Spark Configuration🔗

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

Info

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:

Property	Values	Description
spark.sql.catalog.catalog-name.type	`hive`, `hadoop` or `rest`	The underlying Iceberg catalog implementation, `HiveCatalog`, `HadoopCatalog`, `RESTCatalog` or left unset if using a custom catalog
spark.sql.catalog.catalog-name.catalog-impl		The custom Iceberg catalog implementation. If `type` is null, `catalog-impl` must not be null.
spark.sql.catalog.catalog-name.io-impl		The custom FileIO implementation.
spark.sql.catalog.catalog-name.metrics-reporter-impl		The custom MetricsReporter implementation.
spark.sql.catalog.catalog-name.default-namespace	default	The default current namespace for the catalog
spark.sql.catalog.catalog-name.uri	thrift://host:port	Hive metastore URL for hive typed catalog, REST URL for REST typed catalog
spark.sql.catalog.catalog-name.warehouse	hdfs://nn:8020/warehouse/path	Base path for the warehouse directory
spark.sql.catalog.catalog-name.cache-enabled	`true` or `false`	Whether to enable catalog cache, default value is `true`
spark.sql.catalog.catalog-name.cache.expiration-interval-ms	`30000` (30 seconds)	Duration after which cached catalog entries are expired; Only effective if `cache-enabled` is `true`. `-1` disables cache expiration and `0` disables caching entirely, irrespective of `cache-enabled`. Default is `30000` (30 seconds)
spark.sql.catalog.catalog-name.table-default.propertyKey		Default Iceberg table property value for property key propertyKey, which will be set on tables created by this catalog if not overridden
spark.sql.catalog.catalog-name.table-override.propertyKey		Enforced Iceberg table property value for property key propertyKey, which cannot be overridden by user

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:

Spark extensions property	Iceberg extensions implementation
`spark.sql.extensions`	`org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions`

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")

Spark option	Default	Description
snapshot-id	(latest)	Snapshot ID of the table snapshot to read
as-of-timestamp	(latest)	A timestamp in milliseconds; the snapshot used will be the snapshot current at this time.
split-size	As per table property	Overrides this table's read.split.target-size and read.split.metadata-target-size
lookback	As per table property	Overrides this table's read.split.planning-lookback
file-open-cost	As per table property	Overrides this table's read.split.open-file-cost
vectorization-enabled	As per table property	Overrides this table's read.parquet.vectorization.enabled
batch-size	As per table property	Overrides this table's read.parquet.vectorization.batch-size
stream-from-timestamp	(none)	A timestamp in milliseconds to stream from; if before the oldest known ancestor snapshot, the oldest will be used

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")

Spark option	Default	Description
write-format	Table write.format.default	File format to use for this write operation; parquet, avro, or orc
target-file-size-bytes	As per table property	Overrides this table's write.target-file-size-bytes
check-nullability	true	Sets the nullable check on fields
snapshot-property.custom-key	null	Adds an entry with custom-key and corresponding value in the snapshot summary (the `snapshot-property.` prefix is only required for DSv2)
fanout-enabled	false	Overrides this table's write.spark.fanout.enabled
check-ordering	true	Checks if input schema and table schema are same
isolation-level	null	Desired isolation level for Dataframe overwrite operations. `null` => no checks (for idempotent writes), `serializable` => check for concurrent inserts or deletes in destination partitions, `snapshot` => checks for concurrent deletes in destination partitions.
validate-from-snapshot-id	null	If isolation level is set, id of base snapshot from which to check concurrent write conflicts into a table. Should be the snapshot before any reads from the table. Can be obtained via Table API or Snapshots table. If null, the table's oldest known snapshot is used.
compression-codec	Table write.(fileformat).compression-codec	Overrides this table's compression codec for this write
compression-level	Table write.(fileformat).compression-level	Overrides this table's compression level for Parquet and Avro tables for this write
compression-strategy	Table write.orc.compression-strategy	Overrides this table's compression strategy for ORC tables for this write

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);