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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In this blog post, you will be using AWS Controllers for Kubernetes on an Amazon EKS cluster to put together a solution wherein data from an Amazon SQS queue is processed by an AWS Lambda function and persisted to a DynamoDB table.
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. The idea behind ACK is to enable Kubernetes users to describe the desired state of AWS resources using the Kubernetes API and configuration language. ACK will then take care of provisioning and managing the AWS resources to match the desired state. This is achieved by using Service controllers that are responsible for managing the lifecycle of a particular AWS service. Each ACK service controller is packaged into a separate container image that is published in a public repository corresponding to an individual ACK service controller.
This blog post is for folks interested in learning how to use Golang and AWS Lambda to build a serverless solution. You will be using the aws-lambda-go library along with the AWS Go SDK v2 for an application that will process records from an Amazon Kinesis data stream and store them in a DynamoDB table. But that’s not all! You will also use Go bindings for AWS CDK to implement “Infrastructure-as-code” for the entire solution and deploy it with the AWS CDK CLI.
[Read More]…With the managed runtime for Go
In this blog post you will learn how to run a Go application to AWS App Runner using the Go platform runtime. You will start with an existing Go application on GitHub and deploy it to AWS App Runner. The application is based on the URL shortener application (with some changes) that persists data in DynamoDB.
What’s covered?
AWS App Runner can create and manage services based on two types of service sources:
[Read More]A previous blog post covered how to deploy a Go Lambda function and trigger it in response to events sent to a topic in a MSK Serverless cluster.
This blog will take it a notch further.
DynamoDB, you will also need to configure appropriate IAM roles.All the components in this solution have been written in Go.
[Read More]In this blog post you will learn how to deploy a Go Lambda function and trigger it in response to events sent to a topic in a MSK Serverless cluster.
The following topics have been covered:
Cloud9 etc.Cloud9 to access MSK using IAM roles and fine-grained permissions.You will need an AWS account, install AWS CLI as well a recent version of Go (1.18 or above).
Say you have a web service running as a Kubernetes Deployment. There are a bunch of ways to access it over a public URL, but Kubexpose makes it easy to do so. It’s a Kubernetes Operator backed by a Custom Resource Definition and the corresponding controller implementation.
Kubexposebuilt using kubebuilder and available on GitHub
To try it out, jump into the next section or scroll down to How it works? to learn more
[Read More]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).
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”.
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
In this part, we look at the service which interacts with the Twitter Streaming API to consume tweets and move them on to the next part in the processing pipeline.
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