Blog , p.2

July 15, 2021   •   PostgreSQL
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 .
July 1, 2021   •   PostgreSQL
Last time, we discussed object-level locks and in particular relation-level locks. In this article, we will see how row-level locks are organized in PostgreSQL and how they are used together with object-level locks. We will also talk of wait queues and of those who jumps the queue.
June 17, 2021   •   PostgreSQL
The previous two series of articles covered isolation and multiversion concurrency control and logging . In this series, we will discuss locks . This series will consist of four articles: Relation-level locks (this article). Row-level locks. Locks on other objects and predicate locks. Locks in RAM.  
June 3, 2021   •   PostgreSQL
In this blog post, we're explaining to those new to pg_profile why you might need this PostgreSQL extension in your daily work, on a real-world example.
May 13, 2021   •   PostgreSQL
So, we got acquainted with the structure of the buffer cache and in this context concluded that if all the RAM contents got lost due to failure, the write-ahead log (WAL) was required to recover. The size of the necessary WAL files and the recovery time are limited thanks to the checkpoint performed from time to time. In the previous articles we already reviewed quite a few important settings that anyway relate to WAL. In this article (being the last in this series) we will discuss problems of WAL setup that are unaddressed yet: WAL levels and their purpose, as well as the reliability and performance of write-ahead logging.
April 23, 2021   •   PostgreSQL
We already got acquainted with the structure of the buffer cache  — one of the main objects of the shared memory — and concluded that to recover after failure when all the RAM contents get lost, the write-ahead log (WAL) must be maintained. The problem yet unaddressed, where we left off last time, is that we are unaware of where to start playing back WAL records during the recovery. To begin from the beginning, as the King from Lewis Caroll's Alice advised, is not an option: it is impossible to keep all the WAL records from the server start — this is potentially both a huge memory size and equally huge duration of the recovery. We need such a point that is gradually moving forward and that we can start the recovery at (and safely remove all the previous WAL records, accordingly). And this is the checkpoint , to be discussed below.
April 15, 2021   •   PostgreSQL
Last time we got acquainted with the structure of an important component of the shared memory — the buffer cache. A risk of losing information from RAM is the main reason why we need techniques to recover data after failure. Now we will discuss these techniques.
April 1, 2021   •   PostgreSQL
WAL in PostgreSQL: 1. Buffer Cache
March 19, 2021   •   PostgreSQL
We started with problems related to isolation , made a digression about low-level data structure , discussed row versions in detail and observed how data snapshots are obtained from row versions. Then we covered different vacuuming techniques: in-page vacuum (along with HOT updates), vacuum and autovacuum . Now we've reached the last topic of this series. We will talk on the transaction id wraparound and freezing. Transaction ID wraparound PostgreSQL uses 32-bit transaction IDs. This is a pretty large number (about 4 billion), but with intensive work of the server, this number is not unlikely to get exhausted. For example: with the workload of 1000 transactions a second, this will happen as early as in one month and a half of continuous work. But we've mentioned that multiversion concurrency control relies on the sequential numbering, which means that of two transactions the one with a smaller number can be considered to have started earlier. Therefore, it is clear that it is not an option to just reset the counter and start the numbering from scratch. ...
March 12, 2021   •   News

PGConf.Online 2021 wrap-up: 57 talks, 30 international speakers, 2000+ registrants!

PGConf.Online wrapped up 10 days ago, and we want to share some important stats with you. We were glad to have welcomed 2000+ registrants this year with 700-900 of them joining us online each day! The total attendance/registration rate for all three days was near 75%, which is amazing!
March 8, 2021   •   News

Celebrating Women Who Code in Postgres Pro

In early March, before International Women’s Day, we asked our female tech professionals a few questions and were surprised by some of them providing us answers :) Let’s celebrate women who code working here at Postgres Pro and have a look at what they are up to!  
February 18, 2021   •   News

Postgres Professional is now sponsoring Psycopg development!

Postgres Professional has become one of the main sponsors backing the development of the Psycopg library, the most popular PostgreSQL adapter for the Python programming language.
February 9, 2021   •   PostgreSQL
To remind you, we started with problems related to isolation , made a digression about low-level data structure , discussed row versions in detail and observed how data snapshots are obtained from row versions. Then we explored in-page vacuum (and HOT updates) and vacuum . Now we'll look into autovacuum. Autovacuum We've already mentioned that normally (i. e., when nothing holds the transaction horizon for a long time) VACUUM usually does its job. The problem is how often to call it. If we vacuum a changing table too rarely, its size will grow more than desired. Besides, a next vacuum operation may require several passes through indexes if too many changes were done. If we vacuum the table too often, the server will constantly do maintenance rather than useful work — and this is no good either. Note that launching VACUUM on schedule by no means resolves the issue because the workload can change with time. If the table starts to change more intensively, it must be vacuumed more often. Autovacuum is exactly the technique that enables us to launch vacuuming depending on how intensive the table changes are. ...
February 3, 2021   •   News

Postgres Professional at FOSDEM 2021

It’s this time of the year again, and FOSDEM is coming! In 2021, Postgres Professional is really well-represented in the PostgreSQL devroom , as we have 5 talks accepted by the Committee. Let’s take a look at all presentations to be given by our team at this conference.
January 18, 2021   •   PostgreSQL
We started with problems related to isolation , made a digression about low-level data structure , then discussed row versions and observed how data snapshots are obtained from row versions. Last time we talked about HOT updates and in-page vacuuming, and today we'll proceed to a well-known vacuum vulgaris . Really, so much has already been written about it that I can hardly add anything new, but the beauty of a full picture requires sacrifice. So keep patience. Vacuum What does vacuum do? In-page vacuum works fast, but frees only part of the space. It works within one table page and does not touch indexes. The basic, "normal" vacuum is done using the VACUUM command, and we will call it just "vacuum" (leaving "autovacuum" for a separate discussion). So, vacuum processes the entire table. It vacuums away not only dead tuples, but also references to them from all indexes. Vacuuming is concurrent with other activities in the system. The table and indexes can be used in a regular way both for reads and updates (however, concurrent execution of commands such as CREATE INDEX, ALTER TABLE and some others is impossible). Only those table pages are looked through where some activities took place. To detect them, the visibility map is used (to remind you, the map tracks those pages that contain pretty old tuples, which are visible in all data snapshots for sure). Only those pages are processed that are not tracked by the visibility map, and the map itself gets updated. The free space map also gets updated in the process to reflect the extra free space in the pages. ...