Tune Linux Kernel Parameters For PostgreSQL Optimization and better System Performance

Introduction

In my previous Article i explained  Tuning PostgreSQL Database Memory Configuration Parameters to Optimize Performance and as i said  Database performance does not only depend on Postgresql configurations but also on system parameters .Poorly configured OS kernel parameters can cause degradation in database server performance. Therefore, it is imperative that these parameters are configured according to the database server and its workload. In this article  i will be talking about centos/redhat  linux system specially . 

Story

I will start the article with small story where on one of our client huge amount of writes were there and customer have provided us 200 GB of RAM for that dedicated database server , So there were no problem of resources. 

Now what was happening that after sometime system loads get increased so much and on debugging we found no special query  around the time when load increases . Somewhere over internet we found if we clear the system cache  regularly then issue will be resolved . 

We then schedule a cron to clear system cache after some regular interval and issue got resolved . 

Now  the question is why issue was not coming after this ? ? ?

And the Answer is  that due to large cache size as we have so much of ram available   lots of data is collected in RAM (in GB’s) and when this whole data flushes out on to the disk   ,  system load becomes high at that time 

So from that we came to know that we also need to tune some system parameters also to optimize system and database(postgresql) performance . 

In above case we tuned vm.dirty_background_ratio and vm.dirty_ratio , these two system(os) parameters to resolve the issue . 

Kernel parameters Tuning

Now what values we set for these above two parameters described in story and what are all other  important Linux kernel parameters that can affect database server performance which we can tune are described as follows : 

vm.dirty_background_ratio / vm.dirty_background_bytes

The vm.dirty_background_ratio is the percentage of memory filled with dirty pages that need to be flushed to disk. Flushing is done in the background. The value of this parameter ranges from 0 to 100; however, a value lower than 5 may not be effective and some kernels do not internally support it. The default value is 10 on most Linux systems. You can gain performance for write-intensive operations with a lower ratio, which means that Linux flushes dirty pages in the background.

You need to set a value of vm.dirty_background_bytes depending on your disk speed.

There are no “good” values for these two parameters since both depend on the hardware. However, setting vm.dirty_background_ratio to 5 and vm.dirty_background_bytes to 25% of your disk speed improves performance by up to ~25% in most cases.

vm.dirty_ratio / dirty_bytes

This is the same as vm.dirty_background_ratio / dirty_background_bytes except that the flushing is done in the foreground, blocking the application. So vm.dirty_ratio should be higher than vm.dirty_background_ratio. This will ensure that background processes kick in before the foreground processes to avoid blocking the application, as much as possible. You can tune the difference between the two ratios depending on your disk IO

 vm.swappiness

vm.swappiness is another kernel parameter that can affect the performance of the database. This parameter is used to control the swappiness (swapping pages to and from swap memory into RAM) behavior on a Linux system. The value ranges from 0 to 100. It controls how much memory will be swapped or paged out. Zero means disable swap and 100 means aggressive swapping.

You may get good performance by setting lower values.

Setting a value of 0 in newer kernels may cause the OOM Killer (out of memory killer process in Linux) to kill the process. Therefore, you can be on the safe side and set the value to 1 if you want to minimize swapping. The default value on a Linux system is 60. A higher value causes the MMU (memory management unit) to utilize more swap space than RAM, whereas a lower value preserves more data/code in memory.

A smaller value is a good bet to improve performance in PostgreSQL.

vm.overcommit_memory / vm.overcommit_ratio

Applications acquire memory and free that memory when it is no longer needed. But in some cases, an application acquires too much memory and does not release it.  This can invoke the OOM killer. Here are the possible values for vm.overcommit_memory parameter with a description for each:

  1. Heuristic overcommit, Do it intelligently (default); based kernel heuristics
  2. Allow overcommit anyway
  3. Don’t over commit beyond the overcommit ratio.

Reference: https://www.kernel.org/doc/Documentation/vm/overcommit-accounting

vm.overcommit_ratio is the percentage of RAM that is available for overcommitment. A value of 50% on a system with 2 GB of RAM may commit up to 3 GB of RAM.

A value of 2 for vm.overcommit_memory yields better performance for PostgreSQL. This value maximizes RAM utilization by the server process without any significant risk of getting killed by the OOM killer process. An application will be able to overcommit, but only within the overcommit ratio, thus reducing the risk of having OOM killer kill the process. Hence a value to 2 gives better performance than the default 0 value. However, reliability can be improved by ensuring that memory beyond an allowable range is not overcommitted. It avoids the risk of the process being killed by OOM-killer.

On systems without swap, one may experience a problem when vm.overcommit_memory is 2.

https://www.postgresql.org/docs/current/static/kernel-resources.html#LINUX-MEMORY-OVERCOMMIT

Generally speaking almost all applications which uses more memory depends on this , For example  , In Redis setting this value 1 is best . 

Turn On Huge Pages

Linux, by default, uses 4K memory pages, BSD has Super Pages, whereas Windows has Large Pages. A page is a chunk of RAM that is allocated to a process. A process may own more than one page depending on its memory requirements. The more memory a process needs the more pages that are allocated to it. The OS maintains a table of page allocation to processes. The smaller the page size, the bigger the table, the more time required to look up a page in that page table. Therefore, huge pages make it possible to use a large amount of memory with reduced overheads; fewer page lookups, fewer page faults, faster read/write operations through larger buffers. This results in improved performance.

PostgreSQL has support for bigger pages on Linux only. By default, Linux uses 4K of memory pages, so in cases where there are too many memory operations, there is a need to set bigger pages. Performance gains have been observed by using huge pages with sizes 2 MB and up to 1 GB. The size of Huge Page can be set boot time. You can easily check the huge page settings and utilization on your Linux box using cat /proc/meminfo | grep -i huge command.

Get HugePage Info – On Linux (only)

Note: This is only for Linux, for other OS this operation is ignored
$ cat /proc/meminfo | grep -i huge
AnonHugePages:         0 kB
ShmemHugePages:        0 kB
HugePages_Total:       0
HugePages_Free:        0
HugePages_Rsvd:        0
HugePages_Surp:        0
Hugepagesize:       2048 kB

In this example, although huge page size is set at 2,048 (2 MB), the total number of huge pages has a value of 0. which signifies that huge pages are disabled.

Script to quantify Huge Pages

This is a simple script which returns the number of Huge Pages required. Execute the script on your Linux box while your PostgreSQL is running. Ensure that $PGDATA environment variable is set to PostgreSQL’s data directory.

Get Number of Required HugePages

!/bin/bash
pid=head -1 $PGDATA/postmaster.pid
echo “Pid:            $pid”
peak=grep ^VmPeak /proc/$pid/status | awk '{ print $2 }'
echo “VmPeak:            $peak kB”
hps=grep ^Hugepagesize /proc/meminfo | awk '{ print $2 }'
echo “Hugepagesize:   $hps kB”
hp=$((peak/hps))
echo Set Huge Pages:     $hp

The output of the script looks like this:

Script Output

Pid:            12737
VmPeak:         180932 kB
Hugepagesize:   2048 kB
Set Huge Pages: 88

The recommended huge pages are 88, therefore you should set the value to 88.

Set HugePages Command :

sysctl -w vm.nr_hugepages= 88

Check the huge pages now, you will see no huge page is in use (HugePages_Free = HugePages_Total).

Again Get HugePage Info – On Linux (only)

$ cat /proc/meminfo | grep -i huge
AnonHugePages:         0 kB
ShmemHugePages:        0 kB
HugePages_Total:      88
HugePages_Free:       88
HugePages_Rsvd:        0
HugePages_Surp:        0
Hugepagesize:       2048 kB

Now set the parameter huge_pages “on” in $PGDATA/postgresql.conf and restart the server.

And Again Get HugePage Info – On Linux (only)

$ cat /proc/meminfo | grep -i huge
AnonHugePages:         0 kB
ShmemHugePages:        0 kB
HugePages_Total:      88
HugePages_Free:       81
HugePages_Rsvd:       64
HugePages_Surp:        0
Hugepagesize:       2048 kB

Now you can see that a very few of the huge pages are used. Let’s now try to add some data into the database.

Some DB Operations to Utilise HugePages

postgres=# CREATE TABLE foo(a INTEGER);
CREATE TABLE
postgres=# INSERT INTO foo VALUES(generate_Series(1,10000000));
INSERT 0 10000000

Let’s see if we are now using more huge pages than before.

Once More Get HugePage Info – On Linux (only)

$ cat /proc/meminfo | grep -i huge
AnonHugePages:         0 kB
ShmemHugePages:        0 kB
HugePages_Total:      88
HugePages_Free:       18
HugePages_Rsvd:        1
HugePages_Surp:        0
Hugepagesize:       2048 kB

Now you can see that most of the huge pages are in use.

Note: The sample value for HugePages used here is very low, which is not a normal value for a big production machine. Please assess the required number of pages for your system and set those accordingly depending on your system’s workload and resources.

Now, Tuning Postgresql parameters and kernel parameters is not enough for good Postgresql performance there are many other things like

  • How you are making Query
  • Proper Indexing — For this you can follow indexing series on our blog
  • Proper partitioning and sharding accroding to business usecase
  • and many more .

Stay tuned to get more blogs on optimizing postgresql performance

Refrences : https://www.percona.com/blog/2018/08/29/tune-linux-kernel-parameters-for-postgresql-optimization/

Tuning PostgreSQL Database Memory Configuration Parameters to Optimize Performance

Introduction 

Out of the box, the default PostgreSQL configuration is not tuned for any particular workload. Default values are set to ensure that PostgreSQL runs everywhere, with the least resources it can consume and so that it doesn’t cause any vulnerabilities. It has default settings for all of the database parameters. It is primarily the responsibility of the database administrator or developer to tune PostgreSQL according to their system’s workload. In this blog, we will establish basic guidelines for setting PostgreSQL database parameters to improve database performance according to workload.

Bear in mind that while optimizing PostgreSQL server configuration improves performance, a database developer must also be diligent when writing queries for the application. If queries perform full table scans where an index could be used or perform heavy joins or expensive aggregate operations, then the system can still perform poorly even if the database parameters are tuned. It is important to pay attention to performance when writing database queries.

Nevertheless, database parameters are very important too, so let’s take a look at the eight that have the greatest potential to improve performance , But before that it is important to understand the memory architecture  basic of postgres  : 

Memory in PostgreSQL can be classified into two categories:

  1. Local Memory area: It is allocated by each backend process for its own use.
  2. Shared memory area: It is used by all processes of a PostgreSQL server.

So Now i will explain tunable parameters and what should be the value of these tunable parameters   : . 

shared_buffer

PostgreSQL uses its own buffer and also uses kernel buffered IO. That means data is stored in memory twice, first in PostgreSQL buffer and then kernel buffer. Unlike other databases, PostgreSQL does not provide direct IO. This is called double buffering. The PostgreSQL buffer is called shared_buffer which is the most effective tunable parameter for most operating systems. This parameter sets how much dedicated memory will be used by PostgreSQL for cache.

The default value of shared_buffer is set very low and you will not get much benefit from that. It’s low because certain machines and operating systems do not support higher values. But in most modern machines, you need to increase this value for optimal performance.

The recommended value is 25% of your total machine RAM. You should try some lower and higher values because in some cases we achieve good performance with a setting over 25%. The configuration really depends on your machine and the working data set. If your working set of data can easily fit into your RAM, then you might want to increase the shared_buffer value to contain your entire database, so that the whole working set of data can reside in cache. That said, you obviously do not want to reserve all RAM for PostgreSQL.

In production environments, it is observed that a large value for shared_buffer gives really good performance, though you should always benchmark to find the right balance.

Alternatively, while a larger shared_buffers value can increase performance in ‘read heavy’ use cases, having a large shared_buffer value can be detrimental for ‘write heavy’ use cases, as the entire contents of shared_buffers must be processed during writes.

Please note that the database server needs to be restarted after this change.

testdb=# SHOW shared_buffers;
shared_buffers
----------------
128MB
(1 row)

wal_buffers

Write-Ahead Logging (WAL) is a standard method for ensuring integrity of data. Much like in the shared_buffers setting, PostgreSQL writes WAL records into buffers and then these buffers are flushed to disk.

The default size of the buffer is set by the  wal_buffers setting- initially at 16MB. If the system being tuned has a large number of concurrent connections, then a higher value for  wal_buffers can provide better performance.

effective_cache_size

effective_cache_size has the reputation of being a confusing PostgreSQL settings, and as such, many times the setting is left to the default value.

The effective_cache_size value provides a ‘rough estimate’ of the number of how much memory is available for disk caching by the operating system and within the database itself, after taking into account what’s used by the OS itself and other applications.

This value is used only by the PostgreSQL query planner to figure out whether plans it’s considering would be expected to fit in RAM or not. As such, it’s a bit of a fuzzy number to define for general use cases.

A conservative value for  effective_cache_size  would be ½(50%) of the total memory available on the system. Most commonly, the value is set to 75% of the total system memory on a dedicated DB server, but can vary depending on the specific discrete needs on a particular server workload.

If the value for effective_cache_size  is too low, then the query planner may decide not to use some indexes, even if they would help greatly increase query speed.

So Conclusively , General recommendation for effective_cache_size is as follows.

  • Set the value to the amount of file system cache available. On UNIX/Linux like systems, add the free+cached numbers from free or top commands to get an estimate
  • If you don’t know, set the value to the 50%  or 75%  of total system memory specially if dedicated DB server

work_mem

This configuration is used for complex sorting. If you have to do complex sorting then increase the value of work_mem for good results. In-memory sorts are much faster than sorts spilling to disk. Setting a very high value can cause a memory bottleneck for your deployment environment because this parameter is per user sort operation. Therefore, if you have many users trying to execute sort operations, then the system will allocate work_mem * total sort operations  for all users. Setting this parameter globally can cause very high memory usage. So it is highly recommended to modify this at the session level.

12345678910testdb=# SET work_mem TO “2MB”;testdb=# EXPLAIN SELECT * FROM bar ORDER BY bar.b;                                    QUERY PLAN                                     ———————————————————————————–Gather Merge  (cost=509181.84..1706542.14 rows=10000116 width=24)   Workers Planned: 4   ->  Sort  (cost=508181.79..514431.86 rows=2500029 width=24)         Sort Key: b         ->  Parallel Seq Scan on bar  (cost=0.00..88695.29 rows=2500029 width=24)(5 rows)

The initial query’s sort node has an estimated cost of 514431.86. Cost is an arbitrary unit of computation. For the above query, we have a work_mem of only 2MB. For testing purposes, let’s increase this to 256MB and see if there is any impact on cost.

123456789testdb=# SET work_mem TO “256MB”;testdb=# EXPLAIN SELECT * FROM bar ORDER BY bar.b;                                    QUERY PLAN                                     ———————————————————————————–Gather Merge  (cost=355367.34..1552727.64 rows=10000116 width=24)   Workers Planned: 4   ->  Sort  (cost=354367.29..360617.36 rows=2500029 width=24)         Sort Key: b         ->  Parallel Seq Scan on bar  (cost=0.00..88695.29 rows=2500029 width=24)

The query cost is reduced to 360617.36 from 514431.86 — a 30% reduction.

So Conclusively , Setting the value Higher alway results beer sorting and hashing , But setting it in local queries is always recommended . Set value high in queries where you expect high sorting otherwise low global value is good . 

maintenance_work_mem

maintenance_work_mem is a memory setting used for maintenance tasks. The default value is 64MB. Setting a large value helps in tasks like VACUUM, RESTORE, CREATE INDEX, ADD FOREIGN KEY and ALTER TABLE.

It is necessary to remember that when autovacuum runs, up to autovacuum_max_workers times this memory may be allocated, so be careful not to set the default value too high.

The default value of maintenance_work_mem = 64MB.

General recommendation to set maintenance_work_mem is as follows.

  • Set the value 10% of system memory, up to 1GB
  • Maybe you can set it even higher if you are having VACUUM problems

We can also temporarily increase this memory while creating indexes or at the time of dump restores or while performing full vacuums . 

synchronous_commit

This is used to enforce that commit will wait for WAL to be written on disk before returning a success status to the client. This is a trade-off between performance and reliability. If your application is designed such that performance is more important than the reliability, then turn off synchronous_commit. This means that there will be a time gap between the success status and a guaranteed write to disk. In the case of a server crash, data might be lost even though the client received a success message on commit. In this case, a transaction commits very quickly because it will not wait for a WAL file to be flushed, but reliability is compromised.

Temp_buffers

This parameter sets the maximum number of temporary buffers used by each database session. The session local buffers are used only for access to temporary tables. The setting of this parameter can be changed within individual sessions but only before the first use of temporary tables within the session.

PostgreSQL database utilizes this memory area for holding the temporary tables of each session, these will be cleared when the connection is closed.

The default value of temp_buffer = 8MB.

Conclusion

There are more parameters that can be tuned to gain better performance but those have less impact than the ones highlighted here. In the end, we must always keep in mind that not all parameters are relevant for all applications types. Some applications perform better by tuning a parameter and some don’t. Tuning PostgreSQL Database Parameters must be done for the specific needs of an application and the OS it runs on.  

Also ,  Performance tuning does not only depends on postgres configuration parameters ,  there are many system parameters also on which postgresql performance depends So, in my next blog i will explain some system parameters which can affect postgresql performance .