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Version: Current

Metadata Table

Motivation for a Metadata Table

The Apache Hudi Metadata Table can significantly improve read/write performance of your queries. The main purpose of the Metadata Table is to eliminate the requirement for the "list files" operation.

When reading and writing data, file listing operations are performed to get the current view of the file system. When data sets are large, listing all the files may be a performance bottleneck, but more importantly in the case of cloud storage systems like AWS S3, the large number of file listing requests sometimes causes throttling due to certain request limits. The Metadata Table will instead proactively maintain the list of files and remove the need for recursive file listing operations

Some numbers from a study:

Running a TPCDS benchmark the p50 list latencies for a single folder scales ~linearly with the amount of files/objects:

Number of files/objects1001K10K100K
P50 list latency50ms131ms1062ms9932ms

Whereas listings from the Metadata Table will not scale linearly with file/object count and instead take about 100-500ms per read even for very large tables. Even better, the timeline server caches portions of the metadata (currently only for writers), and provides ~10ms performance for listings.

Supporting Multi-Modal Index

Multi-modal index can drastically improve the lookup performance in file index and query latency with data skipping. Bloom filter index containing the file-level bloom filter facilitates the key lookup and file pruning. The column stats index containing the statistics of all columns improves file pruning based on key and column value range in both the writer and the reader, in query planning in Spark for example. Multi-modal index is implemented as independent partitions containing the indices in the metadata table.

Enable Hudi Metadata Table and Multi-Modal Index

Since 0.11.0, the metadata table with synchronous updates and metadata-table-based file listing are enabled by default. There are prerequisite configurations and steps in Deployment considerations to safely use this feature. The metadata table and related file listing functionality can still be turned off by setting hoodie.metadata.enable to false. For 0.10.1 and prior releases, metadata table is disabled by default, and you can turn it on by setting the same config to true.

If you turn off the metadata table after enabling, be sure to wait for a few commits so that the metadata table is fully cleaned up, before re-enabling the metadata table again.

The multi-modal index is introduced in 0.11.0 release. They are disabled by default. You can choose to enable bloom filter index by setting hoodie.metadata.index.bloom.filter.enable to true and enable column stats index by setting hoodie.metadata.index.column.stats.enable to true, when metadata table is enabled. In 0.11.0 release, data skipping to improve queries in Spark now relies on the column stats index in metadata table. The enabling of metadata table and column stats index is prerequisite to enabling data skipping with hoodie.enable.data.skipping.

Deployment considerations

To ensure that Metadata Table stays up to date, all write operations on the same Hudi table need additional configurations besides the above in different deployment models. Before enabling metadata table, all writers on the same table must be stopped.

Deployment Model A: Single writer with inline table services

If your current deployment model is single writer and all table services (cleaning, clustering, compaction) are configured to be inline, such as Deltastreamer sync-once mode and Spark Datasource with default configs, there is no additional configuration required. After setting hoodie.metadata.enable to true, restarting the single writer is sufficient to safely enable metadata table.

Deployment Model B: Single writer with async table services

If your current deployment model is single writer along with async table services (such as cleaning, clustering, compaction) running in the same process, such as Deltastreamer continuous mode writing MOR table, Spark streaming (where compaction is async by default), and your own job setup enabling async table services inside the same writer, it is a must to have the optimistic concurrency control, the lock provider, and lazy failed write clean policy configured before enabling metadata table as follows. This is to guarantee the proper behavior of optimistic concurrency control when enabling metadata table. Failing to follow the configuration guide leads to loss of data. Note that these configurations are required only if metadata table is enabled in this deployment model.

hoodie.write.concurrency.mode=optimistic_concurrency_control
hoodie.cleaner.policy.failed.writes=LAZY
hoodie.write.lock.provider=org.apache.hudi.client.transaction.lock.InProcessLockProvider

If multiple writers in different processes are present, including one writer with async table services, please refer to Deployment Model C: Multi-writer for configs, with the difference of using a distributed lock provider. Note that running a separate compaction (HoodieCompactor) or clustering (HoodieClusteringJob) job apart from the ingestion writer is considered as multi-writer deployment, as they are not running in the same process which cannot rely on the in-process lock provider.

Deployment Model C: Multi-writer

If your current deployment model is multi-writer along with a lock provider and other required configs set for every writer as follows, there is no additional configuration required. You can bring up the writers sequentially after stopping the writers for enabling metadata table. Applying the proper configurations to only partial writers leads to loss of data from the inconsistent writer. So, ensure you enable metadata table across all writers.

hoodie.write.concurrency.mode=optimistic_concurrency_control
hoodie.cleaner.policy.failed.writes=LAZY
hoodie.write.lock.provider=<distributed-lock-provider-classname>

Note that there are 3 different distributed lock providers available to choose from: ZookeeperBasedLockProvider, HiveMetastoreBasedLockProvider, and DynamoDBBasedLockProvider.