Delta Connectors 0.3.0 release
Allison Portis
Write functionality - This release introduces new APIs to support creating and writing Delta tables without Apache Spark™. External processing engines can write parquet data files themselves and then use the APIs to add the files to the Delta table atomically. Following the Delta Transaction Log Protocol, the implementation uses optimistic concurrency control to manage multiple writers, automatically generates checkpoint files, and manages log and checkpoint cleanup according to the protocol. The main Java class exposed is OptimisticTransaction, which is accessed via DeltaLog.startTransaction().
OptimisticTransaction.markFilesAsRead(readPredicates) must be used to read all metadata during the transaction (and not the DeltaLog). It is used to detect concurrent updates and determine if logical conflicts between this transaction and previously-committed transactions can be resolved.
OptimisticTransaction.commit(actions, operation, engineInfo) is used to commit changes to the table. If a conflicting transaction has been committed first (see above) an exception is thrown, otherwise the table version that was committed is returned.
Idempotent writes can be implemented using OptimisticTransaction.txnVersion(appId) to check for version increases committed by the same application.
Each commit must specify the Operation being performed by the transaction.
Transactional guarantees for concurrent writes on Microsoft Azure and Amazon S3. This release includes custom extensions to support concurrent writes on Azure and S3 storage systems, which on their own do not have the necessary atomicity and durability guarantees. Please note that transactional guarantees are only provided for concurrent writes on S3 from a single cluster.
Memory-optimized iterator implementation for reading files in a snapshot - DeltaScan introduces an iterator implementation for reading the AddFiles in a snapshot with support for partition pruning. It can be accessed via Snapshot.scan() or Snapshot.scan(predicate), the latter of which filters files based on the predicate and any partition columns in the file metadata. This API significantly reduces the memory footprint when reading the files in a Snapshot and when instantiating a DeltaLog (due to internal utilization).
Partition filtering for metadata reads and conflict detection in writes - This release introduces a simple expression framework for partition pruning in metadata queries. When reading files in a snapshot, filter the returned AddFiles on partition columns by passing a predicate into Snapshot.scan(predicate). When updating a table during a transaction, specify which partitions were read by passing a readPredicate into OptimisticTransaction.markFilesAsRead(readPredicate) to detect logical conflicts and avoid transaction conflicts when possible.
Miscellaneous updates:
ParquetSchemaConverter converts a StructType schema to a Parquet schema.
Iterator<VersionLog> DeltaLog.getChanges() exposes an incremental metadata changes API. VersionLog wraps the version number, and the list of actions in that version.
Fix for RowRecord so that values in partition columns can be read.
Miscellaneous bug fixes.
Hive 3 support for the Hive Connector
Microsoft PowerBI connector for reading Delta tables natively - Read Delta tables directly from PowerBI from any storage system supported by PowerBI without running a Spark cluster. Features include online/scheduled refresh in the PowerBI service, support for Delta Lake time travel (e.g. VERSION AS OF), and partition elimination using the partition schema of the Delta table. For more details see the dedicated README.md.
Artur Brandys
Tathagata Das
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