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Version: Latest-3.2

Expression partitioning (recommended)

Since v3.0, StarRocks supports expression partitioning (previously known as automatic partitioning), which is more flexible and user-friendly. This partitioning method is suitable for most scenarios such as querying and managing data based on continuous time ranges or enum values.

You only need to specify a simple partition expression (either a time function expression or a column expression) at table creation. During data loading, StarRocks will automatically create partitions based on the data and the rule defined in the partition expression. You no longer need to manually create numerous partitions at table creation, nor configure dynamic partition properties.

Partitioning based on a time function expression

If you frequently query and manage data based on continuous time ranges, you only need to specify a date type (DATE or DATETIME) column as the partition column and specify year, month, day, or hour as the partition granularity in the time function expression. StarRocks will automatically create partitions and set the partitions' start and end dates or datetime based on the loaded data and partition expression.

However, in some special scenarios, such as partitioning historical data into partitions by month and recent data into partitions by day, you must use range partitioning to create partitions.

Syntax

PARTITION BY expression

expression ::=
{ date_trunc ( <time_unit> , <partition_column> ) |
time_slice ( <partition_column> , INTERVAL <N> <time_unit> [ , boundary ] ) }

Parameters

ParametersRequiredDescription
expressionYESCurrently, only the date_trunc and time_slice functions are supported. If you use the function time_slice, you do not need to pass the boundary parameter. It is because in this scenario, the default and valid value for this parameter is floor, and the value cannot be ceil.
time_unitYESThe partition granularity, which can be hour, day, month or year. The week partition granularity is not supported. If the partition granularity is hour, the partition column must be of the DATETIME data type and cannot be of the DATE data type.
partition_columnYESThe name of the partition column.
  • The partition column can only be of the DATE or DATETIME data type. The partition column allows NULL values.
  • The partition column can be of the DATE or DATETIME data type if the date_trunc function is used. The partition column must be of the DATETIME data type if the time_slice function is used.
  • If the partition column is of the DATE data type, the supported range is [0000-01-01 ~ 9999-12-31]. If the partition column is of the DATETIME data type, the supported range is [0000-01-01 01:01:01 ~ 9999-12-31 23:59:59].
  • Currently, you can specify only one partition column and multiple partition columns are not supported.

Usage notes

  • During data loading, StarRocks automatically creates some partitions based on the loaded data, but if the load job fails for some reason, the partitions that are automatically created by StarRocks cannot be automatically deleted.
  • StarRocks sets the default maximum number of automatically created partitions to 4096, which can be configured by the FE parameter max_automatic_partition_number. This parameter can prevent you from accidentally creating too many partitions.
  • The naming rule for partitions is consistent with the naming rule for dynamic partitioning.

Examples

Example 1: Suppose you frequently query data by day. You can use the partition expression date_trunc() and set the partition column as event_day and the partition granularity as day at table creation. Data is automatically partitioned based on dates during loading. Data of the same day is stored in one partition and partition pruning can be used to significantly improve query efficiency.

CREATE TABLE site_access1 (
event_day DATETIME NOT NULL,
site_id INT DEFAULT '10',
city_code VARCHAR(100),
user_name VARCHAR(32) DEFAULT '',
pv BIGINT DEFAULT '0'
)
DUPLICATE KEY(event_day, site_id, city_code, user_name)
PARTITION BY date_trunc('day', event_day)
DISTRIBUTED BY HASH(event_day, site_id);

For example, when the following two data rows are loaded, StarRocks will automatically create two partitions, p20230226 and p20230227, with ranges [2023-02-26 00:00:00, 2023-02-27 00:00:00) and [2023-02-27 00:00:00, 2023-02-28 00:00:00) respectively. If subsequent loaded data falls within these ranges, they are automatically routed to the corresponding partitions.

-- insert two data rows
INSERT INTO site_access1
VALUES ("2023-02-26 20:12:04",002,"New York","Sam Smith",1),
("2023-02-27 21:06:54",001,"Los Angeles","Taylor Swift",1);

-- view partitions
mysql > SHOW PARTITIONS FROM site_access1;
+-------------+---------------+----------------+---------------------+--------------------+--------+--------------+------------------------------------------------------------------------------------------------------+--------------------+---------+----------------+---------------+---------------------+--------------------------+----------+------------+----------+
| PartitionId | PartitionName | VisibleVersion | VisibleVersionTime | VisibleVersionHash | State | PartitionKey | Range | DistributionKey | Buckets | ReplicationNum | StorageMedium | CooldownTime | LastConsistencyCheckTime | DataSize | IsInMemory | RowCount |
+-------------+---------------+----------------+---------------------+--------------------+--------+--------------+------------------------------------------------------------------------------------------------------+--------------------+---------+----------------+---------------+---------------------+--------------------------+----------+------------+----------+
| 17138 | p20230226 | 2 | 2023-07-19 17:53:59 | 0 | NORMAL | event_day | [types: [DATETIME]; keys: [2023-02-26 00:00:00]; ..types: [DATETIME]; keys: [2023-02-27 00:00:00]; ) | event_day, site_id | 6 | 3 | HDD | 9999-12-31 23:59:59 | NULL | 0B | false | 0 |
| 17113 | p20230227 | 2 | 2023-07-19 17:53:59 | 0 | NORMAL | event_day | [types: [DATETIME]; keys: [2023-02-27 00:00:00]; ..types: [DATETIME]; keys: [2023-02-28 00:00:00]; ) | event_day, site_id | 6 | 3 | HDD | 9999-12-31 23:59:59 | NULL | 0B | false | 0 |
+-------------+---------------+----------------+---------------------+--------------------+--------+--------------+------------------------------------------------------------------------------------------------------+--------------------+---------+----------------+---------------+---------------------+--------------------------+----------+------------+----------+
2 rows in set (0.00 sec)

Example 2: Suppose you frequently query data by week. You can use the partition expression time_slice() and set the partition column as event_day and the partition granularity to seven days at table creation. Data of one week is stored in one partition and partition pruning can be used to significantly improve query efficiency.

CREATE TABLE site_access(
event_day DATETIME NOT NULL,
site_id INT DEFAULT '10',
city_code VARCHAR(100),
user_name VARCHAR(32) DEFAULT '',
pv BIGINT DEFAULT '0'
)
DUPLICATE KEY(event_day, site_id, city_code, user_name)
PARTITION BY time_slice(event_day, INTERVAL 7 day)
DISTRIBUTED BY HASH(event_day, site_id)

Partitioning based on the column expression (since v3.1)

If you frequently query and manage data of specific type, you only need to specify the column representing the type as the partition column. StarRocks will automatically create partitions based on the partition column values of the loaded data.

However, in some special scenarios, such as when the table contains a column city, and you frequently query and manage data based on countries and cities. You must use list partitioning to store data of multiple cities within the same country in one partition.

Syntax

PARTITION BY expression

expression ::=
( partition_columns )

partition_columns ::=
<column>, [ <column> [,...] ]

Parameters

ParametersRequiredDescription
partition_columnsYESThe names of partition columns.
  • The partition column values can be string (BINARY not supported), date or datetime, integer, and boolean values. The partition column allows NULL values.
  • Each partition can only contain data with the same value for a partition column. To include data with different values in a partition column in a partition, see List partitioning.

Usage notes

  • During data loading, StarRocks automatically creates some partitions based on the loaded data, but if the load job fails for some reason, the partitions that are automatically created by StarRocks cannot be automatically deleted.
  • StarRocks sets the default maximum number of automatically created partitions to 4096, which can be configured by the FE parameter max_automatic_partition_number. This parameter can prevent you from accidentally creating too many partitions.
  • The naming rule for partitions: if multiple partition columns are specified, the values of different partition columns are connected with an underscore _ in the partition name, and the format is p<value in partition column 1>_<value in partition column 2>_.... For example, if two columns dt and province are specified as partition columns, both of which are string types, and a data row with values 2022-04-01 and beijing is loaded, the corresponding partition automatically created is named p20220401_beijing.

Examples

Example 1: Suppose you frequently query details of the data center billing based on time ranges and specific cities. At table creation, you can use a partition expression to specify the first partition columns as dt and city . This way, data belonging to the same date and city are routed into the same partition, and partition pruning can be used to significantly improve query efficiency.

CREATE TABLE t_recharge_detail1 (
id bigint,
user_id bigint,
recharge_money decimal(32,2),
city varchar(20) not null,
dt varchar(20) not null
)
DUPLICATE KEY(id)
PARTITION BY (dt,city)
DISTRIBUTED BY HASH(`id`);

Insert a single data row into the table.

INSERT INTO t_recharge_detail1 
VALUES (1, 1, 1, 'Houston', '2022-04-01');

View the partitions. The result shows that StarRocks automatically creates a partition p20220401_Houston1 based on the loaded data. During subsequent loading, data with the values 2022-04-01 and Houston in the partition columns dt and city are stored in this partition.

NOTE

Each partition can only contain data with the specified one value for the partition column. To specify multiple values for a partition column in a partition, see List partitions.

MySQL > SHOW PARTITIONS from t_recharge_detail1\G
*************************** 1. row ***************************
PartitionId: 16890
PartitionName: p20220401_Houston
VisibleVersion: 2
VisibleVersionTime: 2023-07-19 17:24:53
VisibleVersionHash: 0
State: NORMAL
PartitionKey: dt, city
List: (('2022-04-01', 'Houston'))
DistributionKey: id
Buckets: 6
ReplicationNum: 3
StorageMedium: HDD
CooldownTime: 9999-12-31 23:59:59
LastConsistencyCheckTime: NULL
DataSize: 2.5KB
IsInMemory: false
RowCount: 1
1 row in set (0.00 sec)

Manage partitions

Load data into partitions

During data loading, StarRocks will automatically create partitions based on the loaded data and partition rule defined by the partition expression.

Note that if you use expression partitioning at table creation and need to use INSERT OVERWRITE to overwrite data in a specific partition, whether the partition has been created or not, you currently need to explicitly provide an partition range in PARTITION(). This is different from Range Partitioning or List Partitioning, which allow you only to provide the partition name in PARTITION (<partition_name>).

If you use a time function expression at table creation and want to overwrite data in a specific partition, you need to provide the starting date or datetime of that partition (the partition granularity configured at table creation). If the partition does not exist, it can be automatically created during data loading.

INSERT OVERWRITE site_access1 PARTITION(event_day='2022-06-08 20:12:04')
SELECT * FROM site_access2 PARTITION(p20220608);

If you use column expression at table creation and want to overwrite data in a specific partition, you need to provide the partition column values that the partition contains. If the partition does not exist, it can be automatically created during data loading.

INSERT OVERWRITE t_recharge_detail1 PARTITION(dt='2022-04-02',city='texas')
SELECT * FROM t_recharge_detail2 PARTITION(p20220402_texas);

View partitions

When you want to view specific information about automatically created partitions, you need to use the SHOW PARTITIONS FROM <table_name> statement. The SHOW CREATE TABLE <table_name> statement only returns the syntax for expression partitioning that is configured at table creation.

MySQL > SHOW PARTITIONS FROM t_recharge_detail1;
+-------------+-------------------+----------------+---------------------+--------------------+--------+--------------+-----------------------------+-----------------+---------+----------------+---------------+---------------------+--------------------------+----------+------------+----------+
| PartitionId | PartitionName | VisibleVersion | VisibleVersionTime | VisibleVersionHash | State | PartitionKey | List | DistributionKey | Buckets | ReplicationNum | StorageMedium | CooldownTime | LastConsistencyCheckTime | DataSize | IsInMemory | RowCount |
+-------------+-------------------+----------------+---------------------+--------------------+--------+--------------+-----------------------------+-----------------+---------+----------------+---------------+---------------------+--------------------------+----------+------------+----------+
| 16890 | p20220401_Houston | 2 | 2023-07-19 17:24:53 | 0 | NORMAL | dt, city | (('2022-04-01', 'Houston')) | id | 6 | 3 | HDD | 9999-12-31 23:59:59 | NULL | 2.5KB | false | 1 |
| 17056 | p20220402_texas | 2 | 2023-07-19 17:27:42 | 0 | NORMAL | dt, city | (('2022-04-02', 'texas')) | id | 6 | 3 | HDD | 9999-12-31 23:59:59 | NULL | 2.5KB | false | 1 |
+-------------+-------------------+----------------+---------------------+--------------------+--------+--------------+-----------------------------+-----------------+---------+----------------+---------------+---------------------+--------------------------+----------+------------+----------+
2 rows in set (0.00 sec)

Limits

  • Since v3.1.0, StarRocks's shared-data mode supports the time function expression. And since v3.1.1, StarRocks's shared-data mode further supports the column expression.
  • Currently, using CTAS to create tables configured expression partitioning is not supported.
  • Currently, using Spark Load to load data to tables that use expression partitioning is not supported.
  • When the ALTER TABLE <table_name> DROP PARTITION <partition_name> statement is used to delete a partition created by using the column expression, data in the partition is directly removed and cannot be recovered.
  • Currently you cannot backup and restore partitions created by the expression partitioning.