f you’ve used Go for a while you’re probably aware of some of the basic Go concurrency primitives:
gokeyword for spawning goroutines
- Channels, for communicating between goroutines
contextpackage for propagating cancellation
sync/atomicpackages 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.
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, from
pprof 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.
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:
- What is single-table design
- Why single-table design is needed
- The downsides of single-table design
- The two times where the downsides of single-table design outweigh the benefits
“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.”
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:
- These type of changes often involve diving into and understanding things one is not familiar with.
- 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.
Using Z-order indexing, you can efficiently run range queries on any combination of fields in your schema. Although Amazon DynamoDB doesn’t natively support Z-order indexing, you can implement the functionality entirely from the client side. A single Z-order index can outperform and even replace entire collections of secondary indexes, saving you money and improving your throughput.
In a previous AWS Database Blog post, I introduced Z-order indexing, a way in which you can sort your data to efficiently query an Amazon DynamoDB table by using range bounds on multiple attributes. In this post, we explore the process of creating a schema for your index. We look at how to decide which attributes to include in your schema, how your index’s schema impacts query efficiency, and how to work with a variety of data types.
This post builds on concepts that are described in Part 1, so I recommend taking some time to review it before diving in.
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.
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.