CREATE DATASOURCE TABLE
CREATE TABLE statement defines a new table using a Data Source.
CREATE TABLE [ IF NOT EXISTS ] table_identifier [ ( col_name1 col_type1 [ COMMENT col_comment1 ], ... ) ] USING data_source [ OPTIONS ( key1=val1, key2=val2, ... ) ] [ PARTITIONED BY ( col_name1, col_name2, ... ) ] [ CLUSTERED BY ( col_name3, col_name4, ... ) [ SORTED BY ( col_name [ ASC | DESC ], ... ) ] INTO num_buckets BUCKETS ] [ LOCATION path ] [ COMMENT table_comment ] [ TBLPROPERTIES ( key1=val1, key2=val2, ... ) ] [ AS select_statement ]
Note that, the clauses between the USING clause and the AS SELECT clause can come in as any order. For example, you can write COMMENT table_comment after TBLPROPERTIES.
Specifies a table name, which may be optionally qualified with a database name.
[ database_name. ] table_name
Data Source is the input format used to create the table. Data source can be CSV, TXT, ORC, JDBC, PARQUET, etc.
Partitions are created on the table, based on the columns specified.
Partitions created on the table will be bucketed into fixed buckets based on the column specified for bucketing.
NOTE: Bucketing is an optimization technique that uses buckets (and bucketing columns) to determine data partitioning and avoid data shuffle.
Specifies an ordering of bucket columns. Optionally, one can use ASC for an ascending order or DESC for a descending order after any column names in the SORTED BY clause. If not specified, ASC is assumed by default.
INTO num_buckets BUCKETS
Specifies buckets numbers, which is used in
Path to the directory where table data is stored, which could be a path on distributed storage like HDFS, etc.
A string literal to describe the table.
A list of key-value pairs that is used to tag the table definition.
The table is populated using the data from the select statement.
Data Source Interaction
A Data Source table acts like a pointer to the underlying data source. For example, you can create a table “foo” in Spark which points to a table “bar” in MySQL using JDBC Data Source. When you read/write table “foo”, you actually read/write table “bar”.
In general CREATE TABLE is creating a “pointer”, and you need to make sure it points to something existing. An exception is file source such as parquet, json. If you don’t specify the LOCATION, Spark will create a default table location for you.
For CREATE TABLE AS SELECT, Spark will overwrite the underlying data source with the data of the input query, to make sure the table gets created contains exactly the same data as the input query.
--Use data source CREATE TABLE student (id INT, name STRING, age INT) USING CSV; --Use data from another table CREATE TABLE student_copy USING CSV AS SELECT * FROM student; --Omit the USING clause, which uses the default data source (parquet by default) CREATE TABLE student (id INT, name STRING, age INT); --Specify table comment and properties CREATE TABLE student (id INT, name STRING, age INT) USING CSV COMMENT 'this is a comment' TBLPROPERTIES ('foo'='bar'); --Specify table comment and properties with different clauses order CREATE TABLE student (id INT, name STRING, age INT) USING CSV TBLPROPERTIES ('foo'='bar') COMMENT 'this is a comment'; --Create partitioned and bucketed table CREATE TABLE student (id INT, name STRING, age INT) USING CSV PARTITIONED BY (age) CLUSTERED BY (Id) INTO 4 buckets; --Create partitioned and bucketed table through CTAS CREATE TABLE student_partition_bucket USING parquet PARTITIONED BY (age) CLUSTERED BY (id) INTO 4 buckets AS SELECT * FROM student; --Create bucketed table through CTAS and CTE CREATE TABLE student_bucket USING parquet CLUSTERED BY (id) INTO 4 buckets ( WITH tmpTable AS ( SELECT * FROM student WHERE id > 100 ) SELECT * FROM tmpTable );