SQL Performance Best Practices

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This page provides best practices for optimizing query performance in CockroachDB.

DML best practices

Use multi-row statements instead of multiple single-row statements

For INSERT, UPSERT, and DELETE statements, a single multi-row statement is faster than multiple single-row statements. Whenever possible, use multi-row statements for DML queries instead of multiple single-row statements.

For more information, see:

• Insert Data
• Update Data
• Delete Data
• How to improve IoT application performance with multi-row DML

Use UPSERT instead of INSERT ON CONFLICT on tables with no secondary indexes

When inserting/updating all columns of a table, and the table has no secondary indexes, we recommend using an UPSERT statement instead of the equivalent INSERT ON CONFLICT statement. Whereas INSERT ON CONFLICT always performs a read to determine the necessary writes, the UPSERT statement writes without reading, making it faster. For tables with secondary indexes, there is no performance difference between UPSERT and INSERT ON CONFLICT.

This issue is particularly relevant when using a simple SQL table of two columns to simulate direct KV access. In this case, be sure to use the UPSERT statement.

Bulk-insert best practices

Use multi-row INSERT statements for bulk-inserts into existing tables

To bulk-insert data into an existing table, batch multiple rows in one multi-row INSERT statement. Experimentally determine the optimal batch size for your application by monitoring the performance for different batch sizes (10 rows, 100 rows, 1000 rows). Do not include bulk INSERT statements within an explicit transaction.

For more information, see Insert Multiple Rows.

Use IMPORT instead of INSERT for bulk-inserts into new tables

To bulk-insert data into a brand new table, the IMPORT statement performs better than INSERT.

Bulk-update best practices

Use batch updates to delete a large number of rows

To delete a large number of rows, we recommend iteratively deleting batches of rows until all of the unwanted rows are deleted. For an example, see Bulk-update Data.

Bulk-delete best practices

Use TRUNCATE instead of DELETE to delete all rows in a table

The TRUNCATE statement removes all rows from a table by dropping the table and recreating a new table with the same name. This performs better than using DELETE, which performs multiple transactions to delete all rows.

Use batch deletes to delete a large number of rows

To delete a large number of rows, we recommend iteratively deleting batches of rows until all of the unwanted rows are deleted. For an example, see Bulk-delete Data.

Batch delete "expired" data

CockroachDB does not support Time to Live (TTL) on table rows. To delete "expired" rows, we recommend automating a batch delete process with a job scheduler like cron. For an example, see Batch-delete "expired" data.

Assign column families

A column family is a group of columns in a table that is stored as a single key-value pair in the underlying key-value store.

When a table is created, all columns are stored as a single column family. This default approach ensures efficient key-value storage and performance in most cases. However, when frequently updated columns are grouped with seldom updated columns, the seldom updated columns are nonetheless rewritten on every update. Especially when the seldom updated columns are large, it's therefore more performant to assign them to a distinct column family.

Unique ID best practices

The best practices for generating unique IDs in a distributed database like CockroachDB are very different than for a legacy single-node database. Traditional approaches for generating unique IDs for legacy single-node databases include:

1. Using the SERIAL pseudo-type for a column to generate random unique IDs. This can result in a performance bottleneck because IDs generated temporally near each other have similar values and are located physically near each other in a table's storage.
2. Generating monotonically increasing INT IDs by using transactions with roundtrip SELECTs, e.g., INSERT INTO tbl (id, …) VALUES ((SELECT max(id)+1 FROM tbl), …). This has a very high performance cost since it makes all INSERT transactions wait for their turn to insert the next ID. You should only do this if your application really does require strict ID ordering. In some cases, using Change Data Capture (CDC) can help avoid the requirement for strict ID ordering. If you can avoid the requirement for strict ID ordering, you can use one of the higher performance ID strategies outlined below.

The approaches described above are likely to create hotspots for both reads and writes in CockroachDB. To avoid this issue, we recommend the following approaches (listed in order from best to worst performance).

Use multi-column primary keys

A well-designed multi-column primary key can yield even better performance than a UUID primary key, but it requires more up-front schema design work. To get the best performance, ensure that any monotonically increasing field is located after the first column of the primary key. When done right, such a composite primary key should result in:

• Enough randomness in your primary key to spread the table data / query load relatively evenly across the cluster, which will avoid hotspots. By "enough randomness" we mean that the prefix of the primary key should be relatively uniformly distributed over its domain. Its domain should have at least as many elements as you have nodes.
• A monotonically increasing column that is part of the primary key (and thus indexed) which is also useful in your queries.

For example, consider a social media website. Social media posts are written by users, and on login the user's last 10 posts are displayed. A good choice for a primary key might be (username, post_timestamp). For example:

> CREATE TABLE posts (
    username STRING,
    post_timestamp TIMESTAMP,
    post_id INT,
    post_content STRING,
    CONSTRAINT posts_pk PRIMARY KEY(username, post_timestamp)
);

This would make the following query efficient.

> SELECT * FROM posts
          WHERE username = 'alyssa'
       ORDER BY post_timestamp DESC
          LIMIT 10;

  username |      post_timestamp       | post_id | post_content
+----------+---------------------------+---------+--------------+
  alyssa   | 2019-07-31 18:01:00+00:00 |    ...  | ...
  alyssa   | 2019-07-30 10:22:00+00:00 |    ...  | ...
  alyssa   | 2019-07-30 09:12:00+00:00 |    ...  | ...
  alyssa   | 2019-07-29 13:48:00+00:00 |    ...  | ...
  alyssa   | 2019-07-29 13:47:00+00:00 |    ...  | ...
  alyssa   | 2019-07-29 13:46:00+00:00 |    ...  | ...
  alyssa   | 2019-07-29 13:43:00+00:00 |    ...  | ...
  ...

Time: 924µs

To see why, let's look at the EXPLAIN output. It shows that the query is fast because it does a point lookup on the indexed column username (as shown by the line spans | /"alyssa"-...). Furthermore, the column post_timestamp is already in an index, and sorted (since it's a monotonically increasing part of the primary key).

> EXPLAIN (VERBOSE)
    SELECT * FROM posts
            WHERE username = 'alyssa'
         ORDER BY post_timestamp DESC
            LIMIT 10;

   tree   |        field        |          description          |                      columns                      |    ordering
----------+---------------------+-------------------------------+---------------------------------------------------+------------------
          | distribution        | local                         |                                                   |
          | vectorized          | false                         |                                                   |
  revscan |                     |                               | (username, post_timestamp, post_id, post_content) | -post_timestamp
          | estimated row count | 10 (missing stats)            |                                                   |
          | table               | posts@posts_pk                |                                                   |
          | spans               | /"alyssa"-/"alyssa"/PrefixEnd |                                                   |
          | limit               | 10                            |                                                   |
(7 rows)

Note that the above query also follows the indexing best practice of indexing all columns in the WHERE clause.

Use UUID to generate unique IDs

To auto-generate unique row identifiers, use the UUID column with the gen_random_uuid() function as the default value:

> CREATE TABLE t1 (id UUID PRIMARY KEY DEFAULT gen_random_uuid(), name STRING);

> INSERT INTO t1 (name) VALUES ('a'), ('b'), ('c');

> SELECT * FROM t1;

+--------------------------------------+------+
|                  id                  | name |
+--------------------------------------+------+
| 60853a85-681d-4620-9677-946bbfdc8fbc | c    |
| 77c9bc2e-76a5-4ebc-80c3-7ad3159466a1 | b    |
| bd3a56e1-c75e-476c-b221-0da9d74d66eb | a    |
+--------------------------------------+------+
(3 rows)

Use INSERT with the RETURNING clause to generate unique IDs

If something prevents you from using multi-column primary keys or UUIDs to generate unique IDs, you might resort to using INSERTs with SELECTs to return IDs. Instead, use the RETURNING clause with the INSERT statement as shown below for improved performance.

Generate monotonically-increasing unique IDs

Suppose the table schema is as follows:

> CREATE TABLE X (
    ID1 INT,
    ID2 INT,
    ID3 INT DEFAULT 1,
    PRIMARY KEY (ID1,ID2)
  );

The common approach would be to use a transaction with an INSERT followed by a SELECT:

> BEGIN;

> INSERT INTO X VALUES (1,1,1)
    ON CONFLICT (ID1,ID2)
    DO UPDATE SET ID3=X.ID3+1;

> SELECT * FROM X WHERE ID1=1 AND ID2=1;

> COMMIT;

However, the performance best practice is to use a RETURNING clause with INSERT instead of the transaction:

> INSERT INTO X VALUES (1,1,1),(2,2,2),(3,3,3)
    ON CONFLICT (ID1,ID2)
    DO UPDATE SET ID3=X.ID3 + 1
    RETURNING ID1,ID2,ID3;

Generate random unique IDs

Suppose the table schema is as follows:

> CREATE TABLE X (
    ID1 INT,
    ID2 INT,
    ID3 INT DEFAULT unique_rowid(),
    PRIMARY KEY (ID1,ID2)
  );

The common approach to generate random Unique IDs is a transaction using a SELECT statement:

> BEGIN;

> INSERT INTO X VALUES (1,1);

> SELECT * FROM X WHERE ID1=1 AND ID2=1;

> COMMIT;

However, the performance best practice is to use a RETURNING clause with INSERT instead of the transaction:

> INSERT INTO X VALUES (1,1),(2,2),(3,3)
    RETURNING ID1,ID2,ID3;

Secondary index best practices

See Secondary Index Best Practices.

Join best practices

See Join Performance Best Practices.

Subquery best practices

See Subquery Performance Best Practices.

Table scans best practices

Avoid SELECT * for large tables

For large tables, avoid table scans (that is, reading the entire table data) whenever possible. Instead, define the required fields in a SELECT statement.

Example

Suppose the table schema is as follows:

> CREATE TABLE accounts (
    id INT,
    customer STRING,
    address STRING,
    balance INT
    nominee STRING
    );

Now if we want to find the account balances of all customers, an inefficient table scan would be:

> SELECT * FROM ACCOUNTS;

This query retrieves all data stored in the table. A more efficient query would be:

 > SELECT CUSTOMER, BALANCE FROM ACCOUNTS;

This query returns the account balances of the customers.

Avoid SELECT DISTINCT for large tables

SELECT DISTINCT allows you to obtain unique entries from a query by removing duplicate entries. However, SELECT DISTINCT is computationally expensive. As a performance best practice, use SELECT with the WHERE clause instead.

Use AS OF SYSTEM TIME to decrease conflicts with long-running queries

If you have long-running queries (such as analytics queries that perform full table scans) that can tolerate slightly out-of-date reads, consider using the ... AS OF SYSTEM TIME clause. Using this, your query returns data as it appeared at a distinct point in the past and will not cause conflicts with other concurrent transactions, which can increase your application's performance.

However, because AS OF SYSTEM TIME returns historical data, your reads might be stale.

Understanding and avoiding transaction contention

Transaction contention occurs when the following three conditions are met:

• There are multiple concurrent transactions or statements (sent by multiple clients connected simultaneously to a single CockroachDB cluster).
• They operate on the same data, specifically over table rows with the same index key values (either on primary keys or secondary indexes) or using index key values that are close to each other, and thus place the indexed data on the same data ranges.
• At least some of the transactions write or modify the data.

A set of transactions that all contend on the same keys will be limited in performance to the maximum processing speed of a single node (limited horizontal scalability). Non-contended transactions are not affected in this way.

There are two levels of contention:

• Transactions that operate on the same range but different index key values will be limited by the overall hardware capacity of a single node (the range lease holder).

• Transactions that operate on the same index key values (specifically, that operate on the same column family for a given index key) will be more strictly serialized to obey transaction isolation semantics.

Transaction contention can also increase the rate of transaction restarts, and thus make the proper implementation of client-side transaction retries more critical.

To avoid contention, multiple strategies can be applied:

• Use index key values with a more random distribution of values, so that transactions over different rows are more likely to operate on separate data ranges. See the SQL FAQs on row IDs for suggestions.

• Make transactions smaller, so that each transaction has less work to do. In particular, avoid multiple client-server exchanges per transaction. For example, use common table expressions to group multiple SELECT and INSERT/UPDATE/DELETE/UPSERT clauses together in a single SQL statement.

  • For an example showing how to break up large transactions in an application, see Break up large transactions into smaller units of work.
  • If you are experiencing contention (retries) when doing bulk deletes, see Bulk-delete data.

• In combination with the above, if you are able to send all of the statements in your transaction in a single batch, CockroachDB can automatically retry the transaction for you.

• Use the SELECT FOR UPDATE statement in scenarios where a transaction performs a read and then updates the row(s) it just read. It orders transactions by controlling concurrent access to one or more rows of a table. It works by locking the rows returned by a selection query, such that other transactions trying to access those rows are forced to wait for the transaction that locked the rows to finish. These other transactions are effectively put into a queue that is ordered based on when they try to read the value of the locked row(s).

• When replacing values in a row, use UPSERT and specify values for all columns in the inserted rows. This will usually have the best performance under contention, compared to combinations of SELECT, INSERT, and UPDATE.

• Increase normalization of the data to place parts of the same records that are modified by different transactions in different tables. Note however that this is a double-edged sword, because denormalization can also increase performance by creating multiple copies of often-referenced data in separate ranges.

• If the application strictly requires operating on very few different index key values, consider using ALTER ... SPLIT AT so that each index key value can be served by a separate group of nodes in the cluster.

It is always best to avoid contention as much as possible via the design of the schema and application. However, sometimes contention is unavoidable. To maximize performance in the presence of contention, you'll need to maximize the performance of a single range. To achieve this, multiple strategies can be applied:

• Minimize the network distance between the replicas of a range, possibly using zone configs and partitioning.
• Use the fastest storage devices available.
• If the contending transactions operate on different keys within the same range, add more CPU power (more cores) per node. Note however that this is less likely to provide an improvement if the transactions all operate on the same key.

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