Iceberg catalogs support using catalog properties to configure catalog behaviors. Here is a list of commonly used catalog properties:
| Property | Default | Description |
|---|---|---|
| catalog-impl | null | a custom Catalog implementation to use by an engine |
| io-impl | null | a custom FileIO implementation to use in a catalog |
| warehouse | null | the root path of the data warehouse |
| uri | null | a URI string, such as Hive metastore URI |
| clients | 2 | client pool size |
HadoopCatalog and HiveCatalog can access the properties in their constructors. Any other custom catalog can access the properties by implementing Catalog.initialize(catalogName, catalogProperties). The properties can be manually constructed or passed in from a compute engine like Spark or Flink. Spark uses its session properties as catalog properties, see more details in the Spark configuration section. Flink passes in catalog properties through CREATE CATALOG statement, see more details in the Flink section.
Here are the catalog properties related to locking. They are used by some catalog implementations to control the locking behavior during commits.
| Property | Default | Description |
|---|---|---|
| lock-impl | null | a custom implementation of the lock manager, the actual interface depends on the catalog used |
| lock.table | null | an auxiliary table for locking, such as in AWS DynamoDB lock manager |
| lock.acquire-interval-ms | 5 seconds | the interval to wait between each attempt to acquire a lock |
| lock.acquire-timeout-ms | 3 minutes | the maximum time to try acquiring a lock |
| lock.heartbeat-interval-ms | 3 seconds | the interval to wait between each heartbeat after acquiring a lock |
| lock.heartbeat-timeout-ms | 15 seconds | the maximum time without a heartbeat to consider a lock expired |
Iceberg tables support table properties to configure table behavior, like the default split size for readers.
| Property | Default | Description |
|---|---|---|
| read.split.target-size | 134217728 (128 MB) | Target size when combining data input splits |
| read.split.metadata-target-size | 33554432 (32 MB) | Target size when combining metadata input splits |
| read.split.planning-lookback | 10 | Number of bins to consider when combining input splits |
| read.split.open-file-cost | 4194304 (4 MB) | The estimated cost to open a file, used as a minimum weight when combining splits. |
| Property | Default | Description |
|---|---|---|
| write.format.default | parquet | Default file format for the table; parquet, avro, or orc |
| write.parquet.row-group-size-bytes | 134217728 (128 MB) | Parquet row group size |
| write.parquet.page-size-bytes | 1048576 (1 MB) | Parquet page size |
| write.parquet.dict-size-bytes | 2097152 (2 MB) | Parquet dictionary page size |
| write.parquet.compression-codec | gzip | Parquet compression codec |
| write.parquet.compression-level | null | Parquet compression level |
| write.avro.compression-codec | gzip | Avro compression codec |
| write.location-provider.impl | null | Optional custom implemention for LocationProvider |
| write.metadata.compression-codec | none | Metadata compression codec; none or gzip |
| write.metadata.metrics.default | truncate(16) | Default metrics mode for all columns in the table; none, counts, truncate(length), or full |
| write.metadata.metrics.column.col1 | (not set) | Metrics mode for column ‘col1’ to allow per-column tuning; none, counts, truncate(length), or full |
| write.target-file-size-bytes | Long.MAX_VALUE | Controls the size of files generated to target about this many bytes |
| write.wap.enabled | false | Enables write-audit-publish writes |
| write.summary.partition-limit | 0 | Includes partition-level summary stats in snapshot summaries if the changed partition count is less than this limit |
| write.metadata.delete-after-commit.enabled | false | Controls whether to delete the oldest version metadata files after commit |
| write.metadata.previous-versions-max | 100 | The max number of previous version metadata files to keep before deleting after commit |
| write.spark.fanout.enabled | false | Enables Partitioned-Fanout-Writer writes in Spark |
| Property | Default | Description |
|---|---|---|
| commit.retry.num-retries | 4 | Number of times to retry a commit before failing |
| commit.retry.min-wait-ms | 100 | Minimum time in milliseconds to wait before retrying a commit |
| commit.retry.max-wait-ms | 60000 (1 min) | Maximum time in milliseconds to wait before retrying a commit |
| commit.retry.total-timeout-ms | 1800000 (30 min) | Maximum time in milliseconds to wait before retrying a commit |
| commit.manifest.target-size-bytes | 8388608 (8 MB) | Target size when merging manifest files |
| commit.manifest.min-count-to-merge | 100 | Minimum number of manifests to accumulate before merging |
| commit.manifest-merge.enabled | true | Controls whether to automatically merge manifests on writes |
| history.expire.max-snapshot-age-ms | 432000000 (5 days) | Default max age of snapshots to keep while expiring snapshots |
| history.expire.min-snapshots-to-keep | 1 | Default min number of snapshots to keep while expiring snapshots |
| Property | Default | Description |
|---|---|---|
| compatibility.snapshot-id-inheritance.enabled | false | Enables committing snapshots without explicit snapshot IDs |
The following properties from the Hadoop configuration are used by the Hive Metastore connector.
| Property | Default | Description |
|---|---|---|
| iceberg.hive.client-pool-size | 5 | The size of the Hive client pool when tracking tables in HMS |
| iceberg.hive.lock-timeout-ms | 180000 (3 min) | Maximum time in milliseconds to acquire a lock |
| iceberg.hive.lock-check-min-wait-ms | 50 | Minimum time in milliseconds to check back on the status of lock acquisition |
| iceberg.hive.lock-check-max-wait-ms | 5000 | Maximum time in milliseconds to check back on the status of lock acquisition |
Note: iceberg.hive.lock-check-max-wait-ms should be less than the transaction timeout of the Hive Metastore (hive.txn.timeout or metastore.txn.timeout in the newer versions). Otherwise, the heartbeats on the lock (which happens during the lock checks) would end up expiring in the Hive Metastore before the lock is retried from Iceberg.
Spark catalogs are configured using Spark session properties.
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:
| Property | Values | Description |
|---|---|---|
| spark.sql.catalog.catalog-name.type | hive or hadoop | The underlying Iceberg catalog implementation, HiveCatalog or HadoopCatalog |
| spark.sql.catalog.catalog-name.catalog-impl | The underlying Iceberg catalog implementation. When set, the value of type property is ignored | |
| spark.sql.catalog.catalog-name.default-namespace | default | The default current namespace for the catalog |
| spark.sql.catalog.catalog-name.uri | thrift://host:port | URI for the Hive Metastore; default from hive-site.xml (Hive only) |
| spark.sql.catalog.catalog-name.warehouse | hdfs://nn:8020/warehouse/path | Base path for the warehouse directory (Hadoop only) |
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 |
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 |