DDL

Spark DDL #

To use Iceberg in Spark, first configure Spark catalogs.

Iceberg uses Apache Spark’s DataSourceV2 API for data source and catalog implementations. Spark DSv2 is an evolving API with different levels of support in Spark versions. Spark 2.4 does not support SQL DDL.

Spark 2.4 can’t create Iceberg tables with DDL, instead use Spark 3.x or the Iceberg API.

CREATE TABLE #

Spark 3.0 can create tables in any Iceberg catalog with the clause USING iceberg:

CREATE TABLE prod.db.sample (
    id bigint COMMENT 'unique id',
    data string)
USING iceberg

Iceberg will convert the column type in Spark to corresponding Iceberg type. Please check the section of type compatibility on creating table for details.

Table create commands, including CTAS and RTAS, support the full range of Spark create clauses, including:

  • PARTITION BY (partition-expressions) to configure partitioning
  • LOCATION '(fully-qualified-uri)' to set the table location
  • COMMENT 'table documentation' to set a table description
  • TBLPROPERTIES ('key'='value', ...) to set table configuration

Create commands may also set the default format with the USING clause. This is only supported for SparkCatalog because Spark handles the USING clause differently for the built-in catalog.

PARTITIONED BY #

To create a partitioned table, use PARTITIONED BY:

CREATE TABLE prod.db.sample (
    id bigint,
    data string,
    category string)
USING iceberg
PARTITIONED BY (category)

The PARTITIONED BY clause supports transform expressions to create hidden partitions.

CREATE TABLE prod.db.sample (
    id bigint,
    data string,
    category string,
    ts timestamp)
USING iceberg
PARTITIONED BY (bucket(16, id), days(ts), category)

Supported transformations are:

  • years(ts): partition by year
  • months(ts): partition by month
  • days(ts) or date(ts): equivalent to dateint partitioning
  • hours(ts) or date_hour(ts): equivalent to dateint and hour partitioning
  • bucket(N, col): partition by hashed value mod N buckets
  • truncate(L, col): partition by value truncated to L
    • Strings are truncated to the given length
    • Integers and longs truncate to bins: truncate(10, i) produces partitions 0, 10, 20, 30, …

CREATE TABLE ... AS SELECT #

Iceberg supports CTAS as an atomic operation when using a SparkCatalog. CTAS is supported, but is not atomic when using SparkSessionCatalog.

CREATE TABLE prod.db.sample
USING iceberg
AS SELECT ...

REPLACE TABLE ... AS SELECT #

Iceberg supports RTAS as an atomic operation when using a SparkCatalog. RTAS is supported, but is not atomic when using SparkSessionCatalog.

Atomic table replacement creates a new snapshot with the results of the SELECT query, but keeps table history.

REPLACE TABLE prod.db.sample
USING iceberg
AS SELECT ...
REPLACE TABLE prod.db.sample
USING iceberg
PARTITIONED BY (part)
TBLPROPERTIES ('key'='value')
AS SELECT ...
CREATE OR REPLACE TABLE prod.db.sample
USING iceberg
AS SELECT ...

The schema and partition spec will be replaced if changed. To avoid modifying the table’s schema and partitioning, use INSERT OVERWRITE instead of REPLACE TABLE. The new table properties in the REPLACE TABLE command will be merged with any existing table properties. The existing table properties will be updated if changed else they are preserved.

DROP TABLE #

To delete a table, run:

DROP TABLE prod.db.sample

ALTER TABLE #

Iceberg has full ALTER TABLE support in Spark 3, including:

  • Renaming a table
  • Setting or removing table properties
  • Adding, deleting, and renaming columns
  • Adding, deleting, and renaming nested fields
  • Reordering top-level columns and nested struct fields
  • Widening the type of int, float, and decimal fields
  • Making required columns optional

In addition, SQL extensions can be used to add support for partition evolution and setting a table’s write order

ALTER TABLE ... RENAME TO #

ALTER TABLE prod.db.sample RENAME TO prod.db.new_name

ALTER TABLE ... SET TBLPROPERTIES #

ALTER TABLE prod.db.sample SET TBLPROPERTIES (
    'read.split.target-size'='268435456'
)

Iceberg uses table properties to control table behavior. For a list of available properties, see Table configuration.

UNSET is used to remove properties:

ALTER TABLE prod.db.sample UNSET TBLPROPERTIES ('read.split.target-size')

ALTER TABLE ... ADD COLUMN #

To add a column to Iceberg, use the ADD COLUMNS clause with ALTER TABLE:

ALTER TABLE prod.db.sample
ADD COLUMNS (
    new_column string comment 'new_column docs'
  )

Multiple columns can be added at the same time, separated by commas.

Nested columns should be identified using the full column name:

-- create a struct column
ALTER TABLE prod.db.sample
ADD COLUMN point struct<x: double, y: double>;

-- add a field to the struct
ALTER TABLE prod.db.sample
ADD COLUMN point.z double
-- create a nested array column of struct
ALTER TABLE prod.db.sample
ADD COLUMN points array<struct<x: double, y: double>>;

-- add a field to the struct within an array. Using keyword 'element' to access the array's element column.
ALTER TABLE prod.db.sample
ADD COLUMN points.element.z double
-- create a map column of struct key and struct value
ALTER TABLE prod.db.sample
ADD COLUMN points map<struct<x: int>, struct<a: int>>;

-- add a field to the value struct in a map. Using keyword 'value' to access the map's value column.
ALTER TABLE prod.db.sample
ADD COLUMN points.value.b int

Note: Altering a map ‘key’ column by adding columns is not allowed. Only map values can be updated.

In Spark 2.4.4 and later, you can add columns in any position by adding FIRST or AFTER clauses:

ALTER TABLE prod.db.sample
ADD COLUMN new_column bigint AFTER other_column
ALTER TABLE prod.db.sample
ADD COLUMN nested.new_column bigint FIRST

ALTER TABLE ... RENAME COLUMN #

Iceberg allows any field to be renamed. To rename a field, use RENAME COLUMN:

ALTER TABLE prod.db.sample RENAME COLUMN data TO payload
ALTER TABLE prod.db.sample RENAME COLUMN location.lat TO latitude

Note that nested rename commands only rename the leaf field. The above command renames location.lat to location.latitude

ALTER TABLE ... ALTER COLUMN #

Alter column is used to widen types, make a field optional, set comments, and reorder fields.

Iceberg allows updating column types if the update is safe. Safe updates are:

  • int to bigint
  • float to double
  • decimal(P,S) to decimal(P2,S) when P2 > P (scale cannot change)
ALTER TABLE prod.db.sample ALTER COLUMN measurement TYPE double

To add or remove columns from a struct, use ADD COLUMN or DROP COLUMN with a nested column name.

Column comments can also be updated using ALTER COLUMN:

ALTER TABLE prod.db.sample ALTER COLUMN measurement TYPE double COMMENT 'unit is bytes per second'
ALTER TABLE prod.db.sample ALTER COLUMN measurement COMMENT 'unit is kilobytes per second'

Iceberg allows reordering top-level columns or columns in a struct using FIRST and AFTER clauses:

ALTER TABLE prod.db.sample ALTER COLUMN col FIRST
ALTER TABLE prod.db.sample ALTER COLUMN nested.col AFTER other_col

Nullability can be changed using SET NOT NULL and DROP NOT NULL:

ALTER TABLE prod.db.sample ALTER COLUMN id DROP NOT NULL
ALTER COLUMN is not used to update struct types. Use ADD COLUMN and DROP COLUMN to add or remove struct fields.

ALTER TABLE ... DROP COLUMN #

To drop columns, use ALTER TABLE ... DROP COLUMN:

ALTER TABLE prod.db.sample DROP COLUMN id
ALTER TABLE prod.db.sample DROP COLUMN point.z

ALTER TABLE SQL extensions #

These commands are available in Spark 3.x when using Iceberg SQL extensions.

ALTER TABLE ... ADD PARTITION FIELD #

Iceberg supports adding new partition fields to a spec using ADD PARTITION FIELD:

ALTER TABLE prod.db.sample ADD PARTITION FIELD catalog -- identity transform

Partition transforms are also supported:

ALTER TABLE prod.db.sample ADD PARTITION FIELD bucket(16, id)
ALTER TABLE prod.db.sample ADD PARTITION FIELD truncate(data, 4)
ALTER TABLE prod.db.sample ADD PARTITION FIELD years(ts)
-- use optional AS keyword to specify a custom name for the partition field 
ALTER TABLE prod.db.sample ADD PARTITION FIELD bucket(16, id) AS shard

Adding a partition field is a metadata operation and does not change any of the existing table data. New data will be written with the new partitioning, but existing data will remain in the old partition layout. Old data files will have null values for the new partition fields in metadata tables.

Dynamic partition overwrite behavior will change when the table’s partitioning changes because dynamic overwrite replaces partitions implicitly. To overwrite explicitly, use the new DataFrameWriterV2 API.

To migrate from daily to hourly partitioning with transforms, it is not necessary to drop the daily partition field. Keeping the field ensures existing metadata table queries continue to work.
Dynamic partition overwrite behavior will change when partitioning changes For example, if you partition by days and move to partitioning by hours, overwrites will overwrite hourly partitions but not days anymore.

ALTER TABLE ... DROP PARTITION FIELD #

Partition fields can be removed using DROP PARTITION FIELD:

ALTER TABLE prod.db.sample DROP PARTITION FIELD catalog
ALTER TABLE prod.db.sample DROP PARTITION FIELD bucket(16, id)
ALTER TABLE prod.db.sample DROP PARTITION FIELD truncate(data, 4)
ALTER TABLE prod.db.sample DROP PARTITION FIELD years(ts)
ALTER TABLE prod.db.sample DROP PARTITION FIELD shard

Note that although the partition is removed, the column will still exist in the table schema.

Dropping a partition field is a metadata operation and does not change any of the existing table data. New data will be written with the new partitioning, but existing data will remain in the old partition layout.

Dynamic partition overwrite behavior will change when partitioning changes For example, if you partition by days and move to partitioning by hours, overwrites will overwrite hourly partitions but not days anymore.
Be careful when dropping a partition field because it will change the schema of metadata tables, like files, and may cause metadata queries to fail or produce different results.

ALTER TABLE ... WRITE ORDERED BY #

Iceberg tables can be configured with a sort order that is used to automatically sort data that is written to the table in some engines. For example, MERGE INTO in Spark will use the table ordering.

To set the write order for a table, use WRITE ORDERED BY:

ALTER TABLE prod.db.sample WRITE ORDERED BY category, id
-- use optional ASC/DEC keyword to specify sort order of each field (default ASC)
ALTER TABLE prod.db.sample WRITE ORDERED BY category ASC, id DESC
-- use optional NULLS FIRST/NULLS LAST keyword to specify null order of each field (default FIRST)
ALTER TABLE prod.db.sample WRITE ORDERED BY category ASC NULLS LAST, id DESC NULLS FIRST
Table write order does not guarantee data order for queries. It only affects how data is written to the table.

WRITE ORDERED BY sets a global ordering where rows are ordered across tasks, like using ORDER BY in an INSERT command:

INSERT INTO prod.db.sample
SELECT id, data, category, ts FROM another_table
ORDER BY ts, category

To order within each task, not across tasks, use LOCALLY ORDERED BY:

ALTER TABLE prod.db.sample WRITE LOCALLY ORDERED BY category, id

ALTER TABLE ... WRITE DISTRIBUTED BY PARTITION #

WRITE DISTRIBUTED BY PARTITION will request that each partition is handled by one writer, the default implementation is hash distribution.

ALTER TABLE prod.db.sample WRITE DISTRIBUTED BY PARTITION

DISTRIBUTED BY PARTITION and LOCALLY ORDERED BY may be used together, to distribute by partition and locally order rows within each task.

ALTER TABLE prod.db.sample WRITE DISTRIBUTED BY PARTITION LOCALLY ORDERED BY category, id