CREATE TABLE AS SELECT

Syntax

CREATE TABLE
    table_name
[WITH (table_parameter = value [, ... ])] 
AS select_statement;

Description

CREATE TABLE AS is a statement that:

• Generates a DDL statement to create a new Table.

• Launches a new query to write the results of the SELECT statement into the newly created Table.

Arguments

table_name

This specifies the name of the new Table. Optionally, use <database_name>.<schema_name> as the prefix to the name to create the Relation in that namespace. For case-sensitive names, the name must be wrapped in double quotes; otherwise, the lowercase name will be used.

WITH (<table_parameter> = <value> [, …​ ])

Optionally, this clause specifies Table Parameters.

select_statement

This statement specifies the SELECT statement to run.

Table Parameters

Parameter Name	Description
store	The name of the Store that hosts the Entity for this Stream. Required: No Default value: User’s default Store. Type: String Valid values: See LIST STORES

Snowflake Specific Parameters

Parameter Name	Description
snowflake.db.name	The name of the Snowflake database that would host the Snowflake Table. Required: Yes Default values: None Type: String Valid values: Database names available from LIST ENTITIES.
snowflake.schema.name	The name of the Snowflake schema that would host the Snowflake Table. Required: Yes Default value: None Type: String Valid values: Schema names available from LIST ENTITIES under the snowflake.db.name database name.
snowflake.table.name	The name of the Snowflake table to use when creating the Table. If the Table doesn't exist in the Store, a Table with the table_name is created in the corresponding Store. Required: No Default value: table_name Type: String
snowflake.buffer.millis	The amount of time to buffer events with the sink Table before writing the data into the corresponding Snowflake table. It is important to note that the longer events are buffered, the larger the internal state of the query gets. Depending on the volume and size of the events for the corresponding query, memory limitations may be reached. Required: No Default value: 1000 Type: Long Valid values: (0, ...]

Snowflake stores provide a delivery guarantee of at_least_once when producing events into a sink Table. Snowflake Stores provide an insert-only mode when writing to Snowflake tables.

Databricks Specific Parameters

Parameter Name	Description
databricks.catalog.name	The name of the Databricks catalog that would host the Databricks Table. Required: Yes Default values: None Type: String Valid values: Database names available from LIST ENTITIES.
databricks.schema.name	The name of the Databricks schema that would host the Databricks Table. Required: Yes Default value: None Type: String Valid values: Schema names available from LIST ENTITIES under the databricks.catalog.name catalog name.
databricks.table.name	The name of the Databricks table to use when creating the Table. A Table with the table_name is created in the corresponding Store. If that Table already exists, then an error is reported back to the user. Required: No Default value: table_name Type: String
table.data.file.location	The S3 directory location for the Delta formatted data to be written. The credentials for writing to S3 is given during Store creation (see CREATE STORE). Note that the S3 bucket from the location specified by this parameter must match the databricks.cloud.s3.bucket property defined in the Store. Required: Yes Default value: None Type: String

Databricks stores provide a delivery guarantee of exactly_once when producing events into a sink Table. Databricks Stores provide an insert-only mode when writing to Databricks tables.

Examples

Create a copy of a Stream in a Snowflake Table

The following creates a replica of the source Stream, pageviews in the Snowflake Table, PV_TABLE:

CREATE TABLE "PV_TABLE" WITH (
  'store' = 'sfstore',
  'snowflake.db.name' = 'DELTA_STREAMING',
  'snowflake.schema.name' = 'PUBLIC'
) AS SELECT * FROM pageviews;

Create a stream of changes for a Changelog in a Snowflake Table

The following CTAS query creates a new Snowflake Table to store incremental changes resulting from a grouping aggregation on the transactions Stream:

CREATE TABLE "CC_TYPE_USAGE" WITH (
  'store' = 'sfstore',
  'snowflake.db.name' = 'DELTA_STREAMING',
  'snowflake.schema.name' = 'PUBLIC'
) AS SELECT
  cc_type AS "CREDIT_TYPE",
  tx_time AS "TRANSACTION_TS",
  tx_id AS "TRANSACTION_ID"
FROM transactions
GROUP BY cc_type;

This query stores all changes to the grouping column cc_type to the sink Table CC_TYPE_USAGE.

Create a copy of a Stream in a Databricks Table

The following creates a replica of the source Stream, pageviews in the Databricks Table, pageviews_db:

CREATE TABLE pageviews_db WITH (
  'store' = 'databricks_store', 
  'databricks.catalog.name' = 'catalog1', 
  'databricks.schema.name' = 'schema1', 
  'databricks.table.name' = 'pageviews', 
  'table.data.file.location' = 's3://mybucket/test/0/pageviews'
) AS SELECT * FROM pageviews;

Upon issuing this query, a Databricks Table will be created in catalog1.schema1.pageviews which uses s3://mybucket/test/0/pageviews as its external location. This query will write the Delta formatted parquet files and update the Delta log in that S3 location.