Using Impala with Iceberg Tables

• ACID compliance: DML operations are atomic, queries always read a consistent snapshot.
• Hidden partitioning: Iceberg produces partition values by taking a column value and optionally transforming it. Partition information is stored in the Iceberg metadata files. Iceberg is able to TRUNCATE column values or calculate a hash of them and use it for partitioning. Readers don't need to be aware of the partitioning of the table.
• Partition layout evolution: When the data volume or the query patterns change you can update the layout of a table. Since hidden partitioning is used, you don't need to rewrite the data files during partition layout evolution.
• Schema evolution: supports add, drop, update, or rename schema elements, and has no side-effects.
• Time travel: enables reproducible queries that use exactly the same table snapshot, or lets users easily examine changes.
• Cloning Iceberg tables: create an empty Iceberg table based on the definition of another Iceberg table.

When you have an existing Iceberg table that is not yet present in the Hive Metastore, you can use the CREATE EXTERNAL TABLE command in Impala to add the table to the Hive Metastore and make Impala able to interact with this table. Currently Impala supports HadoopTables, HadoopCatalog, and HiveCatalog. If you have an existing table in HiveCatalog, and you are using the same Hive Metastore, you need no further actions.

• HadoopTables. When the table already exists in a HadoopTable it means there is a location on the file system that contains your table. Use the following command to add this table to Impala's catalog:

  
  CREATE EXTERNAL TABLE ice_hadoop_tbl
  STORED AS ICEBERG
  LOCATION '/path/to/table'
  TBLPROPERTIES('iceberg.catalog'='hadoop.tables');
            

• HadoopCatalog. A table in HadoopCatalog means that there is a catalog location in the file system under which Iceberg tables are stored. Use the following command to add a table in a HadoopCatalog to Impala:

  
  CREATE EXTERNAL TABLE ice_hadoop_cat
  STORED AS ICEBERG
  TBLPROPERTIES('iceberg.catalog'='hadoop.catalog',
                'iceberg.catalog_location'='/path/to/catalog',
                'iceberg.table_identifier'='namespace.table');
            

• Alternatively, you can also use custom catalogs to use existing tables. It means you need to define your catalog in hive-site.xml. The adventage of this method is that other engines are more likely to be able to interact with this table. To globally register different catalogs, set the following Hadoop configurations:

  Config Key	Description
  iceberg.catalog.<catalog_name>.type	type of catalog: hive, hadoop, or left unset if using a custom catalog
  iceberg.catalog.<catalog_name>.catalog-impl	catalog implementation, must not be null if type is empty
  iceberg.catalog.<catalog_name>.<key>	any config key and value pairs for the catalog

  For example, to register a HadoopCatalog called 'hadoop', set the following properties in hive-site.xml:

  
  iceberg.catalog.hadoop.type=hadoop;
  iceberg.catalog.hadoop.warehouse=hdfs://example.com:8020/warehouse;
              

  Then in the CREATE TABLE statement you can just refer to the catalog name:

  
  CREATE EXTERNAL TABLE ice_catalogs STORED AS ICEBERG TBLPROPERTIES('iceberg.catalog'='<CATALOG-NAME>');
              

• If the table already exists in HiveCatalog then Impala should be able to see it without any additional commands.

CREATE TABLE ice_t (i INT) STORED AS ICEBERG;
        

By default Impala assumes that the Iceberg table uses Parquet data files. ORC is also supported, but we need to tell Impala via setting the table property 'write.format.default' to 'ORC'.

CREATE TABLE ice_ctas STORED AS ICEBERG AS SELECT i, b FROM value_tbl;

CREATE TABLE ice_ctas_part PARTITIONED BY(d) STORED AS ICEBERG AS SELECT s, ts, d FROM value_tbl;

CREATE TABLE ice_ctas_part_spec PARTITIONED BY SPEC (truncate(3, s)) STORED AS ICEBERG AS SELECT cast(t as INT), s, d FROM value_tbl;
        

          DROP TABLE ice_t;
        

When external.table.purge table property is set to true, then the DROP TABLE statement will also delete the data files. This property is set to true when Impala creates the Iceberg table via CREATE TABLE. When CREATE EXTERNAL TABLE is used (the table already exists in some catalog) then this external.table.purge is set to false, i.e. DROP TABLE doesn't remove any files, only the table definition in HMS.

You can get information about the supported Iceberg data tyeps in the Iceberg spec.

See schema evolution for more details.

The Iceberg spec has information about partitioning Iceberg tables. With Iceberg, we are not limited to value-based partitioning, we can also partition our tables via several partition transforms.

CREATE TABLE ice_p (i INT, d DATE, s STRING, t TIMESTAMP)
PARTITIONED BY SPEC (BUCKET(5, i), MONTH(d), TRUNCATE(3, s), HOUR(t))
STORED AS ICEBERG;
        

ALTER TABLE ice_p SET PARTITION SPEC (VOID(i), VOID(d), TRUNCATE(3, s), HOUR(t), i);
        

CREATE TABLE ice_p (i INT, b INT) PARTITIONED BY (p1 INT, p2 STRING) STORED AS ICEBERG;
        

One can inspect a table's partition spec by the SHOW PARTITIONS or SHOW CREATE TABLE commands.

Impala is also able to insert new data to Iceberg tables. Currently the INSERT INTO and INSERT OVERWRITE DML statements are supported. One can also remove the contents of an Iceberg table via the TRUNCATE command.

CREATE TABLE ice_p (i INT, b INT) PARTITIONED BY SPEC (bucket(17, i)) STORED AS ICEBERG;
INSERT INTO ice_p VALUES (1, 2);
        

INSERT OVERWRITE statements can replace data in the table with the result of a query. For partitioned tables Impala does a dynamic overwrite, which means partitions that have rows produced by the SELECT query will be replaced. And partitions that have no rows produced by the SELECT query remain untouched. INSERT OVERWRITE is not allowed for tables that use the BUCKET partition transform because dynamic overwrite behavior would be too random in this case. If one needs to replace all contents of a table, they can still use TRUNCATE and INSERT INTO.

Impala can only write Iceberg tables with Parquet data files.

Iceberg stores the table states in a chain of snapshots. By default, Impala uses the current snapshot of the table. But for Iceberg tables, it is also possible to query an earlier state of the table.

SELECT * FROM ice_t FOR SYSTEM_TIME AS OF '2022-01-04 10:00:00';
SELECT * FROM ice_t FOR SYSTEM_TIME AS OF now() - interval 5 days;
SELECT * FROM ice_t FOR SYSTEM_VERSION AS OF 123456;
        

DESCRIBE HISTORY [db_name.]table_name
  [FROM timestamp];

DESCRIBE HISTORY [db_name.]table_name
  [BETWEEN timestamp AND timestamp]
        

DESCRIBE HISTORY ice_t FROM '2022-01-04 10:00:00';
DESCRIBE HISTORY ice_t FROM now() - interval 5 days;
DESCRIBE HISTORY ice_t BETWEEN '2022-01-04 10:00:00' AND '2022-01-05 10:00:00';

Please note that during time travel, Impala uses the current table schema to query an older snapshot of the table which might have had a different schema in the past.

CREATE TABLE new_ice_tbl LIKE orig_ice_tbl;
        

Because of the Data Types of Iceberg and Impala do not correspond one by one, Impala can only clone between Iceberg tables.

ALTER TABLE ice_tbl EXECUTE expire_snapshots('2022-01-04 10:00:00');
ALTER TABLE ice_tbl EXECUTE expire_snapshots(now() - interval 5 days);
        

Old metadata file clean up can be configured with write.metadata.delete-after-commit.enabled=true and write.metadata.previous-versions-max table properties. This allows automatic metadata file removal after operations that modify metadata such as expiring snapshots or inserting data.