Tag Archives: Amazon OpenSearch Service

Introducing highly durable Amazon OpenSearch Service clusters with 30% price/performance improvement

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/introducing-highly-durable-amazon-opensearch-service-clusters-with-30-price-performance-improvement/

You can use the new OR1 instances to create Amazon OpenSearch Service clusters that use Amazon Simple Storage Service (Amazon S3) for primary storage. You can ingest, store, index, and access just about any imaginable amount of data, while also enjoying a 30% price/performance improvement over existing instance types, eleven nines of data durability, and a zero-time Recovery Point Objective (RPO). You can use this to perform interactive log analytics, monitor application in real time, and more.

New OR1 Instances
These benefits are all made possible by the new OR1 instances, which are available in eight sizes and used for the data nodes of the cluster:

Instance Name vCPUs
 Memory
 EBS Storage Max (gp3)
or1.medium.search 1 8 GiB 400 GiB
or1.large.search 2 16 GiB 800 GiB
or1.xlarge.search 4 32 GiB 1.5 TiB
or1.2xlarge.search 8 64 GiB 3 TiB
or1.4xlarge.search 16 128 GiB 6 TiB
or1.8xlarge.search 32 256 GiB 12 TiB
or1.12xlarge.search 48 384 GiB 18 TiB
or1.16xlarge.search 64 512 GiB 24 TiB

To choose a suitable instance size, read Sizing Amazon OpenSearch Service domains.

The Amazon Elastic Block Store (Amazon EBS) volumes are used for primary storage, with data copied synchronously to S3 as it arrives. The data in S3 is used to create replicas and to rehydrate EBS after shards are moved between instances as a result of a node failure or a routine rebalancing operation. This is made possible by the remote-backed storage and segment replication features that were recently released for OpenSearch.

Creating a Domain
To create a domain I open the Amazon OpenSearch Service Console, select Managed clusters, and click Create domain:

I enter a name for my domain (my-domain), select Standard create, and use the Production template:

Then I choose the Domain with standby deployment option. This option will create active data nodes in two Availability Zones and a standby one in a third. I also choose the latest engine version:

Then I select the OR1 instance family and (for my use case) configure 500 GiB of EBS storage per data node:

I set the other settings as needed, and click Create to proceed:

I take a quick lunch break and when i come back my domain is ready:

Things to Know
Here are a couple of things to know about this new storage option:

Engine Versions – Amazon OpenSearch Service engines version 2.11 and above support OR1 instances.

Regions – The OR1 instance family is available for use with OpenSearch in the US East (Ohio, N. Virginia), US West (N. California, Oregon), Asia Pacific (Mumbai, Singapore, Sydney, Tokyo), and Europe (Frankfurt, Ireland, Spain, Stockholm) AWS Regions.

Pricing – You pay On-Demand or Reserved prices for data nodes, and you also pay for EBS storage. See the Amazon OpenSearch Service Pricing page for more information.

— Jeff;

Announcing Amazon OpenSearch Service zero-ETL integration with Amazon S3 (preview)

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/amazon-opensearch-service-zero-etl-integration-with-amazon-s3-preview/

Today we are announcing a preview of Amazon OpenSearch Service zero-ETL integration with Amazon S3, a new way to query operational logs in Amazon S3 and S3-based data lakes without needing to switch between services. You can now analyze infrequently queried data in cloud object stores and simultaneously use the operational analytics and visualization capabilities of OpenSearch Service.

Amazon OpenSearch Service direct queries with Amazon S3 provides a zero-ETL integration to reduce the operational complexity of duplicating data or managing multiple analytics tools by enabling customers to directly query their operational data, reducing costs and time to action. This zero-ETL integration will be configurable within OpenSearch Service, where you can take advantage of various log type templates, including predefined dashboards, and configure data accelerations tailored to that log type. Templates include VPC Flow Logs, Elastic Load Balancing logs, and NGINX logs, and accelerations include skipping indexes, materialized views, and covered indexes.

With direct queries with Amazon S3, you can perform complex queries critical to security forensic and threat analysis that correlate data across multiple data sources, which aids teams in investigating service downtime and security events. After creating an integration, you can start querying their data directly from the OpenSearch Dashboards or OpenSearch API. You can easily audit connections to ensure that they are set up in a scalable, cost-efficient, and secure way.

Getting started with direct queries with Amazon S3
You can easily get started by creating a new Amazon S3 direct query data source for OpenSearch Service through the AWS Management Console or the API. Each new data source uses AWS Glue Data Catalog to manage tables that represent S3 buckets. Once you create a data source, you can configure Amazon S3 tables and data indexing and query data in OpenSearch Dashboards.

1. Create a data source in OpenSearch Service
Before you create a data source, you should have an OpenSearch Service domain with version 2.11 or later and a target Amazon S3 table in AWS Glue Data Catalog with the appropriate IAM permissions. IAM will need access to the desired S3 bucket(s) and read and write access to AWS Glue Data Catalog. To learn more about IAM prerequisites, see Creating a data source in the AWS documentation.

Go to the OpenSearch Service console and choose the domain you want to set up a new data source for. In the domain details page, choose the Connections tab below the general information and see the Direct Query section.

To create a new data source, choose Create, input the name of your new data source, select the data source type as Amazon S3 with AWS Glue Data Catalog, and choose the IAM role for your data source.

Once you create a data source, you can go to the OpenSearch Dashboards of the domain, which you use to configure access control, define tables, set up log type–based dashboards for popular log types, and query your data.

2. Configuring your data source in OpenSearch Dashboards
To configure data source in OpenSearch Dashboards, choose Configure in the console and go to OpenSearch Dashboards. In the left-hand navigation of OpenSearch Dashboards, under Management, choose Data sources. Under Manage data sources, choose the name of the data source you created in the console.

Direct queries from OpenSearch Service to Amazon S3 use Spark tables within AWS Glue Data Catalog. To create a new table you want to direct query, go to the Query Workbench in the Open Search Plugins menu.

Now run as in the following SQL statement to create http_logs table and run MSCK REPAIR TABLE mys3.default.http_logs command to update the metadata in the catalog

CREATE EXTERNAL TABLE IF NOT EXISTS mys3.default.http_logs (
   `@timestamp` TIMESTAMP,
    clientip STRING,
    request STRING, 
    status INT, 
    size INT, 
    year INT, 
    month INT, 
    day INT) 
USING json PARTITIONED BY(year, month, day) OPTIONS (path 's3://mys3/data/http_log/http_logs_partitioned_json_bz2/', compression 'bzip2')

To ensure a fast experience with your data in Amazon S3, you can set up any of three different types of accelerations to index data into OpenSearch Service, such as skipping indexes, materialized views, and covering indexes. To create OpenSearch indexes from external data connections for better performance, choose the Accelerate Table.

  • Skipping indexes allow you to index only the metadata of the data stored in Amazon S3. Skipping indexes help quickly identify data stored by narrowing down a specific location of where the data is stored.
  • Materialized views enable you to use complex queries such as aggregations, which can be used for querying or powering dashboard visualizations. Materialized views ingest data into OpenSearch Service for anomaly detection or geospatial capabilities.
  • Covering indexes will ingest all the data from the specified table column. Covering indexes are the most performant of the three indexing types.

3. Query your data source in OpenSearch Dashboards
After you set up your tables, you can query your data using Discover. You can run a sample SQL query for the http_logs table you created in AWS Glue Data Catalog tables.

To learn more, see Working with Amazon OpenSearch Service direct queries with Amazon S3 in the AWS documentation.

Join the preview
Amazon OpenSearch Service zero-ETL integration with Amazon S3 is now previewed in the AWS US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), Europe (Frankfurt), and Europe (Ireland) Regions.

OpenSearch Service separately charges for only the compute needed as OpenSearch Compute Units to query your external data as well as maintain indexes in OpenSearch Service. For more information, see Amazon OpenSearch Service Pricing.

Give it a try and send feedback to the AWS re:Post for Amazon OpenSearch Service or through your usual AWS Support contacts.

— Channy

Vector engine for Amazon OpenSearch Serverless is now available

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/vector-engine-for-amazon-opensearch-serverless-is-now-generally-available/

Today we are announcing the general availability of the vector engine for Amazon OpenSearch Serverless with new features. In July 2023, we introduced the preview release of the vector engine for Amazon OpenSearch Serverless, a simple, scalable, and high-performing similarity search capability. The vector engine makes it easy for you to build modern machine learning (ML) augmented search experiences and generative artificial intelligence (generative AI) applications without needing to manage the underlying vector database infrastructure.

You can now store, update, and search billions of vector embeddings with thousands of dimensions in milliseconds. The highly performant similarity search capability of vector engine enables generative AI-powered applications to deliver accurate and reliable results with consistent milliseconds-scale response times.

The vector engine also enables you to optimize and tune results with hybrid search by combining vector search and full-text search in the same query, removing the need to manage and maintain separate data stores or a complex application stack. The vector engine provides a secure, reliable, scalable, and enterprise-ready platform to cost effectively build a prototyping application and then seamlessly scale to production.

You can now get started in minutes with the vector engine by creating a specialized vector engine–based collection, which is a logical grouping of embeddings that works together to support a workload.

The vector engine uses OpenSearch Compute Units (OCUs), compute capacity unit, to ingest and run similarity search queries. One OCU can handle up to 2 million vectors for 128 dimensions or 500,000 for 768 dimensions at 99 percent recall rate.

The vector engine built on OpenSearch Serverless is a highly available service by default. It requires a minimum of four OCUs (2 OCUs for the ingest, including primary and standby, and 2 OCUs for the search with two active replicas across Availability Zones) for the first collection in an account. All subsequent collections using the same AWS Key Management Service (AWS KMS) key can share those OCUs.

What’s new at GA?
Since the preview, the vector engine for Amazon OpenSearch Serverless became one of the vector database options in the knowledge base of Amazon Bedrock to build generative AI applications using a Retrieval Augmented Generation (RAG) concept.

Here are some new or improved features for this GA release:

Disable redundant replica (development and test focused) option
As we announced in our preview blog post, this feature eliminates the need to have redundant OCUs in another Availability Zone solely for availability purposes. A collection can be deployed with two OCUs – one for indexing and one for search. This cuts the costs in half compared to default deployment with redundant replicas. The reduced cost makes this configuration suitable and economical for development and testing workloads.

With this option, we will still provide durability guarantees since the vector engine persists all the data in Amazon S3, but single-AZ failures would impact your availability.

If you want to disable a redundant replica, uncheck Enable redundancy when creating a new vector search
collection.

Fractional OCU for the development and test focused option
Support for fractional OCU billing for development and test focused workloads (that is, no redundant replica option) reduces the floor price for vector search collection. The vector engine will initially deploy smaller 0.5 OCUs while providing the same capabilities at lower scale and will scale up to a full OCU and beyond to meet your workload demand. This option will further reduce the monthly costs when experimenting with using the vector engine.

Automatic scaling for a billion scale
With vector engine’s seamless auto-scaling, you no longer have to reindex for scaling purposes. At preview, we were supporting about 20 million vector embeddings. With the general availability of vector engine, we have raised the limits to support a billion vector scale.

Now available
The vector engine for Amazon OpenSearch Serverless is now available in all AWS Regions where Amazon OpenSearch Serverless is available.

To get started, you can refer to the following resources:

Give it a try and send feedback to AWS re:Post for Amazon OpenSearch Service or through your usual AWS support contacts.

— Channy

Amazon DynamoDB zero-ETL integration with Amazon OpenSearch Service is now available

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/amazon-dynamodb-zero-etl-integration-with-amazon-opensearch-service-is-now-generally-available/

Today, we are announcing the general availability of Amazon DynamoDB zero-ETL integration with Amazon OpenSearch Service, which lets you perform a search on your DynamoDB data by automatically replicating and transforming it without custom code or infrastructure. This zero-ETL integration reduces the operational burden and cost involved in writing code for a data pipeline architecture, keeping the data in sync, and updating code with frequent application changes, enabling you to focus on your application.

With this zero-ETL integration, Amazon DynamoDB customers can now use the powerful search features of Amazon OpenSearch Service, such as full-text search, fuzzy search, auto-complete, and vector search for machine learning (ML) capabilities to offer new experiences that boost user engagement and improve satisfaction with their applications.

This zero-ETL integration uses Amazon OpenSearch Ingestion to synchronize the data between Amazon DynamoDB and Amazon OpenSearch Service. You choose the DynamoDB table whose data needs to be synchronized and Amazon OpenSearch Ingestion synchronizes the data to an Amazon OpenSearch managed cluster or serverless collection within seconds of it being available.

You can also specify index mapping templates to ensure that your Amazon DynamoDB fields are mapped to the correct fields in your Amazon OpenSearch Service indexes. Also, you can synchronize data from multiple DynamoDB tables into one Amazon OpenSearch Service managed cluster or serverless collection to offer holistic insights across several applications.

Getting started with this zero-ETL integration
With a few clicks, you can synchronize data from DynamoDB to OpenSearch Service. To create an integration between DynamoDB and OpenSearch Service, choose the Integrations menu in the left pane of the DynamoDB console and the DynamoDB table whose data you want to synchronize.

You must turn on point-in-time recovery (PITR) and the DynamoDB Streams feature. This feature allows you to capture item-level changes in your table and push the changes to a stream. Choose Turn on for PITR and enable DynamoDB Streams in the Exports and streams tab.

After turning on PITR and DynamoDB Stream, choose Create to set up an OpenSearch Ingestion pipeline in your account that replicates the data to an OpenSearch Service managed domain.

In the first step, enter a unique pipeline name and set up pipeline capacity and compute resources to automatically scale your pipeline based on the current ingestion workload.

Now you can configure the pre-defined pipeline configuration in YAML file format. You can browse resources to look up and paste information to build the pipeline configuration. This pipeline is a combination of a source part from DyanmoDB settings and a sink part for OpenSearch Service.

You must set multiple IAM roles (sts_role_arn) with the necessary permissions to read data from the DynamoDB table and write to an OpenSearch domain. This role is then assumed by OpenSearch Ingestion pipelines to ensure that the right security posture is always maintained when moving the data from source to destination. To learn more, see Setting up roles and users in Amazon OpenSearch Ingestion in the AWS documentation.

After entering all required values, you can validate the pipeline configuration to ensure that your configuration is valid. To learn more, see Creating Amazon OpenSearch Ingestion pipelines in the AWS documentation.

Take a few minutes to set up the OpenSearch Ingestion pipeline, and you can see your integration is completed in the DynamoDB table.

Now you can search synchronized items in the OpenSearch Dashboards.

Things to know
Here are a couple of things that you should know about this feature:

  • Custom schema – You can specify your custom data schema along with the index mappings used by OpenSearch Ingestion when writing data from Amazon DynamoDB to OpenSearch Service. This experience is added to the console within Amazon DynamoDB so that you have full control over the format of indices that are created on OpenSearch Service.
  • Pricing – There will be no additional cost to use this feature apart from the cost of the existing underlying components. Note that Amazon OpenSearch Ingestion charges OpenSearch Compute Units (OCUs) which will be used to replicate data between Amazon DynamoDB and Amazon OpenSearch Service. Furthermore, this feature uses Amazon DynamoDB streams for the change data capture (CDC) and you will incur the standard costs for Amazon DynamoDB Streams.
  • Monitoring – You can monitor the state of the pipelines by checking the status of the integration on the DynamoDB console or using the OpenSearch Ingestion dashboard. Additionally, you can use Amazon CloudWatch to provide real-time metrics and logs, which lets you to set up alerts in case of a breach of user-defined thresholds.

Now available
Amazon DynamoDB zero-ETL integration with Amazon OpenSearch Service is now generally available in all AWS Regions where OpenSearch Ingestion is available today.

— Channy

Introducing persistent buffering for Amazon OpenSearch Ingestion

Post Syndicated from Muthu Pitchaimani original https://aws.amazon.com/blogs/big-data/introducing-persistent-buffering-for-amazon-opensearch-ingestion/

Amazon OpenSearch Ingestion is a fully managed, serverless pipeline that delivers real-time log, metric, and trace data to Amazon OpenSearch Service domains and OpenSearch Serverless collections.

Customers use Amazon OpenSearch Ingestion pipelines to ingest data from a variety of data sources, both pull-based and push-based. When ingesting data from pull-based sources, such as Amazon Simple Storage Service (Amazon S3) and Amazon MSK using Amazon OpenSearch Ingestion, the source handles the data durability and retention. Push-based sources, however, stream records directly to ingestion endpoints, and typically don’t have a means of persisting data once it is generated.

To address this need for such sources, a common architectural pattern is to add a persistent standalone buffer for enhanced durability and reliability of data ingestion. A durable, persistent buffer can mitigate the impact of ingestion spikes, buffer data during downtime, and reduce the need to expand capacity using in-memory buffers which can overflow. Customers use popular buffering technologies like Apache Kafka or RabbitMQ to add durability to their data flowing through their Amazon OpenSearch Ingestion pipelines. However, these tools add complexity to the data ingestion pipeline architecture and can be time consuming to setup, right-size, and maintain.

Solution overview

Today we’re introducing persistent buffering for Amazon OpenSearch Ingestion to enhance data durability and simplify data ingestion architectures for Amazon OpenSearch Service customers. You can use persistent buffering to ingest data for all push-based sources supported by Amazon OpenSearch Ingestion without the need to set up a standalone buffer. These include HTTP sources and OTEL sources for logs, traces and metrics. Persistent buffering in Amazon OpenSearch Ingestion is serverless and scales elastically to meet the throughput needs of even the most demanding workloads. You can now focus on your core business logic when ingesting data at scale in Amazon OpenSearch Service without worrying about the undifferentiated heavy lifting of provisioning and managing servers to add durability to your ingest pipeline.

Walkthrough

Enable persistent buffering

You can turn on the persistent buffering for existing or new pipelines using the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS SDK. If you choose not to enable persistent buffering, then the pipelines continue to use an in-memory buffer.

By default, persistent data is encrypted at rest with a key that AWS owns and manages for you. You can optionally choose your own customer managed key (KMS key) to encrypt data by selecting the checkbox labeled Customize encryption settings and selecting Choose a different AWS KMS key. Please note that if you choose a different KMS key, your pipeline needs additional permission to decrypt and generate data keys. The following snippet shows an example AWS Identity and Access Management (AWS IAM) permission policy that needs to be attached to a role used by the pipeline.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "KeyAccess",
            "Effect": "Allow",
            "Action": [
              "kms:Decrypt",
              "kms:GenerateDataKeyWithoutPlaintext"
            ],
            "Resource": "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"
        }
    ]
}

Provision for persistent buffering

Once persistent buffering is enabled, data is retained in the buffer for 72 hours. Amazon OpenSearch Ingestion keeps track of the data written into a sink and automatically resumes writing from the last successful check point should there be an outage in the sink or other issues that prevents data from being successfully written. There are no additional services or components needed for persistent buffers other than minimum and maximum OpenSearch compute Units (OCU) set for the pipeline. When persistent buffering is turned on, each Ingestion-OCU is now capable of providing persistent buffering along with its existing ability to ingest, transform, and route data. Amazon OpenSearch Ingestion dynamically allocates the buffer from the minimum and maximum allocation of OCUs that you define for the pipelines.

The number of Ingestion-OCUs used for persistent buffering is dynamically calculated based on the source, the transformations on the streaming data, and the sink that the data is written to. Because a portion of the Ingestion-OCUs now applies to persistent buffering, in order to maintain the same ingestion throughput for your pipeline, you need to increase the minimum and maximum Ingestion-OCUs when turning on persistent buffering. This amount of OCUs that you need with persistent buffering depends on the source that you are ingesting data from and also on the type of processing that you are performing on the data. The following table shows the number of OCUs that you need with persistent buffering with different sources and processors.

Sources and processors Ingestion-OCUs with buffering Compared to number of OCUs without persistent buffering needed to achieve similar data throughput
HTTP with no processors 3 times
HTTP wit Grok 2 times
OTel Trace 2 times
OTel Metrics 2 times

You have complete control over how you want to set up OCUs for your pipelines and decide between increasing OCUs for higher throughput or reducing OCUs for cost control at a lower throughput. Also, when you turn on persistent buffering, the minimum OCUs for a pipeline go up from one to two.

Availability and pricing

Persistent buffering is available in the all the AWS Regions where Amazon OpenSearch Ingestion is available as of November 17 2023. These includes US East (Ohio), US East (N. Virginia), US West (Oregon), US West (N. California), Europe (Ireland), Europe (London), Europe (Frankfurt), Asia Pacific (Tokyo), Asia Pacific (Sydney), Asia Pacific (Singapore), Asia Pacific (Mumbai), Asia Pacific (Seoul), and Canada (Central).

Ingestion-OCUs remains at the same price of $0.24 cents per hour. OCUs are billed on an hourly basis with per-minute granularity. You can control the costs OCUs incur by configuring maximum OCUs that a pipeline is allowed to scale.

Conclusion

In this post, we showed you how to configure persistent buffering for Amazon OpenSearch Ingestion to enhance data durability, and simplify data ingestion architecture for Amazon OpenSearch Service. Please refer to the documentation to learn other capabilities provided by Amazon OpenSearch Ingestion to a build sophisticated architecture for your ingestion needs.

About the Authors

Muthu Pitchaimani is a Search Specialist with Amazon OpenSearch Service. He builds large-scale search applications and solutions. Muthu is interested in the topics of networking and security, and is based out of Austin, Texas.

Arjun Nambiar is a Product Manager with Amazon OpenSearch Service. He focusses on ingestion technologies that enable ingesting data from a wide variety of sources into Amazon OpenSearch Service at scale. Arjun is interested in large scale distributed systems and cloud-native technologies and is based out of Seattle, Washington.

Jay is Customer Success Engineering leader for OpenSearch service. He focusses on overall customer experience with the OpenSearch. Jay is interested in large scale OpenSearch adoption, distributed data store and is based out of Northern Virginia.

Rich Giuli is a Principal Solutions Architect at Amazon Web Service (AWS). He works within a specialized group helping ISVs accelerate adoption of cloud services. Outside of work Rich enjoys running and playing guitar.

Build scalable and serverless RAG workflows with a vector engine for Amazon OpenSearch Serverless and Amazon Bedrock Claude models

Post Syndicated from Fraser Sequeira original https://aws.amazon.com/blogs/big-data/build-scalable-and-serverless-rag-workflows-with-a-vector-engine-for-amazon-opensearch-serverless-and-amazon-bedrock-claude-models/

In pursuit of a more efficient and customer-centric support system, organizations are deploying cutting-edge generative AI applications. These applications are designed to excel in four critical areas: multi-lingual support, sentiment analysis, personally identifiable information (PII) detection, and conversational search capabilities. Customers worldwide can now engage with the applications in their preferred language, and the applications can gauge their emotional state, mask sensitive personal information, and provide context-aware responses. This holistic approach not only enhances the customer experience but also offers efficiency gains, ensures data privacy compliance, and drives customer retention and sales growth.

Generative AI applications are poised to transform the customer support landscape, offering versatile solutions that integrate seamlessly with organizations’ operations. By combining the power of multi-lingual support, sentiment analysis, PII detection, and conversational search, these applications promise to be a game-changer. They empower organizations to deliver personalized, efficient, and secure support services while ultimately driving customer satisfaction, cost savings, data privacy compliance, and revenue growth.

Amazon Bedrock and foundation models like Anthropic Claude are poised to enable a new wave of AI adoption by powering more natural conversational experiences. However, a key challenge that has emerged is tailoring these general purpose models to generate valuable and accurate responses based on extensive, domain-specific datasets. This is where the Retrieval Augmented Generation (RAG) technique plays a crucial role.

RAG allows you to retrieve relevant data from databases or document repositories to provide helpful context to large language models (LLMs). This additional context helps the models generate more specific, high-quality responses tuned to your domain.

In this post, we demonstrate building a serverless RAG workflow by combining the vector engine for Amazon OpenSearch Serverless with an LLM like Anthropic Claude hosted by Amazon Bedrock. This combination provides a scalable way to enable advanced natural language capabilities in your applications, including the following:

  • Multi-lingual support – The solution uses the ability of LLMs like Anthropic Claude to understand and respond to queries in multiple languages without any additional training needed. This provides true multi-lingual capabilities out of the box, unlike traditional machine learning (ML) systems that need training data in each language.
  • Sentiment analysis – This solution enables you to detect positive, negative, or neutral sentiment in text inputs like customer reviews, social media posts, or surveys. LLMs can provide explanations for the inferred sentiment, describing which parts of the text contributed to a positive or negative classification. This explainability helps build trust in the model’s predictions. Potential use cases could include analyzing product reviews to identify pain points or opportunities, monitoring social media for brand sentiment, or gathering feedback from customer surveys.
  • PII detection and redaction – The Claude LLM can be accurately prompted to identify various types of PII like names, addresses, Social Security numbers, and credit card numbers and replace it with placeholders or generic values while maintaining readability of the surrounding text. This enables compliance with regulations like GDPR and prevents sensitive customer data from being exposed. This also helps automate the labor-intensive process of PII redaction and reduces risk of exposed customer data across various use cases, such as the following:
    • Processing customer support tickets and automatically redacting any PII before routing to agents.
    • Scanning internal company documents and emails to flag any accidental exposure of customer PII.
    • Anonymizing datasets containing PII before using the data for analytics or ML, or sharing the data with third parties.

Through careful prompt engineering, you can accomplish the aforementioned use cases with a single LLM. The key is crafting prompt templates that clearly articulate the desired task to the model. Prompting allows us to tap into the vast knowledge already present within the LLM for advanced natural language processing (NLP) tasks, while tailoring its capabilities to our particular needs. Well-designed prompts unlock the power and potential of the model.

With the vector database capabilities of Amazon OpenSearch Serverless, you can store vector embeddings of documents, allowing ultra-fast, semantic (rather than keyword) similarity searches to find the most relevant passages to augment prompts.

Read on to learn how to build your own RAG solution using an OpenSearch Serverless vector database and Amazon Bedrock.

Solution overview

The following architecture diagram provides a scalable and fully managed RAG-based workflow for a wide range of generative AI applications, such as language translation, sentiment analysis, PII data detection and redaction, and conversational AI. This pre-built solution operates in two distinct stages. The initial stage involves generating vector embeddings from unstructured documents and saving these embeddings within an OpenSearch Serverless vectorized database index. In the second stage, user queries are forwarded to the Amazon Bedrock Claude model along with the vectorized context to deliver more precise and relevant responses.

In the following sections, we discuss the two core functions of the architecture in more detail:

  • Index domain data
  • Query an LLM with enhanced context

Index domain data

In this section, we discuss the details of the data indexing phase.

Generate embeddings with Amazon Titan

We used Amazon Titan embeddings model to generate vector embeddings. With 1,536 dimensions, the embeddings model captures semantic nuances in meaning and relationships. Embeddings are available via the Amazon Bedrock serverless experience; you can access it using a single API and without managing any infrastructure. The following code illustrates generating embeddings using a Boto3 client.

import boto3
bedrock_client = boto3.client('bedrock-runtime')

## Generate embeddings with Amazon Titan Embeddings model
response = bedrock_client.invoke_model(
            body = json.dumps({"inputText": 'Hello World'}),
            modelId = 'amazon.titan-embed-text-v1',
            accept = 'application/json',
            contentType = 'application/json'
)
result = json.loads(response['body'].read())
embeddings = result.get('embedding')
print(f'Embeddings -> {embeddings}')

Store embeddings in an OpenSearch Serverless vector collection

OpenSearch Serverless offers a vector engine to store embeddings. As your indexing and querying needs fluctuate based on workload, OpenSearch Serverless automatically scales up and down based on demand. You no longer have to predict capacity or manage infrastructure sizing.

With OpenSearch Serverless, you don’t provision clusters. Instead, you define capacity in the form of Opensearch Capacity Units (OCUs). OpenSearch Serverless will scale up to the maximum number of OCUs defined. You’re charged for a minimum of 4 OCUs, which can be shared across multiple collections sharing the same AWS Key Management Service (AWS KMS) key.

The following screenshot illustrates how to configure capacity limits on the OpenSearch Serverless console.

Query an LLM with domain data

In this section, we discuss the details of the querying phase.

Generate query embeddings

When a user queries for data, we first generate an embedding of the query with Amazon Titan embeddings. OpenSearch Serverless vector collections employ an Approximate Nearest Neighbors (A-NN) algorithm to find document embeddings closest to the query embeddings. The A-NN algorithm uses cosine similarity to measure the closeness between the embedded user query and the indexed data. OpenSearch Serverless then returns the documents whose embeddings have the smallest distance, and therefore the highest similarity, to the user’s query embedding. The following code illustrates our vector search query:

vector_query = {
                "size": 5,
                "query": {"knn": {"embedding": {"vector": embedded_search, "k": 2}}},
                "_source": False,
                "fields": ["text", "doc_type"]
            }

Query Anthropic Claude models on Amazon Bedrock

OpenSearch Serverless finds relevant documents for a given query by matching embedded vectors. We enhance the prompt with this context and then query the LLM. In this example, we use the AWS SDK for Python (Boto3) to invoke models on Amazon Bedrock. The AWS SDK provides the following APIs to interact with foundational models on Amazon Bedrock:

The following code invokes our LLM:

import boto3
bedrock_client = boto3.client('bedrock-runtime')
# model_id could be 'anthropic.claude-v2', 'anthropic.claude-v1','anthropic.claude-instant-v1']
response = bedrock_client.invoke_model_with_response_stream(
        body=json.dumps(prompt),
        modelId=model_id,
        accept='application/json',
        contentType='application/json'
    )

Prerequisites

Before you deploy the solution, review the prerequisites.

Deploy the solution

The code sample along with the deployment steps are available in the GitHub repository. The following screenshot illustrates deploying the solution using AWS CloudShell.

Test the solution

The solution provides some sample data for indexing, as shown in the following screenshot. You can also index custom text. Initial indexing of documents may take some time because OpenSearch Serverless has to create a new vector index and then index documents. Subsequent requests are faster. To delete the vector index and start over, choose Reset.

The following screenshot illustrates how you can query your domain data in multiple languages after it’s indexed. You could also try out sentiment analysis or PII data detection and redaction on custom text. The response is streamed over Amazon API Gateway WebSockets.

Clean up

To clean up your resources, delete the following AWS CloudFormation stacks via the AWS CloudFormation console:

  • LlmsWithServerlessRagStack
  • ApiGwLlmsLambda

Conclusion

In this post, we provided an end-to-end serverless solution for RAG-based generative AI applications. This not only offers you a cost-effective option, particularly in the face of GPU cost and hardware availability challenges, but also simplifies the development process and reduces operational costs.

Stay up to date with the latest advancements in generative AI and start building on AWS. If you’re seeking assistance on how to begin, check out the Generative AI Innovation Center.

About the authors

Fraser Sequeira is a Startups Solutions Architect with AWS based in Mumbai, India. In his role at AWS, Fraser works closely with startups to design and build cloud-native solutions on AWS, with a focus on analytics and streaming workloads. With over 10 years of experience in cloud computing, Fraser has deep expertise in big data, real-time analytics, and building event-driven architecture on AWS. He enjoys staying on top of the latest technology innovations from AWS and sharing his learnings with customers. He spends his free time tinkering with new open source technologies.

Kenneth Walsh is a New York-based Sr. Solutions Architect whose focus is AWS Marketplace. Kenneth is passionate about cloud computing and loves being a trusted advisor for his customers. When he’s not working with customers on their journey to the cloud, he enjoys cooking, audiobooks, movies, and spending time with his family and dog.

Max Winter is a Principal Solutions Architect for AWS Financial Services clients. He works with ISV customers to design solutions that allow them to leverage the power of AWS services to automate and optimize their business. In his free time, he loves hiking and biking with his family, music and theater, digital photography, 3D modeling, and imparting a love of science and reading to his two nearly-teenagers.

Manjula Nagineni is a Senior Solutions Architect with AWS based in New York. She works with major financial service institutions, architecting and modernizing their large-scale applications while adopting AWS Cloud services. She is passionate about designing big data workloads cloud-natively. She has over 20 years of IT experience in software development, analytics, and architecture across multiple domains such as finance, retail, and telecom.

AWS Weekly Roundup – EC2 DL2q instances, PartyRock, Amplify’s 6th birthday, and more – November 20, 2023

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-ec2-dl2q-instances-partyrock-amplifys-6th-birthday-and-more-november-20-2023/

Last week I saw an astonishing 160+ new service launches. There were so many updates that we decided to publish a weekly roundup again. This continues the same innovative pace of the previous week as we are getting closer to AWS re:Invent 2023.

Our News Blog team is also finalizing new blog posts for re:Invent to introduce awesome launches with service teams for your reading pleasure. Jeff Barr shared The Road to AWS re:Invent 2023 to explain our blogging journey and process. Please stay tuned in the next week!

Last week’s launches
Here are some of the launches that caught my attention last week:

Amazon EC2 DL2q instances – New DL2q instances are powered by Qualcomm AI 100 Standard accelerators and are the first to feature Qualcomm’s AI technology in the public cloud. With eight Qualcomm AI 100 Standard accelerators and 128 GiB of total accelerator memory, you can run popular generative artificial intelligence (AI) applications and extend to edge devices across smartphones, autonomous driving, personal compute, and extended reality headsets to develop and validate these AI workloads before deploying.

PartyRock for Amazon Bedrock – We introduced PartyRock, a fun and intuitive hands-on, generative AI app-building playground powered by Amazon Bedrock. You can experiment, learn all about prompt engineering, build mini-apps, and share them with your friends—all without writing any code or creating an AWS account.

You also can now access the Meta Llama 2 Chat 13B foundation model and Cohere Command Light, Embed English, and multilingual models for Amazon Bedrock.

AWS Amplify celebrates its sixth birthday – We announced six new launches; a new documentation site, support for Next.js 14 with our hosting and JavaScript library, added custom token providers and an automatic React Native social sign-in update to Amplify Auth, new ChangePassword and DeleteUser account settings components, and updated all Amplify UI packages to use new Amplify JavaScript v6. You can also use wildcard subdomains when using a custom domain with your Amplify application deployed to AWS Amplify Hosting.

Amplify docs site UI

Also check out other News Blog posts about major launches published in the past week:

Other AWS service launches
Here are some other bundled feature launches per AWS service:

Amazon Athena  – You can use a new cost-based optimizer (CBO) to enhance query performance based on table and column statistics, collected by AWS Glue Data Catalog and Athena JDBC 3.x driver, a new alternative that supports almost all authentication plugins. You can also use Amazon EMR Studio to develop and run interactive queries on Amazon Athena.

Amazon CloudWatch – You can use a new CloudWatch metric called EBS Stalled I/O Check to monitor the health of your Amazon EBS volumes, the regular expression for Amazon CloudWatch Logs Live Tail filter pattern syntax to search and match relevant log events, observability of SAP Sybase ASE database in CloudWatch Application Insights, and up to two stats commands in a Log Insights query to perform aggregations on the results.

Amazon CodeCatalyst – You can connect to a Amazon Virtual Private Cloud (Amazon VPC) from CodeCatalyst Workflows, provision infrastructure using Terraform within CodeCatalyst Workflows, access CodeCatalyst with your workforce identities configured in IAM Identity Center, and create teams made up of members of the CodeCatalyst space.

Amazon Connect – You can use a pre-built queue performance dashboard and Contact Lens conversational analytics dashboard to view and compare real-time and historical aggregated queue performance. You can use quick responses for chats, previously written formats such as typing in ‘/#greet’ to insert a personalized response, and scanning attachments to detect malware or other unwanted content.

AWS Glue – AWS Glue for Apache Spark added new six database connectors: Teradata, SAP HANA, Azure SQL, Azure Cosmos DB, Vertica, and MongoDB, as well as the native connectivity to Amazon OpenSearch Service.

AWS Lambda – You can see single pane view of metrics, logs, and traces in the AWS Lambda console and advanced logging controls to natively capture logs in JSON structured format. You can view the SAM template on the Lambda console and export the function’s configuration to AWS Application Composer. AWS Lambda also supports Java 21 and NodeJS 20 versions built on the new Amazon Linux 2023 runtime.

AWS Local Zones in Dallas – You can enable the new Local Zone in Dallas, Texas, us-east-1-dfw-2a, with Amazon EC2 C6i, M6i, R6i, C6gn, and M6g instances and Amazon EBS volume types gp2, gp3, io1, sc1, and st1. You can also access Amazon ECS, Amazon EKS, Application Load Balancer, and AWS Direct Connect in this new Local Zone to support a broad set of workloads at the edge.

Amazon Managed Streaming for Apache Kafka (Amazon MSK) – You can standardize access control to Kafka resources using AWS Identity and Access Management (IAM) and build Kafka clients for Amazon MSK Serverless written in all programming languages. These are open source client helper libraries and code samples for popular languages, including Java, Python, Go, and JavaScript. Also, Amazon MSK now supports an enhanced version of Apache Kafka 3.6.0 that offers generally available Tiered Storage and automatically sends you storage capacity alerts when you are at risk of exhausting your storage.

Amazon OpenSearch Service Ingestion – You can migrate your data from Elasticsearch version 7.x clusters to the latest versions of Amazon OpenSearch Service and use persistent buffering to protect the durability of incoming data.

Amazon RDS –Amazon RDS for MySQL now supports creating active-active clusters using the Group Replication plugin, upgrading MySQL 5.7 snapshots to MySQL 8.0, and Innovation Release version of MySQL 8.1.

Amazon RDS Custom for SQL Server extends point-in-time recovery support for up to 1,000 databases, supports Service Master Key Retention to use transparent data encryption (TDE), table- and column-level encryption, DBMail and linked servers, and use SQL Server Developer edition with the bring your own media (BYOM).

Additionally, Amazon RDS Multi-AZ deployments with two readable standbys now supports minor version upgrades and system maintenance updates with typically less than one second of downtime when using Amazon RDS Proxy.

AWS Partner Central – You can use an improved user experience in AWS Partner Central to build and promote your offerings and the new Investments tab in the Partner Analytics Dashboard to gain actionable insights. You can now link accounts and associated users between Partner Central and AWS Marketplace and use an enhanced co-sell experience with APN Customer Engagements (ACE) manager.

Amazon QuickSight – You can programmatically manage user access and custom permissions support for roles to restrict QuickSight functionality to the QuickSight account for IAM Identity Center and Active Directory using APIs. You can also use shared restricted folders, a Contributor role and support for data source asset types in folders and the Custom Week Start feature, an addition designed to enhance the data analysis experience for customers across diverse industries and social contexts.

AWS Trusted Advisor – You can use new APIs to programmatically access Trusted Advisor best practices checks, recommendations, and prioritized recommendations and 37 new Amazon RDS checks that provide best practices guidance by analyzing DB instance configuration, usage, and performance data.

There’s a lot more launch news that I haven’t covered. See AWS What’s New for more details.

See you virtually in AWS re:Invent
AWS re:Invent 2023Next week we’ll hear the latest from AWS, learn from experts, and connect with the global cloud community in Las Vegas. If you come, check out the agenda, session catalog, and attendee guides before your departure.

If you’re not able to attend re:Invent in person this year, we’re offering the option to livestream our Keynotes and Innovation Talks. With the registration for online pass, you will have access to on-demand keynote, Innovation Talks, and selected breakout sessions after the event.

– Channy

Configure dynamic tenancy for Amazon OpenSearch Dashboards

Post Syndicated from Abhi Kalra original https://aws.amazon.com/blogs/big-data/configure-dynamic-tenancy-for-amazon-opensearch-dashboards/

Amazon OpenSearch Service securely unlocks real-time search, monitoring, and analysis of business and operational data for use cases like application monitoring, log analytics, observability, and website search. In this post, we talk about new configurable dashboards tenant properties.

OpenSearch Dashboards tenants in Amazon OpenSearch Service are spaces for saving index patterns, visualizations, dashboards, and other Dashboards objects. Users can switch between multiple tenants to access and share index patterns and visualizations.

When users use Dashboards, they select their Dashboards tenant view. There are three types of tenant:

  1. Global tenant – This tenant is shared among all the OpenSearch Dashboard users if they have access to it. This tenant is created by default for all domains.
  2. Private tenant – This tenant is exclusive to each user and can’t be shared. It does not allow you to access routes or index patterns created by the global tenant. Private tenants are usually used for exploratory work.
  3. Custom tenants –  Administrators can create custom tenants and assign them to specific roles. Once created, these tenants can then provide spaces for specific groups of users.

One user can have access to multiple tenants, and this property is called multi-tenancy. With the OpenSearch 2.7 launch, administrators can dynamically configure the following tenancy properties:

  1. Enable or disable multi-tenancy.
  2. Enable or disable private tenant.
  3. Change the default tenant.

Why do you need these properties to be dynamic?

Before OpenSearch 2.7, users of open-source OpenSearch, with security permissions, could enable and disable multi-tenancy and private tenant by changing the YAML configuration file and restarting their Dashboards environment. This had some drawbacks:

  1. Users needed to do a Dashboards environment restart, which takes time.
  2. Changing the configuration on large clusters (more than 100 data nodes) was difficult to automate and error-prone.
  3. When configuration changes did not include all nodes due to configuration update failures or a failure to apply changes, the user experience would differ based on which node the request hits.

With OpenSearch 2.7 in Amazon OpenSearch Service, users can change tenancy configurations dynamically from both the REST API and from the Dashboards UI. This provides a faster and more reliable way to manage your Dashboards tenancy.

Introducing a new property: default tenant

Before OpenSearch 2.7, by default, all new users would sign in to their private tenant when accessing OpenSearch Dashboards. With 2.7, we have added a new property, default tenant. Now administrators can set a default tenant for when users sign in to OpenSearch Dashboards, whether it’s their own, private tenant, the global tenant, or a custom tenant.

This feature will serve two basic functions:

  • Remove confusion among new users who don’t have much experience with OpenSearch Dashboards and tenancy. If their usage of Dashboards is limited to visualizations and small modifications of already existing data in a particular tenant, they don’t have to worry about switching tenants and can access the tenant with required data by default.
  • Give more control to administrators. Administrators can decide which tenant should be default for all visualization purposes.

Users will sign in to the default tenant only when they are signing in for the first time or from a new browser. For subsequent sign-ins, the user will sign in to the tenant they previously signed in to, which comes from browser storage.

The user sign-in flow is as follows:

Since even a small change in these configurations can impact all the users accessing Dashboards, take care when configuring and changing these features to ensure smooth use of Dashboards.

Default tenancy configurations

The following shows the default tenancy configuration on domain creation.

  1. “multitenancy_enabled” : true
  2. “private_tenant_eabled”: true
  3. “default_tenant”: “”

This means that by default for each new domain, multi-tenancy and private tenant will be enabled and the default tenant will be the global tenant. You can change this configuration after domain creation with admins or with users with access to the right FGAC or IAM roles.

Changing tenancy configurations using APIs

You can use the following API call in OpenSearch 2.7+ to configure tenancy properties. All three tenancy properties are optional:

PUT _plugins/_security/api/tenancy/config 
{
    "multitenancy_enabled":true,
    "private_tenant_enabled":false,
    "default_tenant":"mary_brown"
}

You can use the following API to retrieve the current tenancy configuration:

GET _plugins/_security/api/tenancy/config 

Changing tenancy configuration from OpenSearch Dashboards

You can also configure tenancy properties from OpenSearch Dashboards. Amazon OpenSearch Service has introduced the option to configure and manage tenancy from the Getting started tab of the Security page. From the Manage tab of the Multi-tenancy page, admins can choose a tenant to be the default tenant and see tenancy status, which will tell whether a tenant is enabled or disabled. Admins can enable and disable multi-tenancy, private tenant, and choose the default tenant from the configure tab.

Summary

Since the release of OpenSearch 2.7, you can set your tenancy configuration dynamically, using both REST APIs and OpenSearch Dashboards. Dynamic, API-driven tenancy configuration will make use of tenancy features and Dashboards simpler and more efficient for both users and administrators. Administrators will have more control over which tenants are accessible to which users.

We would love to hear from you, especially about how this feature has helped your organization simplify your Dashboards usage. If you have other questions, please leave a comment.

To learn more, please visit the Amazon OpenSearch Service page.

About the authors

Abhi Kalra

Prabhat Chaturvedi

AWS Weekly Roundup – re:Post Selections, SNS and SQS FIFO improvements, multi-VPC ENI attachments, and more – October 30, 2023

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-repost-selections-sns-and-sqs-fifo-improvements-multi-vpc-eni-attachments-and-more-october-30-2023/

It’s less than a month to AWS re:Invent, but interesting news doesn’t slow down in the meantime. This week is my turn to help keep you up to date!

Last week’s launches
Here are some of the launches that caught my attention last week:

AWS re:Post – With re:Post, you have access to a community of experts that helps you become even more successful on AWS. With Selections, community members can organize knowledge in an aggregated view to create learning paths or curated content sets.

Amazon SNS – First-in-First-out (FIFO) topics now support the option to store and replay messages without needing to provision a separate archival resource. This improves the durability of your event-driven applications and can help you recover from downstream failure scenarios. Find out more in this AWS Comput Blog post – Archiving and replaying messages with Amazon SNS FIFO. Also, you can now use custom data identifiers to protect not only common sensitive data (such as names, addresses, and credit card numbers) but also domain-specific sensitive data, such as your company’s employee IDs. You can find additional info on this feature in this AWS Security blog post – Mask and redact sensitive data published to Amazon SNS using managed and custom data identifiers.

Amazon SQS – With the increased throughput quota for FIFO high throughput mode, you can process up to 18,000 transactions per second, per API action. Note the throughput quota depends on the AWS Region.

Amazon OpenSearch Service – OpenSearch Serverless now supports automated time-based data deletion with new index lifecycle policies. To determine the best strategy to deliver accurate and low latency vector search queries, OpenSearch can now intelligently evaluate optimal filtering strategies, like pre-filtering with approximate nearest neighbor (ANN) or filtering with exact k-nearest neighbor (k-NN). Also, OpenSearch Service now supports Internet Protocol Version 6 (IPv6).

Amazon EC2 – With multi-VPC ENI attachments, you can launch an instance with a primary elastic network interface (ENI) in one virtual private cloud (VPC) and attach a secondary ENI from another VPC. This helps maintain network-level segregation, but still allows specific workloads (like centralized appliances and databases) to communicate between them.

AWS CodePipeline – With parameterized pipelines, you can dynamically pass input parameters to a pipeline execution. You can now start a pipeline execution when a specific git tag is applied to a commit in the source repository.

Amazon MemoryDB – Now supports Graviton3-based R7g nodes that deliver up to 28 percent increased throughput compared to R6g. These nodes also deliver higher networking bandwidth.

Other AWS news
Here are a few posts from some of the other AWS and cloud blogs that I follow:

Networking & Content Delivery Blog – Some of the technical management and hardware decisions we make when building AWS network infrastructure: A Continuous Improvement Model for Interconnects within AWS Data Centers

Interconnect monitoring service infrastructure diagram

DevOps Blog – To help enterprise customers understand how many of developers use CodeWhisperer, how often they use it, and how often they accept suggestions: Introducing Amazon CodeWhisperer Dashboard and CloudWatch Metrics

Front-End Web & Mobile Blog – How to restrict access to your GraphQL APIs to consumers within a private network: Architecture Patterns for AWS AppSync Private APIs

Architecture Blog – Another post in this super interesting series: Let’s Architect! Designing systems for stream data processing

A serverless streaming data pipeline using Amazon Kinesis and AWS Glue

From Community.AWS: Load Testing WordPress Amazon Lightsail Instances and Future-proof Your .NET Apps With Foundation Model Choice and Amazon Bedrock.

Don’t miss the latest AWS open source newsletter by my colleague Ricardo.

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

AWS Community Days – Join a community-led conference run by AWS user group leaders in your region: Jaipur (November 4), Vadodara (November 4), Brasil (November 4), Central Asia (Kazakhstan, Uzbekistan, Kyrgyzstan, and Mongolia on November 17-18), and Guatemala (November 18).

AWS re:Invent (November 27 – December 1) – Join us to hear the latest from AWS, learn from experts, and connect with the global cloud community. Browse the session catalog and attendee guides and check out the highlights for generative AI.

Here you can browse all upcoming AWS-led in-person and virtual events and developer-focused events.

And that’s all from me for this week. On to the next one!

— 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!

An automated approach to perform an in-place engine upgrade in Amazon OpenSearch Service

Post Syndicated from Prashant Agrawal original https://aws.amazon.com/blogs/big-data/an-automated-approach-to-perform-an-in-place-engine-upgrade-in-amazon-opensearch-service/

Software upgrades bring new features and better performance, and keep you current with the software provider. However, upgrades for software services can be difficult to complete successfully, especially when you can’t tolerate downtime and when the new version’s APIs introduce breaking changes and deprecation that you must remediate. This post shows you how to upgrade from Elasticsearch engine to OpenSearch engine on Amazon OpenSearch Service without needing an intermediate upgrade to Elasticsearch 7.10.

OpenSearch Service supports OpenSearch as an engine, with versions in the 1.x through 2.x series. The service also supports legacy versions of Elasticsearch, versions 1.x through 7.10. Although OpenSearch brings many improvements over earlier engines, it can feel daunting to consider not only upgrading versions, but also changing engines in the process. The good news is that OpenSearch 1.0 is wire compatible with Elasticsearch 7.10, making engine changes straightforward. If you’re running a version of Elasticsearch in the 6.x or early 7.x series on OpenSearch Service, you might think you need to upgrade to Elasticsearch 7.10, and then upgrade to OpenSearch 1.3. However, you can easily upgrade your existing Elasticsearch engine running 6.8, 7.1, 7.2, 7.4, 7.9, and 7.10 in OpenSearch Service to the OpenSearch 1.3 engine.

OpenSearch Service runs a variety of checks before running an actual upgrade:

  • Validation before starting an upgrade
  • Preparing the setup configuration for the desired version
  • Provisioning new nodes with the same hardware configuration
  • Moving shards from old nodes to newly provisioned nodes
  • Removing older nodes and old node references from OpenSearch endpoints

During an upgrade, AWS takes care of the undifferentiated heavy lifting of provisioning, deploying, and moving the data to new domain. You are responsible to make sure there are no breaking changes that affect the data migration and movement to the newer version of the OpenSearch domain. In this post, we discuss the things you must modify and verify before and after running an upgrade from 6.8, 7.1, 7.2, 7.4, 7.9, and 7.10 version of Elasticsearch to 1.3 OpenSearch Service.

Pre-upgrade breaking changes

The following are pre-upgrade breaking changes:

  • Dependency check for language clients and libraries – If you’re using the open-source high-level language clients from Elastic, for example the Java, go, or Python client libraries, AWS recommends moving to the open-source, OpenSearch versions of these clients. (If you don’t use a high-level language client, you can skip this step.) The following are a few steps to perform a dependency check:
    • Determine the client library – Choose an appropriate client library compatible with your programing language. Refer to OpenSearch language clients for a list of all supported client libraries.
    • Add dependencies and resolve conflicts – Update your project’s dependency management system with the necessary dependencies specified by the client library. If your project already has dependencies that conflict with the OpenSearch client library dependencies, you may encounter dependency conflicts. In such cases, you need to resolve the conflicts manually.
    • Test and verify the client – Test the OpenSearch client functionality by establishing a connection, performing some basic operations (like indexing and searching), and verifying the results.
  • Removal of mapping types – Multiple types within an index were deprecated in Elasticsearch version 6.x, and completely removed in version 7.0 or later. OpenSearch indexes can only contain one mapping type. From OpenSearch version 2.x onward, the mapping _type must be _doc. You must check and fix the mapping before upgrading to OpenSearch 1.3.

Complete the following steps to identify and fix mapping issues:

  1. Navigate to dev tools and use the following GET <index> mapping API to fetch the mapping information for all the indexes:
GET /index-name/_mapping

The mapping response will contain a JSON structure that represents the mapping for your index.

  1. Look for the top-level keys in the response JSON; each key represents a custom type within the index.

The _doc type is used for the default type in Elasticsearch 7.x and OpenSearch Service 1.x, but you may see additional types that you defined in earlier versions of Elasticsearch. The following is an example response for an index with two custom types, type1 and type2.

Note that indexes created in 5.x will continue to function in 6.x as they did in 5.x, but indexes created in 6.x only allow a single type per index.

{
  "myindex": {
    "mappings": {
      "type1": {
        "properties": {
          "field1_type1": {
            "type": "text"
          },
          "field2_type1": {
            "type": "integer"
          }
        }
      },
      "type2": {
        "properties": {
          "field1_type2": {
            "type": "keyword"
          },
          "field2_type2": {
            "type": "date"
          }
        }
      }
    }
  }
}

To fix the multiple mapping types in your existing domain, you need to reindex the data, where you can create one index for each mapping. This is a crucial step in the migration process because OpenSearch doesn’t support multiple types within a single index. In the next steps, we convert an index that has multiple mapping types into two separate indexes, each using the _doc type.

  1. You can unify the mapping by using your existing index name as a root and adding the type as a suffix. For example, the following code creates two indexes with myindex as the root name and type1 and type2 as the suffix: 
    # Create an index for "type1"
PUT /myindex_type1

# Create an index for "type2"
PUT /myindex_type2

  2. Use the _reindex API to reindex the data from the original index into the two new indexes. Alternately, you can reload the data from its source, if you’re keeping it in another system. 
    POST _reindex
{
  "source": {
    "index": "myindex",
    "type": "type1"  
  },
  "dest": {
    "index": "myindex_type1",
    "type": "_doc"  
  }
}
POST _reindex
{
  "source": {
    "index": "myindex",
    "type": "type2"  
  },
  "dest": {
    "index": "myindex_type2",
    "type": "_doc"  
  }
}

  3. If your application was previously querying the original index with multiple types, you’ll need to update your queries to specify the new indexes with _doc as the type. For example, if your client was querying using myindex, which has been reindexed to myindex_type1 and myindex_type2, then change your clients to point to myindex*, which will query across both indexes.
  4. After you have verified that the data is successfully reindexed and your application is working as expected with the new indexes, you should delete the original index before starting the upgrade, because it won’t be supported in the new version. Be cautious when deleting data and make sure you have backups if necessary.
  5. As part of this upgrade, Kibana will be replaced with OpenSearch Dashboards. When you’re done with the upgrade in the next step, you should advocate your users to use the new endpoint, which will be _dashboards. If you use a custom endpoint, be sure to update it to point to /_dashboards.
  6. We recommend that you update your AWS Identity and Access Management (IAM) policies to use the renamed API operations. However, OpenSearch Service will continue to respect existing policies by internally replicating the old API permissions. Service control policies (SCPs) introduce an additional layer of complexity compared to standard IAM. To prevent your SCP policies from breaking, you need to add both the old and the new API operations to each of your SCP policies.

Start the upgrade

The upgrade process is irreversible and can’t be paused or cancelled. You should make sure that everything will go smoothly by running a proof of concept (POC) check. By building a POC, you ensure data preservation, avoid compatibility issues, prevent unwanted bugs, and mitigate risks. You can run a POC with a small domain on the new version, and with a small subset of your data. Deploy and run any front-end code that communicates with OpenSearch Service via API against your test domain. Use your ingest pipeline to send data to your test domain as well, ensuring nothing breaks. Import or rebuild dashboards, alerts, anomaly detectors, and so on. This simple precaution can make your upgrade experience smoother and trouble-free while minimizing potential disruptions.

When OpenSearch Service starts the upgrade, it can take from 15 minutes to several hours to complete. OpenSearch Dashboards might be unavailable during some or all of the duration of the upgrade.

In this section, we show how you can start an upgrade using the AWS Management Console. You can also run the upgrade using the AWS Command Line Interface (AWS CLI) or AWS SDK. For more information, refer to Starting an upgrade (CLI) and Starting an upgrade (SDK).

  1. Take a manual snapshot of your domain.

This snapshot serves as a backup that you can restore on a new domain if you want to return to using the prior version.

  1. On the OpenSearch Service console, choose the domain that you want to upgrade.
  2. On the Actions menu, choose Upgrade.
  3. Select the target version as OpenSearch 1.3. If you’re upgrading to an OpenSearch version, then you’ll be able to enable compatibility mode. If you enable this setting, OpenSearch reports its version as 7.10 to allow Elastic’s open-source clients and plugins like Logstash to continue working with your OpenSearch Service domain.
  4. Choose Check Upgrade Eligibility, which helps you identify if there are any breaking changes you still need to fix before running an upgrade.
  5. Choose Upgrade.
  6. Check the status on the domain dashboard to monitor the status of the upgrade.

The following graphic gives a quick demonstration of running and monitoring the upgrade via the preceding steps.

Post-upgrade changes and validations

Now that you have successfully upgraded your domain to OpenSearch Service 1.3, be sure to make the following changes:

  • Custom endpoint – A custom endpoint for your OpenSearch Service domain makes it straightforward for you to refer to your OpenSearch and OpenSearch Dashboards URLs. You can include your company’s branding or just use a shorter, easier-to-remember endpoint than the standard one. In OpenSearch Service, Kibana has been renamed to dashboards. After you upgrade a domain from the Elasticsearch engine to the OpenSearch engine, the /_plugin/kibana endpoint changes to /_dashboards. If you use the Kibana endpoint to set up a custom domain, you need to update it to include the new /_dashboards endpoint.
  • SAML authentication for OpenSearch Dashboards – After you upgrade your domain to OpenSearch Service, you need to change all Kibana URLs configured in your identity provider (IdP) from /_plugin/kibana to /_dashboards. The most common URLs are assertion consumer service (ACS) URLs and recipient URLs.

Conclusion

This post discussed what to consider when planning to upgrade your existing Elasticsearch engine in your OpenSearch Service domain to the OpenSearch engine. OpenSearch Service continues to support older engine versions, including open-source versions of Elasticsearch from 1.x through 7.10. By migrating forward to OpenSearch Service, you will be able to take advantage of bug fixes, performance improvements, new service features, and expanded instance types for your data nodes, like AWS Graviton 2 instances.

If you have feedback about this post, share it in the comments section. If you have questions about this post, start a new thread on the Amazon OpenSearch Service forum or contact AWS Support.

About the Authors

Prashant Agrawal is a Sr. Search Specialist Solutions Architect with Amazon OpenSearch Service. He works closely with customers to help them migrate their workloads to the cloud and helps existing customers fine-tune their clusters to achieve better performance and save on cost. Before joining AWS, he helped various customers use OpenSearch and Elasticsearch for their search and log analytics use cases. When not working, you can find him traveling and exploring new places. In short, he likes doing Eat → Travel → Repeat.

Harsh Bansal is a Solutions Architect at AWS with Analytics as his area of specialty.He has been building solutions to help organizations make data-driven decisions.

Enable cost-efficient operational analytics with Amazon OpenSearch Ingestion

Post Syndicated from Mikhail Vaynshteyn original https://aws.amazon.com/blogs/big-data/enable-cost-efficient-operational-analytics-with-amazon-opensearch-ingestion/

As the scale and complexity of microservices and distributed applications continues to expand, customers are seeking guidance for building cost-efficient infrastructure supporting operational analytics use cases. Operational analytics is a popular use case with Amazon OpenSearch Service. A few of the defining characteristics of these use cases are ingesting a high volume of time series data and a relatively low volume of querying, alerting, and running analytics on ingested data for real-time insights. Although OpenSearch Service is capable of ingesting petabytes of data across storage tiers, you still have to provision capacity to migrate between hot and warm tiers. This adds to the cost of provisioned OpenSearch Service domains.

The time series data often contains logs or telemetry data from various sources with different values and needs. That is, logs from some sources need to be available in a hot storage tier longer, whereas logs from other sources can tolerate a delay in querying and other requirements. Until now, customers were building external ingestion systems with the Amazon Kinesis family of services, Amazon Simple Queue Service (Amazon SQS), AWS Lambda, custom code, and other similar solutions. Although these solutions enable ingestion of operational data with various requirements, they add to the cost of ingestion.

In general, operational analytics workloads use anomaly detection to aid domain operations. This assumes that the data is already present in OpenSearch Service and the cost of ingestion is already borne.

With the addition of a few recent features of Amazon OpenSearch Ingestion, a fully managed serverless pipeline for OpenSearch Service, you can effectively address each of these cost points and build a cost-effective solution. In this post, we outline a solution that does the following:

  • Uses conditional routing of Amazon OpenSearch Ingestion to separate logs with specific attributes and store those, for example, in Amazon OpenSearch Service and archive all events in Amazon S3 to query with Amazon Athena
  • Uses in-stream anomaly detection with OpenSearch Ingestion, thereby removing the cost associated with compute needed for anomaly detection after ingestion

In this post, we use a VPC flow logs use case to demonstrate the solution. The solution and pattern presented in this post is equally applicable to larger operational analytics and observability use cases.

Solution overview

We use VPC flow logs to capture IP traffic and trigger processing notifications to the OpenSearch Ingestion pipeline. The pipeline filters the data, routes the data, and detects anomalies. The raw data will be stored in Amazon S3 for archival purposes, then the pipeline will detect anomalies in the data in near-real time using the Random Cut Forest (RCF) algorithm and send those data records to OpenSearch Service. The raw data stored in Amazon S3 can be inexpensively retained for an extended period of time using tiered storage and queried using the Athena query engine, and also visualized using Amazon QuickSight or other data visualization services. Although this walkthrough uses VPC flow log data, the same pattern applies for use with AWS CloudTrail, Amazon CloudWatch, any log files as well as any OpenTelemetry events, and custom producers.

The following is a diagram of the solution that we configure in this post.

In the following sections, we provide a walkthrough for configuring this solution.

The patterns and procedures presented in this post have been validated with the current version of OpenSearch Ingestion and the Data Prepper open-source project version 2.4.

Prerequisites

Complete the following prerequisite steps:

  1. We will be using a VPC for demonstration purposes for generating data. Set up the VPC flow logs to publish logs to an S3 bucket in text format. To optimize S3 storage costs, create a lifecycle configuration on the S3 bucket to transition the VPC flow logs to different tiers or expire processed logs. Make a note of the S3 bucket name you configured to use in later steps.
  2. Set up an OpenSearch Service domain. Make a note of the domain URL. The domain can be either public or VPC based, which is the preferred configuration.
  3. Create an S3 bucket for storing archived events, and make a note of S3 bucket name. Configure a resource-based policy allowing OpenSearch Ingestion to archive logs and Athena to read the logs.
  4. Configure an AWS Identity and Access Management (IAM) role or separate IAM roles allowing OpenSearch Ingestion to interact with Amazon SQS and Amazon S3. For instructions, refer to Configure the pipeline role.
  5. Configure Athena or validate that Athena is configured on your account. For instructions, refer to Getting started.

Configure an SQS notification

VPC flow logs will write data in Amazon S3. After each file is written, Amazon S3 will send an SQS notification to notify the OpenSearch Ingestion pipeline that the file is ready for processing.

If the data is already stored in Amazon S3, you can use the S3 scan capability for a one-time or scheduled loading of data through the OpenSearch Ingestion pipeline.

Use AWS CloudShell to issue the following commands to create the SQS queues VpcFlowLogsNotifications and VpcFlowLogsNotifications-DLQ that we use for this walkthrough.

Create a dead-letter queue with the following code

export SQS_DLQ_URL=$(aws sqs create-queue --queue-name VpcFlowLogsNotifications-DLQ | jq -r '.QueueUrl')

echo $SQS_DLQ_URL 

export SQS_DLQ_ARN=$(aws sqs get-queue-attributes --queue-url $SQS_DLQ_URL --attribute-names QueueArn | jq -r '.Attributes.QueueArn') 

echo $SQS_DLQ_ARN

Create an SQS queue to receive events from Amazon S3 with the following code:

export SQS_URL=$(aws sqs create-queue --queue-name VpcFlowLogsNotification --attributes '{
"RedrivePolicy": 
"{\"deadLetterTargetArn\":\"'$SQS_DLQ_ARN'\",\"maxReceiveCount\":\"2\"}", 
"Policy": 
  "{\"Version\":\"2012-10-17\",\"Statement\":[{\"Effect\":\"Allow\",\"Principal\":{\"Service\":\"s3.amazonaws.com\"}, \"Action\":\"SQS:SendMessage\",\"Resource\":\"*\"}]}" 
}' | jq -r '.QueueUrl')

echo $SQS_URL

To configure the S3 bucket to send events to the SQS queue, use the following code (provide the name of your S3 bucket used for storing VPC flow logs):

aws s3api put-bucket-notification-configuration --bucket __BUCKET_NAME__ --notification-configuration '{
     "QueueConfigurations": [
         {
             "QueueArn": "'$SQS_URL'",
             "Events": [
                 "s3:ObjectCreated:*"
             ]
         }
     ]
}'

Create the OpenSearch Ingestion pipeline

Now that you have configured Amazon SQS and the S3 bucket notifications, you can configure the OpenSearch Ingestion pipeline.

  1. On the OpenSearch Service console, choose Pipelines under Ingestion in the navigation pane.
  2. Choose Create pipeline.

  1. For Pipeline name, enter a name (for this post, we use stream-analytics-pipeline).
  2. For Pipeline configuration, enter the following code:
version: "2"
entry-pipeline:
  source:
     s3:
       notification_type: sqs
       compression: gzip
       codec:
         newline:
       sqs:
         queue_url: "<strong>__SQS_QUEUE_URL__</strong>"
         visibility_timeout: 180s
       aws:
        region: "<strong>__REGION__</strong>"
        sts_role_arn: "<strong>__STS_ROLE_ARN__</strong>"
  
  processor:
  sink:
    - pipeline:
        name: "archive-pipeline"
    - pipeline:
        name: "data-processing-pipeline"

data-processing-pipeline:
    source: 
        pipeline:
            name: "entry-pipeline"
    processor:
    - grok:
        tags_on_match_failure: [ "grok_match_failure" ]
        match:
          message: [ "%{VPC_FLOW_LOG}" ]
    route:
        - icmp_traffic: '/protocol == 1'
    sink:
        - pipeline:
            name : "icmp-pipeline"
            routes:
                - "icmp_traffic"
        - pipeline:
            name: "analytics-pipeline"
    

archive-pipeline:
  source:
    pipeline:
      name: entry-pipeline
  processor:
  sink:
    - s3:
        aws:
          region: "<strong>__REGION__</strong>"
          sts_role_arn: "<strong>__STS_ROLE_ARN__</strong>"
        max_retries: 16
        bucket: "<strong>__AWS_S3_BUCKET_ARCHIVE__</strong>"
        object_key:
          path_prefix: "vpc-flow-logs-archive/year=%{yyyy}/month=%{MM}/day=%{dd}/"
        threshold:
          maximum_size: 50mb
          event_collect_timeout: 300s
        codec:
          parquet:
            auto_schema: true
      
analytics-pipeline:
  source:
    pipeline:
      name: "data-processing-pipeline"
  processor:
    - drop_events:
        drop_when: "hasTags(\"grok_match_failure\") or \"/log-status\" == \"NODATA\""
    - date:
        from_time_received: true
        destination: "@timestamp"
    - aggregate:
        identification_keys: ["srcaddr", "dstaddr"]
        action:
          tail_sampler:
            percent: 20.0
            wait_period: "60s"
            condition: '/action != "ACCEPT"'
    - anomaly_detector:
        identification_keys: ["srcaddr","dstaddr"]
        keys: ["bytes"]
        verbose: true
        mode:
          random_cut_forest:
  sink:
    - opensearch:
        hosts: [ "<strong>__AMAZON_OPENSEARCH_DOMAIN_URL__</strong>" ]
        index: "flow-logs-anomalies"
        aws:
          sts_role_arn: "<strong>__STS_ROLE_ARN__</strong>"
          region: "<strong>__REGION__</strong>"
          
icmp-pipeline:
  source:
    pipeline:
      name: "data-processing-pipeline"
  processor:
  sink:
    - opensearch:
        hosts: [ "<strong>__AMAZON_OPENSEARCH_DOMAIN_URL__</strong>" ]
        index: "sensitive-icmp-traffic"
        aws:
          sts_role_arn: "<strong>__STS_ROLE_ARN__</strong>"
          region: "<strong>__REGION__</strong>"</code>

Replace the variables in the preceding code with resources in your account:

    • __SQS_QUEUE_URL__ – URL of Amazon SQS for Amazon S3 events
    • __STS_ROLE_ARN__ – AWS Security Token Service (AWS STS) roles for resources to assume
    • __AWS_S3_BUCKET_ARCHIVE__ – S3 bucket for archiving processed events
    • __AMAZON_OPENSEARCH_DOMAIN_URL__ – URL of OpenSearch Service domain
    • __REGION__ – Region (for example, us-east-1)
  1. In the Network settings section, specify your network access. For this walkthrough, we are using VPC access. We provided the VPC and private subnet locations that have connectivity with the OpenSearch Service domain and security groups.
  2. Leave the other settings with default values, and choose Next.
  3. Review the configuration changes and choose Create pipeline.

It will take a few minutes for OpenSearch Service to provision the environment. While the environment is being provisioned, we’ll walk you through the pipeline configuration. Entry-pipeline listens for SQS notifications about newly arrived files and triggers the reading of VPC flow log compressed files:

…
entry-pipeline:
  source:
     s3:
…

The pipeline branches into two sub-pipelines. The first stores original messages for archival purposes in Amazon S3 in read-optimized Parquet format; the other applies analytics routes events to the OpenSearch Service domain for fast querying and alerting:

…
  sink:
    - pipeline:
        name: "archive-pipeline"
    - pipeline:
        name: "data-processing-pipeline"
…

The pipeline archive-pipeline aggregates messages in 50 MB chunks or every 60 seconds and writes a Parquet file to Amazon S3 with the schema inferred from the message. Also, a prefix is added to help with partitioning and query optimization when reading a collection of files using Athena.

…
sink:
    - s3:
…
        object_key:
          path_prefix: " vpc-flow-logs-archive/year=%{yyyy}/month=%{MM}/day=%{dd}/"
        threshold:
          maximum_size: 50mb
          event_collect_timeout: 300s
        codec:
          parquet:
            auto_schema: true
…

Now that we have reviewed the basics, we focus on the pipeline that detects anomalies and sends only high-value messages that deviate from the norm to OpenSearch Service. It also stores Internet Control Message Protocols (ICMP) messages in OpenSearch Service.

We applied a grok processor to parse the message using a predefined regex for parsing VPC flow logs, and also tagged all unparsable messages with the grok_match_failure tag, which we use to remove headers and other records that can’t be parsed:

…
    processor:
    - grok:
        tags_on_match_failure: [ "grok_match_failure" ]
        match:
          message: [ "%{VPC_FLOW_LOG}" ]
…

We then routed all messages with the protocol identifier 1 (ICMP) to icmp-pipeline and all messages to analytics-pipeline for anomaly detection:

…
   route:
        - icmp_traffic: '/protocol == 1'
    sink:
        - pipeline:
            name : "icmp-pipeline"
            routes:
                - "icmp_traffic"
        - pipeline:
            name: "analytics-pipeline"
…

In the analytics pipeline, we dropped all records that can’t be parsed using the hasTags method based on the tag that we assigned at the time of parsing. We also removed all records that don’t contains useful data for anomaly detection.

…
  - drop_events:
        drop_when: "hasTags(\"grok_match_failure\") or \"/log-status\" == \"NODATA\""		
…

Then we applied probabilistic sampling using the tail_sampler processor for all accepted messages grouped by source and destination addresses and sent those to the sink with all messages that were not accepted. This helps reduce the volume of messages within the selected cardinality keys, with a focus on all messages that weren’t accepted, and keeps a sample representation of messages that were accepted.

…
aggregate:
        identification_keys: ["srcaddr", "dstaddr"]
        action:
          tail_sampler:
            percent: 20.0
            wait_period: "60s"
            condition: '/action != "ACCEPT"'
…

Then we used the anomaly detector processor to identify anomalies within the cardinality key pairs or source and destination addresses in our example. The anomaly detector processor creates and trains RCF models for a hashed value of keys, then uses those models to determine whether newly arriving messages have an anomaly based on the trained data. In our demonstration, we use bytes data to detect anomalies:

…
anomaly_detector:
        identification_keys: ["srcaddr","dstaddr"]
        keys: ["bytes"]
        verbose: true
        mode:
          random_cut_forest:
…

We set verbose:true to instruct the detector to emit the message every time an anomaly is detected. Also, for this walkthrough, we used a non-default sample_size for training the model.

When anomalies are detected, the anomaly detector returns a complete record and adds

"deviation_from_expected":value,"grade":value attributes that signify the deviation value and severity of the anomaly. These values can be used to determine routing of such messages to OpenSearch Service, and use per-document monitoring capabilities in OpenSearch Service to alert on specific conditions.

Currently, OpenSearch Ingestion creates up to 5,000 distinct models based on cardinality key values per compute unit. This limit is observed using the anomaly_detector.RCFInstances.value CloudWatch metric. It’s important to select a cardinality key-value pair to avoid exceeding this constraint. As development of the Data Prepper open-source project and OpenSearch Ingestion continues, more configuration options will be added to offer greater flexibility around model training and memory management.

The OpenSearch Ingestion pipeline exposes the anomaly_detector.cardinalityOverflow.count metric through CloudWatch. This metric indicates a number of key value pairs that weren’t run by the anomaly detection processor during a period of time as the maximum number of RCFInstances per compute unit was reached. To avoid this constraint, a number of compute units can be scaled out to provide additional capacity for hosting additional instances of RCFInstances.

In the last sink, the pipeline writes records with detected anomalies along with deviation_from_expected and grade attributes to the OpenSearch Service domain:

…
sink:
    - opensearch:
        hosts: [ "__AMAZON_OPENSEARCH_DOMAIN_URL__" ]
        index: "anomalies"
…

Because only anomaly records are being routed and written to the OpenSearch Service domain, we are able to significantly reduce the size of our domain and optimize the cost of our sample observability infrastructure.

Another sink was used for storing all ICMP records in a separate index in the OpenSearch Service domain:

…
sink:
    - opensearch:
        hosts: [ "__AMAZON_OPENSEARCH_DOMAIN_URL__" ]
        index: " sensitive-icmp-traffic"
…

Query archived data from Amazon S3 using Athena

In this section, we review the configuration of Athena for querying archived events data stored in Amazon S3. Complete the following steps:

  1. Navigate to the Athena query editor and create a new database called vpc-flow-logs-archive-database using the following command:
CREATE DATABASE `vpc-flow-logs-archive`
  1. 2. On the Database menu, choose vpc-flow-logs-archive.
  2. In the query editor, enter the following command to create a table (provide the S3 bucket used for archiving processed events). For simplicity, for this walkthrough, we create a table without partitions.
CREATE EXTERNAL TABLE `vpc-flow-logs-data`(
  `message` string)
ROW FORMAT SERDE 
  'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' 
STORED AS INPUTFORMAT 
  'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' 
OUTPUTFORMAT 
  'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
LOCATION
  's3://__AWS_S3_BUCKET_ARCHIVE__'
TBLPROPERTIES (
  'classification'='parquet', 
  'compressionType'='none'
)
  1. Run the following query to validate that you can query the archived VPC flow log data:
SELECT * FROM "vpc-flow-logs-archive"."vpc-flow-logs-data" LIMIT 10;

Because archived data is stored in its original format, it helps avoid issues related to format conversion. Athena will query and display records in the original format. However, it’s ideal to interact only with a subset of columns or parts of the messages. You can use the regexp_split function in Athena to split the message in the columns and retrieve certain columns. Run the following query to see the source and destination address groupings from the VPC flow log data:

SELECT srcaddr, dstaddr FROM (
   SELECT regexp_split(message, ' ')[4] AS srcaddr, 
          regexp_split(message, ' ')[5] AS dstaddr, 
          regexp_split(message, ' ')[14] AS status  FROM "vpc-flow-logs-archive"."vpc-flow-logs-data" 
) WHERE status = 'OK' 
GROUP BY srcaddr, dstaddr 
ORDER BY srcaddr, dstaddr LIMIT 10;

This demonstrated that you can query all events using Athena, where archived data in its original raw format is used for the analysis. Athena is priced per data scanned. Because the data is stored in a read-optimized format and partitioned, it enables further cost-optimization around on-demand querying of archived streaming and observability data.

Clean up

To avoid incurring future charges, delete the following resources created as part of this post:

  • OpenSearch Service domain
  • OpenSearch Ingestion pipeline
  • SQS queues
  • VPC flow logs configuration
  • All data stored in Amazon S3

Conclusion

In this post, we demonstrated how to use OpenSearch Ingestion pipelines to build a cost-optimized infrastructure for log analytics and observability events. We used routing, filtering, aggregation, and anomaly detection in an OpenSearch Ingestion pipeline, enabling you to downsize your OpenSearch Service domain and create a cost-optimized observability infrastructure. For our example, we used a data sample with 1.5 million events with a pipeline distilling to 1,300 events with predicted anomalies based on source and destination IP pairs. This metric demonstrates that the pipeline identified that less than 0.1% of events were of high importance, and routed those to OpenSearch Service for visualization and alerting needs. This translates to lower resource utilization in OpenSearch Service domains and can lead to provisioning of smaller OpenSearch Service environments.

We encourage you to use OpenSearch Ingestion pipelines to create your purpose-built and cost-optimized observability infrastructure that uses OpenSearch Service for storing and alerting on high-value events. If you have comments or feedback, please leave them in the comments section.

About the Authors

Mikhail Vaynshteyn is a Solutions Architect with Amazon Web Services. Mikhail works with healthcare and life sciences customers to build solutions that help improve patients’ outcomes. Mikhail specializes in data analytics services.

Muthu Pitchaimani is a Search Specialist with Amazon OpenSearch Service. He builds large-scale search applications and solutions. Muthu is interested in the topics of networking and security, and is based out of Austin, Texas.

Build multi-layer maps in Amazon OpenSearch Service

Post Syndicated from Aish Gunasekar original https://aws.amazon.com/blogs/big-data/build-multi-layer-maps-in-amazon-opensearch-service/

With the release of Amazon OpenSearch Service 2.5, you can create maps with multiple layers to visualize your geographical data. You can build each layer from a different index pattern to separate data sources. Organizing the map in layers makes it more straightforward to visualize, view, and analyze geographical data. The layering also helps fetch data from various sources, view different data at different zoom levels, and continuously refresh data for a real-time dataset. Additionally, in OpenSearch 2.6, you can add multi-layer maps to dashboard panes within OpenSearch Dashboards, which makes it more straightforward to analyze your geospatial data in the context of other visualizations. OpenSearch Service provisions all the resources for your OpenSearch cluster so you can focus on building the business value through maps and other features of OpenSearch rather than spending time managing your deployment.

In this post, we show how to use multi-layer maps in OpenSearch Service.

Solution overview

The multi-layer map helps users visualize data and gain insights using specific layers and zoom levels to help emphasize key messages. For our use case, we use multi-layer maps to support an example real-estate application. Your index will have fields such as location, address, availability, number of bedrooms, price, and more. Initially, we develop a map with state boundaries with aggregated data for an overview. As the user zooms in, they’ll see city boundaries and postal codes. As the user continues to zoom, they’ll see each house with information like the price, number of bedrooms, and so on. You will build various data layers from different data sources to accomplish this task. You can also add filter queries; for example, to only show properties that are available.

Prerequisites

Complete the following prerequisite steps to configure the sample data:

  1. Create an OpenSearch Service domain (version 2.7 or higher).
  2. Download the file bulk_request_realestate.txt.
  3. Copy and paste the entire contents of the downloaded file into the OpenSearch Dashboards console.
  4. Run the commands.

These commands create the realestate index and upload records into the catalog.

Now let’s visualize this data in a multi-layer map.

Add layers

To create your map and add layers, complete the following steps:

  1. On OpenSearch Dashboards, in the navigation pane, under OpenSearch plugins, choose Maps.
  2. Choose Create map.

You will see a default map (or basemap) loaded on the page with a Layers pane on the left. This serves as a canvas for the data. The OpenSearch basemap utilizes vector tiles, resulting in quicker loading speeds and seamless zooming compared to raster tile maps. It effectively accommodates zoom levels ranging from 0–22. 0 is the most zoomed out with the global view, and zoom level 22 is roughly a half-inch per pixel resolution. Any additional layers you add will appear in this pane.

  1. Choose Add layer.
  2. In the prompt, choose the option to add documents for the data layer.
  3. Under Documents, choose the index you created (realestate) as the data source.
  4. For the geospatial field, choose the field containing geopoints (such as location in this example).
  5. Keep the remaining settings at their default values, then choose Update.

In the Layers pane, a newly generated layer named New Layer 2 will be visible. Additionally, you will observe the presence of all geopoints on the map (green dots in the following screenshot).

Update the layer name and enable tooltips

For an overview of the various configuration choices accessible for the layers, choose New Layer 2. This action opens another pane containing three tabs: Data, Style, and Settings. Let’s modify the layer’s name to something more relevant and enable tooltips. Complete the following steps:

  1. On the Settings tab, replace New Layer 2 with realestate-data in the Name field.
  2. On the Data tab, scroll down to Tool tips, and select Tool tips.
  3. Enter region as the tooltip.
  4. Choose Update.

The altered name should now be visible in the left pane. The geopoints themselves don’t convey any information. However, with tooltips enabled, you can access comprehensive information depending on the fields selected. As you hover over the geopoints, you will observe the chosen tooltip information—in this instance, region CA.

Adjust zoom levels

The essence of this functionality lies in the ability to observe your data through distinct layers at varying zoom levels. To achieve this, generate a layer using the same process as before. The following example shows a new layer (locality) featuring tooltips displaying locality and postal code. You can also choose the color of your geographical points on the Style tab. On the Settings tab, you’ll encounter options for zoom levels, allowing you to input minimum and maximum values—like 4 and 6, for instance. Consequently, this indicates that the layer will be visible exclusively within this range of zoom levels.

In the Layers pane, you can observe three layers alongside the locality layer created in the previous step. A notification will indicate “Layer is hidden outside of zoom range 1-2.” This layer becomes visible as you zoom in.

The realestate-data layer is set with a default zoom range of 0–22, ensuring visibility across all levels unless manually hidden. The locality layer is configured to be visible exclusively within the zoom range of 1–2.

As shown in the following screenshot, the tooltip for the realestate-data layer remains visible even after the fourth zoom level. To access the tooltip information for the locality layer, choose the eye icon next to realestate-data to manually conceal this layer. Once completed, hovering over the geopoints will reveal the tooltip details for locality (postal code and locality).

The following are some key points to consider:

  • Each layer can be established with distinct colors for its geographical points. For instance, the realestate-data layer is depicted in green, while the locality layer uses orange.
  • It’s possible to observe geopoints in a color that wasn’t directly chosen. In the following screenshot, brown is visible due to the overlapping of two layers at the same zoom level.
  • You can observe the color shift to the layer’s designated color—orange—after realestate-data is manually hidden, because there’s no longer an overlap between the layers.

You can generate an additional layer designed to display tooltip data such as the count of beds, baths, price, and square footage. This layer will be active within the zoom range of 3–4.

To save your project, choose Save. Enter a title, such as realestate-multilayer-map, then choose Save again. Your multilayer map is now successfully saved!

Exploring the multi-level map

After you have established all the layers, take note of how the layers become visible or invisible at each zoom level. Observe the dynamic adjustments in tooltip information that correspond to these changes as you zoom.

Add a filter

After you have generated multiple layers and successfully visualized your geopoints, you might find that you are interested in specific properties, such as within a particular price range.

To add a filter at layer level, complete the following steps:

  1. In the right pane, on the Data tab, choose Filters.
  2. Input price as the filter criteria.
  3. Select is between as the operator.
  4. Enter 800000 for the start of the range and 1400000 for the end of the range.
  5. Choose Save to update the layer.

You’ll immediately observe the filter taking effect, resulting in the display of only the relevant data matching the filter.

An alternative method to establish a filter involves drawing shapes on the map, such as rectangles or polygons. In this instance, you’ll be utilizing the polygon option. (For API-based filtering, refer to APIs).

  1. Choose the polygon icon on the right side of the map.
  2. For Filter label, enter a name for the filter.
  3. Draw the shape over the map area that you want to select.
  4. For a polygon, select any starting point on the map (this point becomes a polygon vertex) and hover (do not drag) to each subsequent vertex and select that point.
  5. Make sure to select the starting point again to close the polygon, as shown in the following screenshot.

Add a map to a dashboard

You can add this map to an existing or a new dashboard. Complete the following steps:

  1. On OpenSearch Dashboards, choose Create and choose Dashboard.

  1. Select Add an existing dashboard.
  2. Choose realestate-multilayer from the list.

You can see the new visualization on your dashboard.

  1. Choose Save and enter a title for the dashboard.

Conclusion

In this post, you effectively established multi-layer maps for data visualization, analyzed geographic data, observed various data at varying zoom levels, added tooltips for supplementary data visualization, and added multi-layer maps to dashboard panes within OpenSearch Dashboards to easily analyze your geospatial data. Refer to Using maps for an alternative use case and detailed information about map features.

About the authors

Aish Gunasekar is a Specialist Solutions Architect with a focus on Amazon OpenSearch Service. Her passion at AWS is to help customers design highly scalable architectures and help them in their cloud adoption journey. Outside of work, she enjoys hiking and baking.

Satish Nandi is a Senior Technical Product Manager for Amazon OpenSearch Service.

Jon Handler is a Senior Principal Solutions Architect at Amazon Web Services based in Palo Alto, CA. Jon works closely with OpenSearch and Amazon OpenSearch Service, providing help and guidance to a broad range of customers who have search and log analytics workloads that they want to move to the AWS Cloud. Prior to joining AWS, Jon’s career as a software developer included 4 years of coding a large-scale, ecommerce search engine. Jon holds a Bachelor of the Arts from the University of Pennsylvania, and a Master of Science and a PhD in Computer Science and Artificial Intelligence from Northwestern University.

SmugMug’s durable search pipelines for Amazon OpenSearch Service

Post Syndicated from Lee Shepherd original https://aws.amazon.com/blogs/big-data/smugmugs-durable-search-pipelines-for-amazon-opensearch-service/

SmugMug operates two very large online photo platforms, SmugMug and Flickr, enabling more than 100 million customers to safely store, search, share, and sell tens of billions of photos. Customers uploading and searching through decades of photos helped turn search into critical infrastructure, growing steadily since SmugMug first used Amazon CloudSearch in 2012, followed by Amazon OpenSearch Service since 2018, after reaching billions of documents and terabytes of search storage.

Here, Lee Shepherd, SmugMug Staff Engineer, shares SmugMug’s search architecture used to publish, backfill, and mirror live traffic to multiple clusters. SmugMug uses these pipelines to benchmark, validate, and migrate to new configurations, including Graviton based r6gd.2xlarge instances from i3.2xlarge, along with testing Amazon OpenSearch Serverless. We cover three pipelines used for publishing, backfilling, and querying without introducing spiky unrealistic traffic patterns, and without any impact on production services.

There are two main architectural pieces critical to the process:

  • A durable source of truth for index data. It’s best practice and part of our backup strategy to have a durable store beyond the OpenSearch index, and Amazon DynamoDB provides scalability and integration with AWS Lambda that simplifies a lot of the process. We use DynamoDB for other non-search services, so this was a natural fit.
  • A Lambda function for publishing data from the source of truth into OpenSearch. Using function aliases helps run multiple configurations of the same Lambda function at the same time and is key to keeping data in sync.

Publishing

The publishing pipeline is driven from events like a user entering keywords or captions, new uploads, or label detection through Amazon Rekognition. These events are processed, combining data from a few other asset stores like Amazon Aurora MySQL Compatible Edition and Amazon Simple Storage Service (Amazon S3), before writing a single item into DynamoDB.

Writing to DynamoDB invokes a Lambda publishing function, through the DynamoDB Streams Kinesis Adapter, that takes a batch of updated items from DynamoDB and indexes them into OpenSearch. There are other benefits to using the DynamoDB Streams Kinesis Adapter such as reducing the number of concurrent Lambdas required.

The publishing Lambda function uses environment variables to determine what OpenSearch domain and index to publish to. A production alias is configured to write to the production OpenSearch domain, off of the DynamoDB table or Kinesis Stream

When testing new configurations or migrating, a migration alias is configured to write to the new OpenSearch domain but use the same trigger as the production alias. This enables dual indexing of data to both OpenSearch Service domains simultaneously.

Here’s an example of the DynamoDB table schema:

 "Id": 123456,  // partition key
 "Fields": {
  "format": "JPG",
  "height": 1024,
  "width": 1536,
  ...
 },
 "LastUpdated": 1600107934,

The ‘LastUpdated’ value is used as the document version when indexing, allowing OpenSearch to reject any out-of-order updates.

Backfilling

Now that changes are being published to both domains, the new domain (index) needs to be backfilled with historical data. To backfill a newly created index, a combination of Amazon Simple Queue Service (Amazon SQS) and DynamoDB is used. A script populates an SQS queue with messages that contain instructions for parallel scanning a segment of the DynamoDB table.

The SQS queue launches a Lambda function that reads the message instructions, fetches a batch of items from the corresponding segment of the DynamoDB table, and writes them into an OpenSearch index. New messages are written to the SQS queue to keep track of progress through the segment. After the segment completes, no more messages are written to the SQS queue and the process stops itself.

Concurrency is determined by the number of segments, with additional controls provided by Lambda concurrency scaling. SmugMug is able to index more than 1 billion documents per hour on their OpenSearch configuration while incurring zero impact to the production domain.

A NodeJS AWS-SDK based script is used to seed the SQS queue. Here’s a snippet of the SQS configuration script’s options:

Usage: queue_segments [options]

Options:
--search-endpoint <url>  OpenSearch endpoint url
--sqs-url <url>          SQS queue url
--index <string>         OpenSearch index name
--table <string>         DynamoDB table name
--key-name <string>      DynamoDB table partition key name
--segments <int>         Number of parallel segments

Along with the format of the resulting SQS message:

{
  searchEndpoint: opts.searchEndpoint,
  sqsUrl: opts.sqsUrl,
  table: opts.table,
  keyName: opts.keyName,
  index: opts.index,
  segment: i,
  totalSegments: opts.segments,
  exclusiveStartKey: <lastEvaluatedKey from previous iteration>
}

As each segment is processed, the ‘lastEvaluatedKey’ from the previous iteration is added to the message as the ‘exclusiveStartKey’ for the next iteration.

Mirroring

Last, our mirrored search query results run by sending an OpenSearch query to an SQS queue, in addition to our production domain. The SQS queue launches a Lambda function that replays the query to the replica domain. The search results from these requests are not sent to any user but allow replicating production load on the OpenSearch service under test without impact to production systems or customers.

Conclusion

When evaluating a new OpenSearch domain or configuration, the main metrics we are interested in are query latency performance, namely the took latencies (latencies per time), and most importantly latencies for searching. In our move to Graviton R6gd, we saw about 40 percent lower P50-P99 latencies, along with similar gains in CPU usage compared to i3’s (ignoring Graviton’s lower costs). Another welcome benefit was the more predictable and monitorable JVM memory pressure with the garbage collection changes from the addition of G1GC on R6gd and other new instances.

Using this pipeline, we’re also testing OpenSearch Serverless and finding its best use-cases. We’re excited about that service and fully intend to have an entirely serverless architecture in time. Stay tuned for results.

About the Authors

Lee Shepherd is a SmugMug Staff Software Engineer

Aydn Bekirov is an Amazon Web Services Principal Technical Account Manager

Unleash the power of Snapshot Management to take automated snapshots using Amazon OpenSearch Service

Post Syndicated from Prashant Agrawal original https://aws.amazon.com/blogs/big-data/unleash-the-power-of-snapshot-management-to-take-automated-snapshots-using-amazon-opensearch-service/

Data is the lifeblood of any organization, and the importance of protecting it cannot be overstated. Starting with OpenSearch v2.5 in Amazon OpenSearch Service, we introduced Snapshot Management, which automates the process of taking snapshots of your domain. Snapshot Management helps you create point-in-time backups of your domain using OpenSearch Dashboards, including both data and configuration settings (for visualizations and dashboards). You can use these snapshots to restore your cluster to a specific state, recover from potential failures, and even clone environments for testing or development purposes.

Before this release, to automate the process of taking snapshots, you needed to use the snapshot action of OpenSearch’s Index State Management (ISM) feature. With ISM, you could only back up a particular index. Automating backup for multiple indexes required you to write custom scripts or use external management tools. With Snapshot Management, you can automate snapshotting across multiple indexes to safeguard your data and ensure its durability and recoverability.

In this post, we share how to use Snapshot Management to take automated snapshots using OpenSearch Service.

Solution overview

We demonstrate the following high-level steps:

  1. Register a snapshot repository in OpenSearch Service (a one-time process).
  2. Configure a sample ISM policy to migrate the indexes from hot storage to the UltraWarm storage tier after the indexes meet a specific condition.
  3. Create a Snapshot Management policy to take an automated snapshot for all indexes present across different storage tiers within a domain.

As of this writing, Snapshot Management doesn’t support single snapshot creation for all indexes present across different storage tiers within OpenSearch Service. For example, if you try to create a snapshot on multiple indexes with * and some indexes are in the warm tier, the snapshot creation will fail.

To overcome this limitation, you can use index aliases, with one index alias for each type of storage tier. For example, every new index created in the cluster will belong to the hot alias. When the index is moved to the UltraWarm tier via ISM, the alias for the index will be modified to warm, and the index will be removed from the hot alias.

Register a manual snapshot repository

To register a manual snapshot repository, you must create and configure an Amazon Simple Storage Service (Amazon S3) bucket and AWS Identity and Access Management (IAM) roles. For more information, refer to Prerequisites. Complete the following steps:

  1. Create an S3 bucket to store snapshots for your OpenSearch Service domain.
  2. Create an IAM role called SnapshotRole with the following IAM policy to delegate permissions to OpenSearch Service (provide the name of your S3 bucket):
{
    "Version": "2012-10-17",
    "Statement": [{
        "Action": ["s3:ListBucket"],
        "Effect": "Allow",
        "Resource": ["arn:aws:s3:::<s3-bucket-name>"]
    }, {
        "Action": ["s3:GetObject", "s3:PutObject", "s3:DeleteObject"],
        "Effect": "Allow",
        "Resource": ["arn:aws:s3:::<s3-bucket-name>/*"]
    }]
}
  1. Set the trust relationship for SnapshotRole as follows:
{
    "Version": "2012-10-17",
    "Statement": [{
        "Sid": "",
        "Effect": "Allow",
        "Principal": {
            "Service": "es.amazonaws.com"
        },
        "Action": "sts:AssumeRole"
    }]
}
  1. Create a new IAM role called RegisterSnapshotRepo, which delegates iam:PassRole and es:ESHttpPut (provide your AWS account and domain name):
{
    "Version": "2012-10-17",
    "Statement": [{
        "Effect": "Allow",
        "Action": "iam:PassRole",
        "Resource": "arn:aws:iam::<aws account id>:role/SnapshotRole"
    }, {
        "Effect": "Allow",
        "Action": "es:ESHttpPut",
        "Resource": "arn:aws:es:region:<aws account id>:domain/<domain-name>/*"
    }]
}
  1. If you have enabled fine-grained access control for your domain, map the snapshot role manage_snapshots to your RegisterSnapshotRepo IAM role in OpenSearch Service.
  2. Now you can use Python code like the following example to register the S3 bucket you created as a snapshot repository for your domain. Provide your host name, Region, snapshot repo name, and S3 bucket. Replace "arn:aws:iam::123456789012:role/SnapshotRole" with the ARN of your SnapshotRole. The Boto3 session should use the RegisterSnapshotRepo IAM role.
import boto3
import requests
from requests_aws4auth import AWS4Auth
 
host = '<host>' # domain endpoint with trailing /
region = '<region>' # e.g. us-west-1
service = 'es'
credentials = boto3.Session().get_credentials()
awsauth = AWS4Auth(credentials.access_key, credentials.secret_key, region, service, session_token=credentials.token)
 
# To Register the repository

path = '_snapshot/<snapshot-repo-name>'
url = host + path
 
payload = {
  "type": "s3",
  "settings": {
    "bucket": "<s3-bucket-name>",
    "region": "<region>",
    "role_arn": "arn:aws:iam::123456789012:role/SnapshotRole"
  }
}
 
headers = {"Content-Type": "application/json"}
 
r = requests.put(url, auth=awsauth, json=payload, headers=headers, timeout=300)
 
print(r.status_code)
print(r.text)

The S3 bucket used as a repository must be in the same Region as your domain. You can run the preceding code in any compute instance that has connectivity to your OpenSearch Service domain, such as Amazon Elastic Compute Cloud (Amazon EC2), AWS Cloud9, or AWS Lambda. If your domain is within a VPC, then the compute instance should be running inside the same VPC or have connectivity to the VPC.

If you have to register the snapshot repository from a local machine, replace the following lines in the preceding code:

credentials = boto3.Session().get_credentials()
awsauth = AWS4Auth(credentials.access_key, credentials.secret_key, region, service, session_token=credentials.token)

Replace this code with the following and assume the RegisterSnapshotRepo role (make sure the IAM entity that you are using has appropriate permission to assume the RegisterSnapshotRepo role). Modify "arn:aws:iam::123456789012:role/RegisterSnapshotRepo" with the ARN of your RegisterSnapshotRepo role.

sts = boto3.Session().client("sts")
response = sts.assume_role(
    RoleArn="arn:aws:iam::123456789012:role/RegisterSnapshotRepo",
    RoleSessionName="Snapshot-Session"
)

awsauth = AWS4Auth(response['Credentials']['AccessKeyId'], response['Credentials']['SecretAccessKey'], region, service, session_token=response['Credentials']['SessionToken'])

Upon successfully running the sample code, you should receive output such as the following:

200
{"acknowledged":true}

You can also verify that you have successfully registered the snapshot repository by accessing OpenSearch Dashboards in your domain and navigating to Snapshots Managements, Repositories.

Create an index template

Index templates enable you to automatically assign configuration when new indexes are created that match a wildcard index pattern.

  1. Navigate to your domain’s OpenSearch Dashboards and choose the Dev Tools tab.
  2. Enter the following text in the left pane and choose the play icon to run it.

The index template applies to newly created indexes that match the pattern of "log*". These indexes are attached to the alias named hot and the replica count is set to 1.

PUT _index_template/snapshot_template
{
  "index_patterns" : ["log*"],
  "template": {
      "settings": {
      "number_of_replicas": 1
    },
    "aliases" : {
        "hot" : {}
    }
  }
}

Note that in the preceding example, you assign the template to indexes that match "log*". You can modify index_patterns for your use case.

Create an ISM policy

In this step, you create the ISM policy that updates the alias of an index before it is migrated to UltraWarm. The policy also performs hot to warm migration. This is to overcome the limitations where a snapshot can’t be taken across two storage tiers (hot and UltraWarm). Complete the following steps:

  1. Navigate to the Dev Tools page of OpenSearch Dashboards.
  2. Create the ISM policy using the following command (modify the values of index_patterns and min_index_age accordingly):
PUT /_plugins/_ism/policies/alias_policy
{
    "policy": {
        "policy_id": "alias_policy",
        "description": "Example Policy for changing the alias and performing the warm migration",
        "default_state": "hot_alias",
        "states": [{
            "name": "hot_alias",
            "actions": [],
            "transitions": [{
                "state_name": "warm",
                "conditions": {
                    "min_index_age": "30d"
                }
            }]
        }, {
            "name": "warm",
            "actions": [{
                "alias": {
                    "actions": [{
                        "remove": {
                            "aliases": ["hot"]
                        }
                    }, {
                        "add": {
                            "aliases": ["warm"]
                        }
                    }]
                }
            }, {
                "retry": {
                    "count": 5,
                    "backoff": "exponential",
                    "delay": "1h"
                },
                "warm_migration": {}
            }],
            "transitions": []
        }],
        "ism_template": [{
            "index_patterns": ["log*"],
            "priority": 100
        }]
    }
}

Create a snapshot policy

In this step, you create a snapshot policy, which takes a snapshot for all the indexes aliased as hot and stores them to your repository at the scheduled time in the cron expression (midnight UTC). Complete the following steps:

  1. Navigate to the Dev Tools page of OpenSearch Dashboards.
  2. Create the snapshot policy using the following command (modify the value of snapshot-repo-name to the name of the snapshot repository you registered previously):
POST _plugins/_sm/policies/daily-snapshot-for-manual-repo
{
    "description": "Policy for Daily Snapshot in the Manual repo",
    "creation": {
      "schedule": {
        "cron": {
          "expression": "0 0 * * *",
          "timezone": "UTC"
        }
      }
    },
    "deletion": {
      "schedule": {
        "cron": {
          "expression": "0 1 * * *",
          "timezone": "UTC"
        }
      },
      "condition": {
        "min_count": 1,
        "max_count": 40
      }
    },
    "snapshot_config": {
      "indices": "hot",
      "repository": "snapshot-repo-name"
    }
}

Clean up

Snapshots that you create incur cost in the S3 bucket used as the repository. With the new Snapshots Management feature, you can easily list and delete unwanted snapshots, and delete the ISM policy to stop taking manual snapshots directly from OpenSearch Dashboards.

Conclusion

With the new Snapshot Management capabilities of OpenSearch Service, you can create regular backups and ensure the availability of your data even in the event of unexpected events or disasters. In this post, we discussed essential concepts such as snapshot repositories, automated snapshot lifecycle policies, and Snapshot Management options, enabling you to make informed decisions when it comes to managing your data backups effectively. As you continue to explore and harness the potential of OpenSearch Service, incorporating Snapshot Management into your data protection strategy will undoubtedly provide you with the resilience and reliability needed to ensure business continuity.

If you have feedback about this post, share it in the comments section. If you have questions about this post, start a new thread on the Amazon OpenSearch Service forum or contact AWS Support.

About the authors

Prashant Agrawal is a Sr. Search Specialist Solutions Architect with Amazon OpenSearch Service. He works closely with customers to help them migrate their workloads to the cloud and helps existing customers fine-tune their clusters to achieve better performance and save on cost. Before joining AWS, he helped various customers use OpenSearch and Elasticsearch for their search and log analytics use cases. When not working, you can find him traveling and exploring new places. In short, he likes doing Eat → Travel → Repeat.

Hendy Wijaya is a Senior OpenSearch Specialist Solutions Architect at Amazon Web Services. Hendy enables customers to leverage AWS services to achieve their business objectives and gain competitive advantages. He is passionate in collaborating with customers in getting the best out of OpenSearch and Amazon OpenSearch

Utkarsh Agarwal is a Cloud Support Engineer in the Support Engineering team at Amazon Web Services. He specializes in Amazon OpenSearch Service. He provides guidance and technical assistance to customers thus enabling them to build scalable, highly available and secure solutions in AWS Cloud. In his free time, he enjoys watching movies, TV series and of course cricket! Lately, he his also attempting to master the art of cooking in his free time – The taste buds are excited, but the kitchen might disagree.

Achieve higher query throughput: Auto scaling in Amazon OpenSearch Serverless now supports shard replica scaling

Post Syndicated from Prashant Agrawal original https://aws.amazon.com/blogs/big-data/achieve-higher-query-throughput-auto-scaling-in-amazon-opensearch-serverless-now-supports-shard-replica-scaling/

Amazon OpenSearch Serverless is the serverless option for Amazon OpenSearch Service that makes it simple for you to run search and analytics workloads without having to think about infrastructure management. We recently announced new enhancements to autoscaling in OpenSearch Serverless that scales capacity automatically in response to your query loads.

At launch, OpenSearch Serverless supported increasing capacity automatically in response to growing data sizes. With the new shard replica scaling feature, OpenSearch Serverless automatically detects shards under duress due to sudden spikes in query rates and dynamically adds new shard replicas to handle the increased query throughput while maintaining fast response times. This approach proves to be more cost-efficient than simply adding new index replicas. With the expanded support for more replicas, OpenSearch Serverless can now handle thousands of query transactions per minute. OpenSearch Serverless will also seamlessly scale the shard replicas back to a minimum of two active replicas across the Availability Zones when the workload demand decreases.

Scaling overview

Consider an ecommerce website that uses OpenSearch Serverless as a backend search engine to host its product catalog.

In the following figure, an index has four shards to handle the product catalog. All four shards fit into one OpenSearch Capacity Unit (OCU). Because OpenSearch Serverless is designed to cater to production systems, it will automatically create an additional set of replicas for these four shards, which are hosted in a separate Availability Zone. Both sets of search replicas will actively respond to the incoming traffic load.
When new products are launched, they often generate more interest, resulting in increased traffic and search queries on the website in the days following the launch. In this scenario, the shards containing the data for the new product will receive significantly higher volume of search requests than other shards within the same index. OpenSearch Serverless will identify these shards as hot shards because they’re close to breaching the system thresholds.
To handle the spike in search requests, OpenSearch Serverless will vertically scale the OCUs and then move the hot shards to a new OCU if required to balance the high query rates. The following figure shows how the shards would be moved to a new OCU along with other normally loaded shards.
If OpenSearch Serverless keeps receiving additional search requests for shards, it will add new replicas for the shard until all shard replicas can effectively handle the incoming query rates without exceeding the system thresholds. Even after the traffic is successfully handled by OpenSearch Serverless, it continues to evaluate the shard state. When the load on the shards reduces, OpenSearch Serverless will scale down the shard replicas to maintain the minimum OCU and replicas required for the workload.

Search performance with replica scale-out

We ran a performance test on a search corpus representing a product catalog with 600,000 documents and approximately 500 MB. The queries were a mix of term, fuzzy, and aggregation queries. OpenSearch Serverless was able to handle 613 transactions per second (TPS) with P50 latency of 2.8 seconds, whereas with replica scaling, we saw the search throughput scale to 1423 TPS with a 100% increase in throughput and P50 latency of 690 milliseconds, leading to a 75% improvement in response times. The following table summarizes our results. Note that you can configure the max OCU limit to control your costs.

. Initial OCUs Scaled OCUs TPS P50 Latency Number of Replicas
With no replica scaling 2 26 613 2.8 secs 2
With replica scaling 2 100 1423 619ms Replica scaling scales the hot shards up to 8 replicas

The following graphs show that under the same load profile, the new autoscaling feature handled a higher number of queries in the period of 24 hours while consistently maintaining lower latency.

The first graph shows the system performance profile without auto scaling.

The second graph shows the system performance profile with replica scaling.

Conclusion

In this post, we showed how the OpenSearch Serverless new shard replica scale-out feature for auto scaling helps you achieve higher throughput while maintaining cost-efficiency for search and time series collections. It automatically scales the replicas for those shards under duress instead of adding replicas for the entire index.

If you have feedback about this post, share it in the comments section. If you have questions about this post, start a new thread on the Amazon OpenSearch Service forum or contact AWS Support.

About the Authors

Prashant Agrawal is a Sr. Search Specialist Solutions Architect with Amazon OpenSearch Service. He works closely with customers to help them migrate their workloads to the cloud and helps existing customers fine-tune their clusters to achieve better performance and save on cost. Before joining AWS, he helped various customers use OpenSearch and Elasticsearch for their search and log analytics use cases. When not working, you can find him traveling and exploring new places. In short, he likes doing Eat → Travel → Repeat.

Satish Nandi is a Senior Technical Product Manager for Amazon OpenSearch Service.

Pavani Baddepudi is a Principal Product Manager working in search services at AWS. Her interests include distributed systems, networking, and security.

AWS Weekly Roundup: AWS Control Tower, Amazon Bedrock, Amazon OpenSearch Service, and More (October 9, 2023)

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-aws-control-tower-amazon-bedrock-amazon-opensearch-service-and-more-october-9-2023/

Pumpkins

As the Northern Hemisphere enjoys early fall and pumpkins take over the local farmers markets and coffee flavors here in the United States, we’re also just 50 days away from re:Invent 2023! But before we officially enter pre:Invent sea­­son, let’s have a look at some of last week’s exciting news and announcements.

Last Week’s Launches
Here are some launches that got my attention:

AWS Control Tower – AWS Control Tower released 22 proactive controls and 10 AWS Security Hub detective controls to help you meet regulatory requirements and meet control objectives such as encrypting data in transit, encrypting data at rest, or using strong authentication. For more details and a list of controls, check out the AWS Control Tower user guide.

Amazon Bedrock – Just a week after Amazon Bedrock became available in AWS Regions US East (N. Virginia) and US West (Oregon), Amazon Bedrock is now also available in the Asia Pacific (Tokyo) AWS Region. To get started building and scaling generative AI applications with foundation models, check out the Amazon Bedrock documentation, explore the generative AI space at community.aws, and get hands-on with the Amazon Bedrock workshop.

Amazon OpenSearch Service – You can now run OpenSearch version 2.9 in Amazon OpenSearch Service with improvements to search, observability, security analytics, and machine learning (ML) capabilities. OpenSearch Service has expanded its geospatial aggregations support in version 2.9 to gather insights on high-level overview of trends and patterns and establish correlations within the data. OpenSearch Service 2.9 now also comes with OpenSearch Service Integrations to take advantage of new schema standards such as OpenTelemetry and supports managing and overlaying alerts and anomalies onto dashboard visualization line charts.

Amazon SageMaker – SageMaker Feature Store now supports a fully managed, in-memory online store to help you retrieve features for model serving in real time for high throughput ML applications. The new online store is powered by ElastiCache for Redis, an in-memory data store built on open-source Redis. The SageMaker developer guide has all the details.

Also, SageMaker Model Registry added support for private model repositories. You can now register models that are stored in private Docker repositories and track all your models across multiple private AWS and non-AWS model repositories in one central service, simplifying ML operations (MLOps) and ML governance at scale. The SageMaker Developer Guide shows you how to get started.

Amazon SageMaker Canvas – SageMaker Canvas expanded its support for ready-to-use models to include foundation models (FMs). You can now access FMs such as Claude 2, Amazon Titan, and Jurassic-2 (powered by Amazon Bedrock) as well as publicly available models such as Falcon and MPT (powered by SageMaker JumpStart) through a no-code chat interface. Check out the SageMaker Developer Guide for more details.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

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

Behind the scenes on AWS contributions to open-source databases – This post shares some of the more substantial open-source contributions AWS has made in the past two years to upstream databases, introduces some key contributors, and shares how AWS approaches upstream work in our database services.

Fast and cost-effective Llama 2 fine-tuning with AWS Trainium – This post shows you how to fine-tune the Llama 2 model from Meta on AWS Trainium, a purpose-built accelerator for LLM training, to reduce training times and costs.

Code Llama code generation models from Meta are now available via Amazon SageMaker JumpStart – You can now deploy Code Llama FMs, developed by Meta, with one click in SageMaker JumpStart. This post walks you through the details.

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

Build On AWS - Generative AIBuild On Generative AI – Season 2 of this weekly Twitch show about all things generative AI is in full swing! Every Monday, 9:00 US PT, my colleagues Emily and Darko look at new technical and scientific patterns on AWS, invite guest speakers to demo their work, and show us how they built something new to improve the state of generative AI. In today’s episode, Emily and Darko discussed how to translate unstructured documents into structured data. Check out show notes and the full list of episodes on community.aws.

AWS Community Days – Join a community-led conference run by AWS user group leaders in your region: DMV (DC, Maryland, Virginia) (October 13), Italy (October 18), UAE (October 21), Jaipur (November 4), Vadodara (November 4), and Brasil (November 4).

AWS InnovateAWS Innovate: Every Application Edition – Join our free online conference to explore cutting-edge ways to enhance security and reliability, optimize performance on a budget, speed up application development, and revolutionize your applications with generative AI. Register for AWS Innovate Online Americas and EMEA on October 19 and AWS Innovate Online Asia Pacific & Japan on October 26.

AWS re:Invent 2023AWS re:Invent (November 27 – December 1) – Join us to hear the latest from AWS, learn from experts, and connect with the global cloud community. Browse the session catalog and attendee guides and check out the re:Invent highlights for generative AI.

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

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

— Antje

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

Network connectivity patterns for Amazon OpenSearch Serverless

Post Syndicated from Salman Ahmed original https://aws.amazon.com/blogs/big-data/network-connectivity-patterns-for-amazon-opensearch-serverless/

Amazon OpenSearch Serverless is an on-demand, auto-scaling configuration for Amazon OpenSearch Service. OpenSearch Serverless enables a broad set of use cases, such as real-time application monitoring, log analytics, and website search. OpenSearch Serverless lets you use OpenSearch without having to worry about scaling and managing an OpenSearch cluster. A collection can be accessed over the public internet or from your VPC. As you start accessing OpenSearch Serverless from different VPCs and accounts or from on premises, your connectivity patterns may change. In this post, we cover connectivity patterns and Domain Name System (DNS) resolution for your OpenSearch Serverless collection—whether accessed over the internet, within the VPC, within AWS, or from your on-premises location.

Foundational concepts

The following foundational concepts will help you better understand OpenSearch Serverless and DNS resolution.

Network access policy

The network access policy for OpenSearch Serverless determines whether the collection can be accessed over the internet or only through OpenSearch Serverless managed VPC endpoints. A single policy can be attached to multiple collections.

OpenSearch Serverless VPC endpoint

To access OpenSearch Serverless collections and dashboards privately from a VPC without using an internet gateway, you can create a VPC interface endpoint. When you create a VPC endpoint, it creates an elastic network interface (ENI) in each subnet that you enable for the VPC endpoint. These are requester-managed ENIs that serve as the entry point for traffic destined for the OpenSearch Serverless collection. When you create an OpenSearch Serverless VPC endpoint, the private DNS name option is enabled by default. This means that OpenSearch Serverless also creates an Amazon Route 53 private hosted zone and associates that with the VPC where the endpoint is created. This private hosted zone has a wildcard DNS record *.<region>.aoss.amazonaws.com pointing to the private DNS of the endpoint.

You create an OpenSearch Serverless VPC endpoint via the OpenSearch Serverless console or the OpenSearch Serverless API. You can’t create an OpenSearch Serverless VPC endpoint from the Amazon Virtual Private Cloud (Amazon VPC) console, although once created, you can see them on the VPC console as well.

Amazon Route 53 Resolver

Let’s understand what Amazon Route 53 Resolver does when an Amazon Elastic Compute Cloud (Amazon EC2) instance queries a DNS name. DNS queries originating from the VPC go to the Route 53 Resolver at the VPC+2 IP address. When a DNS query reaches the resolver, it checks if there are any Route 53 forward rules. If it matches, then it forwards the query to the DNS server provided by that rule. If the query remains unresolved, Route 53 Resolver proceeds to check the private hosted zones associated with the originating VPC. If it still remains unresolved, then it checks for VPC DNS, which helps to resolve EC2 DNS names. Lastly, if the query still isn’t resolved, Route 53 Resolver checks the public DNS. The following diagram illustrates this order or operations.

Route 53 DNS Overview

Route 53 Resolver inbound endpoints

Workloads utilizing resources both in a VPC and on premises need to resolve DNS records hosted on-premises and resources hosted in the VPC. With Route 53 Resolver inbound endpoints, you can resolve DNS queries to your VPC from your on-premises network or another VPC.

In the following sections, we provide an overview of connectivity patterns and DNS resolution.

Access an OpenSearch Serverless collection from Amazon EC2 (via internet gateway)

The following figure demonstrates the connectivity pattern to access an OpenSearch Serverless collection over the internet. The collection has an access type set to public, which allows authorized users to connect to the collection over the internet. An EC2 instance within the VPC can establish a connection to the collection via the internet gateway, and users outside the VPC can also access this collection over the internet.

Access an OpenSearch Serverless collection from Amazon EC2 (via internet gateway)

The workflow has the following steps, as indicated in the preceding diagram:

A. The EC2 instance performs a DNS lookup to Route 53 Resolver at a VPC+2 IP address. Route 53 Resolver returns the public IP addresses of the OpenSearch Serverless collection.

B. The EC2 instance sends a data request via an internet gateway to the OpenSearch Serverless collection using this public IP address.

C. An external client resolves to the public IP addresses of the OpenSearch Serverless collection and reaches it via the internet.

Now let’s perform a dig command for the collection or dashboard URL from the EC2 instance, and we observe that it’s resolving to a public IP address.

The following command uses an OpenSearch Serverless collection:

sh-5.2$ dig +short <collection-id>.<region>.aoss.amazonaws.com
192.0.2.10
198.51.100.204
192.0.2.45
198.51.100.55
192.0.2.100
203.0.113.66

The following command uses an OpenSearch dashboard:

sh-5.2$ dig +short dashboards.<region>.aoss.amazonaws.com
192.0.2.10
198.51.100.204
192.0.2.45
198.51.100.55
192.0.2.100
203.0.113.66

Now that you have implemented an OpenSearch Serverless collection with a network access policy as public, you can make the same collection accessible privately within the VPC. To achieve this, complete the following steps:

  1. Modify the network access policy of the collection and change the access type to VPC.
  2. Select the option Create VPC endpoints.

Access type for OpenSearch Serverless

  1. Choose the VPC and at least two subnets where you would like to have a VPC endpoint ENI for high availability.
  2. Choose Confirm to create the VPC endpoint.

Create VPC endpoints for Amazon OpenSearch Serverless

  1. Lastly, select the VPC endpoint and update the policy.

Access Type VPC Endpoint for Amazon OpenSearch Serverless

With the creation of the VPC endpoint, a Route 53 private hosted zone is also created within your account and associated with your VPC. In this setup, a CNAME record *.us-east-1.aoss.amazonaws.com is created to direct to the Regional AWS PrivateLink endpoint, as depicted in the following screenshot.

Route 53 Private Hosted Zone

Due to the private hosted zone associated with the VPC, Route 53 Resolver gives preference to the private hosted zone to resolve any DNS query originating from the VPC. DNS requests for the OpenSearch Serverless collection originating from the EC2 instance get resolved using this associated private hosted zone and resolve to the private IP addresses of the VPC endpoint, which allows Amazon EC2 to connect to the serverless collection via VPC endpoints vs. the internet gateway. We expand on this in the following section.

Access an OpenSearch Serverless collection from Amazon EC2 (via interface VPC endpoints)

The following figure demonstrates the connectivity pattern to access an OpenSearch Serverless collection privately from the VPC. The collection has an access type set to VPC endpoint, restricting access solely from the resources within the VPC via the VPC endpoint and preventing external users from connecting. With the creation of the VPC endpoint, a private hosted zone is also associated with this VPC. An EC2 instance within the VPC can establish a connection with the collection using the VPC endpoint, but resources outside of the VPC don’t have access to this collection because of the network access policy.

Access an OpenSearch Serverless collection from Amazon EC2 (via interface VPC endpoints)

The workflow consists of the following steps:

A. The EC2 instance performs a DNS lookup to Route 53 Resolver at a VPC+2 IP address. Route 53 Resolver returns the private IP addresses of the VPC endpoint because there is a private hosted zone associated with the VPC containing a CNAME record.

B. The EC2 instance sends a data request via the VPC interface endpoint to the OpenSearch Serverless collection.

C. An external client resolves to the public IP addresses of the OpenSearch Serverless collection but is unable to reach it because the network policy restricts to the VPC.

Now let’s perform a dig command for the collection or dashboard URL from the EC2 instance, and we observe that it’s resolving to the private IP addresses belonging to the VPC endpoints.

Use the following code for an OpenSearch Serverless collection:

sh-5.2$ dig +short <collection-id>.<region>.aoss.amazonaws.com
10.0.1.98
10.0.2.83

Use the following code for an OpenSearch dashboard:

sh-5.2$ dig +short dashboards.<region>.aoss.amazonaws.com
10.0.1.98
10.0.2.83

Access an OpenSearch Serverless collection from many VPCs (via interface VPC endpoints) with a VPC endpoint in each VPC

The following figure demonstrates the connectivity pattern to use the same VPC endpoint to connect to multiple OpenSearch Serverless collections. In this scenario, a VPC endpoint is created in each VPC to enable EC2 instances within the VPCs to utilize the VPC endpoint as the connectivity path to OpenSearch Serverless. A private hosted zone is auto generated for each endpoint and associated with the corresponding VPC. Network policies of OpenSearch Serverless collections are updated to allow both VPC Endpoint-1 and VPC Endpoint-2, which allows the EC2 instance in VPC-1 to access both collections via VPC Endpoint-1 and EC2 instances in VPC-2 to access both collections via VPC Endpoint-2.

Access an OpenSearch Serverless collection from many VPCs (via interface VPC endpoints) with a VPC endpoint in each VPC

The workflow consists of the following steps:

A. The EC2 instance performs a DNS lookup to Route 53 Resolver at a VPC+2 IP address. Route 53 Resolver returns the private IP addresses of the VPC endpoint (the EC2 instance in VPC-1 gets the IP address of VPC Endpoint-1 and the EC2 instance in VPC-2 gets the IP address of VPC Endpoint-2), because there is a private hosted zone associated with each of the VPCs containing a CNAME record.

B. The EC2 instance sends a data request via the VPC interface endpoint to the OpenSearch Serverless collection.

Access an OpenSearch Serverless collection from many VPCs (via interface VPC endpoints) with a VPC endpoint in a centralized VPC

In the previous connectivity pattern, we had one endpoint in each VPC through which VPC resources accessed OpenSearch Serverless collections. Many organizations would like to maintain control of these endpoints and keep these in a centralized VPC.

The following figure demonstrates the connectivity pattern to use a centralized VPC endpoint to connect to multiple OpenSearch Serverless collections from multiple VPCs. In this scenario, a VPC interface endpoint is created in a centralized VPC. A private hosted zone is auto generated for this VPC endpoint and associated with the centralized VPC, and then manually associated with VPC-1 and VPC-2. The DNS query for OpenSearch Serverless collections from VPC-1 and VPC-2 gets resolved to the centralized VPC endpoint due to the private hosted zone association. Network policies for both collections allow access from the centralized VPC endpoint only. All three VPCs (centralized, VPC-1, and VPC-2) are connected via AWS Transit Gateway and route tables have routes to direct traffic between VPC-1 and VPC-2 to the centralized VPC and vice versa.

Access an OpenSearch Serverless collection from many VPCs (via interface VPC endpoints) with a VPC endpoint in a centralized VPC

The workflow consists of the following steps:

A. The EC2 instance performs a DNS lookup to Route 53 Resolver at a VPC+2 IP address. Route 53 Resolver returns the private IP addresses of the centralized VPC endpoint, because there is a private hosted zone associated with each VPC containing a CNAME record.

B. The EC2 instance sends a data request to the Transit Gateway ENI in its own VPC. The Transit Gateway route table is checked and the data request is forwarded to the Transit Gateway ENI in the centralized VPC. The Transit Gateway ENI in the centralized VPC sends it to the OpenSearch Serverless collection via the VPC interface endpoint.

Access an OpenSearch Serverless collection from on premises (via AWS Site-to-Site VPN or AWS Direct Connect)

The following figure demonstrates the connectivity pattern for accessing OpenSearch Serverless collections from on premises. You can use either AWS Direct Connect or AWS Site-to-Site VPN to establish connectivity between on-premises and AWS resources. In the following example, Direct Connect is used for the connectivity between AWS and on premises. An OpenSearch Serverless VPC endpoint is created in the VPC, and a private hosted zone is automatically generated and associated with this VPC. The network policy of the OpenSearch Serverless collection is updated to allow connectivity only from the VPC endpoint.

To access these OpenSearch Serverless collections privately from the on-premises environment, resources need to resolve the OpenSearch Serverless collection DNS to the OpenSearch Serverless VPC endpoint IP address. By default, OpenSearch Serverless DNS resolves to the public IP addresses and attempts to access OpenSearch Serverless via the internet. To ensure that OpenSearch Serverless is accessed via the VPC endpoint from on premises, you need to ensure that DNS queries are resolved to a VPC endpoint’s private IP address. Resources inside the VPC use Route 53 Resolver, available at a VPC+2 IP address, to resolve these queries to the VPC endpoint. Route 53 Resolver checks the associated private hosted zone to resolve the query to the VPC endpoint. However, the VPC+2 IP address is not accessible from on premises. To address this, you can utilize the Route 53 Resolver inbound endpoint.

To achieve this, you can create an inbound endpoint in your VPC by following the steps outlined in Configuring inbound forwarding, and then update the on-premises DNS server to forward all the DNS requests for *.<region>.aoss.amazonaws.com to the IP address of the Route 53 Resolver inbound endpoint. When the on-premises client obtains the IP address of the VPC endpoint, it can use Direct Connect or Site-to-Site VPN to establish a private connection to the OpenSearch Serverless collection.

Access an OpenSearch Serverless collection from on premises (via AWS Site-to-Site VPN or AWS Direct Connect)

The workflow contains the following steps:

A. The on-premises client sends a DNS lookup to the on-premises DNS resolver. The on-premises DNS resolver forwards this request to the Route 53 Resolver inbound endpoint. The Route 53 Resolver inbound endpoint sends a DNS lookup to Route 53 Resolver at a VPC+2 IP address. Route 53 Resolver returns the private IP addresses of the VPC endpoint, because there is a private hosted zone associated with this VPC containing a CNAME record.

B. The on-premises client sends a data request to the OpenSearch Serverless collection, which routes via Direct Connect or Site-to-site VPN to the VPC interface endpoint and finally to the OpenSearch Serverless collection.

Conclusion

In this post, we showed you various connectivity patterns for OpenSearch Serverless. We covered the use of hybrid DNS and using a Route 53 Resolver inbound endpoint to allow connectivity from on premises for OpenSearch Serverless. You can choose from various centralization models for reaching multiple OpenSearch Serverless collections within the AWS Cloud or from on-premises locations. Get started today by connecting to OpenSearch Serverless from the various network patterns we discussed.

About the authors

Salman AhmedSalman Ahmed is a Sr. Technical Account Manager in AWS Enterprise Support. He enjoys working with Enterprise Support customers to help them with design, implementation and supporting cloud infrastructure. He also has a passion for networking services and with 12+ years of experience, he leverages that to help customers with adoption of AWS Transit Gateway, AWS Direct Connect and various other AWS networking services.

Ankush GoyalAnkush Goyal is Enterprise Support Lead in AWS Enterprise Support who helps enterprise support customers streamline their cloud operations on AWS. He is a results-driven IT professional with over 18 years of experience.

Rohit AswaniRohit Aswani is a Senior Specialist Solutions Architect focussed on Networking at AWS, where he helps customers build and design scalable, highly-available, secure, resilient and cost effective networks. He holds a MS in Telecommunication Systems Management from Northeastern University, specializing in Computer Networking. In his spare time, Rohit enjoys hiking, traveling and exploring new coffee places.

Improved resiliency with cluster manager task throttling for Amazon OpenSearch Service

Post Syndicated from Dhwanil Patel original https://aws.amazon.com/blogs/big-data/improved-resiliency-with-cluster-manager-task-throttling-for-amazon-opensearch-service/

Amazon OpenSearch Service is a managed service that makes it simple to secure, deploy, and operate OpenSearch clusters at scale in the AWS Cloud. Amazon OpenSearch clusters are comprised of data nodes and cluster manager nodes. The cluster manager nodes elect a leader among themselves. The leader node is the authority on the metadata in the cluster, which is called cluster state. Any changes to the cluster state are processed by the leader node and broadcasted to all of the nodes in the cluster. The data nodes enqueue a new cluster-level task for any cluster state change like creation of an index, dynamic put-mappings, shard started, etc. in the cluster manager’s unbounded queue. The tasks waiting in this queue are called pending tasks. Because it’s unbounded, a large number of pending tasks can be queued, which overloads the leader node. This can affect the leader’s performance and may also in turn affect the stability and availability of the whole cluster.

We have introduced a throttling mechanism for cluster manager nodes to provide protection against a large number of pending tasks. It acts during task submission to the leader node. This feature is available for Amazon OpenSearch engine version 1.3 and above in Amazon OpenSearch Service.

Cluster manager task throttling

Cluster manager task throttling is a mechanism to protect the cluster manager against submission of too many resource-intensive cluster state update tasks from other nodes. For tasks like put-mapping, data nodes have an existing throttling mechanism for cluster-state tasks that helps avoid overload of the cluster manager. For example, if the cluster manager can handle 10 K requests and the domain has 10 data nodes, then each data node gets a throttle at 1,000 put-mapping requests. If the domain grows to 100 data nodes, then each data node must throttle at 100 requests. To avoid having to modify these throttle limit whenever the cluster changes the number of data nodes and to support more task types, we ‘ve introduced throttling at cluster manager node for self-protection.

The cluster state update tasks are of different types ( create-index, put-mapping, and more) and this throttling mechanism rejects a task based on its type. For any incoming task, the cluster manager evaluates the total number of tasks of the same type in the pending task queue. If this number exceeds the threshold for a task type, then the cluster manager rejects the incoming task.

Amazon OpenSearch Service configures different throttling thresholds for different task types and throttling acts independently on each task type. Rejecting a particular task doesn’t affect other tasks of a different type. For example, if the cluster manager rejects a put-mapping task, it can still accept a concurrent create-index task.

All of the tasks generated by data plane APIs( _mapping/, _setting/ and more) have been onboarded for throttling and are listed here.

When the cluster manager rejects a task, the data node performs retries with exponential back off to resubmit the task to the cluster manager until it is successfully submitted. If retries are unsuccessful within the timeout period, then Amazon OpenSearch returns a cluster timeout error.

Sample of error

{
  "error" : {
    "type" : "process_cluster_event_timeout_exception",
    "reason" : "failed to process cluster event (indices:admin/mapping/put) within 30s",
    "suppressed" : [
      {
        "type" : "cluster_manager_throttling_exception",
        "reason" : "Throttling Exception : Limit exceeded for put-mapping"
      }
    ]
  },
  "status" : 503
}

Handling time out errors

The throttling exception is acted upon by data nodes; they perform the retries on throttled task. If API times out during throttling period, you’ll get process_cluster_event_timeout_exception , which is a 503 error. This is the same error that was thrown earlier as well when tasks are timing out in the cluster manager node’s queue. You can retry the API calls with timeout errors.

This solution will improve this feature by exposing the throttling exception directly as an API error.

Monitoring throttling

You can monitor the detailed throttling stats using the _nodes/stats API.

curl -XGET "https://{endpoint}/_nodes/stats/cluster_manager_throttling?pretty"

You can use the cluster_manager_throttling section in the _nodes/stats response to track, which tasks are getting throttled and how many tasks has been throttled.

Sample response

    "cluster_manager_throttling" : {
        "cluster_manager_stats" : {
          "TotalThrottledTasks" : 18,
          "ThrottledTasksPerTaskType" : {
            "put-mapping" : 18
          }
        }
    }

Conclusion

In this post, we showed you how a throttling mechanism for task submission to the cluster manager node makes it more resilient to a high number of pending tasks in Amazon OpenSearch Service, where we have fine-tuned the thresholds per cluster.

Cluster manager throttling is available in Amazon OpenSearch, and we are always looking for external contributions. You can refer to the RFC (Request For Comment) to get started.

About the Authors

Dhwanil Patel is a Software Developer Engineer working on Amazon OpenSearch Service. He likes to contribute to open-source software development, and is passionate about distributed systems.

Shweta Thareja is a Principal Engineer working on Amazon OpenSearch Service. She is interested in building distributed and autonomous systems. She is a maintainer and an active contributor to OpenSearch.

Jon Handler is a Senior Principal Solutions Architect at Amazon Web Services based in Palo Alto, CA. Jon works closely with OpenSearch and Amazon OpenSearch Service, providing help and guidance to a broad range of customers who have search and log analytics workloads that they want to move to the AWS Cloud. Prior to joining AWS, Jon’s career as a software developer included 4 years of coding a large-scale, ecommerce search engine. Jon holds a Bachelor of the Arts from the University of Pennsylvania, and a Master of Science and a PhD in Computer Science and Artificial Intelligence from Northwestern University.

Managing Amazon EBS volume throughput limits in Amazon OpenSearch Service domains

Post Syndicated from Pranit Kumar original https://aws.amazon.com/blogs/big-data/managing-amazon-ebs-volume-throughput-limits-in-amazon-opensearch-service-domains/

In this blog post, we discuss the impact of Amazon Elastic Block Store (Amazon EBS) volume IOPS and throughput limits on Amazon OpenSearch Service domain and how to prevent/mitigate throughput throttling situation.

Amazon OpenSearch Service is a managed service that makes it easy for you to perform website searches, interactive log analytics, real-time application monitoring, and more. Based on the open source OpenSearch suite, Amazon OpenSearch Service allows you to search, visualize, and analyze up to petabytes of text and unstructured data.

An OpenSearch Service domain primarily contains nodes with the following set of roles.

  • Cluster manager (dedicated master): Responsible for managing the cluster and checking the health of the data nodes in the cluster.
  • Data: Responsible for serving search and indexing requests and storing the indexed data.
  • Ultrawarm: Nodes which use Amazon S3 as a backing store to provide lower-cost storage.

When creating an OpenSearch Service domain, you choose the storage for the data nodes with local Non-Volatile Memory Express (NVMe) or with Amazon EBS volumes.

If the OpenSearch Service data node storage is backed by Amazon EBS volumes, depending on your workload, EBS throughput can heavily influence performance of the OpenSearch Service domain. The EBS volume performance metric is defined by the following two key parameters.

  • IOPS defines the number of IO operations performed per second.
  • Throughput is a measure of how much data can be transferred in a given amount of time. It is usually measured in bytes per second.

Whenever IOPS or throughput of the data node breaches the maximum allowed limit of the EBS volume or the EC2 instance of the data node, then the OpenSearch Service domain experiences IOPS or throughput throttling. This can result in high search and indexing latency and in the worst scenario node crash as well.

Maximum allowed IOPS and throughput for the data node

The maximum allowed value for IOPS or the throughput for the data node in an OpenSearch Service domain is the minimum of the following two values.

Throughput throttling and its impact on an Amazon OpenSearch Service domain

Throughput throttling happens when the total EBS throughput on a data node exceeds the maximum allowed throughput value of that data node in the OpenSearch Service domain.

The ThroughputThrottle metric for the domain or node can be seen in the Amazon CloudWatch console at the following location.

  • Domain: “ES/OpenSearchService > Per-Domain, Per-Client Metrics”
  • Node: “ES/OpenSearchService > ClientId, DomainName, NodeId”

The value of 1 in the ThroughputThrottle metric signifies a throttling event for the domain or node.

If a data node in the domain experiences throughput throttling for a consistent period, it can result in the following performance degradation for the data node.

  • Slower EBS volume performance.
  • High read/write latency.

This can affect the checks performed by the cluster manager or data node. It can result in:

  • FS (file system) health check failure performed by the data node.
  • Follower check failure performed by cluster manager due to high request latency.

This will result in the cluster manager marking such data nodes unhealthy, resulting in the data node being removed from the cluster. This can lead to a yellow or red cluster status.

Throughput value calculation

Total throughput for the data node is the total bytes read and written to the EBS volume per second. The following metrics provides the read and write throughput for the data node in the Amazon Opensearch Service domain.

Total throughput for the data node in the OpenSearch Service domain is calculated as the following.

Throughput = ReadThroughputMicroBursting + WriteThroughputMicroBursting

To get total throughput for the data node, follow these steps.

  1. Go to Amazon Cloudwatch metrics.
  2. Go to ES/OpenSearchService > ClientId, DomainName, NodeId.
  3. Select ReadThroughputMicroBursting and WriteThroughputMicroBursting metric.
  4. Go to Graphed metrics.
  5. Use Add math and create formulas to sum ReadThroughputMicroBursting and WriteThroughputMicroBursting values.

Handling throughput throttle

When the maximum allowed throughput limit is breached on the data node in an OpenSearch Service domain, a disk throughput throttle notification is sent to the AWS console. Throughput throttling on the data node can happen due to various reasons, such as the following.

  • A sudden increase in the index rate or search rate to the data node of the OpenSearch Service domain.
  • A blue/green event happening on the OpenSearch Service domain during peak hours.
  • The OpenSearch Service domain is under-scaled.

We suggest the following measures to prevent throughput throttling for the OpenSearch Service domain.

  • Monitor the traffic to the OpenSearch Service domain and create alarms on the search and index traffic sent to the OpenSearch Service domain.
  • Set up off-peak hours for OpenSearch Service domain so that the updates that lead to blue/green deployments are executed when there is less demand.
  • Monitor the ThroughputThrottle cluster metrics for the OpenSearch Service domain.
  • Monitor shard skewness for the OpenSearch Service domain. Shard skewness can lead to uneven load distribution of traffic to data nodes and can lead to hot nodes in the cluster, which can experience high index and search traffic that results in throttling.
  • If you are hitting EBS Volume or EC2 instance throughput limits for the data node, you will need to scale up the OpenSearch Service domain to avoid throughput throttling. Check the limits provided by EBS volumes and  Amazon EBS optimized instances used by the data node and scale up the OpenSearch cluster accordingly.

Every scenario requires specific investigation and the appropriate measures to resolve it. Still, we suggest the following guidelines as part of a broader approach to handling throughput throttle.

  • If high throughput is seen on a specific set of data nodes most of the time, shard skewness may be causing hot nodes. In such cases, resolving shard skewness will help the situation.
  • If OpenSearch Service domain is experiencing uneven traffic patterns, check for sudden bursts resulting in throttling. In such scenarios, streamlining the traffic pattern can be helpful.
  • If throughput throttling is seen on most of the nodes on the cluster with consistent traffic patterns, scaling up of the OpenSearch Service domain should be considered.

Conclusion

In this post, we covered the Amazon EBS throughput throttling in OpenSearch Service domain, its impact, and ways to monitor and handle it. We provided suggestions that can be used to handle such throttling situations.

Related links

About the Authors

Pranit Kumar is a Sr. Software Dev Engineer working on OpenSearch at Amazon Web Services. He is interested in distributed systems and solving complex problems.

Dhrubajyoti Das is an Engineering Manager working on OpenSearch at Amazon Web Services. He is deeply interested in high scalable systems and infrastructure related challenges.

Use the reverse token filter to enable suffix matching queries in OpenSearch

Post Syndicated from Bharav Patel original https://aws.amazon.com/blogs/big-data/use-the-reverse-token-filter-to-enable-suffix-matching-queries-in-opensearch/

OpenSearch is an open-source RESTful search engine built on top of the Apache Lucene library. OpenSearch full-text search is fast, can give the result of complex queries within a fraction of a second. With OpenSearch, you can convert unstructured text into structured text using different text analyzers, tokenizers, and filters to improve search. OpenSearch uses a default analyzer, called the standard analyzer, which works well for most use cases out of the box. But for some use cases, it may not work best, and you need to use a specific analyzer.

In this post, we show how you can implement a suffix-based search. To find a document with the movie name “saving private ryan” for example, you can use the prefix “saving” with a prefix-based query. Occasionally, you also want to match suffixes as well, such as matching “Harry Potter Goblet of Fire” with the suffix “Fire” To do that, first reverse the string “eriF telboG rettoP yrraH” with the reverse token filter, then query for the prefix “eriF”.

Solution overview

Text analysis involves transforming unstructured text, such as the content of an email or a product description, into a structured format that is finely tuned for effective searching. An analyzer enables the implementation of full-text search using tokenization, which entails breaking down a text into smaller fragments known as tokens, with these tokens commonly representing individual words. To implement a reversed field search, the analyzer does the following.

The analyzer processes text in the following order:

  1. Use a character filter to replace - with _. For example, from “My Driving License Number Is 123-456-789” to “My Driving License Number Is 123_456_789.”
  2. The standard tokenizer splits texts into tokens. For example, from “My Driving License Number Is 123_456_789” to “[ my, driving, license, number, is, 123, 456, 789 ].”
  3. The reverse token filter reverses each token in a stream. For example, from [ my, driving, license, number, is, 123, 456, 789 ] to [ ym, gnivird, esnecil, rebmun, si, 321, 654, 987 ].

The standard analyzer (default analyzer) breaks down input strings into tokens based on word boundaries and removes most punctuation marks. For additional information about analyzers, refer Build-in analyzers.

Indexing and searching

Every document is a collection of fields, each having its own specific data type. When you create a mapping for your data, you create a mapping definition, which contains a list of fields that are pertinent to the document. To know more about index mappings refer to index mapping.

Let’s take the example of an analyzer with the reverse token filter applied on the text field.

  1. First, create an index with mappings as shown in the following code. The new field ‘reverse_title’ is derived from ‘title’ field for suffix search and original field ‘title’ will be used for normal search.
PUT movies
{
  "settings" : {
    "analysis" : {
      "analyzer" : {
        "whitespace_reverse" : {
          "tokenizer" : "whitespace",
          "filter" : ["reverse"]
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "title": { 
        "type": "text",
        "analyzer": "standard",
        "copy_to": "reverse_title"
      },
      "reverse_title": {
        "type": "text",
        "analyzer": "whitespace_reverse"
      }
    }
  }
}

  1. Insert some documents into the index:
POST _bulk
{ "index" : { "_index" : "movies", "_id" : "1" } }
{ "title": "Harry Potter Goblet of Fire" }
{ "index" : { "_index" : "movies", "_id" : "2" } }
{ "title": "Lord of the rings" }
{ "index" : { "_index" : "movies", "_id" : "3" } }
{ "title": "Saving Private Ryan" }
  1. Run the following query to perform a suffix/reverse search on derived field ‘reverse_title’ for “Fire”:
GET movies/_search
{
  "query": {
    "prefix": {
      "reverse_title": {
        "value": "eriF"
      }
    }
  }
}

The following code shows our results:

   {
        "_index": "movies",
        "_id": "1",
        "_score": 1,
        "_source": {
          "title": "Harry Potter Goblet of Fire"
        }
      }
  1. For non-reverse search you can use original field ‘title’.
GET movies/_search
{
  "query": {
    "match": {
      "title": "Fire"
    }
  }
}

The following code shows our result.

{
        "_index": "movies",
        "_id": "1",
        "_score": 0.2876821,
        "_source": {
          "title": "Harry Potter Goblet of Fire"
        }
}

The query returns a document with the movie name “Harry Potter Goblet of Fire”.
If you’re curious to know how search works at high level, refer to A query, or There and Back Again.

Conclusion

In this post, you walked through how text analysis works in OpenSearch and how to implement suffix-based search using a reverse token filter effectively.

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

About the Authors

Bharav Patel is a Specialist Solution Architect, Analytics at Amazon Web Services. He primarily works on Amazon OpenSearch Service and helps customers with key concepts and design principles of running OpenSearch workloads on the cloud. Bharav likes to explore new places and try out different cuisines.