Skip to main content
Version: Latest-3.1

CREATE TABLE

Description

Creates a new table in StarRocks.

NOTE

This operation requires the CREATE TABLE privilege on the destination database.

Syntax

CREATE [EXTERNAL] TABLE [IF NOT EXISTS] [database.]table_name
(column_definition1[, column_definition2, ...]
[, index_definition1[, index_definition12,]])
[ENGINE = [olap|mysql|elasticsearch|hive|hudi|iceberg|jdbc]]
[key_desc]
[COMMENT "table comment"]
[partition_desc]
[distribution_desc]
[rollup_index]
[ORDER BY (column_definition1,...)]
[PROPERTIES ("key"="value", ...)]
[BROKER PROPERTIES ("key"="value", ...)]

Parameters

column_definition

Syntax:

col_name col_type [agg_type] [NULL | NOT NULL] [DEFAULT "default_value"] [AUTO_INCREMENT] [AS generation_expr]

col_name: Column name.

Note that normally you cannot create a column whose name is initiated with __op or __row because these name formats are reserved for special purposes in StarRocks and creating such columns may result in undefined behavior. If you do need to create such column, set the FE dynamic parameter allow_system_reserved_names to TRUE.

col_type: Column type. Specific column information, such as types and ranges:

  • TINYINT (1 byte): Ranges from -2^7 + 1 to 2^7 - 1.

  • SMALLINT (2 bytes): Ranges from -2^15 + 1 to 2^15 - 1.

  • INT (4 bytes): Ranges from -2^31 + 1 to 2^31 - 1.

  • BIGINT (8 bytes): Ranges from -2^63 + 1 to 2^63 - 1.

  • LARGEINT (16 bytes): Ranges from -2^127 + 1 to 2^127 - 1.

  • FLOAT (4 bytes): Supports scientific notation.

  • DOUBLE (8 bytes): Supports scientific notation.

  • DECIMAL[(precision, scale)] (16 bytes)

    • Default value: DECIMAL(10, 0)

    • precision: 1 ~ 38

    • scale: 0 ~ precision

    • Integer part: precision - scale

      Scientific notation is not supported.

  • DATE (3 bytes): Ranges from 0000-01-01 to 9999-12-31.

  • DATETIME (8 bytes): Ranges from 0000-01-01 00:00:00 to 9999-12-31 23:59:59.

  • CHAR[(length)]: Fixed length string. Range: 1 ~ 255. Default value: 1.

  • VARCHAR[(length)]: A variable-length string. The default value is 1. Unit: bytes. In versions earlier than StarRocks 2.1, the value range of length is 1–65533. [Preview] In StarRocks 2.1 and later versions, the value range of length is 1–1048576.

  • HLL (1~16385 bytes): For HLL type, there's no need to specify length or default value. The length will be controlled within the system according to data aggregation. HLL column can only be queried or used by hll_union_agg, Hll_cardinality, and hll_hash.

  • BITMAP: Bitmap type does not require specified length or default value. It represents a set of unsigned bigint numbers. The largest element could be up to 2^64 - 1.

agg_type: aggregation type. If not specified, this column is key column. If specified, it is value column. The aggregation types supported are as follows:

  • SUM, MAX, MIN, REPLACE
  • HLL_UNION (only for HLL type)
  • BITMAP_UNION(only for BITMAP)
  • REPLACE_IF_NOT_NULL: This means the imported data will only be replaced when it is of non-null value. If it is of null value, StarRocks will retain the original value.

NOTE

  • When the column of aggregation type BITMAP_UNION is imported, its original data types must be TINYINT, SMALLINT, INT, and BIGINT.
  • If NOT NULL is specified by REPLACE_IF_NOT_NULL column when the table was created, StarRocks will still convert the data to NULL without sending an error report to the user. With this, the user can import selected columns.

This aggregation type applies ONLY to the Aggregate table whose key_desc type is AGGREGATE KEY.

NULL | NOT NULL: Whether the column is allowed to be NULL. By default, NULL is specified for all columns in a table that uses the Duplicate Key, Aggregate, or Unique Key table. In a table that uses the Primary Key table, by default, value columns are specified with NULL, whereas key columns are specified with NOT NULL. If NULL values are included in the raw data, present them with \N. StarRocks treats \N as NULL during data loading.

DEFAULT "default_value": the default value of a column. When you load data into StarRocks, if the source field mapped onto the column is empty, StarRocks automatically fills the default value in the column. You can specify a default value in one of the following ways:

  • DEFAULT current_timestamp: Use the current time as the default value. For more information, see current_timestamp().
  • DEFAULT <default_value>: Use a given value of the column data type as the default value. For example, if the data type of the column is VARCHAR, you can specify a VARCHAR string, such as beijing, as the default value, as presented in DEFAULT "beijing". Note that default values cannot be any of the following types: ARRAY, BITMAP, JSON, HLL, and BOOLEAN.
  • DEFAULT (<expr>): Use the result returned by a given function as the default value. Only the uuid() and uuid_numeric() expressions are supported.

AUTO_INCREMENT: specifies an AUTO_INCREMENT column. The data types of AUTO_INCREMENT columns must be BIGINT. Auto-incremented IDs start from 1 and increase at a step of 1. For more information about AUTO_INCREMENT columns, see AUTO_INCREMENT. Since v3.0, StarRocks supports AUTO_INCREMENT columns.

AS generation_expr: specifies the generated column and its expression. The generated column can be used to precompute and store the results of expressions, which significantly accelerates queries with the same complex expressions. Since v3.1, StarRocks supports generated columns.

index_definition

You can only create bitmap indexes when you create tables. For more information about parameter descriptions and usage notes, see Bitmap indexing.

INDEX index_name (col_name[, col_name, ...]) [USING BITMAP] COMMENT 'xxxxxx'

ENGINE type

Default value: olap. If this parameter is not specified, an OLAP table (StarRocks native table) is created by default.

Optional value: mysql, elasticsearch, hive, jdbc (2.3 and later), iceberg, and hudi (2.2 and later). If you want to create an external table to query external data sources, specify CREATE EXTERNAL TABLE and set ENGINE to any of these values. You can refer to External table for more information.

From v3.0 onwards, we recommend that you use catalogs to query data from Hive, Iceberg, Hudi, and JDBC data sources. External tables are deprecated. For more information, see Hive catalog, Iceberg catalog, Hudi catalog, and JDBC catalog.

From v3.1 onwards, StarRocks supports creating Parquet-formatted tables in Iceberg catalogs, and you can insert data to these Parquet-formatted Iceberg tables by using INSERT INTO. See Create an Iceberg table.

  • For MySQL, specify the following properties:

    PROPERTIES (
    "host" = "mysql_server_host",
    "port" = "mysql_server_port",
    "user" = "your_user_name",
    "password" = "your_password",
    "database" = "database_name",
    "table" = "table_name"
    )

    Note:

    "table_name" in MySQL should indicate the real table name. In contrast, "table_name" in CREATE TABLE statement indicates the name of this mysql table on StarRocks. They can either be different or the same.

    The aim of creating MySQL tables in StarRocks is to access MySQL database. StarRocks itself does not maintain or store any MySQL data.

  • For Elasticsearch, specify the following properties:

    PROPERTIES (

    "hosts" = "http://192.168.0.1:8200,http://192.168.0.2:8200",
    "user" = "root",
    "password" = "root",
    "index" = "tindex",
    "type" = "doc"
    )
    • hosts: the URL that is used to connect your Elasticsearch cluster. You can specify one or more URLs.
    • user: the account of the root user that is used to log in to your Elasticsearch cluster for which basic authentication is enabled.
    • password: the password of the preceding root account.
    • index: the index of the StarRocks table in your Elasticsearch cluster. The index name is the same as the StarRocks table name. You can set this parameter to the alias of the StarRocks table.
    • type: the type of index. The default value is doc.
  • For Hive, specify the following properties:

    PROPERTIES (

    "database" = "hive_db_name",
    "table" = "hive_table_name",
    "hive.metastore.uris" = "thrift://127.0.0.1:9083"
    )

    Here, database is the name of the corresponding database in Hive table. Table is the name of Hive table. hive.metastore.uris is the server address.

  • For JDBC, specify the following properties:

    PROPERTIES (
    "resource"="jdbc0",
    "table"="dest_tbl"
    )

    resource is the JDBC resource name and table is the destination table.

  • For Iceberg, specify the following properties:

     PROPERTIES (
    "resource" = "iceberg0",
    "database" = "iceberg",
    "table" = "iceberg_table"
    )

    resource is the Iceberg resource name. database is the Iceberg database. table is the Iceberg table.

  • For Hudi, specify the following properties:

      PROPERTIES (
    "resource" = "hudi0",
    "database" = "hudi",
    "table" = "hudi_table"
    )

key_desc

Syntax:

key_type(k1[,k2 ...])

Data is sequenced in specified key columns and has different attributes for different key types:

  • AGGREGATE KEY: Identical content in key columns will be aggregated into value columns according to the specified aggregation type. It usually applies to business scenarios such as financial statements and multi-dimensional analysis.
  • UNIQUE KEY/PRIMARY KEY: Identical content in key columns will be replaced in value columns according to the import sequence. It can be applied to make addition, deletion, modification and query on key columns.
  • DUPLICATE KEY: Identical content in key columns, which also exists in StarRocks at the same time. It can be used to store detailed data or data with no aggregation attributes. DUPLICATE KEY is the default type. Data will be sequenced according to key columns.

NOTE

Value columns do not need to specify aggregation types when other key_type is used to create tables with the exception of AGGREGATE KEY.

COMMENT

You can add a table comment when you create a table, optional. Note that COMMENT must be placed after key_desc. Otherwise, the table cannot be created.

From v3.1 onwards, you can modify the table comment suing ALTER TABLE <table_name> COMMENT = "new table comment".

partition_desc

Partition description can be used in the following ways:

Create partitions dynamically

Dynamic partitioning provides a time-to-live (TTL) management for partitions. StarRocks automatically creates new partitions in advance and removes expired partitions to ensure data freshness. To enable this feature, you can configure Dynamic partitioning related properties at table creation.

Create partitions one by one

Specify only the upper bound for a partition

Syntax:

PARTITION BY RANGE ( <partitioning_column1> [, <partitioning_column2>, ... ] )
PARTITION <partition1_name> VALUES LESS THAN ("<upper_bound_for_partitioning_column1>" [ , "<upper_bound_for_partitioning_column2>", ... ] )
[ ,
PARTITION <partition2_name> VALUES LESS THAN ("<upper_bound_for_partitioning_column1>" [ , "<upper_bound_for_partitioning_column2>", ... ] )
, ... ]
)

Note:

Please use specified key columns and specified value ranges for partitioning.

  • Partition name only supports [A-z0-9_]

  • Columns in Range partition only support the following types: TINYINT, SMALLINT, INT, BIGINT, LARGEINT, DATE, and DATETIME.

  • Partitions are left closed and right open. The left boundary of the first partition is of minimum value.

  • NULL value is stored only in partitions that contain minimum values. When the partition containing the minimum value is deleted, NULL values can no longer be imported.

  • Partition columns can either be single columns or multiple columns. The partition values are the default minimum values.

  • When only one column is specified as the partitioning column, you can set MAXVALUE as the upper bound for the partitioning column of the most recent partition.

    PARTITION BY RANGE (pay_dt) (
    PARTITION p1 VALUES LESS THAN ("20210102"),
    PARTITION p2 VALUES LESS THAN ("20210103"),
    PARTITION p3 VALUES LESS THAN MAXVALUE
    )

Please note:

  • Partitions are often used for managing data related to time.
  • When data backtracking is needed, you may want to consider emptying the first partition for adding partitions later when necessary.

Specify both the lower and upper bounds for a partition

Syntax:

PARTITION BY RANGE ( <partitioning_column1> [, <partitioning_column2>, ... ] )
(
PARTITION <partition_name1> VALUES [( "<lower_bound_for_partitioning_column1>" [ , "<lower_bound_for_partitioning_column2>", ... ] ), ( "<upper_bound_for_partitioning_column1?" [ , "<upper_bound_for_partitioning_column2>", ... ] ) )
[,
PARTITION <partition_name2> VALUES [( "<lower_bound_for_partitioning_column1>" [ , "<lower_bound_for_partitioning_column2>", ... ] ), ( "<upper_bound_for_partitioning_column1>" [ , "<upper_bound_for_partitioning_column2>", ... ] ) )
, ...]
)

Note:

  • Fixed Range is more flexible than LESS THAN. You can customize the left and right partitions.

  • Fixed Range is the same as LESS THAN in the other aspects.

  • When only one column is specified as the partitioning column, you can set MAXVALUE as the upper bound for the partitioning column of the most recent partition.

    PARTITION BY RANGE (pay_dt) (
    PARTITION p202101 VALUES [("20210101"), ("20210201")),
    PARTITION p202102 VALUES [("20210201"), ("20210301")),
    PARTITION p202103 VALUES [("20210301"), (MAXVALUE))
    )

Create multiple partitions in a batch

Syntax

  • If the partitioning column is of a date type.

    PARTITION BY RANGE (<partitioning_column>) (
    START ("<start_date>") END ("<end_date>") EVERY (INTERVAL <N> <time_unit>)
    )
  • If the partitioning column is of an integer type.

    PARTITION BY RANGE (<partitioning_column>) (
    START ("<start_integer>") END ("<end_integer>") EVERY (<partitioning_granularity>)
    )

Description

You can specify the start and end values in START() and END() and the time unit or partitioning granularity in EVERY() to create multiple partitions in a batch.

  • The partitioning column can be of a date or integer type.
  • If the partitioning column is of a date type, you need to use the INTERVAL keyword to specify the time interval. You can specify the time unit as hour (since v3.0), day, week, month, or year. The naming conventions of partitions are the same as those for dynamic partitions.

For more information, see Data distribution.

distribution_desc

StarRocks supports hash bucketing and random bucketing. If you do not configure bucketing, StarRocks uses random bucketing and automatically sets the number of buckets by default.

  • Random bucketing (since v3.1)

    For data in a partition, StarRocks distributes the data randomly across all buckets, which is not based on specific column values. And if you want StarRocks to automatically determine the number of buckets, you do not need to specify any bucketing configurations. If you choose to manually specify the number of buckets, the syntax is as follows:

    DISTRIBUTED BY RANDOM BUCKETS <num>

    However, note that the query performance provided by random bucketing may not be ideal when you query massive amounts of data and frequently use certain columns as conditional columns. In this scenario, it is recommended to use hash bucketing. Because only a small number of buckets need to be scanned and computed, significantly improving query performance.

    Precautions

    • You can only use random bucketing to create Duplicate Key tables.
    • You can not specify a Colocation Group for a table bucketed randomly.
    • Spark Load cannot be used to load data into tables bucketed randomly.
    • Since StarRocks v2.5.7, you do not need to set the number of buckets when you create a table. StarRocks automatically sets the number of buckets. If you want to set this parameter, see Determine the number of buckets.

    For more information, see Random bucketing.

  • Hash bucketing

    Syntax:

    DISTRIBUTED BY HASH (k1[,k2 ...]) [BUCKETS num]

    Data in partitions can be subdivided into buckets based on the hash values of the bucketing columns and the number of buckets. We recommend that you choose the column that meets the following two requirements as the bucketing column.

    • High cardinality column such as ID
    • Column that is often used as a filter in queries

    If such a column does not exist, you can determine the bucketing column according to the complexity of queries.

    • If the query is complex, we recommend that you select a high cardinality column as the bucketing column to ensure balanced data distribution among buckets and improve cluster resource utilization.
    • If the query is relatively simple, we recommend that you select the column that is often used as the query condition as the bucketing column to improve query efficiency.

    If partition data cannot be evenly distributed into each bucket by using one bucketing column, you can choose multiple bucketing columns (at most three). For more information, see Choose bucketing columns.

    Precautions:

    • When you create a table, you must specify its bucketing columns.
    • The values of bucketing columns cannot be updated.
    • Bucketing columns cannot be modified after they are specified.
    • Since StarRocks v2.5.7, you do not need to set the number of buckets when you create a table. StarRocks automatically sets the number of buckets. If you want to set this parameter, see Determine the number of buckets.

ORDER BY

Since version 3.0, the primary key and sort key are decoupled in the Primary Key table. The sort key is specified by the ORDER BY keyword and can be the permutation and combination of any columns.

NOTICE

If the sort key is specified, the prefix index is built according to the sort key; if the sort key is not specified, the prefix index is built according to the primary key.

PROPERTIES

Specify initial storage medium, automatic storage cooldown time, replica number

If the engine type is OLAP, you can specify initial storage medium (storage_medium), automatic storage cooldown time (storage_cooldown_time) or time interval (storage_cooldown_ttl), and replica number (replication_num) when you create a table.

The scope where the properties take effect: If the table has only one partition, the properties belong to the table. If the table is divided into multiple partitions, the properties belong to each partition. And when you need to configure different properties for specified partitions, you can execute ALTER TABLE ... ADD PARTITION or ALTER TABLE ... MODIFY PARTITION after table creation.

Set initial storage medium and automatic storage cooldown time

PROPERTIES (
"storage_medium" = "[SSD|HDD]",
{ "storage_cooldown_ttl" = "<num> { YEAR | MONTH | DAY | HOUR } "
| "storage_cooldown_time" = "yyyy-MM-dd HH:mm:ss" }
)
  • storage_medium: the initial storage medium, which can be set to SSD or HDD. Make sure that the type of storage medium you explicitly specified is consistent with the BE disk types for your StarRocks cluster specified in the BE static parameter storage_root_path.

    If the FE configuration item enable_strict_storage_medium_check is set to true, the system strictly checks BE disk type when you create a table. If the storage medium you specified in CREATE TABLE is inconsistent with BE disk type, an error "Failed to find enough host in all backends with storage medium is SSD|HDD." is returned and table creation fails. If enable_strict_storage_medium_check is set to false, the system ignores this error and forcibly creates the table. However, cluster disk space may be unevenly distributed after data is loaded.

    From v2.3.6, v2.4.2, v2.5.1, and v3.0 onwards, the system automatically infers storage medium based on BE disk type if storage_medium is not explicitly specified.

    • The system automatically sets this parameter to SSD in the following scenarios:

      • The disk types reported by BEs (storage_root_path) contain only SSD.
      • The disk types reported by BEs (storage_root_path) contain both SSD and HDD. Note that from v2.3.10, v2.4.5, v2.5.4, and v3.0 onwards, the system sets storage_medium to SSD when storage_root_path reported by BEs contain both SSD and HDD and the property storage_cooldown_time is specified.
    • The system automatically sets this parameter to HDD in the following scenarios:

      • The disk types reported by BEs (storage_root_path) contain only HDD.
      • From 2.3.10, 2.4.5, 2.5.4, and 3.0 onwards, the system sets storage_medium to HDD when storage_root_path reported by BEs contain both SSD and HDD and the property storage_cooldown_time is not specified.
  • storage_cooldown_ttl or storage_cooldown_time: the automatic storage cooldown time or time interval. Automatic storage cooldown refers to automatically migrate data from SSD to HDD. This feature is only effective when the initial storage medium is SSD.

    Parameter

    • storage_cooldown_ttl:the time interval of automatic storage cooldown for the partitions in this table. If you need to retain the most recent partitions on SSD and automatically cool down older partitions to HDD after a certain time interval, you can use this parameter. The automatic storage cooldown time for each partition is calculated using the value of this parameter plus the upper time bound of the partition.

    The supported values are <num> YEAR, <num> MONTH, <num> DAY, and <num> HOUR. <num> is a non-negative integer. The default value is null, indicating that storage cooldown is not automatically performed.

    For example, you specify the value as "storage_cooldown_ttl"="1 DAY" when creating the table, and the partition p20230801 with a range of [2023-08-01 00:00:00,2023-08-02 00:00:00) exists. The automatic storage cooldown time for this partition is 2023-08-03 00:00:00, which is 2023-08-02 00:00:00 + 1 DAY. If you specify the value as "storage_cooldown_ttl"="0 DAY" when creating the table, the automatic storage cooldown time for this partition is 2023-08-02 00:00:00.

    • storage_cooldown_time: the automatic storage cooldown time (absolute time) when the table is cooled down from SSD to HDD. The specified time needs to be later than the current time. Format: "yyyy-MM-dd HH:mm:ss". When you need to configure different properties for specified partitions, you can execute ALTER TABLE ... ADD PARTITION or ALTER TABLE ... MODIFY PARTITION.

Usages

  • The comparison between the parameters related to automatic storage cooldown is as follows:

    • storage_cooldown_ttl: A table property that specifies the time interval of automatic storage cooldown for partitions in the table. The system automatically cools down a partition at the time the value of this parameter plus the upper time bound of the partition. So automatic storage cooldown is performed at the partition granularity, which is more flexible.
    • storage_cooldown_time: A table property that specifies the automatic storage cooldown time (absolute time) for this table. Also, you can configure different properties for specified partitions after table creation.
    • storage_cooldown_second: A static FE parameter that specifies the automatic storage cooldown latency for all tables within the cluster.
  • The table property storage_cooldown_ttl or storage_cooldown_time takes precedence over the FE static parameter storage_cooldown_second.

  • When configuring these parameters, you need to specify "storage_medium = "SSD".

  • If you do not configure these parameters, automatic storage cooldown is not be automatically performed.

  • Execute SHOW PARTITIONS FROM <table_name> to view the automatic storage cooldown time for each partition.

Limit

  • Expression and List partitioning are not supported.
  • The partition column need to be of date type.
  • Multiple partition columns are not supported.
  • Primary Key tables are not supported.

Set the number of replicas for each tablet in partitions

replication_num: number of replicas for each table in the partitions. Default number: 3.

PROPERTIES (
"replication_num" = "<num>"
)

Add bloom filter index for a column

If the Engine type is olap, you can specify a column to adopt bloom filter indexes.

The following limits apply when you use bloom filter index:

  • You can create bloom filter indexes for all columns of a Duplicate Key or Primary Key table. For an Aggregate table or Unique Key table, you can only create bloom filter indexes for key columns.
  • TINYINT, FLOAT, DOUBLE, and DECIMAL columns do not support creating bloom filter indexes.
  • Bloom filter indexes can only improve the performance of queries that contain the in and = operators, such as Select xxx from table where x in {} and Select xxx from table where column = xxx. More discrete values in this column will result in more precise queries.

For more information, see Bloom filter indexing

PROPERTIES (
"bloom_filter_columns"="k1,k2,k3"
)

Use Colocate Join

If you want to use Colocate Join attributes, specify it in properties.

PROPERTIES (
"colocate_with"="table1"
)

Configure dynamic partitions

If you want to use dynamic partition attributes, please specify it in properties.

PROPERTIES (

"dynamic_partition.enable" = "true|false",
"dynamic_partition.time_unit" = "DAY|WEEK|MONTH",
"dynamic_partition.start" = "${integer_value}",
"dynamic_partition.end" = "${integer_value}",
"dynamic_partition.prefix" = "${string_value}",
"dynamic_partition.buckets" = "${integer_value}"

PROPERTIES

ParameterRequiredDescription
dynamic_partition.enableNoWhether to enable dynamic partitioning. Valid values: TRUE and FALSE. Default value: TRUE.
dynamic_partition.time_unitYesThe time granularity for dynamically created partitions. It is a required parameter. Valid values: DAY, WEEK, and MONTH. The time granularity determines the suffix format for dynamically created partitions.
- If the value is DAY, the suffix format for dynamically created partitions is yyyyMMdd. An example partition name suffix is 20200321.
- If the value is WEEK, the suffix format for dynamically created partitions is yyyy_ww, for example 2020_13 for the 13th week of 2020.
- If the value is MONTH, the suffix format for dynamically created partitions is yyyyMM, for example 202003.
dynamic_partition.startNoThe starting offset of dynamic partitioning. The value of this parameter must be a negative integer. The partitions before this offset will be deleted based on the current day, week, or month which is determined by dynamic_partition.time_unit. The default value is Integer.MIN_VALUE, namely, -2147483648, which means that historical partitions will not be deleted.
dynamic_partition.endYesThe end offset of dynamic partitioning. The value of this parameter must be a positive integer. The partitions from the current day, week, or month to the end offset will be created in advance.
dynamic_partition.prefixNoThe prefix added to the names of dynamic partitions. Default value: p.
dynamic_partition.bucketsNoThe number of buckets per dynamic partition. The default value is the same as the number of buckets determined by the reserved word BUCKETS or automatically set by StarRocks.

Set data compression algorithm

You can specify a data compression algorithm for a table by adding property compression when you create a table.

The valid values of compression are:

  • LZ4: the LZ4 algorithm.
  • ZSTD: the Zstandard algorithm.
  • ZLIB: the zlib algorithm.
  • SNAPPY: the Snappy algorithm.

For more information about how to choose a suitable data compression algorithm, see Data compression.

Set write quorum for data loading

If your StarRocks cluster has multiple data replicas, you can set different write quorum for tables, that is, how many replicas are required to return loading success before StarRocks can determine the loading task is successful. You can specify write quorum by adding the property write_quorum when you create a table. This property is supported from v2.5.

The valid values of write_quorum are:

  • MAJORITY: Default value. When the majority of data replicas return loading success, StarRocks returns loading task success. Otherwise, StarRocks returns loading task failed.
  • ONE: When one of the data replicas returns loading success, StarRocks returns loading task success. Otherwise, StarRocks returns loading task failed.
  • ALL: When all of the data replicas return loading success, StarRocks returns loading task success. Otherwise, StarRocks returns loading task failed.

CAUTION

  • Setting a low write quorum for loading increases the risk of data inaccessibility and even loss. For example, you load data into a table with one write quorum in a StarRocks cluster of two replicas, and the data was successfully loaded into only one replica. Despite that StarRocks determines the loading task succeeded, there is only one surviving replica of the data. If the server which stores the tablets of loaded data goes down, the data in these tablets becomes inaccessible. And if the disk of the server is damaged, the data is lost.
  • StarRocks returns the loading task status only after all data replicas have returned the status. StarRocks will not return the loading task status when there are replicas whose loading status is unknown. In a replica, loading timeout is also considered as loading failed.

Specify data writing and replication mode among replicas

If your StarRocks cluster has multiple data replicas, you can specify the replicated_storage parameter in PROPERTIES to configure the data writing and replication mode among replicas.

  • true (default in v3.0 and later) indicates "single leader replication", which means data is written only to the primary replica. Other replicas synchronize data from the primary replica. This mode significantly reduces CPU cost caused by data writing to multiple replicas. It is supported from v2.5.
  • false (default in v2.5) indicates "leaderless replication", which means data is directly written to multiple replicas, without differentiating primary and secondary replicas. The CPU cost is multiplied by the number of replicas.

In most cases, using the default value gains better data writing performance. If you want to change the data writing and replication mode among replicas, run the ALTER TABLE command. Example:

    ALTER TABLE example_db.my_table
SET ("replicated_storage" = "false");

Create rollup in bulk

You can create rollup in bulk when you create a table.

Syntax:

ROLLUP (rollup_name (column_name1, column_name2, ...)
[FROM from_index_name]
[PROPERTIES ("key"="value", ...)],...)

Define Unique Key constraints and Foreign Key constraints for View Delta Join query rewrite

To enable query rewrite in the View Delta Join scenario, you must define the Unique Key constraints unique_constraints and Foreign Key constraints foreign_key_constraints for the table to be joined in the Delta Join. See Asynchronous materialized view - Rewrite queries in View Delta Join scenario for further information.

PROPERTIES (
"unique_constraints" = "<unique_key>[, ...]",
"foreign_key_constraints" = "
(<child_column>[, ...])
REFERENCES
[catalog_name].[database_name].<parent_table_name>(<parent_column>[, ...])
[;...]
"
)
  • child_column: the Foreign Key of the table. You can define multiple child_column.
  • catalog_name: the name of the catalog where the table to join resides. The default catalog is used if this parameter is not specified.
  • database_name: the name of the database where the table to join resides. The current database is used if this parameter is not specified.
  • parent_table_name: the name of the table to join.
  • parent_column: the column to be joined. They must be the Primary Keys or Unique Keys of the corresponding tables.

CAUTION

  • unique_constraints and foreign_key_constraints are only used for query rewrite. Foreign Key constraints checks are not guaranteed when data is loaded into the table. You must ensure the data loaded into the table meets the constraints.
  • The primary keys of a Primary Key table or the unique keys of a Unique Key table are, by default, the corresponding unique_constraints. You do not need to set it manually.
  • The child_column in a table's foreign_key_constraints must be referenced to a unique_key in another table's unique_constraints.
  • The number of child_column and parent_column must agree.
  • The data types of the child_column and the corresponding parent_column must match.

Create cloud-native tables for StarRocks Shared-data cluster

To use your StarRocks Shared-data cluster, you must create cloud-native tables with the following properties:

PROPERTIES (
"datacache.enable" = "{ true | false }",
"datacache.partition_duration" = "<string_value>",
"enable_async_write_back" = "{ true | false }"
)
  • datacache.enable: Whether to enable the local disk cache. Default: true.

    • When this property is set to true, the data to be loaded is simultaneously written into the object storage and the local disk (as the cache for query acceleration).

    • When this property is set to false, the data is loaded only into the object storage.

    NOTE

    To enable the local disk cache, you must specify the directory of the disk in the BE configuration item storage_root_path. For more information, see BE Configuration items.

  • datacache.partition_duration: The validity duration of the hot data. When the local disk cache is enabled, all data is loaded into the cache. When the cache is full, StarRocks deletes the less recently used data from the cache. When a query needs to scan the deleted data, StarRocks checks if the data is within the duration of validity. If the data is within the duration, StarRocks loads the data into the cache again. If the data is not within the duration, StarRocks does not load it into the cache. This property is a string value that can be specified with the following units: YEAR, MONTH, DAY, and HOUR, for example, 7 DAY and 12 HOUR. If it is not specified, all data is cached as the hot data.

    NOTE

    This property is available only when datacache.enable is set to true.

  • enable_async_write_back: Whether to allow data to be written into object storage asynchronously. Default: false.

    • When this property is set to true, the load task returns success as soon as the data is written into the local disk cache, and the data is written into the object storage asynchronously. This allows better loading performance, but it also risks data reliability under potential system failures.
    • When this property is set to false, the load task returns success only after the data is written into both object storage and the local disk cache. This guarantees higher availability but leads to lower loading performance.

Examples

Create an Aggregate table that uses Hash bucketing and columnar storage

CREATE TABLE example_db.table_hash
(
k1 TINYINT,
k2 DECIMAL(10, 2) DEFAULT "10.5",
v1 CHAR(10) REPLACE,
v2 INT SUM
)
ENGINE=olap
AGGREGATE KEY(k1, k2)
COMMENT "my first starrocks table"
DISTRIBUTED BY HASH(k1)
PROPERTIES ("storage_type"="column");

Create an Aggregate table and set the storage medium and cooldown time

CREATE TABLE example_db.table_hash
(
k1 BIGINT,
k2 LARGEINT,
v1 VARCHAR(2048) REPLACE,
v2 SMALLINT SUM DEFAULT "10"
)
ENGINE=olap
UNIQUE KEY(k1, k2)
DISTRIBUTED BY HASH (k1, k2)
PROPERTIES(
"storage_type"="column",
"storage_medium" = "SSD",
"storage_cooldown_time" = "2015-06-04 00:00:00"
);

Or

CREATE TABLE example_db.table_hash
(
k1 BIGINT,
k2 LARGEINT,
v1 VARCHAR(2048) REPLACE,
v2 SMALLINT SUM DEFAULT "10"
)
ENGINE=olap
PRIMARY KEY(k1, k2)
DISTRIBUTED BY HASH (k1, k2)
PROPERTIES(
"storage_type"="column",
"storage_medium" = "SSD",
"storage_cooldown_time" = "2015-06-04 00:00:00"
);

Create a Duplicate Key table that uses Range partition, Hash bucketing, and column-based storage, and set the storage medium and cooldown time

LESS THAN

CREATE TABLE example_db.table_range
(
k1 DATE,
k2 INT,
k3 SMALLINT,
v1 VARCHAR(2048),
v2 DATETIME DEFAULT "2014-02-04 15:36:00"
)
ENGINE=olap
DUPLICATE KEY(k1, k2, k3)
PARTITION BY RANGE (k1)
(
PARTITION p1 VALUES LESS THAN ("2014-01-01"),
PARTITION p2 VALUES LESS THAN ("2014-06-01"),
PARTITION p3 VALUES LESS THAN ("2014-12-01")
)
DISTRIBUTED BY HASH(k2)
PROPERTIES(
"storage_medium" = "SSD",
"storage_cooldown_time" = "2015-06-04 00:00:00"
);

Note:

This statement will create three data partitions:

( {    MIN     },   {"2014-01-01"} )
[ {"2014-01-01"}, {"2014-06-01"} )
[ {"2014-06-01"}, {"2014-12-01"} )

Data outside these ranges will be not be loaded.

Fixed Range

CREATE TABLE table_range
(
k1 DATE,
k2 INT,
k3 SMALLINT,
v1 VARCHAR(2048),
v2 DATETIME DEFAULT "2014-02-04 15:36:00"
)
ENGINE=olap
DUPLICATE KEY(k1, k2, k3)
PARTITION BY RANGE (k1, k2, k3)
(
PARTITION p1 VALUES [("2014-01-01", "10", "200"), ("2014-01-01", "20", "300")),
PARTITION p2 VALUES [("2014-06-01", "100", "200"), ("2014-07-01", "100", "300"))
)
DISTRIBUTED BY HASH(k2)
PROPERTIES(
"storage_medium" = "SSD"
);

Create a MySQL external table

CREATE EXTERNAL TABLE example_db.table_mysql
(
k1 DATE,
k2 INT,
k3 SMALLINT,
k4 VARCHAR(2048),
k5 DATETIME
)
ENGINE=mysql
PROPERTIES
(
"host" = "127.0.0.1",
"port" = "8239",
"user" = "mysql_user",
"password" = "mysql_passwd",
"database" = "mysql_db_test",
"table" = "mysql_table_test"
)

Create a table that contains HLL columns

CREATE TABLE example_db.example_table
(
k1 TINYINT,
k2 DECIMAL(10, 2) DEFAULT "10.5",
v1 HLL HLL_UNION,
v2 HLL HLL_UNION
)
ENGINE=olap
AGGREGATE KEY(k1, k2)
DISTRIBUTED BY HASH(k1)
PROPERTIES ("storage_type"="column");

Create a table containing BITMAP_UNION aggregation type

The original data type of v1 and v2 columns must be TINYINT, SMALLINT, or INT.

CREATE TABLE example_db.example_table
(
k1 TINYINT,
k2 DECIMAL(10, 2) DEFAULT "10.5",
v1 BITMAP BITMAP_UNION,
v2 BITMAP BITMAP_UNION
)
ENGINE=olap
AGGREGATE KEY(k1, k2)
DISTRIBUTED BY HASH(k1)
PROPERTIES ("storage_type"="column");

Create two tables that support Colocate Join

CREATE TABLE `t1` 
(
`id` int(11) COMMENT "",
`value` varchar(8) COMMENT ""
)
ENGINE=OLAP
DUPLICATE KEY(`id`)
DISTRIBUTED BY HASH(`id`)
PROPERTIES
(
"colocate_with" = "t1"
);

CREATE TABLE `t2`
(
`id` int(11) COMMENT "",
`value` varchar(8) COMMENT ""
)
ENGINE=OLAP
DUPLICATE KEY(`id`)
DISTRIBUTED BY HASH(`id`)
PROPERTIES
(
"colocate_with" = "t1"
);

Create a table with bitmap index

CREATE TABLE example_db.table_hash
(
k1 TINYINT,
k2 DECIMAL(10, 2) DEFAULT "10.5",
v1 CHAR(10) REPLACE,
v2 INT SUM,
INDEX k1_idx (k1) USING BITMAP COMMENT 'xxxxxx'
)
ENGINE=olap
AGGREGATE KEY(k1, k2)
COMMENT "my first starrocks table"
DISTRIBUTED BY HASH(k1)
PROPERTIES ("storage_type"="column");

Create a dynamic partition table

The dynamic partitioning function must be enabled ("dynamic_partition.enable" = "true") in FE configuration. For more information, see Configure dynamic partitions.

This example creates partitions for the next three days and deletes partitions created three days ago. For example, if today is 2020-01-08, partitions with the following names will be created: p20200108, p20200109, p20200110, p20200111, and their ranges are:

[types: [DATE]; keys: [2020-01-08]; ‥types: [DATE]; keys: [2020-01-09]; )
[types: [DATE]; keys: [2020-01-09]; ‥types: [DATE]; keys: [2020-01-10]; )
[types: [DATE]; keys: [2020-01-10]; ‥types: [DATE]; keys: [2020-01-11]; )
[types: [DATE]; keys: [2020-01-11]; ‥types: [DATE]; keys: [2020-01-12]; )
CREATE TABLE example_db.dynamic_partition
(
k1 DATE,
k2 INT,
k3 SMALLINT,
v1 VARCHAR(2048),
v2 DATETIME DEFAULT "2014-02-04 15:36:00"
)
ENGINE=olap
DUPLICATE KEY(k1, k2, k3)
PARTITION BY RANGE (k1)
(
PARTITION p1 VALUES LESS THAN ("2014-01-01"),
PARTITION p2 VALUES LESS THAN ("2014-06-01"),
PARTITION p3 VALUES LESS THAN ("2014-12-01")
)
DISTRIBUTED BY HASH(k2)
PROPERTIES(
"storage_medium" = "SSD",
"dynamic_partition.enable" = "true",
"dynamic_partition.time_unit" = "DAY",
"dynamic_partition.start" = "-3",
"dynamic_partition.end" = "3",
"dynamic_partition.prefix" = "p",
"dynamic_partition.buckets" = "10"
);

Create a table where multiple partitions are created in a batch, and an integer type column is specified as partitioning column

In the following example, the partitioning column datekey is of the INT type. All the partitions are created by only one simple partition clause START ("1") END ("5") EVERY (1). The range of all the partitions starts from 1 and ends at 5, with a partition granularity of 1:

NOTE

The partitioning column values in START() and END() need to be wrapped in quotation marks, while the partition granularity in the EVERY() does not need to be wrapped in quotation marks.

CREATE TABLE site_access (
datekey INT,
site_id INT,
city_code SMALLINT,
user_name VARCHAR(32),
pv BIGINT DEFAULT '0'
)
ENGINE=olap
DUPLICATE KEY(datekey, site_id, city_code, user_name)
PARTITION BY RANGE (datekey) (START ("1") END ("5") EVERY (1)
)
DISTRIBUTED BY HASH(site_id)
PROPERTIES ("replication_num" = "3");

Create a Hive external table

Before you create a Hive external table, you must have created a Hive resource and database. For more information, see External table.

CREATE EXTERNAL TABLE example_db.table_hive
(
k1 TINYINT,
k2 VARCHAR(50),
v INT
)
ENGINE=hive
PROPERTIES
(
"resource" = "hive0",
"database" = "hive_db_name",
"table" = "hive_table_name"
);

Create a Primary Key table and specify the sort key

Suppose that you need to analyze user behavior in real time from dimensions such as users' address and last active time. When you create a table, you can define the user_id column as the primary key and define the combination of the address and last_active columns as the sort key.

create table users (
user_id bigint NOT NULL,
name string NOT NULL,
email string NULL,
address string NULL,
age tinyint NULL,
sex tinyint NULL,
last_active datetime,
property0 tinyint NOT NULL,
property1 tinyint NOT NULL,
property2 tinyint NOT NULL,
property3 tinyint NOT NULL
)
PRIMARY KEY (`user_id`)
DISTRIBUTED BY HASH(`user_id`)
ORDER BY(`address`,`last_active`)
PROPERTIES(
"replication_num" = "3",
"enable_persistent_index" = "true"
);

References