This document describes various guidelines to follow when optimizing SQL queries.
When you are optimizing your SQL queries, there are two dimensions to pay attention to:
- The query execution time. This is paramount as it reflects how the user experiences GitLab.
- The query plan. Optimizing the query plan is important in allowing queries to independently scale over time. Realizing that an index keeps a query performing well as the table grows before the query degrades is an example of why we analyze these plans.
|Query Type||Maximum Query Time||Notes|
|General queries||This is not a hard limit, but if a query is getting above it, it is important to spend time understanding why it can or cannot be optimized.|
|Queries in a migration||This is different than the total migration time.|
|Concurrent operations in a migration||Concurrent operations do not block the database, but they block the GitLab update. This includes operations such as |
|Concurrent operations in a post migration||Concurrent operations do not block the database, but they block the GitLab post update process. This includes operations such as |
|Service Ping||See the Service Ping docs for more details.|
- When analyzing your query’s performance, pay attention to if the time you are seeing is on a cold or warm cache. These guidelines apply for both cache types.
- When working with batched queries, change the range and batch size to see how it effects the query timing and caching.
- If an existing query is not performing well, make an effort to improve it. If it is too complex or would stall development, create a follow-up so it can be addressed in a timely manner. You can always ask the database reviewer or maintainer for help and guidance.
When evaluating query performance it is important to understand the difference between cold and warm cached queries.
The first time a query is made, it is made on a “cold cache”. Meaning it needs to read from disk. If you run the query again, the data can be read from the cache, or what PostgreSQL calls shared buffers. This is the “warm cache” query.
When analyzing an
EXPLAIN plan, you can see
the difference not only in the timing, but by looking at the output for
by running your explain with
EXPLAIN(analyze, buffers). Database Lab
automatically includes these options.
If you are making a warm cache query, you see only the
For example in #database-lab:
Shared buffers: - hits: 36467 (~284.90 MiB) from the buffer pool - reads: 0 from the OS file cache, including disk I/O
Or in the explain plan from
Buffers: shared hit=7323
If the cache is cold, you also see
Shared buffers: - hits: 17204 (~134.40 MiB) from the buffer pool - reads: 15229 (~119.00 MiB) from the OS file cache, including disk I/O
Buffers: shared hit=7202 read=121