At Marqeta, we strive to continually evolve our platform to make it scalable and highly performant. We rely heavily on MySQL, and have many MySQL instances hosted across data centers, as well as on EC2s for various purposes. While refactoring some of our APIs, we thought of giving Amazon Aurora a try. Having heard about Aurora’s performance and high availability, this was definitely a great opportunity. Setting up a single node cluster (one
db.t2.small) via the Console was the first step. After a few clicks, we had our first Aurora Cluster running happily. Next step was to fire up our regression tests while pointing to a schema in Aurora. Our database fixtures worked like a charm, and we were surprised to see all of our (thousand+) tests pass - while we knew it was a MySQL drop in replacement, we still expected some drama. Great first impression!
One of the very common architectural patterns is fan-out where an event is sent to multiple subscribers by a broker. An event can be like placing an order, which can then be handled by inventory service, record-keeping, as well as shipping service concurrently. These events can also be very frequent - like clickstreams, or search strings on a website. AWS allows for fan-out architecture with SNS topics, and SQS queues. SQS queue(s) can subscribe to SNS topics and receive any message sent to the SNS topic(s). It is documented that FIFO queues cannot be SNS subscribers here.
However, as I recently found out, SSE-enabled SQS queues cannot subscribe to SNS either. While AWS stops us from subscribing a FIFO queue to SNS, SSE-enabled queues are allowed to subscribe, but they never get any events. The purpose of this post is to document this previously undocumented behavior.
Its a challenge to log messages with a Lambda, given that there is no server to run the agents or forwarders (splunk, filebeat, etc.) on. Here is a quick and easy tutorial to set up ELK logging by writing directly to logstash via the TCP appender and logback. This is for a Java/Maven based Lambda.
Slides from my talk at Silicon Valley Code Camp 2017.
Serverless is a node.js based framework that makes creating, deploying, and managing serverless functions a breeze. We will use AWS Lambda as our FaaS (Function-as-a-Service) provider, although Serverless supports IBM OpenWhisk and Microsoft Azure as well.
In this session, we will talk about Serverless Applications, and Create and deploy a java-maven based AWS Lambda API. We will also explore the command line interface to manage lambda, which is provided out of the box by serverless framework.
Every service needs encryption at one point or another - passwords to the database, credentials to an external service, or even entire filesystem or files. Sticking the secrets, or keys in configuration files seems a quick and easy option. However, it carries security risks, even if these configurations are managed outside of the source code. On top of it, the keys used to encrypt/decrypt the data bring additional security implications and requirements in terms of storage, audit, and lifecycle management.
AWS KMS, or AWS Key Management Service is a fully managed service to store and manage keys. Any AWS service which supports encryption - S3 buckets, EBS Volumes, SQS, etc. uses KMS under the hood. KMS is more than just a key manager, it can also be used to encrypt large volumes of data, using a technique called Envelope Encryption.
In this post I will cover KMS, and the why, what, and how of Envelope Encryption.