CREATE TABLE
Description
Creates a new table in StarRocks.
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]
[PROPERTIES ("key"="value", ...)]
[BROKER PROPERTIES ("key"="value", ...)]
Parameters
column_definition
Syntax:
col_name col_type [agg_type] [NULL | NOT NULL] [DEFAULT "default_value"]
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 oflength
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 inDEFAULT "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.
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.
-
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 isdoc
.
-
For Hive, specify the following properties:
PROPERTIES (
"database" = "hive_db_name",
"table" = "hive_table_name",
"hive.metastore.uris" = "thrift://xx.xx.xx.xx: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 andtable
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.
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
Syntax:
DISTRIBUTED BY HASH (k1[,k2 ...]) [BUCKETS num]
Data in partitions can be subdivided into tablets 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 tablet by using one bucketing column, you can choose multiple bucketing columns (at most three). For more information, see Choose bucketing columns.
Precautions:
- When a table is created, you must specify the bucketing columns.
- The values of bucketing columns cannot be updated.
- Bucketing columns cannot be modified after they are specified.
- Since StarRocks 2.5, 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 tablets.
PROPERTIES
Specify storage medium, storage cooldown time, replica number
If the engine type is olap
, you can specify storage medium, storage cooldown time, and replica number when you create a table.
NOTE
storage_cooldown_time
can be configured only whenstorage_medium
is set toSSD
. If you want to setstorage_medium
to SSD, make sure that your cluster uses SSD disks, that is,storage_root_path
reported by BEs includes SSD. For more information aboutstorage_root_path
, see Configuration.
PROPERTIES (
"storage_medium" = "[SSD|HDD]",
[ "storage_cooldown_time" = "yyyy-MM-dd HH:mm:ss", ]
[ "replication_num" = "3" ]
)
storage_medium: the initial storage medium, which can be set to SSD or HDD.
NOTE
- From 2.5.1, the system automatically infers storage medium based on BE disk type if
storage_medium
is not explicitly specified. Inference mechanism: Ifstorage_root_path
reported by BEs contain only SSD, the system automatically sets this parameter to SSD. Ifstorage_root_path
reported by BEs contain only HDD, the system automatically sets this parameter to HDD. Ifstorage_root_path
reported by BEs contain both SSD and HDD, the system automatically sets this parameter to SSD. From 2.5.4 onwards, ifstorage_root_path
reported by BEs contain both SSD and HDD and the propertystorage_cooldown_time
is specified,storage_medium
is set to SSD; if the propertystorage_cooldown_time
is not specified,storage_medium
is set to HDD.- If the FE configuration item
enable_strict_storage_medium_check
is set totrue
, 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. Ifenable_strict_storage_medium_check
is set tofalse
, the system ignores this error and forcibly creates the table. However, cluster disk space may be unevenly distributed after data is loaded.
storage_cooldown_time: the storage cooldown time for a partition. If the storage medium is SSD, SSD is switched to HDD after the time specified by this parameter. Format: "yyyy-MM-dd HH:mm:ss". The specified time must be later than the current time. If this parameter is not explicitly specified, storage cooldown is not performed by default.
replication_num: number of replicas in the specified partition. Default number: 3.
If the table has only one partition, the properties belong to the table. If the table has two levels of partitions, the properties belong to each partition. You can also specify different properties for different partitions by using ALTER TABLE ADD PARTITION or ALTER TABLE MODIFY PARTITION.
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 asSelect xxx from table where x in {}
andSelect 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
Parameter | Required | Description |
---|---|---|
dynamic_partition.enable | No | Whether to enable dynamic partitioning. Valid values: TRUE and FALSE . Default value: TRUE . |
dynamic_partition.time_unit | Yes | The 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.start | No | The 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.end | Yes | The 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.prefix | No | The prefix added to the names of dynamic partitions. Default value: p . |
dynamic_partition.buckets | No | The 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
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" = "true");
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", ...)],...)
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) BUCKETS 10
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) BUCKETS 10
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) BUCKETS 10
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) BUCKETS 10
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) BUCKETS 10
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) BUCKETS 10
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) BUCKETS 10
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`) BUCKETS 10
PROPERTIES
(
"colocate_with" = "t1"
);
CREATE TABLE `t2`
(
`id` int(11) COMMENT "",
`value` varchar(8) COMMENT ""
)
ENGINE=OLAP
DUPLICATE KEY(`id`)
DISTRIBUTED BY HASH(`id`) BUCKETS 10
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) BUCKETS 10
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) BUCKETS 10
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"
);