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
{{< hint info >}} The Hive-based catalog only loads Iceberg tables. To load non-Iceberg tables in the same Hive metastore, use a session catalog. {{< /hint >}}
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 catalogorg.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 tablesBoth 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 |
| 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) |
Additional properties can be found in common catalog configuration.
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.
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.
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
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
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.
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.
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 |
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 |
| 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. |