In general, you will want to keep your development environment and your higher environments as similar as makes sense [times it doesn’t make sense: when it costs too much], to catch bugs early and often. Here, we’ll quickly run through how to set up a leader/follower topology for redis using docker/docker-compose on your local machine.
DynamoDB streams record information about what has changed in a DynamoDB table, and AWS lambdas are ways to run code without managing servers yourself. DynamoDB streams also have an integration with AWS Lambdas so that any change to a DynamoDB table can be processed by an AWS Lambda–still without worrying about keeping your servers up or maintaining them. That is the subject of this post.
AWS Lambda functions were the first “serverless” way to run code. Of course, there are still servers, but the point is that you can nearly forget about managing those servers and all of that is owned by AWS.
Amazon SNS is AWS’s solution to pub/sub. In a large, distributed system, decoupling events from services that need to act on those events allows for teams that own different services to better work in parallel, and also prevents the need for coordinating code deploys to deliver new features. If a services is already publishing a generic event, other services can hook into that event and act on them without needing anything but a bit of infrastructure.
Optimistic Locking is a form of concurrency control that basically aims to prevent two different threads from accidentally overwriting data that another thread has already written. I covered optimistic locking in MySQL in a previous blog post, which may or may not be easier to understand based on your background.
DynamoDB Streams is AWS’s home grown Change Data Capture [CDC] mechanism, which allows the consumer of the stream to see records probably in approximately the order they were created [it’s basically impossible, at scale, to guarantee that all records across all partitions will somehow stream the data in exactly the same order that it was written]. This is a pretty fantastic feature because it allows us to reliably do —something— after we add new data, update existing data, or delete existing data. As long as all the stream records are read and processed, we can ensure at least once processing on data changes and then go sleep soundly at night knowing that there is one less edge case in our application. Combine that with the natural scale that DynamoDB provides via its leaderless architecture and you can build this thing once and probably never have to worry about it handling more load ever again.
DynamoDB is a fully managed distributed datastore that does a great job of alleviating the operational overhead of building very scalable systems.
In this article, I’m going to show you how to bootstrap a local kubernetes cluster with a custom image, debug it, deploy a new image, then rollback to the old image.
kind is a tool that spins up a kubernetes cluster of arbitrary size on your local machine. It is, in my experience, more lightweight than minikube, and also allows for a multi node setup.
You can see the source code for this post on Github.
Following up on the last post [ How to Expose Meaningful Prometheus Metrics In a Spring Boot 2.x Application], if we have metrics exposed but they don’t go anywhere, are there metrics exposed at all?
The source code for this post can be found on Github.
Prometheus is a metrics aggregator with its own presumed format. The basic idea is to have the application gather a set of custom metrics, then periodically collect (or “scrape”) the metrics and send them off to a prometheus server. This server will store the data in its database, and you can thus view the evolution of your application’s metrics over time.
The nginx map module is a nifty tool that allows you to programmatically change behavior based on things like http headers that come in.
