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.
Postgres Professional
Разработчик СУБД Postgres Pro
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.
WAL in PostgreSQL: 2. Write-Ahead Log
8 min
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.
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.
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.
WAL in PostgreSQL: 1. Buffer Cache
13 min
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:
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.
This series will consist of four parts:
- Buffer cache (this article).
- Write-ahead log — how it is structured and used to recover the data.
- Checkpoint and background writer — why we need them and how we set them up.
- WAL setup and tuning — levels and problems solved, reliability, and performance.
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.
On recursive queries
25 min
Translation
This article deals with writing recursive queries. This topic was brought up routinely, but the discussion was usually limited to simple cases related to trees: to descend from a vertex to the leaves and to ascend from a vertex to the root. We will address a more complicated case of an arbitrary graph.
Let's start with recalling the theory (very briefly since all of it is trivial), and then we will discuss what to do if it is unclear how to approach a real-life problem or if it seems to be clear, but the query persistently fails to work fine.
For an exercise, we will use the airlines demo database and try to write a query to find the shortest route from one airport to another.
Let's start with recalling the theory (very briefly since all of it is trivial), and then we will discuss what to do if it is unclear how to approach a real-life problem or if it seems to be clear, but the query persistently fails to work fine.
For an exercise, we will use the airlines demo database and try to write a query to find the shortest route from one airport to another.
MVCC in PostgreSQL-8. Freezing
12 min
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.
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.
MVCC in PostgreSQL-7. Autovacuum
10 min
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.
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.
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.
MVCC in PostgreSQL-6. Vacuum
13 min
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.
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.
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.
MVCC in PostgreSQL-5. In-page vacuum and HOT updates
9 min
Translation
Just to remind you, we already discussed issues related to isolation, made a digression regarding low-level data structure, and then explored row versions and observed how data snapshots are obtained from row versions.
Now we will proceed to two closely connected problems: in-page vacuum и HOT updates. Both techniques can be referred to optimizations; they are important, but virtually not covered in the documentation.
When accessing a page for either an update or read, if PostgreSQL understands that the page is running out of space, it can do a fast in-page vacuum. This happens in either of the cases:
Now we will proceed to two closely connected problems: in-page vacuum и HOT updates. Both techniques can be referred to optimizations; they are important, but virtually not covered in the documentation.
In-page vacuum during regular updates
When accessing a page for either an update or read, if PostgreSQL understands that the page is running out of space, it can do a fast in-page vacuum. This happens in either of the cases:
- A previous update in this page did not find enough space to allocate a new row version in the same page. Such a situation is remembered in the page header, and next time the page is vacuumed.
- The page is more than
fillfactorpercent full. In this case, vacuum is performed right away without putting off till next time.