Presto 0.213 Documentation

5.9. Kudu Connector

5.9. Kudu Connector

The Kudu connector allows querying, inserting and deleting data in Apache Kudu

Compatibility

Connector is compatible with all Apache Kudu versions starting from 1.0.

If the connector uses features that are not available on the target server, an error will be returned. Apache Kudu 1.7.0 is currently used for testing.

Configuration

To configure the Kudu connector, create a catalog properties file etc/catalog/kudu.properties with the following contents, replacing the properties as appropriate:

connector.name=kudu

## List of Kudu master addresses, at least one is needed (comma separated)
## Supported formats: example.com, example.com:7051, 192.0.2.1, 192.0.2.1:7051,
##                    [2001:db8::1], [2001:db8::1]:7051, 2001:db8::1
kudu.client.master-addresses=localhost

## Kudu does not support schemas, but the connector can emulate them optionally.
## By default, this feature is disabled, and all tables belong to the default schema.
## For more details see connector documentation.
#kudu.schema-emulation.enabled=false

## Prefix to use for schema emulation (only relevant if `kudu.schema-emulation.enabled=true`)
## The standard prefix is `presto::`. Empty prefix is also supported.
## For more details see connector documentation.
#kudu.schema-emulation.prefix=

#######################
### Advanced Kudu Java client configuration
#######################

## Default timeout used for administrative operations (e.g. createTable, deleteTable, etc.)
#kudu.client.defaultAdminOperationTimeout = 30s

## Default timeout used for user operations
#kudu.client.defaultOperationTimeout = 30s

## Default timeout to use when waiting on data from a socket
#kudu.client.defaultSocketReadTimeout = 10s

## Disable Kudu client's collection of statistics.
#kudu.client.disableStatistics = false

Querying Data

Apache Kudu does not support schemas, i.e. namespaces for tables. The connector can optionally emulate schemas by table naming conventions.

Default behaviour (without schema emulation)

The emulation of schemas is disabled by default. In this case all Kudu tables are part of the default schema.

For example, a Kudu table named orders can be queried in Presto with SELECT * FROM kudu.default.orders or simple with SELECT * FROM orders if catalog and schema are set to kudu and default respectively.

Table names can contain any characters in Kudu. In this case, use double quotes. E.g. To query a Kudu table named special.table! use SELECT * FROM kudu.default."special.table!".

Example

  • Create a users table in the default schema with
CREATE TABLE kudu.default.users (
  user_id int WITH (primary_key = true),
  first_name varchar,
  last_name varchar
) WITH (
  partition_by_hash_columns = ARRAY['user_id'],
  partition_by_hash_buckets = 2
);

On creating a Kudu table you must/can specify addition information about the primary key, encoding, and compression of columns and hash or range partitioning, and the number of replicas. Details see in section Create Table.

  • The table can be described using
DESCRIBE kudu.default.users;

You should get something like

   Column   |  Type   |                      Extra                      | Comment
------------+---------+-------------------------------------------------+---------
 user_id    | integer | primary_key, encoding=auto, compression=default |
 first_name | varchar | nullable, encoding=auto, compression=default    |
 last_name  | varchar | nullable, encoding=auto, compression=default    |
(3 rows)
  • Insert some data with
INSERT INTO kudu.default.users VALUES (1, 'Donald', 'Duck'), (2, 'Mickey', 'Mouse');
  • Select the inserted data
SELECT * FROM kudu.default.users;

Behaviour With Schema Emulation

If schema emulation has been enabled in the connector properties, i.e. etc/catalog/kudu.properties, tables are mapped to schemas depending on some conventions.

  • With kudu.schema-emulation.enabled=true and kudu.schema-emulation.prefix=, the mapping works like:

    Kudu Table Name Presto Qualified Name
    orders kudu.default.orders
    part1.part2 kudu.part1.part2
    x.y.z kudu.x."y.z"

    As schemas are not directly supported by Kudu, a special table named $schemas is created for managing the schemas.

  • With kudu.schema-emulation.enabled=true and kudu.schema-emulation.prefix=presto::, the mapping works like:

    Kudu Table Name Presto Qualified Name
    orders kudu.default.orders
    part1.part2 kudu.default."part1.part2"
    x.y.z kudu.default."x.y.z"
    presto::part1.part2 kudu.part1.part2
    presto:x.y.z kudu.x."y.z"

    As schemas are not directly supported by Kudu, a special table named presto::$schemas is created for managing the schemas.

Data Type Mapping

The data types of Presto and Kudu are mapped as far as possible:

Presto Data Type Kudu Data Type Comment
BOOLEAN BOOL  
TINYINT INT8  
SMALLINT INT16  
INTEGER INT32  
BIGINT INT64  
REAL FLOAT  
DOUBLE DOUBLE  
VARCHAR STRING see [1]
VARBINARY BINARY see [1]
TIMESTAMP UNIXTIME_MICROS µs resolution in Kudu column is reduced to ms resolution
DECIMAL DECIMAL only supported for Kudu server >= 1.7.0
CHAR
not supported
DATE
not supported [2]
TIME
not supported
JSON
not supported
TIME WITH TIMEZONE
not supported
TIMESTAMP WITH TIME ZONE
not supported
INTERVAL YEAR TO MO NTH
not supported
INTERVAL DAY TO SEC OND
not supported
ARRAY
not supported
MAP
not supported
IPADDRESS
not supported
[1](1, 2) On performing CREATE TABLE ... AS ... from a Presto table to Kudu, the optional maximum length is lost
[2]On performing CREATE TABLE ... AS ... from a Presto table to Kudu, a DATE column is converted to STRING

Supported Presto SQL statements

Presto SQL statement Comment
SELECT  
INSERT INTO ... VALUES Behaves like upsert
INSERT INTO ... SELECT ... Behaves like upsert
DELETE  
CREATE SCHEMA Only allowed, if schema emulation is enabled
DROP SCHEMA Only allowed, if schema emulation is enabled
CREATE TABLE See Create Table
CREATE TABLE ... AS  
DROP TABLE  
ALTER TABLE ... RENAME TO ...  
ALTER TABLE ... RENAME COLUMN ... Only allowed, if not part of primary key
ALTER TABLE ... ADD COLUMN ... See Add Column
ALTER TABLE ... DROP COLUMN ... Only allowed, if not part of primary key
SHOW SCHEMAS  
SHOW TABLES  
SHOW CREATE TABLE  
SHOW COLUMNS FROM  
DESCRIBE Same as SHOW COLUMNS FROM
CALL kudu.system.add_range_partition Adds range partition to a table. See Managing range partitions
CALL kudu.system.drop_range_partition Drops a range partition from a table. See Managing range partitions

ALTER SCHEMA ... RENAME TO ... is not supported.

Create Table

On creating a Kudu Table you need to provide the columns and their types, of course, but Kudu needs information about partitioning and optionally for column encoding and compression.

Simple Example:

CREATE TABLE user_events (
  user_id int WITH (primary_key = true),
  event_name varchar WITH (primary_key = true),
  message varchar,
  details varchar WITH (nullable = true, encoding = 'plain')
) WITH (
  partition_by_hash_columns = ARRAY['user_id'],
  partition_by_hash_buckets = 5
);

Here the table is partitioned into five partitions by hash values of the column user_id. Note that the primary key consists of user_id and event_name. The primary key columns must always be the first columns of the column list. All columns used in partitions must be part of the primary key. Kudu supports two different kinds of partitioning: hash and range partitioning. Hash partitioning distributes rows by hash value into one of many buckets. Range partitions distributes rows using a totally-ordered range partition key. The concrete range partitions must be created explicitly. Kudu also supports multi-level partitioning. A table must have at least one partitioning (either hash or range). It can have at most one range partitioning, but multiple hash partitioning ‘levels’.

For more details see Partitioning Design.

Column Properties

Besides column name and type, you can specify some more properties of a column.

Column property name Type Description
primary_key BOOLEAN If true, the column belongs to primary key columns. The Kudu primary key enforces a uniqueness constraint. Inserting a second row with the same primary key results in updating the existing row (‘UPSERT’). See also Primary Key Design in the Kudu documentation.
nullable BOOLEAN If true, the value can be null. Primary key columns must not be nullable.
encoding VARCHAR The column encoding can help to save storage space and to improve query performance. Kudu uses an auto encoding depending on the column type if not specified. Valid values are: 'auto', 'plain', 'bitshuffle', 'runlength', 'prefix', 'dictionary', 'group_varint'. See also Column encoding in the Kudu documentation.
compression VARCHAR The encoded column values can be compressed. Kudu uses a default compression if not specified. Valid values are: 'default', 'no', 'lz4', 'snappy', 'zlib'. See also Column compression in the Kudu documentation.

Example

CREATE TABLE mytable (
  name varchar WITH (primary_key = true, encoding = 'dictionary', compression = 'snappy'),
  index bigint WITH (nullable = true, encoding = 'runlength', compression = 'lz4'),
  comment varchar WITH (nullable = true, encoding = 'plain', compression = 'default'),
   ...
) WITH (...);

Partitioning Design

A table must have at least one partitioning (either hash or range). It can have at most one range partitioning, but multiple hash partitioning ‘levels’. For more details see Apache Kudu documentation: Partitioning

If you create a Kudu table in Presto, the partitioning design is given by several table properties.

Hash partitioning

You can provide the first hash partition group with two table properties:

The partition_by_hash_columns defines the column(s) belonging to the partition group and partition_by_hash_buckets the number of partitions to split the hash values range into. All partition columns must be part of the primary key.

Example:

CREATE TABLE mytable (
  col1 varchar WITH (primary_key=true),
  col2 varchar WITH (primary_key=true),
  ...
) WITH (
  partition_by_hash_columns = ARRAY['col1', 'col2'],
  partition_by_hash_buckets = 4
)

This defines a hash partitioning with the columns col1 and col2 distributed over 4 partitions.

To define two separate hash partition groups use also the second pair of table properties named partition_by_second_hash_columns and partition_by_second_hash_buckets.

Example:

CREATE TABLE mytable (
  col1 varchar WITH (primary_key=true),
  col2 varchar WITH (primary_key=true),
  ...
) WITH (
  partition_by_hash_columns = ARRAY['col1'],
  partition_by_hash_buckets = 2,
  partition_by_second_hash_columns = ARRAY['col2'],
  partition_by_second_hash_buckets = 3
)

This defines a two-level hash partitioning with the first hash partition group over the column col1 distributed over 2 buckets and the second hash partition group over the column col2 distributed over 3 buckets. As a result you have table with 2 x 3 = 6 partitions.

Range partitioning

You can provide at most one range partitioning in Apache Kudu. The columns are defined with the table property partition_by_range_columns. The ranges themselves are given either in the table property range_partitions on creating the table. Or alternatively, the procedures kudu.system.add_range_partition and kudu.system.drop_range_partition can be used to manage range partitions for existing tables. For both ways see below for more details.

Example:

CREATE TABLE events (
  rack varchar WITH (primary_key=true),
  machine varchar WITH (primary_key=true),
  event_time timestamp WITH (primary_key=true),
  ...
) WITH (
  partition_by_hash_columns = ARRAY['rack'],
  partition_by_hash_buckets = 2,
  partition_by_second_hash_columns = ARRAY['machine'],
  partition_by_second_hash_buckets = 3,
  partition_by_range_columns = ARRAY['event_time'],
  range_partitions = '[{"lower": null, "upper": "2018-01-01T00:00:00"}, {"lower": "2018-01-01T00:00:00", "upper": null}]'
)

This defines a tree-level partitioning with two hash partition groups and one range partitioning on the event_time column. Two range partitions are created with a split at “2018-01-01T00:00:00”.

Table property range_partitions

With the range_partitions table property you specify the concrete range partitions to be created. The range partition definition itself must be given in the table property partition_design separately.

Example:

CREATE TABLE events (
  serialno varchar WITH (primary_key = true),
  event_time timestamp WITH (primary_key = true),
  message varchar
) WITH (
  partition_by_hash_columns = ARRAY['serialno'],
  partition_by_hash_buckets = 4,
  partition_by_range_columns = ARRAY['event_time'],
  range_partitions = '[{"lower": null, "upper": "2017-01-01T00:00:00"},
                       {"lower": "2017-01-01T00:00:00", "upper": "2017-07-01T00:00:00"},
                       {"lower": "2017-07-01T00:00:00", "upper": "2018-01-01T00:00:00"}]'
);

This creates a table with a hash partition on column serialno with 4 buckets and range partitioning on column event_time. Additionally three range partitions are created:

  1. for all event_times before the year 2017 (lower bound = null means it is unbound)
  2. for the first half of the year 2017
  3. for the second half the year 2017

This means any try to add rows with event_time of year 2018 or greater will fail, as no partition is defined. The next section shows how to define a new range partition for an existing table.

Managing range partitions

For existing tables, there are procedures to add and drop a range partition.

  • adding a range partition

    CALL kudu.system.add_range_partition(<schema>, <table>, <range_partition_as_json_string>),
    
  • dropping a range partition

    CALL kudu.system.drop_range_partition(<schema>, <table>, <range_partition_as_json_string>)
    
    • <schema>: schema of the table

    • <table>: table names

    • <range_partition_as_json_string>: lower and upper bound of the range partition as json string in the form '{"lower": <value>, "upper": <value>}', or if the range partition has multiple columns: '{"lower": [<value_col1>,...], "upper": [<value_col1>,...]}'. The concrete literal for lower and upper bound values are depending on the column types.

      Examples:

      Presto Data Type JSON string example
      BIGINT ‘{“lower”: 0, “upper”: 1000000}’
      SMALLINT ‘{“lower”: 10, “upper”: null}’
      VARCHAR ‘{“lower”: “A”, “upper”: “M”}’
      TIMESTAMP ‘{“lower”: “2018-02-01T00:00:00.000”, “upper”: “2018-02-01T12:00:00.000”}’
      BOOLEAN ‘{“lower”: false, “upper”: true}’
      VARBINARY values encoded as base64 strings

      To specified an unbounded bound, use the value null.

Example:

CALL kudu.system.add_range_partition('myschema', 'events', '{"lower": "2018-01-01", "upper": "2018-06-01"}')

This would add a range partition for a table events in the schema myschema with the lower bound 2018-01-01 (more exactly 2018-01-01T00:00:00.000) and the upper bound 2018-07-01.

Use the sql statement SHOW CREATE TABLE to query the existing range partitions (they are shown in the table property range_partitions).

Add Column

Adding a column to an existing table uses the SQL statement ALTER TABLE ... ADD COLUMN .... You can specify the same column properties as on creating a table.

Example:

ALTER TABLE mytable ADD COLUMN extraInfo varchar WITH (nullable = true, encoding = 'plain')

See also Column Properties.

Known limitations

  • Only lower case table and column names in Kudu are supported
  • Using a secured Kudu cluster has not been tested.