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CREATE-TABLE

CREATE-TABLE​

Description​

This command is used to create a table. The subject of this document describes the syntax for creating Doris self-maintained tables.

CREATE TABLE [IF NOT EXISTS] [database.]table
(
    column_definition_list
    [, index_definition_list]
)
[engine_type]
[keys_type]
[table_comment]
[partition_info]
distribution_desc
[rollup_list]
[properties]
[extra_properties]

column_definition_list​

Column definition list:

column_definition[, column_definition]

  • column_definition

    Column definition:

    column_name column_type [KEY] [aggr_type] [NULL] [AUTO_INCREMENT(auto_inc_start_value)] [default_value] [on update current_timestamp] [column_comment]

    • column_type

      Column type, the following types are supported:

      TINYINT (1 byte)
          Range: -2^7 + 1 ~ 2^7-1
      SMALLINT (2 bytes)
          Range: -2^15 + 1 ~ 2^15-1
      INT (4 bytes)
          Range: -2^31 + 1 ~ 2^31-1
      BIGINT (8 bytes)
          Range: -2^63 + 1 ~ 2^63-1
      LARGEINT (16 bytes)
          Range: -2^127 + 1 ~ 2^127-1
      FLOAT (4 bytes)
          Support scientific notation
      DOUBLE (12 bytes)
          Support scientific notation
      DECIMAL[(precision, scale)] (16 bytes)
          The decimal type with guaranteed precision. The default is DECIMAL(10, 0)
          precision: 1 ~ 27
          scale: 0 ~ 9
          Where the integer part is 1 ~ 18
          Does not support scientific notation
      DATE (3 bytes)
          Range: 0000-01-01 ~ 9999-12-31
      DATETIME (8 bytes)
          Range: 0000-01-01 00:00:00 ~ 9999-12-31 23:59:59
      CHAR[(length)]
          Fixed-length character string. Length range: 1 ~ 255. Default is 1
      VARCHAR[(length)]
          Variable length character string. Length range: 1 ~ 65533. Default is 65533
      HLL (1~16385 bytes)
          HyperLogLog column type, do not need to specify the length and default value. The length is controlled within the system according to the degree of data aggregation.
          Must be used with HLL_UNION aggregation type.
      BITMAP
          The bitmap column type does not need to specify the length and default value. Represents a collection of integers, and the maximum number of elements supported is 2^64-1.
          Must be used with BITMAP_UNION aggregation type.

    • aggr_type

      Aggregation type, the following aggregation types are supported:

      SUM: Sum. Applicable numeric types.
      MIN: Find the minimum value. Suitable for numeric types.
      MAX: Find the maximum value. Suitable for numeric types.
      REPLACE: Replace. For rows with the same dimension column, the index column will be imported in the order of import, and the last imported will replace the first imported.
      REPLACE_IF_NOT_NULL: non-null value replacement. The difference with REPLACE is that there is no replacement for null values. It should be noted here that the default value should be NULL, not an empty string. If it is an empty string, you should replace it with an empty string.
      HLL_UNION: The aggregation method of HLL type columns, aggregated by HyperLogLog algorithm.
      BITMAP_UNION: The aggregation mode of BIMTAP type columns, which performs the union aggregation of bitmaps.

    • AUTO_INCREMENT(auto_inc_start_value)

      To indicate if the column is a auto-increment column. Auto-increment column can be used to generate a unique identity for new row. If no values are assgined for auto-increment column when inserting, Doris will generate sequence numbers automatically. You can also assign the auto-increment column with NULL literal to indicate Doris to generate sequence numbers. It should be noted that, for performance reasons, BE will cache some values of auto-increment column in memory. Therefore, the values generated by auto-increment column can only guarantee monotonicity and uniqueness, but not strict continuity. A table can have at most one auto-incremnt column. The auto-increment column should be BIGINT type and be NOT NULL. Both Duplicate model table and Unique model table support auto-increment column. You can specify the starting value for an auto-increment column by providing `auto_inc_start_value. If not specified, the default starting value is 1.

    • default_value

      Default value of the column. If the load data does not specify a value for this column, the system will assign a default value to this column.

      The syntax is: default default_value.

      Currently, the default value supports two forms:

      1. The user specifies a fixed value, such as:
      	k1 INT DEFAULT '1',
          k2 CHAR(10) DEFAULT 'aaaa'
      1. Keywords are provided by the system. Currently, the following keywords are supported:
          // This keyword is used only for DATETIME type. If the value is missing, the system assigns the current timestamp.
          dt DATETIME DEFAULT CURRENT_TIMESTAMP

    • on update current_timestamp

      To indicate that whether the value of this column should be updated to the current timestamp (current_timestamp) when there is an update on the row. The feature is only available on unique table with merge-on-write enabled. Columns with this feature enabled must declare a default value, and the default value must be current_timestamp. If the precision of the timestamp is declared here, the timestamp precision in the default value of the column must be the same as the precision declared here."

    Example:

      ```
      k1 TINYINT,
      k2 DECIMAL(10,2) DEFAULT "10.5",
      k4 BIGINT NULL DEFAULT "1000" COMMENT "This is column k4",
      v1 VARCHAR(10) REPLACE NOT NULL,
      v2 BITMAP BITMAP_UNION,
      v3 HLL HLL_UNION,
      v4 INT SUM NOT NULL DEFAULT "1" COMMENT "This is column v4"
      dt datetime(6) default current_timestamp(6) on update current_timestamp(6)
      ```

index_definition_list​

Index list definition:

index_definition[, index_definition]

  • index_definition

    Index definition:

    INDEX index_name (col_name) [USING INVERTED] COMMENT'xxxxxx'

    Example:

    INDEX idx1 (k1) USING INVERTED COMMENT "This is a inverted index1",
    INDEX idx2 (k2) USING INVERTED COMMENT "This is a inverted index2",
    ...

engine_type​

Table engine type. All types in this document are OLAP. Example:

`ENGINE=olap`

keys_type​

Data model.

key_type(col1, col2, ...)

key_type supports the following models:

  • DUPLICATE KEY (default): The subsequent specified column is the sorting column.
  • AGGREGATE KEY: The specified column is the dimension column.
  • UNIQUE KEY: The subsequent specified column is the primary key column.

NOTE: when set table property "enable_duplicate_without_keys_by_default" = "true", will create a duplicate model without sorting columns and prefix indexes by default.

Example:

DUPLICATE KEY(col1, col2),
AGGREGATE KEY(k1, k2, k3),
UNIQUE KEY(k1, k2)

table_comment​

Table notes. Example:

```
COMMENT "This is my first DORIS table"
```

partition_info​

Partition information supports three writing methods:

  1. LESS THAN: Only define the upper boundary of the partition. The lower bound is determined by the upper bound of the previous partition.

    PARTITION BY RANGE(col1[, col2, ...])
    (
        PARTITION partition_name1 VALUES LESS THAN MAXVALUE|("value1", "value2", ...),
        PARTITION partition_name2 VALUES LESS THAN MAXVALUE|("value1", "value2", ...)
    )

  2. FIXED RANGE: Define the left closed and right open interval of the zone.

    PARTITION BY RANGE(col1[, col2, ...])
    (
        PARTITION partition_name1 VALUES [("k1-lower1", "k2-lower1", "k3-lower1",...), ("k1-upper1", "k2-upper1", "k3-upper1", ... )),
        PARTITION partition_name2 VALUES [("k1-lower1-2", "k2-lower1-2", ...), ("k1-upper1-2", MAXVALUE, ))
    )

  3. MULTI RANGE: Multi build RANGE partitions,Define the left closed and right open interval of the zone, Set the time unit and step size, the time unit supports year, month, day, week and hour.

Tips

This feature is supported since the Apache Doris 1.2 version

```
PARTITION BY RANGE(col)
(
   FROM ("2000-11-14") TO ("2021-11-14") INTERVAL 1 YEAR,
   FROM ("2021-11-14") TO ("2022-11-14") INTERVAL 1 MONTH,
   FROM ("2022-11-14") TO ("2023-01-03") INTERVAL 1 WEEK,
   FROM ("2023-01-03") TO ("2023-01-14") INTERVAL 1 DAY
)
```

4. MULTI RANGE: Multi build integer RANGE partitions,Define the left closed and right open interval of the zone, and step size.

```
PARTITION BY RANGE(int_col)
(
    FROM (1) TO (100) INTERVAL 10
)
```

distribution_desc​

Define the data bucketing method.

  1. Hash Syntax: DISTRIBUTED BY HASH (k1[,k2 ...]) [BUCKETS num|auto] Explain: Hash bucketing using the specified key column.

  2. Random Syntax: DISTRIBUTED BY RANDOM [BUCKETS num|auto] Explain: Use random numbers for bucketing.

rollup_list​

Multiple materialized views (ROLLUP) can be created at the same time as the table is built.

ROLLUP (rollup_definition[, rollup_definition, ...])

  • rollup_definition

    rollup_name (col1[, col2, ...]) [DUPLICATE KEY(col1[, col2, ...])] [PROPERTIES("key" = "value")]

    Example:

    ROLLUP (
        r1 (k1, k3, v1, v2),
        r2 (k1, v1)
    )

properties​

Set table properties. The following attributes are currently supported:

  • replication_num

    Number of copies. The default number of copies is 3. If the number of BE nodes is less than 3, you need to specify that the number of copies is less than or equal to the number of BE nodes.

    After version 0.15, this attribute will be automatically converted to the replication_allocation attribute, such as:

    "replication_num" = "3" will be automatically converted to "replication_allocation" = "tag.location.default:3"

  • replication_allocation

    Set the copy distribution according to Tag. This attribute can completely cover the function of the replication_num attribute.

  • min_load_replica_num

    The minimum required successful replica num for loading data. The default value is -1. If set less than or equal to 0, loading data requires a majority replicas to succeed.

  • is_being_synced

    Used to identify whether this table is copied by CCR and is being synchronized by syncer. The default is false.

    If set to true:
    colocate_with, storage_policy properties will be erased
    dynamic partition, auto bucket features will be disabled, that is, they will be displayed as enabled in show create table, but will not actually take effect. When is_being_synced is set to false, these features will resume working.

    This property is for CCR peripheral modules only and should not be manually set during CCR synchronization.

  • storage_medium/storage_cooldown_time

    Data storage medium. storage_medium is used to declare the initial storage medium of the table data, and storage_cooldown_time is used to set the expiration time. Example:

    "storage_medium" = "SSD",
    "storage_cooldown_time" = "2020-11-20 00:00:00"

    This example indicates that the data is stored in the SSD and will be automatically migrated to the HDD storage after the expiration of 2020-11-20 00:00:00.

  • colocate_with

    When you need to use the Colocation Join function, use this parameter to set the Colocation Group.

    "colocate_with" = "group1"

  • bloom_filter_columns

    The user specifies the list of column names that need to be added to the Bloom Filter index. The Bloom Filter index of each column is independent, not a composite index.

    "bloom_filter_columns" = "k1, k2, k3"

  • in_memory

    Deprecated.

  • function_column.sequence_col

    When using the UNIQUE KEY model, you can specify a sequence column. When the KEY columns are the same, REPLACE will be performed according to the sequence column (the larger value replaces the smaller value, otherwise it cannot be replaced)

    The function_column.sequence_col is used to specify the mapping of the sequence column to a column in the table, which can be integral and time (DATE, DATETIME). The type of this column cannot be changed after creation. If function_column.sequence_col is set, function_column.sequence_type is ignored.

    "function_column.sequence_col" ='column_name'

  • function_column.sequence_type

    When using the UNIQUE KEY model, you can specify a sequence column. When the KEY columns are the same, REPLACE will be performed according to the sequence column (the larger value replaces the smaller value, otherwise it cannot be replaced)

    Here we only need to specify the type of sequence column, support time type or integer type. Doris will create a hidden sequence column.

    "function_column.sequence_type" ='Date'

  • compression

    The default compression method for Doris tables is LZ4. After version 1.1, it is supported to specify the compression method as ZSTD to obtain a higher compression ratio.

    "compression"="zstd"

  • enable_unique_key_merge_on_write

    Wheather the unique table use merge-on-write implementation.

    The property is disabled by default before version 2.1 and is enabled by default since version 2.1.

  • light_schema_change

    Whether to use the Light Schema Change optimization.

    If set to true, the addition and deletion of value columns can be done more quickly and synchronously.

    "light_schema_change"="true"

    This feature is enabled by default after v2.0.0.

  • disable_auto_compaction

    Whether to disable automatic compaction for this table.

    If this property is set to 'true', the background automatic compaction process will skip all the tables of this table.

    "disable_auto_compaction" = "false"

  • enable_single_replica_compaction

    Whether to enable single replica compaction for this table.

    If this property is set to 'true', all replicas of the tablet will only have one replica performing compaction, while the others fetch rowsets from that replica.

    "enable_single_replica_compaction" = "false"

  • enable_duplicate_without_keys_by_default

    When true, if Unique, Aggregate, or Duplicate is not specified when creating a table, a Duplicate model table without sorting columns and prefix indexes will be created by default.

    "enable_duplicate_without_keys_by_default" = "false"

  • skip_write_index_on_load

    Whether to enable skip inverted index on load for this table.

    If this property is set to 'true', skip writting index (only inverted index now) on first time load and delay writting index to compaction. It can reduce CPU and IO resource usage for high throughput load.

    "skip_write_index_on_load" = "false"

  • compaction_policy

    Configure the compaction strategy in the compression phase. Only support configuring the compaction policy as "time_series" or "size_based".

    time_series: When the disk size of a rowset accumulates to a certain threshold, version merging takes place. The merged rowset is directly promoted to the base compaction stage. This approach effectively reduces the write amplification rate of compaction, especially in scenarios with continuous imports in a time series context.

    In the case of time series compaction, the execution of compaction is adjusted using parameters that have the prefix time_series_compaction.

    "compaction_policy" = ""

  • group_commit_interval_ms

    Configures the group commit batch interval for this table. The unit is milliseconds, with a default value of 10000ms (10 seconds).

    The flushing timing of group commit depends on which value is reached first: group_commit_interval_ms or group_commit_data_bytes.

    "group_commit_interval_ms" = "10000"

  • group_commit_data_bytes

    Configures the group commit batch data size for this table. The unit is bytes, with a default value of 134217728 bytes (128MB).

    The flushing timing of group commit depends on which value is reached first: group_commit_interval_ms or group_commit_data_bytes.

    "group_commit_data_bytes" = "134217728"

  • time_series_compaction_goal_size_mbytes

    Time series compaction policy will utilize this parameter to adjust the size of input files for each compaction. The output file size will be approximately equal to the input file size.

    "time_series_compaction_goal_size_mbytes" = "1024"

  • time_series_compaction_file_count_threshold

    Time series compaction policy will utilize this parameter to adjust the minimum number of input files for each compaction.

    If the number of files in a tablet exceeds the configured threshold, it will trigger a compaction process.

    "time_series_compaction_file_count_threshold" = "2000"

  • time_series_compaction_time_threshold_seconds

    When time series compaction policy is applied, a significant duration passes without a compaction being executed, a compaction will be triggered.

    "time_series_compaction_time_threshold_seconds" = "3600"

  • enable_mow_light_delete

    If modify delete predicate for the DELETE statement on the unique merge-on-write table. If enabled, it will improve the performance of the DELETE statement, but errors may occur in partial column updates after deletion. If disabled, it will reduce the performance of the DELETE statement to ensure correctness.

    The default value for this property is false.

    This propertiy can only be enabled on unique merge-on-write tables.

    "enable_mow_light_delete" = "true"

  • Dynamic partition related

References related to dynamic partitioningData Partitioning-Dynamic partitioning

Example​

  1. Create a detailed model table

    CREATE TABLE example_db.table_hash
    (
        k1 TINYINT,
        k2 DECIMAL(10, 2) DEFAULT "10.5",
        k3 CHAR(10) COMMENT "string column",
        k4 INT NOT NULL DEFAULT "1" COMMENT "int column"
    )
    COMMENT "my first table"
    DISTRIBUTED BY HASH(k1) BUCKETS 32

  2. Create a detailed model table, partition, specify the sorting column, and set the number of copies to 1

    CREATE TABLE example_db.table_hash
    (
        k1 DATE,
        k2 DECIMAL(10, 2) DEFAULT "10.5",
        k3 CHAR(10) COMMENT "string column",
        k4 INT NOT NULL DEFAULT "1" COMMENT "int column"
    )
    DUPLICATE KEY(k1, k2)
    COMMENT "my first table"
    PARTITION BY RANGE(k1)
    (
        PARTITION p1 VALUES LESS THAN ("2020-02-01"),
        PARTITION p2 VALUES LESS THAN ("2020-03-01"),
        PARTITION p3 VALUES LESS THAN ("2020-04-01")
    )
    DISTRIBUTED BY HASH(k1) BUCKETS 32
    PROPERTIES (
        "replication_num" = "1"
    );

  3. Create a table with a unique model of the primary key, set the initial storage medium and cooling time

    CREATE TABLE example_db.table_hash
    (
        k1 BIGINT,
        k2 LARGEINT,
        v1 VARCHAR(2048),
        v2 SMALLINT DEFAULT "10"
    )
    UNIQUE KEY(k1, k2)
    DISTRIBUTED BY HASH (k1, k2) BUCKETS 32
    PROPERTIES(
        "storage_medium" = "SSD",
        "storage_cooldown_time" = "2015-06-04 00:00:00"
    );

  4. Create an aggregate model table, using a fixed range partition description

    CREATE TABLE table_range
    (
        k1 DATE,
        k2 INT,
        k3 SMALLINT,
        v1 VARCHAR(2048) REPLACE,
        v2 INT SUM DEFAULT "1"
    )
    AGGREGATE 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 32

  5. Create an aggregate model table with HLL and BITMAP column types

    CREATE TABLE example_db.example_table
    (
        k1 TINYINT,
        k2 DECIMAL(10, 2) DEFAULT "10.5",
        v1 HLL HLL_UNION,
        v2 BITMAP BITMAP_UNION
    )
    ENGINE=olap
    AGGREGATE KEY(k1, k2)
    DISTRIBUTED BY HASH(k1) BUCKETS 32

  6. Create two self-maintained tables of the same Colocation Group.

    CREATE TABLE t1 (
        id int(11) COMMENT "",
        value varchar(8) COMMENT ""
    )
    DUPLICATE KEY(id)
    DISTRIBUTED BY HASH(id) BUCKETS 10
    PROPERTIES (
        "colocate_with" = "group1"
    );

    CREATE TABLE t2 (
        id int(11) COMMENT "",
        value1 varchar(8) COMMENT "",
        value2 varchar(8) COMMENT ""
    )
    DUPLICATE KEY(`id`)
    DISTRIBUTED BY HASH(`id`) BUCKETS 10
    PROPERTIES (
        "colocate_with" = "group1"
    );

  7. Create a table with inverted index and bloom filter 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 INVERTED COMMENT'my first index'
    )
    AGGREGATE KEY(k1, k2)
    DISTRIBUTED BY HASH(k1) BUCKETS 32
    PROPERTIES (
        "bloom_filter_columns" = "k2"
    );

  8. Create a dynamic partition table.

    The table creates partitions 3 days in advance every day, and deletes the partitions 3 days ago. For example, if today is 2020-01-08, partitions named p20200108, p20200109, p20200110, p20200111 will be created. The partition 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"
    )
    DUPLICATE KEY(k1, k2, k3)
    PARTITION BY RANGE (k1) ()
    DISTRIBUTED BY HASH(k2) BUCKETS 32
    PROPERTIES(
        "dynamic_partition.time_unit" = "DAY",
        "dynamic_partition.start" = "-3",
        "dynamic_partition.end" = "3",
        "dynamic_partition.prefix" = "p",
        "dynamic_partition.buckets" = "32"
    );

  9. Create a table with a materialized view (ROLLUP).

    CREATE TABLE example_db.rolup_index_table
    (
        event_day DATE,
        siteid INT DEFAULT '10',
        citycode SMALLINT,
        username VARCHAR(32) DEFAULT'',
        pv BIGINT SUM DEFAULT '0'
    )
    AGGREGATE KEY(event_day, siteid, citycode, username)
    DISTRIBUTED BY HASH(siteid) BUCKETS 10
    ROLLUP (
        r1(event_day,siteid),
        r2(event_day,citycode),
        r3(event_day)
    )
    PROPERTIES("replication_num" = "3");

  10. Set the replica of the table through the replication_allocation property.

    CREATE TABLE example_db.table_hash
    (
      k1 TINYINT,
    	k2 DECIMAL(10, 2) DEFAULT "10.5"
    )
    DISTRIBUTED BY HASH(k1) BUCKETS 32
    PROPERTIES (
        "replication_allocation"="tag.location.group_a:1, tag.location.group_b:2"
    );
    CREATE TABLE example_db.dynamic_partition
    (
    	k1 DATE,
    	k2 INT,
    	k3 SMALLINT,
    	v1 VARCHAR(2048),
    	v2 DATETIME DEFAULT "2014-02-04 15:36:00"
    )
    PARTITION BY RANGE (k1) ()
    DISTRIBUTED BY HASH(k2) BUCKETS 32
    PROPERTIES(
        "dynamic_partition.time_unit" = "DAY",
        "dynamic_partition.start" = "-3",
        "dynamic_partition.end" = "3",
        "dynamic_partition.prefix" = "p",
        "dynamic_partition.buckets" = "32",
        "dynamic_partition.replication_allocation" = "tag.location.group_a:3"
     );

  11. Set the table hot and cold separation policy through the storage_policy property.

        CREATE TABLE IF NOT EXISTS create_table_use_created_policy 
        (
            k1 BIGINT,
            k2 LARGEINT,
            v1 VARCHAR(2048)
        )
        UNIQUE KEY(k1)
        DISTRIBUTED BY HASH (k1) BUCKETS 3
        PROPERTIES(
            "storage_policy" = "test_create_table_use_policy",
            "replication_num" = "1"
        );

NOTE: Need to create the s3 resource and storage policy before the table can be successfully associated with the migration policy

  1. Add a hot and cold data migration strategy for the table partition 
        CREATE TABLE create_table_partion_use_created_policy
        (
            k1 DATE,
            k2 INT,
            V1 VARCHAR(2048) REPLACE
        ) PARTITION BY RANGE (k1) (
            PARTITION p1 VALUES LESS THAN ("2022-01-01") ("storage_policy" = "test_create_table_partition_use_policy_1" ,"replication_num"="1"),
            PARTITION p2 VALUES LESS THAN ("2022-02-01") ("storage_policy" = "test_create_table_partition_use_policy_2" ,"replication_num"="1")
        ) DISTRIBUTED BY HASH(k2) BUCKETS 1;

NOTE: Need to create the s3 resource and storage policy before the table can be successfully associated with the migration policy

  1. Multi Partition by a partition desc
Tips

This feature is supported since the Apache Doris 1.2 version

```sql
    CREATE TABLE create_table_multi_partion_date
    (
        k1 DATE,
        k2 INT,
        V1 VARCHAR(20)
    ) PARTITION BY RANGE (k1) (
        FROM ("2000-11-14") TO ("2021-11-14") INTERVAL 1 YEAR,
        FROM ("2021-11-14") TO ("2022-11-14") INTERVAL 1 MONTH,
        FROM ("2022-11-14") TO ("2023-01-03") INTERVAL 1 WEEK,
        FROM ("2023-01-03") TO ("2023-01-14") INTERVAL 1 DAY,
        PARTITION p_20230114 VALUES [('2023-01-14'), ('2023-01-15'))
    ) DISTRIBUTED BY HASH(k2) BUCKETS 1
    PROPERTIES(
        "replication_num" = "1"
    );
```
```sql
    CREATE TABLE create_table_multi_partion_date_hour
    (
        k1 DATETIME,
        k2 INT,
        V1 VARCHAR(20)
    ) PARTITION BY RANGE (k1) (
        FROM ("2023-01-03 12") TO ("2023-01-14 22") INTERVAL 1 HOUR
    ) DISTRIBUTED BY HASH(k2) BUCKETS 1
    PROPERTIES(
        "replication_num" = "1"
    );
```
```sql
    CREATE TABLE create_table_multi_partion_integer
    (
        k1 BIGINT,
        k2 INT,
        V1 VARCHAR(20)
    ) PARTITION BY RANGE (k1) (
        FROM (1) TO (100) INTERVAL 10
    ) DISTRIBUTED BY HASH(k2) BUCKETS 1
    PROPERTIES(
        "replication_num" = "1"
    );
```

NOTE: Multi Partition can be mixed with conventional manual creation of partitions. When using, you need to limit the partition column to only one, The default maximum number of partitions created in multi partition is 4096, This parameter can be adjusted in fe configuration max_multi_partition_num.

  1. Add a duplicate without sorting column table

    CREATE TABLE example_db.table_hash
    (
        k1 DATE,
        k2 DECIMAL(10, 2) DEFAULT "10.5",
        k3 CHAR(10) COMMENT "string column",
        k4 INT NOT NULL DEFAULT "1" COMMENT "int column"
    )
    COMMENT "duplicate without keys"
    PARTITION BY RANGE(k1)
    (
        PARTITION p1 VALUES LESS THAN ("2020-02-01"),
        PARTITION p2 VALUES LESS THAN ("2020-03-01"),
        PARTITION p3 VALUES LESS THAN ("2020-04-01")
    )
    DISTRIBUTED BY HASH(k1) BUCKETS 32
    PROPERTIES (
        "replication_num" = "1",
        "enable_duplicate_without_keys_by_default" = "true"
    );

Keywords​

CREATE, TABLE

Best Practice​

Partitioning and bucketing​

A table must specify the bucket column, but it does not need to specify the partition. For the specific introduction of partitioning and bucketing, please refer to the Data Partitioning document.

Tables in Doris can be divided into partitioned tables and non-partitioned tables. This attribute is determined when the table is created and cannot be changed afterwards. That is, for partitioned tables, you can add or delete partitions in the subsequent use process, and for non-partitioned tables, you can no longer perform operations such as adding partitions afterwards.

At the same time, partitioning columns and bucketing columns cannot be changed after the table is created. You can neither change the types of partitioning and bucketing columns, nor do any additions or deletions to these columns.

Therefore, it is recommended to confirm the usage method to build the table reasonably before building the table.

Dynamic Partition​

The dynamic partition function is mainly used to help users automatically manage partitions. By setting certain rules, the Doris system regularly adds new partitions or deletes historical partitions. Please refer to Dynamic Partition document for more help.

Auto Partition​

See the Auto Partition document.

Materialized View​

Users can create multiple materialized views (ROLLUP) while building a table. Materialized views can also be added after the table is built. It is convenient for users to create all materialized views at one time by writing in the table creation statement.

If the materialized view is created when the table is created, all subsequent data import operations will synchronize the data of the materialized view to be generated. The number of materialized views may affect the efficiency of data import.

If you add a materialized view in the subsequent use process, if there is data in the table, the creation time of the materialized view depends on the current amount of data.

For the introduction of materialized views, please refer to the Materialized View document.

Index​

Users can create indexes on multiple columns while building a table. Indexes can also be added after the table is built.

If you add an index in the subsequent use process, if there is data in the table, you need to rewrite all the data, so the creation time of the index depends on the current data volume.

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