We continue to follow the news of the PostgreSQL 16 release, and today, the results of the fourth CommitFest are on the table. Let's have a look.

If you missed the previous CommitFests, check out our reviews for 2022-07, 2022-09 and 2022-11.

We continue to follow the news of the upcoming PostgreSQL 16. The third CommitFest concluded in early December. Let's look at the results.

If you missed the previous CommitFests, check out our reviews: 2022-07, 2022-09.

Here are the patches I want to talk about:

meson: a new source code build system
Documentation: a new chapter on transaction processing
psql: \d+ indicates foreign partitions in a partitioned table
psql: extended query protocol support
Predicate locks on materialized views
Tracking last scan time of indexes and tables
pg_buffercache: a new function pg_buffercache_summary
walsender displays the database name in the process status
Reducing the WAL overhead of freezing tuples
Reduced power consumption when idle
postgres_fdw: batch mode for COPY
Modernizing the GUC infrastructure
Hash index build optimization
MAINTAIN ― a new privilege for table maintenance
SET ROLE: better role change management
Support for file inclusion directives in pg_hba.conf and pg_ident.conf
Regular expressions support in pg_hba.conf

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

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.

August was a special month in PostgreSQL release cycle as the first CommitFest for the 16th PostgreSQL release was held.

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

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.

Queries in PostgreSQL. Hashing

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

Queries in PostgreSQL. Index scan

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.

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.

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.

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.