScheduleGroup", {
  scheduleGroupName: "Europe-London",
});
 
new Schedule(this, "Schedule", {
  schedule: ScheduleExpression.cron({
minute: "0",
hour: "23",
timeZone: cdk.TimeZone.EUROPE_LONDON,
  }),
  target: new targets.Universal({
service: "ec2",
action: "stopInstances",
input: ScheduleTargetInput.fromObject({
  InstanceIds: [ec2Instance.instanceId],
}),
  }),
  scheduleGroup: group,
});
 
group.grantReadSchedules(lambdaFunction);

Similar to the first example, the ScheduleExpression and ScheduleTargetInput help users to define the correct input types. The universal target is one of the options allowed by the scheduler-target constructs that allow users to perform SDK API operations on AWS services such as Amazon EC2.

The ScheduleGroup construct is used to create the group, which is used as a property on the Schedule construct. The group implements convenience methods that allow simplified permissions management. The example above grants read permissions for the schedule group to an Amazon Lambda function, which is applied to the resources without additional configuration.

Community Shout-Outs

The CDK team would like to give a huge shout-out to the awesome members of the community that contributed to this construct to help get it where it is today! Thank you to:

sakurai-ryo

Kenta Goto

Hiroki Yamazaki

Joshua Weber

Manuel

Jacco Kulman

Conclusion

In this post, we introduced the general availability of the AWS CDK L2 construct for Amazon EventBridge Scheduler and Targets. We showcased practical implementations of the new construct, leveraging two example use cases. For more details on the EventBridge Scheduler L2 construct and examples of its use, see the Scheduler CDK Documentation.

If you’re new to AWS CDK and want to get started, we highly recommend checking out the CDK documentation and the CDK workshop.

Serverless ICYMI 2025 Q1

Post Syndicated from Julian Wood original https://aws.amazon.com/blogs/compute/serverless-icymi-2025-q1/

Welcome to the 28th edition of the AWS Serverless ICYMI (in case you missed it) quarterly recap. At the end of a quarter, we share the most recent product launches, feature enhancements, blog posts, videos, live streams, and other interesting things that you might have missed!

In case you missed our last ICYMI, check out what happened in Q4 2024 here.

Serverless calendar Q1 2025

Serverless calendar Q1 2025

AWS Step Functions

The AWS Step Functions team continues to improve developer experience. Workflow Studio is now available within Visual Studio Code (VS Code) through the AWS Toolkit extension.

AWS Step Functions in IDE

AWS Step Functions in IDE

You can now design, test, and deploy your Step Functions workflows without leaving your IDE. The extension provides a drag-and-drop interface with all the familiar Workflow Studio capabilities, making it even easier to build state machines locally.

To get started, install the AWS Toolkit for Visual Studio Code and visit the user guide on Workflow Studio integration.

Step Functions private integrations now allows you to integrate applications seamlessly across private networks, on-premises infrastructure, and cloud platforms. Learn more in a blog post and explanation video.

AWS Step Functions private integrations video

AWS Step Functions private integrations video

Step Functions now integrates with 36 more AWS services that support user messaging capabilities. You can orchestrate notifications through Amazon SNS, Amazon SQS, Amazon EventBridge, Amazon Pinpoint, and more, all using the optimized integrations you’re familiar with.

Step Functions has increased the default quota for state machines and activities from 10,000 to 100,000 per AWS account. This tenfold increase means you can create more workflows to automate your business processes without worrying about hitting quota limits.

Distributed Map is expanding capabilities by adding support for JSON Lines (JSONL) format. JSONL, a highly efficient text-based format, stores structured data as individual JSON objects separated by newlines, making it particularly suitable for processing large datasets.

AWS Step Functions Distributed Map

AWS Step Functions Distributed Map

Distributed Map can also process data from a broader range of delimited file formats stored in Amazon S3 and offers new output transformations for greater control over result formatting.

Developer Tools

Serverless Land patterns are now available directly within VS Code.

You no longer need to switch between your IDE and external resources when building serverless architectures. Browse, search, and implement pre-built serverless patterns directly in VS Code.

Example Serverless Pattern

Example Serverless Pattern

AWS Lambda

Learn how AWS Lambda handles billions of invocations.

AWS Lambda asynchronous invocations

AWS Lambda asynchronous invocations

This blog post provides recommendations and insights for implementing highly distributed applications based on the Lambda service team’s experience building its robust asynchronous event processing system. It dives into challenges you might face, solution techniques, and best practices for handling noisy neighbors.

A new video walks through using the enhanced local IDE experience for Lambda developers.

AWS Lambda new IDE experience

AWS Lambda new IDE experience

The VS Code extension for Lambda now supports live tailing of CloudWatch Logs directly in your IDE following on from previous support for Live Tail in the Lambda console. Watch logs in real-time as your functions execute, making debugging and troubleshooting more efficient than ever.

You can now enable Application Performance Monitoring (APM) for Java and .NET runtimes using Amazon CloudWatch Application Signals.

Amazon CloudWatch Application Signals for Java and .NET AWS Lambda runtimes

Amazon CloudWatch Application Signals for Java and .NET AWS Lambda runtimes

This provides deep visibility into your function’s performance, including method-level tracing, memory profiling, and automated anomaly detection.

Amazon Bedrock features

Multi-agent collaboration is now available in Bedrock as a preview, enabling you to create systems where multiple AI agents work together to solve complex problems. Agents can specialize in different domains, share context, and coordinate their actions to achieve goals that would be difficult for a single agent.

RAG evaluation is now generally available. This provides metrics to assess and improve your retrieval augmented generation pipelines. GraphRAG for Bedrock Knowledge Bases is now generally available, allowing you to enhance retrievals with graph-based context.

Amazon Bedrock Flows now supports multi-turn conversations, allowing you to build dynamic AI applications that maintain context across multiple user interactions. Bedrock data automation is now generally available, streamlining the process of preparing, ingesting, and maintaining data for your GenAI applications. Bedrock now offers LLM-as-a-judge capability for model evaluation, providing automated assessment of model outputs without requiring human reviewers. Compare different models or prompt strategies against your specific criteria at scale.

Bedrock’s capabilities are now integrated into the Amazon SageMaker Unified Studio, creating a seamless experience for machine learning practitioners who want to incorporate foundation models into their workflows. Access Bedrock models, fine-tuning, and evaluation directly from SageMaker.

Amazon Nova is a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry leading price-performance. Nova has expanded its tool use and converse API capabilities, making it easier for developers to build AI assistants that can use external tools to complete tasks.

Amazon Bedrock Guardrails image content filters are now generally available. Define and enforce boundaries for your AI applications with controls for both text and image content, ensuring outputs align with your organization’s policies.

Bedrock Knowledge Bases now supports using your existing OpenSearch clusters as the vector storage backend. This integration allows you to leverage your investments in OpenSearch while benefiting from the managed RAG capabilities of Bedrock.

New Amazon Bedrock models

  • Anthropic’s Claude 3.7 Sonnet hybrid reasoning allows you to toggle between standard and extended thinking modes. In standard mode, it functions as an upgraded version of Claude 3.5 Sonnet. While in extended thinking mode, it employs self-reflection to achieve improved results across a wide range of tasks.
  • DeepSeek R1, an advanced model specialized in research and scientific reasoning excels at complex problem-solving tasks and technical content generation.
  • Cohere Embed 3 models are now available in both multilingual and English-specific versions. These embedding models support text and images, providing more accurate representation for multimodal content and improving retrieval augmented generation (RAG) applications.
  • Ray2, Luma AI’s new visual AI model is capable of creating realistic visuals with fluid, natural movement. You can use it for image understanding, 3D scene reconstruction, and visual content generation, opening new possibilities for immersive and visual applications.
  • Bedrock now supports fine-tuning of Meta’s latest Llama 3.2 models. These upgraded models deliver improved performance across reasoning, coding, and multilingual tasks while being more efficient with computational resources.

Amazon Q Developer

Amazon Q Developer is now available as a CLI agent, bringing AI-assisted development to the command line. Get contextual recommendations, generate shell commands, and solve coding problems without leaving your terminal.

Amazon Q CLI

Amazon Q CLI

Amazon Q Developer transformation now supports upgrading Java applications using Maven to Java 21. It offers enhanced code suggestions, refactoring, and optimization recommendations for applications using the latest Java features, like virtual threads and pattern matching.

AWS AppSync

AWS AppSync Events now supports events publishing for WebSocket APIs, enabling real-time publish-subscribe functionality. This feature makes it easier to build applications requiring instant updates, like chat applications, collaborative tools, and real-time dashboards.

AWS AppSync Events

AWS AppSync Events

There are new AWS Cloud Development Kit (AWS CDK) L2 constructs for AppSync WebSocket APIs. These make it simpler to define and deploy real-time APIs using infrastructure as code. These high-level constructs handle the details of WebSocket connections, authorization, and messaging patterns.

Amazon SNS

Amazon SNS now supports high throughput mode for SNS FIFO topics, with default throughput matching SNS standard topics. When you enable high-throughput mode, SNS FIFO topics will maintain order within message group, while reducing the de-duplication scope to the message-group level.

Amazon EventBridge

Amazon EventBridge now supports direct delivery to targets across AWS accounts, simplifying multi-account architectures. This reduces latency and improves reliability when routing events between accounts in your organization.

Amazon EventBridge cross account

Amazon EventBridge cross account

The EventBridge console now features event source discovery, making it easier to find and visualize available event sources in your AWS environment. This tool helps you identify potential event producers and understand the event schemas they emit.

AWS Amplify

AWS Amplify now offers a TypeScript data client optimized for server-side Lambda functions, providing type-safe access to your data sources. This client reduces code complexity and improves reliability when working with databases and APIs in server environments.

Serverless compute blog posts

January

February

March

Serverless Office Hours weekly livestream

February

March

Still looking for more?

The Serverless landing page has more information. The Lambda resources page contains case studies, webinars, whitepapers, customer stories, reference architectures, and even more Getting Started tutorials.

You can also follow the Developer Advocacy team members who work on Serverless to see the latest news, follow conversations, and interact with the team.

And finally, visit the Serverless Land  for all your serverless needs.

Validate Your Lambda Runtime with CloudFormation Lambda Hooks

Post Syndicated from Matteo Luigi Restelli original https://aws.amazon.com/blogs/devops/validate-your-lambda-runtime-with-cloudformation-lambda-hooks/

Introduction

This post demonstrates how to leverage AWS CloudFormation Lambda Hooks to enforce compliance rules at provisioning time, enabling you to evaluate and validate Lambda function configurations against custom policies before deployment. Often these policies impact the way a software should be built, restricting language versions and runtimes. A great example is applying those policies on AWS Lambda, a serverless compute service for running code without having to provision or manage servers. While AWS Lambda already manages the deprecation of runtimes, preventing you from deploying unsupported runtimes, organizations may need to provide and enforce their specific compliance rules not directly linked to the deprecation of a specific language version.

Introducing Lambda Hooks

AWS CloudFormation Lambda Hooks are a powerful feature that allows developers to evaluate CloudFormation and AWS Cloud Control API operations against custom code implemented as Lambda functions. This capability enables proactive inspection of resource configurations before provisioning, enhancing security, compliance, and operational efficiency.

Lambda Hooks provide a mechanism to intercept and evaluate various CloudFormation operations, including resource operations, stack operations, and change set operations (they can also be used with Cloud Control API, but in this post we’re focusing on CloudFormation). By activating a Lambda Hook, CloudFormation creates an entry in your account’s registry as a private Hook, allowing you to configure it for specific AWS accounts and regions. When configuring Lambda Hooks, you can specify one or more Lambda functions to be invoked during the evaluation process. These functions can be in the same AWS account and Region as the Hook, or in another Account you own, provided proper permissions are set up. The evaluation process occurs at specific points in the CloudFormation Stack lifecycle. For instance, during stack creation, update, or deletion, the configured Lambda functions are invoked to assess the proposed changes against your defined compliance rules. Based on the evaluation results, the hook can either block the operation or issue a warning, allowing the operation to proceed.

Lambda Hooks evaluate resources before they are provisioned through CloudFormation, providing a pre-emptive layer of governance. This means that non-compliant resources are caught and prevented from being deployed, rather than requiring retroactive fixes. By leveraging Lambda Hooks, organizations can automate and standardize their compliance checks across all AWS accounts and regions. This centralized approach to policy enforcement ensures consistency and reduces the overhead of managing compliance manually.

Solution Overview

The following sections demonstrate a practical use case for AWS CloudFormation Lambda Hooks, focusing on enforcing compliance rules on AWS Lambda runtimes.

Meet AnyCompany, a forward-thinking enterprise with a robust set of compliance rules governing their software development practices. Among these rules is a strict policy on the use of specific AWS Lambda runtimes.

As they continue to embrace serverless architecture, AnyCompany faces a challenge: how to prevent the deployment of Lambda functions that use non-compliant runtimes. Given their commitment to AWS CloudFormation for deploying Lambda functions, AnyCompany is keen to leverage the power of AWS CloudFormation Lambda Hooks.

We’ll explore the setup process, demonstrate the hook in action, and discuss the broader implications for maintaining compliance in a dynamic cloud environment.

Architecture

The following architecture highlights the implementation of the Lambda Hook. In this implementation, we are using AWS CloudFormation Lambda Hooks to intercept the deployment of Lambda Functions and perform the compliance checks on these resources. The Lambda Hook will interact with an AWS Lambda Function, which will perform the compliance checks. Finally, we’re using AWS Systems Manager Parameter Store to store the Configuration Parameter which contains the list of permitted Lambda Runtimes.

Figure 1: Architecture of the Solution

Figure 1: Architecture of the Solution

  1. A Developer (or a CI/CD pipeline) deploys a CloudFormation stack containing Lambda functions.
  2. CloudFormation invokes the respective Lambda Hook, which is configured to intercept operations on AWS Lambda Resources. We are setting this hook to “FAIL” deployment in case checks are not successful.
  3. The Lambda Hook checks if the runtime of the Lambda is admitted or violates Company’s compliance. To do this, it checks if the runtime is present on a pre-configured list of admitted runtimes saved as Parameter in AWS Systems Manager Parameter Store. Keep in mind that we’re using SSM Parameter Store to store the configuration for this specific example, but other alternatives may be viable as well (Amazon DynamoDB, AWS Secrets Manager, or AppConfig lambda-function-settings-check Preventive Rule)
  4. The Lambda Hook, after checking runtime compliance, replies:
    • With a failure, if the Lambda runtime is not compliant
    • With a success, if the Lambda runtime is compliant
  5. Depending on the response of the Lambda Hook, the deployment may or not take place.

Repository Structure

You can find all the code for this solution at this link. Here’s the repository structure:

.
├── README.md
├── deploy.sh
├── cleanup.sh
├── hook-lambda
│ ├── index.ts
│ ├── package.json
│ ├── services
│ │ └── parameter-store.ts
│ └── tsconfig.json
├── sample
│ ├── deploy_sample.sh
│ ├── cleanup_sample.sh
│ └── lambda_template.yml
└── template.yml
  • hook-lambda: directory containing all the code related to the CloudFormation Lambda Hook (Validation Lambda Function, and the CloudFormation template for the Solution)
  • sample: directory containing the code of the sample used to test the CloudFormation Lambda Hook
  • deploy.sh: utility script to deploy the Solution via AWS CLI
  • cleanup.sh: utility script to clean up the AWS CloudFormation Hook infrastructure via the AWS CLI
  • template.yml: AWS CloudFormation Template containing all the AWS Resources involved in the Solution

Prerequisites

You must have the following prerequisites for this solution:

  • An AWS account or sign up to create and activate one.
  • The following software installed on your development machine:
  • Install the AWS Command Line Interface (AWS CLI) and configure it to point to your AWS account.
  • Install Node.js and use a package manager such as npm.
  • Appropriate AWS credentials for interacting with resources in your AWS account.

Walkthrough

Creating the AWS Lambda Validation Function – Lambda Code

The CloudFormation Lambda Hook interacts with a specific Lambda (referred to as Validation Lambda throughout the rest of this post), which gets invoked during CloudFormation CREATE and UPDATE STACK operations involving Lambda Functions. The goal is to check if these Lambda functions have runtimes that comply with AnyCompany’s rules.

Below is the detailed description of the steps that the Validation Lambda function handler follows (the code is written in Typescript).

First, the Validation Lambda retrieves an environment variable containing the SSM Parameter Store parameter name which contains the compliant runtimes list. Additionally, safety checks ensure that only Lambda Resources are considered and that their Runtime property is defined.

Note that both safety checks could be skipped, since the Hook should already be configured to interact only with Lambda Resources and the Lambda’s Runtime property is always required. However, they remain in place to demonstrate how to retrieve this information from the Lambda Hook event in your handler.

const parameterName = process.env.PERMITTED_RUNTIMES_PARAM;
if (!parameterName) {
	throw new Error('Permitted Runtimes Parameter is not set');
}

const resourceProperties = event.requestData.targetModel.resourceProperties;
// Check if this is a Lambda function resource
if (event.requestData.targetType !== 'AWS::Lambda::Function') {
console.log("Resource is not a Lambda function, skipping");
	return {
		hookStatus: 'SUCCESS',
		message: 'Not a Lambda function resource, skipping validation',
		clientRequestToken: event.clientRequestToken
	}
}

// Check runtime version compliance
const runtime = resourceProperties.Runtime;
if (!runtime) {
	console.log("Runtime not defined, failing");
	return {
		hookStatus: 'FAILURE',
		errorCode: 'NonCompliant',
		message: 'Runtime is required for Lambda functions',
		clientRequestToken: event.clientRequestToken
	}
}

Then the Validation Lambda retrieves the value of the Configuration Parameter from SSM Parameter Store through a utility class called ParameterStoreService. For this post, consider that the value inside that Configuration Parameter is a list of strings, where each string contains one of the possible Lambda runtime values that you can find here (e.g. nodejs22.x,nodejs20.x,python3.11,python3.10,java17,java11,dotnet6). After retrieving the value, the Validation Lambda checks if the runtime of the Lambda Resource complies with the configured admitted runtimes. If the runtime is not compliant, you’ll receive a properly formatted response with FAILURE as hookStatus, otherwise the response will contain a SUCCESS hookStatus.

// Retrieve configuration from Parameter Store
const compliantRuntimes = await parameterStoreService.getParameterFromStore(parameterName);

// Check if Lambda runtime is permitted or not
if (!compliantRuntimes.includes(runtime)) {
console.log("Runtime " + runtime + " not compliant ");
	return {
		hookStatus: 'FAILURE',
		errorCode: 'NonCompliant',
		message: `Runtime ${runtime} is not compliant. Please use one of: ${compliantRuntimes.join(', ')}`,
		clientRequestToken: event.clientRequestToken
	}
}

return {
	hookStatus: 'SUCCESS',
	message: 'Runtime version compliance check passed',
	clientRequestToken: event.clientRequestToken
}

For more information about the possible response values of CloudFormation Lambda Hooks Lambda, have a look at this link.

Creating the validation Lambda – Lambda CloudFormation definition

The Validation Lambda function will be deployed via CloudFormation, in the same Stack with the CloudFormation Lambda Hook definition and the AWS Systems Manager Parameter Store Parameter. Here’s the fragment of the CloudFormation Template containing its definition:

# Lambda Function
ValidationFunction:
	Type: AWS::Lambda::Function
	Properties:
		Handler: index.handler
		Role: !GetAtt LambdaExecutionRole.Arn
		Code:
			S3Bucket: !Ref DeploymentBucket
			S3Key: hook-lambda.zip
		Runtime: nodejs22.x
		Timeout: 60
		MemorySize: 128
		Environment:
			Variables:
				PERMITTED_RUNTIMES_PARAM: !Ref ParameterStoreParamName

You’ll need to associate an IAM Role with proper permissions to access the AWS Systems Manager Parameter Store Parameter:

# Lambda Function Role
LambdaExecutionRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Principal:
              Service: lambda.amazonaws.com
            Action: sts:AssumeRole
      ManagedPolicyArns:
        - arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole
		
# IAM Policy to access Parameter Store
ParameterStoreAccessPolicy:
    Type: AWS::IAM::RolePolicy
    Properties:
      RoleName: !Ref LambdaExecutionRole
      PolicyName: ParameterStoreAccess
      PolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Action:
              - ssm:GetParameter
            Resource: !Sub arn:aws:ssm:${AWS::Region}:${AWS::AccountId}:parameter${ParameterStoreParamName}

Creating the CloudFormation Lambda Hook

At this point, you only need to author a proper CloudFormation Lambda Hook. The Hook requires:

  • To be activated during the CREATE and UPDATE CloudFormation operations,
  • To consider only AWS::Lambda::Function CloudFormation resources
  • To act during Pre Provisioning of CloudFormation templates
  • To target Stack and Resource Operations
  • Target the already defined Lambda Validation function

Here’s the definition in the CloudFormation template:

# Lambda Hook
ValidationHook:
    Type: AWS::CloudFormation::LambdaHook
    Properties:
      Alias: Private::Lambda::LambdaResourcesComplianceValidationHook
      LambdaFunction: !GetAtt ValidationFunction.Arn
      ExecutionRole: !GetAtt HookExecutionRole.Arn
      FailureMode: FAIL
      HookStatus: ENABLED
      TargetFilters:
        Actions:
          - CREATE
          - UPDATE
        InvocationPoints:
          - PRE_PROVISION
        TargetNames:
          - AWS::Lambda::Function
      TargetOperations:
        - RESOURCE
        - STACK

Please note that the above template contains a reference to an IAM Role because the Hook requires proper permissions to call the target (Lambda Function). Here’s the IAM Role definition:

# Hook Execution Role
HookExecutionRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Principal:
              Service: hooks.cloudformation.amazonaws.com
            Action: sts:AssumeRole

# IAM Policy for Lambda Invocation
LambdaInvokePolicy:
    Type: AWS::IAM::RolePolicy
    Properties:
      RoleName: !Ref HookExecutionRole
      PolicyName: LambdaInvokePolicy
      PolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Action:
              - lambda:InvokeFunction
            Resource: !GetAtt ValidationFunction.Arn

Configuring the compliant runtimes – Using Systems Manager Parameter Store

AWS Systems Manager Parameter Store is a secure, hierarchical storage service for configuration data management and secrets management, allowing users to store and retrieve data such as configurations, database strings etc. as parameter values.

In this specific example, we’ll leverage Parameter Store to store our permitted Lambda runtimes configuration. This configuration value is a StringList parameter, containing a comma-separated list of permitted runtimes. Here’s the fragment of the CloudFormation template that defines the Parameter:

# Parameter Store Parameter
ConfigParameter:
    Type: AWS::SSM::Parameter
    Properties:
      Name: !Ref ParameterStoreParamName
      Type: StringList
      Value: !Ref ParameterStoreDefaultValue
      Description: "Configuration for Lambda Hook"

Please note the usage of CloudFormation parameters for the ‘Name’ and ‘Value’ properties, allowing for dynamic input when deploying the CloudFormation template.

Deploying the Solution

To deploy the solution you can leverage the script deploy.sh in the root folder of the repository. This script will perform the following actions:

  • Compile and build the Validation Lambda Function
  • Create an Amazon S3 Bucket to store the CloudFormation Template
  • Upload the CloudFormation template and Lambda code to the S3 Bucket
  • Deploy the CloudFormation template

Testing the Lambda Hook

To test the CloudFormation Lambda Hook, deploy a simple testing CloudFormation template containing a Hello World Lambda function. First, test the Lambda configured with a permitted Lambda runtime, then modify the template to configure the Lambda with a non-compliant runtime.

Here’s the initial definition of the testing CloudFormation Template:

# Lambda Function
HelloWorldFunction:
    Type: AWS::Lambda::Function
    Properties:
      FunctionName: hello-world-function
      Runtime: nodejs22.x
      Handler: index.handler
      Role: !GetAtt LambdaExecutionRole.Arn
      Code:
        ZipFile: |
          exports.handler = async (event, context) => {
              console.log('Hello World!');
              const response = {
                  statusCode: 200,
                  body: JSON.stringify('Hello World!')
              };
              return response;
          };
      Timeout: 30
      MemorySize: 128

Please note that the Runtime value is nodejs22.x, which is currently in the list of permitted runtimes. The expectation is that the deployment of this function will succeed.

Deploy this template via the AWS CLI:

aws cloudformation deploy \
--template-file ./lambda_template.yml \
--capabilities CAPABILITY_IAM \
--stack-name lambda-sample

Check the CloudFormation Console:

CloudFormation Console showing successful Stack deployment

Figure 2: CloudFormation Console showing successful Stack deployment

As expected, the deployment was successful. You can also see that the CloudFormation Lambda Hook has been invoked by taking a look at the CloudWatch Logs:

Validation Lambda Function Logs with successful validation

Figure 3: Validation Lambda Function Logs with successful validation

Now modify the original sample Template in order to set a Lambda Runtime which is not inside the list of permitted runtimes:

# Lambda Function
HelloWorldFunction:
    Type: AWS::Lambda::Function
    Properties:
      FunctionName: hello-world-function
      Runtime: nodejs18.x
      Handler: index.handler
      Role: !GetAtt LambdaExecutionRole.Arn
      Code:
        ZipFile: |
          exports.handler = async (event, context) => {
              console.log('Hello World!');
              const response = {
                  statusCode: 200,
                  body: JSON.stringify('Hello World!')
              };
              return response;
          };
      Timeout: 30
      MemorySize: 128

Deploy this template via AWS CLI with the same command used before and check the CloudFormation Console:

CloudFormation Console showing failed Stack deployment due to Hook intervention

Figure 4: CloudFormation Console showing failed Stack deployment due to Hook intervention

As expected, the deployment was not successful. The CloudFormation Lambda Hook has been invoked, and since the Lambda Runtime was not present in the permitted runtimes list, the deployment failed.

You can also see that the hook failed In the CloudWatch Logs:

Validation Lambda Function Logs with validation error

Figure 5: Validation Lambda Function Logs with validation error

Cleaning up

To clean up the resources related to the sample, you can run the script cleanup_sample.sh inside the sample folder. This script will delete the sample’s CloudFormation Template through the AWS CLI.

To cleanup the resources related to the solution described above and based on AWS CloudFormation Lambda Hook, you can leverage the script cleanup.sh in the root folder of the repository. This script will perform the following actions:

  • Delete the CloudFormation Stack
  • Empty the S3 Bucket used for the deployment of the Stack
  • Delete the S3 Bucket

Conclusion

In this post, you explored the implementation of CloudFormation Hooks to enforce runtime compliance in Lambda functions across your AWS infrastructure. By leveraging the Lambda hook’s capabilities, you learned how to create a preventative control that validates Lambda runtime configurations before deployment.

By activating the Lambda hook and implementing a custom Lambda function validator, you established an automated mechanism to ensure that only compliant runtimes are used within your organization’s Lambda functions during CloudFormation stack creation and updates. The solution’s integration with common development tools like AWS CLI, AWS SAM, CI/CD pipelines, and AWS CDK makes it straightforward to implement these controls within existing workflows, eliminating the need for manual runtime checks or post-deployment remediation.

The validation approach demonstrated in this post extends beyond Lambda runtimes and can be adapted to different AWS Resources supported by CloudFormation, allowing you to enforce policies on different infrastructure components offered by AWS.

About the author

Matteo Luigi Restelli

Matteo Luigi Restelli is an AWS Sr. Partner Solutions Architect. He mainly focuses on Italian Consulting AWS Partners and is also specialized in Infrastructure as Code, Cloud Native App Development and DevOps. Outside of work, he enjoys swimming, rock & roll music and learning something new everyday especially in Computer Science space.

Stella Hie

Stella Hie is a Sr. Product Manager Technical for AWS Infrastructure as Code. She focuses on proactive control and governance space, working on delivering the best experience for customers to use AWS solutions safely. Outside of work, she enjoys hiking, playing piano, and watching live shows.

From virtual machine to Kubernetes to serverless: How dacadoo saved 78% on cloud costs and automated operations

Post Syndicated from Andreas Gehrig original https://aws.amazon.com/blogs/architecture/from-virtual-machine-to-kubernetes-to-serverless-how-dacadoo-saved-78-on-cloud-costs-and-automated-operations/

dacadoo is a global Swiss-based technology company that develops solutions for digital health engagement and health risk quantification. Their products include a software-as-a-service (SaaS)-based digital health engagement platform that uses behavioral science, AI, and gamification to help end users improve their health outcomes.

The company embarked on a journey to modernize an API to quantify health and lifestyle data plus a risk engine to calculate mortality and morbidity probabilities based on years of scientific research data.

To transform a virtual machine–based API service into a globally redundant, scalable health score and risk calculation solution dacadoo chose Amazon Web Services (AWS) technology. The service handles highly sensitive health data from a global customer base and must comply with regional regulations.

The result is a cost reduction of 78% and an infrastructure maintenance effort of less than an hour per year , allowing dacadoo to deliver and operate more AWS infrastructure without scaling its site reliability engineering (SRE) team, thanks to a high level of automation and an agile mindset.

In this post, we walk you step-by-step through dacadoo’s journey of embracing managed services, highlighting their architectural decisions as we go.

Background

The solution architecture went through a three-stage journey:

  1. Incubation – Single virtual machine on premises with disaster recovery (DR) in Switzerland
  2. Global and scalable – Multiple global Kubernetes clusters
  3. Operational excellence – Fully serverless and geo-redundant on AWS

Stage 1: Incubation with a virtual machine

After years of scientific research and development, the service was launched, running on a single on-premises virtual machine that used hypervisor technology to provide disaster recovery (DR). However, it had no high availability (HA) capability and it required manual recovery.

The application serving the API requests and the NoSQL database were both running on the same host. Software deployment and operating system maintenance were performed manually using Secure Shell (SSH)—a typical low-automation setup that also included downtime.

The following architecture diagram shows a virtual machine encompassing the monolithic application and its database.

Monolithic architecture

Challenges

A single virtual machine was quick to set up and inexpensive to operate, but it had considerable shortcomings. The health API was only available in Switzerland, infrastructure maintenance was performed manually, and software deployment was handled manually. Additionally, database backups were done using virtual machine snapshots, uptime monitoring only, and testing was conducted on the developer workstation.

Stage 2: Global and scalable with Kubernetes

At that time, dacadoo made a strategic decision to heavily invest in Kubernetes for managing containerized workloads on a global scale. As part of this technology rollout, the health score and risk service were migrated to Kubernetes.

Due to the geographically distributed customer base and low latency requirements, three Kubernetes clusters were deployed, one on each continent. The NoSQL database was hosted in proximity to the workload to reduce service latency and keep the migration effort low.

To reduce the operational maintenance, the NoSQL database was integrated as a SaaS offering, and monitoring was centralized using Datadog.

All cloud infrastructure was provisioned exclusively with Terraform, covering the Kubernetes cluster, NoSQL database , and integration with GitLab and Datadog.

dacadoo containerized the API service and used Gitlab continuous integration and continuous deployment (CI/CD) pipelines to deploy multiple environments and clusters on a global hyperscaler.

In retrospect, this was a typical replatform modernization project from virtual machine to Kubernetes, with a high level of automation and a SaaS-first approach.

The following diagram is the architecture for the container solution with managed NoSQL database.

Containers architecture

Challenges

The service faced several challenges, including increased costs from deploying three regional Kubernetes clusters across three environments, resulting in 27 cluster nodes and additional expenses from managing NoSQL database SaaS instances for each cluster. The complexity of CI/CD pipelines for multi-environment multi-cluster deployments added to the difficulty. Significant operational effort was required to keep infrastructure and Kubernetes components up to date.

Stage 3: Operational excellence with serverless

The Kubernetes-based architecture met the requirements, but some features in the dacadoo API service backlog needed to fit better with the application architecture at the time.

This was the right moment to take a holistic view of the infrastructure and software architecture and refactor the solution according to the latest AWS technologies and best practices, the next frontier for dacadoo’s engineering team.

Solution requirements

Requirements for the solution refactoring were as follows:

  • Keep the functionality of the API unmodified
  • Constrain data processing to a region of choice for compliance with local data protection laws
  • Avoid weekly patch cycles by exclusively using managed serverless services
  • Reduce costs by choosing services with a pay-as-you-go billing model
  • Delegate authentication to a dedicated service
  • Use an established web framework with an extensive ecosystem

Refactoring the apps

The API service has two components: a developer portal and the health score and risk calculations API. The database is only required for API keys, algorithm parameters, quotas, and usage statistics. Health data is processed regionally by the compute layer but not persisted, opening the door for a distributed database: Amazon DynamoDB global tables is the perfect fit for the solution. Writes are distributed to all connected Regions, whereas reads are local, providing low latency for complying with dacadoo service level agreements (SLAs).

The developer portal is a web UI with API documentation and API key management features. AWS Lambda is a great fit because it scales automatically and has a pay-per-request billing model.

The health and risk API uses algorithms implemented in the C programming language for short bursting, compute-intense simulations. These calls are wrapped by a REST API using the Python FastAPI framework. These characteristics make AWS Lambda a great fit.

Serverless architecture

HTTP requests are routed to the Lambda functions using Amazon API Gateway with AWS WAF for protection from malicious requests and attacks. Static assets are served from an Amazon Simple Storage Service (Amazon S3) bucket through API Gateway. The additional features of Amazon CloudFront aren’t required, and Amazon S3 reduces the complexity.

Amazon Route 53 provides a powerful feature known as latency-based routing, which allows it to direct DNS queries to the endpoint that offers the lowest latency for the requester.

This feature provides Regional high availability for API users without data processing location requirements. Alternatively, the user can call specific Regional endpoints to make sure requests are processed in the desired Region.

API authorization is HTTP header-based and is performed in the application with data stored in Amazon DynamoDB.

The following diagram is the architecture for a geo-redundant fully serverless solution.

Serverless architecture

With a dacadoo SRE team proficient in Python, they opted for Pulumi for its advanced features such as programming language flow control constructs, powerful configuration capabilities, and multi-cloud support.

For continuous integration, GitLab CI compiles the algorithm library, tests the FastAPI applications and packages everything. The application deployment is just an update of the AWS Lambda, a simple and reliable workflow.

Summary

The solution evolved from a managed infrastructure setup, where the customer held most of the responsibility, to an AWS managed service architecture.

Infrastructure provisioning evolved from manual, error-prone processes to powerful code-driven workflows in Pulumi. The SRE needed to enhance their software engineering skills to adopt Pulumi, transitioning from configuration-based approaches to designing and maintaining an infrastructure code base using object-oriented Python. This was part of dacadoo’s investment in the SRE team and broader modernization efforts. The serverless architecture enabled a GitOps engineering culture focused on productivity.

The transformation maximized scalability and availability while reducing costs and operational effort:

Virtual machine

  • Scalability: Low
  • Availability: Best effort
  • Infrastructure costs: Low
  • Maintenance effort: High

Kubernetes

  • Scalability: High
  • Availability: 99.95%
  • Infrastructure costs: High
  • Maintenance effort: Medium

Serverless

  • Scalability: Very high
  • Availability: 99.999% (with failover to another AWS Region)
  • Infrastructure costs: Low
  • Maintenance effort: Very low

The global redundancy elevates availability to an impressive 99.999% while keeping the costs low.

Conclusion

Migrating from a virtual machine to Kubernetes and ultimately to AWS Lambda demonstrates the progression of cloud engineering toward enhanced efficiency and scalability.

Each step in this journey reduced the complexity of managing resources while increasing flexibility and automation. Transitioning dacadoo’s API service to a fully serverless, geo-redundant architecture not only advanced the platform but also upskilled engineers, maintained a lean SRE team, and kept infrastructure costs low. Get started with your own AWS serverless solution.


About the Authors

Optimizing network footprint in serverless applications

Post Syndicated from Chris McPeek original https://aws.amazon.com/blogs/compute/optimizing-network-footprint-in-serverless-applications/

This post is authored by Anton Aleksandrov, Principal Solution Architect, AWS Serverless and Daniel Abib, Senior Specialist Solutions Architect, AWS

Serverless application developers may commonly encounter scenarios where they need to transport large payloads, especially when building modern cloud applications that need rich data. Examples include analytics services with detailed reports, e-commerce platforms with extensive product catalogs, healthcare applications transmitting patient records, or financial services aggregating transactional data.

Many serverless services have a well-defined maximum payload size. For example, AWS Lambda maximum request/response payload size is 6 MB, and Amazon Simple Queue Service (Amazon SQS) and Amazon EventBridge maximum message size is 256 KB. In this post, you will learn how to use data compression techniques to reduce your network footprint and transport larger payloads under existing constraints.

Overview

Cloud applications evolve continuously and need to be adjusted frequently for new requirements, such as new business features or new Service Level Objectives (SLO) for higher throughput and lower latency. As new use cases and data patterns are added, it is common to see request and response payload sizes increase. At some point, you might hit the maximum service payload size limits, such as 6 MB for synchronous Lambda function invokes, 10 MB for Amazon API Gateway, and 256 KB for Amazon SQS, EventBridge, and asynchronous Lambda invokes.

There are several techniques you can apply when dealing with large payloads. If your payloads are tens of MBs or more, or you need to transport large binary objects with API Gateway, you can store the payload on Amazon Simple Storage Service (Amazon S3) and use pre-signed URLs for clients to directly upload and download from S3.

A sample of architecture for handling large payloads.

Figure 1. A sample of architecture for handling large payloads

Lambda function URLs response streaming supports up to 20 MB responses. For handling large messages with services such as SQS or EventBridge, you can store the message in S3 and pass a reference. The downstream consumer will use the reference to download the message directly from S3. One common characteristic of these techniques is that they introduce architectural complexity and may necessitate modifications to your existing solution architecture and data flow patterns.

Furthermore, as your payloads grow in size, you will see increased data transfer costs, especially if your solution is transporting data through Amazon Virtual Private Cloud (VPC) NAT Gateways, VPC endpoints, or sending data across AWS Regions. For example, it is common for VPC-based solutions to have Lambda functions in their architecture. A container running on Amazon Elastic Kubernetes Service (Amazon EKS) might need to invoke a Lambda function, or a VPC-attached Lambda function might need to reach out to the public internet.

Examples of using virtual network appliances with serverless applications.

Figure 2. Examples of using virtual network appliances with serverless applications

Both NAT Gateway and VPC Endpoint are billed per GB of data processed, which makes data compression a valuable optimization technique. Go to NAT Gateway pricing and VPC Endpoint pricing for details.

The following sections explore data compression techniques and demonstrate how to apply them in your serverless applications. You can learn how to send larger payloads within the existing payload size boundaries and reduce your network footprint without significant architectural changes. This post discusses compression techniques in the context of Lambda and API Gateway, but the same principles can be applied to other services, such as SQS, EventBridge, and AWS AppSync. Understanding compression concepts better equips you to optimize your application’s data-handling capabilities.

What is data compression?

Compression is a widely used approach to reduce data size in order to improve cost-effectiveness and performance for data storage and transmission. Many tools and frameworks incorporate data compression techniques, such as gzip or zstd. It is thoroughly documented in the official IANA specification and IETF RFC 9110. Browsers such as Chrome and Firefox, HTTP toolkits such as curl and Postman, and runtimes such as Node.js and Python natively handle compression, often without user involvement.

Consider HTTP protocol. When a client wants to send a compressed payload, it specifies it in the Content-Type header. To receive a compressed response, the client specifies supported compression methods in the Accept-Encoding request header.

Accept-Encoding request header specifying supported compression methods.

Figure 3. Accept-Encoding request header specifying supported compression methods

The server compresses the response payload using one of the supported methods and uses the Content-Encoding response header to indicate the method to the client.

Content-Encoding response header specifying compression method.

Figure 4. Content-Encoding response header specifying compression method

This mechanism can accelerate client-server communications by reducing the number of bytes transmitted over the network. Compression efficiency depends on the data type. Text-based formats like JSON, XML, HTML, and YAML compress well, while binary data such as PDF and JPEG generally compress less effectively.

Data compression with API Gateway

API Gateway provides built-in compression support. Use the minimumCompressionSize configuration to set the smallest payload size to compress automatically. The value can be between 0 bytes to 10 MB. Compressing very small payloads might actually increase the final payload size, and you should always test with your real payload patterns to determine the optimal threshold.

Handling data compression in API Gateway.

Figure 5. Handling data compression in API Gateway

API Gateway enables clients to interact with your API using compressed payloads through supported content encodings. The compression mechanism works bi-directionally. For JSON payloads, API Gateway seamlessly handles compression and decompression, maintaining compatibility with mapping templates. It decompresses incoming payloads before applying request mapping templates and compresses outgoing responses after applying response mapping templates. This automated compression optimizes data transfer:

  • When sending compressed data, clients supply the appropriate Content-Encoding header. API Gateway handles the decompression and applies configured mapping templates before forwarding the request to the integration.
  • When API Gateway receives an integration response and compression is enabled, it compresses the response payload and returns it to the client, provided that the client has included a matching Accept-Encoding header.

A sample test using the compression technique with API Gateway and JSON payload yielded the following results.

  • Compression disabled. Response size = 1 MB, response latency = 660 ms
  • Compression enabled. Response size = 220 KB, response latency = 550 ms

Compressing data resulted in 78% network footprint reduction and improved latency by 110 ms.

This configuration-based technique uses the API Gateway native compression. However, payloads are decompressed before being delivered to downstream integrations, thus they still remain subject to Lambda’s 6 MB max payload size. To address this, you can configure binaryMediaTypes in the API Gateway to pass compressed payloads to Lambda directly, enabling the function to handle decompression.

CDK code to configure API Gateway for data compression and binary data passthrough.

Figure 6. CDK code to configure API Gateway for data compression and binary data passthrough

Handling compressed data in Lambda functions

The Lambda Invoke API supports payloads in plain-text formats, such as JSON. The maximum payload size is 6 MB for synchronous invocations and 256 KB for asynchronous. Although the Invoke API supports uncompressed text-based payloads, you can introduce data compression in your function code and use API Gateway or Function URLs to facilitate content conversion, as illustrated in the following figure.

Transporting compressed payloads in a serverless applications.

Figure 7. Transporting compressed payloads in a serverless applications

Handling data compression in your Lambda function code can be done through libraries commonly embedded in the runtime. The following code snippet shows the compressing response payload using Node.js. Similar techniques can be applied to other runtimes.

Sample code implementing response payload compression in a Lambda function.

Figure 8. Sample code implementing response payload compression in a Lambda function

  • Line 1: Import gzip functionality from the zlib module.
  • Lines 11: Compress and Base64-encode data. Gzip compression, similar to many other compression methods, produces a binary stream. Base64 encoding converts it to the text-based format expected by the Lambda service
  • Lines 13-21: Response object is created with isBase64Encoded=true and response headers telling the client that the response is a gzip-encoded JSON object.

The following screenshot shows the result: 20 MB uncompressed JSON returned from a Lambda function as a 2.5 MB compressed response body. Network footprint reduced by over 80%.

A screenshot from Postman showing the original and compressed payload size.

Figure 9. A screenshot from Postman showing the original and compressed payload size

Using this technique, you can reduce your network footprint and transport payload sizes several times higher than the Lambda maximum payload size.

Using Function URLs with compressed payloads

Transporting compressed payloads through Lambda Function URLs doesn’t necessitate any extra configuration. For handler responses, your code needs to compress and Base64-encode the data as shown in the preceding figure. For invocation requests, the Function URL endpoint recognizes the incoming compressed payload as binary and passes it to your handler as a Base64 encoded string in the event body.

Sample code implementing request payload decompression in a Lambda function.

Figure 10. Sample code implementing request payload decompression in a Lambda function

Trade-offs and testing results

Compressing data in function code is a CPU-intensive activity, potentially increasing invocation duration and, as a result, function cost. This, however, can be balanced by the benefits of data compression. As you’ve seen in previous sections, while compressing data adds compute latency, transporting smaller payloads over the network reduces network latency. The following section summarizes a series of tests performed to estimate the impact of data compression on Lambda function invocation duration, Lambda function invocation cost, and data transfer savings with both NAT Gateway and VPC Endpoint. The tests were performed with several assumptions and randomly generated JSON data. You can see full testing results in the sample GitHub.com repo.

Test results demonstrated that the impact on function latency and cost primarily depends on two key factors: payload size and allocated memory (which determines vCPU capacity). Using a Node.js runtime with ARM architecture as an example, compressing a 1 MB JSON object in a function with 1 GB of allocated memory resulted in 124 ms of added processing time on average. For 10 million invocations, this extra processing time adds approximately $16. At the same time, the compression yielded a 70% reduction in payload size. With the same number of invocations, this translates to approximately $300 in savings when using NAT Gateway and $70 in savings when using VPC Endpoints (depending on the number of Availability Zones (AZs)).

AWS Service pricing is updated regularly, you should always consult the respective pricing pages for the latest information. Moreover, you should conduct your own performance and cost estimates using payloads that represent your workloads. Compression effectiveness varies significantly depending on the data type: payloads with low compression rates might not benefit from this technique.

Sample application

Follow the instructions in this GitHub repository to provision the sample in your AWS account. The project creates two Lambda functions to demonstrate receiving and returning compressed JSON using Function URLs and API Gateway.

The sample shows how to GET and POST JSON payloads using gzip compression to reduce the network footprint by over 80%.

A screenshot from Postman showing the original and compressed payload size.

Figure 11. A screenshot from Postman showing the original and compressed payload size

Conclusion

Data compression enables larger payload transfers and reduces network footprint. It can help to lower network latencies and optimize data transfer costs. When implementing compression within Lambda functions, it is important to consider its CPU-bound nature, which may increase function duration and costs. You should always evaluate the added compute cost against potential data transfer savings to make sure the technique benefits your use case.

Compression is most effective for handling large text-based payloads and when a slight increase in compute latency balanced by reduced network latency is acceptable.

To learn more about Serverless architectures and asynchronous Lambda invocation patterns, see Serverless Land.

Handling billions of invocations – best practices from AWS Lambda

Post Syndicated from Chris McPeek original https://aws.amazon.com/blogs/compute/handling-billions-of-invocations-best-practices-from-aws-lambda/

This post is written by Anton Aleksandrov, Principal Solution Architect, AWS Serverless and Rajesh Kumar Pandey, Principal Engineer, AWS Lambda

AWS Lambda is a highly scalable and resilient serverless compute service. With over 1.5 million monthly active customers and tens of trillions of invocations processed, scalability and reliability are two of the most important service tenets. This post provides recommendations and insights for implementing highly distributed applications based on the Lambda service team’s experience building its robust asynchronous event processing system. It dives into challenges you might face, solution techniques, and best practices for handling noisy neighbors.

Overview

Developers building serverless applications create Lambda functions to run their code in the cloud. After uploading the code, the functions are invoked using synchronous or asynchronous mode.

Synchronous invocations are commonly used for interactive applications that expect immediate responses, such as web APIs. The Lambda service receives the invocation request, invokes the function handler, waits for the handler response, and returns it in response to the original request. With synchronous invocations, the client waits for the function handler to return, and is responsible for managing timeouts and retries for failed invocations.

Synchronous invocation sequence diagram.

Figure 1. Synchronous invocation sequence diagram

Asynchronous invocations enable decoupled function executions. Clients submit payloads for processing without expecting immediate responses. This is used for scenarios like asynchronous data processing or order/job submissions. The Lambda service immediately returns a confirmation for accepted invocation and proceeds to manage further handler invocation, timeouts, and retries asynchronously.

Asynchronous invocation sequence diagram.

Figure 2. Asynchronous invocation sequence diagram

Asynchronous invocations under-the-hood

To accommodate asynchronous invocations, the Lambda service places requests into its internal queue and immediately returns HTTP 202 back to the client. After that, a separate internal poller component reads messages from the queue and synchronously invokes the function.

Asynchronous invocations workflow high-level topology.

Figure 3. Asynchronous invocations workflow high-level topology

The same system also takes care of timeouts and retries in case of handler exceptions. When code execution completes, the system sends handler response to either onSuccess or onFailure destination, if configured.

Asynchronous invocations workflow detailed sequence diagram.

Figure 4. Asynchronous invocations workflow detailed sequence diagram

Scaling highly distributed systems for billions of asynchronous requests presents unique challenges, such as managing noisy neighbors and potential traffic spikes to prevent system overload. Solutions vary by scale – what works for millions of requests may not suite billions. As workload size increases, solutions typically become more complex and costly, so right-sizing the approach is critical and should evolve with changing needs.

Simple queueing

A simple implementation of an asynchronous architecture can start with a single shared queue. This is a common approach for many asynchronous systems, particularly in early stages. It is effective when you’re not concerned about tenant isolation and when capacity planning indicates that a single queue can handle estimated incoming traffic efficiently.

Asynchronous workflow with a single queue.

Figure 5. Asynchronous workflow with a single queue

Even with this simple setup, it is critical to instrument your solution for observability to detect potential issues as soon as possible. You should monitor key metrics like queue backlog size, processing time, and errors, to indicate insufficient processing capacity early. Periods of unexpected traffic spikes and degraded performance may be a signal you have noisy neighbors impacting other tenants.Top of FormBottom of Form

To address this, you can scale your solution horizontally. You can implement random request placement across multiple queues to spread the load. Using a serverless service like Amazon SQS allows you to easily add and remove queues on-demand. One notable benefit of this approach is its simplicity – you do not need to introduce any complex routing mechanisms; requests are evenly spread across the queues. The downside is that you still do not have tenant boundaries. As your system grows, high-volume tenants and noisy neighbors can potentially affect all queues, thus impacting all tenants.

Asynchronous workflow with multiple queues and random request placement.

Figure 6. Asynchronous workflow with multiple queues and random request placement

Intelligent partitioning with consistent hashing

In order to further reduce potential impact, you can partition your tenants using sticky tenant-to-partition assignment with a hashing technique such as consistent hashing. This method uses a hash function to assign each tenant to a queue on a consistent hash ring.

Asynchronous workflow with multiple queues and consistent hashing placement.

Figure 7. Asynchronous workflow with multiple queues and consistent hashing placement

This technique ensures individual tenants stay in their queue partitions without the risk of disturbing the whole system. It helps to solve the problem where a few noisy neighbors have the potential to overflow all queues and as such impact all other tenants.

The consistent hashing approach proved to be efficient and enabled Lambda to offer robust asynchronous invocation performance to customers. As the volume of traffic and number of customers continued to grow, the Lambda service team came up with an innovative shuffle-sharding technique to further optimize the experience, and proactively eliminate any potential noisy-neighbor issues.

Shuffle-sharding

Drawing inspiration from the “The Power of Two Random Choices” paper, the Lambda team explored the shuffle-sharding technique for its asynchronous invocations processing. Using this technique, you shuffle-shard tenants into several randomly assigned queues. Upon receiving an asynchronous invocation, you place the message in the queue with the smallest backlog to optimize load distribution. This approach helps to minimize the likelihood of assigning tenants to a busy queue.

Asynchronous workflow with multiple queues and shuffle-sharding placement.

Figure 8. Asynchronous workflow with multiple queues and shuffle-sharding placement

To illustrate the benefit of this approach, consider a scenario where you’re using а 100 queues. The following formula helps to calculate the number of unique queue shards (combinations), where n is the total number of queues and r is the shard size (the number of queues you’re assigning per tenant).

Formula to show queue shard calculation.

With n=100, r=2 (each tenant is assigned randomly to 2 out of 100 queues), you get 4,950 unique combinations (shards). The probability of two tenants assigned to exactly the same shard is 0.02%. In case of r=3, the number of combinations spikes to 161,700. The probability of two tenants assigned to exactly the same shard drops to 0.0006%.

The shuffle-sharding technique proved remarkably effective. By distributing tenants across shards, the approach ensures that only a very small subset of tenants could be affected by a noisy neighbor. The potential impact is also minimized since each affected tenant maintains access to unaffected queues. As your workloads grow, increasing the number of queues enhances resilience and further reduces the probability of multiple tenants being assigned to the same shard. This significantly lowers the risk of a single point of failure, making shuffle sharding a robust strategy for workload isolation and fault tolerance.

Proactive detection, automated isolation, sidelining

Many distributed services will have a cohort of tenants with legitimate spiky asynchronous invocation traffic. This can be driven by seasonal factors, such as holiday shopping, or periodical batch processing. Recognizing these as real business needs, not malicious actions, you want to improve service quality for these tenants as well, while maintaining the overall system stability. For example, you can further improve solution performance by continuously monitoring queue depth to detect traffic spikes and route traffic to dynamically allocated dedicated queues. When you use Lambda asynchronous invocations, this internal complexity is managed for you by the service, ensuring seamless consumption experience.

Tenant D is automatically reallocated to a dedicated queue.

Figure 9. Tenant D is automatically reallocated to a dedicated queue

Resilience and failure handling

“Everything fails, all the time” is a famous quote from Amazon’s Chief Technology Officer Werner Vogels. Lambda’s distributed and resilient architecture is built to withstand potential outages of its dependencies and internal components to limit the fallout for customers. Specifically for asynchronous invocation processing, the frontend service builds a processing backlog during an outage, allowing the backend to gradually recover without losing any in-flight messages.

Lambda service maintains resilience during component outage.

Figure 10. Lambda service maintains resilience during component outage

Upon recovery, the service gradually ramps up the traffic to process the accumulated backlog. During this time, automated mechanisms are in place to coordinate between system components, preventing inadvertently DDoSing itself.

To further improve the recovery ramp-up process and provide a smooth restoration of normal operations, the Lambda service uses load-shedding technique to ensure fair resource allocation during recovery. While trying to drain the backlog as fast as possible, the service ensures that no single customer ends up consuming an outsized share of the available resources. Adopting such techniques can help you to improve your mean-time-to-recovery (MTTR).

Observability for asynchronous invocations processing

When using the Lambda service for asynchronous processing, you want to monitor your invocations for situational awareness and potential slowdowns. Use metrics such as AsyncEventReceived, AsyncEventAge, and AsyncEventDropped to get insights about internal processing.

AsyncEventReceived tracks the number of async invocations the Lambda service was able to successfully queue for processing. A drop in this metric indicates that invocations are not being delivered to the Lambda service and you should check your invocation source. Potential issues include misconfigurations, invalid access permissions, or throttling. Check your invocation source configuration, logs, and the function resource policy for further analysis.

AsyncEventAge tracks how long has a message spent in the internal queue before being processed by a function. This metric increases when async invocations processing is delayed due to insufficient concurrency, execution failures, or throttles. Increase your function concurrency to process more asynchronous invocations at a time and optimize function performance for better throughput, i.e. by increasing memory allocation to add more vCPU capacity. Experiment with adjusting batch size to enable functions to process more messages at a time. Use invocation logs to identify whether the problem is caused by function code throwing exceptions. Check Throttles and Errors metrics for further analysis.

AsyncEventDropped tracks the number of messages in the internal queue that were dropped because Lambda could not process them. This can be due to throttling, exceeding number of retries, exceeding maximum message age, or function code throwing an exception. Configure OnFailure destination or a dead-letter queue to avoid losing data and save dropped messages for re-processing. Use function logs and metrics described above to investigate whether you can address the issue by increasing function concurrency or allocating more memory.

By monitoring these metrics and addressing the underlying issues, you can ensure that your Lambda functions run smoothly, with minimal event processing delays and failures. You can also enable AWS X-Ray tracing to capture Lambda service traces. The AWS::Lambda trace segment captures the breakdown of the time that Lambda service spends routing requests to internal queues, the time a message spends in a queue, and the time before a function is invoked. This is a powerful tool to get insights into Lambda’s internal processing.

Conclusion

AWS Lambda processes tens of trillions of monthly invocations across more than 1.5 million active customers, demonstrating its exceptional scalability and resilience. Gaining an understanding of the underlying mechanisms of AWS services like Lambda enables you to proactively address potential challenges in your own applications. By learning how these services handle traffic, manage resources, and recover from failures, you can incorporate similar capabilities into your own solutions. For instance, leveraging Lambda’s asynchronous invocation metrics allows you to optimize workflow performance. This knowledge empowers you to implement strategies such as automated scaling, proactive monitoring, and graceful recovery during outages.

See below resources to learn about using queues and shuffle sharding at scale at Amazon

To learn more about Serverless architectures and asynchronous Lambda invocation patterns, see Serverless Land.

Build an enterprise API management solution using Amazon API Gateway

Post Syndicated from Roger Zhang original https://aws.amazon.com/blogs/architecture/build-an-enterprise-api-management-solution-using-amazon-api-gateway/

Enterprises face many challenges when they build and manage application programming interfaces (APIs). These challenges include security controls, version management, traffic control, and usage analytics. As digital businesses expand, a mature API management (APIM) solution is crucial for ensuring scalability, security, and operational efficiency.

This blog post shows how you can use Amazon API Gateway—along with AWS Lambda, Amazon DynamoDB, and other AWS services—to create a comprehensive and customizable APIM solution. This solution addresses the complex requirements of large enterprises managing APIs at scale.

Core features of APIM

API Management (APIM) centralizes the management and publishing of APIs for the entire enterprise, acting as a hub between clients, applications, and administrators on one side, and internal services, external systems, and large language models (LLMs) on the other, as shown in the following figure.

APIM capabilities

The key features of APIM include:

  • Security and governance
    • Authentication, authorization, rate limiting, and security policy enforcement.
    • Helps ensure APIs meet organizational or industry standards.
  • Monitoring and logging
    • Provides monitoring, alarms, and logging to track API performance and troubleshoot issues quickly.
  • Customization and transformation
    • Offers protocol and field transformations, plus orchestration and aggregation.
    • Makes it easier to integrate with different systems and meet various client needs.
  • API lifecycle management
    • Publishing, rollback, version control, and documentation.
    • Streamlines development and maintenance throughout the API lifecycle.
  • Developer and business tools
    • Portals for developers, business owners, and administrators to manage documentation, billing, and analytics.
  • Integration with LLMs
    • Specialized adapters, proxy configurations, and switching to integrate AI models seamlessly.
  • Flexible deployment options
    • Canary releases, pipeline automation, and other advanced release strategies.
    • Helps ensure stable, controlled API updates.

Unified management of multiple API gateways

API Gateway enforces resource limits of 300 resources per gateway, with a hard limit of 600. For enterprises that require more resources, managing multiple gateways individually can be time-consuming and error prone. APIM simplifies this by integrating API Gateway, Lambda, and DynamoDB; creating a centralized platform for managing APIs across multiple gateways. This integration streamlines the process, making it easier to scale and maintain APIs.

API lifecycle management

Managing API versions, publishing updates, and maintaining documentation often requires separate tools and manual processes, leading to inefficiencies. APIM centralizes these tasks in one portal, offering version control, publishing workflows, and rollback options. This streamlines the API lifecycle, ensuring consistency and reducing the chances for errors.

Enhanced security

Enterprises often need to implement different authentication strategies for various clients. These configurations typically require custom Lambda logic and database lookups, adding complexity and cost. APIM introduces configurable security policies that allow client-specific authentication without the need for additional custom code, reducing both complexity and operational overhead.

Customization and transformation

Enterprises frequently handle diverse client requests that involve different formats and protocols. Traditional API management approaches might struggle to support such variations. APIM allows for seamless protocol and field transformations, enabling integrations that meet a wide range of client requirements without additional development effort.

Developer portal

Developers need clear documentation, easy testing environments, and efficient API key management to work effectively. Traditional systems often lack these features, slowing down adoption. APIM provides a developer portal that consolidates API documentation, offers sandbox environments for testing, and simplifies API key management, reducing onboarding time and improving the developer experience.

Logging and monitoring

Log management is key to maintaining API performance, diagnosing issues, and gaining insights into usage. APIM uses API Gateway custom access logging, allowing teams to define logs based on business needs; whether creating separate CloudWatch metrics for each API path or exporting data to external platforms like ELK or Grafana.

Architecture overview

The APIM architecture, shown in the following figure, includes a management state (represented by numbers) and a runtime state (represented by letters). Both parts use a serverless paradigm.

APIM Architecture

Management state

The management state includes the following elements:

  1. Administrator portal access: Administrators access the APIM solution through a secured web portal.
  2. API Requests to APIM Lambda: Requests from the administrator’s API go through API Gateway, which then invokes the APIM Lambda function. This function handles logic related to configuration changes and other administrative actions.

In the following example, we show you how the APIM Lambda function dynamically applies different middleware based on the route configuration. This approach allows for flexible handling of authentication, client access restrictions, and request/response transformations. Here’s a quick breakdown of the key elements:

// If the route requires OIDC (OpenID Connect) authentication,
// add the OIDC authentication middleware to the route.
if route.Auth == "OIDC" {
    r.Use(middleware.OidcAuthenticator)
}
// If the route configuration specifies a list of allowed clients
// and the list is not empty, add a middleware to restrict access
// to only the specified clients.
if route.Allow.Clients != nil && len(route.Allow.Clients) != 0 {
    r.Use(middleware.AllowClients(route.Allow.Clients, cfg.Clients))
}
// Remove specific headers injected by the API Gateway
// to reduce exposure of internal details to downstream systems.
r.Use(middleware.RemoveGatewayHeaders)

// Add additional middleware for handling outbound logic.
// This could include retries, logging, or other outbound-specific functionality.
r.Use(outboundMiddlewares)
// Dynamically constructs and applies a chain of middlewares 
// based on the outbound configuration associated with the current request.
func outboundMiddlewares(next http.Handler) http.Handler {
    return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
        // Retrieve the outbound configuration from the request context.
        outbound, _ := r.Context().Value(selectedOutboundContext).(config.Outbound)

        // Initialize a slice to store the middlewares to be applied.
        middlewares := []func(http.Handler) http.Handler{}

        // Middleware to rewrite the HTTP request based on the outbound configuration.
        middlewares = append(middlewares, middleware.ProxyRequestRewrite(&outbound))

        // Add a middleware for mapping request data if specified in the outbound configuration.
        if len(outbound.Convert.Request) != 0 {
            middlewares = append(middlewares, middleware.RequestDataMapping(outbound.Convert.Request))
        }

        // Middleware to log the outbound response for monitoring or debugging purposes.
        middlewares = append(middlewares, middleware.OutboundResponseLog)

        // Add a middleware for mapping response data if specified in the outbound configuration.
        if len(outbound.Convert.Response) != 0 {
            middlewares = append(middlewares, middleware.ResponseDataMapping(outbound.Convert.Response))
        }

        // Add a middleware for modifying the response if a modification function is defined.
        if outbound.ModifyResponse != "" {
            f, ok := system.MODIFY[outbound.ModifyResponse]
            if ok {
                middlewares = append(middlewares, f())
            }
        }

        // Chain the constructed middlewares together and apply them to the request.
        chain := chi.Chain(middlewares...)
        chain.Handler(next).ServeHTTP(w, r)
    })
}

By using a middleware chain, you can customize how each request and response is processed on a per-route basis. This architecture not only keeps your code organized but also makes the API Gateway-integrated Lambda function far more adaptable to changing requirements. You can add or remove configurations from APIM portal as new use cases emerge—such as data transformations, custom logging, or additional security checks—without rewriting core logic.

  1. Configuration management: Administrators set up server-side and client-side settings, such as API Gateway parameters, authentication requirements, transformations, and more.
  2. Persistence:  DynamoDB stores these configurations, providing persistent data storage and auditing capabilities.
  3. Asynchronous resource provisioning: After administrators save configurations and release them from the APIM portal, APIM creates or updates AWS resources—such as API Gateway, Lambda functions, and AWS Identity and Access Management (IAM). Lambda runs these updates in the background, so administrators can continue working uninterrupted.

Runtime state

The runtime state includes the following elements:

A. Client request: Clients send requests to the APIM endpoint.

B. Routing to the correct gateway: APIM uses the URI prefix in the API mappings associated with custom domain names to route requests to the appropriate API gateway, as shown in the following figure. Each mapping defines a specific API, stage, and an optional path. When a request arrives, APIM checks the path and directs the request to the correct stage and API if it matches. Unmatched requests default to the mapping with no path defined.

C. APIM core processing: A Lambda function (APIM CORE) uses DynamoDB configurations to handle authentication, authorization, protocol conversion, field transformation, and routing.

D. Downstream service call: APIM forwards each request to the configured internal or external endpoint.

E. Logging and monitoring: API Gateway access logs and custom logs track requests in detail.

F. Alarm: Metrics and alarms detect anomalies and notify stakeholders. Use Amazon CloudWatch or self-hosted solutions such as ELK to enable real-time monitoring and alerting.

api-mapping

Conclusion

In this post, we’ve demonstrated how to build an enterprise API management (APIM) solution using Amazon API Gateway, AWS Lambda, Amazon DynamoDB, and other AWS services. We’ve also shown how APIM centralizes critical features—such as version management, security policies, and request/response transformations—to accommodate large-scale enterprise requirements.

You can use the APIM portal to store and manage configurations in DynamoDB, dynamically applying these settings to multiple API gateways without rewriting code. This approach ensures consistent governance across diverse client types and business scenarios, helping to keep APIs both secure and flexible.

Finally, you’ve seen how the APIM architecture unifies the management state and runtime state, streamlines administrative tasks, and provides end-to-end monitoring and alerting. By adopting these best practices, your enterprise can establish a robust, scalable, and secure API management foundation, all within a serverless paradigm.


About the Authors

AWS Weekly Roundup: Amazon Q CLI agent, AWS Step Functions, AWS Lambda, and more (March 10, 2025)

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-q-cli-agent-aws-step-functions-aws-lambda-and-more-march-10-2025/

As the weather improves in the Northern hemisphere, there are more opportunities to learn and connect. This week, I’ll be in San Francisco, and we can meet at the Nova Networking Night at the AWS GenAI Loft where we’ll dive into the world of Amazon Nova foundation models (FMs) with live demos and real-world implementations.

AWS Pi Day is now a yearly tradition. It started in 2021 as a celebration of the 15th anniversary of Amazon S3. This year, there will be in-depth discussions with AWS product teams on how to build a data foundation for a unified seamless experience, managing and using data for analytics and AI workloads. Join us online to learn about the latest innovations through hands-on demos, and ask questions during our interactive livestream.

Last week’s launches
Another busy week, here are the launches that got my attention.

Amazon Q Developer – You can now use an enhanced agent within the Amazon Q command line interface (CLI) to give you more dynamic conversations, help you read and write files locally, query AWS resources, or create code. This enhanced CLI agent is powered by Anthropic’s most intelligent model to date, Claude 3.7 Sonnet. Read more about this agenic coding experience and how to try it out. Here’s a visual demo of the new capabilities of Amazon Q CLI, by Nathan Peck.

Amazon Q Business – Now supports the ingestion of audio and video data. This capability streamlines information retrieval, enhances knowledge sharing, and improves decision-making processes, by making multimedia content as searchable and accessible as text-based documents.

Amazon Bedrock – Bedrock Data Automation is now generally available, so you can automate the generation of valuable insights from unstructured multimodal content such as documents, images, video, and audio files. Learn more and see code examples in my blog post. Amazon Bedrock Knowledge Bases support for GraphRAG is now also generally available. GraphRAG is a capability that enhances Retrieval-Augmented Generation (RAG) by incorporating graph data and delivers more comprehensive, relevant, and explainable responses by leveraging relationships within your data, improving how Generative AI applications retrieve and synthesize information.

Amazon Nova – The Amazon Nova Pro foundation model now supports latency-optimized inference in preview on Amazon Bedrock, enabling faster response times and improved responsiveness for generative AI applications.

AWS Step Functions – Workflow Studio for VS Code is now available, a visual builder you can use to compose workflows on a canvas. You can generate workflow definitions in the background to create workflows in your local development environment. Read more about this enhanced local IDE experience.

AWS Lambda – Now supports Amazon CloudWatch Logs Live Tail in VS Code. We previously introduced support for Live Tail in the Lambda console to simplify how you can view and analyze Lambda logs in real time. Now, you can also monitor Lambda function logs in real time while staying within the VS Code development environment.

AWS Amplify – Now supports HttpOnly cookies for server-rendered Next.js applications when using Amazon Cognito’s managed login. Because cookies with the HttpOnly attribute can’t be accessed by JavaScript, your applications can gain an additional layer of protection against cross-site scripting (XSS) attacks.

Amazon Cognito – You can now customize access tokens for machine-to-machine (M2M) flows, enabling you to implement fine-grained authorization in your applications, APIs, and workloads. M2M authorization is commonly used for automated processes such as scheduled data synchronization tasks, event-driven workflows, microservices communication, or real-time data streaming between systems.

AWS CodeBuild – Now supports builds on Linux x86, Arm, and Windows on-demand fleets directly on the host operating system without containerization. In this way, you can now execute build commands that require direct access to the host system resources or have specific requirements that make containerization challenging. For example, this is useful when building device drivers, running system-level tests, or working with tools that require host machine access. CodeBuild has also added support for Node 22, Python 3.13, and Go 1.23 in Linux x86, Arm, Windows, and macOS platforms.

Bottlerocket – The open source Linux-based operating system purpose-built for containers now supports NVIDIA’s Multi-Instance GPU (MIG) to help partition NVIDIA GPUs into multiple GPU instances on Kubernetes nodes and maximize GPU resource utilization. Bottlerocket now also supports AWS Neuron accelerated instance types and provides a default bootstrap container image that simplifies system setup tasks.

Amazon GameLift – Introducing Amazon GameLift Streams, a new managed capability that developers can use to stream games at up to 1080p resolution and 60 frames per second to any device with a WebRTC-enabled browser. To learn more, explore Donnie’s blog post.

Amazon FSx for NetApp ONTAP – Starting March 5, 2025, the SnapLock licensing fees for data stored in SnapLock volumes has been eliminated, making it more cost-effective.

Other AWS news
Here are some additional projects, blog posts, and news items that you might find interesting:

Accelerate AWS Well-Architected reviews with Generative AI – In this post, we explore a generative AI solution to streamline the Well-Architected Framework Reviews (WAFRs) process. We demonstrate how to build an intelligent, scalable system that analyzes architecture documents and generates insightful recommendations based on best practices.

Architectural diagram

Build a Multi-Agent System with LangGraph and Mistral on AWS – The Multi-Agent City Information System demonstrated in this post exemplifies the potential of agent-based architectures to create sophisticated, adaptable, and highly capable AI applications.

Reference architecture

Evaluate RAG responses with Amazon Bedrock, LlamaIndex and RAGAS – How to enhance your Retrieval Augmented Generation (RAG) implementations with practical techniques to evaluate and optimize your AI systems and enable more accurate, context-aware responses that align with your specific needs.

Architectural diagram

From community.aws
Here are some of my favorite posts from community.aws. Create your AWS Builder ID to start sharing your tips and connect with fellow builders. Your Builder ID is a universal login credential that gives you access, beyond the AWS Management Console, to AWS tools and resources, including over 600 free training courses, community features, and developer tools such as Amazon Q Developer.

Optimize AWS Lambda Costs with Automated Compute Optimizer Insights (Zechariah Kasina) – An automated and scalable method for optimizing AWS Lambda memory configurations to enhance cost efficiency and performance.

Optimize AWS Costs: Auto-Shutdown for EC2 Instances (Adeleke Adebowale Julius) – Using Amazon CloudWatch alarms to dynamically shut down instances based on inactivity.

The Evolution of the Developer Role in an AI-Assisted Future (Aaron Sempf) – While AI is transforming software development, the need for developing talent remains crucial.

Amazon Q Developer CLI – More coffee, less remembering commands (Cobus Bernard) – Now that you can use Amazon Q Developer directly from your terminal to interact with your files, so let’s add some convenience automations.

Upcoming AWS events
Check your calendars and sign up for these upcoming AWS events:

AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Milan, Italy (April 2), Bay Area – Security Edition (April 4), Timișoara, Romania (April 10), and Prague, Czech Republic (April 29).

AWS Innovate: Generative AI + Data – Join a free online conference focusing on generative AI and data innovations. Available in multiple geographic regions: North America (March 13), Greater China Region (March 14), and Latin America (April 8).

AWS Summits – The AWS Summit season is coming along! Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Paris (April 9), Amsterdam (April 16), London (April 30), and Poland (May 5).

AWS re:Inforce (June 16–18) – Our annual learning event devoted to all things AWS Cloud security. This year is in Philadelphia, PA. Registration opens in March, so be ready to join more than 5,000 security builders and leaders.

AWS DevDays are free, technical events where developers can learn about some of the hottest topics in cloud computing. DevDays offer hands-on workshops, technical sessions, live demos, and networking with AWS technical experts and your peers. Register to access AWS DevDays sessions on demand.

That’s all for this week. Check back next Monday for another Weekly Roundup!

– Danilo

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

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