Do you have scheduled or long-running task on AWS ECS cluster and want to get notified when it fails? You can subscribe to ECS event stream with AWS CloudWatch Event rules and use Amazon SNS to send notifications to your email when container state changes.
The following example uses Serverless Framework to set up a service that sends an email to you when the container stops with the non-zero exit status. You find the sources for this example from GitHub. It is the same service that we are going to install here with Serverless Framework.
You might have already heard about our new project, Serverless Components. Our goal was to encapsulate common functionality into so-called “components”, which could then be easily re-used, extended and shared with other developers and other serverless applications.
In this post, I’m going to show you how to compose a fully-fledged, REST API-powered application, all by using several pre-built components from the component registry.
Using Amazon DynamoDB Accelerator (DAX) from AWS Lambda has several benefits for serverless applications that also use Amazon DynamoDB. DAX can improve the response time of your application by dramatically reducing read latency, as compared to using DynamoDB. Using DAX can also lower the cost of DynamoDB by reducing the amount of provisioned read throughput needed for read-heavy applications. For serverless applications, DAX provides an additional benefit: Lower latency results in shorter Lambda execution times, which means lower costs.
Connecting to a DAX cluster from Lambda functions requires some special configuration. In this post, I show an example URL-shortening application based on the AWS Serverless Application Model (AWS SAM). The application uses Amazon API Gateway, Lambda, DynamoDB, DAX, and AWS CloudFormation to demonstrate how to access DAX from Lambda.
Corral is a MapReduce framework designed to be deployed to serverless platforms, like AWS Lambda. It presents a lightweight alternative to Hadoop MapReduce. Much of the design philosophy was inspired by Yelp’s mrjob — corral retains mrjob’s ease-of-use while gaining the type safety and speed of Go.
Corral’s runtime model consists of stateless, transient executors controlled by a central driver. Currently, the best environment for deployment is AWS Lambda, but corral is modular enough that support for other serverless platforms can be added as support for Go in cloud functions improves.
Corral is best suited for data-intensive but computationally inexpensive tasks, such as ETL jobs.
More details about corral’s internals can be found in this blog post.
In the previous posts, we explored availability and reliability and the needs and means of building a multi-region, active-active architecture on AWS. In this blog post, I will walk you through the steps needed to build and deploy a serverless multi-region, active-active backend.
This actually blows my mind — since I will be able to explain this in one blog post and you should be able to deploy a fully functional backend in about an hour. In contrast, few years ago it would have required a lot more expertise, work, time and money!
Earlier this year, Amazon DynamoDB released Time to Live (TTL) functionality, which automatically deletes expired items from your tables, at no additional cost. TTL eliminates the complexity and cost of scanning tables and deleting items that you don’t want to retain, saving you money on provisioned throughput and storage. One AWS customer, TUNE, purged 85 terabytes of stale data and reduced their costs by over $200K per year.
Today, DynamoDB made TTL better with the release of a new CloudWatch metric for tracking the number of items deleted by TTL, which is also viewable for no additional charge. This new metric helps you monitor the rate of TTL deletions to validate that TTL is working as expected. For example, you could set a CloudWatch alarm to fire if too many or too few automated deletes occur, which might indicate an issue in how you set expiration time stamps for your items.
In this post, I’ll walk through an example of a serverless application using TTL to automate a common database management task: moving old data from your database into archival storage automatically. Archiving old data helps reduce costs and meet regulatory requirements governing data retention or deletion policies. I’ll show how TTL—combined with DynamoDB Streams, AWS Lambda, and Amazon Kinesis Firehose—facilitates archiving data to a low-cost storage service like Amazon S3, a data warehouse like Amazon Redshift, or to Amazon Elasticsearch Service.