TABLESAMPLE clause

Applies to: Databricks SQL Databricks Runtime

The TABLESAMPLE statement is used to sample the relation.

Syntax

TABLESAMPLE ( { percentage PERCENT ) |
                num_rows ROWS |
                BUCKET fraction OUT OF total } )
            [ REPEATABLE ( seed ) ]

Parameters

• percentage PERCENT

  An INTEGER or DECIMAL constant percentage between 0 and 100 specifying which percentage of the table's rows to sample.

• num_rows ROWS

  A constant positive INTEGER expression num_rows specifying an absolute number of rows out of all rows to sample.

• BUCKET fraction OUT OF total

  An INTEGER constant fraction specifying the portion out of the INTEGER constant total to sample.

• REPEATABLE ( seed )

  Applies to: Databricks SQL Databricks Runtime 11.3 LTS and above

  An optional positive INTEGER constant seed, used to always produce the same set of rows. Use this clause when you want to reissue the query multiple times, and you expect the same set of sampled rows.

note

• TABLESAMPLE returns the approximate number of rows or fraction requested.
• Always use TABLESAMPLE (percent PERCENT) if randomness is important. TABLESAMPLE (num_rows ROWS) is not a simple random sample but instead is implemented using LIMIT.
• TABLESAMPLE does not support filter pushdowns as the sampling precedes the application of any other filters, such as WHERE clauses.

Examples

SQL

> CREATE TEMPORARY VIEW test(id, name) AS
  VALUES ( 1, 'Lisa'),
         ( 2, 'Mary'),
         ( 3, 'Evan'),
         ( 4, 'Fred'),
         ( 5, 'Alex'),
         ( 6, 'Mark'),
         ( 7, 'Lily'),
         ( 8, 'Lucy'),
         ( 9, 'Eric'),
         (10, 'Adam');
> SELECT * FROM test;
  5 Alex
  8 Lucy
  2 Mary
  4 Fred
  1 Lisa
  9 Eric
 10 Adam
  6 Mark
  7 Lily
  3 Evan

> SELECT * FROM test TABLESAMPLE (30 PERCENT) REPEATABLE (123);
  1     Lisa
  2     Mary
  3     Evan
  5     Alex
  8     Lucy

> SELECT * FROM test TABLESAMPLE (5 ROWS);
  5 Alex
  8 Lucy
  2 Mary
  4 Fred
  1 Lisa

> SELECT * FROM test TABLESAMPLE (BUCKET 4 OUT OF 10);
  8 Lucy
  2 Mary
  9 Eric
  6 Mark

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