Spark Configuration¶

Catalogs¶

Spark 3.0 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

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

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 or hadoop	The underlying Iceberg catalog implementation, HiveCatalog, HadoopCatalog or left unset if using a custom catalog
spark.sql.catalog.catalog-name.catalog-impl		The underlying Iceberg catalog 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	Metastore connect URI; default from hive-site.xml
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

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

Catalogs in Spark 2.4¶

When using Iceberg 0.11.0 and later, Spark 2.4 can load tables from multiple Iceberg catalogs or from table locations.

Catalogs in 2.4 are configured just like catalogs in 3.0, but only Iceberg catalogs are supported.

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

SQL extensions are not available for Spark 2.4.

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

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
fanout-enabled	false	Overrides this table’s write.spark.fanout.enabled
check-ordering	true	Checks if input schema and table schema are same