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Using PGBouncer With CockroachDB Serverless

Given CockroachDB scales with vCPU, there's a hard limit to how many active connections we can support per vCPU before a serious problem arises. PGBouncer stretches the limits a bit making it a cost-effective option. In serverless architectures, there is no client-side connection pooling, and using middleware like PGBouncer can alleviate the problem of connection storms. Please see my previous articles on the topic for more details.

Previous Articles

  1. Using PGBouncer with CockroachDB
  2. Using PGBouncer with Cockroach Cloud Free Tier
  3. Exploring PGBouncer auth_type(s) with CockroachDB

Motivation

We've covered how to deploy PGBouncer with a self-hosted CockroachDB cluster. Today, I'm going to demonstrate how to run PGBouncer along with the Cockroach Cloud free-forever tier database. The overall concepts are identical, but we will highlight some of the major differences in deploying PGBouncer with a cloud product.

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Using CockroachDB Workloads With Kerberos

GSSAPI authentication is becoming increasingly popular as CockroachDB starting to make inroads in Fortune 2000 customer bases and financial services segment. That said, ecosystem coverage for GSS needs to improve for parity with other authN methods. Today, we are providing a workaround and a look at the future. By the way, do you realize this is my 15th article on Kerberos and CockroachDB?

Articles Covering CockroachDB and Kerberos

I find the topic of Kerberos very interesting and my colleagues commonly refer to me for help with this complex topic. I am by no means an expert at Kerberos, I am however familiar enough with it to be dangerous. That said, I've written multiple articles on the topic which you may find below:

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Secrets Detection: Optimizing Filter Processes

While increasing both the precision and the recall of our secrets detection engine, we felt the need to keep a close eye on speed. In a gearbox, if you want to increase torque, you need to decrease speed. So it wasn’t a surprise to find that our engine had the same problem: more power, less speed. At roughly 10 thousand public documents scanned every minute, this eventually led to a bottleneck.

In a previous article, we explained how we built benchmarks to keep track of those three metrics: precision, recall, and the most important here, speed. These benchmarks taught us a lot about the true internals of our engine at runtime and led to our first improvements.

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TiFS: A TiKV-Based, Partition Tolerant, Strictly Consistent File System

TiKV is a distributed key-value storage engine, featuring strong consistency and partition tolerance. It can act either as the storage engine for TiDB or as an independent transactional key-value database. Do you know what else it is capable of?

At TiDB Hackathon 2020, our team built a TiKV-based distributed POSIX file system, TiFS, which inherits the powerful features of TiKV and also taps into TiKV's possibilities beyond data storage.

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Optimizing a Simple Ray-Tracer Written in Go Part 2

This is the second part of my mini-series on how I used the go profiling and built-in benchmarking tools to optimize a naive ray-tracer written in Go. For part 1, click here.

  1. Sorting efficiently
  2. Slices vs Arrays
  3. Pointers or values?
  4. AVX2 using c2goasm
  5. A final word on threading
  6. Conclusion
(Note: Most of this blog post was written based on using Go 1.13)

This part takes off directly after part 1.

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Introduction to Benchmarking in Julia

For the number of years I’ve been programming using Julia, I’ve never really been concerned with performance. Which is to say, I’ve appreciated that other people are interested in performance and have proven that Julia can be as fast as any other performance language out there. But I’ve never been one to pour over the Performance Tips section of the Julia manual trying to squeeze every last bit of performance.

But now that I’ve released OmniSci.jl, and as a company one of our major selling points is accelerated analytics, I figured it was time to stop assuming I wrote decent-ish code and pay attention to performance. This post highlights my experience as a beginner and hopefully will show how others can get started in learning to optimize their Julia code.