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PostgreSQL 18: Part 1 or CommitFest 2024-07

Level of difficultyMedium
Reading time10 min
Reach and readers915

PostgreSQL 17: Part 3 or Commitfest 2023-11

Level of difficultyMedium
Reading time11 min
Reach and readers1.3K

PostgreSQL 17: Part 2 or Commitfest 2023-09

Reading time11 min
Reach and readers1.6K

PostgreSQL 17: Part 1 or Commitfest 2023-07

Level of difficultyMedium
Reading time8 min
Reach and readers1.4K
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We continue to follow the news in the world of PostgreSQL. The PostgreSQL 16 Release Candidate 1 was rolled out on August 31. If all is well, PostgreSQL 16 will officially release on September 14.


What has changed in the upcoming release after the April code freeze? What's getting into PostgreSQL 17 after the first commitfest? Read our latest review to find out!

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PostgreSQL 16: Part 5 or CommitFest 2023-03

Level of difficultyMedium
Reading time28 min
Reach and readers1.8K

The end of the March Commitfest concludes the acceptance of patches for PostgreSQL 16. Let’s take a look at some exciting new updates it introduced.

I hope that this review together with the previous articles in the series (2022-072022-092022-112023-01) will give you a coherent idea of the new features of PostgreSQL 16.

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PostgreSQL 16: Part 3 or CommitFest 2022-11

Reading time10 min
Reach and readers1.7K

PostgreSQL 16: Part 2 or CommitFest 2022-09

Reading time13 min
Reach and readers2.2K


It's official! PostgreSQL 15 is out, and the community is abuzz discussing all the new features of the fresh release.


Meanwhile, the October CommitFest for PostgreSQL 16 had come and gone, with its own notable additions to the code.


If you missed the July CommitFest, our previous article will get you up to speed in no time.


Here are the patches I want to talk about:


SYSTEM_USER function
Frozen pages/tuples information in autovacuum's server log
pg_stat_get_backend_idset returns the actual backend ID
Improved performance of ORDER BY / DISTINCT aggregates
Faster bulk-loading into partitioned tables
Optimized lookups in snapshots
Bidirectional logical replication
pg_auth_members: pg_auth_members: role membership granting management
pg_auth_members: role membership and privilege inheritance
pg_receivewal and pg_recvlogical can now handle SIGTERM

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Queries in PostgreSQL. Nested Loop

Reading time17 min
Reach and readers3.7K

So far we've discussed query execution stagesstatistics, 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.

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Queries in PostgreSQL. Sort and merge

Reading time19 min
Reach and readers2.8K


In the previous articles, we have covered query execution stages, statistics, sequential and index scan, and two of the three join methods: nested loop and hash join.


This last article of the series will cover the merge algorithm and sorting. I will also demonstrate how the three join methods compare against each other.

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Queries in PostgreSQL. Sequential Scan

Reading time15 min
Reach and readers3.1K

Queries in PostgreSQL. Sequential scan


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.

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Queries in PostgreSQL. Statistics

Reading time18 min
Reach and readers7K

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.

Dive in to learn what types of statistics PostgreSQL collects when planning queries, and how they improve query cost assessment and execution times.

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Queries in PostgreSQL. Query execution stages

Reading time15 min
Reach and readers6.3K

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

In the first article we will split the query execution process into stages and discuss what exactly happens at each stage.

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Locks in PostgreSQL: 4. Locks in memory

Reading time10 min
Reach and readers17K
To remind you, we've already talked about relation-level locks, row-level locks, locks on other objects (including predicate locks) and interrelationships of different types of locks.

The following discussion of locks in RAM finishes this series of articles. We will consider spinlocks, lightweight locks and buffer pins, as well as events monitoring tools and sampling.


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Locks in PostgreSQL: 3. Other locks

Reading time14 min
Reach and readers14K
We've already discussed some object-level locks (specifically, relation-level locks), as well as row-level locks with their connection to object-level locks and also explored wait queues, which are not always fair.

We have a hodgepodge this time. We'll start with deadlocks (actually, I planned to discuss them last time, but that article was excessively long in itself), then briefly review object-level locks left and finally discuss predicate locks.

Deadlocks


When using locks, we can confront a deadlock. It occurs when one transaction tries to acquire a resource that is already in use by another transaction, while the second transaction tries to acquire a resource that is in use by the first. The figure on the left below illustrates this: solid-line arrows indicate acquired resources, while dashed-line arrows show attempts to acquire a resource that is already in use.

To visualize a deadlock, it is convenient to build the wait-for graph. To do this, we remove specific resources, leave only transactions and indicate which transaction waits for which other. If a graph contains a cycle (from a vertex, we can get to itself in a walk along arrows), this is a deadlock.


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