CREATE TABLE

On this page

The CREATE TABLE statement creates a new table in a database.

Note:

This statement performs a schema change. For more information about how online schema changes work in CockroachDB, see Online Schema Changes.

Required privileges

The user must have the CREATE privilege on the parent database.

Synopsis

opt_temp_create_table ::=

column_def ::=

col_qualification ::=

index_def ::=

family_def ::=

table_constraint ::=

opt_interleave ::=

Tip:

To create a table from the results of a SELECT statement, use CREATE TABLE AS.

Parameters

Parameter	Description
`IF NOT EXISTS`	Create a new table only if a table of the same name does not already exist in the database; if one does exist, do not return an error. Note that `IF NOT EXISTS` checks the table name only; it does not check if an existing table has the same columns, indexes, constraints, etc., of the new table.
`table_name`	The name of the table to create, which must be unique within its database and follow these identifier rules. When the parent database is not set as the default, the name must be formatted as `database.name`. The `UPSERT` and `INSERT ON CONFLICT` statements use a temporary table called `excluded` to handle uniqueness conflicts during execution. It's therefore not recommended to use the name `excluded` for any of your tables.
`column_def`	A comma-separated list of column definitions. Each column requires a name/identifier and data type; optionally, a column-level constraint or other column qualification (e.g., computed columns) can be specified. Column names must be unique within the table but can have the same name as indexes or constraints. Any `PRIMARY KEY`, `UNIQUE`, and `CHECK` constraints defined at the column level are moved to the table-level as part of the table's creation. Use the `SHOW CREATE` statement to view them at the table level.
`index_def`	An optional, comma-separated list of index definitions. For each index, the column(s) to index must be specified; optionally, a name can be specified. Index names must be unique within the table and follow these identifier rules. See the Create a Table with Secondary Indexes and Inverted Indexes example below. New in v20.1: To enable hash-sharded indexes, set the `experimental_enable_hash_sharded_indexes` session variable to `on`. For examples, see Create a table with hash-sharded indexes below. The `CREATE INDEX` statement can be used to create an index separate from table creation.
`family_def`	An optional, comma-separated list of column family definitions. Column family names must be unique within the table but can have the same name as columns, constraints, or indexes. A column family is a group of columns that are stored as a single key-value pair in the underlying key-value store. CockroachDB automatically groups columns into families to ensure efficient storage and performance. However, there are cases when you may want to manually assign columns to families. For more details, see Column Families.
`table_constraint`	An optional, comma-separated list of table-level constraints. Constraint names must be unique within the table but can have the same name as columns, column families, or indexes.
`opt_interleave`	You can potentially optimize query performance by interleaving tables, which changes how CockroachDB stores your data. Note: Hash-sharded indexes cannot be interleaved.
`opt_partition_by`	An enterprise-only option that lets you define table partitions at the row level. You can define table partitions by list or by range. See Define Table Partitions for more information.
`opt_temp_create_table`	New in v20.1: Defines the table as a session-scoped temporary table. For more information, see Temporary Tables. Support for temporary tables is experimental.

Table-level replication

By default, tables are created in the default replication zone but can be placed into a specific replication zone. See Create a Replication Zone for a Table for more information.

Row-level replication

CockroachDB allows enterprise users to define table partitions, thus providing row-level control of how and where the data is stored. See Create a Replication Zone for a Table Partition for more information.

Note:

The primary key required for partitioning is different from the conventional primary key. To define the primary key for partitioning, prefix the unique identifier(s) in the primary key with all columns you want to partition and subpartition the table on, in the order in which you want to nest your subpartitions. See Partition using Primary Key for more details.

Examples

Create a table

In this example, we create the users table with a single primary key column defined. In CockroachDB, every table requires a primary key. If one is not explicitly defined, a column called rowid of the type INT is added automatically as the primary key, with the unique_rowid() function used to ensure that new rows always default to unique rowid values. The primary key is automatically indexed.

For performance recommendations on primary keys, see the Primary Key Constraint page and the SQL Performance Best Practices page.

Note:

New in v20.1: If no primary key is explicitly defined in a CREATE TABLE statement, you can add a primary key to the table with ADD CONSTRAINT ... PRIMARY KEY or ALTER PRIMARY KEY. If the ADD or ALTER statement follows the CREATE TABLE statement, and is part of the same transaction, no default primary key will be created. If the table has already been created and the transaction committed, the ADD or ALTER statements replace the default primary key.

Note:

Strictly speaking, a primary key's unique index is not created; it is derived from the key(s) under which the data is stored, so it takes no additional space. However, it appears as a normal unique index when using commands like SHOW INDEX.

> CREATE TABLE users (
        id UUID PRIMARY KEY,
        city STRING,
        name STRING,
        address STRING,
        credit_card STRING,
        dl STRING
);

> SHOW COLUMNS FROM users;

  column_name | data_type | is_nullable | column_default | generation_expression |  indices  | is_hidden
+-------------+-----------+-------------+----------------+-----------------------+-----------+-----------+
  id          | UUID      |    false    | NULL           |                       | {primary} |   false
  city        | STRING    |    true     | NULL           |                       | {}        |   false
  name        | STRING    |    true     | NULL           |                       | {}        |   false
  address     | STRING    |    true     | NULL           |                       | {}        |   false
  credit_card | STRING    |    true     | NULL           |                       | {}        |   false
  dl          | STRING    |    true     | NULL           |                       | {}        |   false
(6 rows)

> SHOW INDEX FROM users;

  table_name | index_name | non_unique | seq_in_index | column_name | direction | storing | implicit
+------------+------------+------------+--------------+-------------+-----------+---------+----------+
  users      | primary    |   false    |            1 | id          | ASC       |  false  |  false
(1 row)

Create a table with secondary and inverted indexes

In this example, we create secondary and inverted indexes during table creation. Secondary indexes allow efficient access to data with keys other than the primary key. Inverted indexes allow efficient access to the schemaless data in a JSONB column.

> CREATE TABLE vehicles (
        id UUID NOT NULL,
        city STRING NOT NULL,
        type STRING,
        owner_id UUID,
        creation_time TIMESTAMP,
        status STRING,
        current_location STRING,
        ext JSONB,
        CONSTRAINT "primary" PRIMARY KEY (city ASC, id ASC),
        INDEX index_status (status),
        INVERTED INDEX ix_vehicle_ext (ext),
        FAMILY "primary" (id, city, type, owner_id, creation_time, status, current_location, ext)
);

> SHOW INDEX FROM vehicles;

  table_name |   index_name   | non_unique | seq_in_index | column_name | direction | storing | implicit
-------------+----------------+------------+--------------+-------------+-----------+---------+-----------
  vehicles   | primary        |   false    |            1 | city        | ASC       |  false  |  false
  vehicles   | primary        |   false    |            2 | id          | ASC       |  false  |  false
  vehicles   | index_status   |    true    |            1 | status      | ASC       |  false  |  false
  vehicles   | index_status   |    true    |            2 | city        | ASC       |  false  |   true
  vehicles   | index_status   |    true    |            3 | id          | ASC       |  false  |   true
  vehicles   | ix_vehicle_ext |    true    |            1 | ext         | ASC       |  false  |  false
  vehicles   | ix_vehicle_ext |    true    |            2 | city        | ASC       |  false  |   true
  vehicles   | ix_vehicle_ext |    true    |            3 | id          | ASC       |  false  |   true
(8 rows)

We also have other resources on indexes:

Create indexes for existing tables using CREATE INDEX.
Learn more about indexes.

Create a table with auto-generated unique row IDs

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

> CREATE TABLE users (
        id UUID NOT NULL DEFAULT gen_random_uuid(),
        city STRING NOT NULL,
        name STRING NULL,
        address STRING NULL,
        credit_card STRING NULL,
        CONSTRAINT "primary" PRIMARY KEY (city ASC, id ASC),
        FAMILY "primary" (id, city, name, address, credit_card)
);

> INSERT INTO users (name, city) VALUES ('Petee', 'new york'), ('Eric', 'seattle'), ('Dan', 'seattle');

> SELECT * FROM users;

                   id                  |   city   | name  | address | credit_card
+--------------------------------------+----------+-------+---------+-------------+
  cf8ee4e2-cd74-449a-b6e6-a0fb2017baa4 | new york | Petee | NULL    | NULL
  2382564e-702f-42d9-a139-b6df535ae00a | seattle  | Eric  | NULL    | NULL
  7d27e40b-263a-4891-b29b-d59135e55650 | seattle  | Dan   | NULL    | NULL
(3 rows)

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

> CREATE TABLE users2 (
        id BYTES DEFAULT uuid_v4(),
        city STRING NOT NULL,
        name STRING NULL,
        address STRING NULL,
        credit_card STRING NULL,
        CONSTRAINT "primary" PRIMARY KEY (city ASC, id ASC),
        FAMILY "primary" (id, city, name, address, credit_card)
);

> INSERT INTO users2 (name, city) VALUES ('Anna', 'new york'), ('Jonah', 'seattle'), ('Terry', 'chicago');

> SELECT * FROM users;

                        id                       |   city   | name  | address | credit_card
+------------------------------------------------+----------+-------+---------+-------------+
  4\244\277\323/\261M\007\213\275*\0060\346\025z | chicago  | Terry | NULL    | NULL
  \273*t=u.F\010\274f/}\313\332\373a             | new york | Anna  | NULL    | NULL
  \004\\\364nP\024L)\252\364\222r$\274O0         | seattle  | Jonah | NULL    | NULL
(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 512 MiB 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.

This approach has the disadvantage of creating a primary key that may not be useful in a query directly, which can require a join with another table or a secondary index.

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 users3 (
        id INT DEFAULT unique_rowid(),
        city STRING NOT NULL,
        name STRING NULL,
        address STRING NULL,
        credit_card STRING NULL,
        CONSTRAINT "primary" PRIMARY KEY (city ASC, id ASC),
        FAMILY "primary" (id, city, name, address, credit_card)
);

> INSERT INTO users3 (name, city) VALUES ('Blake', 'chicago'), ('Hannah', 'seattle'), ('Bobby', 'seattle');

> SELECT * FROM users3;

          id         |  city   |  name  | address | credit_card
+--------------------+---------+--------+---------+-------------+
  469048192112197633 | chicago | Blake  | NULL    | NULL
  469048192112263169 | seattle | Hannah | NULL    | NULL
  469048192112295937 | seattle | Bobby  | NULL    | NULL
(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.

Create a table with a foreign key constraint

Foreign key constraints guarantee a column uses only values that already exist in the column it references, which must be from another table. This constraint enforces referential integrity between the two tables.

There are a number of rules that govern foreign keys, but the two most important are:

Foreign key columns must be indexed. If no index is defined in the CREATE TABLE statement using INDEX, PRIMARY KEY, or UNIQUE, a secondary index is automatically created on the foreign key columns.
Referenced columns must contain only unique values. This means the REFERENCES clause must use exactly the same columns as a primary key or unique constraint.

You can include a foreign key action to specify what happens when a column referenced by a foreign key constraint is updated or deleted. The default actions are ON UPDATE NO ACTION and ON DELETE NO ACTION.

In this example, we use ON DELETE CASCADE (i.e., when row referenced by a foreign key constraint is deleted, all dependent rows are also deleted).

> CREATE TABLE users (
        id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
        city STRING,
        name STRING,
        address STRING,
        credit_card STRING,
        dl STRING UNIQUE CHECK (LENGTH(dl) < 8)
);

> CREATE TABLE vehicles (
        id UUID NOT NULL DEFAULT gen_random_uuid(),
        city STRING NOT NULL,
        type STRING,
        owner_id UUID REFERENCES users(id) ON DELETE CASCADE,
        creation_time TIMESTAMP,
        status STRING,
        current_location STRING,
        ext JSONB,
        CONSTRAINT "primary" PRIMARY KEY (city ASC, id ASC),
        INDEX vehicles_auto_index_fk_city_ref_users (city ASC, owner_id ASC),
        INVERTED INDEX ix_vehicle_ext (ext),
        FAMILY "primary" (id, city, type, owner_id, creation_time, status, current_location, ext)
);

> SHOW CREATE TABLE vehicles;

  table_name |                                          create_statement
+------------+-----------------------------------------------------------------------------------------------------+
  vehicles   | CREATE TABLE vehicles (
             |     id UUID NOT NULL DEFAULT gen_random_uuid(),
             |     city STRING NOT NULL,
             |     type STRING NULL,
             |     owner_id UUID NULL,
             |     creation_time TIMESTAMP NULL,
             |     status STRING NULL,
             |     current_location STRING NULL,
             |     ext JSONB NULL,
             |     CONSTRAINT "primary" PRIMARY KEY (city ASC, id ASC),
             |     INDEX vehicles_auto_index_fk_city_ref_users (city ASC, owner_id ASC),
             |     INVERTED INDEX ix_vehicle_ext (ext),
             |     CONSTRAINT fk_owner_id_ref_users FOREIGN KEY (owner_id) REFERENCES users(id) ON DELETE CASCADE,
             |     INDEX vehicles_auto_index_fk_owner_id_ref_users (owner_id ASC),
             |     FAMILY "primary" (id, city, type, owner_id, creation_time, status, current_location, ext)
             | )
(1 row)

> INSERT INTO users (name, dl) VALUES ('Annika', 'ABC-123');

> SELECT * FROM users;

                   id                  | city |  name  | address | credit_card |   dl
+--------------------------------------+------+--------+---------+-------------+---------+
  26da1fce-59e1-4290-a786-9068242dd195 | NULL | Annika | NULL    | NULL        | ABC-123
(1 row)

> INSERT INTO vehicles (city, owner_id) VALUES ('seattle', '26da1fce-59e1-4290-a786-9068242dd195');

> SELECT * FROM vehicles;

                   id                  |  city   | type |               owner_id               | creation_time | status | current_location | ext
+--------------------------------------+---------+------+--------------------------------------+---------------+--------+------------------+------+
  fc6f7a8c-4ba9-42e1-9c37-7be3c906050c | seattle | NULL | 26da1fce-59e1-4290-a786-9068242dd195 | NULL          | NULL   | NULL             | NULL
(1 row)

> DELETE FROM users WHERE id = '26da1fce-59e1-4290-a786-9068242dd195';

> SELECT * FROM vehicles;

  id | city | type | owner_id | creation_time | status | current_location | ext
+----+------+------+----------+---------------+--------+------------------+-----+
(0 rows)

Create a table with a check constraint

In this example, we create the users table, but with some column constraints. One column is the primary key, and another column is given a unique constraint and a check constraint that limits the length of the string. Primary key columns and columns with unique constraints are automatically indexed.

> CREATE TABLE users (
        id UUID PRIMARY KEY,
        city STRING,
        name STRING,
        address STRING,
        credit_card STRING,
        dl STRING UNIQUE CHECK (LENGTH(dl) < 8)
);

> SHOW COLUMNS FROM users;

  column_name | data_type | is_nullable | column_default | generation_expression |        indices         | is_hidden
+-------------+-----------+-------------+----------------+-----------------------+------------------------+-----------+
  id          | UUID      |    false    | NULL           |                       | {primary,users_dl_key} |   false
  city        | STRING    |    true     | NULL           |                       | {}                     |   false
  name        | STRING    |    true     | NULL           |                       | {}                     |   false
  address     | STRING    |    true     | NULL           |                       | {}                     |   false
  credit_card | STRING    |    true     | NULL           |                       | {}                     |   false
  dl          | STRING    |    true     | NULL           |                       | {users_dl_key}         |   false
(6 rows)

> SHOW INDEX FROM users;

  table_name |  index_name  | non_unique | seq_in_index | column_name | direction | storing | implicit
+------------+--------------+------------+--------------+-------------+-----------+---------+----------+
  users      | primary      |   false    |            1 | id          | ASC       |  false  |  false
  users      | users_dl_key |   false    |            1 | dl          | ASC       |  false  |  false
  users      | users_dl_key |   false    |            2 | id          | ASC       |  false  |   true
(3 rows)

Create a table that mirrors key-value storage

CockroachDB is a distributed SQL database built on a transactional and strongly-consistent key-value store. Although it is not possible to access the key-value store directly, you can mirror direct access using a "simple" table of two columns, with one set as the primary key:

> CREATE TABLE kv (k INT PRIMARY KEY, v BYTES);

When such a "simple" table has no indexes or foreign keys, INSERT/UPSERT/UPDATE/DELETE statements translate to key-value operations with minimal overhead (single digit percent slowdowns). For example, the following UPSERT to add or replace a row in the table would translate into a single key-value Put operation:

> UPSERT INTO kv VALUES (1, b'hello')

This SQL table approach also offers you a well-defined query language, a known transaction model, and the flexibility to add more columns to the table if the need arises.

Create a table from a `SELECT` statement

You can use the CREATE TABLE AS statement to create a new table from the results of a SELECT statement. For example, suppose you have a number of rows of user data in the users table, and you want to create a new table from the subset of users that are located in New York.

> SELECT * FROM users WHERE city = 'new york';

                   id                  |   city   |       name       |           address           | credit_card
+--------------------------------------+----------+------------------+-----------------------------+-------------+
  00000000-0000-4000-8000-000000000000 | new york | Robert Murphy    | 99176 Anderson Mills        | 8885705228
  051eb851-eb85-4ec0-8000-000000000001 | new york | James Hamilton   | 73488 Sydney Ports Suite 57 | 8340905892
  0a3d70a3-d70a-4d80-8000-000000000002 | new york | Judy White       | 18580 Rosario Ville Apt. 61 | 2597958636
  0f5c28f5-c28f-4c00-8000-000000000003 | new york | Devin Jordan     | 81127 Angela Ferry Apt. 8   | 5614075234
  147ae147-ae14-4b00-8000-000000000004 | new york | Catherine Nelson | 1149 Lee Alley              | 0792553487
(5 rows)

> CREATE TABLE users_ny AS SELECT * FROM users WHERE city = 'new york';

> SELECT * FROM users_ny;

                   id                  |   city   |       name       |           address           | credit_card
+--------------------------------------+----------+------------------+-----------------------------+-------------+
  00000000-0000-4000-8000-000000000000 | new york | Robert Murphy    | 99176 Anderson Mills        | 8885705228
  051eb851-eb85-4ec0-8000-000000000001 | new york | James Hamilton   | 73488 Sydney Ports Suite 57 | 8340905892
  0a3d70a3-d70a-4d80-8000-000000000002 | new york | Judy White       | 18580 Rosario Ville Apt. 61 | 2597958636
  0f5c28f5-c28f-4c00-8000-000000000003 | new york | Devin Jordan     | 81127 Angela Ferry Apt. 8   | 5614075234
  147ae147-ae14-4b00-8000-000000000004 | new york | Catherine Nelson | 1149 Lee Alley              | 0792553487
(5 rows)

Create a table with a computed column

In this example, let's create a simple table with a computed column:

> CREATE TABLE users (
        id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
        city STRING,
        first_name STRING,
        last_name STRING,
        full_name STRING AS (CONCAT(first_name, ' ', last_name)) STORED,
        address STRING,
        credit_card STRING,
        dl STRING UNIQUE CHECK (LENGTH(dl) < 8)
);

Then, insert a few rows of data:

> INSERT INTO users (first_name, last_name) VALUES
    ('Lola', 'McDog'),
    ('Carl', 'Kimball'),
    ('Ernie', 'Narayan');

> SELECT * FROM users;

                   id                  | city | first_name | last_name |   full_name   | address | credit_card |  dl
+--------------------------------------+------+------------+-----------+---------------+---------+-------------+------+
  5740da29-cc0c-47af-921c-b275d21d4c76 | NULL | Ernie      | Narayan   | Ernie Narayan | NULL    | NULL        | NULL
  e7e0b748-9194-4d71-9343-cd65218848f0 | NULL | Lola       | McDog     | Lola McDog    | NULL    | NULL        | NULL
  f00e4715-8ca7-4d5a-8de5-ef1d5d8092f3 | NULL | Carl       | Kimball   | Carl Kimball  | NULL    | NULL        | NULL
(3 rows)

The full_name column is computed from the first_name and last_name columns without the need to define a view.

Create a table with partitions

Note:

Create a table with partitions by list

In this example, we create a table and define partitions by list.

> CREATE TABLE rides (
        id UUID NOT NULL,
        city STRING NOT NULL,
        vehicle_city STRING,
        rider_id UUID,
        vehicle_id UUID,
        start_address STRING,
        end_address STRING,
        start_time TIMESTAMP,
        end_time TIMESTAMP,
        revenue DECIMAL(10,2),
        CONSTRAINT "primary" PRIMARY KEY (city ASC, id ASC),
        INDEX rides_auto_index_fk_city_ref_users (city ASC, rider_id ASC),
        INDEX rides_auto_index_fk_vehicle_city_ref_vehicles (vehicle_city ASC, vehicle_id ASC),
        FAMILY "primary" (id, city, vehicle_city, rider_id, vehicle_id, start_address, end_address, start_time, end_time, revenue),
        CONSTRAINT check_vehicle_city_city CHECK (vehicle_city = city))
        PARTITION BY LIST (city)
          (PARTITION new_york VALUES IN ('new york'),
          PARTITION chicago VALUES IN ('chicago'),
          PARTITION seattle VALUES IN ('seattle'));

Create a table with partitions by range

In this example, we create a table and define partitions by range.

> CREATE TABLE rides (
        id UUID NOT NULL,
        city STRING NOT NULL,
        vehicle_city STRING,
        rider_id UUID,
        vehicle_id UUID,
        start_address STRING,
        end_address STRING,
        start_time TIMESTAMP,
        end_time TIMESTAMP,
        ride_length INTERVAL as (start_time - end_time) STORED,
        revenue DECIMAL(10,2),
        CONSTRAINT "primary" PRIMARY KEY (ride_length ASC, city ASC, id ASC),
        INDEX rides_auto_index_fk_city_ref_users (city ASC, rider_id ASC),
        INDEX rides_auto_index_fk_vehicle_city_ref_vehicles (vehicle_city ASC, vehicle_id ASC),
        FAMILY "primary" (id, city, vehicle_city, rider_id, vehicle_id, start_address, end_address, start_time, end_time, revenue),
        CONSTRAINT check_vehicle_city_city CHECK (vehicle_city = city))
        PARTITION BY RANGE (ride_length)
          (PARTITION short_rides VALUES FROM ('0 seconds') TO ('30 minutes'),
          PARTITION long_rides VALUES FROM ('30 minutes') TO (MAXVALUE));

Show the definition of a table

To show the definition of a table, use the SHOW CREATE statement. The contents of the create_statement column in the response is a string with embedded line breaks that, when echoed, produces formatted output.

> SHOW CREATE rides;

  table_name |                                                               create_statement
+------------+----------------------------------------------------------------------------------------------------------------------------------------------+
  rides      | CREATE TABLE rides (
             |     id UUID NOT NULL,
             |     city STRING NOT NULL,
             |     vehicle_city STRING NULL,
             |     rider_id UUID NULL,
             |     vehicle_id UUID NULL,
             |     start_address STRING NULL,
             |     end_address STRING NULL,
             |     start_time TIMESTAMP NULL,
             |     end_time TIMESTAMP NULL,
             |     ride_length INTERVAL NOT NULL AS (start_time - end_time) STORED,
             |     revenue DECIMAL(10,2) NULL,
             |     CONSTRAINT "primary" PRIMARY KEY (ride_length ASC, city ASC, id ASC),
             |     INDEX rides_auto_index_fk_city_ref_users (city ASC, rider_id ASC),
             |     INDEX rides_auto_index_fk_vehicle_city_ref_vehicles (vehicle_city ASC, vehicle_id ASC),
             |     FAMILY "primary" (id, city, vehicle_city, rider_id, vehicle_id, start_address, end_address, start_time, end_time, revenue, ride_length),
             |     CONSTRAINT check_vehicle_city_city CHECK (vehicle_city = city)
             | ) PARTITION BY RANGE (ride_length) (
             |     PARTITION short_rides VALUES FROM ('00:00:00') TO ('00:30:00'),
             |     PARTITION long_rides VALUES FROM ('00:30:00') TO (MAXVALUE)
             | )
(1 row)

Create a table with a hash-sharded primary index

For performance reasons, we discourage indexing on sequential keys. If, however, you are working with a table that must be indexed on sequential keys, you should use hash-sharded indexes. Hash-sharded indexes distribute sequential traffic uniformly across ranges, eliminating single-range hotspots and improving write performance on sequentially-keyed indexes at a small cost to read performance.

> SET experimental_enable_hash_sharded_indexes=on;

> CREATE TABLE events (
    ts DECIMAL PRIMARY KEY USING HASH WITH BUCKET_COUNT=8,
    product_id INT8
    );

> SHOW INDEX FROM events;

        column_name        | data_type | is_nullable | column_default |       generation_expression       |  indices  | is_hidden
---------------------------+-----------+-------------+----------------+-----------------------------------+-----------+------------
  crdb_internal_ts_shard_8 | INT4      |    false    | NULL           | mod(fnv32(CAST(ts AS STRING)), 8) | {primary} |   true
  ts                       | DECIMAL   |    false    | NULL           |                                   | {primary} |   false
  product_id               | INT8      |    true     | NULL           |                                   | {}        |   false
(3 rows)

> SHOW COLUMNS FROM events;

  table_name | index_name | non_unique | seq_in_index |       column_name        | direction | storing | implicit
-------------+------------+------------+--------------+--------------------------+-----------+---------+-----------
  events     | primary    |   false    |            1 | crdb_internal_ts_shard_8 | ASC       |  false  |  false
  events     | primary    |   false    |            2 | ts                       | ASC       |  false  |  false
(2 rows)

Create a table with a hash-sharded secondary index

> SET experimental_enable_hash_sharded_indexes=on;

> CREATE TABLE events (
    product_id INT8,
    owner UUID,
    serial_number VARCHAR,
    event_id UUID,
    ts TIMESTAMP,
    data JSONB,
    PRIMARY KEY (product_id, owner, serial_number, ts, event_id),
    INDEX (ts) USING HASH WITH BUCKET_COUNT=8
);

> SHOW INDEX FROM events;

  table_name |               index_name               | non_unique | seq_in_index |       column_name        | direction | storing | implicit
-------------+----------------------------------------+------------+--------------+--------------------------+-----------+---------+-----------
  events     | primary                                |   false    |            1 | product_id               | ASC       |  false  |  false
  events     | primary                                |   false    |            2 | owner                    | ASC       |  false  |  false
  events     | primary                                |   false    |            3 | serial_number            | ASC       |  false  |  false
  events     | primary                                |   false    |            4 | ts                       | ASC       |  false  |  false
  events     | primary                                |   false    |            5 | event_id                 | ASC       |  false  |  false
  events     | events_crdb_internal_ts_shard_8_ts_idx |    true    |            1 | crdb_internal_ts_shard_8 | ASC       |  false  |  false
  events     | events_crdb_internal_ts_shard_8_ts_idx |    true    |            2 | ts                       | ASC       |  false  |  false
  events     | events_crdb_internal_ts_shard_8_ts_idx |    true    |            3 | product_id               | ASC       |  false  |   true
  events     | events_crdb_internal_ts_shard_8_ts_idx |    true    |            4 | owner                    | ASC       |  false  |   true
  events     | events_crdb_internal_ts_shard_8_ts_idx |    true    |            5 | serial_number            | ASC       |  false  |   true
  events     | events_crdb_internal_ts_shard_8_ts_idx |    true    |            6 | event_id                 | ASC       |  false  |   true
(11 rows)

> SHOW COLUMNS FROM events;

        column_name        | data_type | is_nullable | column_default |       generation_expression       |                     indices                      | is_hidden
---------------------------+-----------+-------------+----------------+-----------------------------------+--------------------------------------------------+------------
  product_id               | INT8      |    false    | NULL           |                                   | {primary,events_crdb_internal_ts_shard_8_ts_idx} |   false
  owner                    | UUID      |    false    | NULL           |                                   | {primary,events_crdb_internal_ts_shard_8_ts_idx} |   false
  serial_number            | VARCHAR   |    false    | NULL           |                                   | {primary,events_crdb_internal_ts_shard_8_ts_idx} |   false
  event_id                 | UUID      |    false    | NULL           |                                   | {primary,events_crdb_internal_ts_shard_8_ts_idx} |   false
  ts                       | TIMESTAMP |    false    | NULL           |                                   | {primary,events_crdb_internal_ts_shard_8_ts_idx} |   false
  data                     | JSONB     |    true     | NULL           |                                   | {}                                               |   false
  crdb_internal_ts_shard_8 | INT4      |    false    | NULL           | mod(fnv32(CAST(ts AS STRING)), 8) | {events_crdb_internal_ts_shard_8_ts_idx}         |   true
(7 rows)

Pricing

Contact us

Sign In

CREATE TABLE

Required privileges

Synopsis

Parameters

Table-level replication

Row-level replication

Examples

Create a table

Create a table with secondary and inverted indexes

Create a table with auto-generated unique row IDs

Create a table with a foreign key constraint

Create a table with a check constraint

Create a table that mirrors key-value storage

Create a table from a `SELECT` statement

Create a table with a computed column

Create a table with partitions

Create a table with partitions by list

Create a table with partitions by range

Show the definition of a table

Create a table with a hash-sharded primary index

Create a table with a hash-sharded secondary index

See also

CREATE TABLE

Required privileges

Synopsis

Parameters

Table-level replication

Row-level replication

Examples

Create a table

Create a table with secondary and inverted indexes

Create a table with auto-generated unique row IDs

Create a table with a foreign key constraint

Create a table with a check constraint

Create a table that mirrors key-value storage

Create a table from a SELECT statement

Create a table with a computed column

Create a table with partitions

Create a table with partitions by list

Create a table with partitions by range

Show the definition of a table

Create a table with a hash-sharded primary index

Create a table with a hash-sharded secondary index

See also

Create a table from a `SELECT` statement