Advanced Go Concurrency

f you’ve used Go for a while you’re probably aware of some of the basic Go concurrency primitives:

  • The go keyword for spawning goroutines
  • Channels, for communicating between goroutines
  • The context package for propagating cancellation
  • The sync and sync/atomic packages for lower-level primitives such as mutexes and atomic memory access

These language features and packages combine to provide a very rich set of tools for building concurrent applications. What you might not have discovered yet is a set of higher-level concurrency primitives available in the “extended standard library” available at golang.org/x/sync. We’ll be taking a look at these in this article.

https://encore.dev/blog/advanced-go-concurrency

Go: Discovery of the Trace Package

This article is based on Go 1.13.

Go provides us a tool to enable tracing during the runtime and get a detailed view of the execution of our program. This tool can be enabled by flag -trace with the tests, frompprof to get live tracing, or anywhere in our code thanks to the trace package. This tool can be even more powerful since you can enhance it with your own traces. Let’s review how it works.

https://medium.com/a-journey-with-go/go-discovery-of-the-trace-package-e5a821743c3c

The What, Why, and When of Single-Table Design with DynamoDB

Yet data modeling with DynamoDB is tricky for those used to the relational databases that have dominated for the past few decades. There are a number of quirks around data modeling with DynamoDB, but the biggest one is the recommendation from AWS to use a single table for all of your records.

In this post, we’ll do a deep dive on the concepts behind single-table design. You’ll learn:

https://www.alexdebrie.com/posts/dynamodb-single-table/

Internals of Google Cloud Spanner

“I have learned a lot more internal things about Google Cloud Spanner from past two days. I read some of the portions of the Spanner white paper and the deep internal things from the Google Cloud Next event videos from Youtube. I’ll share the video links here, but I want to summarize all the learnings in one place. That’s why I wrote this blog post. A special thanks to Deepti Srivastava(Product Manager for Spanner) who presented the Spanner Deep Dive sessions in the Google Cloud Next Event.”

https://thedataguy.in/internals-of-google-cloud-spanner/

 

Go memory ballast: How I learnt to stop worrying and love the heap

Go memory ballast: How I learned to stop worrying and love the heap

I’m a big fan of small code changes that can have large impact. This may seem like an obvious thing to state, but let me explain:

  1. These type of changes often involve diving into and understanding things one is not familiar with.
  2. Even with the most well factored code, there is a maintenance cost to each optimization you add, and it’s usually (although not always) pretty linear with the amount of lines of code you end up adding/changing.

We recently rolled out a small change that reduced the CPU utilization of our API frontend servers at Twitch by ~30% and reduced overall 99th percentile API latency during peak load by ~45%.

This blog post is about the change, the process of finding it and explaining how it works.

https://blog.twitch.tv/en/2019/04/10/go-memory-ballast-how-i-learnt-to-stop-worrying-and-love-the-heap-26c2462549a2/