#Article

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.

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. ...

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 ...

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: ...

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. ...