I’m pleased to announce that Part II of the “PostgreSQL 14 Internals” book is available now. This part explores the purpose and design of the buffer cache and explains the need for write-ahead logging.

Please download the book freely in PDF. There are three more parts to come, so stay tuned!

Thanks to Matthew Daniel who has spotted a blunder in typesetting of Part I. Your comments are much appreciated; write us to edu@postgrespro.ru.

August is a special month in PostgreSQL release cycle. PostgreSQL 15 isn't even officially out yet, but the first CommitFest for the 16th release has already been held.

Let's compile the server and check out the cool new stuff!

So far we have covered query execution stages, statistics, sequential and index scan, and have moved on to joins.

The previous article focused on the nested loop join, and in this one I will explain the hash join. I will also briefly mention group-bys and distincs.

This is a short note to inform that we are translating my book “PostgreSQL 14 Internals,” recently published in Russian. Liudmila Mantrova, who helped me a lot with the editing of the original, is working on the translation, too. My gratitude to her is beyond words.

The book is largely based on the articles I’ve published here and training courses my colleagues and I are developing. It dives deep into the problems of data consistency and isolation, explaining implementation details of multiversion concurrency control and snapshot isolation, buffer cache and write-ahead log, and then moves on to the twists and turns of the locking system. The book also covers the questions of planning and executing SQL queries, including the discussion of data access and join methods, statistics, and various index types.

The book is freely available in PDF. The translation is in progress, and for now only Part I of the book is ready. Other parts will follow soon, so stay tuned!

So far we've discussed query execution stages, statistics, and the two basic data access methods: Sequential scan and Index scan.

The next item on the list is join methods. This article will remind you what logical join types are out there, and then discuss one of three physical join methods, the Nested loop join. Additionally, we will check out the row memoization feature introduced in PostgreSQL 14.

Joins

Joins are the primary feature of SQL, the foundation that enables its power and agility. Sets of rows (whether pulled directly from a table or formed as a result of an operation) are always joined together in pairs. There are several types of joins.

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While preparing for my talk to be given at PGCon 2022, I decided to share some important thoughts with the Postgres community. We’re going to change the way TOAST works, and it will have a positive impact on lives of many PostgreSQL contributors, developers and users in the upcoming years. It is also the rare case when I tackle commercial aspects of open source development, so it’s worth reading for everyone.

In previous articles we discussed query execution stages and statistics. Last time, I started on data access methods, namely Sequential scan. Today we will cover Index Scan. This article requires a basic understanding of the index method interface. If words like "operator class" and "access method properties" don't ring a bell, check out my article on indexes from a while back for a refresher.

Plain Index Scan

Indexes return row version IDs (tuple IDs, or TIDs for short), which can be handled in one of two ways. The first one is Index scan. Most (but not all) index methods have the INDEX SCAN property and support this approach. The operation is represented in the plan with an Index Scan node

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As PostgreSQL 15 moves to the feature freeze stage, we’d like to share the news about our team’s JSONB-related patches committed to version 15. This milestone marks years of work for many people, so we will name and thank them all in this blog post.

In previous articles we discussed how the system plans a query execution and how it collects statistics to select the best plan. The following articles, starting with this one, will focus on what a plan actually is, what it consists of and how it is executed.

In this article, I will demonstrate how the planner calculates execution costs. I will also discuss access methods and how they affect these costs, and use the sequential scan method as an illustration. Lastly, I will talk about parallel execution in PostgreSQL, how it works and when to use it.

I will use several seemingly complicated math formulas later in the article. You don't have to memorize any of them to get to the bottom of how the planner works; they are merely there to show where I get my numbers from.

Pluggable storage engines

The PostgreSQL's approach to storing data on disk will not be optimal for every possible type of load. Thankfully, you have options. Delivering on its promise of extensibility, PostgreSQL 12 and higher supports custom table access methods (storage engines), although it ships only with the stock one, heap:

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Despite the ongoing tragic events, we continue the series. In the last article we reviewed the stages of query execution. Before we move on to plan node operations (data access and join methods), let's discuss the bread and butter of the cost optimizer: statistics.

As usual, I use the demo database for all my examples. You can download it and follow along.

You will see a lot of execution plans here today. We will discuss how the plans work in more detail in later articles. For now just pay attention to the numbers that you see in the first line of each plan, next to the word rows. These are row number estimates, or cardinality.

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This time we decided to create a blog post on constraints using one of the most popular PGConf.Online talks explaining how you can try to gain peace of mind by using constraints in PostgreSQL.

Hello! I'm kicking off another article series about the internals of PostgreSQL. This one will focus on query planning and execution mechanics.

This series will cover: query execution stages (this article), statistics, sequential and index scans, nested-loop, hash, and merge joins.

Many thanks to Alexander Meleshko for the translation of this series into English.

This article borrows from our course QPT Query Optimization (available in English soon), but focuses mostly on the internal mechanisms of query execution, leaving the optimization aspect aside. Please also note that this article series is written with PostgreSQL 14 in mind.

Simple query protocol

The fundamental purpose of the PostgreSQL client-server protocol is twofold: it sends SQL queries to the server, and it receives the entire execution result in response. The query received by the server for execution goes through several stages.

Parsing

First, the query text is parsed, so that the server understands exactly what needs to be done.

Lexer and parser. The lexer is responsible for recognizing lexemes in the query string (such as SQL keywords, string and numeric literals, etc.), and the parser makes sure that the resulting set of lexemes is grammatically valid. The parser and lexer are implemented using the standard tools Bison and Flex.

The parsed query is represented as an abstract syntax tree.

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Postgres Professional's support team has shared a real-world case of auditing the performance of our customer's 180-TB database deployment.

This is a summary of Postgres Professional’s more than 6 years field experience of providing HA/DR solutions to our customers and it covers both PostgreSQL and Postgres Pro databases.

While most of our customers are making progress on their journeys to the cloud, many of the HA/DR solutions they use from the on-premises world can be used in the cloud as well.