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Domain-specific languageused in programming and designed for managing data held in a relational database management system, or for stream processing in a relational data stream management system

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Queries in PostgreSQL. Sequential Scan

Reading time15 min
Views2.6K
Postgres Professional corporate blogPostgreSQL*SQL*
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.

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Total votes 3: ↑3 and ↓0+3
Comments0

WAL in PostgreSQL: 2. Write-Ahead Log

Reading time8 min
Views7.5K
Postgres Professional corporate blogPostgreSQL*SQL*
Translation
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.

The log


Sadly, there's no such thing as miracles: to survive the loss of information in RAM, everything needed must be duly saved to disk (or other nonvolatile media).

Therefore, the following was done. Along with changing data, the log of these changes is maintained. When we change something on a page in the buffer cache, we create a record of this change in the log. The record contains the minimum information sufficient to redo the change if the need arises.

For this to work, the log record must obligatory get to disk before the changed page gets there. And this explains the name: write-ahead log (WAL).

In case of failure, the data on disk appear to be inconsistent: some pages were written earlier, and others later. But WAL remains, which we can read and redo the operations that were performed before the failure but their result was late to reach the disk.
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Total votes 3: ↑3 and ↓0+3
Comments0

Queries in PostgreSQL. Statistics

Reading time18 min
Views6.3K
Postgres Professional corporate blogPostgreSQL*SQL*
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.

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Total votes 3: ↑2 and ↓1+2
Comments0

Locks in PostgreSQL: 1. Relation-level locks

Reading time13 min
Views19K
Postgres Professional corporate blogPostgreSQL*SQL*
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:

  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

WAL in PostgreSQL: 4. Setup and Tuning

Reading time17 min
Views9.6K
Postgres Professional corporate blogPostgreSQL*SQL*
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.

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.
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Total votes 2: ↑2 and ↓0+2
Comments0

WAL in PostgreSQL: 3. Checkpoint

Reading time11 min
Views6.9K
Postgres Professional corporate blogPostgreSQL*SQL*
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.

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.
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Total votes 2: ↑1 and ↓1+2
Comments0

WAL in PostgreSQL: 1. Buffer Cache

Reading time13 min
Views7.9K
Postgres Professional corporate blogPostgreSQL*SQL*
Translation
The previous series addressed isolation and multiversion concurrency control, and now we start a new series: on write-ahead logging. To remind you, the material 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.

This series will consist of four parts:


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

Why do we need write-ahead logging?


Part of the data that a DBMS works with is stored in RAM and gets written to disk (or other nonvolatile storage) asynchronously, i. e., writes are postponed for some time. The more infrequently this happens the less is the input/output and the faster the system operates.

But what will happen in case of failure, for example, power outage or an error in the code of the DBMS or operating system? All the contents of RAM will be lost, and only data written to disk will survive (disks are not immune to certain failures either, and only a backup copy can help if data on disk are affected). In general, it is possible to organize input/output in such a way that data on disk are always consistent, but this is complicated and not that much efficient (to my knowledge, only Firebird chose this option).

Usually, and specifically in PostgreSQL, data written to disk appear to be inconsistent, and when recovering after failure, special actions are required to restore data consistency. Write-ahead logging (WAL) is just a feature that makes it possible.
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Total votes 2: ↑1 and ↓1+2
Comments0

MVCC in PostgreSQL-7. Autovacuum

Reading time10 min
Views2.7K
Postgres Professional corporate blogPostgreSQL*SQL*
Translation
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.
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Total votes 2: ↑2 and ↓0+2
Comments0

Database selection cheat sheet: SQL or NoSQL?

Reading time9 min
Views5.2K
PostgreSQL*Java*SQL*NoSQL*Go*

This is a series of articles dedicated to the optimal choice between different systems on a real project or an architectural interview.

This topic seemed relevant to me because such tasks can be encountered both at work and at an interview for System Design Interview and you will have to choose between these two types of DBMS. I plunged into this issue and will tell you what and how. What is better in each case, what are the advantages and disadvantages of these systems and which one to choose, I will show with several examples at the end of the article.

SQL or NoSQL?

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Total votes 1: ↑1 and ↓0+1
Comments0

PostgreSQL 16: Part 3 or CommitFest 2022-11

Reading time10 min
Views1.4K
Postgres Professional corporate blogPostgreSQL*SQL*
Translation

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
Views1.8K
Postgres Professional corporate blogSQL*PostgreSQL*
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

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Total votes 1: ↑1 and ↓0+1
Comments0

MVCC in PostgreSQL-8. Freezing

Reading time12 min
Views6.1K
Postgres Professional corporate blogPostgreSQL*SQL*
Translation
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.
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Total votes 1: ↑1 and ↓0+1
Comments0

MVCC in PostgreSQL-6. Vacuum

Reading time13 min
Views3.7K
Postgres Professional corporate blogPostgreSQL*SQL*
Translation
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.
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Total votes 1: ↑1 and ↓0+1
Comments1
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