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Articles tagged: Postgres locks

Marco Slot

When Postgres blocks: 7 tips for dealing with locks

Written byBy Marco Slot | February 22, 2018Feb 22, 2018

Last week I wrote about locking behaviour in Postgres, which commands block each other, and how you can diagnose blocked commands. Of course, after the diagnosis you may also want a cure. With Postgres it is possible to shoot yourself in the foot, but Postgres also offers you a way to stay on target. These are some of the important do’s and don’ts that we’ve seen as helpful when working with users to migrate from their single node Postgres database to Citus or when building new real-time analytics apps on Citus.

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Marco Slot

PostgreSQL rocks, except when it blocks: Understanding locks

Written byBy Marco Slot | February 15, 2018Feb 15, 2018

On the Citus open source team, we engineers take an active role in helping our users scale out their Postgres database, be it for migrating an existing application or building a new application from scratch. This means we help you with distributing your relational data model—and also with getting the most out of Postgres.

One problem I often see users struggle with when it comes to Postgres is locks. While Postgres is amazing at running multiple operations at the same time, there are a few cases in which Postgres needs to block an operation using a lock. You therefore have to be careful about which locks your transactions take, but with the high-level abstractions that PostgreSQL provides, it can be difficult to know exactly what will happen. This post aims to demystify the locking behaviors in Postgres, and to give advice on how to avoid common problems.

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Ozgun Erdogan

How Citus Executes Distributed Transactions on Postgres

Written byBy Ozgun Erdogan | November 22, 2017Nov 22, 2017

Distributed transactions are one of the meanest, baddest problems in relational databases. With the release of Citus 7.1, distributed transactions are now available to all our users. In this article, we are going to describe how we built distributed transaction support into Citus by using PostgreSQL modules. But first, let’s give an overview of what a distributed transaction is.

(If this sounds familiar, that’s because we first announced distributed transactions as part of last week’s Citus Cloud 2 announcement. The Citus Cloud announcement centered on other new useful capabilities —such as our warp feature to streamline migrations from single-node Postgres deployments to Citus Cloud — but it seems worthwhile to dedicate an entire post to distributed transactions.)

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“Thirty years ago, my older brother was trying to get a report on birds written that he’d had three months to write. It was due the next day.

We were out at our family cabin in Bolinas, and he was at the kitchen table close to tears, surrounded by binder paper and pencils and unopened books on birds, immobilized by the hugeness of the task ahead. Then my father sat down beside him, put his arm around my brother’s shoulder, and said, ‘Bird by bird, buddy. Just take it bird by bird.’”

Bird by Bird: Some Instructions on Writing and Life, by Anne LaMott

When we started working on Citus, our vision was to combine the power of relational databases with the elastic scale of NoSQL. To do this, we took a different approach. Instead of building a new database from scratch, we leveraged PostgreSQL’s new extension APIs. This way, Citus would make Postgres a distributed database and integrate with the rich ecosystem of tools you already use.

When PostgreSQL is involved, executing on this vision isn’t a simple task. The PostgreSQL manual offers 3,558 pages of features built over two decades. The tools built around Postgres use and combine these features in unimaginable ways.

After our Citus open source announcement, we talked to many of you about scaling out your relational database. In every conversation, we’d hear about different Postgres features that needed to scale out of the box. We’d take notes from our meeting and add these features into an internal document. The list would keep getting longer, and longer, and longer.

Like the child writing a report on birds, the task ahead felt insurmountable. So how do you take a solid relational database and make sure that all those complex features scale? You take it bird by bird. We broke down the problem of scaling into five hundred smaller ones and started implementing these features one by one.

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Marco Slot

Databases and Distributed Deadlocks: A FAQ

Written byBy Marco Slot | August 31, 2017Aug 31, 2017

Since Citus is a distributed database, we often hear questions about distributed transactions. Specifically, people ask us about transactions that modify data living on different machines.

So we started to work on distributed transactions. We then identified distributed deadlock detection as the building block to enable distributed transactions in Citus.

First some background: At Citus we focus on scaling out Postgres. We want to make Postgres performance & Postgres scale something you never have to worry about. We even have a cloud offering, a fully-managed database as a service, to make Citus even more worry-free. We carry the pager so you don’t have to and all that. And because we’ve built Citus using the PostgreSQL extension APIs, Citus stays in sync with all the latest Postgres innovations as they are released (aka we are not a fork.) Yes, we’re excited for Postgres 10 like all the rest of you :)

Back to distributed deadlocks: As we began working on distributed deadlock detection, we realized that we needed to clarify certain concepts. So we created a simple FAQ for the Citus development team. And we found ourselves referring back to the FAQ over and over again. So we decided to share it here on our blog, in the hopes you find it useful.

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