Partitioning for Kudu Tables

Kudu tables use special mechanisms to distribute data among the underlying tablet servers. Although referred as partitioned tables, they are distinguished from traditional Impala partitioned tables with the different syntax in CREATE TABLE statement.

Kudu tables use PARTITION BY, HASH, RANGE, and range specification clauses rather than the PARTITIONED BY clause for HDFS-backed tables, which specifies only a column name and creates a new partition for each different value.

To see the current partitioning scheme for a Kudu table, you can use the SHOW CREATE TABLE statement or the SHOW PARTITIONS statement. The CREATE TABLE syntax displayed by this statement includes all the hash, range, or both clauses that reflect the original table structure plus any subsequent ALTER TABLE statements that changed the table structure.

To see the underlying buckets and partitions for a Kudu table, use the SHOW TABLE STATS or SHOW PARTITIONS statement.

Hash Partitioning

Hash partitioning is the simplest type of partitioning for Kudu tables. For hash-partitioned Kudu tables, inserted rows are divided up between a fixed number of “buckets” by applying a hash function to the values of the columns specified in the HASH clause. Hashing ensures that rows with similar values are evenly distributed, instead of clumping together all in the same bucket. Spreading new rows across the buckets this way lets insertion operations work in parallel across multiple tablet servers. Separating the hashed values can impose additional overhead on queries, where queries with range-based predicates might have to read multiple tablets to retrieve all the relevant values.


-- 1M rows with 50 hash partitions = approximately 20,000 rows per partition.
-- The values in each partition are not sequential, but rather based on a hash function.
-- Rows 1, 99999, and 123456 might be in the same partition.
CREATE TABLE million_rows (id string primary key, s string)
  PARTITION BY HASH(id) PARTITIONS 50
  STORED AS KUDU;

-- Because the ID values are unique, we expect the rows to be roughly
-- evenly distributed between the buckets in the destination table.
INSERT INTO million_rows SELECT * FROM billion_rows ORDER BY id LIMIT 1e6;

The largest number of buckets that you can create with a PARTITIONS clause varies depending on the number of tablet servers in the cluster, while the smallest is 2. For large tables, prefer to use roughly 10 partitions per server in the cluster.

Range Partitioning

Range partitioning lets you specify partitioning precisely, based on single values or ranges of values within one or more columns. You add one or more RANGE clauses to the CREATE TABLE statement, following the PARTITION BY clause. The RANGE clause includes a combination of constant expressions, VALUE or VALUES keywords, and comparison operators.


CREATE TABLE t1 (id STRING PRIMARY KEY, s STRING)
  PARTITION BY RANGE (PARTITION 'a' <= VALUES < '{', PARTITION 'A' <= VALUES < '[', PARTITION VALUES = '00000')
  STORED AS KUDU;

For range-partitioned Kudu tables, an appropriate range must exist before a data value can be created in the table. Any INSERT, UPDATE, or UPSERT statements fail if they try to create column values that fall outside the specified ranges. The error checking for ranges is performed on the Kudu side. Impala passes the specified range information to Kudu, and passes back any error or warning if the ranges are not valid. (A nonsensical range specification causes an error for a DDL statement, but only a warning for a DML statement.)

Partition ranges can be non-contiguous:


PARTITION BY RANGE (year) (PARTITION 1885 <= VALUES <= 1889, PARTITION 1893 <= VALUES <= 1897)

The ALTER TABLE statement with the ADD PARTITION or DROP PARTITION clauses can be used to add or remove ranges from an existing Kudu table.


ALTER TABLE foo ADD PARTITION 30 <= VALUES < 50;
ALTER TABLE foo DROP PARTITION 1 <= VALUES < 5;

When a range is added, the new range must not overlap with any of the previous ranges; that is, it can only fill in gaps within the previous ranges.

When a range is removed, all the associated rows in the table are deleted regardless whether the table is internal or external.

Kudu tables can also use a combination of hash and range partitioning. For example:


PARTITION BY HASH (school) PARTITIONS 10,
  RANGE (letter_grade) (PARTITION VALUE = 'A', PARTITION VALUE = 'B',
    PARTITION VALUE = 'C', PARTITION VALUE = 'D', PARTITION VALUE = 'F')