Understanding EXPLAIN plans

PostgreSQL allows you to obtain query plans using the EXPLAIN command. This command can be invaluable when trying to determine how a query performs. You can use this command directly in your SQL query, as long as the query starts with it: 

EXPLAIN
SELECT COUNT(*)
FROM projects
WHERE visibility_level IN (0, 20);

When running this on GitLab.com, we are presented with the following output: 

Aggregate  (cost=922411.76..922411.77 rows=1 width=8)
  ->  Seq Scan on projects  (cost=0.00..908044.47 rows=5746914 width=0)
        Filter: (visibility_level = ANY ('{0,20}'::integer[]))

When using just EXPLAIN, PostgreSQL does not actually execute our query, instead it produces an estimated execution plan based on the available statistics. This means the actual plan can differ quite a bit. Fortunately, PostgreSQL provides us with the option to execute the query as well. To do so, we need to use EXPLAIN ANALYZE instead of just EXPLAIN: 

EXPLAIN ANALYZE
SELECT COUNT(*)
FROM projects
WHERE visibility_level IN (0, 20);

This produces: 

Aggregate  (cost=922420.60..922420.61 rows=1 width=8) (actual time=3428.535..3428.535 rows=1 loops=1)
  ->  Seq Scan on projects  (cost=0.00..908053.18 rows=5746969 width=0) (actual time=0.041..2987.606 rows=5746940 loops=1)
        Filter: (visibility_level = ANY ('{0,20}'::integer[]))
        Rows Removed by Filter: 65677
Planning time: 2.861 ms
Execution time: 3428.596 ms

As we can see this plan is quite different, and includes a lot more data. Let’s discuss this step by step.

Because EXPLAIN ANALYZE executes the query, care should be taken when using a query that writes data or might time out. If the query modifies data, consider wrapping it in a transaction that rolls back automatically like so: 

BEGIN;
EXPLAIN ANALYZE
DELETE FROM users WHERE id = 1;
ROLLBACK;

The EXPLAIN command also takes additional options, such as BUFFERS: 

EXPLAIN (ANALYZE, BUFFERS)
SELECT COUNT(*)
FROM projects
WHERE visibility_level IN (0, 20);

This then produces: 

Aggregate  (cost=922420.60..922420.61 rows=1 width=8) (actual time=3428.535..3428.535 rows=1 loops=1)
  Buffers: shared hit=208846
  ->  Seq Scan on projects  (cost=0.00..908053.18 rows=5746969 width=0) (actual time=0.041..2987.606 rows=5746940 loops=1)
        Filter: (visibility_level = ANY ('{0,20}'::integer[]))
        Rows Removed by Filter: 65677
        Buffers: shared hit=208846
Planning time: 2.861 ms
Execution time: 3428.596 ms

For more information, refer to the official EXPLAIN documentation and using EXPLAIN guide.

Nodes

Every query plan consists of nodes. Nodes can be nested, and are executed from the inside out. This means that the innermost node is executed before an outer node. This can be best thought of as nested function calls, returning their results as they unwind. For example, a plan starting with an Aggregate followed by a Nested Loop, followed by an Index Only scan can be thought of as the following Ruby code: 

aggregate(
  nested_loop(
    index_only_scan()
    index_only_scan()
  )
)

Nodes are indicated using a -> followed by the type of node taken. For example: 

Aggregate  (cost=922411.76..922411.77 rows=1 width=8)
  ->  Seq Scan on projects  (cost=0.00..908044.47 rows=5746914 width=0)
        Filter: (visibility_level = ANY ('{0,20}'::integer[]))

Here the first node executed is Seq scan on projects. The Filter: is an additional filter applied to the results of the node. A filter is very similar to Ruby’s Array#select: it takes the input rows, applies the filter, and produces a new list of rows. After the node is done, we perform the Aggregate above it.

Nested nodes look like this: 

Aggregate  (cost=176.97..176.98 rows=1 width=8) (actual time=0.252..0.252 rows=1 loops=1)
  Buffers: shared hit=155
  ->  Nested Loop  (cost=0.86..176.75 rows=87 width=0) (actual time=0.035..0.249 rows=36 loops=1)
        Buffers: shared hit=155
        ->  Index Only Scan using users_pkey on users users_1  (cost=0.43..4.95 rows=87 width=4) (actual time=0.029..0.123 rows=36 loops=1)
              Index Cond: (id < 100)
              Heap Fetches: 0
        ->  Index Only Scan using users_pkey on users  (cost=0.43..1.96 rows=1 width=4) (actual time=0.003..0.003 rows=1 loops=36)
              Index Cond: (id = users_1.id)
              Heap Fetches: 0
Planning time: 2.585 ms
Execution time: 0.310 ms

Here we first perform two separate “Index Only” scans, followed by performing a “Nested Loop” on the result of these two scans.

Node statistics

Each node in a plan has a set of associated statistics, such as the cost, the number of rows produced, the number of loops performed, and more. For example: 

Seq Scan on projects  (cost=0.00..908044.47 rows=5746914 width=0)

Here we can see that our cost ranges from 0.00..908044.47 (we cover this in a moment), and we estimate (since we’re using EXPLAIN and not EXPLAIN ANALYZE) a total of 5,746,914 rows to be produced by this node. The width statistics describes the estimated width of each row, in bytes.

The costs field specifies how expensive a node was. The cost is measured in arbitrary units determined by the query planner’s cost parameters. What influences the costs depends on a variety of settings, such as seq_page_cost, cpu_tuple_cost, and various others. The format of the costs field is as follows: 

STARTUP COST..TOTAL COST

The startup cost states how expensive it was to start the node, with the total cost describing how expensive the entire node was. In general: the greater the values, the more expensive the node.

When using EXPLAIN ANALYZE, these statistics also include the actual time (in milliseconds) spent, and other runtime statistics (for example, the actual number of produced rows): 

Seq Scan on projects  (cost=0.00..908053.18 rows=5746969 width=0) (actual time=0.041..2987.606 rows=5746940 loops=1)

Here we can see we estimated 5,746,969 rows to be returned, but in reality we returned 5,746,940 rows. We can also see that just this sequential scan took 2.98 seconds to run.

Using EXPLAIN (ANALYZE, BUFFERS) also gives us information about the number of rows removed by a filter, the number of buffers used, and more. For example: 

Seq Scan on projects  (cost=0.00..908053.18 rows=5746969 width=0) (actual time=0.041..2987.606 rows=5746940 loops=1)
  Filter: (visibility_level = ANY ('{0,20}'::integer[]))
  Rows Removed by Filter: 65677
  Buffers: shared hit=208846

Here we can see that our filter has to remove 65,677 rows, and that we use 208,846 buffers. Each buffer in PostgreSQL is 8 KB (8192 bytes), meaning our above node uses 1.6 GB of buffers. That’s a lot!

Keep in mind that some statistics are per-loop averages, while others are total values:

Field name Value type
Actual Total Time per-loop average
Actual Rows per-loop average
Buffers Shared Hit total value
Buffers Shared Read total value
Buffers Shared Dirtied total value
Buffers Shared Written total value
I/O Read Time total value
I/O Read Write total value

For example: 

 ->  Index Scan using users_pkey on public.users  (cost=0.43..3.44 rows=1 width=1318) (actual time=0.025..0.025 rows=1 loops=888)
       Index Cond: (users.id = issues.author_id)
       Buffers: shared hit=3543 read=9
       I/O Timings: read=17.760 write=0.000

Here we can see that this node used 3552 buffers (3543 + 9), returned 888 rows (888 * 1), and the actual duration was 22.2 milliseconds (888 * 0.025). 17.76 milliseconds of the total duration was spent in reading from disk, to retrieve data that was not in the cache.

Node types

There are quite a few different types of nodes, so we only cover some of the more common ones here.

A full list of all the available nodes and their descriptions can be found in the PostgreSQL source file plannodes.h. The pgMustard EXPLAIN documentation also offers detailed look into nodes and their fields.

Seq Scan

A sequential scan over (a chunk of) a database table. This is like using Array#each, but on a database table. Sequential scans can be quite slow when retrieving lots of rows, so it’s best to avoid these for large tables.

Index Only Scan

A scan on an index that did not require fetching anything from the table. In certain cases an index only scan may still fetch data from the table, in this case the node includes a Heap Fetches: statistic.

Index Scan

A scan on an index that required retrieving some data from the table.

Bitmap Index Scan and Bitmap Heap scan

Bitmap scans fall between sequential scans and index scans. These are typically used when we would read too much data from an index scan, but too little to perform a sequential scan. A bitmap scan uses what is known as a bitmap index to perform its work.

The source code of PostgreSQL states the following on bitmap scans:

Bitmap Index Scan delivers a bitmap of potential tuple locations; it does not access the heap itself. The bitmap is used by an ancestor Bitmap Heap Scan node, possibly after passing through intermediate Bitmap Or and/or Bitmap And nodes to combine it with the results of other Bitmap Index Scans.

Limit

Applies a LIMIT on the input rows.

Sort

Sorts the input rows as specified using an ORDER BY statement.

Nested Loop

A nested loop executes its child nodes for every row produced by a node that precedes it. For example: 

->  Nested Loop  (cost=0.86..176.75 rows=87 width=0) (actual time=0.035..0.249 rows=36 loops=1)
      Buffers: shared hit=155
      ->  Index Only Scan using users_pkey on users users_1  (cost=0.43..4.95 rows=87 width=4) (actual time=0.029..0.123 rows=36 loops=1)
            Index Cond: (id < 100)
            Heap Fetches: 0
      ->  Index Only Scan using users_pkey on users  (cost=0.43..1.96 rows=1 width=4) (actual time=0.003..0.003 rows=1 loops=36)
            Index Cond: (id = users_1.id)
            Heap Fetches: 0

Here the first child node (Index Only Scan using users_pkey on users users_1) produces 36 rows, and is executed once (rows=36 loops=1). The next node produces 1 row (rows=1), but is repeated 36 times (loops=36). This is because the previous node produced 36 rows.

This means that nested loops can quickly slow the query down if the various child nodes keep producing many rows.

Optimizing queries

With that out of the way, let’s see how we can optimize a query. Let’s use the following query as an example: 

SELECT COUNT(*)
FROM users
WHERE twitter != '';

This query counts the number of users that have a Twitter profile set. Let’s run this using EXPLAIN (ANALYZE, BUFFERS): 

EXPLAIN (ANALYZE, BUFFERS)
SELECT COUNT(*)
FROM users
WHERE twitter != '';

This produces the following plan: 

Aggregate  (cost=845110.21..845110.22 rows=1 width=8) (actual time=1271.157..1271.158 rows=1 loops=1)
  Buffers: shared hit=202662
  ->  Seq Scan on users  (cost=0.00..844969.99 rows=56087 width=0) (actual time=0.019..1265.883 rows=51833 loops=1)
        Filter: ((twitter)::text <> ''::text)
        Rows Removed by Filter: 2487813
        Buffers: shared hit=202662
Planning time: 0.390 ms
Execution time: 1271.180 ms

From this query plan we can see the following:

  1. We need to perform a sequential scan on the users table.
  2. This sequential scan filters out 2,487,813 rows using a Filter.
  3. We use 202,622 buffers, which equals 1.58 GB of memory.
  4. It takes us 1.2 seconds to do all of this.

Considering we are just counting users, that’s quite expensive!

Before we start making any changes, let’s see if there are any existing indexes on the users table that we might be able to use. We can obtain this information by running \d users in a psql console, then scrolling down to the Indexes: section: 

Indexes:
    "users_pkey" PRIMARY KEY, btree (id)
    "index_users_on_confirmation_token" UNIQUE, btree (confirmation_token)
    "index_users_on_email" UNIQUE, btree (email)
    "index_users_on_reset_password_token" UNIQUE, btree (reset_password_token)
    "index_users_on_static_object_token" UNIQUE, btree (static_object_token)
    "index_users_on_unlock_token" UNIQUE, btree (unlock_token)
    "index_on_users_name_lower" btree (lower(name::text))
    "index_users_on_admin" btree (admin)
    "index_users_on_created_at" btree (created_at)
    "index_users_on_email_trigram" gin (email gin_trgm_ops)
    "index_users_on_feed_token" btree (feed_token)
    "index_users_on_group_view" btree (group_view)
    "index_users_on_incoming_email_token" btree (incoming_email_token)
    "index_users_on_managing_group_id" btree (managing_group_id)
    "index_users_on_name" btree (name)
    "index_users_on_name_trigram" gin (name gin_trgm_ops)
    "index_users_on_public_email" btree (public_email) WHERE public_email::text <> ''::text
    "index_users_on_state" btree (state)
    "index_users_on_state_and_user_type" btree (state, user_type)
    "index_users_on_unconfirmed_email" btree (unconfirmed_email) WHERE unconfirmed_email IS NOT NULL
    "index_users_on_user_type" btree (user_type)
    "index_users_on_username" btree (username)
    "index_users_on_username_trigram" gin (username gin_trgm_ops)
    "tmp_idx_on_user_id_where_bio_is_filled" btree (id) WHERE COALESCE(bio, ''::character varying)::text IS DISTINCT FROM ''::text

Here we can see there is no index on the twitter column, which means PostgreSQL has to perform a sequential scan in this case. Let’s try to fix this by adding the following index: 

CREATE INDEX CONCURRENTLY twitter_test ON users (twitter);

If we now re-run our query using EXPLAIN (ANALYZE, BUFFERS) we get the following plan: 

Aggregate  (cost=61002.82..61002.83 rows=1 width=8) (actual time=297.311..297.312 rows=1 loops=1)
  Buffers: shared hit=51854 dirtied=19
  ->  Index Only Scan using twitter_test on users  (cost=0.43..60873.13 rows=51877 width=0) (actual time=279.184..293.532 rows=51833 loops=1)
        Filter: ((twitter)::text <> ''::text)
        Rows Removed by Filter: 2487830
        Heap Fetches: 26037
        Buffers: shared hit=51854 dirtied=19
Planning time: 0.191 ms
Execution time: 297.334 ms

Now it takes just under 300 milliseconds to get our data, instead of 1.2 seconds. However, we still use 51,854 buffers, which is about 400 MB of memory. 300 milliseconds is also quite slow for such a simple query. To understand why this query is still expensive, let’s take a look at the following: 

Index Only Scan using twitter_test on users  (cost=0.43..60873.13 rows=51877 width=0) (actual time=279.184..293.532 rows=51833 loops=1)
  Filter: ((twitter)::text <> ''::text)
  Rows Removed by Filter: 2487830

We start with an index only scan on our index, but we somehow still apply a Filter that filters out 2,487,830 rows. Why is that? Well, let’s look at how we created the index: 

CREATE INDEX CONCURRENTLY twitter_test ON users (twitter);

We told PostgreSQL to index all possible values of the twitter column, even empty strings. Our query in turn uses WHERE twitter != ''. This means that the index does improve things, as we don’t need to do a sequential scan, but we may still encounter empty strings. This means PostgreSQL has to apply a Filter on the index results to get rid of those values.

Fortunately, we can improve this even further using “partial indexes”. Partial indexes are indexes with a WHERE condition that is applied when indexing data. For example: 

CREATE INDEX CONCURRENTLY some_index ON users (email) WHERE id < 100

This index would only index the email value of rows that match WHERE id < 100. We can use partial indexes to change our Twitter index to the following: 

CREATE INDEX CONCURRENTLY twitter_test ON users (twitter) WHERE twitter != '';

After being created, if we run our query again we are given the following plan: 

Aggregate  (cost=1608.26..1608.27 rows=1 width=8) (actual time=19.821..19.821 rows=1 loops=1)
  Buffers: shared hit=44036
  ->  Index Only Scan using twitter_test on users  (cost=0.41..1479.71 rows=51420 width=0) (actual time=0.023..15.514 rows=51833 loops=1)
        Heap Fetches: 1208
        Buffers: shared hit=44036
Planning time: 0.123 ms
Execution time: 19.848 ms

That’s a lot better! Now it only takes 20 milliseconds to get the data, and we only use about 344 MB of buffers (instead of the original 1.58 GB). The reason this works is that now PostgreSQL no longer needs to apply a Filter, as the index only contains twitter values that are not empty.

Keep in mind that you shouldn’t just add partial indexes every time you want to optimize a query. Every index has to be updated for every write, and they may require quite a bit of space, depending on the amount of indexed data. As a result, first check if there are any existing indexes you may be able to reuse. If there aren’t any, check if you can perhaps slightly change an existing one to fit both the existing and new queries. Only add a new index if none of the existing indexes can be used in any way.

When comparing execution plans, don’t take timing as the only important metric. Good timing is the main goal of any optimization, but it can be too volatile to be used for comparison (for example, it depends a lot on the state of cache). When optimizing a query, we usually need to reduce the amount of data we’re dealing with. Indexes are the way to work with fewer pages (buffers) to get the result, so, during optimization, look at the number of buffers used (read and hit), and work on reducing these numbers. Reduced timing is the consequence of reduced buffer numbers. Database Lab Engine guarantees that the plan is structurally identical to production (and overall number of buffers is the same as on production), but difference in cache state and I/O speed may lead to different timings.

Queries that can’t be optimized

Now that we have seen how to optimize a query, let’s look at another query that we might not be able to optimize: 

EXPLAIN (ANALYZE, BUFFERS)
SELECT COUNT(*)
FROM projects
WHERE visibility_level IN (0, 20);

The output of EXPLAIN (ANALYZE, BUFFERS) is as follows: 

Aggregate  (cost=922420.60..922420.61 rows=1 width=8) (actual time=3428.535..3428.535 rows=1 loops=1)
  Buffers: shared hit=208846
  ->  Seq Scan on projects  (cost=0.00..908053.18 rows=5746969 width=0) (actual time=0.041..2987.606 rows=5746940 loops=1)
        Filter: (visibility_level = ANY ('{0,20}'::integer[]))
        Rows Removed by Filter: 65677
        Buffers: shared hit=208846
Planning time: 2.861 ms
Execution time: 3428.596 ms

Looking at the output we see the following Filter: 

Filter: (visibility_level = ANY ('{0,20}'::integer[]))
Rows Removed by Filter: 65677

Looking at the number of rows removed by the filter, we may be tempted to add an index on projects.visibility_level to somehow turn this Sequential scan + filter into an index-only scan.

Unfortunately, doing so is unlikely to improve anything. Contrary to what some might believe, an index being present does not guarantee that PostgreSQL actually uses it. For example, when doing a SELECT * FROM projects it is much cheaper to just scan the entire table, instead of using an index and then fetching data from the table. In such cases PostgreSQL may decide to not use an index.

Second, let’s think for a moment what our query does: it gets all projects with visibility level 0 or 20. In the above plan we can see this produces quite a lot of rows (5,745,940), but how much is that relative to the total? Let’s find out by running the following query: 

SELECT visibility_level, count(*) AS amount
FROM projects
GROUP BY visibility_level
ORDER BY visibility_level ASC;

For GitLab.com this produces: 

 visibility_level | amount
------------------+---------
                0 | 5071325
               10 |   65678
               20 |  674801

Here the total number of projects is 5,811,804, and 5,746,126 of those are of level 0 or 20. That’s 98% of the entire table!

So no matter what we do, this query retrieves 98% of the entire table. Since most time is spent doing exactly that, there isn’t really much we can do to improve this query, other than not running it at all.

What is important here is that while some may recommend to straight up add an index the moment you see a sequential scan, it is much more important to first understand what your query does, how much data it retrieves, and so on. After all, you cannot optimize something you do not understand.

Cardinality and selectivity

Earlier we saw that our query had to retrieve 98% of the rows in the table. There are two terms commonly used for databases: cardinality, and selectivity. Cardinality refers to the number of unique values in a particular column in a table.

Selectivity is the number of unique values produced by an operation (for example, an index scan or filter), relative to the total number of rows. The higher the selectivity, the more likely PostgreSQL is able to use an index.

In the above example, there are only 3 unique values: 0, 10, and 20. This means the cardinality is 3. The selectivity in turn is also very low: 0.0000003% (2 / 5,811,804), because our Filter only filters using two values (0 and 20). With such a low selectivity value it’s not surprising that PostgreSQL decides using an index is not worth it, because it would produce almost no unique rows.

Rewriting queries

So the above query can’t really be optimized as-is, or at least not much. But what if we slightly change the purpose of it? What if instead of retrieving all projects with visibility_level 0 or 20, we retrieve those that a user interacted with somehow?

Prior to GitLab 16.7, GitLab used a table named user_interacted_projects to track user interactions with projects. This table had the following schema: 

Table "public.user_interacted_projects"
   Column   |  Type   | Modifiers
------------+---------+-----------
 user_id    | integer | not null
 project_id | integer | not null
Indexes:
    "index_user_interacted_projects_on_project_id_and_user_id" UNIQUE, btree (project_id, user_id)
    "index_user_interacted_projects_on_user_id" btree (user_id)
Foreign-key constraints:
    "fk_rails_0894651f08" FOREIGN KEY (user_id) REFERENCES users(id) ON DELETE CASCADE
    "fk_rails_722ceba4f7" FOREIGN KEY (project_id) REFERENCES projects(id) ON DELETE CASCADE

Let’s rewrite our query to JOIN this table onto our projects, and get the projects for a specific user: 

EXPLAIN ANALYZE
SELECT COUNT(*)
FROM projects
INNER JOIN user_interacted_projects ON user_interacted_projects.project_id = projects.id
WHERE projects.visibility_level IN (0, 20)
AND user_interacted_projects.user_id = 1;

What we do here is the following:

  1. Get our projects.
  2. INNER JOIN user_interacted_projects, meaning we’re only left with rows in projects that have a corresponding row in user_interacted_projects.
  3. Limit this to the projects with visibility_level of 0 or 20, and to projects that the user with ID 1 interacted with.

If we run this query we get the following plan: 

 Aggregate  (cost=871.03..871.04 rows=1 width=8) (actual time=9.763..9.763 rows=1 loops=1)
   ->  Nested Loop  (cost=0.86..870.52 rows=203 width=0) (actual time=1.072..9.748 rows=143 loops=1)
         ->  Index Scan using index_user_interacted_projects_on_user_id on user_interacted_projects  (cost=0.43..160.71 rows=205 width=4) (actual time=0.939..2.508 rows=145 loops=1)
               Index Cond: (user_id = 1)
         ->  Index Scan using projects_pkey on projects  (cost=0.43..3.45 rows=1 width=4) (actual time=0.049..0.050 rows=1 loops=145)
               Index Cond: (id = user_interacted_projects.project_id)
               Filter: (visibility_level = ANY ('{0,20}'::integer[]))
               Rows Removed by Filter: 0
 Planning time: 2.614 ms
 Execution time: 9.809 ms

Here it only took us just under 10 milliseconds to get the data. We can also see we’re retrieving far fewer projects: 

Index Scan using projects_pkey on projects  (cost=0.43..3.45 rows=1 width=4) (actual time=0.049..0.050 rows=1 loops=145)
  Index Cond: (id = user_interacted_projects.project_id)
  Filter: (visibility_level = ANY ('{0,20}'::integer[]))
  Rows Removed by Filter: 0

Here we see we perform 145 loops (loops=145), with every loop producing 1 row (rows=1). This is much less than before, and our query performs much better!

If we look at the plan we also see our costs are very low: 

Index Scan using projects_pkey on projects  (cost=0.43..3.45 rows=1 width=4) (actual time=0.049..0.050 rows=1 loops=145)

Here our cost is only 3.45, and it takes us 7.25 milliseconds to do so (0.05 * 145). The next index scan is a bit more expensive: 

Index Scan using index_user_interacted_projects_on_user_id on user_interacted_projects  (cost=0.43..160.71 rows=205 width=4) (actual time=0.939..2.508 rows=145 loops=1)

Here the cost is 160.71 (cost=0.43..160.71), taking about 2.5 milliseconds (based on the output of actual time=....).

The most expensive part here is the “Nested Loop” that acts upon the result of these two index scans: 

Nested Loop  (cost=0.86..870.52 rows=203 width=0) (actual time=1.072..9.748 rows=143 loops=1)

Here we had to perform 870.52 disk page fetches for 203 rows, 9.748 milliseconds, producing 143 rows in a single loop.

The key takeaway here is that sometimes you have to rewrite (parts of) a query to make it better. Sometimes that means having to slightly change your feature to accommodate for better performance.

What makes a bad plan

This is a bit of a difficult question to answer, because the definition of “bad” is relative to the problem you are trying to solve. However, some patterns are best avoided in most cases, such as:

  • Sequential scans on large tables
  • Filters that remove a lot of rows
  • Performing a certain step that requires a lot of buffers (for example, an index scan for GitLab.com that requires more than 512 MB).

As a general guideline, aim for a query that:

  1. Takes no more than 10 milliseconds. Our target time spent in SQL per request is around 100 milliseconds, so every query should be as fast as possible.
  2. Does not use an excessive number of buffers, relative to the workload. For example, retrieving ten rows shouldn’t require 1 GB of buffers.
  3. Does not spend a long amount of time performing disk IO operations. The setting track_io_timing must be enabled for this data to be included in the output of EXPLAIN ANALYZE.
  4. Applies a LIMIT when retrieving rows without aggregating them, such as SELECT * FROM users.
  5. Doesn’t use a Filter to filter out too many rows, especially if the query does not use a LIMIT to limit the number of returned rows. Filters can usually be removed by adding a (partial) index.

These are guidelines and not hard requirements, as different needs may require different queries. The only rule is that you must always measure your query (preferably using a production-like database) using EXPLAIN (ANALYZE, BUFFERS) and related tools such as:

Producing query plans

There are a few ways to get the output of a query plan. Of course you can directly run the EXPLAIN query in the psql console, or you can follow one of the other options below.

Database Lab Engine

GitLab team members can use Database Lab Engine, and the companion SQL optimization tool - Joe Bot.

Database Lab Engine provides developers with their own clone of the production database, while Joe Bot helps with exploring execution plans.

Joe Bot is available through its web interface.

With Joe Bot you can execute DDL statements (like creating indexes, tables, and columns) and get query plans for SELECT, UPDATE, and DELETE statements.

For example, to test new index on a column that is not existing on production yet, you can do the following:

Create the column: 

exec ALTER TABLE projects ADD COLUMN last_at timestamp without time zone

Create the index: 

exec CREATE INDEX index_projects_last_activity ON projects (last_activity_at) WHERE last_activity_at IS NOT NULL

Analyze the table to update its statistics: 

exec ANALYZE projects

Get the query plan: 

explain SELECT * FROM projects WHERE last_activity_at < CURRENT_DATE

Once done you can rollback your changes: 

reset

For more information about the available options, run: 

help

The web interface comes with the following execution plan visualizers included:

Tips & Tricks

The database connection is now maintained during your whole session, so you can use exec set ... for any session variables (such as enable_seqscan or work_mem). These settings are applied to all subsequent commands until you reset them. For example you can disable parallel queries with 

exec SET max_parallel_workers_per_gather = 0

Rails console

Using the activerecord-explain-analyze you can directly generate the query plan from the Rails console: 

pry(main)> require 'activerecord-explain-analyze'
=> true
pry(main)> Project.where('build_timeout > ?', 3600).explain(analyze: true)
  Project Load (1.9ms)  SELECT "projects".* FROM "projects" WHERE (build_timeout > 3600)
  ↳ (pry):12
=> EXPLAIN for: SELECT "projects".* FROM "projects" WHERE (build_timeout > 3600)
Seq Scan on public.projects  (cost=0.00..2.17 rows=1 width=742) (actual time=0.040..0.041 rows=0 loops=1)
  Output: id, name, path, description, created_at, updated_at, creator_id, namespace_id, ...
  Filter: (projects.build_timeout > 3600)
  Rows Removed by Filter: 14
  Buffers: shared hit=2
Planning time: 0.411 ms
Execution time: 0.113 ms

Further reading

A more extensive guide on understanding query plans can be found in the presentation from Dalibo.org.

The Depesz blog also has a good section dedicated to query plans.