Redis

I recently presented this talk at the Conf42 Golang 2023 and I thought it might be a good idea to turn it into a blog post for folks who don’t want to spend 40+ mins watching the talk (it’s ok, I understand 😉) or just staring at slides trying to imagine what I was saying.

So here you go….

By the way, you are still welcome to watch the talk or download the slides! 🙌 There are a lot of great talks that you can get from this playlist.

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Its hard to operate stateful distributed systems at scale and Redis is no exception. Managed databases make life easier by taking on much of the heavy lifting. But you still need a sound architecture and apply best practices both on the server (Redis) as well as client (application).

This blog covers a range of Redis related best practices, tips and tricks including cluster scalability, client side configuration, integration, metrics etc. Although I will be citing Amazon MemoryDB and ElastiCache for Redis from time to time, most (if not all) will be applicable to Redis clusters in general.

Using AWS Controller for Kubernetes and CDK for Kubernetes

In this blog post, you will learn how to use ACK with Amazon EKS for creating a Redis cluster on AWS (with Amazon MemoryDB).

AWS Controllers for Kubernetes (also known as ACK) leverage Kubernetes Custom Resource and Custom Resource Definitions and give you the ability to manage and use AWS services directly from Kubernetes without needing to define resources outside of the cluster. It supports many AWS services including S3, DynamoDB, MemoryDB etc.

This is the second blog post of this two-part series that uses a practical application to demonstrate how to integrate Redis with AWS Lambda. The first part was about the solution overview, deployment and hopefully you were able to try it out end to end. As promised, the second part (this one) will cover the Infrastructure aspects (IaaC to be specific) which is comprised of three (CDK) Stacks (in the context of a single CDK App).

Run your Go app on AWS App Runner service, integrate it with MemoryDB for Redis and use AWS CDK to package and deploy the app along with infrastructure

AWS App Runner allows you to deploy and run cloud-native applications in a fast, simple, and cost-effective manner. You can choose the programming language of your choice since App Runner can deploy directly from source code (in GitHub for example) or a Docker container image (from private or public repo in ECR) - all this without worrying about provisioning and managing the underlying infrastructure.

Hello and welcome 👋🏼 to this two-part blog series that uses a practical application to demonstrate how to integrate Redis with AWS Lambda. The first part (this one) covers the application - by the end of this blog, you should have the solution deployed, played around with it and in the process, have a good overview of the solution.

The second part is about the Infrastructure (IaaC to be specific) aspects - its mostly centred around AWS CDK along with some code walkthrough.

This quick-start uses AWS Cloud9 IDE to help you get up and running with MemoryDB for Redis, quickly

When I was initially exploring some of the AWS services (such as MemoryDB, MSK, Elasticache for Redis etc.), I usually followed the documentation that involved setting up EC2, SSH-ing into the instance, install/copy stuff (language, runtime, code, client etc.) and then try things out. Most often, the first step is the hardest, and it’s important for developers to have the least amount of friction as possible to “get going”.

Welcome to this series of blog posts which covers Redis Streams with the help of a practical example. We will use a sample application to make Twitter data available for search and query in real-time. RediSearch and Redis Streams serve as the backbone of this solution that consists of several co-operating components, each of which will we covered in a dedicated blog post.

The code is available in this GitHub repo - https://github.com/abhirockzz/redis-streams-in-action

We will continue from where we left off in the previous blog post and see how to build a monitoring app to make the overall system more robust in the face of high load or failure scenarios. This is because our very often, data processing applications either slow down (due to high data volumes) or may even crash/stop due to circumstances beyond our control. If this happens with our Tweets processing application, the messages that were assigned to a specific instance will be left unprocessed. The monitoring component covered in this blog post, checks pending Tweets (using XPENDING), claims (XCLAIM), processes (store them as HASH using HSET) and finally acknowledges them (XACK).

RedisTimeSeries is a Redis Module that brings native Time Series data structure to Redis. Time Series solutions which were earlier built on top of Sorted Sets (or Redis Streams) can benefit from RedisTimeSeries features such as high volume inserts, low latency reads, flexible query language, down-sampling and much more!

Generally speaking, Time Series data is (relatively) simple. Having said that, we need to factor in other characteristics as well:

Welcome to this series of blog posts which covers Redis Streams with the help of a practical example. We will use a sample application to make Twitter data available for search and query in real-time. RediSearch and Redis Streams serve as the backbone of this solution that consists of several co-operating components, each of which will we covered in a dedicated blog post.

The code is available in this GitHub repo - https://github.com/abhirockzz/redis-streams-in-action

This blog post will cover a Java based Tweets processor application whose role is to pick up tweets from Redis Streams and store them (as a HASH) so that they can be queried using RediSearch (the accurate term for this is “indexing documents” in RediSearch). You will deploy the application to Azure, validate it, run a few RediSearch queries to search tweets. Finally, there is a section where we will walk through the code to understand “how things work”.