Tableinstance. Please refer Java API quickstart page to refer how to load an existing table.
Each write to an Iceberg table creates a new snapshot, or version, of a table. Snapshots can be used for time-travel queries, or the table can be rolled back to any valid snapshot.
Snapshots accumulate until they are expired by the
expireSnapshots operation. Regularly expiring snapshots is recommended to delete data files that are no longer needed, and to keep the size of table metadata small.
This example expires snapshots that are older than 1 day:
Table table = ... long tsToExpire = System.currentTimeMillis() - (1000 * 60 * 60 * 24); // 1 day table.expireSnapshots() .expireOlderThan(tsToExpire) .commit();
ExpireSnapshots Javadoc to see more configuration options.
There is also a Spark action that can run table expiration in parallel for large tables:
Table table = ... SparkActions .get() .expireSnapshots(table) .expireOlderThan(tsToExpire) .execute();
Expiring old snapshots removes them from metadata, so they are no longer available for time travel queries.
Remove old metadata files
Iceberg keeps track of table metadata using JSON files. Each change to a table produces a new metadata file to provide atomicity.
Old metadata files are kept for history by default. Tables with frequent commits, like those written by streaming jobs, may need to regularly clean metadata files.
To automatically clean metadata files, set
write.metadata.delete-after-commit.enabled=true in table properties. This will keep some metadata files (up to
write.metadata.previous-versions-max) and will delete the oldest metadata file after each new one is created.
|Whether to delete old metadata files after each table commit|
|The number of old metadata files to keep|
See table write properties for more details.
Delete orphan files
In Spark and other distributed processing engines, task or job failures can leave files that are not referenced by table metadata, and in some cases normal snapshot expiration may not be able to determine a file is no longer needed and delete it.
To clean up these “orphan” files under a table location, use the
Table table = ... SparkActions .get() .deleteOrphanFiles(table) .execute();
See the DeleteOrphanFiles Javadoc to see more configuration options.
This action may take a long time to finish if you have lots of files in data and metadata directories. It is recommended to execute this periodically, but you may not need to execute this often.
Some tables require additional maintenance. For example, streaming queries may produce small data files that should be compacted into larger files. And some tables can benefit from rewriting manifest files to make locating data for queries much faster.
Compact data files
Iceberg tracks each data file in a table. 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 can compact data files in parallel using Spark with the
rewriteDataFiles action. This will combine small files into larger files to reduce metadata overhead and runtime file open cost.
Table table = ... SparkActions .get() .rewriteDataFiles(table) .filter(Expressions.equal("date", "2020-08-18")) .option("target-file-size-bytes", Long.toString(500 * 1024 * 1024)) // 500 MB .execute();
files metadata table is useful for inspecting data file sizes and determining when to compact partitions.
RewriteDataFiles Javadoc to see more configuration options.
Iceberg uses metadata in its manifest list and manifest files speed up query planning and to prune unnecessary data files. The metadata tree functions as an index over a table’s data.
Manifests in the metadata tree are automatically compacted in the order they are added, which makes queries faster when the write pattern aligns with read filters. For example, writing hourly-partitioned data as it arrives is aligned with time range query filters.
When a table’s write pattern doesn’t align with the query pattern, metadata can be rewritten to re-group data files into manifests using
rewriteManifests or the
rewriteManifests action (for parallel rewrites using Spark).
This example rewrites small manifests and groups data files by the first partition field.
Table table = ... SparkActions .get() .rewriteManifests(table) .rewriteIf(file -> file.length() < 10 * 1024 * 1024) // 10 MB .execute();
RewriteManifests Javadoc to see more configuration options.