Basic partitioning

In basic partitioning, you can either partition by range, or partition by hash.

PARTITION BY RANGE

You can specify range partitions for one or more primary key columns. Range partitioning in Kudu allows splitting a table based on specific values or ranges of values of the chosen partition keys. This allows you to balance parallelism in writes with scan efficiency.

For instance, if you have a table that has the columns state, name, and purchase_count, and you partition the table by state, it will create 50 tablets, one for each US state.

CREATE TABLE customers (
  state STRING,
  name STRING,
  purchase_count int,
  PRIMARY KEY (state, name)
)
PARTITION BY RANGE (state)
(
  PARTITION VALUE = 'al',
  PARTITION VALUE = 'ak',
  PARTITION VALUE = 'ar',
  ...
  ...
  PARTITION VALUE = 'wv',
  PARTITION VALUE = 'wy'
)
STORED AS KUDU;

PARTITION BY HASH

Instead of distributing by an explicit range, or in combination with range distribution, you can distribute into a specific number of partitions by hash. You specify the primary key columns you want to partition by, and the number of partitions you want to use. Rows are distributed by hashing the specified key columns. Assuming that the values being hashed do not themselves exhibit significant skew, this will serve to distribute the data evenly across all partitions.

You can specify multiple definitions, and you can specify definitions which use compound primary keys. However, one column cannot be mentioned in multiple hash definitions. Consider two columns, a and b:

HASH(a), HASH(b) -- will succeed
HASH(a,b) -- will succeed
HASH(a), HASH(a,b) -- will fail

Hash partitioning is a reasonable approach if primary key values are evenly distributed in their domain and no data skew is apparent, such as timestamps or serial IDs.

The following example creates 16 tablets by hashing the id column. A maximum of 16 tablets can be written to in parallel. In this example, a query for a range of sku values is likely to need to read from all 16 tablets, so this may not be the optimum schema for this table. See the Advanced partinioning section for an extended example.

CREATE TABLE cust_behavior (
  id BIGINT,
  sku STRING,
  salary STRING,
  edu_level INT,
  usergender STRING,
  `group` STRING,
  city STRING,
  postcode STRING,
  last_purchase_price FLOAT,
  last_purchase_date BIGINT,
  category STRING,
  rating INT,
  fulfilled_date BIGINT,
  PRIMARY KEY (id, sku)
)
PARTITION BY HASH PARTITIONS 16
STORED AS KUDU;