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PostgreSQL 17: Part 3 or Commitfest 2023-11

Level of difficultyMedium
Reading time11 min
Views616


The November commitfest is ripe with new interesting features! Without further ado, let's proceed with the review.


If you missed our July and September commitfest reviews, you can check them out here: 2023-07, 2023-09.


ON LOGIN trigger
Event triggers for REINDEX
ALTER OPERATOR: commutator, negator, hashes, merges
pg_dump --filter=dump.txt
psql: displaying default privileges
pg_stat_statements: track statement entry timestamps and reset min/max statistics
pg_stat_checkpointer: checkpointer process statistics
pg_stats: statistics for range type columns
Planner: exclusion of unnecessary table self-joins
Planner: materialized CTE statistics
Planner: accessing a table with multiple clauses
Index range scan optimization
dblink, postgres_fdw: detailed wait events
Logical replication: migration of replication slots during publisher upgrade
Replication slot use log
Unicode: new information functions
New function: xmltext
AT LOCAL support
Infinite intervals
ALTER SYSTEM with unrecognized custom parameters
Building the server from source

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Total votes 3: ↑3 and ↓0+3
Comments0

PostgreSQL 17: Part 2 or Commitfest 2023-09

Reading time11 min
Views1K


We continue to follow the news of the PostgreSQL 17 development. Let's find out what the September commitfest brings to the table.


If you missed our July commitfest review, you can check it out here: 2023-07.


Removed the parameter old_snapshot_threshold
New parameter event_triggers
New functions to_bin and to_oct
New system view pg_wait_events
EXPLAIN: a JIT compilation time counter for tuple deforming
Planner: better estimate of the initial cost of the WindowAgg node
pg_constraint: NOT NULL constraints
Normalization of CALL, DEALLOCATE and two-phase commit control commands
unaccent: the target rule expressions now support values in quotation marks
COPY FROM: FORCE_NOT_NULL * and FORCE_NULL *
Audit of connections without authentication
pg_stat_subscription: new column worker_type
The behaviour of pg_promote in case of unsuccessful switchover to a replica
Choosing the disk synchronization method in server utilities
pg_restore: optimization of parallel recovery of a large number of tables
pg_basebackup and pg_receivewal with the parameter dbname
Parameter names for a number of built-in functions
psql: \watch min_rows

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Total votes 3: ↑3 and ↓0+3
Comments2

PostgreSQL 17: Part 1 or Commitfest 2023-07

Level of difficultyMedium
Reading time8 min
Views833
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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
Views1K

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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Total votes 10: ↑10 and ↓0+10
Comments0

PostgreSQL 16: Part 3 or CommitFest 2022-11

Reading time10 min
Views1.3K

image


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

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Total votes 1: ↑1 and ↓0+1
Comments1

PostgreSQL 16: Part 2 or CommitFest 2022-09

Reading time13 min
Views1.7K


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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Total votes 1: ↑1 and ↓0+1
Comments0

Queries in PostgreSQL. Nested Loop

Reading time17 min
Views2.4K

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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Total votes 4: ↑4 and ↓0+4
Comments0

Queries in PostgreSQL. Sort and merge

Reading time19 min
Views1.7K


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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Total votes 3: ↑3 and ↓0+3
Comments0

Queries in PostgreSQL. Sequential Scan

Reading time15 min
Views2.2K

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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Total votes 3: ↑3 and ↓0+3
Comments0

Queries in PostgreSQL. Statistics

Reading time18 min
Views5.5K

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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Total votes 4: ↑3 and ↓1+2
Comments0

Locks in PostgreSQL: 4. Locks in memory

Reading time10 min
Views15K
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
Views8.1K
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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Total votes 6: ↑6 and ↓0+6
Comments0

Locks in PostgreSQL: 2. Row-level locks

Reading time14 min
Views13K
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.



Row-level locks


Organization


Let's recall a few weighty conclusions of the previous article.

  • A lock must be available somewhere in the shared memory of the server.
  • The higher granularity of locks, the lower the contention among concurrent processes.
  • On the other hand, the higher the granularity, the more of the memory is occupied by locks.

There is no doubt that we want a change of one row not block other rows of the same table. But we cannot afford to have its own lock for each row either.

There are different approaches to solving this problem. Some database management systems apply escalation of locks: if the number of row-level locks gets too high, they are replaced with one, more general lock (for example: a page-level or an entire table-level).

As we will see later, PostgreSQL also applies this technique, but only for predicate locks. The situation with row-level locks is different.
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Total votes 4: ↑4 and ↓0+4
Comments2

Locks in PostgreSQL: 1. Relation-level locks

Reading time13 min
Views16K
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:

  1. Relation-level locks (this article).
  2. Row-level locks.
  3. Locks on other objects and predicate locks.
  4. Locks in RAM.

The material of all the articles is based on training courses on administration that Pavel pluzanov and I are creating (mostly in Russian, although one course is available in English), but does not repeat them verbatim and is intended for careful reading and self-experimenting.

Many thanks to Elena Indrupskaya for the translation of these articles into English.



General information on locks


PostgreSQL has a wide variety of techniques that serve to lock something (or are at least called so). Therefore, I will first explain in the most general terms why locks are needed at all, what kinds of them are available and how they differ from one another. Then we will figure out what of this variety is used in PostgreSQL and only after that we will start discussing different kinds of locks in detail.
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Total votes 2: ↑2 and ↓0+2
Comments0

Parallelism in PostgreSQL: treatment of trees and conscience

Reading time10 min
Views3.4K


Database scaling is a continually coming future. DBMS get improved and better scaled on hardware platforms, while the hardware platforms themselves increase the performance, number of cores, and memory — Achilles is trying to catch up with the turtle, but has not caught up yet. The database scaling challenge manifests itself in all its magnitude.

Postgres Professional had to face the scaling problem not only theoretically, but also in practice: through their customers. Even more than once. It's one of these real-life cases that this article
will discuss.
Many thanks to Elena Indrupskaya for the translation. Russian version is here.
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Total votes 9: ↑8 and ↓1+7
Comments2

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