| Queries in PostgreSQL: 3. 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.
Pluggable storage engines
The PostgreSQL's approach to storing data on disk will not be optimal for every possible type of load. Thankfully, you have options. Delivering on its promise of extensibility, PostgreSQL 12 and higher supports custom table access methods (storage engines), although it ships only with the stock one, heap:
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| Queries in PostgreSQL: 2. Statistics
Despite the ongoing tragic events, we continue the series. 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.
As usual, I use the demo database for all my examples. You can download it and follow along.
You will see a lot of execution plans here today. We will discuss how the plans work in more detail in later articles. For now just pay attention to the numbers that you see in the first line of each plan, next to the word rows. These are row number estimates, or cardinality.
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| How JSONB in PostgreSQL Helps Businesses Solve Today’s Data Challenges
How JSONB in PostgreSQL Helps Businesses Solve Today’s Data Challenges
| Trying to gain peace of mind by using constraints in PostgreSQL
This time we decided to create a blog post on constraints using one of the most popular PGConf.Online talks explaining how you can try to gain peace of mind by using constraints in PostgreSQL.
| Queries in PostgreSQL: 1. Query execution stages
Hello! I'm kicking off another article series about the internals of PostgreSQL. This one will focus on query planning and execution mechanics.
This series will cover: query execution stages (this article), statistics, sequential and index scans, nested-loop, hash, and merge joins.
Many thanks to Alexander Meleshko for the translation of this series into English.
This article borrows from our course QPT Query Optimization (available in English soon), but focuses mostly on the internal mechanisms of query execution, leaving the optimization aspect aside. Please also note that this article series is written with PostgreSQL 14 in mind.
Simple query protocol
The fundamental purpose of the PostgreSQL client-server protocol is twofold: it sends SQL queries to the server, and it receives the entire execution result in response. The query received by the server for execution goes through several stages.
Parsing
First, the query text is parsed, so that the server understands exactly what needs to be done.
Lexer and parser. The lexer is responsible for recognizing lexemes in the query string (such as SQL keywords, string and numeric literals, etc.), and the parser makes sure that the resulting set of lexemes is grammatically valid. The parser and lexer are implemented using the standard tools Bison and Flex.
The parsed query is represented as an abstract syntax tree.
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| Battles in the ZFS Field
Postgres Professional's support team has shared a real-world case of auditing the performance of our customer's 180-TB database deployment.
| 2022 will mark the beginning of the end of the open source software model we treasure
2022 will mark the beginning of the end of the open source software model we treasure
| Using pg_profile for Historical Workload Analysis in PostgreSQL
Using pg_profile for Historical Workload Analysis in PostgreSQL
| High availability and disaster recovery (HA/DR) for Postgres databases
This is a summary of Postgres Professional’s more than 6 years field experience of providing HA/DR solutions to our customers and it covers both PostgreSQL and Postgres Pro databases.
While most of our customers are making progress on their journeys to the cloud, many of the HA/DR solutions they use from the on-premises world can be used in the cloud as well.
| А deep dive into PostgreSQL query optimizations
During PostgreSQL maintenance, resource-consuming queries occur inevitably. Therefore, it's vital for every Database Engineer, DBA, or Software Developer to detect and fix them as soon as possible. In this article, we'll list various extensions and monitoring tools used to collect information about queries and display them in a human-readable way.
Then we will cover typical query writing mistakes and explain how to correct them. We will also consider cases where extended statistics are used for more accurate row count estimates. Finally, you will see an example of how PostgreSQL planner excludes redundant filter clauses during its work.
| Optimizing PostgreSQL queries: questions and answers
During our webinar on PostgreSQL query optimization, we found many of your questions interesting and helpful to the public, if properly answered. Get more useful links and working expert advice from Peter Petrov, our Database Engineer.
| Locks in PostgreSQL: 4. Locks in memory
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
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.
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