CREATE TABLE

Introduced: v1.1.56

ENTERPRISE EDITION FEATURE

COMPUTED COLUMNS requires Enterprise Edition. To inquire about upgrading, please contact Databend Support.

Creating tables is one of the most complicated operations for many databases because you might need to:

Manually specify the engine
Manually specify the indexes
And even specify the data partitions or data shard

Databend aims to be easy to use by design and does NOT require any of those operations when you create a table. Moreover, the CREATE TABLE statement provides these options to make it much easier for you to create tables in various scenarios:

CREATE TABLE: Creates a table from scratch.
CREATE TABLE ... LIKE: Creates a table with the same column definitions as an existing one.
CREATE TABLE ... AS: Creates a table and inserts data with the results of a SELECT query.
CREATE TRANSIENT TABLE: Creates a table without storing its historical data for Time Travel..
CREATE TABLE ... EXTERNAL_LOCATION: Creates a table and specifies an S3 bucket for the data storage instead of the FUSE engine.

CREATE TABLE

CREATE [TRANSIENT] TABLE [IF NOT EXISTS] [db.]table_name
(
    <column_name> <data_type> [ NOT NULL | NULL] [ { DEFAULT <expr> }] [AS (<expr>) STORED | VIRTUAL],
    <column_name> <data_type> [ NOT NULL | NULL] [ { DEFAULT <expr> }] [AS (<expr>) STORED | VIRTUAL],
    ...
)

note

For available data types in Databend, see Data Types.
Databend suggests avoiding special characters as much as possible when naming columns. However, if special characters are necessary in some cases, the alias should be enclosed in backticks, like this: CREATE TABLE price(`$CA` int);
Databend will automatically convert column names into lowercase. For example, if you name a column as Total, it will appear as total in the result.

CREATE TABLE ... LIKE

Creates a table with the same column definitions as an existing table. Column names, data types, and their non-NUll constraints of the existing will be copied to the new table.

Syntax:

CREATE TABLE [IF NOT EXISTS] [db.]table_name
LIKE [db.]origin_table_name

This command does not include any data or attributes (such as CLUSTER BY, TRANSIENT, and COMPRESSION) from the original table, and instead creates a new table using the default system settings.

WORKAROUND

TRANSIENT and COMPRESSION can be explicitly specified when you create a new table with this command. For example,

create transient table t_new like t_old;

create table t_new compression='lz4' like t_old;

CREATE TABLE ... AS

Creates a table and fills it with data computed by a SELECT command.

Syntax:

CREATE TABLE [IF NOT EXISTS] [db.]table_name
AS SELECT query

This command does not include any attributes (such as CLUSTER BY, TRANSIENT, and COMPRESSION) from the original table, and instead creates a new table using the default system settings.

WORKAROUND

TRANSIENT and COMPRESSION can be explicitly specified when you create a new table with this command. For example,

create transient table t_new as select * from t_old;

create table t_new compression='lz4' as select * from t_old;

CREATE TRANSIENT TABLE

Creates a transient table.

Transient tables are used to hold transitory data that does not require a data protection or recovery mechanism. Dataebend does not hold historical data for a transient table so you will not be able to query from a previous version of the transient table with the Time Travel feature, for example, the AT clause in the SELECT statement will not work for transient tables. Please note that you can still drop and undrop a transient table.

Transient tables help save your storage expenses because they do not need extra space for historical data compared to non-transient tables. See example for detailed explanations.

Syntax:

CREATE TRANSIENT TABLE ...

CREATE TABLE ... EXTERNAL_LOCATION

Creates a table and specifies an S3 bucket for the data storage instead of the FUSE engine.

Databend stores the table data in the location configured in the file databend-query.toml by default. This option enables you to store the data (in parquet format) in a table in another bucket instead of the default one.

Syntax:

CREATE TABLE [IF NOT EXISTS] [db.]table_name

    <column_name> <data_type> [ NOT NULL | NULL] [ { DEFAULT <expr> }],
    <column_name> <data_type> [ NOT NULL | NULL] [ { DEFAULT <expr> }],
    ...

's3://<bucket>/[<path>]' 
CONNECTION = (
        ENDPOINT_URL = 'https://<endpoint-URL>'
        ACCESS_KEY_ID = '<your-access-key-ID>'
        SECRET_ACCESS_KEY = '<your-secret-access-key>'
        REGION = '<region-name>'
        ENABLE_VIRTUAL_HOST_STYLE = 'true'|'false'
  );

Parameter	Description	Required
`s3://<bucket>/[<path>]`	Files are in the specified external location (S3-like bucket)	YES
ENDPOINT_URL	The bucket endpoint URL starting with "https://". To use a URL starting with "http://", set `allow_insecure` to `true` in the [storage] block of the file `databend-query-node.toml`.	Optional
ACCESS_KEY_ID	Your access key ID for connecting the AWS S3 compatible object storage. If not provided, Databend will access the bucket anonymously.	Optional
SECRET_ACCESS_KEY	Your secret access key for connecting the AWS S3 compatible object storage.	Optional
REGION	AWS region name. For example, us-east-1.	Optional
ENABLE_VIRTUAL_HOST_STYLE	If you use virtual hosting to address the bucket, set it to "true".	Optional

Column Nullable

By default, all columns are not nullable(NOT NULL), if you want to specify a column default to NULL, please use:

CREATE TABLE [IF NOT EXISTS] [db.]table_name
(
    <column_name> <data_type> NULL,
     ...
)

Let check it out how difference the column is NULL or NOT NULL.

Create a table t_not_null which column with NOT NULL(Databend Column is NOT NULL by default):

CREATE TABLE t_not_null(a INT);

DESC t_not_null;
+-------+-------+------+---------+
| Field | Type  | Null | Default |
+-------+-------+------+---------+
| a     | Int32 | NO   | 0       |
+-------+-------+------+---------+

Create another table t_null column with NULL:

CREATE TABLE t_null(a INT NULL);

DESC t_null;
+-------+-------+------+---------+
| Field | Type  | Null | Default |
+-------+-------+------+---------+
| a     | Int32 | YES  | NULL    |
+-------+-------+------+---------+

Default Values

DEFAULT <expr>

Specify a default value inserted in the column if a value is not specified via an INSERT or CREATE TABLE AS SELECT statement.

For example:

CREATE TABLE t_default_value(a TINYINT UNSIGNED, b VARCHAR DEFAULT 'b');

Desc the t_default_value table:

DESC t_default_value;
+-------+------------------+------+---------+-------+
| Field | Type             | Null | Default | Extra |
+-------+------------------+------+---------+-------+
| a     | TINYINT UNSIGNED | NO   | 0       |       |
| b     | VARCHAR          | NO   | b       |       |
+-------+------------------+------+---------+-------+

Insert a value:

INSERT INTO T_default_value(a) VALUES(1);

Check the table values:

SELECT * FROM t_default_value;
+------+------+
| a    | b    |
+------+------+
|    1 | b    |
+------+------+

Computed Columns

Computed columns are columns that are generated from other columns in a table using a scalar expression. When data in any of the columns used in the computation is updated, the computed column will automatically recalculate its value to reflect the update.

Databend supports two types of computed columns: stored and virtual. Stored computed columns are physically stored in the database and occupy storage space, while virtual computed columns are not physically stored and their values are calculated on the fly when accessed.

Databend supports two syntax options for creating computed columns: one using AS (<expr>) and the other using GENERATED ALWAYS AS (<expr>). Both syntaxes allow specifying whether the computed column is stored or virtual.

CREATE TABLE [IF NOT EXISTS] [db.]table_name
(
    <column_name> <data_type> [ NOT NULL | NULL] AS (<expr>) STORED | VIRTUAL,
    <column_name> <data_type> [ NOT NULL | NULL] AS (<expr>) STORED | VIRTUAL,
    ...
)

CREATE TABLE [IF NOT EXISTS] [db.]table_name
(
    <column_name> <data_type> [NOT NULL | NULL] GENERATED ALWAYS AS (<expr>) STORED | VIRTUAL,
    <column_name> <data_type> [NOT NULL | NULL] GENERATED ALWAYS AS (<expr>) STORED | VIRTUAL,
    ...
)

The following is an example of creating a stored computed column: Whenever the values of the "price" or "quantity" columns are updated, the "total_price" column will automatically recalculate and update its stored value.

CREATE TABLE IF NOT EXISTS products (
  id INT,
  price FLOAT64,
  quantity INT,
  total_price FLOAT64 AS (price * quantity) STORED
);

The following is an example of creating a virtual computed column: The "full_name" column is dynamically calculated based on the current values of the "first_name" and "last_name" columns. It does not occupy additional storage space. Whenever the "first_name" or "last_name" values are accessed, the "full_name" column will be computed and returned.

CREATE TABLE IF NOT EXISTS employees (
  id INT,
  first_name VARCHAR,
  last_name VARCHAR,
  full_name VARCHAR AS (CONCAT(first_name, ' ', last_name)) VIRTUAL
);

STORED or VIRTUAL?

When choosing between stored computed columns and virtual computed columns, consider the following factors:

Storage Space: Stored computed columns occupy additional storage space in the table because their computed values are physically stored. If you have limited database space or want to minimize storage usage, virtual computed columns can be a better choice.
Real-time Updates: Stored computed columns update their computed values immediately when the dependent columns are updated. This ensures that you always have the latest computed values when querying. Virtual computed columns, on the other hand, compute their values dynamically during queries, which may slightly increase the processing time.
Data Integrity and Consistency: Stored computed columns maintain immediate data consistency since their computed values are updated upon write operations. Virtual computed columns, however, calculate their values on-the-fly during queries, which means there might be a momentary inconsistency between write operations and subsequent queries.

MySQL Compatibility

Databend’s syntax is difference from MySQL mainly in the data type and some specific index hints.

Examples

Create Table

Create a table with a default value for a column (in this case, the genre column has 'General' as the default value):

CREATE TABLE books (
    id BIGINT UNSIGNED,
    title VARCHAR,
    genre VARCHAR DEFAULT 'General'
);

Describe the table to confirm the structure and the default value for the genre column:

DESC books;
+-------+-----------------+------+---------+-------+
| Field | Type            | Null | Default | Extra |
+-------+-----------------+------+---------+-------+
| id    | BIGINT UNSIGNED | NO   | 0       |       |
| title | VARCHAR         | NO   | ""      |       |
| genre | VARCHAR         | NO   | 'General'|       |
+-------+-----------------+------+---------+-------+

Insert a row without specifying the genre:

INSERT INTO books(id, title) VALUES(1, 'Invisible Stars');

Query the table and notice that the default value 'General' has been set for the genre column:

SELECT * FROM books;
+----+----------------+---------+
| id | title          | genre   |
+----+----------------+---------+
|  1 | Invisible Stars| General |
+----+----------------+---------+

Create Table ... Like

Create a new table (books_copy) with the same structure as an existing table (books):

CREATE TABLE books_copy LIKE books;

Check the structure of the new table:

DESC books_copy;
+-------+-----------------+------+---------+-------+
| Field | Type            | Null | Default | Extra |
+-------+-----------------+------+---------+-------+
| id    | BIGINT UNSIGNED | NO   | 0       |       |
| title | VARCHAR         | NO   | ""      |       |
| genre | VARCHAR         | NO   | 'General'|       |
+-------+-----------------+------+---------+-------+

Insert a row into the new table and notice that the default value for the genre column has been copied:

INSERT INTO books_copy(id, title) VALUES(1, 'Invisible Stars');

SELECT * FROM books_copy;
+----+----------------+---------+
| id | title          | genre   |
+----+----------------+---------+
|  1 | Invisible Stars| General |
+----+----------------+---------+

Create Table ... As

Create a new table (books_backup) that includes data from an existing table (books):

CREATE TABLE books_backup AS SELECT * FROM books;

Describe the new table and notice that the default value for the genre column has NOT been copied:

DESC books_backup;
+-------+-----------------+------+---------+-------+
| Field | Type            | Null | Default | Extra |
+-------+-----------------+------+---------+-------+
| id    | BIGINT UNSIGNED | NO   | 0       |       |
| title | VARCHAR         | NO   | ""      |       |
| genre | VARCHAR         | NO   | NULL    |       |
+-------+-----------------+------+---------+-------+

Query the new table and notice that the data from the original table has been copied:

SELECT * FROM books_backup;
+----+----------------+---------+
| id | title          | genre   |
+----+----------------+---------+
|  1 | Invisible Stars| General |
+----+----------------+---------+

Create Transient Table

Create a transient table (temporary table) that automatically deletes data after a specified period of time:

-- Create a transient table
CREATE TRANSIENT TABLE visits (
  visitor_id BIGINT
);

-- Insert values
INSERT INTO visits VALUES(1);
INSERT INTO visits VALUES(2);
INSERT INTO visits VALUES(3);

-- Check the inserted data
SELECT * FROM visits;
+-----------+
| visitor_id |
+-----------+
|         1 |
|         2 |
|         3 |
+-----------+

Create Table ... External_Location

Create a table with data stored on an external location, such as Amazon S3:

-- Create a table named `mytable` and specify the location `s3://testbucket/admin/data/` for the data storage
CREATE TABLE mytable (
  a INT
) 
's3://testbucket/admin/data/' 
CONNECTION=(
  ACCESS_KEY_ID='<your_aws_key_id>' 
  SECRET_ACCESS_KEY='<your_aws_secret_key>' 
  ENDPOINT_URL='https://s3.amazonaws.com'
);

Create Table ... Column As STORED | VIRTUAL

The following example demonstrates a table with a stored computed column that automatically recalculates based on updates to the "price" or "quantity" columns:

-- Create the table with a stored computed column
CREATE TABLE IF NOT EXISTS products (
  id INT,
  price FLOAT64,
  quantity INT,
  total_price FLOAT64 AS (price * quantity) STORED
);

-- Insert data into the table
INSERT INTO products (id, price, quantity)
VALUES (1, 10.5, 3),
       (2, 15.2, 5),
       (3, 8.7, 2);

-- Query the table to see the computed column
SELECT id, price, quantity, total_price
FROM products;

---
+------+-------+----------+-------------+
| id   | price | quantity | total_price |
+------+-------+----------+-------------+
|    1 |  10.5 |        3 |        31.5 |
|    2 |  15.2 |        5 |        76.0 |
|    3 |   8.7 |        2 |        17.4 |
+------+-------+----------+-------------+

In this example, we create a table called student_profiles with a Variant type column named profile to store JSON data. We also add a virtual computed column named age that extracts the age property from the profile column and casts it to an integer.

-- Create the table with a virtual computed column
CREATE TABLE student_profiles (
    id STRING,
    profile VARIANT,
    age INT NULL AS (profile['age']::INT) VIRTUAL
);

-- Insert data into the table
INSERT INTO student_profiles (id, profile) VALUES
    ('d78236', '{"id": "d78236", "name": "Arthur Read", "age": "16", "school": "PVPHS", "credits": 120, "sports": "none"}'),
    ('f98112', '{"name": "Buster Bunny", "age": "15", "id": "f98112", "school": "TEO", "credits": 67, "clubs": "MUN"}'),
    ('t63512', '{"name": "Ernie Narayan", "school" : "Brooklyn Tech", "id": "t63512", "sports": "Track and Field", "clubs": "Chess"}');

-- Query the table to see the computed column
SELECT * FROM student_profiles;

+--------+------------------------------------------------------------------------------------------------------------+------+
| id     | profile                                                                                                    | age  |
+--------+------------------------------------------------------------------------------------------------------------+------+
| d78236 | {"age":"16","credits":120,"id":"d78236","name":"Arthur Read","school":"PVPHS","sports":"none"}             |   16 |
| f98112 | {"age":"15","clubs":"MUN","credits":67,"id":"f98112","name":"Buster Bunny","school":"TEO"}                 |   15 |
| t63512 | {"clubs":"Chess","id":"t63512","name":"Ernie Narayan","school":"Brooklyn Tech","sports":"Track and Field"} | NULL |
+--------+------------------------------------------------------------------------------------------------------------+------+

CREATE TABLE

CREATE TABLE

CREATE TABLE ... LIKE

CREATE TABLE ... AS

CREATE TRANSIENT TABLE

CREATE TABLE ... EXTERNAL_LOCATION

Column Nullable

Default Values

Computed Columns

MySQL Compatibility

Examples

Create Table

Create Table ... Like

Create Table ... As

Create Transient Table

Create Table ... External_Location

Create Table ... Column As STORED | VIRTUAL

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CREATE TABLE​

CREATE TABLE ... LIKE​

CREATE TABLE ... AS​

CREATE TRANSIENT TABLE​

CREATE TABLE ... EXTERNAL_LOCATION​

Column Nullable​

Default Values​

Computed Columns​

MySQL Compatibility​

Examples​

Create Table​

Create Table ... Like​

Create Table ... As​

Create Transient Table​

Create Table ... External_Location​

Create Table ... Column As STORED | VIRTUAL​

Join our growing community

GitHub

Slack

Twitter

YouTube

Explore Databend Cloud for FREE

CREATE TABLE

CREATE TABLE ... LIKE

CREATE TABLE ... AS

CREATE TRANSIENT TABLE

CREATE TABLE ... EXTERNAL_LOCATION

Column Nullable

Default Values

Computed Columns

MySQL Compatibility

Examples

Create Table

Create Table ... Like

Create Table ... As

Create Transient Table

Create Table ... External_Location

Create Table ... Column As STORED | VIRTUAL