We’re just over one month away from PGConf Silicon Valley. The schedule is live, speakers are prepping their talks, and all the last minute preparations are being made. This year the conference is one day longer to pack in even more great talks. It all starts with a day of tutorials followed by two days of talks from Postgres core committers to many others in the community.
We’ve already got a great group of people coming, but we’d love to see you there as well. While early bird ticket sales have ended we thought it’d be nice to provide a coupon code just for Citus Data readers. In case you forgot to grab your ticket before, make sure to do so with this code to get 10% off: "CitusNewsletter" (which is only good for one week).
In B2B applications, most information relates to central tenant/customer/account tables. As these tables grow, developers need to painfully re-architect their database to scale out. In this webinar we’ll walk through various design patterns to plan for scale, the trade-offs of each, and best practices you should be following.
Last month we introduced the private beta of Citus MX. Citus MX builds on the earlier work of the Citus extension even further scaling your writes by allowing you to read or write from any node within the cluster. In benchmarks we saw over 500k durable writes per second on a 32 node cluster, and much higher rates by using copy. If you have a need for very high write throughput and want to get started with Citus MX reach out to us.
Multi-tenant applications aren’t new, and if you’ve built a SaaS application you either implicitly or explicitly have the notion of tenants built-in. With smaller data (under 10 GB) you don’t have to think much about this, but as you scale you have to make choices and trade-offs to keep your database performant. In this deep dive we walk through the three common options and trade-offs you face with each approach.
SQL is a powerful language for reporting and analytics. At the core of SQL is the idea of joins and how you combine various tables together. One such type of join: outer joins are useful when we need to retain rows, even if it has no match on the other side. In this post we provide a quick overview of join types, then look at how they actually work in a distributed nature within Citus.
Calculating counts in Postgres often has a reputation for being slow. And in reality distinct counts are often one of the slower queries you may run. But there are other options, here we break down all the various ways you can perform counts in Postgres, how they perform, and when they make sense.