# SQL Performance Best Practices

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

Tip:

For a demonstration of some of these techniques, see Performance Tuning.

## Multi-row DML best practices

### Use multi-row DML instead of multiple single-row DMLs

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

For more information, see:

- Insert Multiple Rows
- Upsert Multiple Rows
- Delete Multiple Rows
- How to improve IoT application performance with multi-row DML

### 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.

## 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 and do not include the `INSERT` statements within a transaction. Experimentally determine the optimal batch size for your application by monitoring the performance for different batch sizes (10 rows, 100 rows, 1000 rows). 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`.

## 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.

## Interleave tables

Interleaving tables improves query performance by optimizing the key-value structure of closely related tables, attempting to keep data on the same key-value range if it's likely to be read and written together. This is particularly helpful if the tables are frequently joined on the columns that consist of the interleaving relationship.

However, the above is only true for tables where all operations (e.g., `SELECT` or `INSERT`) are performed on a single value shared between both tables. The following types of operations may actually become slower after interleaving:

- Operations that span multiple values.
- Operations that do not specify the interleaved parent ID.

This happens because when data is interleaved, queries that work on the parent table(s) will need to "skip over" the data in interleaved children, which increases the read and write latencies to the parent in proportion to the number of interleaved values.

## Unique ID best practices

A traditional approach for generating unique IDs is one of the following:

- Monotonically increase `INT` IDs by using transactions with roundtrip `SELECT`s.
- Use the `SERIAL` pseudo-type for a column to generate random unique IDs.

The first approach does not take advantage of the parallelization possible in a distributed database like CockroachDB. It also causes all new records to be inserted onto the same node.

The bottleneck with the second approach is that IDs generated temporally near each other have similar values and are located physically near each other in a table. This can cause a hotspot for reads and writes in a table.

There are two schema design patterns that can mitigate these issues.

One pattern in CockroachDB is to generate unique IDs for the primary key using the `UUID` type, which generates random unique IDs in parallel, thus improving performance. This will distribute the insert load as well as any later queries of the table, but has the disadvantage of creating a primary key that may not be useful in a query directly, and will require a join with another table or a secondary index.

An alternative pattern which requires careful planning at the schema design phase but which can yield even better performance would be to use a multi-column Primary Key that is unique, but which is also useful in a query. The trick is to ensure that any monotonically increasing field is located after the first column.

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)`. This would make the following query efficient:

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

### Use `UUID` to generate unique IDs

To auto-generate unique row IDs, 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)

Alternatively, you can use the `BYTES` column with the `uuid_v4()` function as the default value instead:

    > CREATE TABLE t2 (id BYTES PRIMARY KEY DEFAULT uuid_v4(), name STRING);

    > INSERT INTO t2 (name) VALUES ('a'), ('b'), ('c');

    > SELECT * FROM t2;

    +---------------------------------------------------+------+
    |                        id                         | name |
    +---------------------------------------------------+------+
    | "\x9b\x10\xdc\x11\x9a\x9cGB\xbd\x8d\t\x8c\xf6@vP" | a    |
    | "\xd9s\xd7\x13\n_L*\xb0\x87c\xb6d\xe1\xd8@"       | c    |
    | "\uac74\x1dd@B\x97\xac\x04N&\x9eBg\x86"           | b    |
    +---------------------------------------------------+------+
    (3 rows)

In either case, generated IDs will be 128-bit, large enough for there to be virtually no chance of generating non-unique values. Also, once the table grows beyond a single key-value range (more than 64MB by default), new IDs will be scattered across all of the table's ranges and, therefore, likely across different nodes. This means that multiple nodes will share in the load.

If it is important for generated IDs to be stored in the same key-value range, you can use an integer type with the `unique_rowid()` function as the default value, either explicitly or via the `SERIAL` pseudo-type:

    > CREATE TABLE t3 (id INT PRIMARY KEY DEFAULT unique_rowid(), name STRING);

    > INSERT INTO t3 (name) VALUES ('a'), ('b'), ('c');

    > SELECT * FROM t3;

    +--------------------+------+
    |         id         | name |
    +--------------------+------+
    | 293807573840855041 | a    |
    | 293807573840887809 | b    |
    | 293807573840920577 | c    |
    +--------------------+------+
    (3 rows)

Upon insert or upsert, the `unique_rowid()` function generates a default value from the timestamp and ID of the node executing the insert. Such time-ordered values are likely to be globally unique except in cases where a very large number of IDs (100,000+) are generated per node per second. Also, there can be gaps and the order is not completely guaranteed.

### Use `INSERT` with the `RETURNING` clause to generate unique IDs

If something prevents you from using `UUID` to generate unique IDs, you might resort to using `INSERT`s with `SELECT`s to return IDs. Instead, use the `RETURNING` clause with the `INSERT` statement 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;

## Indexes best practices

### Use secondary indexes

You can use secondary indexes to improve the performance of queries using columns not in a table's primary key. You can create them:

- At the same time as the table with the `INDEX` clause of `CREATE TABLE`. In addition to explicitly defined indexes, CockroachDB automatically creates secondary indexes for columns with the `UNIQUE` constraint.
- For existing tables with `CREATE INDEX`.
- By applying the `UNIQUE` constraint to columns with `ALTER TABLE`, which automatically creates an index of the constrained columns.

To create the most useful secondary indexes, check out our best practices.

### Use indexes for faster joins

See Join Performance Best Practices.

### Drop unused indexes

Though indexes improve read performance, they incur an overhead for every write. In some cases, like the use cases discussed above, the tradeoff is worth it. However, if an index is unused, it slows down DML operations. Therefore, drop unused indexes whenever possible.

## 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 via interleaving) 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.

- 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.