Abhishek's blog

Learn with practical code samples

DynamoDB provides a rich set of data types including Strings, Numbers, Sets, Lists, Maps etc. In the Go SDK for DynamoDB, the types package contains Go representations of these data types and the attributevalue module provides functions to work with Go and DynamoDB types.

This blog post will demonstrate how to handle conversions between Go types in your application and DynamoDB. We will start off with simple code snippets to introduce some of the API constructs and wrap up with a example of how to use these Go SDK features in the context of a complete application (including a code walk though).

In an earlier blog post you saw how to use cdk8s with AWS Controllers for Kubernetes (also known as ACK), thanks to the fact that you can import existing Kubernetes Custom Resource Definitions using cdk8s! This made is possible to deploy DynamoDB along with a client application, from using cdk8s and Kubernetes.

But, what if you continue using AWS CDK for AWS infrastructure and harness the power cdk8s (and cdk8s-plus!) to define Kubernetes resources using regular code? Thanks to the native integration between the AWS EKS module and cdk8s, you can have the best of both worlds!

Deploy DynamoDB and a client app using cdk8s along with AWS Controller for Kubernetes

AWS Controllers for Kubernetes (also known as ACK) are built around the Kubernetes extension concepts of Custom Resource and Custom Resource Definitions. You can use ACK to define and use AWS services directly from Kubernetes. This helps you take advantage of managed AWS services for your Kubernetes applications without needing to define resources outside of the cluster.

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.

Earlier, I wrote about a Serverless URL shortener application on AWS using DynamoDB, AWS Lambda and API Gateway.

In this blog post, we will deploy that as a REST API on AWS App Runner and continue to use DynamoDB as the database. AWS App Runner is a compute service that makes it easy to deploy applications from a container image (or source code), manage their scalability, deployment pipelines and more.

With the help of a practical example presented in this blog, you will:

Build a Wordpress deployment as a cdk8s construct

Constructs are the fundamental building block of cdk8s (Cloud Development Kit for Kubernetes) - an open-source framework (part of CNCF) with which you can define your Kubernetes applications using regular programming languages (instead of yaml). In Getting started with cdk8s, you saw how to use the core cdk8s library.

You can also use the cdk8s-plus library (also covered this a previous blog) to reduce the amount of boilerplate code you need to write. With cdk8s-plus, creating a Kubernetes Deployment, specifying it’s container (and other properties) and exposing it via a Service is three function calls away.

Using AWS Lambda, DynamoDB and API Gateway

This blog post covers how to build a Serverless URL shortener application using Go. It leverages AWS Lambda for business logic, DynamoDB for persistence and API Gateway to provide the HTTP endpoints to access and use the application. The sample application presented in this blog is a trimmed down version of bit.ly or other solutions you may have used or encountered.

It’s structured as follows:

• I will start off with a quick introduction and dive into how to deploy try the solution.
• After that, I will focus on the code itself. This will cover:
  • The part which is used to write the infrastructure (using Go bindings for AWS CDK)
  • And also the core business logic which contains the Lambda function (using Lambda Go support) as well as the DynamoDB operations (using the DynamoDB Go SDK)

In this blog, you will learn:

cdk8s (Cloud Development Kit for Kubernetes) is an an open-source framework (part of CNCF) using which you can define your Kubernetes applications with regular programming languages (instead of yaml). Some of the previous blogs on this topic covered the getting started experience and using cdk8s-plus library to further improve upon the core cdk8s library features. We are going to continue and push cdk8s even further. This blog post will demonstrate how you can use Kubernetes Custom Resource Definitions with cdk8s. We will start off with a simple Nginx example and then you will use the combination of Strimzi project CRDs along with Go and cdk8s to define and deploy a Kafka cluster on Kubernetes!

Replicate DynamoDB data from one table to another

This blog post will help you get quickly started with DynamoDB Streams and AWS Lambda using Go. It will cover how to deploy the entire solution using AWS CDK.

The use case presented here is pretty simple. There are a couple of DynamoDB tables and the goal is to capture the data in one of those tables (also referred to as the source table) and replicate them to another table (also referred to as the target table) so that it can serve different queries. To demonstrate an end-to-end flow, there is also an Amazon API Gateway that front ends a Lambda function which persists data in the source DynamoDB table. Changes in this table will trigger another Lambda function (thanks to DynamoDB Streams) which will finally replicate the data into the target table.