May 2018 Newsletter from the database builders at Citus Data - Citus Data

Citus 7.4: Move fast and reduce technical debt

We are happy to announce the latest version of the Citus distributed database, Citus 7.4. Over the last 2 years, our focus on shorter release cycles & a myriad of new features caused us to accumulate some technical debt. So, in Citus 7.4, we focused on reducing technical debt in our planner logic, and in code paths that became obsolete over the years. All this helps us maintain our release velocity so we can add even more new features faster. See what we did.
Ozgun’s write-up on Citus 7.4 →

Pex credits Citus for their existence. Humbled.

Performing analytics on multimedia content across multiple platforms requires some heavy lifting within Pex’s technology stack. After evaluating several potential solutions, the team at Pex chose the Citus database. Here’s why. Faster, better analytics. Massive scale. Database ops without the worry. Who could ask for more?
Read the deets →

Fun with SQL: recursive CTEs in Postgres

This post on recursive CTEs hit the front page of Hacker News last week. For applications that deal with ecommerce and shopping categories, recursive CTEs are a great help. Craig explains why next time you need to do some recursive computation across your data, you should consider doing it directly in SQL rather than loading all that data into your app.
Craig’s post on recursive CTEs →

So happy Freshworks chose Citus to power their real-time chat analytics

Freshchat by Freshworks is a messaging service that provides near real-time and historical analytics. The Freshchat goal: to enable customers to measure response times and better understand the end user experience. Companies using Freshchat can easily determine whether they’re meeting service-level agreements (SLAs), decide whether they need to increase or reduce staff, and quantify Freshchat’s impact on the organization. So happy that Citus plays a role in Freshchat’s mission.
Why Freshworks chose Citus →

Preparing your multi-tenant app for scale

This blog answers the question “What do I need to do now to make sure I can scale my multi-tenant application later?” We’ve written before about approaches not to take, such as schema-based sharding or one-database-per-customer. Here Craig digs into three key steps to take to prepare to scale.
Three key steps to prepare for scale →

All about PostgreSQL data types

In this post, Citus engineer Dimitri Fontaine neatly recaps a series of posts covering the various data types in PostgreSQL. Using the right data type allows you to rely on Postgres to handle more complex queries.
Dimitri’s post on data types →

Podcast: Distributed PostgreSQL with Big Data

In this episode of Data Engineering Podcast, Ozgun Erdogan (our CTO) and Craig Kerstiens (our Head of Cloud) discuss how Citus Data got started, what we are doing to scale out Postgres, and how you can start using the Citus database in your environment. Tune in.
Listen to the podcast →

It’s the future (for databases)

For many developers, the default is to “just use Postgres.” Here’s what a few questions on schema designs can lead to. Yes, it’s satire. From Craig, of course.
Read Craig’s post on the future (of databases) →

Throwback Thursday! dotScale Paris

dotScale Paris is soon! So here’s a throwback to a video of Citus Data Principal Engineer Marco Slot presenting at last year’s event, on Scaling Out (Postgre)SQL.
Watch the video →

ICYMI: Monitoring Citus with Datadog

Citus developed a turnkey integration for one of the more popular tools out there to monitor Citus Cloud database clusters: Datadog. This post explains how you can get key stats on your cloud database with our Datadog integration, so you can easily monitor status before issues arise.
All about Datadog and Citus →

Looking forwards, looking backwards.

Is Citus right for you?

Want to explore whether Citus is right for you and your use case? Learn more about the Citus product, check out the documentation, or let us know if you want to book a demo.