Postgres Professional
Разработчик СУБД Postgres Pro
PostgreSQL 16: Part 3 or CommitFest 2022-11
10 min
Translation
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
PostgreSQL 16: Part 2 or CommitFest 2022-09
13 min
Translation
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
Queries in PostgreSQL. Nested Loop
17 min
Translation
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.
PostgreSQL 16: Part 1 or CommitFest 2022-07
7 min
Translation
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!
Queries in PostgreSQL. Sort and merge
19 min
Translation
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
18 min
Translation
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
18 min
Translation
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
15 min
Translation
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.
Queries in PostgreSQL. Statistics
18 min
Translation
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.
Queries in PostgreSQL. Query execution stages
15 min
Translation
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.
Locks in PostgreSQL: 4. Locks in memory
10 min
Translation
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.
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.
Locks in PostgreSQL: 3. Other locks
14 min
Translation
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.
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.
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.
Locks in PostgreSQL: 2. Row-level locks
14 min
Translation
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.
Let's recall a few weighty conclusions of the previous article.
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.
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.
Locks in PostgreSQL: 1. Relation-level locks
13 min
Translation
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:
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.
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.
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.
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.
Parallelism in PostgreSQL: treatment of trees and conscience
10 min
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.
JSONPath in PostgreSQL: committing patches and selecting apartments
10 min
This article was written in Russian in 2019 after the PostgreSQL 12 feature freeze, and it is still up-to-date. Unfortunately other patches of the SQL/JSON will not get even into version 13.
Many thanks to Elena Indrupskaya for the translation.
JSONPath
All that relates to JSON(B) is relevant and of high demand in the world and in Russia, and it is one of the key development areas in Postgres Professional. The jsonb type, as well as functions and operators to manipulate JSON/JSONB, appeared as early as in PostgreSQL 9.4. They were developed by the team lead by Oleg Bartunov.
The SQL/2016 standard provides for JSON usage: the standard mentions JSONPath — a set of functionalities to address data inside JSON; JSONTABLE — capabilities for conversion of JSON to usual database tables; a large family of functions and operators. Although JSON has long been supported in Postgres, in 2017 Oleg Bartunov with his colleagues started their work to support the standard. Of all described in the standard, only one patch, but a critical one, got into version 12; it is JSONPath, which we will, therefore, describe here.
What is Baked in the Baker's Dozen?
31 min
On April 8, PostgreSQL feature freeze took place, so only features committed earlier will get into version PostgreSQL 13. Probably, this version can hardly be considered revolutionary, since it has no conceptual changes. Some of critical patches were late to get into it, such as Table and Functions for the JSON/SQL standard, which had been desirable to be part of PostgreSQL 12, along with the JSONPath patch; plug-in warehouses did not appear either — only the interface is being finalized. The list of improvements is still impressive. We prepared a pretty complete overview of the patches included in the Baker's Dozen.
WAL in PostgreSQL: 4. Setup and Tuning
17 min
Translation
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.
The main WAL task is to ensure recovery after a failure. But once we have to maintain the log anyway, we can also adapt it to other tasks by adding some more information to it. There are several logging levels. The wal_level parameter specifies the level, and each next level includes everything that gets into WAL of the preceding level plus something new.
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.
WAL levels
The main WAL task is to ensure recovery after a failure. But once we have to maintain the log anyway, we can also adapt it to other tasks by adding some more information to it. There are several logging levels. The wal_level parameter specifies the level, and each next level includes everything that gets into WAL of the preceding level plus something new.
WAL in PostgreSQL: 3. Checkpoint
11 min
Translation
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.
What features must the checkpoint have? We must be sure that all the WAL records starting with the checkpoint will be applied to the pages flushed to disk. If it were not the case, during recovery, we could read from disk a version of the page that is too old, apply the WAL record to it and by doing so, irreversibly hurt the data.
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.
Checkpoint
What features must the checkpoint have? We must be sure that all the WAL records starting with the checkpoint will be applied to the pages flushed to disk. If it were not the case, during recovery, we could read from disk a version of the page that is too old, apply the WAL record to it and by doing so, irreversibly hurt the data.