Hive¶

Hive read support¶

Iceberg supports the reading of Iceberg tables from Hive by using a StorageHandler. Please note that only Hive 2.x versions are currently supported.

Table creation¶

This section explains the various steps needed in order to overlay a Hive table “on top of” an existing Iceberg table. Iceberg tables are created using either a Catalog or an implementation of the Tables interface and Hive needs to be configured accordingly to read data from these different types of table.

Add the Iceberg Hive Runtime jar file to the Hive classpath¶

Regardless of the table type, the HiveIcebergStorageHandler and supporting classes need to be made available on Hive’s classpath. These are provided by the iceberg-hive-runtime jar file. For example, if using the Hive shell, this can be achieved by issuing a statement like so:

add jar /path/to/iceberg-hive-runtime.jar;

There are many others ways to achieve this including adding the jar file to Hive’s auxiliary classpath (so it is available by default) - please refer to Hive’s documentation for more information.

Using Hadoop Tables¶

Iceberg tables created using HadoopTables are stored entirely in a directory in a filesystem like HDFS.

Create an Iceberg table¶

The first step is to create an Iceberg table using the Spark/Java/Python API and HadoopTables. For the purposes of this documentation we will assume that the table is called table_a and that the table location is hdfs://some_path/table_a.

Create a Hive table¶

Now overlay a Hive table on top of this Iceberg table by issuing Hive DDL like so:

CREATE EXTERNAL TABLE table_a 
STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler' 
LOCATION 'hdfs://some_bucket/some_path/table_a';

Query the Iceberg table via Hive¶

You should now be able to issue Hive SQL SELECT queries using the above table and see the results returned from the underlying Iceberg table.

SELECT * from table_a;

Using Hive Catalog¶

Iceberg tables created using HiveCatalog are automatically registered with Hive.

Create an Iceberg table¶

The first step is to create an Iceberg table using the Spark/Java/Python API and HiveCatalog. For the purposes of this documentation we will assume that the table is called table_b and that the table location is s3://some_path/table_b. In order for Iceberg to correctly set up the Hive table for querying some configuration values need to be set, the two options for this are described below - you can use either or the other depending on your use case.

Hive Configuration¶

The value iceberg.engine.hive.enabled needs to be set to true and added to the Hive configuration file on the classpath of the application creating or modifying (altering, inserting etc.) the table. This can be done by modifying the relevant hive-site.xml. Alternatively this can be done programmatically like so:

Configuration hadoopConfiguration = spark.sparkContext().hadoopConfiguration();
hadoopConfiguration.set(ConfigProperties.ENGINE_HIVE_ENABLED, "true"); //iceberg.engine.hive.enabled=true
HiveCatalog catalog = new HiveCatalog(hadoopConfiguration);
...
catalog.createTable(tableId, schema, spec);

Table Property Configuration¶

The property engine.hive.enabled needs to be set to true and added to the table properties when creating the Iceberg table. This can be done like so:

    Map<String, String> tableProperties = new HashMap<String, String>();
    tableProperties.put(TableProperties.ENGINE_HIVE_ENABLED, "true"); //engine.hive.enabled=true
    catalog.createTable(tableId, schema, spec, tableProperties);

Query the Iceberg table via Hive¶

In order to query a Hive table created by either of the HiveCatalog methods described above you need to first set a Hive configuration value like so:

SET iceberg.mr.catalog=hive;

You should now be able to issue Hive SQL SELECT queries using the above table and see the results returned from the underlying Iceberg table.

SELECT * from table_b;

Using Hadoop Catalog¶

Iceberg tables created using HadoopCatalog are stored entirely in a directory in a filesystem like HDFS.

Create an Iceberg table¶

The first step is to create an Iceberg table using the Spark/Java/Python API and HadoopCatalog. For the purposes of this documentation we will assume that the fully qualified table identifier is database_a.table_c and that the Hadoop Catalog warehouse location is hdfs://some_bucket/path_to_hadoop_warehouse. Iceberg will therefore create the table at the location hdfs://some_bucket/path_to_hadoop_warehouse/database_a/table_c.

Create a Hive table¶

Now overlay a Hive table on top of this Iceberg table by issuing Hive DDL like so:

CREATE EXTERNAL TABLE database_a.table_c 
STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler' 
LOCATION 'hdfs://some_bucket/path_to_hadoop_warehouse/database_a/table_c'
TBLPROPERTIES (
  'iceberg.mr.catalog'='hadoop', 
  'iceberg.mr.catalog.hadoop.warehouse.location'='hdfs://some_bucket/path_to_hadoop_warehouse')
;

Note that the Hive database and table name must match the values used in the Iceberg TableIdentifier when the table was created.

It is possible to omit either or both of the table properties but instead you will then need to set these when reading from the table. Generally it is recommended to set them at table creation time so you can query tables created by different catalogs.

Query the Iceberg table via Hive¶

You should now be able to issue Hive SQL SELECT queries using the above table and see the results returned from the underlying Iceberg table.

SELECT * from database_a.table_c;

Features¶

Predicate pushdown¶

Pushdown of the Hive SQL WHERE clause has been implemented so that these filters are used at the Iceberg TableScan level as well as by the Parquet and ORC Readers.

Column Projection¶

Columns from the Hive SQL SELECT clause are projected down to the Iceberg readers to reduce the number of columns read.

Hive Query Engines¶

Both the Map Reduce and Tez query execution engines are supported.