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The Ubiquity of the Delta Standalone Venture for Delta Lake

We’re excited for the discharge of Delta Connectors 0.3.0, which introduces assist for writing Delta tables. The important thing options on this launch are:

Delta Standalone

  • Write performance – This launch introduces new APIs to assist creating and writing Delta tables with out Apache Spark™. Exterior processing engines can write Parquet knowledge recordsdata after which use the APIs to commit the recordsdata to the Delta desk atomically. Following the Delta Transaction Log Protocol, the implementation makes use of optimistic concurrency management to handle a number of writers, mechanically generates checkpoint recordsdata, and manages log and checkpoint cleanup in response to the protocol. The primary Java class uncovered is OptimisticTransaction, which is accessed by way of DeltaLog.startTransaction().
    • OptimisticTransaction.markFilesAsRead(readPredicates) should be used to learn all metadata throughout the transaction (and never the DeltaLog. It’s used to detect concurrent updates and decide if logical conflicts between this transaction and previously-committed transactions may be resolved.
    • OptimisticTransaction.commit(actions, operation, engineInfo) is used to commit modifications to the desk. If a conflicting transaction has been dedicated first (see above) an exception is thrown, in any other case, the desk model that was dedicated is returned.
    • Idempotent writes may be applied utilizing OptimisticTransaction.txnVersion(appId) to examine for model will increase dedicated by the identical software.
    • Every commit should specify the Operation being carried out by the transaction.
    • Transactional ensures for concurrent writes on Microsoft Azure and Amazon S3. This launch contains customized extensions to assist concurrent writes on Azure and S3 storage methods, which on their very own wouldn’t have the mandatory atomicity and sturdiness ensures. Please notice that transactional ensures are solely offered for concurrent writes on S3 from a single cluster.
  • Reminiscence-optimized iterator implementation for studying recordsdata in a snapshot: DeltaScan introduces an iterator implementation for studying the AddFiles in a snapshot with assist for partition pruning. It may be accessed by way of Snapshot.scan() or Snapshot.scan(predicate), the latter of which filters recordsdata primarily based on the predicate and any partition columns within the file metadata. This API considerably reduces the reminiscence footprint when studying the recordsdata in a snapshot and instantiating a DeltaLog (attributable to inside utilization).
  • Partition filtering for metadata reads and battle detection in writes: This launch introduces a easy expression framework for partition pruning in metadata queries. When studying recordsdata in a snapshot, filter the returned AddFiles on partition columns by passing a predicate into Snapshot.scan(predicate). When updating a desk throughout a transaction, specify which partitions had been learn by passing a readPredicate into OptimisticTransaction.markFilesAsRead(readPredicate) to detect logical conflicts and keep away from transaction conflicts when potential.
  • Miscellaneous updates:
    • DeltaLog.getChanges() exposes an incremental metadata modifications API. VersionLog wraps the model quantity and the checklist of actions in that model.
    • ParquetSchemaConverter converts a StructType schema to a Parquet schema.
    • Repair #197 for RowRecord in order that values in partition columns may be learn.
    • Miscellaneous bug fixes.

Delta Connectors

  • Hive 3 assist for the Hive Connector
  • Microsoft PowerBI connector for studying Delta tables natively: Learn Delta tables immediately from PowerBI from any storage supported system with out working a Spark cluster. Options embrace on-line/scheduled refresh within the PowerBI service, assist for Delta Lake time journey (e.g., VERSION AS OF), and partition elimination utilizing the partition schema of the Delta desk. For extra particulars see the devoted README.md.
  • What’s Delta Standalone?

    The Delta Standalone venture in Delta connectors, previously often known as Delta Standalone Reader (DSR), is a JVM library that can be utilized to learn and write Delta Lake tables. In contrast to Delta Lake Core, this venture doesn’t use Spark to learn or write tables and has just a few transitive dependencies. It may be utilized by any software that can’t use a Spark cluster (learn extra: The way to Natively Question Your Delta Lake with Scala, Java, and Python).

    The venture permits builders to construct a Delta connector for an exterior processing engine following the Delta protocol with out utilizing a manifest file. The reader element ensures builders can learn the set of parquet recordsdata related to the Delta desk model requested. As a part of Delta Standalone 0.3.0, the reader features a memory-optimized, lazy iterator implementation for DeltaScan.getFiles (PR #194). The next code pattern reads Parquet recordsdata in a distributed method the place Delta Standalone (as of 0.3.0) contains Snapshot::scan(filter)::getFiles, which helps partition pruning and an optimized inside iterator implementation.

    import io.delta.standalone.Snapshot;
    DeltaLog log = DeltaLog.forTable(new Configuration(), "$TABLE_PATH$");
    Snapshot latestSnapshot = log.replace();
    StructType schema = latestSnapshot.getMetadata().getSchema();
    DeltaScan scan = latestSnapshot.scan(
        new And(
            new And(
                new EqualTo(schema.column("12 months"), Literal.of(2021)),
                new EqualTo(schema.column("month"), Literal.of(11))),
            new EqualTo(schema.column("buyer"), Literal.of("XYZ"))
    CloseableIterator iter = scan.getFiles();
    strive {
        whereas (iter.hasNext()) {
            AddFile addFile = iter.subsequent();
            // Zappy engine to deal with studying knowledge in `addFile.getPath()` and apply any `scan.getResidualPredicate()`
    } lastly {

    As properly, Delta Standalone 0.3.0 features a new author element that enables builders to generate parquet recordsdata themselves and add these recordsdata to a Delta desk atomically, with assist for idempotent writes (learn extra: Delta Standalone Author design doc). The next code snippet exhibits tips on how to decide to the transaction log so as to add the brand new recordsdata and take away the previous incorrect recordsdata after writing Parquet recordsdata to storage.

    import io.delta.standalone.Operation;
    import io.delta.standalone.actions.RemoveFile;
    import io.delta.standalone.exceptions.DeltaConcurrentModificationException;
    import io.delta.standalone.sorts.StructType;
    Record removeOldFiles = existingFiles.stream()
        .map(path -> addFileMap.get(path).take away())
    Record addNewFiles = newDataFiles.getNewFiles()
        .map(file ->
            new AddFile(
                true, // isDataChange
                null, // stats
                null  // tags
    Record totalCommitFiles = new ArrayList();
    // Zippy is in reference to a generic engine
    strive {
        txn.commit(totalCommitFiles, new Operation(Operation.Identify.UPDATE), "Zippy/1.0.0");
    } catch (DeltaConcurrentModificationException e) {
        // deal with exception right here

    Hive 3 utilizing Delta Standalone

    Delta Standalone 0.3.0 helps Hive 2 and three permitting Hive to natively learn a Delta desk. The next is an instance of tips on how to create a Hive exterior desk to entry your Delta desk.

    CREATE EXTERNAL TABLE deltaTable(col1 INT, col2 STRING)
    STORED BY 'io.delta.hive.DeltaStorageHandler'
    LOCATION '/delta/desk/path'

    For extra particulars on tips on how to arrange Hive, please check with Delta Connectors > Hive Connector. You will need to notice this connector solely helps Apache Hive; it doesn't assist Apache Spark or Presto.

    Studying Delta Lake from PrestoDB

    As demonstrated in PrestoCon 2021 session Delta Lake Connector for Presto, the just lately merged Presto/Delta connector makes use of the Delta Standalone venture to natively learn the Delta transaction log with out the necessity of a manifest file. The memory-optimized, lazy iterator included in Delta Standalone 0.3.0 permits PrestoDB to effectively iterate by the Delta transaction log metadata and avoids OOM points when studying massive Delta tables.

    With the Presto/Delta connector, along with querying your Delta tables natively with Presto, you should utilize the @ syntax to carry out time journey queries and question earlier variations of your Delta desk by model or timestamp. The next code pattern is querying earlier variations of the identical NYCTaxi 2019 dataset utilizing model.

    # Model 1 of s3://…/nyctaxi_2019_part desk 
    WITH nyctaxi_2019_part AS (
      SELECT * FROM deltas3."$path$"."s3://…/nyctaxi_2019_part@v1)
    SELECT COUNT(1) FROM nyctaxi_2019_part;
    # output
    # Model 5 of s3://…/nyctaxi_2019_part desk
    WITH nyctaxi_2019_part AS (
      SELECT * FROM deltas3."$path$"."s3://…/nyctaxi_2019_part@v5)
    SELECT COUNT(1) FROM nyctaxi_2019_part;
    # output

    With this connector, you'll be able to each specify the desk out of your metastore and question the Delta desk immediately from the file path utilizing the syntax of deltas3."$path$"."s3://…

    For extra details about the PrestoDB/Delta connector:

    Word, we're presently working with the Trino (right here’s the present department that accommodates the Trino 359 Delta Lake reader) and Athena communities to supply native Delta Lake connectivity.

    Studying Delta Lake from Energy BI Natively

    We additionally needed to offer a shout-out to Gerhard Brueckl (github: gbrueckl) for persevering with to enhance Energy BI connectivity to Delta Lake. As a part of Delta Connectors 0.3.0, the Energy BI connector contains on-line/scheduled refresh within the PowerBI service, assist for Delta Lake time journey, and partition elimination utilizing the partition schema of the Delta desk.

     Reading Delta Lake Tables natively in PowerBI

    Supply: Studying Delta Lake Tables natively in PowerBI

    For extra data, check with Studying Delta Lake Tables natively in PowerBI or take a look at the code-base.


    We're actually excited concerning the speedy adoption of Delta Lake by the info engineering and knowledge sciences group. Should you’re eager about studying extra about Delta Standalone or any of those Delta connectors, take a look at the next assets:

    We wish to thank the next contributors for updates, doc modifications, and contributions in Delta Standalone 0.3.0: Alex, Allison Portis, Denny Lee, Gerhard Brueckl, Pawel Kubit, Scott Sandre, Shixiong Zhu, Wang Wei, Yann Byron, Yuhong Chen, and gurunath.

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