2016-08-11 12:22:21 +00:00
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# Adding Database Indexes
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Indexes can be used to speed up database queries, but when should you add a new
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index? Traditionally the answer to this question has been to add an index for
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every column used for filtering or joining data. For example, consider the
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following query:
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```sql
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SELECT *
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FROM projects
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WHERE user_id = 2;
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```
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Here we are filtering by the `user_id` column and as such a developer may decide
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to index this column.
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While in certain cases indexing columns using the above approach may make sense
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it can actually have a negative impact. Whenever you write data to a table any
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existing indexes need to be updated. The more indexes there are the slower this
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can potentially become. Indexes can also take up quite some disk space depending
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on the amount of data indexed and the index type. For example, PostgreSQL offers
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"GIN" indexes which can be used to index certain data types that can not be
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2020-06-10 03:08:17 +00:00
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indexed by regular B-tree indexes. These indexes however generally take up more
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data and are slower to update compared to B-tree indexes.
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2016-08-11 12:22:21 +00:00
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Because of all this one should not blindly add a new index for every column used
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to filter data by. Instead one should ask themselves the following questions:
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1. Can I write my query in such a way that it re-uses as many existing indexes
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as possible?
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2018-11-13 00:39:21 +00:00
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1. Is the data going to be large enough that using an index will actually be
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2016-08-11 12:22:21 +00:00
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faster than just iterating over the rows in the table?
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2018-11-13 00:39:21 +00:00
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1. Is the overhead of maintaining the index worth the reduction in query
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2016-08-11 12:22:21 +00:00
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timings?
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We'll explore every question in detail below.
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## Re-using Queries
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The first step is to make sure your query re-uses as many existing indexes as
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possible. For example, consider the following query:
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```sql
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SELECT *
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FROM todos
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WHERE user_id = 123
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AND state = 'open';
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```
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Now imagine we already have an index on the `user_id` column but not on the
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`state` column. One may think this query will perform badly due to `state` being
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unindexed. In reality the query may perform just fine given the index on
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`user_id` can filter out enough rows.
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The best way to determine if indexes are re-used is to run your query using
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`EXPLAIN ANALYZE`. Depending on any extra tables that may be joined and
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other columns being used for filtering you may find an extra index is not going
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to make much (if any) difference. On the other hand you may determine that the
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index _may_ make a difference.
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In short:
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1. Try to write your query in such a way that it re-uses as many existing
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indexes as possible.
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2018-11-13 00:39:21 +00:00
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1. Run the query using `EXPLAIN ANALYZE` and study the output to find the most
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2016-08-11 12:22:21 +00:00
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ideal query.
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## Data Size
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A database may decide not to use an index despite it existing in case a regular
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sequence scan (= simply iterating over all existing rows) is faster. This is
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especially the case for small tables.
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If a table is expected to grow in size and you expect your query has to filter
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out a lot of rows you may want to consider adding an index. If the table size is
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2020-02-20 18:08:51 +00:00
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very small (e.g. less than `1,000` records) or any existing indexes filter out
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2016-08-11 12:22:21 +00:00
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enough rows you may _not_ want to add a new index.
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## Maintenance Overhead
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Indexes have to be updated on every table write. In case of PostgreSQL _all_
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existing indexes will be updated whenever data is written to a table. As a
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result of this having many indexes on the same table will slow down writes.
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Because of this one should ask themselves: is the reduction in query performance
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worth the overhead of maintaining an extra index?
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If adding an index reduces SELECT timings by 5 milliseconds but increases
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INSERT/UPDATE/DELETE timings by 10 milliseconds then the index may not be worth
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it. On the other hand, if SELECT timings are reduced but INSERT/UPDATE/DELETE
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timings are not affected you may want to add the index after all.
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## Finding Unused Indexes
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To see which indexes are unused you can run the following query:
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```sql
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SELECT relname as table_name, indexrelname as index_name, idx_scan, idx_tup_read, idx_tup_fetch, pg_size_pretty(pg_relation_size(indexrelname::regclass))
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FROM pg_stat_all_indexes
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WHERE schemaname = 'public'
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AND idx_scan = 0
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AND idx_tup_read = 0
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AND idx_tup_fetch = 0
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ORDER BY pg_relation_size(indexrelname::regclass) desc;
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```
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This query outputs a list containing all indexes that are never used and sorts
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2020-02-11 21:08:44 +00:00
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them by indexes sizes in descending order. This query can be useful to
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2016-08-11 12:22:21 +00:00
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determine if any previously indexes are useful after all. More information on
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the meaning of the various columns can be found at
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2019-09-27 12:06:07 +00:00
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<https://www.postgresql.org/docs/current/monitoring-stats.html>.
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2016-08-11 12:22:21 +00:00
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Because the output of this query relies on the actual usage of your database it
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may be affected by factors such as (but not limited to):
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2018-11-13 06:07:16 +00:00
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- Certain queries never being executed, thus not being able to use certain
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2016-08-11 12:22:21 +00:00
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indexes.
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2018-11-13 06:07:16 +00:00
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- Certain tables having little data, resulting in PostgreSQL using sequence
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2016-08-11 12:22:21 +00:00
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scans instead of index scans.
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In other words, this data is only reliable for a frequently used database with
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plenty of data and with as many GitLab features enabled (and being used) as
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possible.
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