After rewrite_data_files, position delete records pointing to the rewritten data files are not always marked for removal, and can remain tracked by the table’s live snapshot metadata. Remove Dangling Deletes: Filter out position delete records that refer to data files that are no longer live.This reduces the size of metadata stored in manifest files and overhead of opening small delete files. Minor Compaction: Compact small position delete files into larger ones.Iceberg can rewrite position delete files, which serves two purposes: system.rewrite_manifests( 'db.sample', false) Rewrite the data files in table db.sample using the default rewrite algorithm of bin-packing to combine small filesĪnd also split large files according to the default write size of the table.ĬALL catalog_name. Number of new data files which were written by this command Number of data which were re-written by this command See the RewriteDataFiles Javadoc, BinPackStrategy Javadocįor list of all the supported options for this action. Note that all files that may contain data matching the filter will be selected for rewriting Predicate as a string used for filtering the files. NullOrder can be NULLS FIRST or NULLS LAST. Else, Comma separated sort orders in the format (ColumnName SortDirection NullOrder). (Supported in Spark 3.2 and Above) Example: zorder(c1,c2,c3). Defaults to binpack strategyįor Zorder use a comma separated list of columns within zorder(). This will combine small files into larger files to reduce metadata overhead and runtime file open cost. Iceberg can compact data files in parallel using Spark with the rewriteDataFiles action. More data files leads to more metadata stored in manifest files, and small data files causes an unnecessary amount of metadata and less efficient queries from file open costs. Iceberg tracks each data file in a table. system.remove_orphan_files( table => 'db.sample', location => 'tablelocation/data') Remove snapshots older than specific day and time, but retain the last 100 snapshots:ĬALL catalog_name. Number of manifest List files deleted by this operation Number of manifest files deleted by this operation Number of equality delete files deleted by this operation Number of position delete files deleted by this operation Number of data files deleted by this operation If older_than and retain_last are omitted, the table’s expiration properties will be used. This option is recommended to set to true to prevent Spark driver OOM from large file size When true, deletion files will be sent to Spark driver by RDD partition (by default, all the files will be sent to Spark driver). Size of the thread pool used for delete file actions (by default, no thread pool is used) Number of ancestor snapshots to preserve regardless of older_than (defaults to 1) Timestamp before which snapshots will be removed (Default: 5 days ago) The expire_snapshots procedure will never remove files which are still required by a non-expired snapshot. This procedure will remove old snapshots and data files which are uniquely required by those old snapshots. The expire_snapshots procedure can be used to remove older snapshotsĪnd their files which are no longer needed. expire_snapshotsĮach write/update/delete/upsert/compaction in Iceberg produces a new snapshot while keeping the old data and metadataĪround for snapshot isolation and time travel. Many maintenance actions can be performed using Iceberg stored procedures. system.cherrypick_snapshot(snapshot_id => 1, table => 'my_table' )
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