Tag Archives: CloudFormation templates

Stream Amazon CloudWatch Logs to a Centralized Account for Audit and Analysis

Post Syndicated from David Bailey original https://aws.amazon.com/blogs/architecture/stream-amazon-cloudwatch-logs-to-a-centralized-account-for-audit-and-analysis/

A key component of enterprise multi-account environments is logging. Centralized logging provides a single point of access to all salient logs generated across accounts and regions, and is critical for auditing, security and compliance. While some customers use the built-in ability to push Amazon CloudWatch Logs directly into Amazon Elasticsearch Service for analysis, others would prefer to move all logs into a centralized Amazon Simple Storage Service (Amazon S3) bucket location for access by several custom and third-party tools. In this blog post, I will show you how to forward existing and any new CloudWatch Logs log groups created in the future to a cross-account centralized logging Amazon S3 bucket.

The streaming architecture I use in the destination logging account is a streamlined version of the architecture and AWS CloudFormation templates from the Central logging in Multi-Account Environments blog post by Mahmoud Matouk. This blog post assumes some knowledge of CloudFormation, Python3 and the boto3 AWS SDK. You will need to have or configure an AWS working account and logging account, an IAM access and secret key for those accounts, and a working environment containing Python and the boto3 SDK. (For assistance, see the Getting Started Resource Center and Start Building with SDKs and Tools.) All CloudFormation templates and Python code used in this article can be found in this GitHub Repository.

Setting Up the Solution

You need to create or use an existing S3 bucket for storing CloudFormation templates and Python code for an AWS Lambda function. This S3 bucket is referred to throughout the blog post as the <S3 infrastructure-bucket>. Ensure that the bucket does not block new bucket policies or cross-account access by checking the bucket’s Permissions tab and the Public access settings button.

You also need a bucket policy that allows each account that needs to stream logs to access it when we create the AWS Lambda function below. To do so, update your bucket policy to include each new account you create and the <S3 infrastructure-bucket> ARN from the top of the Bucket policy editor page to modify this template:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "AWS": [
                  "03XXXXXXXX85",
                  "29XXXXXXXX02",
                  "13XXXXXXXX96",
                  "37XXXXXXXX30",
                  "86XXXXXXXX95"
                ]
            },
            "Action": [
                "s3:Get*",
                "s3:List*"
            ],
            "Resource": [
                "arn:aws:s3:::<S3 infrastructure-bucket>",
                "arn:aws:s3:::<S3 infrastructure-bucket>/*"
            ]
        }
    ]
}

Clone a local copy of the CloudFormation templates and Python code from the GitHub repository. Compress the CentralLogging.py and lambda.py into a .zip file for the lambda function we create below and name it AddSubscriptionFilter.zip. Load these local files into the <S3 infrastructure-bucket>. I recommend using folders called /python for the .py files, /lambdas for the AddSubscriptionFilter.zip file and /cfn for the CloudFormation templates.

Multi-Account Configuration and the Central Logging Account

One form of multi-account configuration is the Landing Zone offering, which provides a core logging account for storing all logs for auditing. I use this account configuration as an example in this blog post. Initially, the Landing Zone setup creates several stack sets and resources, including roles, security groups, alarms, lambda functions, a cloud trail stream and an S3 bucket.

If you are not using a Landing Zone, create an appropriately named S3 bucket in the account you have chosen as a logging account. This S3 bucket will be referred to later as the <LoggingS3Bucket>. To mimic what the Landing Zone calls its logging bucket, you can use the format aws-landing-zone-logs-<Account Number><Region>, or simply pick an appropriate name for the centralized logging location. In a production environment, remember that it is critical to lock down the access to logging resources and the permissions allowed within the account to prevent deletion or tampering with the logs.

Figure 1 - Initial Landing Zone logging account resources

Figure 1 – Initial Landing Zone logging account resources

The S3 bucket – aws-landing-zone-logs-<Account Number><Region> is the most important resource created by the stack-sets for logging purposes. It contains all of the logs streamed to it from all of the accounts. Initially, the Landing Zone only sends the AWS CloudTrail and AWS Config logs to this S3 bucket.

In order to send all of the other CloudWatch Logs that are necessary for auditing, we need to add a destination and streaming mechanism to the logging account.

Logging Account Insfrastructure

The additional infrastructure required in the central logging account provides a destination for the log group subscription filters and a stream for log events that are sent from all accounts and appropriate regions to load them into the <LoggingS3Bucket> repository. The selection of these particular AWS resources is important, because Kinesis Data Streams is the only resource currently supported as a destination for cross-account CloudWatch Logs subscription filters.

The centralLogging.yml CloudFormation template automates the creation of the entire required infrastructure in the core logging account. Make sure to run it in each of the regions in which you need to centralize logs. The log group subscription filter and destination regions must match in order to successfully stream the logs.

Installation Instructions:

  1. Modify the centralLogging.yml template to add your account numbers for all of the accounts you want to stream logs from into the DestinationPolicy where you see the <AccountNumberHere> placeholders. Remove any unused placeholders.
  2. In the same DestinationPolicy, modify the final arn statement, replacing <region> with the region it will be run in (e.g., us-east-1), and the <logging account number> with the account number of the logging account where this template is to be run.
  3. Log in to the core logging account and access the AWS management console using administrator credentials.
  4. Navigate to CloudFormation and click the Create Stack button.
  5. Select Specify an Amazon S3 template URL and enter the Link for the centralLogging.yml template found in the <S3 infrastructure-bucket>.
  6. Enter a stack name, such as CentralizedLogging, and the one parameter called LoggingS3Bucket. Enter in the ARN of the logging bucket: arn:aws:s3::: <LoggingS3Bucket>. This can be obtained by opening the S3 console, clicking on the bucket icon next to this bucket, and then clicking the Copy Bucket ARN button.
  7. Skip the next page, acknowledge the creation of IAM resources, and Create the stack.
  8. When the stack completes, select the stack name to go to stack details and open the Outputs. Copy the value of the DestinationArnExport, which will be needed as a parameter for the script in the next section.

Upon successful creation of this CloudFormation stack, the following new resources will be created:

  • Amazon CloudWatch Logs Destination
  • Amazon Kinesis Stream
  • Amazon Kinesis Firehose Stream
  • Two AWS Identity and Access Management (IAM) Roles
Figure 2 - New infrastructure required in the centralized logging account

Figure 2 – New infrastructure required in the centralized logging account

Because the Landing Zone is a multi-account offering, the Log Destination is required to be the destination for all subscription filters. The key feature of the destination is its DestinationPolicy. Whenever a new account is added to the environment, its account number needs to be added to this DestinationPolicy in order for logs to be sent to it from the new account. Add the new account number in the centralLogging.yml CloudFormation template, and run an update in CloudFormation to complete the addition. A sample Destination Policy looks like this:

{
  "Version" : "2012-10-17",
  "Statement" : [
    {
      "Effect" : "Allow",
      "Principal" : {
        "AWS" : [
          "03XXXXXXXX85",
          "29XXXXXXXX02",
          "13XXXXXXXX96",
          "37XXXXXXXX30",
          "86XXXXXXXX95"
        ]
      },
      "Action" : "logs:PutSubscriptionFilter",
      "Resource" : "arn:aws:logs:<Region>:<LoggingAccountNumber>:destination:CentralLogDestination"
    }
  ]
}

The Kinesis Stream get records from the Logs Destination and holds them for 48 hours. Kinesis Streams scale by adding shards. The CloudFormation template starts the stream with two shards. You need to monitor this as instances and applications are deployed into the accounts, however, because all CloudWatch log objects will flow through this stream, and it will need to be scaled up at some point. To scale, change the number of shards (ShardCount) in the Kinesis Stream resource (KinesisLoggingStream) to the required number. See the Amazon Kinesis Data Streams FAQ documentation to confirm the capacity and throughput of each shard.

Kinesis Firehose provides a simple and efficient mechanism to retrieve the records from the Kinesis Stream and load them into the <LoggingS3Bucket> repository. It uses the CloudFormation template parameter to know where to load the logs. All of the CloudWatch logs loaded by Firehose will be under the prefix /CentralizedAccountsLog. The buffering hints for Firehose suggest that the logs be loaded every 5 minutes or 50 MB. Leave the CompressionFormat UNCOMPRESSED, since the logs are already compressed.

There are two AWS Identity and Access Management (IAM) roles created for this infrastructure. The first, CWLtoKinesisRole is used by the destination to allow CloudWatch Logs from all regions to use the destination to put the log object records into the Kinesis Stream, as well as to pass the role. The second, FirehoseDeliveryRole, allows Firehose to get the log object records from the Kinesis Stream, and then to load them into S3 logging bucket.

Once you have successfully created this infrastructure, the next step is to add the subscription filters to existing log groups.

Adding Subscription Filters to Existing Log Groups

The next step in the process is to add subscription filters for the Log Destination in the core logging account to all existing log groups. Several log groups are created by the Landing Zone, or you may have created them by using various AWS services or by logging application events. For every new AWS account, you will need to run the init_account_central_logging.py Python script to add the subscription filters to all the existing log groups.

The init_account_central_logging.py script takes one parameter, which is the Log Destination ARN. Use the Destination ARN you copied from the stack details output in the previous section as the parameter to the script.

The init_account_central_logging.py script first adds this Destination ARN to the AWS Systems Manager Parameter Store so that the core logic that creates the subscription filter can use it. The script then gets a list of all existing log groups, iterates over them, deletes any existing subscription filters (because there can only be one subscription filter per log group and attempting to create another would cause an error), and then adds the new subscription filter to the centralized logging account to the Log Destination.

Figure 3 - Run script to add subscription filters to existing log groups

Figure 3 – Run script to add subscription filters to existing log groups

Installation Instructions:

  1. Make sure that Python and boto3 are installed and accessible in the client computer – consider loading into a virtual environment to keep dependencies separate.
  2. Set the AWS_PROFILE environment variable to the appropriate AWS account profile.
  3. Log in to the proper account, and obtain administrator or other credentials with appropriate permissions, and add the account access key and secret key to the AWS credentials file.
  4. Set the region and output in the AWS config file.
  5. Download and place two python files into a working directory: init_account_central_logging.py and CentralLogging.py.
  6. Run the script using the command python3 ./init_account_central_logging.py -d <LogDestinationArn>.

Use the AWS Management Console to validate the results. Navigate to CloudWatch Logs and view all of the log groups. Each one should now have a subscription filter named “Logs (CentralLogDestination).”

Automatically Adding Subscription Filters to New Log Groups

The final step to set up the centralized log streaming capability is to run a CloudFormation script to create resources that automatically add subscription filters to new log groups. New log groups are created in accounts by resources (e.g., Lambda functions) and by applications. A subscription filter must be added to every new log group in order to deliver its log events to the logging account,

The AddSubscriptionFilter.yml CloudFormation template contains resources to automatically add subscription filters.

First, it creates a role that allows it to access the lambda code that is stored in a centralized location – the <S3 infrastructure-bucket>. (Remember that its S3 bucket policy must contain this account number in order to access the lambda code.)

Second, the template creates the AddSubscriptionLambda, which reuses the core logic shared by the script in the last section. It retrieves the proper destination from the Parameter Store, deletes any existing subscription filter from the log group, and adds the new subscription filter to the newly created log group. This lambda function is triggered by a CloudWatch event rule.

Third, the CloudFormation creates a Lambda Permission, which allows the event trigger to invoke this particular lambda.

Finally, the CloudFormation template creates an Amazon CloudWatch Events Rule that acts as a trigger for the lambda. This rule looks for an event coming from CloudTrail that signals the creation of a new log group. For each create log group event found, it invokes the AddSubscriptionLambda.

Figure 4 - Infrastructure to automatically add a subscription filter to a new log group and the log flow to the centralized account

Figure 4 – Infrastructure to automatically add a subscription filter to a new log group and the log flow to the centralized account

Installation Instructions:

(Important note: This functionality requires that the LogDestination parameter be properly set to the LogDestinationArn in the Parameter Store before the Lambda will run successfully. The script in the previous step sets this parameter, or it can be done manually. Make certain that the destination specified is in this same region.)

  1. Ensure that the <S3 infrastructure-bucket> has the AddSubscriptionFilter.zip file containing the Python code files lambda.py and CentralLogging.py.
  2. Log in to the appropriate account, and access using administrator credentials. Make sure that the region is set properly.
  3. Navigate to Cloudformation and click the Create Stack button.
  4. Select Specify an Amazon S3 template URL and enter the Link for the AddSubscriptionFilter.yml template found in <S3 infrastructure-bucket>
  5. Enter a stack name, such as AddSubscription.
  6. Enter the two parameters, the <S3 infrastructure-bucket> name (not ARN) and the folder and file name (e.g., lambdas/AddSubscriptionFilter.zip)
  7. Skip the next page, acknowledge the creation of IAM resources, and Create the stack.

In order to test that the automated addition of subscription filters is working properly, use the AWS Management Console to navigate to CloudWatch Logs and click the Actions button. Select Create New Log Group and enter a random log group name, such as “testLogGroup.” When first created, the log group will not have a subscription filter. After a few minutes, refresh the display and you should see the new subscription filter on the log group. At this point, you can delete the test log group.

New Account Setup

As a reminder, when you add new accounts that you want to have stream log events to the central logging account, you will need to configure the new accounts in two places in order for this functionality to work properly.

First, add the account number to the LoggingDestination property DestinationPolicy in the centralLogging.yml template. Then, update the CloudFormation stack.

Second, modify the bucket policy for the <S3 infrastructure-bucket>. Select the Permissions tab, then the Bucket Policy button. Add the new account to allow cross-account access to the lambda code by adding the line “arn:aws:iam::<new account number>:root” to the Principal.AWS list.

Conclusion

Centralized logging is a key component in enterprise multi-account architectures. In this blog post, I have built on the central logging in multi-account environments streaming architecture to automatically subscribe all CloudWatch Logs log groups to send all log events to an S3 bucket in a designated logging account. The solution uses a script to add subscription filters to existing log groups, and a lambda function to automatically place a subscription filter on all new log groups created within the account. This can be used to forward application logs, security logs, VPC flow logs, or any other important logs that are required for audit, security, or compliance purposes.

About the author

David BaileyDavid Bailey is a Cloud Infrastructure Architect with AWS Professional Services specializing in serverless application architecture, IoT, and artificial intelligence. He has spent decades architecting and developing complex custom software applications, as well as teaching internationally on object-oriented design, expert systems, and neural networks.

 

 

Get Started with Blockchain Using the new AWS Blockchain Templates

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/get-started-with-blockchain-using-the-new-aws-blockchain-templates/

Many of today’s discussions around blockchain technology remind me of the classic Shimmer Floor Wax skit. According to Dan Aykroyd, Shimmer is a dessert topping. Gilda Radner claims that it is a floor wax, and Chevy Chase settles the debate and reveals that it actually is both! Some of the people that I talk to see blockchains as the foundation of a new monetary system and a way to facilitate international payments. Others see blockchains as a distributed ledger and immutable data source that can be applied to logistics, supply chain, land registration, crowdfunding, and other use cases. Either way, it is clear that there are a lot of intriguing possibilities and we are working to help our customers use this technology more effectively.

We are launching AWS Blockchain Templates today. These templates will let you launch an Ethereum (either public or private) or Hyperledger Fabric (private) network in a matter of minutes and with just a few clicks. The templates create and configure all of the AWS resources needed to get you going in a robust and scalable fashion.

Launching a Private Ethereum Network
The Ethereum template offers two launch options. The ecs option creates an Amazon ECS cluster within a Virtual Private Cloud (VPC) and launches a set of Docker images in the cluster. The docker-local option also runs within a VPC, and launches the Docker images on EC2 instances. The template supports Ethereum mining, the EthStats and EthExplorer status pages, and a set of nodes that implement and respond to the Ethereum RPC protocol. Both options create and make use of a DynamoDB table for service discovery, along with Application Load Balancers for the status pages.

Here are the AWS Blockchain Templates for Ethereum:

I start by opening the CloudFormation Console in the desired region and clicking Create Stack:

I select Specify an Amazon S3 template URL, enter the URL of the template for the region, and click Next:

I give my stack a name:

Next, I enter the first set of parameters, including the network ID for the genesis block. I’ll stick with the default values for now:

I will also use the default values for the remaining network parameters:

Moving right along, I choose the container orchestration platform (ecs or docker-local, as I explained earlier) and the EC2 instance type for the container nodes:

Next, I choose my VPC and the subnets for the Ethereum network and the Application Load Balancer:

I configure my keypair, EC2 security group, IAM role, and instance profile ARN (full information on the required permissions can be found in the documentation):

The Instance Profile ARN can be found on the summary page for the role:

I confirm that I want to deploy EthStats and EthExplorer, choose the tag and version for the nested CloudFormation templates that are used by this one, and click Next to proceed:

On the next page I specify a tag for the resources that the stack will create, leave the other options as-is, and click Next:

I review all of the parameters and options, acknowledge that the stack might create IAM resources, and click Create to build my network:

The template makes use of three nested templates:

After all of the stacks have been created (mine took about 5 minutes), I can select JeffNet and click the Outputs tab to discover the links to EthStats and EthExplorer:

Here’s my EthStats:

And my EthExplorer:

If I am writing apps that make use of my private network to store and process smart contracts, I would use the EthJsonRpcUrl.

Stay Tuned
My colleagues are eager to get your feedback on these new templates and plan to add new versions of the frameworks as they become available.

Jeff;

 

AWS AppSync – Production-Ready with Six New Features

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-appsync-production-ready-with-six-new-features/

If you build (or want to build) data-driven web and mobile apps and need real-time updates and the ability to work offline, you should take a look at AWS AppSync. Announced in preview form at AWS re:Invent 2017 and described in depth here, AWS AppSync is designed for use in iOS, Android, JavaScript, and React Native apps. AWS AppSync is built around GraphQL, an open, standardized query language that makes it easy for your applications to request the precise data that they need from the cloud.

I’m happy to announce that the preview period is over and that AWS AppSync is now generally available and production-ready, with six new features that will simplify and streamline your application development process:

Console Log Access – You can now see the CloudWatch Logs entries that are created when you test your GraphQL queries, mutations, and subscriptions from within the AWS AppSync Console.

Console Testing with Mock Data – You can now create and use mock context objects in the console for testing purposes.

Subscription Resolvers – You can now create resolvers for AWS AppSync subscription requests, just as you can already do for query and mutate requests.

Batch GraphQL Operations for DynamoDB – You can now make use of DynamoDB’s batch operations (BatchGetItem and BatchWriteItem) across one or more tables. in your resolver functions.

CloudWatch Support – You can now use Amazon CloudWatch Metrics and CloudWatch Logs to monitor calls to the AWS AppSync APIs.

CloudFormation Support – You can now define your schemas, data sources, and resolvers using AWS CloudFormation templates.

A Brief AppSync Review
Before diving in to the new features, let’s review the process of creating an AWS AppSync API, starting from the console. I click Create API to begin:

I enter a name for my API and (for demo purposes) choose to use the Sample schema:

The schema defines a collection of GraphQL object types. Each object type has a set of fields, with optional arguments:

If I was creating an API of my own I would enter my schema at this point. Since I am using the sample, I don’t need to do this. Either way, I click on Create to proceed:

The GraphQL schema type defines the entry points for the operations on the data. All of the data stored on behalf of a particular schema must be accessible using a path that begins at one of these entry points. The console provides me with an endpoint and key for my API:

It also provides me with guidance and a set of fully functional sample apps that I can clone:

When I clicked Create, AWS AppSync created a pair of Amazon DynamoDB tables for me. I can click Data Sources to see them:

I can also see and modify my schema, issue queries, and modify an assortment of settings for my API.

Let’s take a quick look at each new feature…

Console Log Access
The AWS AppSync Console already allows me to issue queries and to see the results, and now provides access to relevant log entries.In order to see the entries, I must enable logs (as detailed below), open up the LOGS, and check the checkbox. Here’s a simple mutation query that adds a new event. I enter the query and click the arrow to test it:

I can click VIEW IN CLOUDWATCH for a more detailed view:

To learn more, read Test and Debug Resolvers.

Console Testing with Mock Data
You can now create a context object in the console where it will be passed to one of your resolvers for testing purposes. I’ll add a testResolver item to my schema:

Then I locate it on the right-hand side of the Schema page and click Attach:

I choose a data source (this is for testing and the actual source will not be accessed), and use the Put item mapping template:

Then I click Select test context, choose Create New Context, assign a name to my test content, and click Save (as you can see, the test context contains the arguments from the query along with values to be returned for each field of the result):

After I save the new Resolver, I click Test to see the request and the response:

Subscription Resolvers
Your AWS AppSync application can monitor changes to any data source using the @aws_subscribe GraphQL schema directive and defining a Subscription type. The AWS AppSync client SDK connects to AWS AppSync using MQTT over Websockets and the application is notified after each mutation. You can now attach resolvers (which convert GraphQL payloads into the protocol needed by the underlying storage system) to your subscription fields and perform authorization checks when clients attempt to connect. This allows you to perform the same fine grained authorization routines across queries, mutations, and subscriptions.

To learn more about this feature, read Real-Time Data.

Batch GraphQL Operations
Your resolvers can now make use of DynamoDB batch operations that span one or more tables in a region. This allows you to use a list of keys in a single query, read records multiple tables, write records in bulk to multiple tables, and conditionally write or delete related records across multiple tables.

In order to use this feature the IAM role that you use to access your tables must grant access to DynamoDB’s BatchGetItem and BatchPutItem functions.

To learn more, read the DynamoDB Batch Resolvers tutorial.

CloudWatch Logs Support
You can now tell AWS AppSync to log API requests to CloudWatch Logs. Click on Settings and Enable logs, then choose the IAM role and the log level:

CloudFormation Support
You can use the following CloudFormation resource types in your templates to define AWS AppSync resources:

AWS::AppSync::GraphQLApi – Defines an AppSync API in terms of a data source (an Amazon Elasticsearch Service domain or a DynamoDB table).

AWS::AppSync::ApiKey – Defines the access key needed to access the data source.

AWS::AppSync::GraphQLSchema – Defines a GraphQL schema.

AWS::AppSync::DataSource – Defines a data source.

AWS::AppSync::Resolver – Defines a resolver by referencing a schema and a data source, and includes a mapping template for requests.

Here’s a simple schema definition in YAML form:

  AppSyncSchema:
    Type: "AWS::AppSync::GraphQLSchema"
    DependsOn:
      - AppSyncGraphQLApi
    Properties:
      ApiId: !GetAtt AppSyncGraphQLApi.ApiId
      Definition: |
        schema {
          query: Query
          mutation: Mutation
        }
        type Query {
          singlePost(id: ID!): Post
          allPosts: [Post]
        }
        type Mutation {
          putPost(id: ID!, title: String!): Post
        }
        type Post {
          id: ID!
          title: String!
        }

Available Now
These new features are available now and you can start using them today! Here are a couple of blog posts and other resources that you might find to be of interest:

Jeff;

 

 

How to retain system tables’ data spanning multiple Amazon Redshift clusters and run cross-cluster diagnostic queries

Post Syndicated from Karthik Sonti original https://aws.amazon.com/blogs/big-data/how-to-retain-system-tables-data-spanning-multiple-amazon-redshift-clusters-and-run-cross-cluster-diagnostic-queries/

Amazon Redshift is a data warehouse service that logs the history of the system in STL log tables. The STL log tables manage disk space by retaining only two to five days of log history, depending on log usage and available disk space.

To retain STL tables’ data for an extended period, you usually have to create a replica table for every system table. Then, for each you load the data from the system table into the replica at regular intervals. By maintaining replica tables for STL tables, you can run diagnostic queries on historical data from the STL tables. You then can derive insights from query execution times, query plans, and disk-spill patterns, and make better cluster-sizing decisions. However, refreshing replica tables with live data from STL tables at regular intervals requires schedulers such as Cron or AWS Data Pipeline. Also, these tables are specific to one cluster and they are not accessible after the cluster is terminated. This is especially true for transient Amazon Redshift clusters that last for only a finite period of ad hoc query execution.

In this blog post, I present a solution that exports system tables from multiple Amazon Redshift clusters into an Amazon S3 bucket. This solution is serverless, and you can schedule it as frequently as every five minutes. The AWS CloudFormation deployment template that I provide automates the solution setup in your environment. The system tables’ data in the Amazon S3 bucket is partitioned by cluster name and query execution date to enable efficient joins in cross-cluster diagnostic queries.

I also provide another CloudFormation template later in this post. This second template helps to automate the creation of tables in the AWS Glue Data Catalog for the system tables’ data stored in Amazon S3. After the system tables are exported to Amazon S3, you can run cross-cluster diagnostic queries on the system tables’ data and derive insights about query executions in each Amazon Redshift cluster. You can do this using Amazon QuickSight, Amazon Athena, Amazon EMR, or Amazon Redshift Spectrum.

You can find all the code examples in this post, including the CloudFormation templates, AWS Glue extract, transform, and load (ETL) scripts, and the resolution steps for common errors you might encounter in this GitHub repository.

Solution overview

The solution in this post uses AWS Glue to export system tables’ log data from Amazon Redshift clusters into Amazon S3. The AWS Glue ETL jobs are invoked at a scheduled interval by AWS Lambda. AWS Systems Manager, which provides secure, hierarchical storage for configuration data management and secrets management, maintains the details of Amazon Redshift clusters for which the solution is enabled. The last-fetched time stamp values for the respective cluster-table combination are maintained in an Amazon DynamoDB table.

The following diagram covers the key steps involved in this solution.

The solution as illustrated in the preceding diagram flows like this:

  1. The Lambda function, invoke_rs_stl_export_etl, is triggered at regular intervals, as controlled by Amazon CloudWatch. It’s triggered to look up the AWS Systems Manager parameter store to get the details of the Amazon Redshift clusters for which the system table export is enabled.
  2. The same Lambda function, based on the Amazon Redshift cluster details obtained in step 1, invokes the AWS Glue ETL job designated for the Amazon Redshift cluster. If an ETL job for the cluster is not found, the Lambda function creates one.
  3. The ETL job invoked for the Amazon Redshift cluster gets the cluster credentials from the parameter store. It gets from the DynamoDB table the last exported time stamp of when each of the system tables was exported from the respective Amazon Redshift cluster.
  4. The ETL job unloads the system tables’ data from the Amazon Redshift cluster into an Amazon S3 bucket.
  5. The ETL job updates the DynamoDB table with the last exported time stamp value for each system table exported from the Amazon Redshift cluster.
  6. The Amazon Redshift cluster system tables’ data is available in Amazon S3 and is partitioned by cluster name and date for running cross-cluster diagnostic queries.

Understanding the configuration data

This solution uses AWS Systems Manager parameter store to store the Amazon Redshift cluster credentials securely. The parameter store also securely stores other configuration information that the AWS Glue ETL job needs for extracting and storing system tables’ data in Amazon S3. Systems Manager comes with a default AWS Key Management Service (AWS KMS) key that it uses to encrypt the password component of the Amazon Redshift cluster credentials.

The following table explains the global parameters and cluster-specific parameters required in this solution. The global parameters are defined once and applicable at the overall solution level. The cluster-specific parameters are specific to an Amazon Redshift cluster and repeat for each cluster for which you enable this post’s solution. The CloudFormation template explained later in this post creates these parameters as part of the deployment process.

Parameter name Type Description
Global parametersdefined once and applied to all jobs
redshift_query_logs.global.s3_prefix String The Amazon S3 path where the query logs are exported. Under this path, each exported table is partitioned by cluster name and date.
redshift_query_logs.global.tempdir String The Amazon S3 path that AWS Glue ETL jobs use for temporarily staging the data.
redshift_query_logs.global.role> String The name of the role that the AWS Glue ETL jobs assume. Just the role name is sufficient. The complete Amazon Resource Name (ARN) is not required.
redshift_query_logs.global.enabled_cluster_list StringList A comma-separated list of cluster names for which system tables’ data export is enabled. This gives flexibility for a user to exclude certain clusters.
Cluster-specific parametersfor each cluster specified in the enabled_cluster_list parameter
redshift_query_logs.<<cluster_name>>.connection String The name of the AWS Glue Data Catalog connection to the Amazon Redshift cluster. For example, if the cluster name is product_warehouse, the entry is redshift_query_logs.product_warehouse.connection.
redshift_query_logs.<<cluster_name>>.user String The user name that AWS Glue uses to connect to the Amazon Redshift cluster.
redshift_query_logs.<<cluster_name>>.password Secure String The password that AWS Glue uses to connect the Amazon Redshift cluster’s encrypted-by key that is managed in AWS KMS.

For example, suppose that you have two Amazon Redshift clusters, product-warehouse and category-management, for which the solution described in this post is enabled. In this case, the parameters shown in the following screenshot are created by the solution deployment CloudFormation template in the AWS Systems Manager parameter store.

Solution deployment

To make it easier for you to get started, I created a CloudFormation template that automatically configures and deploys the solution—only one step is required after deployment.

Prerequisites

To deploy the solution, you must have one or more Amazon Redshift clusters in a private subnet. This subnet must have a network address translation (NAT) gateway or a NAT instance configured, and also a security group with a self-referencing inbound rule for all TCP ports. For more information about why AWS Glue ETL needs the configuration it does, described previously, see Connecting to a JDBC Data Store in a VPC in the AWS Glue documentation.

To start the deployment, launch the CloudFormation template:

CloudFormation stack parameters

The following table lists and describes the parameters for deploying the solution to export query logs from multiple Amazon Redshift clusters.

Property Default Description
S3Bucket mybucket The bucket this solution uses to store the exported query logs, stage code artifacts, and perform unloads from Amazon Redshift. For example, the mybucket/extract_rs_logs/data bucket is used for storing all the exported query logs for each system table partitioned by the cluster. The mybucket/extract_rs_logs/temp/ bucket is used for temporarily staging the unloaded data from Amazon Redshift. The mybucket/extract_rs_logs/code bucket is used for storing all the code artifacts required for Lambda and the AWS Glue ETL jobs.
ExportEnabledRedshiftClusters Requires Input A comma-separated list of cluster names from which the system table logs need to be exported.
DataStoreSecurityGroups Requires Input A list of security groups with an inbound rule to the Amazon Redshift clusters provided in the parameter, ExportEnabledClusters. These security groups should also have a self-referencing inbound rule on all TCP ports, as explained on Connecting to a JDBC Data Store in a VPC.

After you launch the template and create the stack, you see that the following resources have been created:

  1. AWS Glue connections for each Amazon Redshift cluster you provided in the CloudFormation stack parameter, ExportEnabledRedshiftClusters.
  2. All parameters required for this solution created in the parameter store.
  3. The Lambda function that invokes the AWS Glue ETL jobs for each configured Amazon Redshift cluster at a regular interval of five minutes.
  4. The DynamoDB table that captures the last exported time stamps for each exported cluster-table combination.
  5. The AWS Glue ETL jobs to export query logs from each Amazon Redshift cluster provided in the CloudFormation stack parameter, ExportEnabledRedshiftClusters.
  6. The IAM roles and policies required for the Lambda function and AWS Glue ETL jobs.

After the deployment

For each Amazon Redshift cluster for which you enabled the solution through the CloudFormation stack parameter, ExportEnabledRedshiftClusters, the automated deployment includes temporary credentials that you must update after the deployment:

  1. Go to the parameter store.
  2. Note the parameters <<cluster_name>>.user and redshift_query_logs.<<cluster_name>>.password that correspond to each Amazon Redshift cluster for which you enabled this solution. Edit these parameters to replace the placeholder values with the right credentials.

For example, if product-warehouse is one of the clusters for which you enabled system table export, you edit these two parameters with the right user name and password and choose Save parameter.

Querying the exported system tables

Within a few minutes after the solution deployment, you should see Amazon Redshift query logs being exported to the Amazon S3 location, <<S3Bucket_you_provided>>/extract_redshift_query_logs/data/. In that bucket, you should see the eight system tables partitioned by customer name and date: stl_alert_event_log, stl_dlltext, stl_explain, stl_query, stl_querytext, stl_scan, stl_utilitytext, and stl_wlm_query.

To run cross-cluster diagnostic queries on the exported system tables, create external tables in the AWS Glue Data Catalog. To make it easier for you to get started, I provide a CloudFormation template that creates an AWS Glue crawler, which crawls the exported system tables stored in Amazon S3 and builds the external tables in the AWS Glue Data Catalog.

Launch this CloudFormation template to create external tables that correspond to the Amazon Redshift system tables. S3Bucket is the only input parameter required for this stack deployment. Provide the same Amazon S3 bucket name where the system tables’ data is being exported. After you successfully create the stack, you can see the eight tables in the database, redshift_query_logs_db, as shown in the following screenshot.

Now, navigate to the Athena console to run cross-cluster diagnostic queries. The following screenshot shows a diagnostic query executed in Athena that retrieves query alerts logged across multiple Amazon Redshift clusters.

You can build the following example Amazon QuickSight dashboard by running cross-cluster diagnostic queries on Athena to identify the hourly query count and the key query alert events across multiple Amazon Redshift clusters.

How to extend the solution

You can extend this post’s solution in two ways:

  • Add any new Amazon Redshift clusters that you spin up after you deploy the solution.
  • Add other system tables or custom query results to the list of exports from an Amazon Redshift cluster.

Extend the solution to other Amazon Redshift clusters

To extend the solution to more Amazon Redshift clusters, add the three cluster-specific parameters in the AWS Systems Manager parameter store following the guidelines earlier in this post. Modify the redshift_query_logs.global.enabled_cluster_list parameter to append the new cluster to the comma-separated string.

Extend the solution to add other tables or custom queries to an Amazon Redshift cluster

The current solution ships with the export functionality for the following Amazon Redshift system tables:

  • stl_alert_event_log
  • stl_dlltext
  • stl_explain
  • stl_query
  • stl_querytext
  • stl_scan
  • stl_utilitytext
  • stl_wlm_query

You can easily add another system table or custom query by adding a few lines of code to the AWS Glue ETL job, <<cluster-name>_extract_rs_query_logs. For example, suppose that from the product-warehouse Amazon Redshift cluster you want to export orders greater than $2,000. To do so, add the following five lines of code to the AWS Glue ETL job product-warehouse_extract_rs_query_logs, where product-warehouse is your cluster name:

  1. Get the last-processed time-stamp value. The function creates a value if it doesn’t already exist.

salesLastProcessTSValue = functions.getLastProcessedTSValue(trackingEntry=”mydb.sales_2000",job_configs=job_configs)

  1. Run the custom query with the time stamp.

returnDF=functions.runQuery(query="select * from sales s join order o where o.order_amnt > 2000 and sale_timestamp > '{}'".format (salesLastProcessTSValue) ,tableName="mydb.sales_2000",job_configs=job_configs)

  1. Save the results to Amazon S3.

functions.saveToS3(dataframe=returnDF,s3Prefix=s3Prefix,tableName="mydb.sales_2000",partitionColumns=["sale_date"],job_configs=job_configs)

  1. Get the latest time-stamp value from the returned data frame in Step 2.

latestTimestampVal=functions.getMaxValue(returnDF,"sale_timestamp",job_configs)

  1. Update the last-processed time-stamp value in the DynamoDB table.

functions.updateLastProcessedTSValue(“mydb.sales_2000",latestTimestampVal[0],job_configs)

Conclusion

In this post, I demonstrate a serverless solution to retain the system tables’ log data across multiple Amazon Redshift clusters. By using this solution, you can incrementally export the data from system tables into Amazon S3. By performing this export, you can build cross-cluster diagnostic queries, build audit dashboards, and derive insights into capacity planning by using services such as Athena. I also demonstrate how you can extend this solution to other ad hoc query use cases or tables other than system tables by adding a few lines of code.


Additional Reading

If you found this post useful, be sure to check out Using Amazon Redshift Spectrum, Amazon Athena, and AWS Glue with Node.js in Production and Amazon Redshift – 2017 Recap.


About the Author

Karthik Sonti is a senior big data architect at Amazon Web Services. He helps AWS customers build big data and analytical solutions and provides guidance on architecture and best practices.

 

 

 

 

Amazon S3 Update: New Storage Class and General Availability of S3 Select

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/amazon-s3-update-new-storage-class-general-availability-of-s3-select/

I’ve got two big pieces of news for anyone who stores and retrieves data in Amazon Simple Storage Service (S3):

New S3 One Zone-IA Storage Class – This new storage class is 20% less expensive than the existing Standard-IA storage class. It is designed to be used to store data that does not need the extra level of protection provided by geographic redundancy.

General Availability of S3 Select – This unique retrieval option lets you retrieve subsets of data from S3 objects using simple SQL expressions, with the possibility for a 400% performance improvement in the process.

Let’s take a look at both!

S3 One Zone-IA (Infrequent Access) Storage Class
This new storage class stores data in a single AWS Availability Zone and is designed to provide eleven 9’s (99.99999999%) of data durability, just like the other S3 storage classes. Unlike those other classes, it is not designed to be resilient to the physical loss of an AZ due to major event such as an earthquake or a flood, and data could be lost in the unlikely event that an AZ is destroyed. S3 One Zone-IA storage gives you a lower cost option for secondary backups of on-premises data and for data that can be easily re-created. You can also use it as the target of S3 Cross-Region Replication from another AWS region.

You can specify the use of S3 One Zone-IA storage when you upload a new object to S3:

You can also make use of it as part of an S3 lifecycle rule:

You can set up a lifecycle rule that moves previous versions of an object to S3 One Zone-IA after 30 or more days:

And you can modify the storage class of an existing object:

You can also manage storage classes using the S3 API, CLI, and CloudFormation templates.

The S3 One Zone-IA storage class can be used in all public AWS regions. As I noted earlier, pricing is 20% lower than for the S3 Standard-IA storage class (see the S3 Pricing page for more info). There’s a 30 day minimum retention period, and a 128 KB minimum object size.

General Availability of S3 Select
Randall wrote a detailed introduction to S3 Select last year and showed you how you can use it to retrieve selected data from within S3 objects. During the preview we added support for server-side encryption and the ability to run queries from the S3 Console.

I used a CSV file of airport codes to exercise the new console functionality:

This file contains listings for over 9100 airports, so it makes for useful test data but it definitely does not test the limits of S3 Select in any way. I select the file, open the More menu, and choose Select from:

The console sets the file format and compression according to the file name and the encryption status. I set delimiter and click Show file preview to verify that my settings are correct. Then I click Next to proceed:

I type SQL expressions in the SQL editor and click Run SQL to issue the query:

Or:

I can also issue queries from the AWS SDKs. I initiate the select operation:

s3 = boto3.client('s3', region_name='us-west-2')

r = s3.select_object_content(
        Bucket='jbarr-us-west-2',
        Key='sample-data/airportCodes.csv',
        ExpressionType='SQL',
        Expression="select * from s3object s where s.\"Country (Name)\" like '%United States%'",
        InputSerialization = {'CSV': {"FileHeaderInfo": "Use"}},
        OutputSerialization = {'CSV': {}},
)

And then I process the stream of results:

for event in r['Payload']:
    if 'Records' in event:
        records = event['Records']['Payload'].decode('utf-8')
        print(records)
    elif 'Stats' in event:
        statsDetails = event['Stats']['Details']
        print("Stats details bytesScanned: ")
        print(statsDetails['BytesScanned'])
        print("Stats details bytesProcessed: ")
        print(statsDetails['BytesProcessed'])

S3 Select is available in all public regions and you can start using it today. Pricing is based on the amount of data scanned and the amount of data returned.

Jeff;

Our Newest AWS Community Heroes (Spring 2018 Edition)

Post Syndicated from Betsy Chernoff original https://aws.amazon.com/blogs/aws/our-newest-aws-community-heroes-spring-2018-edition/

The AWS Community Heroes program helps shine a spotlight on some of the innovative work being done by rockstar AWS developers around the globe. Marrying cloud expertise with a passion for community building and education, these Heroes share their time and knowledge across social media and in-person events. Heroes also actively help drive content at Meetups, workshops, and conferences.

This March, we have five Heroes that we’re happy to welcome to our network of cloud innovators:

Peter Sbarski

Peter Sbarski is VP of Engineering at A Cloud Guru and the organizer of Serverlessconf, the world’s first conference dedicated entirely to serverless architectures and technologies. His work at A Cloud Guru allows him to work with, talk and write about serverless architectures, cloud computing, and AWS. He has written a book called Serverless Architectures on AWS and is currently collaborating on another book called Serverless Design Patterns with Tim Wagner and Yochay Kiriaty.

Peter is always happy to talk about cloud computing and AWS, and can be found at conferences and meetups throughout the year. He helps to organize Serverless Meetups in Melbourne and Sydney in Australia, and is always keen to share his experience working on interesting and innovative cloud projects.

Peter’s passions include serverless technologies, event-driven programming, back end architecture, microservices, and orchestration of systems. Peter holds a PhD in Computer Science from Monash University, Australia and can be followed on Twitter, LinkedIn, Medium, and GitHub.

 

 

 

Michael Wittig

Michael Wittig is co-founder of widdix, a consulting company focused on cloud architecture, DevOps, and software development on AWS. widdix maintains several AWS related open source projects, most notably a collection of production-ready CloudFormation templates. In 2016, widdix released marbot: a Slack bot supporting your DevOps team to detect and solve incidents on AWS.

In close collaboration with his brother Andreas Wittig, the Wittig brothers are actively creating AWS related content. Their book Amazon Web Services in Action (Manning) introduces AWS with a strong focus on automation. Andreas and Michael run the blog cloudonaut.io where they share their knowledge about AWS with the community. The Wittig brothers also published a bunch of video courses with O’Reilly, Manning, Pluralsight, and A Cloud Guru. You can also find them speaking at conferences and user groups in Europe. Both brothers are co-organizing the AWS user group in Stuttgart.

 

 

 

 

Fernando Hönig

Fernando is an experienced Infrastructure Solutions Leader, holding 5 AWS Certifications, with extensive IT Architecture and Management experience in a variety of market sectors. Working as a Cloud Architect Consultant in United Kingdom since 2014, Fernando built an online community for Hispanic speakers worldwide.

Fernando founded a LinkedIn Group, a Slack Community and a YouTube channel all of them named “AWS en Español”, and started to run a monthly webinar via YouTube streaming where different leaders discuss aspects and challenges around AWS Cloud.

During the last 18 months he’s been helping to run and coach AWS User Group leaders across LATAM and Spain, and 10 new User Groups were founded during this time.

Feel free to follow Fernando on Twitter, connect with him on LinkedIn, or join the ever-growing Hispanic Community via Slack, LinkedIn or YouTube.

 

 

 

Anders Bjørnestad

Anders is a consultant and cloud evangelist at Webstep AS in Norway. He finished his degree in Computer Science at the Norwegian Institute of Technology at about the same time the Internet emerged as a public service. Since then he has been an IT consultant and a passionate advocate of knowledge-sharing.

He architected and implemented his first customer solution on AWS back in 2010, and is essential in building Webstep’s core cloud team. Anders applies his broad expert knowledge across all layers of the organizational stack. He engages with developers on technology and architectures and with top management where he advises about cloud strategies and new business models.

Anders enjoys helping people increase their understanding of AWS and cloud in general, and holds several AWS certifications. He co-founded and co-organizes the AWS User Groups in the largest cities in Norway (Oslo, Bergen, Trondheim and Stavanger), and also uses any opportunity to engage in events related to AWS and cloud wherever he is.

You can follow him on Twitter or connect with him on LinkedIn.

To learn more about the AWS Community Heroes Program and how to get involved with your local AWS community, click here.

 

 

 

 

 

 

 

 

Task Networking in AWS Fargate

Post Syndicated from Nathan Peck original https://aws.amazon.com/blogs/compute/task-networking-in-aws-fargate/

AWS Fargate is a technology that allows you to focus on running your application without needing to provision, monitor, or manage the underlying compute infrastructure. You package your application into a Docker container that you can then launch using your container orchestration tool of choice.

Fargate allows you to use containers without being responsible for Amazon EC2 instances, similar to how EC2 allows you to run VMs without managing physical infrastructure. Currently, Fargate provides support for Amazon Elastic Container Service (Amazon ECS). Support for Amazon Elastic Container Service for Kubernetes (Amazon EKS) will be made available in the near future.

Despite offloading the responsibility for the underlying instances, Fargate still gives you deep control over configuration of network placement and policies. This includes the ability to use many networking fundamentals such as Amazon VPC and security groups.

This post covers how to take advantage of the different ways of networking your containers in Fargate when using ECS as your orchestration platform, with a focus on how to do networking securely.

The first step to running any application in Fargate is defining an ECS task for Fargate to launch. A task is a logical group of one or more Docker containers that are deployed with specified settings. When running a task in Fargate, there are two different forms of networking to consider:

  • Container (local) networking
  • External networking

Container Networking

Container networking is often used for tightly coupled application components. Perhaps your application has a web tier that is responsible for serving static content as well as generating some dynamic HTML pages. To generate these dynamic pages, it has to fetch information from another application component that has an HTTP API.

One potential architecture for such an application is to deploy the web tier and the API tier together as a pair and use local networking so the web tier can fetch information from the API tier.

If you are running these two components as two processes on a single EC2 instance, the web tier application process could communicate with the API process on the same machine by using the local loopback interface. The local loopback interface has a special IP address of 127.0.0.1 and hostname of localhost.

By making a networking request to this local interface, it bypasses the network interface hardware and instead the operating system just routes network calls from one process to the other directly. This gives the web tier a fast and efficient way to fetch information from the API tier with almost no networking latency.

In Fargate, when you launch multiple containers as part of a single task, they can also communicate with each other over the local loopback interface. Fargate uses a special container networking mode called awsvpc, which gives all the containers in a task a shared elastic network interface to use for communication.

If you specify a port mapping for each container in the task, then the containers can communicate with each other on that port. For example the following task definition could be used to deploy the web tier and the API tier:

{
  "family": "myapp"
  "containerDefinitions": [
    {
      "name": "web",
      "image": "my web image url",
      "portMappings": [
        {
          "containerPort": 80
        }
      ],
      "memory": 500,
      "cpu": 10,
      "esssential": true
    },
    {
      "name": "api",
      "image": "my api image url",
      "portMappings": [
        {
          "containerPort": 8080
        }
      ],
      "cpu": 10,
      "memory": 500,
      "essential": true
    }
  ]
}

ECS, with Fargate, is able to take this definition and launch two containers, each of which is bound to a specific static port on the elastic network interface for the task.

Because each Fargate task has its own isolated networking stack, there is no need for dynamic ports to avoid port conflicts between different tasks as in other networking modes. The static ports make it easy for containers to communicate with each other. For example, the web container makes a request to the API container using its well-known static port:

curl 127.0.0.1:8080/my-endpoint

This sends a local network request, which goes directly from one container to the other over the local loopback interface without traversing the network. This deployment strategy allows for fast and efficient communication between two tightly coupled containers. But most application architectures require more than just internal local networking.

External Networking

External networking is used for network communications that go outside the task to other servers that are not part of the task, or network communications that originate from other hosts on the internet and are directed to the task.

Configuring external networking for a task is done by modifying the settings of the VPC in which you launch your tasks. A VPC is a fundamental tool in AWS for controlling the networking capabilities of resources that you launch on your account.

When setting up a VPC, you create one or more subnets, which are logical groups that your resources can be placed into. Each subnet has an Availability Zone and its own route table, which defines rules about how network traffic operates for that subnet. There are two main types of subnets: public and private.

Public subnets

A public subnet is a subnet that has an associated internet gateway. Fargate tasks in that subnet are assigned both private and public IP addresses:


A browser or other client on the internet can send network traffic to the task via the internet gateway using its public IP address. The tasks can also send network traffic to other servers on the internet because the route table can route traffic out via the internet gateway.

If tasks want to communicate directly with each other, they can use each other’s private IP address to send traffic directly from one to the other so that it stays inside the subnet without going out to the internet gateway and back in.

Private subnets

A private subnet does not have direct internet access. The Fargate tasks inside the subnet don’t have public IP addresses, only private IP addresses. Instead of an internet gateway, a network address translation (NAT) gateway is attached to the subnet:

 

There is no way for another server or client on the internet to reach your tasks directly, because they don’t even have an address or a direct route to reach them. This is a great way to add another layer of protection for internal tasks that handle sensitive data. Those tasks are protected and can’t receive any inbound traffic at all.

In this configuration, the tasks can still communicate to other servers on the internet via the NAT gateway. They would appear to have the IP address of the NAT gateway to the recipient of the communication. If you run a Fargate task in a private subnet, you must add this NAT gateway. Otherwise, Fargate can’t make a network request to Amazon ECR to download the container image, or communicate with Amazon CloudWatch to store container metrics.

Load balancers

If you are running a container that is hosting internet content in a private subnet, you need a way for traffic from the public to reach the container. This is generally accomplished by using a load balancer such as an Application Load Balancer or a Network Load Balancer.

ECS integrates tightly with AWS load balancers by automatically configuring a service-linked load balancer to send network traffic to containers that are part of the service. When each task starts, the IP address of its elastic network interface is added to the load balancer’s configuration. When the task is being shut down, network traffic is safely drained from the task before removal from the load balancer.

To get internet traffic to containers using a load balancer, the load balancer is placed into a public subnet. ECS configures the load balancer to forward traffic to the container tasks in the private subnet:

This configuration allows your tasks in Fargate to be safely isolated from the rest of the internet. They can still initiate network communication with external resources via the NAT gateway, and still receive traffic from the public via the Application Load Balancer that is in the public subnet.

Another potential use case for a load balancer is for internal communication from one service to another service within the private subnet. This is typically used for a microservice deployment, in which one service such as an internet user account service needs to communicate with an internal service such as a password service. Obviously, it is undesirable for the password service to be directly accessible on the internet, so using an internet load balancer would be a major security vulnerability. Instead, this can be accomplished by hosting an internal load balancer within the private subnet:

With this approach, one container can distribute requests across an Auto Scaling group of other private containers via the internal load balancer, ensuring that the network traffic stays safely protected within the private subnet.

Best Practices for Fargate Networking

Determine whether you should use local task networking

Local task networking is ideal for communicating between containers that are tightly coupled and require maximum networking performance between them. However, when you deploy one or more containers as part of the same task they are always deployed together so it removes the ability to independently scale different types of workload up and down.

In the example of the application with a web tier and an API tier, it may be the case that powering the application requires only two web tier containers but 10 API tier containers. If local container networking is used between these two container types, then an extra eight unnecessary web tier containers would end up being run instead of allowing the two different services to scale independently.

A better approach would be to deploy the two containers as two different services, each with its own load balancer. This allows clients to communicate with the two web containers via the web service’s load balancer. The web service could distribute requests across the eight backend API containers via the API service’s load balancer.

Run internet tasks that require internet access in a public subnet

If you have tasks that require internet access and a lot of bandwidth for communication with other services, it is best to run them in a public subnet. Give them public IP addresses so that each task can communicate with other services directly.

If you run these tasks in a private subnet, then all their outbound traffic has to go through an NAT gateway. AWS NAT gateways support up to 10 Gbps of burst bandwidth. If your bandwidth requirements go over this, then all task networking starts to get throttled. To avoid this, you could distribute the tasks across multiple private subnets, each with their own NAT gateway. It can be easier to just place the tasks into a public subnet, if possible.

Avoid using a public subnet or public IP addresses for private, internal tasks

If you are running a service that handles private, internal information, you should not put it into a public subnet or use a public IP address. For example, imagine that you have one task, which is an API gateway for authentication and access control. You have another background worker task that handles sensitive information.

The intended access pattern is that requests from the public go to the API gateway, which then proxies request to the background task only if the request is from an authenticated user. If the background task is in a public subnet and has a public IP address, then it could be possible for an attacker to bypass the API gateway entirely. They could communicate directly to the background task using its public IP address, without being authenticated.

Conclusion

Fargate gives you a way to run containerized tasks directly without managing any EC2 instances, but you still have full control over how you want networking to work. You can set up containers to talk to each other over the local network interface for maximum speed and efficiency. For running workloads that require privacy and security, use a private subnet with public internet access locked down. Or, for simplicity with an internet workload, you can just use a public subnet and give your containers a public IP address.

To deploy one of these Fargate task networking approaches, check out some sample CloudFormation templates showing how to configure the VPC, subnets, and load balancers.

If you have questions or suggestions, please comment below.

Now Available: A New AWS Quick Start Reference Deployment for CJIS

Post Syndicated from Emil Lerch original https://aws.amazon.com/blogs/security/now-available-a-new-aws-quick-start-reference-deployment-for-cjis/

CJIS logo

As part of the AWS Compliance Quick Start program, AWS has published a new Quick Start reference deployment for customers who need to align with Criminal Justice Information Services (CJIS) Security Policy 5.6 and process Criminal Justice Information (CJI) in accordance with this policy. The new Quick Start is AWS Enterprise Accelerator – Compliance: CJIS, and it makes it easier for you to address the list of supported controls you will find in the security controls matrix that accompanies the Quick Start.

As all AWS Quick Starts do, this Quick Start helps you automate the building of a recommended architecture that, when deployed as a package, provides a baseline AWS configuration. The Quick Start uses sets of nested AWS CloudFormation templates and user data scripts to create an example environment with a two-VPC, multi-tiered web service.

The new Quick Start also includes:

The recommended architecture built by the Quick Start supports a wide variety of AWS best practices (all of which are detailed in the Quick Start), including the use of multiple Availability Zones, isolation using public and private subnets, load balancing, and Auto Scaling.

The Quick Start package also includes a deployment guide with detailed instructions and a security controls matrix that describes how the deployment addresses CJIS Security Policy 5.6 controls. You should have your IT security assessors and risk decision makers review the security controls matrix so that they can understand the extent of the implementation of the controls within the architecture. The matrix also identifies the specific resources in the CloudFormation templates that affect each control, and contains cross-references to the CJIS Security Policy 5.6 security controls.

If you have questions about this new Quick Start, contact the AWS Compliance Quick Start team. For more information about the AWS CJIS program, see CJIS Compliance.

– Emil

Building a Multi-region Serverless Application with Amazon API Gateway and AWS Lambda

Post Syndicated from Stefano Buliani original https://aws.amazon.com/blogs/compute/building-a-multi-region-serverless-application-with-amazon-api-gateway-and-aws-lambda/

This post written by: Magnus Bjorkman – Solutions Architect

Many customers are looking to run their services at global scale, deploying their backend to multiple regions. In this post, we describe how to deploy a Serverless API into multiple regions and how to leverage Amazon Route 53 to route the traffic between regions. We use latency-based routing and health checks to achieve an active-active setup that can fail over between regions in case of an issue. We leverage the new regional API endpoint feature in Amazon API Gateway to make this a seamless process for the API client making the requests. This post does not cover the replication of your data, which is another aspect to consider when deploying applications across regions.

Solution overview

Currently, the default API endpoint type in API Gateway is the edge-optimized API endpoint, which enables clients to access an API through an Amazon CloudFront distribution. This typically improves connection time for geographically diverse clients. By default, a custom domain name is globally unique and the edge-optimized API endpoint would invoke a Lambda function in a single region in the case of Lambda integration. You can’t use this type of endpoint with a Route 53 active-active setup and fail-over.

The new regional API endpoint in API Gateway moves the API endpoint into the region and the custom domain name is unique per region. This makes it possible to run a full copy of an API in each region and then use Route 53 to use an active-active setup and failover. The following diagram shows how you do this:

Active/active multi region architecture

  • Deploy your Rest API stack, consisting of API Gateway and Lambda, in two regions, such as us-east-1 and us-west-2.
  • Choose the regional API endpoint type for your API.
  • Create a custom domain name and choose the regional API endpoint type for that one as well. In both regions, you are configuring the custom domain name to be the same, for example, helloworldapi.replacewithyourcompanyname.com
  • Use the host name of the custom domain names from each region, for example, xxxxxx.execute-api.us-east-1.amazonaws.com and xxxxxx.execute-api.us-west-2.amazonaws.com, to configure record sets in Route 53 for your client-facing domain name, for example, helloworldapi.replacewithyourcompanyname.com

The above solution provides an active-active setup for your API across the two regions, but you are not doing failover yet. For that to work, set up a health check in Route 53:

Route 53 Health Check

A Route 53 health check must have an endpoint to call to check the health of a service. You could do a simple ping of your actual Rest API methods, but instead provide a specific method on your Rest API that does a deep ping. That is, it is a Lambda function that checks the status of all the dependencies.

In the case of the Hello World API, you don’t have any other dependencies. In a real-world scenario, you could check on dependencies as databases, other APIs, and external dependencies. Route 53 health checks themselves cannot use your custom domain name endpoint’s DNS address, so you are going to directly call the API endpoints via their region unique endpoint’s DNS address.

Walkthrough

The following sections describe how to set up this solution. You can find the complete solution at the blog-multi-region-serverless-service GitHub repo. Clone or download the repository locally to be able to do the setup as described.

Prerequisites

You need the following resources to set up the solution described in this post:

  • AWS CLI
  • An S3 bucket in each region in which to deploy the solution, which can be used by the AWS Serverless Application Model (SAM). You can use the following CloudFormation templates to create buckets in us-east-1 and us-west-2:
    • us-east-1:
    • us-west-2:
  • A hosted zone registered in Amazon Route 53. This is used for defining the domain name of your API endpoint, for example, helloworldapi.replacewithyourcompanyname.com. You can use a third-party domain name registrar and then configure the DNS in Amazon Route 53, or you can purchase a domain directly from Amazon Route 53.

Deploy API with health checks in two regions

Start by creating a small “Hello World” Lambda function that sends back a message in the region in which it has been deployed.


"""Return message."""
import logging

logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)

def lambda_handler(event, context):
    """Lambda handler for getting the hello world message."""

    region = context.invoked_function_arn.split(':')[3]

    logger.info("message: " + "Hello from " + region)
    
    return {
		"message": "Hello from " + region
    }

Also create a Lambda function for doing a health check that returns a value based on another environment variable (either “ok” or “fail”) to allow for ease of testing:


"""Return health."""
import logging
import os

logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)

def lambda_handler(event, context):
    """Lambda handler for getting the health."""

    logger.info("status: " + os.environ['STATUS'])
    
    return {
		"status": os.environ['STATUS']
    }

Deploy both of these using an AWS Serverless Application Model (SAM) template. SAM is a CloudFormation extension that is optimized for serverless, and provides a standard way to create a complete serverless application. You can find the full helloworld-sam.yaml template in the blog-multi-region-serverless-service GitHub repo.

A few things to highlight:

  • You are using inline Swagger to define your API so you can substitute the current region in the x-amazon-apigateway-integration section.
  • Most of the Swagger template covers CORS to allow you to test this from a browser.
  • You are also using substitution to populate the environment variable used by the “Hello World” method with the region into which it is being deployed.

The Swagger allows you to use the same SAM template in both regions.

You can only use SAM from the AWS CLI, so do the following from the command prompt. First, deploy the SAM template in us-east-1 with the following commands, replacing “<your bucket in us-east-1>” with a bucket in your account:


> cd helloworld-api
> aws cloudformation package --template-file helloworld-sam.yaml --output-template-file /tmp/cf-helloworld-sam.yaml --s3-bucket <your bucket in us-east-1> --region us-east-1
> aws cloudformation deploy --template-file /tmp/cf-helloworld-sam.yaml --stack-name multiregionhelloworld --capabilities CAPABILITY_IAM --region us-east-1

Second, do the same in us-west-2:


> aws cloudformation package --template-file helloworld-sam.yaml --output-template-file /tmp/cf-helloworld-sam.yaml --s3-bucket <your bucket in us-west-2> --region us-west-2
> aws cloudformation deploy --template-file /tmp/cf-helloworld-sam.yaml --stack-name multiregionhelloworld --capabilities CAPABILITY_IAM --region us-west-2

The API was created with the default endpoint type of Edge Optimized. Switch it to Regional. In the Amazon API Gateway console, select the API that you just created and choose the wheel-icon to edit it.

API Gateway edit API settings

In the edit screen, select the Regional endpoint type and save the API. Do the same in both regions.

Grab the URL for the API in the console by navigating to the method in the prod stage.

API Gateway endpoint link

You can now test this with curl:


> curl https://2wkt1cxxxx.execute-api.us-west-2.amazonaws.com/prod/helloworld
{"message": "Hello from us-west-2"}

Write down the domain name for the URL in each region (for example, 2wkt1cxxxx.execute-api.us-west-2.amazonaws.com), as you need that later when you deploy the Route 53 setup.

Create the custom domain name

Next, create an Amazon API Gateway custom domain name endpoint. As part of using this feature, you must have a hosted zone and domain available to use in Route 53 as well as an SSL certificate that you use with your specific domain name.

You can create the SSL certificate by using AWS Certificate Manager. In the ACM console, choose Get started (if you have no existing certificates) or Request a certificate. Fill out the form with the domain name to use for the custom domain name endpoint, which is the same across the two regions:

Amazon Certificate Manager request new certificate

Go through the remaining steps and validate the certificate for each region before moving on.

You are now ready to create the endpoints. In the Amazon API Gateway console, choose Custom Domain Names, Create Custom Domain Name.

API Gateway create custom domain name

A few things to highlight:

  • The domain name is the same as what you requested earlier through ACM.
  • The endpoint configuration should be regional.
  • Select the ACM Certificate that you created earlier.
  • You need to create a base path mapping that connects back to your earlier API Gateway endpoint. Set the base path to v1 so you can version your API, and then select the API and the prod stage.

Choose Save. You should see your newly created custom domain name:

API Gateway custom domain setup

Note the value for Target Domain Name as you need that for the next step. Do this for both regions.

Deploy Route 53 setup

Use the global Route 53 service to provide DNS lookup for the Rest API, distributing the traffic in an active-active setup based on latency. You can find the full CloudFormation template in the blog-multi-region-serverless-service GitHub repo.

The template sets up health checks, for example, for us-east-1:


HealthcheckRegion1:
  Type: "AWS::Route53::HealthCheck"
  Properties:
    HealthCheckConfig:
      Port: "443"
      Type: "HTTPS_STR_MATCH"
      SearchString: "ok"
      ResourcePath: "/prod/healthcheck"
      FullyQualifiedDomainName: !Ref Region1HealthEndpoint
      RequestInterval: "30"
      FailureThreshold: "2"

Use the health check when you set up the record set and the latency routing, for example, for us-east-1:


Region1EndpointRecord:
  Type: AWS::Route53::RecordSet
  Properties:
    Region: us-east-1
    HealthCheckId: !Ref HealthcheckRegion1
    SetIdentifier: "endpoint-region1"
    HostedZoneId: !Ref HostedZoneId
    Name: !Ref MultiregionEndpoint
    Type: CNAME
    TTL: 60
    ResourceRecords:
      - !Ref Region1Endpoint

You can create the stack by using the following link, copying in the domain names from the previous section, your existing hosted zone name, and the main domain name that is created (for example, hellowordapi.replacewithyourcompanyname.com):

The following screenshot shows what the parameters might look like:
Serverless multi region Route 53 health check

Specifically, the domain names that you collected earlier would map according to following:

  • The domain names from the API Gateway “prod”-stage go into Region1HealthEndpoint and Region2HealthEndpoint.
  • The domain names from the custom domain name’s target domain name goes into Region1Endpoint and Region2Endpoint.

Using the Rest API from server-side applications

You are now ready to use your setup. First, demonstrate the use of the API from server-side clients. You can demonstrate this by using curl from the command line:


> curl https://hellowordapi.replacewithyourcompanyname.com/v1/helloworld/
{"message": "Hello from us-east-1"}

Testing failover of Rest API in browser

Here’s how you can use this from the browser and test the failover. Find all of the files for this test in the browser-client folder of the blog-multi-region-serverless-service GitHub repo.

Use this html file:


<!DOCTYPE HTML>
<html>
<head>
    <meta charset="utf-8"/>
    <meta http-equiv="X-UA-Compatible" content="IE=edge"/>
    <meta name="viewport" content="width=device-width, initial-scale=1"/>
    <title>Multi-Region Client</title>
</head>
<body>
<div>
   <h1>Test Client</h1>

    <p id="client_result">

    </p>

    <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.3/jquery.min.js"></script>
    <script src="settings.js"></script>
    <script src="client.js"></script>
</body>
</html>

The html file uses this JavaScript file to repeatedly call the API and print the history of messages:


var messageHistory = "";

(function call_service() {

   $.ajax({
      url: helloworldMultiregionendpoint+'v1/helloworld/',
      dataType: "json",
      cache: false,
      success: function(data) {
         messageHistory+="<p>"+data['message']+"</p>";
         $('#client_result').html(messageHistory);
      },
      complete: function() {
         // Schedule the next request when the current one's complete
         setTimeout(call_service, 10000);
      },
      error: function(xhr, status, error) {
         $('#client_result').html('ERROR: '+status);
      }
   });

})();

Also, make sure to update the settings in settings.js to match with the API Gateway endpoints for the DNS-proxy and the multi-regional endpoint for the Hello World API: var helloworldMultiregionendpoint = "https://hellowordapi.replacewithyourcompanyname.com/";

You can now open the HTML file in the browser (you can do this directly from the file system) and you should see something like the following screenshot:

Serverless multi region browser test

You can test failover by changing the environment variable in your health check Lambda function. In the Lambda console, select your health check function and scroll down to the Environment variables section. For the STATUS key, modify the value to fail.

Lambda update environment variable

You should see the region switch in the test client:

Serverless multi region broker test switchover

During an emulated failure like this, the browser might take some additional time to switch over due to connection keep-alive functionality. If you are using a browser like Chrome, you can kill all the connections to see a more immediate fail-over: chrome://net-internals/#sockets

Summary

You have implemented a simple way to do multi-regional serverless applications that fail over seamlessly between regions, either being accessed from the browser or from other applications/services. You achieved this by using the capabilities of Amazon Route 53 to do latency based routing and health checks for fail-over. You unlocked the use of these features in a serverless application by leveraging the new regional endpoint feature of Amazon API Gateway.

The setup was fully scripted using CloudFormation, the AWS Serverless Application Model (SAM), and the AWS CLI, and it can be integrated into deployment tools to push the code across the regions to make sure it is available in all the needed regions. For more information about cross-region deployments, see Building a Cross-Region/Cross-Account Code Deployment Solution on AWS on the AWS DevOps blog.

Automating Amazon EBS Snapshot Management with AWS Step Functions and Amazon CloudWatch Events

Post Syndicated from Andy Katz original https://aws.amazon.com/blogs/compute/automating-amazon-ebs-snapshot-management-with-aws-step-functions-and-amazon-cloudwatch-events/

Brittany Doncaster, Solutions Architect

Business continuity is important for building mission-critical workloads on AWS. As an AWS customer, you might define recovery point objectives (RPO) and recovery time objectives (RTO) for different tier applications in your business. After the RPO and RTO requirements are defined, it is up to your architects to determine how to meet those requirements.

You probably store persistent data in Amazon EBS volumes, which live within a single Availability Zone. And, following best practices, you take snapshots of your EBS volumes to back up the data on Amazon S3, which provides 11 9’s of durability. If you are following these best practices, then you’ve probably recognized the need to manage the number of snapshots you keep for a particular EBS volume and delete older, unneeded snapshots. Doing this cleanup helps save on storage costs.

Some customers also have policies stating that backups need to be stored a certain number of miles away as part of a disaster recovery (DR) plan. To meet these requirements, customers copy their EBS snapshots to the DR region. Then, the same snapshot management and cleanup has to also be done in the DR region.

All of this snapshot management logic consists of different components. You would first tag your snapshots so you could manage them. Then, determine how many snapshots you currently have for a particular EBS volume and assess that value against a retention rule. If the number of snapshots was greater than your retention value, then you would clean up old snapshots. And finally, you might copy the latest snapshot to your DR region. All these steps are just an example of a simple snapshot management workflow. But how do you automate something like this in AWS? How do you do it without servers?

One of the most powerful AWS services released in 2016 was Amazon CloudWatch Events. It enables you to build event-driven IT automation, based on events happening within your AWS infrastructure. CloudWatch Events integrates with AWS Lambda to let you execute your custom code when one of those events occurs. However, the actions to take based on those events aren’t always composed of a single Lambda function. Instead, your business logic may consist of multiple steps (like in the case of the example snapshot management flow described earlier). And you may want to run those steps in sequence or in parallel. You may also want to have retry logic or exception handling for each step.

AWS Step Functions serves just this purpose―to help you coordinate your functions and microservices. Step Functions enables you to simplify your effort and pull the error handling, retry logic, and workflow logic out of your Lambda code. Step Functions integrates with Lambda to provide a mechanism for building complex serverless applications. Now, you can kick off a Step Functions state machine based on a CloudWatch event.

In this post, I discuss how you can target Step Functions in a CloudWatch Events rule. This allows you to have event-driven snapshot management based on snapshot completion events firing in CloudWatch Event rules.

As an example of what you could do with Step Functions and CloudWatch Events, we’ve developed a reference architecture that performs management of your EBS snapshots.

Automating EBS Snapshot Management with Step Functions

This architecture assumes that you have already set up CloudWatch Events to create the snapshots on a schedule or that you are using some other means of creating snapshots according to your needs.

This architecture covers the pieces of the workflow that need to happen after a snapshot has been created.

  • It creates a CloudWatch Events rule to invoke a Step Functions state machine execution when an EBS snapshot is created.
  • The state machine then tags the snapshot, cleans up the oldest snapshots if the number of snapshots is greater than the defined number to retain, and copies the snapshot to a DR region.
  • When the DR region snapshot copy is completed, another state machine kicks off in the DR region. The new state machine has a similar flow and uses some of the same Lambda code to clean up the oldest snapshots that are greater than the defined number to retain.
  • Also, both state machines demonstrate how you can use Step Functions to handle errors within your workflow. Any errors that are caught during execution result in the execution of a Lambda function that writes a message to an SNS topic. Therefore, if any errors occur, you can subscribe to the SNS topic and get notified.

The following is an architecture diagram of the reference architecture:

Creating the Lambda functions and Step Functions state machines

First, pull the code from GitHub and use the AWS CLI to create S3 buckets for the Lambda code in the primary and DR regions. For this example, assume that the primary region is us-west-2 and the DR region is us-east-2. Run the following commands, replacing the italicized text in <> with your own unique bucket names.

git clone https://github.com/awslabs/aws-step-functions-ebs-snapshot-mgmt.git

cd aws-step-functions-ebs-snapshot-mgmt/

aws s3 mb s3://<primary region bucket name> --region us-west-2

aws s3 mb s3://<DR region bucket name> --region us-east-2

Next, use the Serverless Application Model (SAM), which uses AWS CloudFormation to deploy the Lambda functions and Step Functions state machines in the primary and DR regions. Replace the italicized text in <> with the S3 bucket names that you created earlier.

aws cloudformation package --template-file PrimaryRegionTemplate.yaml --s3-bucket <primary region bucket name>  --output-template-file tempPrimary.yaml --region us-west-2

aws cloudformation deploy --template-file tempPrimary.yaml --stack-name ebsSnapshotMgmtPrimary --capabilities CAPABILITY_IAM --region us-west-2

aws cloudformation package --template-file DR_RegionTemplate.yaml --s3-bucket <DR region bucket name> --output-template-file tempDR.yaml  --region us-east-2

aws cloudformation deploy --template-file tempDR.yaml --stack-name ebsSnapshotMgmtDR --capabilities CAPABILITY_IAM --region us-east-2

CloudWatch event rule verification

The CloudFormation templates deploy the following resources:

  • The Lambda functions that are coordinated by Step Functions
  • The Step Functions state machine
  • The SNS topic
  • The CloudWatch Events rules that trigger the state machine execution

So, all of the CloudWatch event rules have been created for you by performing the preceding commands. The next section demonstrates how you could create the CloudWatch event rule manually. To jump straight to testing the workflow, see the “Testing in your Account” section. Otherwise, you begin by setting up the CloudWatch event rule in the primary region for the createSnapshot event and also the CloudWatch event rule in the DR region for the copySnapshot command.

First, open the CloudWatch console in the primary region.

Choose Create Rule and create a rule for the createSnapshot command, with your newly created Step Function state machine as the target.

For Event Source, choose Event Pattern and specify the following values:

  • Service Name: EC2
  • Event Type: EBS Snapshot Notification
  • Specific Event: createSnapshot

For Target, choose Step Functions state machine, then choose the state machine created by the CloudFormation commands. Choose Create a new role for this specific resource. Your completed rule should look like the following:

Choose Configure Details and give the rule a name and description.

Choose Create Rule. You now have a CloudWatch Events rule that triggers a Step Functions state machine execution when the EBS snapshot creation is complete.

Now, set up the CloudWatch Events rule in the DR region as well. This looks almost same, but is based off the copySnapshot event instead of createSnapshot.

In the upper right corner in the console, switch to your DR region. Choose CloudWatch, Create Rule.

For Event Source, choose Event Pattern and specify the following values:

  • Service Name: EC2
  • Event Type: EBS Snapshot Notification
  • Specific Event: copySnapshot

For Target, choose Step Functions state machine, then select the state machine created by the CloudFormation commands. Choose Create a new role for this specific resource. Your completed rule should look like in the following:

As in the primary region, choose Configure Details and then give this rule a name and description. Complete the creation of the rule.

Testing in your account

To test this setup, open the EC2 console and choose Volumes. Select a volume to snapshot. Choose Actions, Create Snapshot, and then create a snapshot.

This results in a new execution of your state machine in the primary and DR regions. You can view these executions by going to the Step Functions console and selecting your state machine.

From there, you can see the execution of the state machine.

Primary region state machine:

DR region state machine:

I’ve also provided CloudFormation templates that perform all the earlier setup without using git clone and running the CloudFormation commands. Choose the Launch Stack buttons below to launch the primary and DR region stacks in Dublin and Ohio, respectively. From there, you can pick up at the Testing in Your Account section above to finish the example. All of the code for this example architecture is located in the aws-step-functions-ebs-snapshot-mgmt AWSLabs repo.

Launch EBS Snapshot Management into Ireland with CloudFormation
Primary Region eu-west-1 (Ireland)

Launch EBS Snapshot Management into Ohio with CloudFormation
DR Region us-east-2 (Ohio)

Summary

This reference architecture is just an example of how you can use Step Functions and CloudWatch Events to build event-driven IT automation. The possibilities are endless:

  • Use this pattern to perform other common cleanup type jobs such as managing Amazon RDS snapshots, old versions of Lambda functions, or old Amazon ECR images—all triggered by scheduled events.
  • Use Trusted Advisor events to identify unused EC2 instances or EBS volumes, then coordinate actions on them, such as alerting owners, stopping, or snapshotting.

Happy coding and please let me know what useful state machines you build!

Manage Kubernetes Clusters on AWS Using CoreOS Tectonic

Post Syndicated from Arun Gupta original https://aws.amazon.com/blogs/compute/kubernetes-clusters-aws-coreos-tectonic/

There are multiple ways to run a Kubernetes cluster on Amazon Web Services (AWS). The first post in this series explained how to manage a Kubernetes cluster on AWS using kops. This second post explains how to manage a Kubernetes cluster on AWS using CoreOS Tectonic.

Tectonic overview

Tectonic delivers the most current upstream version of Kubernetes with additional features. It is a commercial offering from CoreOS and adds the following features over the upstream:

  • Installer
    Comes with a graphical installer that installs a highly available Kubernetes cluster. Alternatively, the cluster can be installed using AWS CloudFormation templates or Terraform scripts.
  • Operators
    An operator is an application-specific controller that extends the Kubernetes API to create, configure, and manage instances of complex stateful applications on behalf of a Kubernetes user. This release includes an etcd operator for rolling upgrades and a Prometheus operator for monitoring capabilities.
  • Console
    A web console provides a full view of applications running in the cluster. It also allows you to deploy applications to the cluster and start the rolling upgrade of the cluster.
  • Monitoring
    Node CPU and memory metrics are powered by the Prometheus operator. The graphs are available in the console. A large set of preconfigured Prometheus alerts are also available.
  • Security
    Tectonic ensures that cluster is always up to date with the most recent patches/fixes. Tectonic clusters also enable role-based access control (RBAC). Different roles can be mapped to an LDAP service.
  • Support
    CoreOS provides commercial support for clusters created using Tectonic.

Tectonic can be installed on AWS using a GUI installer or Terraform scripts. The installer prompts you for the information needed to boot the Kubernetes cluster, such as AWS access and secret key, number of master and worker nodes, and instance size for the master and worker nodes. The cluster can be created after all the options are specified. Alternatively, Terraform assets can be downloaded and the cluster can be created later. This post shows using the installer.

CoreOS License and Pull Secret

Even though Tectonic is a commercial offering, a cluster for up to 10 nodes can be created by creating a free account at Get Tectonic for Kubernetes. After signup, a CoreOS License and Pull Secret files are provided on your CoreOS account page. Download these files as they are needed by the installer to boot the cluster.

IAM user permission

The IAM user to create the Kubernetes cluster must have access to the following services and features:

  • Amazon Route 53
  • Amazon EC2
  • Elastic Load Balancing
  • Amazon S3
  • Amazon VPC
  • Security groups

Use the aws-policy policy to grant the required permissions for the IAM user.

DNS configuration

A subdomain is required to create the cluster, and it must be registered as a public Route 53 hosted zone. The zone is used to host and expose the console web application. It is also used as the static namespace for the Kubernetes API server. This allows kubectl to be able to talk directly with the master.

The domain may be registered using Route 53. Alternatively, a domain may be registered at a third-party registrar. This post uses a kubernetes-aws.io domain registered at a third-party registrar and a tectonic subdomain within it.

Generate a Route 53 hosted zone using the AWS CLI. Download jq to run this command:

ID=$(uuidgen) && \
aws route53 create-hosted-zone \
--name tectonic.kubernetes-aws.io \
--caller-reference $ID \
| jq .DelegationSet.NameServers

The command shows an output such as the following:

[
  "ns-1924.awsdns-48.co.uk",
  "ns-501.awsdns-62.com",
  "ns-1259.awsdns-29.org",
  "ns-749.awsdns-29.net"
]

Create NS records for the domain with your registrar. Make sure that the NS records can be resolved using a utility like dig web interface. A sample output would look like the following:

The bottom of the screenshot shows NS records configured for the subdomain.

Download and run the Tectonic installer

Download the Tectonic installer (version 1.7.1) and extract it. The latest installer can always be found at coreos.com/tectonic. Start the installer:

./tectonic/tectonic-installer/$PLATFORM/installer

Replace $PLATFORM with either darwin or linux. The installer opens your default browser and prompts you to select the cloud provider. Choose Amazon Web Services as the platform. Choose Next Step.

Specify the Access Key ID and Secret Access Key for the IAM role that you created earlier. This allows the installer to create resources required for the Kubernetes cluster. This also gives the installer full access to your AWS account. Alternatively, to protect the integrity of your main AWS credentials, use a temporary session token to generate temporary credentials.

You also need to choose a region in which to install the cluster. For the purpose of this post, I chose a region close to where I live, Northern California. Choose Next Step.

Give your cluster a name. This name is part of the static namespace for the master and the address of the console.

To enable in-place update to the Kubernetes cluster, select the checkbox next to Automated Updates. It also enables update to the etcd and Prometheus operators. This feature may become a default in future releases.

Choose Upload “tectonic-license.txt” and upload the previously downloaded license file.

Choose Upload “config.json” and upload the previously downloaded pull secret file. Choose Next Step.

Let the installer generate a CA certificate and key. In this case, the browser may not recognize this certificate, which I discuss later in the post. Alternatively, you can provide a CA certificate and a key in PEM format issued by an authorized certificate authority. Choose Next Step.

Use the SSH key for the region specified earlier. You also have an option to generate a new key. This allows you to later connect using SSH into the Amazon EC2 instances provisioned by the cluster. Here is the command that can be used to log in:

ssh –i <key> [email protected]<ec2-instance-ip>

Choose Next Step.

Define the number and instance type of master and worker nodes. In this case, create a 6 nodes cluster. Make sure that the worker nodes have enough processing power and memory to run the containers.

An etcd cluster is used as persistent storage for all of Kubernetes API objects. This cluster is required for the Kubernetes cluster to operate. There are three ways to use the etcd cluster as part of the Tectonic installer:

  • (Default) Provision the cluster using EC2 instances. Additional EC2 instances are used in this case.
  • Use an alpha support for cluster provisioning using the etcd operator. The etcd operator is used for automated operations of the etcd master nodes for the cluster itself, in addition to for etcd instances that are created for application usage. The etcd cluster is provisioned within the Tectonic installer.
  • Bring your own pre-provisioned etcd cluster.

Use the first option in this case.

For more information about choosing the appropriate instance type, see the etcd hardware recommendation. Choose Next Step.

Specify the networking options. The installer can create a new public VPC or use a pre-existing public or private VPC. Make sure that the VPC requirements are met for an existing VPC.

Give a DNS name for the cluster. Choose the domain for which the Route 53 hosted zone was configured earlier, such as tectonic.kubernetes-aws.io. Multiple clusters may be created under a single domain. The cluster name and the DNS name would typically match each other.

To select the CIDR range, choose Show Advanced Settings. You can also choose the Availability Zones for the master and worker nodes. By default, the master and worker nodes are spread across multiple Availability Zones in the chosen region. This makes the cluster highly available.

Leave the other values as default. Choose Next Step.

Specify an email address and password to be used as credentials to log in to the console. Choose Next Step.

At any point during the installation, you can choose Save progress. This allows you to save configurations specified in the installer. This configuration file can then be used to restore progress in the installer at a later point.

To start the cluster installation, choose Submit. At another time, you can download the Terraform assets by choosing Manually boot. This allows you to boot the cluster later.

The logs from the Terraform scripts are shown in the installer. When the installation is complete, the console shows that the Terraform scripts were successfully applied, the domain name was resolved successfully, and that the console has started. The domain works successfully if the DNS resolution worked earlier, and it’s the address where the console is accessible.

Choose Download assets to download assets related to your cluster. It contains your generated CA, kubectl configuration file, and the Terraform state. This download is an important step as it allows you to delete the cluster later.

Choose Next Step for the final installation screen. It allows you to access the Tectonic console, gives you instructions about how to configure kubectl to manage this cluster, and finally deploys an application using kubectl.

Choose Go to my Tectonic Console. In our case, it is also accessible at http://cluster.tectonic.kubernetes-aws.io/.

As I mentioned earlier, the browser does not recognize the self-generated CA certificate. Choose Advanced and connect to the console. Enter the login credentials specified earlier in the installer and choose Login.

The Kubernetes upstream and console version are shown under Software Details. Cluster health shows All systems go and it means that the API server and the backend API can be reached.

To view different Kubernetes resources in the cluster choose, the resource in the left navigation bar. For example, all deployments can be seen by choosing Deployments.

By default, resources in the all namespace are shown. Other namespaces may be chosen by clicking on a menu item on the top of the screen. Different administration tasks such as managing the namespaces, getting list of the nodes and RBAC can be configured as well.

Download and run Kubectl

Kubectl is required to manage the Kubernetes cluster. The latest version of kubectl can be downloaded using the following command:

curl -LO https://storage.googleapis.com/kubernetes-release/release/$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)/bin/darwin/amd64/kubectl

It can also be conveniently installed using the Homebrew package manager. To find and access a cluster, Kubectl needs a kubeconfig file. By default, this configuration file is at ~/.kube/config. This file is created when a Kubernetes cluster is created from your machine. However, in this case, download this file from the console.

In the console, choose admin, My Account, Download Configuration and follow the steps to download the kubectl configuration file. Move this file to ~/.kube/config. If kubectl has already been used on your machine before, then this file already exists. Make sure to take a backup of that file first.

Now you can run the commands to view the list of deployments:

~ $ kubectl get deployments --all-namespaces
NAMESPACE         NAME                                    DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
kube-system       etcd-operator                           1         1         1            1           43m
kube-system       heapster                                1         1         1            1           40m
kube-system       kube-controller-manager                 3         3         3            3           43m
kube-system       kube-dns                                1         1         1            1           43m
kube-system       kube-scheduler                          3         3         3            3           43m
tectonic-system   container-linux-update-operator         1         1         1            1           40m
tectonic-system   default-http-backend                    1         1         1            1           40m
tectonic-system   kube-state-metrics                      1         1         1            1           40m
tectonic-system   kube-version-operator                   1         1         1            1           40m
tectonic-system   prometheus-operator                     1         1         1            1           40m
tectonic-system   tectonic-channel-operator               1         1         1            1           40m
tectonic-system   tectonic-console                        2         2         2            2           40m
tectonic-system   tectonic-identity                       2         2         2            2           40m
tectonic-system   tectonic-ingress-controller             1         1         1            1           40m
tectonic-system   tectonic-monitoring-auth-alertmanager   1         1         1            1           40m
tectonic-system   tectonic-monitoring-auth-prometheus     1         1         1            1           40m
tectonic-system   tectonic-prometheus-operator            1         1         1            1           40m
tectonic-system   tectonic-stats-emitter                  1         1         1            1           40m

This output is similar to the one shown in the console earlier. Now, this kubectl can be used to manage your resources.

Upgrade the Kubernetes cluster

Tectonic allows the in-place upgrade of the cluster. This is an experimental feature as of this release. The clusters can be updated either automatically, or with manual approval.

To perform the update, choose Administration, Cluster Settings. If an earlier Tectonic installer, version 1.6.2 in this case, is used to install the cluster, then this screen would look like the following:

Choose Check for Updates. If any updates are available, choose Start Upgrade. After the upgrade is completed, the screen is refreshed.

This is an experimental feature in this release and so should only be used on clusters that can be easily replaced. This feature may become a fully supported in a future release. For more information about the upgrade process, see Upgrading Tectonic & Kubernetes.

Delete the Kubernetes cluster

Typically, the Kubernetes cluster is a long-running cluster to serve your applications. After its purpose is served, you may delete it. It is important to delete the cluster as this ensures that all resources created by the cluster are appropriately cleaned up.

The easiest way to delete the cluster is using the assets downloaded in the last step of the installer. Extract the downloaded zip file. This creates a directory like <cluster-name>_TIMESTAMP. In that directory, give the following command to delete the cluster:

TERRAFORM_CONFIG=$(pwd)/.terraformrc terraform destroy --force

This destroys the cluster and all associated resources.

You may have forgotten to download the assets. There is a copy of the assets in the directory tectonic/tectonic-installer/darwin/clusters. In this directory, another directory with the name <cluster-name>_TIMESTAMP contains your assets.

Conclusion

This post explained how to manage Kubernetes clusters using the CoreOS Tectonic graphical installer.  For more details, see Graphical Installer with AWS. If the installation does not succeed, see the helpful Troubleshooting tips. After the cluster is created, see the Tectonic tutorials to learn how to deploy, scale, version, and delete an application.

Future posts in this series will explain other ways of creating and running a Kubernetes cluster on AWS.

Arun

From Data Lake to Data Warehouse: Enhancing Customer 360 with Amazon Redshift Spectrum

Post Syndicated from Dylan Tong original https://aws.amazon.com/blogs/big-data/from-data-lake-to-data-warehouse-enhancing-customer-360-with-amazon-redshift-spectrum/

Achieving a 360o-view of your customer has become increasingly challenging as companies embrace omni-channel strategies, engaging customers across websites, mobile, call centers, social media, physical sites, and beyond. The promise of a web where online and physical worlds blend makes understanding your customers more challenging, but also more important. Businesses that are successful in this medium have a significant competitive advantage.

The big data challenge requires the management of data at high velocity and volume. Many customers have identified Amazon S3 as a great data lake solution that removes the complexities of managing a highly durable, fault tolerant data lake infrastructure at scale and economically.

AWS data services substantially lessen the heavy lifting of adopting technologies, allowing you to spend more time on what matters most—gaining a better understanding of customers to elevate your business. In this post, I show how a recent Amazon Redshift innovation, Redshift Spectrum, can enhance a customer 360 initiative.

Customer 360 solution

A successful customer 360 view benefits from using a variety of technologies to deliver different forms of insights. These could range from real-time analysis of streaming data from wearable devices and mobile interactions to historical analysis that requires interactive, on demand queries on billions of transactions. In some cases, insights can only be inferred through AI via deep learning. Finally, the value of your customer data and insights can’t be fully realized until it is operationalized at scale—readily accessible by fleets of applications. Companies are leveraging AWS for the breadth of services that cover these domains, to drive their data strategy.

A number of AWS customers stream data from various sources into a S3 data lake through Amazon Kinesis. They use Kinesis and technologies in the Hadoop ecosystem like Spark running on Amazon EMR to enrich this data. High-value data is loaded into an Amazon Redshift data warehouse, which allows users to analyze and interact with data through a choice of client tools. Redshift Spectrum expands on this analytics platform by enabling Amazon Redshift to blend and analyze data beyond the data warehouse and across a data lake.

The following diagram illustrates the workflow for such a solution.

This solution delivers value by:

  • Reducing complexity and time to value to deeper insights. For instance, an existing data model in Amazon Redshift may provide insights across dimensions such as customer, geography, time, and product on metrics from sales and financial systems. Down the road, you may gain access to streaming data sources like customer-care call logs and website activity that you want to blend in with the sales data on the same dimensions to understand how web and call center experiences maybe correlated with sales performance. Redshift Spectrum can join these dimensions in Amazon Redshift with data in S3 to allow you to quickly gain new insights, and avoid the slow and more expensive alternative of fully integrating these sources with your data warehouse.
  • Providing an additional avenue for optimizing costs and performance. In cases like call logs and clickstream data where volumes could be many TBs to PBs, storing the data exclusively in S3 yields significant cost savings. Interactive analysis on massive datasets may now be economically viable in cases where data was previously analyzed periodically through static reports generated by inexpensive batch processes. In some cases, you can improve the user experience while simultaneously lowering costs. Spectrum is powered by a large-scale infrastructure external to your Amazon Redshift cluster, and excels at scanning and aggregating large volumes of data. For instance, your analysts maybe performing data discovery on customer interactions across millions of consumers over years of data across various channels. On this large dataset, certain queries could be slow if you didn’t have a large Amazon Redshift cluster. Alternatively, you could use Redshift Spectrum to achieve a better user experience with a smaller cluster.

Proof of concept walkthrough

To make evaluation easier for you, I’ve conducted a Redshift Spectrum proof-of-concept (PoC) for the customer 360 use case. For those who want to replicate the PoC, the instructions, AWS CloudFormation templates, and public data sets are available in the GitHub repository.

The remainder of this post is a journey through the project, observing best practices in action, and learning how you can achieve business value. The walkthrough involves:

  • An analysis of performance data from the PoC environment involving queries that demonstrate blending and analysis of data across Amazon Redshift and S3. Observe that great results are achievable at scale.
  • Guidance by example on query tuning, design, and data preparation to illustrate the optimization process. This includes tuning a query that combines clickstream data in S3 with customer and time dimensions in Amazon Redshift, and aggregates ~1.9 B out of 3.7 B+ records in under 10 seconds with a small cluster!
  • Guidance and measurements to help assess deciding between two options: accessing and analyzing data exclusively in Amazon Redshift, or using Redshift Spectrum to access data left in S3.

Stream ingestion and enrichment

The focus of this post isn’t stream ingestion and enrichment on Kinesis and EMR, but be mindful of performance best practices on S3 to ensure good streaming and query performance:

  • Use random object keys: The data files provided for this project are prefixed with SHA-256 hashes to prevent hot partitions. This is important to ensure that optimal request rates to support PUT requests from the incoming stream in addition to certain queries from large Amazon Redshift clusters that could send a large number of parallel GET requests.
  • Micro-batch your data stream: S3 isn’t optimized for small random write workloads. Your datasets should be micro-batched into large files. For instance, the “parquet-1” dataset provided batches >7 million records per file. The optimal file size for Redshift Spectrum is usually in the 100 MB to 1 GB range.

If you have an edge case that may pose scalability challenges, AWS would love to hear about it. For further guidance, talk to your solutions architect.

Environment

The project consists of the following environment:

  • Amazon Redshift cluster: 4 X dc1.large
  • Data:
    • Time and customer dimension tables are stored on all Amazon Redshift nodes (ALL distribution style):
      • The data originates from the DWDATE and CUSTOMER tables in the Star Schema Benchmark
      • The customer table contains attributes for 3 million customers.
      • The time data is at the day-level granularity, and spans 7 years, from the start of 1992 to the end of 1998.
    • The clickstream data is stored in an S3 bucket, and serves as a fact table.
      • Various copies of this dataset in CSV and Parquet format have been provided, for reasons to be discussed later.
      • The data is a modified version of the uservisits dataset from AMPLab’s Big Data Benchmark, which was generated by Intel’s Hadoop benchmark tools.
      • Changes were minimal, so that existing test harnesses for this test can be adapted:
        • Increased the 751,754,869-row dataset 5X to 3,758,774,345 rows.
        • Added surrogate keys to support joins with customer and time dimensions. These keys were distributed evenly across the entire dataset to represents user visits from six customers over seven years.
        • Values for the visitDate column were replaced to align with the 7-year timeframe, and the added time surrogate key.

Queries across the data lake and data warehouse 

Imagine a scenario where a business analyst plans to analyze clickstream metrics like ad revenue over time and by customer, market segment and more. The example below is a query that achieves this effect: 

The query part highlighted in red retrieves clickstream data in S3, and joins the data with the time and customer dimension tables in Amazon Redshift through the part highlighted in blue. The query returns the total ad revenue for three customers over the last three months, along with info on their respective market segment.

Unfortunately, this query takes around three minutes to run, and doesn’t enable the interactive experience that you want. However, there’s a number of performance optimizations that you can implement to achieve the desired performance.

Performance analysis

Two key utilities provide visibility into Redshift Spectrum:

  • EXPLAIN
    Provides the query execution plan, which includes info around what processing is pushed down to Redshift Spectrum. Steps in the plan that include the prefix S3 are executed on Redshift Spectrum. For instance, the plan for the previous query has the step “S3 Seq Scan clickstream.uservisits_csv10”, indicating that Redshift Spectrum performs a scan on S3 as part of the query execution.
  • SVL_S3QUERY_SUMMARY
    Statistics for Redshift Spectrum queries are stored in this table. While the execution plan presents cost estimates, this table stores actual statistics for past query runs.

You can get the statistics of your last query by inspecting the SVL_S3QUERY_SUMMARY table with the condition (query = pg_last_query_id()). Inspecting the previous query reveals that the entire dataset of nearly 3.8 billion rows was scanned to retrieve less than 66.3 million rows. Improving scan selectivity in your query could yield substantial performance improvements.

Partitioning

Partitioning is a key means to improving scan efficiency. In your environment, the data and tables have already been organized, and configured to support partitions. For more information, see the PoC project setup instructions. The clickstream table was defined as:

CREATE EXTERNAL TABLE clickstream.uservisits_csv10
…
PARTITIONED BY(customer int4, visitYearMonth int4)

The entire 3.8 billion-row dataset is organized as a collection of large files where each file contains data exclusive to a particular customer and month in a year. This allows you to partition your data into logical subsets by customer and year/month. With partitions, the query engine can target a subset of files:

  • Only for specific customers
  • Only data for specific months
  • A combination of specific customers and year/months

You can use partitions in your queries. Instead of joining your customer data on the surrogate customer key (that is, c.c_custkey = uv.custKey), the partition key “customer” should be used instead:

SELECT c.c_name, c.c_mktsegment, t.prettyMonthYear, SUM(uv.adRevenue)
…
ON c.c_custkey = uv.customer
…
ORDER BY c.c_name, c.c_mktsegment, uv.yearMonthKey  ASC

This query should run approximately twice as fast as the previous query. If you look at the statistics for this query in SVL_S3QUERY_SUMMARY, you see that only half the dataset was scanned. This is expected because your query is on three out of six customers on an evenly distributed dataset. However, the scan is still inefficient, and you can benefit from using your year/month partition key as well:

SELECT c.c_name, c.c_mktsegment, t.prettyMonthYear, SUM(uv.adRevenue)
…
ON c.c_custkey = uv.customer
…
ON uv.visitYearMonth = t.d_yearmonthnum
…
ORDER BY c.c_name, c.c_mktsegment, uv.visitYearMonth ASC

All joins between the tables are now using partitions. Upon reviewing the statistics for this query, you should observe that Redshift Spectrum scans and returns the exact number of rows, 66,270,117. If you run this query a few times, you should see execution time in the range of 8 seconds, which is a 22.5X improvement on your original query!

Predicate pushdown and storage optimizations 

Previously, I mentioned that Redshift Spectrum performs processing through large-scale infrastructure external to your Amazon Redshift cluster. It is optimized for performing large scans and aggregations on S3. In fact, Redshift Spectrum may even out-perform a medium size Amazon Redshift cluster on these types of workloads with the proper optimizations. There are two important variables to consider for optimizing large scans and aggregations:

  • File size and count. As a general rule, use files 100 MB-1 GB in size, as Redshift Spectrum and S3 are optimized for reading this object size. However, the number of files operating on a query is directly correlated with the parallelism achievable by a query. There is an inverse relationship between file size and count: the bigger the files, the fewer files there are for the same dataset. Consequently, there is a trade-off between optimizing for object read performance, and the amount of parallelism achievable on a particular query. Large files are best for large scans as the query likely operates on sufficiently large number of files. For queries that are more selective and for which fewer files are operating, you may find that smaller files allow for more parallelism.
  • Data format. Redshift Spectrum supports various data formats. Columnar formats like Parquet can sometimes lead to substantial performance benefits by providing compression and more efficient I/O for certain workloads. Generally, format types like Parquet should be used for query workloads involving large scans, and high attribute selectivity. Again, there are trade-offs as formats like Parquet require more compute power to process than plaintext. For queries on smaller subsets of data, the I/O efficiency benefit of Parquet is diminished. At some point, Parquet may perform the same or slower than plaintext. Latency, compression rates, and the trade-off between user experience and cost should drive your decision.

To help illustrate how Redshift Spectrum performs on these large aggregation workloads, run a basic query that aggregates the entire ~3.7 billion record dataset on Redshift Spectrum, and compared that with running the query exclusively on Amazon Redshift:

SELECT uv.custKey, COUNT(uv.custKey)
FROM <your clickstream table> as uv
GROUP BY uv.custKey
ORDER BY uv.custKey ASC

For the Amazon Redshift test case, the clickstream data is loaded, and distributed evenly across all nodes (even distribution style) with optimal column compression encodings prescribed by the Amazon Redshift’s ANALYZE command.

The Redshift Spectrum test case uses a Parquet data format with each file containing all the data for a particular customer in a month. This results in files mostly in the range of 220-280 MB, and in effect, is the largest file size for this partitioning scheme. If you run tests with the other datasets provided, you see that this data format and size is optimal and out-performs others by ~60X. 

Performance differences will vary depending on the scenario. The important takeaway is to understand the testing strategy and the workload characteristics where Redshift Spectrum is likely to yield performance benefits. 

The following chart compares the query execution time for the two scenarios. The results indicate that you would have to pay for 12 X DC1.Large nodes to get performance comparable to using a small Amazon Redshift cluster that leverages Redshift Spectrum. 

Chart showing simple aggregation on ~3.7 billion records

So you’ve validated that Spectrum excels at performing large aggregations. Could you benefit by pushing more work down to Redshift Spectrum in your original query? It turns out that you can, by making the following modification:

The clickstream data is stored at a day-level granularity for each customer while your query rolls up the data to the month level per customer. In the earlier query that uses the day/month partition key, you optimized the query so that it only scans and retrieves the data required, but the day level data is still sent back to your Amazon Redshift cluster for joining and aggregation. The query shown here pushes aggregation work down to Redshift Spectrum as indicated by the query plan:

In this query, Redshift Spectrum aggregates the clickstream data to the month level before it is returned to the Amazon Redshift cluster and joined with the dimension tables. This query should complete in about 4 seconds, which is roughly twice as fast as only using the partition key. The speed increase is evident upon reviewing the SVL_S3QUERY_SUMMARY table:

  • Bytes scanned is 21.6X less because of the Parquet data format.
  • Only 90 records are returned back to the Amazon Redshift cluster as a result of the push-down, instead of ~66.2 million, leading to substantially less join overhead, and about 530 MB less data sent back to your cluster.
  • No adverse change in average parallelism.

Assessing the value of Amazon Redshift vs. Redshift Spectrum

At this point, you might be asking yourself, why would I ever not use Redshift Spectrum? Well, you still get additional value for your money by loading data into Amazon Redshift, and querying in Amazon Redshift vs. querying S3.

In fact, it turns out that the last version of our query runs even faster when executed exclusively in native Amazon Redshift, as shown in the following chart:

Chart comparing Amazon Redshift vs. Redshift Spectrum with pushdown aggregation over 3 months of data

As a general rule, queries that aren’t dominated by I/O and which involve multiple joins are better optimized in native Amazon Redshift. For instance, the performance difference between running the partition key query entirely in Amazon Redshift versus with Redshift Spectrum is twice as large as that that of the pushdown aggregation query, partly because the former case benefits more from better join performance.

Furthermore, the variability in latency in native Amazon Redshift is lower. For use cases where you have tight performance SLAs on queries, you may want to consider using Amazon Redshift exclusively to support those queries.

On the other hand, when you perform large scans, you could benefit from the best of both worlds: higher performance at lower cost. For instance, imagine that you wanted to enable your business analysts to interactively discover insights across a vast amount of historical data. In the example below, the pushdown aggregation query is modified to analyze seven years of data instead of three months:

SELECT c.c_name, c.c_mktsegment, t.prettyMonthYear, uv.totalRevenue
…
WHERE customer <= 3 and visitYearMonth >= 199201
… 
FROM dwdate WHERE d_yearmonthnum >= 199201) as t
…
ORDER BY c.c_name, c.c_mktsegment, uv.visitYearMonth ASC

This query requires scanning and aggregating nearly 1.9 billion records. As shown in the chart below, Redshift Spectrum substantially speeds up this query. A large Amazon Redshift cluster would have to be provisioned to support this use case. With the aid of Redshift Spectrum, you could use an existing small cluster, keep a single copy of your data in S3, and benefit from economical, durable storage while only paying for what you use via the pay per query pricing model.

Chart comparing Amazon Redshift vs. Redshift Spectrum with pushdown aggregation over 7 years of data

Summary

Redshift Spectrum lowers the time to value for deeper insights on customer data queries spanning the data lake and data warehouse. It can enable interactive analysis on datasets in cases that weren’t economically practical or technically feasible before.

There are cases where you can get the best of both worlds from Redshift Spectrum: higher performance at lower cost. However, there are still latency-sensitive use cases where you may want native Amazon Redshift performance. For more best practice tips, see the 10 Best Practices for Amazon Redshift post.

Please visit the Amazon Redshift Spectrum PoC Environment Github page. If you have questions or suggestions, please comment below.

 


Additional Reading

Learn more about how Amazon Redshift Spectrum extends data warehousing out to exabytes – no loading required.


About the Author

Dylan Tong is an Enterprise Solutions Architect at AWS. He works with customers to help drive their success on the AWS platform through thought leadership and guidance on designing well architected solutions. He has spent most of his career building on his expertise in data management and analytics by working for leaders and innovators in the space.

 

 

Deploy a Data Warehouse Quickly with Amazon Redshift, Amazon RDS for PostgreSQL and Tableau Server

Post Syndicated from Jorge A. Lopez original https://aws.amazon.com/blogs/big-data/deploy-a-data-warehouse-quickly-with-amazon-redshift-amazon-rds-for-postgresql-and-tableau-server/

One of the benefits of a data warehouse environment using both Amazon Redshift and Amazon RDS for PostgreSQL is that you can leverage the advantages of each service. Amazon Redshift is a high performance, petabyte-scale data warehouse service optimized for the online analytical processing (OLAP) queries typical of analytic reporting and business intelligence applications. On the other hand, a service like RDS excels at transactional OLTP workloads such as inserting, deleting, or updating rows.

In the recent JOIN Amazon Redshift AND Amazon RDS PostgreSQL WITH dblink post, we showed how you can deploy such an environment. Now, you can deploy a similar architecture using the Modern Data Warehouse on AWS Quick Start. The Quick Start is an automated deployment that uses AWS CloudFormation templates to launch, configure, and run the services required to deploy a data warehousing environment on AWS, based on Amazon Redshift and RDS for PostgreSQL.

The Quick Start also includes an instance of Tableau Server, running on Amazon EC2. This gives you the ability to host and serve analytic dashboards, workbooks and visualizations, supported by a trial license. You can play with the sample data source and dashboard, or create your own analyses by uploading your own data sets.

For more information about the Modern Data Warehouse on AWS Quick Start, download the full deployment guide. If you’re ready to get started, use one of the buttons below:

Option 1: Deploy Quick Start into a new VPC on AWS

Option 2: Deploy Quick Start into an existing VPC

If you have questions, please leave a comment below.


Next Steps

You can also join us for the webinar Unlock Insights and Reduce Costs by Modernizing Your Data Warehouse on AWS on Tuesday, August 22, 2017. Pearson, the education and publishing company, will present best practices and lessons learned during their journey to Amazon Redshift and Tableau.