Plumbing At Scale – Event Sourcing and Stream Processing Pipelines at Grab

As custodians and builders of the streaming platform at Grab operating at massive scale (think terabytes of data ingress each hour), the Coban team’s mission is to provide a NoOps, managed platform for seamless, secure access to event streams in real-time, for every team at Grab.

Coban Sewu Waterfall In Indonesia
Coban Sewu Waterfall In Indonesia. (Streams, get it?)

Streaming systems are often at the heart of event-driven architectures, and what starts as a need for a simple message bus for asynchronous processing of events quickly evolves into one that requires a more sophisticated stream processing paradigms. Earlier this year, we saw common patterns of event processing emerge across our Go backend ecosystem, including:

  • Filtering and mapping stream events of one type to another
  • Aggregating events into time windows and materializing them back to the event log or to various types of transactional and analytics databases

Generally, a class of problems surfaced which could be elegantly solved through an event sourcing1 platform with a stream processing framework built over it, similar to the Keystone platform at Netflix2.

This article details our journey building and deploying an event sourcing platform in Go, building a stream processing framework over it, and then scaling it (reliably and efficiently) to service over 300 billion events a week.

https://engineering.grab.com/plumbing-at-scale

Using artificial intelligence to detect product defects with AWS Step Functions

Factories that produce a high volume of inventory must ensure that defective products are not shipped. This is often accomplished with human workers on the assembly line or through computer vision.

You can build an application that uses a custom image classification model to detect and report back any defects in a product, then takes appropriate action. This method provides a powerful, scalable, and simple solution for quality control. It uses Amazon S3Amazon SQSAWS LambdaAWS Step Functions, and Amazon SageMaker.

To simulate a production scenario, the model is trained using an example dataset containing images of an open-source printed circuit board, with defects and without. An accompanying AWS Serverless Application Repository application deploys the Step Functions workflow for handling image classification and notifications.

https://aws.amazon.com/blogs/compute/using-artificial-intelligence-to-detect-product-defects-with-aws-step-functions/

Re-Architecting Cash and Digital Wallet Payments for India with Uber Engineering

Uber is developing a payment platform for India that enables operations teams to more seamlessly collect and distribute cash and digital wallet payments to drivers. In this article, San Francisco-based software engineer Yijun Liu reflects on his experiences working with the Uber India Engineering team in Bangalore to architect this revamped payment system.

https://eng.uber.com/india-payments/

URL shortening service written in Go and React

Short backend is built on top of Uncle Bob’s Clean Architecture, the central objective of which is separation of concerns.

Short adopts Microservices Architecture to organize dependent services around business capabilities and to enable independent deployment of each service.

Short leverages Kubernetes to automate deployment, scaling, and management of containerized microservices.

Short is maintained by a small team of talented software engineers working at Google, Uber, and Vmware as a side project.

https://github.com/short-d/short

Make resilient Go net/http servers using timeouts, deadlines and context cancellation

When it comes to timeouts, there are two types of people: those who know how tricky they can be, and those who are yet to find out.

As tricky as they are, timeouts are a reality in the connected world we live in. As I am writing this, on the other side of the table, two persons are typing on their smartphones, probably chatting to people very far from them. All made possible because of networks.

Networks and all their intricacies are here to stay, and we, who write servers for the web, have to know how to use them efficiently and guard against their deficiencies.

Without further ado, let’s look at timeouts and how they affect our net/http servers.

https://ieftimov.com/post/make-resilient-golang-net-http-servers-using-timeouts-deadlines-context-cancellation/

Microservices in Golang

Getting started, gRPC: https://ewanvalentine.io/microservices-in-golang-part-1/
Docker and micro: https://ewanvalentine.io/microservices-in-golang-part-2/
Docker compose and datastores: https://ewanvalentine.io/microservices-in-golang-part-3/
Authentication and JWT: https://ewanvalentine.io/microservices-in-golang-part-4/
Event brokering: https://ewanvalentine.io/microservices-in-golang-part-5/
Web Clients: https://ewanvalentine.io/microservices-in-golang-part-6/
Terraform: https://ewanvalentine.io/microservices-in-golang-part-7/
Kubernetes: https://ewanvalentine.io/microservices-in-golang-part-8/
CircleCI: https://ewanvalentine.io/microservices-in-golang-part-9/
Summary: https://ewanvalentine.io/microservices-in-golang-part-10/