What is postgresql performance tuning ?
Performance tuning is the process of changing the configuration in an effort to get better performance out of your database. This requires a thorough understanding of how the database works, what each configuration parameter does, and what values need to be used.
After describing what performance tuning is we are going to have a deep dive into what are the common issues/reasons that may lead to performance tuning .
Common issues that can lead to performance tuning your PostgreSQL database include:
1.Slow queries: This is the most common performance issue that people experience with PostgreSQL. Slow queries can be caused by a variety of factors, such as poor database design, inefficient queries, or hardware problems.
2.Deadlocks: Deadlocks occur when two or more transactions are waiting for each other to finish. This can lead to the database becoming unresponsive and requiring a restart.
3.High CPU usage: PostgreSQL uses the CPU to execute queries and process data. If the database is running complex queries or processing large amounts of data, this can lead to high CPU usage and performance problems.
4.High memory usage: PostgreSQL uses memory to cache data and indexes. If the database has too much data or indexes in memory, this can lead to high memory usage and performance problems.
After getting to know the common issues/causes of database performance we are going to explore what are some of the best ways/tools that we can use to performance tune our postgresql database.
There are various ways developers and database administrators can performance tune their postgresql database and this can be divided into categories.
The categories of performance tuning include:
- Database Design.
- Analyzing PostgreSQL Performance.
- Hardware Optimization and operating system optimization.
- Reporting and logging.
Technique 1: Database Design
Designing the databases is one integral and most important step in optimizing the performance of any database.In postgresql this can be easily achieved by partition the data into multiple logically separated tables instead of having one big table. This usually shows immediate and significant improvement in query performance.
second common technique under database design that can help performance tune your postgresql database is by Creating partial indexes. This again depends heavily on the use case and the queries you’ll be running often. The idea here is to filter as much data as possible so that there’s less data to work with. So, you should create the indexes on columns that are typically used as filters in the most frequently run queries.
Something to note with having enough indexes is that make sure to use them with caution. Overusing indexes will cause adverse effects. Creating and maintaining indexes is a costly operation, and creating too many indexes will deteriorate the overall performance of the database.
Technique 2: Reporting and Logging
Logs are one method of accomplishing this. When an application acts, it leaves behind a log of short information statements. Postgresql offers some configuration parameters for logging too which can help improve the maintenance of a PostgreSQL database.This parameters include:
log_statement :controls what types of queries are logged into log files. The options here include none, ddl, mod, and all. DDLs are queries that are used to create, alter, or drop tables. MOD queries are used to insert, update, delete, truncate, and other similar operations on a table.
logging_collector : lets you catch all the log messages sent to the standard error logger and then redirects those to a log file. This way, we can be sure that all log messages are captured, even those that don’t appear in the syslog.
log_checkpoints :logs checkpoints and restart points to the server log. Along with this, the number of buffers written and the time taken to write these buffers are also included in the log message, allowing for better understanding and debugging.
*log_min_error_statement *:sets the minimum logging level at which SQL queries generating such errors are logged into the system log. Values include DEBUG1, DEBUG2, INFO, NOTICE, WARNING, ERROR, FATAL, etc.
log_line_prefix : Sets the format of the database logs. It can be used to improve readability.
log_lock_waits : Identifies slow performance and lock waits due to locking delays.
Technique 3:Hardware Optimization and operating system optimization.
The underlying hardware definitely has its part to play in optimizing the performance of PostgreSQL. Developers need to take into account data partitioning, indexing, configuration, and hardware capacity while designing queries.Some of the major hardware components that can really help to improve performance include:
Choosing the proper and high CPU for PostgreSQL performance could be critical. When dealing with huge amounts of data, CPU speed is important, but CPUs with larger L3 caches will also improve performance. Having more and faster cores will assist the operating system and PostgreSQL is more efficient in utilizing them for OLTP performance.
Choosing a faster drive set will greatly increase performance if the application is I/O bound (read and/or write-intensive). There are a variety of options, including NMVe and SSD drives.
Even while network cards appear to be unrelated to PostgreSQL performance, as data expands rapidly, faster or bonded network cards will also speed up base backups.
The cheapest of the existing hardware, as well as the best for PostgreSQL performance. Operating systems aim to cache as much data as possible using the available memory. Less disk I/O and faster query times will result from more caching. When purchasing new hardware, we recommend starting with as much RAM as possible.
when choosing an operating system
PostgreSQL query performance greatly depends on the operating system and file systems it is running on. For example, with the Linux operating system enabling huge pages available in this operating system can improve PostgreSQL performance, while disabling data files will save CPU cycles.
and lastly ...
Technique 4:Analyzing PostgreSQL Performance.
First things first we should understand how postgresql executes our query on the data so that you can tune the query or the database itself to perform better. There are a few commands that help you automatically optimize query performance.
Explain is the logical next step to PostgreSQL query performance tuning after the analyze command. This outputs the plan that the database forms after using the stats calculated by the analyze command. This should give us a clear idea as to how the query is going to perform, if it’s going to use an index scan or a table scan, etc. Based on this, we can either alter the query for better performance, or update the stats by running analyze again.
Analyze is a command you can run on your databases and tables to measure some statistics about the tables. These calculated statistics are then stored in a PostgreSQL maintained table, which will then be used by the query planner to better plan query execution. When you submit a query, it’s first transformed into PostgreSQL. This then goes to the planner, which comes up with a plan for the execution of the query.
The planner uses the stats of a table to form the execution plan. But with time, the stats become stale and the execution plan might not be accurate or performant. So you need to make sure the stats are always updated.
Indexing is everything when it comes to query performance. We can see an immense difference in query performance with and without indexes. You need to understand how indexing can affect query performance based on a number of factors, such as the size of the table, how you are going to access the data in a table, what the indexing algorithm is, etc.
This requires a thorough understanding of all the use cases on a table. Also, you need to note that whenever the data is updated in an indexed table, the indexes need to be updated as well, which adds overhead.
Tuning PostgreSQL Database Parameters is necessary for the specific needs of an application and the operating system on which it operates. Finally, we must remember that not all metrics are significant for all types of applications. Some applications benefit from parameter adjustment, while others do not so we should consider all techniques suitability when performance tuning.