Catalogs

Spark adds an API to plug in table catalogs that are used to load, create, and manage Iceberg tables. Spark catalogs are configured by setting Spark properties under spark.sql.catalog.

This creates an Iceberg catalog named hive_prod that loads tables from a Hive metastore:

spark.sql.catalog.hive_prod = org.apache.iceberg.spark.SparkCatalog
spark.sql.catalog.hive_prod.type = hive
spark.sql.catalog.hive_prod.uri = thrift://metastore-host:port
# omit uri to use the same URI as Spark: hive.metastore.uris in hive-site.xml

Below is an example for a REST catalog named rest_prod that loads tables from REST URL http://localhost:8080:

spark.sql.catalog.rest_prod = org.apache.iceberg.spark.SparkCatalog
spark.sql.catalog.rest_prod.type = rest
spark.sql.catalog.rest_prod.uri = http://localhost:8080

Iceberg also supports a directory-based catalog in HDFS that can be configured using type=hadoop:

spark.sql.catalog.hadoop_prod = org.apache.iceberg.spark.SparkCatalog
spark.sql.catalog.hadoop_prod.type = hadoop
spark.sql.catalog.hadoop_prod.warehouse = hdfs://nn:8020/warehouse/path

The Hive-based catalog only loads Iceberg tables. To load non-Iceberg tables in the same Hive metastore, use a session catalog.

Catalog configuration

A catalog is created and named by adding a property spark.sql.catalog.(catalog-name) with an implementation class for its value.

Iceberg supplies two implementations:

• org.apache.iceberg.spark.SparkCatalog supports a Hive Metastore or a Hadoop warehouse as a catalog
• org.apache.iceberg.spark.SparkSessionCatalog adds support for Iceberg tables to Spark’s built-in catalog, and delegates to the built-in catalog for non-Iceberg tables

Both catalogs are configured using properties nested under the catalog name. Common configuration properties for Hive and Hadoop are:

Additional properties can be found in common catalog configuration.

Using catalogs

Catalog names are used in SQL queries to identify a table. In the examples above, hive_prod and hadoop_prod can be used to prefix database and table names that will be loaded from those catalogs.

SELECT * FROM hive_prod.db.table; -- load db.table from catalog hive_prod

Spark 3 keeps track of the current catalog and namespace, which can be omitted from table names.

USE hive_prod.db;
SELECT * FROM table; -- load db.table from catalog hive_prod

To see the current catalog and namespace, run SHOW CURRENT NAMESPACE.

Replacing the session catalog

To add Iceberg table support to Spark’s built-in catalog, configure spark_catalog to use Iceberg’s SparkSessionCatalog.

spark.sql.catalog.spark_catalog = org.apache.iceberg.spark.SparkSessionCatalog
spark.sql.catalog.spark_catalog.type = hive

Spark’s built-in catalog supports existing v1 and v2 tables tracked in a Hive Metastore. This configures Spark to use Iceberg’s SparkSessionCatalog as a wrapper around that session catalog. When a table is not an Iceberg table, the built-in catalog will be used to load it instead.

This configuration can use same Hive Metastore for both Iceberg and non-Iceberg tables.

Using catalog specific Hadoop configuration values

Similar to configuring Hadoop properties by using spark.hadoop.*, it’s possible to set per-catalog Hadoop configuration values when using Spark by adding the property for the catalog with the prefix spark.sql.catalog.(catalog-name).hadoop.*. These properties will take precedence over values configured globally using spark.hadoop.* and will only affect Iceberg tables.

spark.sql.catalog.hadoop_prod.hadoop.fs.s3a.endpoint = http://aws-local:9000

Loading a custom catalog

Spark supports loading a custom Iceberg Catalog implementation by specifying the catalog-impl property. Here is an example:

spark.sql.catalog.custom_prod = org.apache.iceberg.spark.SparkCatalog
spark.sql.catalog.custom_prod.catalog-impl = com.my.custom.CatalogImpl
spark.sql.catalog.custom_prod.my-additional-catalog-config = my-value

SQL Extensions

Iceberg 0.11.0 and later add an extension module to Spark to add new SQL commands, like CALL for stored procedures or ALTER TABLE ... WRITE ORDERED BY.

Using those SQL commands requires adding Iceberg extensions to your Spark environment using the following Spark property:

Runtime configuration

Read options

Spark read options are passed when configuring the DataFrameReader, like this:

// time travel
spark.read
    .option("snapshot-id", 10963874102873L)
    .table("catalog.db.table")

Write options

Spark write options are passed when configuring the DataFrameWriter, like this:

// write with Avro instead of Parquet
df.write
    .option("write-format", "avro")
    .option("snapshot-property.key", "value")
    .insertInto("catalog.db.table")

CommitMetadata provides an interface to add custom metadata to a snapshot summary during a SQL execution, which can be beneficial for purposes such as auditing or change tracking. If properties start with snapshot-property., then that prefix will be removed from each property. Here is an example:

import org.apache.iceberg.spark.CommitMetadata;
Map<String, String> properties = Maps.newHashMap();
properties.put("property_key", "property_value");
CommitMetadata.withCommitProperties(properties,
        () -> {
            spark.sql("DELETE FROM " + tableName + " where id = 1");
            return 0;
        },
        RuntimeException.class);