gitlab-org--gitlab-foss/doc/development/iterating_tables_in_batches.md

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Iterating Tables In Batches

Rails provides a method called in_batches that can be used to iterate over rows in batches. For example:

User.in_batches(of: 10) do |relation|
  relation.update_all(updated_at: Time.now)
end

Unfortunately this method is implemented in a way that is not very efficient, both query and memory usage wise.

To work around this you can include the EachBatch module into your models, then use the each_batch class method. For example:

class User < ActiveRecord::Base
  include EachBatch
end

User.each_batch(of: 10) do |relation|
  relation.update_all(updated_at: Time.now)
end

This will end up producing queries such as:

User Load (0.7ms)  SELECT  "users"."id" FROM "users" WHERE ("users"."id" >= 41654)  ORDER BY "users"."id" ASC LIMIT 1 OFFSET 1000
  (0.7ms)  SELECT COUNT(*) FROM "users" WHERE ("users"."id" >= 41654) AND ("users"."id" < 42687)

The API of this method is similar to in_batches, though it doesn't support all of the arguments that in_batches supports. You should always use each_batch unless you have a specific need for in_batches.

Column definition

EachBatch uses the primary key of the model by default for the iteration. This works most of the cases, however in some cases, you might want to use a different column for the iteration.

Project.distinct.each_batch(column: :creator_id, of: 10) do |relation|
  puts User.where(id: relation.select(:creator_id)).map(&:id)
end

The query above iterates over the project creators and prints them out without duplications.

NOTE: In case the column is not unique (no unique index definition), calling the distinct method on the relation is necessary.

EachBatch in data migrations

When dealing with data migrations the preferred way to iterate over large volume of data is using EachBatch.

A special case of data migration is a background migration where the actual data modification is executed in a background job. The migration code that determines the data ranges (slices) and schedules the background jobs uses each_batch.

Efficient usage of each_batch

EachBatch helps iterating over large tables. It's important to highlight that EachBatch is not going to magically solve all iteration related performance problems and it might not help at all in some scenarios. From the database point of view, correctly configured database indexes are also necessary to make EachBatch perform well.

Example 1: Simple iteration

Let's consider that we want to iterate over the users table and print the User records to the standard output. The users table contains millions of records, thus running one query to fetch the users will likely time out.

This is a simplified version of the users table which contains several rows. We have a few smaller gaps in the id column to make the example a bit more realistic (a few records were already deleted). Currently we have one index on the id field.

Loading all users into memory (avoid):

users = User.all

users.each { |user| puts user.inspect }

Use each_batch:

# Note: for this example I picked 5 as the batch size, the default is 1_000
User.each_batch(of: 5) do |relation|
  relation.each { |user| puts user.inspect }
end

How does each_batch work?

As the first step, it finds the lowest id (start id) in the table by executing the following database query:

SELECT "users"."id" FROM "users" ORDER BY "users"."id" ASC LIMIT 1

Notice that the query only reads data from the index (INDEX ONLY SCAN), the table is not accessed. Database indexes are sorted so taking out the first item is a very cheap operation.

The next step is to find the next id (end id) which should respect the batch size configuration. In this example we used batch size of 5. EachBatch uses the OFFSET clause to get a "shifted" id value.

SELECT "users"."id" FROM "users" WHERE "users"."id" >= 1 ORDER BY "users"."id" ASC LIMIT 1 OFFSET 5

Again, the query only looks into the index. The OFFSET 5 takes out the sixth id value: this query reads a maximum of six items from the index regardless of the table size or the iteration count.

At this point we know the id range for the first batch. Now it's time to construct the query for the relation block.

SELECT "users".* FROM "users" WHERE "users"."id" >= 1 AND "users"."id" < 302

Notice the < sign. Previously six items were read from the index and in this query the last value is "excluded". The query will look at the index to get the location of the five user rows on the disk and read the rows from the table. The returned array is processed in Ruby.

The first iteration is done. For the next iteration, the last id value is reused from the previous iteration in order to find out the next end id value.

SELECT "users"."id" FROM "users" WHERE "users"."id" >= 302 ORDER BY "users"."id" ASC LIMIT 1 OFFSET 5

Now we can easily construct the users query for the second iteration.

SELECT "users".* FROM "users" WHERE "users"."id" >= 302 AND "users"."id" < 353

Example 2: Iteration with filters

Building on top of the previous example, we want to print users with zero sign-in count. We keep track of the number of sign-ins in the sign_in_count column so we write the following code:

users = User.where(sign_in_count: 0)

users.each_batch(of: 5) do |relation|
  relation.each { |user| puts user.inspect }
end

each_batch will produce the following SQL query for the start id value:

SELECT "users"."id" FROM "users" WHERE "users"."sign_in_count" = 0 ORDER BY "users"."id" ASC LIMIT 1

Selecting only the id column and ordering by id is going to "force" the database to use the index on the id (primary key index) column, however we also have an extra condition on the sign_in_count column. The column is not part of the index, so the database needs to look into the actual table to find the first matching row.

NOTE: The number of scanned rows depends on the data distribution in the table.

• Best case scenario: the first user was never logged in. The database reads only one row.
• Worst case scenario: all users were logged in at least once. The database reads all rows.

In this particular example the database had to read 10 rows (regardless of our batch size setting) to determine the first id value. In a "real-world" application it's hard to predict whether the filtering is going to cause problems or not. In case of GitLab, verifying the data on a production replica is a good start, but keep in mind that data distribution on GitLab.com can be different from self-managed instances.

Improve filtering with each_batch

Specialized conditional index

CREATE INDEX index_on_users_never_logged_in ON users (id) WHERE sign_in_count = 0

This is how our table and the newly created index looks like:

This index definition covers the conditions on the id and sign_in_count columns thus makes the each_batch queries very effective (similar to the simple iteration example).

It's rare when a user was never signed in so we anticipate small index size. Including only the id in the index definition also helps keeping the index size small.

Index on columns

Later on we might want to iterate over the table filtering for different sign_in_count values, in those cases we cannot use the previously suggested conditional index because the WHERE condition does not match with our new filter (sign_in_count > 10).

To address this problem, we have two options:

• Create another, conditional index to cover the new query.
• Replace the index with more generalized configuration.

NOTE: Having multiple indexes on the same table and on the same columns could be a performance bottleneck when writing data.

Let's consider the following index (avoid):

CREATE INDEX index_on_users_never_logged_in ON users (id, sign_in_count)

The index definition starts with the id column which makes the index very inefficient from data selectivity point of view.

SELECT "users"."id" FROM "users" WHERE "users"."sign_in_count" = 0 ORDER BY "users"."id" ASC LIMIT 1

Executing the query above results in an INDEX ONLY SCAN. However, the query still needs to iterate over unknown number of entries in the index, and then find the first item where the sign_in_count is 0.

We can improve the query significantly by swapping the columns in the index definition (prefer).

CREATE INDEX index_on_users_never_logged_in ON users (sign_in_count, id)

The following index definition is not going to work well with each_batch (avoid).

CREATE INDEX index_on_users_never_logged_in ON users (sign_in_count)

Since each_batch builds range queries based on the id column, this index cannot be used efficiently. The DB reads the rows from the table or uses a bitmap search where the primary key index is also read.

"Slow" iteration

Slow iteration means that we use a good index configuration to iterate over the table and apply filtering on the yielded relation.

User.each_batch(of: 5) do |relation|
  relation.where(sign_in_count: 0).each { |user| puts user inspect }
end

The iteration uses the primary key index (on the id column) which makes it safe from statement timeouts. The filter (sign_in_count: 0) is applied on the relation where the id is already constrained (range). The number of rows are limited.

Slow iteration generally takes more time to finish. The iteration count is higher and one iteration could yield fewer records than the batch size. Iterations may even yield 0 records. This is not an optimal solution; however, in some cases (especially when dealing with large tables) this is the only viable option.

Using Subqueries

Using subqueries in your each_batch query does not work well in most cases. Consider the following example:

projects = Project.where(creator_id: Issue.where(confidential: true).select(:author_id))

projects.each_batch do |relation|
  # do something
end

The iteration uses the id column of the projects table. The batching does not affect the subquery. This means for each iteration, the subquery is executed by the database. This adds a constant "load" on the query which often ends up in statement timeouts. We have an unknown number of confidential issues, the execution time and the accessed database rows depends on the data distribution in the issues table.

NOTE: Using subqueries works only when the subquery returns a small number of rows.

Improving Subqueries

When dealing with subqueries, a slow iteration approach could work: the filter on creator_id can be part of the generated relation object.

projects = Project.all

projects.each_batch do |relation|
  relation.where(creator_id: Issue.where(confidential: true).select(:author_id))
end

If the query on the issues table itself is not performant enough, a nested loop could be constructed. Try to avoid it when possible.

projects = Project.all

projects.each_batch do |relation|
  issues = Issue.where(confidential: true)

  issues.each_batch do |issues_relation|
    relation.where(creator_id: issues_relation.select(:author_id))
  end
end

If we know that the issues table has many more rows than projects, it would make sense to flip the queries, where the issues table is batched first.

Using JOIN and EXISTS

When to use JOINS:

• When there's a 1:1 or 1:N relationship between the tables where we know that the joined record (almost) always exists. This works well for "extension-like" tables:
  • projects - project_settings
  • users - user_details
  • users - user_statuses
• LEFT JOIN works well in this case. Conditions on the joined table need to go to the yielded relation so the iteration is not affected by the data distribution in the joined table.

Example:

users = User.joins("LEFT JOIN personal_access_tokens on personal_access_tokens.user_id = users.id")

users.each_batch do |relation|
  relation.where("personal_access_tokens.name = 'name'")
end

EXISTS queries should be added only to the inner relation of the each_batch query:

User.each_batch do |relation|
  relation.where("EXISTS (SELECT 1 FROM ...")
end

Complex queries on the relation object

When the relation object has several extra conditions, the execution plans might become "unstable".

Example:

Issue.each_batch do |relation|
  relation
    .joins(:metrics)
    .joins(:merge_requests_closing_issues)
    .where("id IN (SELECT ...)")
    .where(confidential: true)
end

Here, we expect that the relation query reads the BATCH_SIZE of user records and then filters down the results according to the provided queries. The planner might decide that using a bitmap index lookup with the index on the confidential column is a better way to execute the query. This can cause unexpectedly high amount of rows to be read and the query could time out.

Problem: we know for sure that the relation is returning maximum BATCH_SIZE of records, however the planner does not know this.

Common table expression (CTE) trick to force the range query to execute first:

Issue.each_batch(of: 1000) do |relation|
  cte = Gitlab::SQL::CTE.new(:batched_relation, relation.limit(1000))

  scope = cte
    .apply_to(Issue.all)
    .joins(:metrics)
    .joins(:merge_requests_closing_issues)
    .where("id IN (SELECT ...)")
    .where(confidential: true)

  puts scope.to_a
end

EachBatch vs BatchCount

When adding new counters for usage ping, the preferred way to count records is using the Gitlab::Database::BatchCount class. The iteration logic implemented in BatchCount has similar performance characteristics like EachBatch. Most of the tips and suggestions for improving BatchCount mentioned above applies to BatchCount as well.