Iceberg supports reading and writing Iceberg tables through Hive by using a StorageHandler.
Iceberg compatibility with Hive 2.x and Hive 3.1.2/3 supports the following features:
- Creating a table
- Dropping a table
- Reading a table
- Inserting into a table (INSERT INTO)
With Hive version 4.0.0-alpha-2 and above, the Iceberg integration when using HiveCatalog supports the following additional features:
- Altering a table with expiring snapshots.
- Create a table like an existing table (CTLT table)
- Support adding parquet compression type via Table properties Compression types
- Altering a table metadata location
- Supporting table rollback
- Honours sort orders on existing tables when writing a table Sort orders specification
With Hive version 4.0.0-alpha-1 and above, the Iceberg integration when using HiveCatalog supports the following additional features:
- Creating an Iceberg identity-partitioned table
- Creating an Iceberg table with any partition spec, including the various transforms supported by Iceberg
- Creating a table from an existing table (CTAS table)
- Altering a table while keeping Iceberg and Hive schemas in sync
- Altering the partition schema (updating columns)
- Altering the partition schema by specifying partition transforms
- Truncating a table
- Migrating tables in Avro, Parquet, or ORC (Non-ACID) format to Iceberg
- Reading the schema of a table
- Querying Iceberg metadata tables
- Time travel applications
- Inserting into a table (INSERT INTO)
- Inserting data overwriting existing data (INSERT OVERWRITE)
Enabling Iceberg support in Hive
Hive 4.0.0-alpha-1 comes with the Iceberg 0.13.1 included. No additional downloads or jars are needed.
Hive 2.3.x, Hive 3.1.x
In order to use Hive 2.3.x or Hive 3.1.x, you must load the Iceberg-Hive runtime jar and enable Iceberg support, either globally or for an individual table using a table property.
Loading runtime jar
To enable Iceberg support in Hive, 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.
If the Iceberg storage handler is not in Hive’s classpath, then Hive cannot load or update the metadata for an Iceberg table when the storage handler is set. To avoid the appearance of broken tables in Hive, Iceberg will not add the storage handler to a table unless Hive support is enabled. The storage handler is kept in sync (added or removed) every time Hive engine support for the table is updated, i.e. turned on or off in the table properties. There are two ways to enable Hive support: globally in Hadoop Configuration and per-table using a table property.
To enable Hive support globally for an application, set
iceberg.engine.hive.enabled=true in its Hadoop configuration.
For example, setting this in the
hive-site.xml loaded by Spark will enable the storage handler for all tables created
0.11.0, when using Hive with Tez you also have to disable vectorization (
Table property configuration
Alternatively, the property
engine.hive.enabled can be set to
true and added to the table properties when creating
the Iceberg table. Here is an example of doing it programmatically:
Catalog catalog=...; Map<String, String> tableProperties=Maps.newHashMap(); tableProperties.put(TableProperties.ENGINE_HIVE_ENABLED,"true"); // engine.hive.enabled=true catalog.createTable(tableId,schema,spec,tableProperties);
The table level configuration overrides the global Hadoop configuration.
Hive on Tez configuration
To use the Tez engine on Hive
3.1.2 or later, Tez needs to be upgraded to >=
0.10.1 which contains a necessary fix TEZ-4248.
To use the Tez engine on Hive
2.3.x, you will need to manually build Tez from the
branch-0.9 branch due to a
backwards incompatibility issue with Tez
In both cases, you will also need to set the following property in the
tez-site.xml configuration file:
Global Hive catalog
From the Hive engine’s perspective, there is only one global data catalog that is defined in the Hadoop configuration in the runtime environment. In contrast, Iceberg supports multiple different data catalog types such as Hive, Hadoop, AWS Glue, or custom catalog implementations. Iceberg also allows loading a table directly based on its path in the file system. Those tables do not belong to any catalog. Users might want to read these cross-catalog and path-based tables through the Hive engine for use cases like join.
To support this, a table in the Hive metastore can represent three different ways of loading an Iceberg table, depending
on the table’s
- The table will be loaded using a
HiveCatalogthat corresponds to the metastore configured in the Hive environment if no
- The table will be loaded using a custom catalog if
iceberg.catalogis set to a catalog name (see below)
- The table can be loaded directly using the table’s root location if
iceberg.catalogis set to
For cases 2 and 3 above, users can create an overlay of an Iceberg table in the Hive metastore, so that different table types can work together in the same Hive environment. See CREATE EXTERNAL TABLE and CREATE TABLE for more details.
Custom Iceberg catalogs
To globally register different catalogs, set the following Hadoop configurations:
|iceberg.catalog.<catalog_name>.type||type of 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|
Here are some examples using Hive CLI:
SET iceberg.catalog.another_hive.type=hive; SET iceberg.catalog.another_hive.uri=thrift://example.com:9083; SET iceberg.catalog.another_hive.clients=10; SET iceberg.catalog.another_hive.warehouse=hdfs://example.com:8020/warehouse;
SET iceberg.catalog.hadoop.type=hadoop; SET iceberg.catalog.hadoop.warehouse=hdfs://example.com:8020/warehouse;
Register an AWS
SET iceberg.catalog.glue.catalog-impl=org.apache.iceberg.aws.glue.GlueCatalog; SET iceberg.catalog.glue.warehouse=s3://my-bucket/my/key/prefix; SET iceberg.catalog.glue.lock.table=myGlueLockTable;
Not all the features below are supported with Hive 2.3.x and Hive 3.1.x. Please refer to the Feature support paragraph for further details.
One generally applicable difference is that Hive 4.0.0-alpha-1 provides the possibility to use
STORED BY ICEBERG instead of the old
STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler'
Non partitioned tables
CREATE EXTERNAL TABLE command creates an Iceberg table when you specify the storage handler as follows:
CREATE EXTERNAL TABLE x (i int) STORED BY ICEBERG;
If you want to create external tables using CREATE TABLE, configure the MetaStoreMetadataTransformer on the cluster,
CREATE TABLE commands are transformed to create external tables. For example:
CREATE TABLE x (i int) STORED BY ICEBERG;
You can specify the default file format (Avro, Parquet, ORC) at the time of the table creation. The default is Parquet:
CREATE TABLE x (i int) STORED BY ICEBERG STORED AS ORC;
You can create Iceberg partitioned tables using a command familiar to those who create non-Iceberg tables:
CREATE TABLE x (i int) PARTITIONED BY (j int) STORED BY ICEBERG;
Use the DESCRIBE command to get information about the Iceberg identity partitions:
The result is:
|# Partition Transform Information||NULL||NULL|
You can create Iceberg partitions using the following Iceberg partition specification syntax (supported only from Hive 4.0.0-alpha-1):
CREATE TABLE x (i int, ts timestamp) PARTITIONED BY SPEC (month(ts), bucket(2, i)) STORED AS ICEBERG; DESCRIBE x;
The result is:
|# Partition Transform Information||NULL||NULL|
The supported transformations for Hive are the same as for Spark:
- 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
CREATE TABLE AS SELECT operation resembles the native Hive operation with a single important difference.
The Iceberg table and the corresponding Hive table are created at the beginning of the query execution.
The data is inserted / committed when the query finishes. So for a transient period the table already exists but contains no data.
CREATE TABLE target PARTITIONED BY SPEC (year(year_field), identity_field) STORED BY ICEBERG AS SELECT * FROM source;
CREATE TABLE LIKE TABLE
CREATE TABLE target LIKE source STORED BY ICEBERG;
CREATE EXTERNAL TABLE overlaying an existing Iceberg table
CREATE EXTERNAL TABLE command is used 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
operate on these different types of table.
Hive catalog tables
As described before, tables created by the
HiveCatalog with Hive engine feature enabled are directly visible by the
Hive engine, so there is no need to create an overlay.
Custom catalog tables
For a table in a registered catalog, specify the catalog name in the statement using table property
For example, the SQL below creates an overlay for a table in a
hadoop type catalog named
SET iceberg.catalog.hadoop_cat.type=hadoop; SET iceberg.catalog.hadoop_cat.warehouse=hdfs://example.com:8020/hadoop_cat; CREATE EXTERNAL TABLE database_a.table_a STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler' TBLPROPERTIES ('iceberg.catalog'='hadoop_cat');
iceberg.catalog is missing from both table properties and the global Hadoop configuration,
HiveCatalog will be
used as default.
Path-based Hadoop tables
Iceberg tables created using
HadoopTables are stored entirely in a directory in a filesystem like HDFS. These tables
are considered to have no catalog. To indicate that, set
iceberg.catalog property to
CREATE EXTERNAL TABLE table_a STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler' LOCATION 'hdfs://some_bucket/some_path/table_a' TBLPROPERTIES ('iceberg.catalog'='location_based_table');
CREATE TABLE overlaying an existing Iceberg table
You can also create a new table that is managed by a custom catalog. For example, the following code creates a table in a custom Hadoop catalog:
SET iceberg.catalog.hadoop_cat.type=hadoop; SET iceberg.catalog.hadoop_cat.warehouse=hdfs://example.com:8020/hadoop_cat; CREATE TABLE database_a.table_a ( id bigint, name string ) PARTITIONED BY ( dept string ) STORED BY 'org.apache.iceberg.mr.hive.HiveIcebergStorageHandler' TBLPROPERTIES ('iceberg.catalog'='hadoop_cat');
EXTERNALkeyword when creating an overlay table. However, this is not recommended because creating managed overlay tables could pose a risk to the shared data files in case of accidental drop table commands from the Hive side, which would unintentionally remove all the data in the table.
For HiveCatalog tables the Iceberg table properties and the Hive table properties stored in HMS are kept in sync.
ALTER TABLE t SET TBLPROPERTIES('...'='...');
The Hive table schema is kept in sync with the Iceberg table. If an outside source (Impala/Spark/Java API/etc) changes the schema, the Hive table immediately reflects the changes. You alter the table schema using Hive commands:
- Add a column
ALTER TABLE orders ADD COLUMNS (nickname string);
- Rename a column
ALTER TABLE orders CHANGE COLUMN item fruit string;
- Reorder columns
ALTER TABLE orders CHANGE COLUMN quantity quantity int AFTER price;
- Change a column type - only if the Iceberg defined the column type change as safe
ALTER TABLE orders CHANGE COLUMN price price long;
- Drop column by using REPLACE COLUMN to remove the old column
ALTER TABLE orders REPLACE COLUMNS (remaining string);
You change the partitioning schema using the following commands:
- Change the partitioning schema to new identity partitions:
ALTER TABLE default.customers SET PARTITION SPEC (last_name);
- Alternatively, provide a partition specification:
ALTER TABLE order SET PARTITION SPEC (month(ts));
You can migrate Avro / Parquet / ORC external tables to Iceberg tables using the following command:
ALTER TABLE t SET TBLPROPERTIES ('storage_handler'='org.apache.iceberg.mr.hive.HiveIcebergStorageHandler');
During the migration the data files are not changed, only the appropriate Iceberg metadata files are created. After the migration, handle the table as a normal Iceberg table.
The following command truncates the Iceberg table:
TRUNCATE TABLE t;
Using a partition specification is not allowed.
Tables can be dropped using the
DROP TABLE command:
DROP TABLE [IF EXISTS] table_name [PURGE];
The metadata location (snapshot location) only can be changed if the new path contains the exact same metadata json. It can be done only after migrating the table to Iceberg, the two operation cannot be done in one step.
ALTER TABLE t set TBLPROPERTIES ('metadata_location'='<path>/hivemetadata/00003-a1ada2b8-fc86-4b5b-8c91-400b6b46d0f2.metadata.json');
Select statements work the same on Iceberg tables in Hive. You will see the Iceberg benefits over Hive in compilation and execution:
- No file system listings - especially important on blob stores, like S3
- No partition listing from the Metastore
- Advanced partition filtering - the partition keys are not needed in the queries when they could be calculated
- Could handle higher number of partitions than normal Hive tables
Here are the features highlights for Iceberg Hive read support:
- Predicate pushdown: Pushdown of the Hive SQL
WHEREclause has been implemented so that these filters are used at the Iceberg
TableScanlevel as well as by the Parquet and ORC Readers.
- Column projection: Columns from the Hive SQL
SELECTclause are projected down to the Iceberg readers to reduce the number of columns read.
- Hive query engines:
- With Hive 2.3.x, 3.1.x both the MapReduce and Tez query execution engines are supported.
- With Hive 4.0.0-alpha-1 Tez query execution engine is supported.
Some of the advanced / little used optimizations are not yet implemented for Iceberg tables, so you should check your individual queries. Also currently the statistics stored in the MetaStore are used for query planning. This is something we are planning to improve in the future.
Hive supports the standard single-table INSERT INTO operation:
INSERT INTO table_a VALUES ('a', 1); INSERT INTO table_a SELECT...;
Multi-table insert is also supported, but it will not be atomic. Commits occur one table at a time. Partial changes will be visible during the commit process and failures can leave partial changes committed. Changes within a single table will remain atomic.
Here is an example of inserting into multiple tables at once in Hive SQL:
FROM customers INSERT INTO target1 SELECT customer_id, first_name INSERT INTO target2 SELECT last_name, customer_id;
INSERT OVERWRITE can replace data in the table with the result of a query. Overwrites are atomic operations for Iceberg tables. For nonpartitioned tables the content of the table is always removed. For partitioned tables the partitions that have rows produced by the SELECT query will be replaced.
INSERT OVERWRITE TABLE target SELECT * FROM source;
QUERYING METADATA TABLES
Hive supports querying of the Iceberg Metadata tables. The tables could be used as normal Hive tables, so it is possible to use projections / joins / filters / etc. To reference a metadata table the full name of the table should be used, like: <DB_NAME>.<TABLE_NAME>.<METADATA_TABLE_NAME>.
Currently the following metadata tables are available in Hive:
SELECT * FROM default.table_a.files;
Hive supports snapshot id based and time base timetravel queries. For these views it is possible to use projections / joins / filters / etc. The function is available with the following syntax:
SELECT * FROM table_a FOR SYSTEM_TIME AS OF '2021-08-09 10:35:57'; SELECT * FROM table_a FOR SYSTEM_VERSION AS OF 1234567;
You can expire snapshots of an Iceberg table using an ALTER TABLE query from Hive. You should periodically expire snapshots to delete data files that is no longer needed, and reduce the size of table metadata.
Each write to an Iceberg table from Hive creates a new snapshot, or version, of a table. Snapshots can be used for time-travel queries, or the table can be rolled back to any valid snapshot. Snapshots accumulate until they are expired by the expire_snapshots operation.
Enter a query to expire snapshots having the following timestamp:
ALTER TABLE test_table EXECUTE expire_snapshots('2021-12-09 05:39:18.689000000');
Hive and Iceberg support different set of types. Iceberg can perform type conversion automatically, but not for all combinations, so you may want to understand the type conversion in Iceberg in prior to design the types of columns in your tables. You can enable auto-conversion through Hadoop configuration (not enabled by default):
|iceberg.mr.schema.auto.conversion||false||if Hive should perform type auto-conversion|
Hive type to Iceberg type
This type conversion table describes how Hive types are converted to the Iceberg types. The conversion applies on both creating Iceberg table and writing to Iceberg table via Hive.
|timestamp||timestamp without timezone|
|timestamplocaltz||timestamp with timezone||Hive 3 only|
Rolling back iceberg table’s data to the state at an older table snapshot.
Rollback to the last snapshot before a specific timestamp
ALTER TABLE ice_t EXECUTE ROLLBACK('2022-05-12 00:00:00')
Rollback to a specific snapshot ID
ALTER TABLE ice_t EXECUTE ROLLBACK(1111);