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How to Fix PostgreSQL Performance Issues with PG Extras

 
PostgreSQL performance checklist is represented by a notebook Photo by Aaron Burden on Unsplash


PostgreSQL database queries are a common performance bottleneck for web apps. Before you resort to more complex optimization techniques like caching or read replicas, you should double-check if your database engine is correctly tuned and queries are not underperforming.

PG Extras is a tool that allows you to spot common PostgreSQL pitfalls. Ruby, Rails, Elixir, NodeJS, Python and Haskell implementations are currently available.

In this blog post, I present a step by step guide on using PG Extras library to spot and resolve common PostgreSQL database performance issues.


How to start using PG Extras

Please refer to READMEs of respective implementations for installation details. API is almost identical for all the versions. Let’s compare the invocation of the cache_hit method:

Ruby

RubyPGExtras.cache_hit

Elixir

EctoPSQLExtras.query(:cache_hit, YourApp.Repo)

NodeJS

PostgresExtras.cache_hit()

Python

PGExtras.query('cache_hit')

Haskell

extrasCacheHit databaseUrl

In this blog post, I’ll be using examples from the pure Ruby version.

Enable pg_stat_statements extension

Some of the PG Extras methods depend on the pg_stat_statements extension. If you are using a default Heroku PostgreSQL plugin or AWS RDS, you should be good to go without making any changes.

To check if the extension is already enabled you can use PG Extras itself:

RubyPGExtras.extensions

...
| pg_stat_statements | 1.7 | 1.7 | track execution statistics of all SQL statements executed
...

If pg_stat_statements is not listed you should check out these docs for info on installing it.

Now that you’ve set up the library in the language of your choice let’s start checking our database’s health.

[Important] Make certain to run all the checks on a warmed up production database. Under the hood, PG Extras performs a lightweight queries on PostgreSQL metadata tables. It will not impact your production workload.

1) Validate your database specs with cache hit ratios

In theory, the simplest solution to optimize the underperforming database is to scale it up vertically. Before you start throwing money at your performance issues, it’s good to check if it will actually help.

PostgreSQL tracks access patterns of your data and keeps frequently read chunks in a memory cache. A reliable indicator that a database should be scaled up is an invalid cache hit ratio.

You can check index and table cache hit ratios using the following code:

RubyPGExtras.cache_hit

      name      |         ratio
----------------+------------------------
 index hit rate |        0.999577
 table hit rate |        0.988721

If you want to drill down into each individual’s table and index cache hit ratios, you can use table_cache_hit and index_cache_hit methods.

The rule of the thumb is that values should be above 99%. If your database cache hit ratios are lower, it’s either not correctly configured or should be scaled up to increase the performance.

Heroku PostgreSQL ships with already optimized settings and does not allow you to change them. If you see low cache hit ratios, your best bet is to provision a more powerful database instance.

Amazon RDS is notorious for shipping the database instances with incorrect default settings. If you’re using it, make sure to tweak them before deciding to scale up the instance. PGTune is the best tool to help you tweak the most important Postgres buttons and dials to the correct values.

2) Remove unused indexes

Overusing indexes is a recipe for a sluggish web app.

The more indexes you add, the more write operations have to be performed on each data update. Misconfigured indexes also tend to unecessarily bloat the size of a database, slowing down the backup/restore/upgrade operations.

It’s entirely possible that some of your indexes and not used and can be safely removed.

PG Extras unused_indexes method can help you spot them:

RubyPGExtras.unused_indexes

          table      |                       index                | index_size | index_scans
---------------------+--------------------------------------------+------------+-------------
 public.grade_levels | index_placement_attempts_on_grade_level_id | 97 MB      |           0
 public.observations | observations_attrs_grade_resources         | 33 MB      |           0
 public.messages     | user_resource_id_idx                       | 12 MB      |           0

Few index_scans on an index that has been around for a while means that it should be removed. If the index is large, remember to use the CONCURRENTLY option when dropping it, to avoid exclusively blocking the whole related table.

index_size method can give you a quick overview of how much space your database indexes are taking:

RubyPGExtras.index_size

     name            |  size
-----------------------------------------------
 index_a_name        | 5196 MB
 index_b_name        | 4045 MB
 index_b_name        | 2611 MB

3) Add missing indexes

Now that we’ve removed unused indexes, let’s add some new ones. We don’t want them to share the fate of their recently deprovisioned cousins. Let’s look at PG Extras seq_scans and calls methods before deciding on what should be indexed.

A sequential scan is an action that Postgres performs if it cannot find an index necessary to fulfill the query condition. For the following query:

SELECT * FROM USERS WHERE AGE = 18;

the related EXPLAIN ANALYZE query output will show Seq scan on users Filter: AGE = 18 or Index Scan using users_age_index Index Cond: AGE = 18 depending on whether the index on age column is present or not.

seq_scans method displays the number of Seq Scan operations for each table:

RubyPGExtras.seq_scans

               name                |  count
-----------------------------------+----------
 learning_coaches                  | 44820063
 states                            | 36794975
 grade_levels                      | 13972293
 charities_customers               |  8615277

Now that we know which tables are often read inefficiently, we can use calls and outliers methods to list the most often executed and most time-consuming queries.

Both of those methods let you extract the raw query string. You can use it to perform EXPLAIN ANALYZE and check if the query planner does Seq scan on one of the tables.

By correlating all those sources of data, you should be able to spot queries that are consuming a lot of your database resources and are potentially missing an index.

Watch out to avoid premature optimization by adding unnecessary indexes. PostgreSQL will often fallback to Seq Scan instead of Index Scan on small tables, for which using the index would be less efficient than reading the whole table row by row.

4) Identify deadlocks

PostgreSQL uses locks to ensure data consistency in multithreaded environments. There are different kinds of locks, but we’ll focus on ExclusiveLock and RowExclusiveLock. A healthy web app should never lock for more than a couple of hundred of miliseconds.

Deadlock is two more or database locks blocking each other and not able to continue execution. An implementation error that results in a deadlock might have disastrous consequences for your application. The queue of requests not able to proceed could start piling up and eventually crash your servers.

Common reasons for deadlocks and locks that are granted for too long:

  • too broad database transaction scope
  • adding or removing index without using the CONCURRENTLY option
  • updating lots of rows at once
  • adding a new column with the default value (before PostgreSQL 12)

How to detect locks and deadlocks

You can use locks method to see all the currently obtained locks together with the source query:

RubyPGExtras.locks

 procpid | relname | transactionid | granted |     query_snippet     | mode             |       age
---------+---------+---------------+---------+-----------------------+-------------------------------------
   31776 |         |               | t       | <IDLE> in transaction | ExclusiveLock    |  00:19:29.837898
   31776 |         |          1294 | t       | <IDLE> in transaction | RowExclusiveLock |  00:19:29.837898
   31912 |         |               | t       | select * from hello;  | ExclusiveLock    |  00:19:17.94259
    3443 |         |               | t       |                      +| ExclusiveLock    |  00:00:00

The mere presence of locks does not mean that something is wrong with your database. Only locks that are granted for too long are potentially problematic. You can use the following Ruby snippet integrated into the background job to alert you if this happens:

TRESHOLD_SECONDS = 1

long_locks = RubyPGExtras.locks(in_format: :hash).select do |lock|
  Time.parse(lock.fetch("age")).seconds_since_midnight > TRESHOLD_SECONDS
end

raise "Long running locks: #{long_locks}" if long_locks.present?

If you notice extended locks, you can use the blocking method to check which SQL statements cannot continue execution because of a lock:

RubyPGExtras.blocking

 blocked_pid |    blocking_statement    | blocking_duration | blocking_pid |                                        blocked_statement                           | blocked_duration
-------------+--------------------------+-------------------+--------------+------------------------------------------------------------------------------------+------------------
         461 | select count(*) from app | 00:00:03.838314   |        15682 | UPDATE "app" SET "updated_at" = '2013-03-04 15:07:04.746688' WHERE "id" = 12823149 | 00:00:03.821826

If your app is crashing because of deadlocks, you can use the kill_all to terminate all the database processes before you manage to resolve the underlying cause.

5) Get rid of unnecessary bloat

The way PostgreSQL works is that it never updates or removes the data in place but instead marks each row as visible or not for transactions using two meta columns xmin and xmax. Rows no longer visible for any of the currently active transactions are called dead rows or bloat.

Dead rows are regularly garbage collected by a process called AUTOVACUUM, and space they previously occupied can be reclaimed for new data. If autovacuuming is misconfigured, it might result in your table consisting of mostly dead rows that are blocking the disk space and slowing down queries.

To check if some of your tables are overly bloated, you can use the bloat method:

RubyPGExtras.bloat

 type  | schemaname |           object_name         | bloat |   waste
-------+------------+-------------------------------+-------+----------
 table | public     | bloated_table                 |   8.1 | 98 MB
 table | public     | other_bloated_table           |   1.1 | 58 MB
 table | public     | clean_table                   |   0.2 | 3808 kB
 table | public     | other_clean_table             |   0.3 | 1576 kB

If bloat ratio (i.e. size of bloat related to the size of the whole table) is close to the value of 10 you should have a closer look at your vacuum settings. vacuum_stats method can help you with that:

RubyPGExtras.vacuum_stats

 schema |         table         | last_vacuum | last_autovacuum  |    rowcount    | dead_rowcount  | autovacuum_threshold | expect_autovacuum
--------+-----------------------+-------------+------------------+----------------+----------------+----------------------+-------------------
 public | bloated_table         |             | 2020-04-26 17:37 |            100 |            810 |           3000       |
 public | other_bloated_table   |             | 2020-04-26 13:09 |             79 |             98 |            166       |
 public | clean_table           |             | 2020-04-26 11:41 |             41 |             11 |             58       |
 public | other_clean_table     |             | 2020-04-26 17:39 |             12 |              4 |             52       |

On frequently updated tables the bloat could be significant but autovacumming should be getting rid of it on a regular basis.

If you see that your bloated table has not been autovacuumed for a while and autovacuum is not expected, it means that something might be misconfigured in your autovacuum settings. You should check the PostgreSQL docs on automatic vacuuming for more info on config settings that could cause that.

To VACUUM FULL or not

If your table has bloat, although vacuum_stats indicate that it has recently been vacuumed, it means that table has a lot of free disk space that has been previously restored from the vacuumed rows.

It is not necessarily a bad thing. PostgreSQL will reuse that disk space for new data. If you’d like to reclaim this space for the operating system, you need to perform the VACUUM FULL. Contrary to VACUUM the VACUUM FULL grants an exclusive table lock, so doing it on a live production database could cause a disaster.

To perform VACUUM FULL without locking the underlying table, you could use pg_repack.

Generally, it is not recommended to run VACUUM FULL on a regular basis. You should consider it only in some cases, i.e., you’ve removed most of the data from a huge table.

6) Remove unneeded objects

It might seem like obvious advice, but I have a feeling that it is often getting overlooked. Do you really need to keep all the data of a user who signed up two years ago and has not visited ever since? Regularly purging unnecessary data will keep both your database and GDPR inspectors happy.

table_size method can help you get a quick overview of how big your tables actually are:

RubyPGExtras.table_size

            name             |  size
---------------------------------------+
 learning_coaches            |  196 MB
 states                      |  145 MB
 grade_levels                |  111 MB
 charities_customers         |   73 MB
 charities                   |   66 MB

E.g., in my project Abot I regularly remove all the data of teams who deactivated the Slack integration. This is both performance and GDPR friendly practice.

Summary

I hope that this checklist will help you keep your Postgres databases running at their best possible performance.

I understand that manually checking all those stats is a tedious and repetitive task. I’m currently working on a new API for PG Extras that will allow you to automate the database health checks and alert when the numbers are off. You can follow me on Twitter to get updated when it’s ready.



Pawel Urbanek Full Stack Ruby on Rails developer avatar

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