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Delta Lake 3.3.0

06 Jan 22:04
@allisonport-db allisonport-db
v3.3.0
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We are excited to announce the release of Delta Lake 3.3.0! This release includes several exciting new features.

Highlights

Details by each component.

Delta Spark

Delta Spark 3.3.0 is built on Apache Spark™ 3.5.3. Similarly to Apache Spark, we have released Maven artifacts for both Scala 2.12 and Scala 2.13.

The key features of this release are:

  • Support for Identity Column: Delta Lake identity columns are a type of generated column that automatically assigns unique values to each record inserted into a table. Users do not need to explicitly provide values for these columns during data insertion. They offer a straightforward and efficient mechanism to generate unique keys for table rows, combining ease of use with high performance. See the documentation for more information.
  • Support VACUUM LITE to deliver faster VACUUM for periodically run VACUUM commands. When running VACUUM in LITE mode, instead of finding all files in the table directory, VACUUM LITE uses the Delta transaction log to identify and remove files no longer referenced by any table versions within the retention duration.
  • Support for Row Tracking Backfill:Row Tracking feature can now be used on existing Delta Lake tables to track row-level lineage in Delta Spark, previously it was only possible for new tables. Users can now use ALTER TABLE table_name SET TBLPROPERTIES (delta.enableRowTracking = true) syntax to alter an existing table to enable Row Tracking. When enabled, users can identify rows across multiple versions of the table and can access this tracking information using the two metadata fields _metadata.row_id and _metadata.row_commit_version. Refer to the documentation on Row Tracking for more information and examples.
  • Delta Lake now generates version checksums for each table commit, providing stronger consistency guarantees and improved debugging capabilities. It tracks detailed table metrics including file counts, table size, data distribution histograms, etc. This enables automatic detection of potential state inconsistencies and helps maintain table integrity in distributed environments. The state validation is performed on every checkpoint. The Checksum is also used to bypass the initial Spark query that retrieves the Protocol and Metadata actions, resulting in a decreased snapshot initialization latency.
  • Liquid clustering updates:
    • Support OPTIMIZE FULL to fully recluster a Liquid table. This command optimizes all records in a table that uses liquid clustering, including data that might have previously been clustered.
    • Support enabling liquid clustering on an existing unpartitioned Delta table using ALTER TABLE <table_name> CLUSTER BY (<clustering_columns>). Previously, liquid clustering could only be enabled upon table creation.
    • Support creating clustered table from an external location
  • The In-Commit Timestamp table feature is no longer in preview When enabled, this feature persists monotonically increasing timestamps within Delta commits, ensuring they are not affected by file operations. With this, time travel queries yield consistent results, even if the table directory is relocated. This feature was available as a preview feature in Delta 3.2 and is now generally available in Delta 3.3. See the documentation for more information.

Other notable changes include:

  • Protocol upgrade/downgrade improvements
    • Support dropping table features for columnMapping, vacuumProtocolCheck, and checkConstraints.
    • Improve table protocol transitions to simplify the CUJ when altering the table protocol.
    • Support protocol version downgrades when the existing table features exist in the lower protocol version.
    • Update protocol upgrades behavior such that when enabling a legacy feature via a table property (e.g. setting delta.enableChangeDataFeed=true) the protocol is upgraded to (1,7) and only the legacy feature is enabled. Previously the minimum protocol version would be selected and all preceding legacy features enabled.
    • Support enabling a table feature on a table using the Python DeltaTable API with deltaTable.addFeatureSupport(...).
  • Type-widening improvements
    • Support automatic type widening in Delta Sink when type widening is enabled on the table and schema evolution is enabled on the sink.
    • Support type widening on nested fields when other nested fields in the same struct are referenced by check constraints or generated column expressions.
    • Fix type-widening operation validation for map, array or struct columns used in generated column expressions or check constraints.
    • Fix to directly read the file schema from the parquet footers when identifying the files to be rewritten when dropping the type widening table feature.
    • Fix using type widening on a table containing a char/varchar column.
  • Liquid clustering improvements
    • Fix liquid clustering to automatically fall back to Z-order clustering when clustering on a single column. Previously, any attempts to optimize the table would fail.
    • Support RESTORE on clustered tables. Previously, RESTORE operations would not restore clustering metadata.
    • Support SHOW TBLPROPERTIES for clustered tables.
    • Support for partition-like data skipping filters (preview): When enabled by setting spark.databricks.delta.skipping.partitionLikeFilters.enabled, applies arbitrary data skipping filters referencing Liquid clustering columns to files with the same min and max values on clustering columns. This may decrease the files scanned for selective queries on large Liquid tables.
  • Performance improvements
    • Improve the performance of finding the last complete checkpoint with more efficient file listing.
    • Pushdown query filters when reading CDF so the filters can be used for partition pruning...
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Delta Lake 3.2.1

26 Sep 21:00
@vkorukanti vkorukanti
v3.2.1
c8697c5
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Delta Lake 3.2.1

We are excited to announce the release of Delta Lake 3.2.1! This release contains important bug fixes to 3.2.0 and it is recommended that users upgrade to 3.2.1.

Details by each component.

Delta Spark

Delta Spark 3.2.1 is built on Apache Spark™ 3.5.3. Similar to Apache Spark, we have released Maven artifacts for both Scala 2.12 and Scala 2.13.

The key changes of this release are:

  • Support for Apache Spark™ 3.5.3.
  • Fix MERGE operation not being recorded in QueryExecutionListener when submitted through Scala/Python API.
  • Support RESTORE on a Delta table with clustering enabled
  • Fix replacing the clustered table with non-clustered table.
  • Fix an issue when running clustering on table with single column selected as clustering columns.

Delta Universal Format (UniForm)

The key changes of this release are:

  • Added the support to enable Uniform Iceberg on existing Delta tables by ALTER table instead of REORG, which rewrites data files.
  • Fixed a bug that Uniform iceberg conversion transaction should not convert commit with only AddFiles without data change

Delta Sharing Spark

The key changes of this release are:

  • Upgrade delta-sharing-client to version 1.1.1 which removes the pre-signed URL address from the error message on access errors.
  • Fix an issue with DeltaSharingLogFileStatus

Delta Kernel

The key changes of this release are:

  • Fix comparison issues with string values having characters with surrogate pairs. This fixes a corner case with wrong results when comparing characters (e.g. emojis) that have surrogate pairs in UTF-16 representation.
  • Fix ClassNotFoundException issue when loading LogStores in Kernel default Engine module. This issue happens in some environments where the thread local class loader is not set.
  • Fix error when querying tables with spaces in the path name. Now you can query tables with paths having any valid path characters.
  • Fix an issue with writing decimal as binary when writing decimals with certain scale and precision when writing them to the Parquet file.
  • Throw proper exception when unsupported VOID data type is encountered in Delta tables when reading.
  • Handle long type values in field metadata of columns in schema. Earlier Kernel was throwing a parsing exception, now Kernel handles long types.
  • Fix an issue where Kernel retries multiple times when _last_checkpoint file is not found. Now Kernel tries just once when file not found exception is thrown.
  • Support reading Parquet files with legacy map type physical formats. Earlier Kernel used to throw errors, now Kernel can read data from file containing legacy map physical formats.
  • Support reading Parquet files with legacy 3-level repeated type physical formats.
  • Write timestamp data to Parquet file as INT64 physical format instead of INT96 physical format. INT96 is a legacy physical format that is deprecated.

For more information, refer to:

  • User guide on step-by-step process of using Kernel in a standalone Java program or in a distributed processing connector.
  • Slides explaining the rationale behind Kernel and the API design.
  • Example Java programs that illustrate how to read Delta tables using the Kernel APIs.
  • Table and default Engine API Java documentation

Delta Standalone (deprecated in favor of Delta Kernel)

This release does not update Standalone. Standalone is being deprecated in favor of Delta Kernel, which supports advanced features in Delta tables.

Delta Storage

Artifacts: delta-storage, delta-storage-s3-dynamodb

The key changes of this release are:

  • Fix an issue with VACUUM when using the S3DynamoDBLogStore where the LogStore made unnecessary listFrom calls to DynamoDB, causing a ProvisionedThroughputExceededException

Credits

Abhishek Radhakrishnan, Allison Portis, Charlene Lyu, Fred Storage Liu, Jiaheng Tang, Johan Lasperas, Lin Zhou, Marko Ilić, Scott Sandre, Tathagata Das, Tom van Bussel, Venki Korukanti, Wenchen Fan, Zihao Xu

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Delta Lake 4.0.0 Preview

13 Jun 16:28
@vkorukanti vkorukanti
v4.0.0rc1
6c81c59
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Delta Lake 4.0.0 Preview Pre-release
Pre-release

We are excited to announce the preview release of Delta Lake 4.0.0 on the preview release of Apache Spark 4.0.0! This release gives a preview of the following exciting new features.

  • Support for Spark Connect (aka Delta Connect) is an extension for Spark Connect which enables the usage of Delta over Spark Connect, allowing Delta to be used with the decoupled client-server architecture of Spark Connect.
  • Support for Type Widening to allow users to change the type of columns without having to rewrite data.
  • Support for the Variant data type to enable semi-structured storage and data processing, for flexibility and performance.
  • Support for Coordinated Commits table feature which makes the commit protocol very flexible and allows reliable multi-cloud and multi-engine writes.

Read below for more details. In addition, few existing artifacts are unavailable in this release that are listed at the end.

Delta Spark

Delta Spark 4.0 preview is built on Apache Spark™ 4.0.0-preview1. Similar to Apache Spark, we have released Maven artifacts for Scala 2.13.

The key features of this release are:

  • Support for Spark Connect (aka Delta Connect): Spark Connect is a new initiative in Apache Spark that adds a decoupled client-server infrastructure which allows Spark applications to connect remotely to a Spark server and run SQL / Dataframe operations. Delta Connect allows Delta operations to be made in applications running in such client-server mode. For more information on how to use Delta Connect see the Delta Connect documentation.
  • Support for Coordinated Commits: Coordinated Commits is a new writer table feature which allows users to designate a “Commit Coordinator” for their Delta table. A commit coordinator is an entity with a unique identifier which maintains information about commits. Once a commit coordinator has been set for a table, all writes to the table must be coordinated through it. This single point of ownership of commits for the table makes cross-environment (e.g. cross cloud) writes safe. Examples of Commit Coordinators are catalogs (Hive Metastore, Unity Catalog, etc.), DynamoDB, or any system which can implement the commit coordinator API. This release also adds a DynamoDB Commit Coordinator which can use a DynamoDB table to coordinate commits for a table. Delta tables with commit coordinators are still readable through the object storage paths, making reads backward compatible. See the Delta Coordinated Commits documentation for more details.
  • Support for Type Widening: Delta Spark can now change the type of a column to a wider type using the ALTER TABLE t CHANGE COLUMN col TYPE type command or with schema evolution during MERGE and INSERT operations. See the type widening documentation for a list of all supported type changes and additional information. The table will be readable by Delta 4.0 readers without requiring the data to be rewritten. For compatibility with older versions, a rewrite of the data can be triggered using the ALTER TABLE t DROP FEATURE 'typeWidening' command.
  • Support for Variant data type: The Variant data type is a new Apache Spark data type. The Variant data type enables flexible, and efficient processing of semi-structured data, without a user-specified schema. Variant data does not require a fixed schema on write. Instead, Variant data is queried using the schema-on-read approach. The Variant data type allows flexible ingestion by not requiring a write schema, and enables faster processing with the Spark Variant binary encoding format. Please see the documentation and the example for more details.

Other notable changes include:

  • Support protocol version downgrades when the existing table features exist in the lower protocol version.
  • Support dropping table features for columnMapping and vacuumProtocolCheck.
  • Support CREATE TABLE LIKE with user provided properties. Previously any properties that were provided in the SQL command were ignored and only the properties from the source table were used.
  • Fix liquid clustering to automatically fall back to Z-order clustering when clustering on a single column. Previously, any attempts to optimize the table would fail.
  • Pushdown query filters when reading CDF so the filters can be used for partition pruning and row group skipping.
  • Improve the performance of finding the last complete checkpoint with more efficient file listing.
  • Fix a bug where providing a query filter that compares two Literal expressions would cause an infinite loop when constructing data skipping filters.
  • Fix In-Commit Timestamps to use clock.currentTimeMillis() instead of System.nanoTime() for large commits since some systems return a very small number when System.nanoTime() is called.
  • Fix streaming CDF queries to not read log entries beyond endOffset for reduced processing time.

More features to come in the final release of Delta 4.0!

Delta Kernel Java

The Delta Kernel project is a set of Java and Rust libraries for building Delta connectors that can read and write to Delta tables without the need to understand the Delta protocol details.

This release of Delta Kernel Java contains the following changes:

  • Write timestamps using the INT64 physical format in Parquet in the DefaultParquetHandler. Previously they were written as INT96 which is an outdated and deprecated format for timestamps.
  • Lazily evaluate comparator expressions in the DefaultExpressionHandler. Previously expressions would be eagerly evaluated for every row in the underlying vectors.
  • Support SQL expression LIKE in the DefaultExpressionHandler.
  • Support legacy Parquet schemas for map type and array type in the DefaultParquetHandler.

In addition to the above Delta Kernel Java changes, Delta Kernel Rust released its first version 0.1, which is available at https://crates.io/crates/delta_kernel.

Limitations

The following features from Delta 3.2 are not supported in this preview release. We are working with the community to address the following gaps by the final release of Delta 4.0:

  • In Delta Spark, Uniform with Iceberg and Hudi is unavailable yet due to lack of their support for Spark 4.0.
  • Delta Flink, Delta Standalone, and Delta Hive are not available yet.

Credits

Abhishek Radhakrishnan, Allison Portis, Ami Oka, Andreas Chatzistergiou, Anish, Carmen Kwan, Chirag Singh, Christos Stavrakakis, Dhruv Arya, Felipe Pessoto, Fred Storage Liu, Hyukjin Kwon, James DeLoye, Jiaheng Tang, Johan Lasperas, Jun, Kaiqi Jin, Krishnan Paranji Ravi, Lin Zhou, Lukas Rupprecht, Ole Sasse, Paddy Xu, Prakhar Jain, Qianru Lao, Richard Chen, Sabir Akhadov, Scott Sandre, Sergiu Pocol, Sumeet Varma, Tai Le Manh, Tathagata Das, Thang Long Vu, Tom van Bussel,...

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Delta Lake 3.2.0

09 May 19:55
@scottsand-db scottsand-db
v3.2.0
4e7a342
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Delta Lake 3.2.0

We are excited to announce the release of Delta Lake 3.2.0! This release includes several exciting new features.

Highlights

Delta Spark

Delta Spark 3.2.0 is built on Apache Spark™ 3.5. Similar to Apache Spark, we have released Maven artifacts for both Scala 2.12 and Scala 2.13.

The key features of this release are:

  • Support for Liquid clustering: This allows for incremental clustering based on ZCubes and reduces the write amplification by not touching files already well clustered (i.e., files in stable ZCubes). Users can now use the ALTER TABLE CLUSTER BY syntax to change clustering columns and use the DESCRIBE DETAIL command to check the clustering columns. In addition, Delta Spark now supports DeltaTable clusterBy API in both Python and Scala to allow creating clustered tables using DeltaTable API. See the documentation and examples for more information.
  • Preview support for Type Widening: Delta Spark can now change the type of a column from byte to short to integer using the ALTER TABLE t CHANGE COLUMN col TYPE type command or with schema evolution during MERGE and INSERT operations. The table remains readable by Delta 3.2 readers without requiring the data to be rewritten. For compatibility with older versions, a rewrite of the data can be triggered using the ALTER TABLE t DROP FEATURE 'typeWidening-preview’ command.
    • Note that this feature is in preview and that tables created with this preview feature enabled may not be compatible with future Delta Spark releases.
  • Support for Vacuum Inventory: Delta Spark now extends the VACUUM SQL command to allow users to specify an inventory table in a VACUUM command. When an inventory table is provided, VACUUM will consider the files listed there instead of doing the full listing of the table directory, which can be time consuming for very large tables. See the docs here.
  • Support for Vacuum Writer Protocol Check: Delta Spark can now  support vacuumProtocolCheck ReaderWriter feature which ensures consistent application of reader and writer protocol checks during VACUUM operations, addressing potential protocol discrepancies and mitigating the risk of data corruption due to skipped writer checks.
  • Preview support for In-Commit Timestamps: When enabled, this preview feature persists monotonically increasing timestamps within Delta commits, ensuring they are not affected by file operations. When enabled, time travel queries will yield consistent results, even if the table directory is relocated.
    • Note that this feature is in preview and that tables created with this preview feature enabled may not be compatible with future Delta Spark releases.
  • Deletion Vectors Read Performance Improvements: Two improvements were introduced to DVs in Delta 3.2.
  • Support for Row Tracking: Delta Spark can now write to tables that maintain information that allows identifying rows across multiple versions of a Delta table. Delta Spark can now also access this tracking information using the two metadata fields _metadata.row_id and _metadata.row_commit_version.

Other notable changes include:

  • Delta Sharing: reduce the minimum RPC interval in delta sharing streaming from 30 seconds to 10 seconds
  • Improve the performance of write operations by skipping collecting commit stats
  • New SQL configurations to specify Delta Log cache size (spark.databricks.delta.delta.log.cacheSize) and retention duration (spark.databricks.delta.delta.log.cacheRetentionMinutes)
  • Fix bug in plan validation due to inconsistent field metadata in MERGE
  • Improved metrics during VACUUM for better visibility
  • Hive Metastore schema sync: The truncation threshold for schemas with long fields is now user configurable

Delta Universal Format (UniForm)

Hudi is now supported by Delta Universal format in addition to Iceberg. Writing to a Delta UniForm table can generate Hudi metadata, alongside Delta. This feature is contributed by XTable.

Create a UniForm-enabled that automatically generates Hudi metadata using the following command:

CREATE TABLE T (c1 INT) USING DELTA TBLPROPERTIES ('delta.universalFormat.enabledFormats' = hudi);

See the documentation here for more details.

Other notable changes include:

  • Throw a better error if Iceberg conversion fails during initial sync
  • Fix a bug in Delta Universal Format to support correct table overwrites

Delta Kernel

The Delta Kernel project is a set of Java libraries (Rust will be coming soon!) for building Delta connectors that can read (and, soon, write to) Delta tables without the need to understand the Delta protocol details). In this release,e we improved the read support to make it production-ready by adding numerous performance improvements, additional functionality, and improved protocol support.

  • Support for time travel. Now you can read a table snapshot at a version id or snapshot at a timestamp.

  • Improved Delta protocol support.

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Delta Lake 3.1.0

30 Jan 22:13
@vkorukanti vkorukanti
v3.1.0
71b09f0
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Delta Lake 3.1.0

We are excited to announce the release of Delta Lake 3.1.0. This release includes several exciting new features.

Few Highlights

  • Delta-Spark: Support for merge with deletion vectors to reduce the write overhead for merge operations. This feature improves the performance of merge by several folds.
  • Delta-Spark: Support for optimizing min/max aggregation queries using the table metadata which improves the performance of simple aggregations queries (e.g SELECT min(x) FROM deltaTable) by up to 100x.
  • Delta-Spark: Support for querying tables shared through Delta Sharing protocol.
  • Kernel: Support for data skipping for given query predicates to reduce the number of files read during the table scan.
  • Uniform: Enhanced Iceberg support for Delta tables that enables MAP and LIST types and ease of use improvements to enable Uniform on a Delta table.
  • Delta-Flink: Flink write job startup time latency improvement using Kernel.

Details by each component.

Delta Spark

Delta Spark 3.1.0 is built on Apache Spark™ 3.5. Similar to Apache Spark, we have released Maven artifacts for both Scala 2.12 and Scala 2.13.

The key features of this release are:

  • Support for merge with deletion vectors to reduce the write overhead for merge operations. This feature improves the performance of merge by several folds. Refer to the documentation on deletion vectors for more information.
  • Support for optimizing min/max aggregation queries using the table metadata which improves the performance of simple aggregations queries (e.g SELECT min(x) FROM deltaTable) by up to 100x.
  • (Preview) Liquid clustering for better table layout Now Delta allows clustering the data in a Delta table for better data skipping. Currently this is an experimental feature. See documentation and example for how to try out this feature.
  • Support for DEFAULT value columns. Delta supports defining default expressions for columns on Delta tables. Delta will generate default values for columns when users do not explicitly provide values for them when writing to such tables, or when the user explicitly specifies the DEFAULT SQL keyword for any such column. See documentation on how to enable this feature and try out.
  • Support for Hive Metastore schema sync. Adds a mechanism for syncing the table schema to HMS. External tools can now directly consume the schema from HMS instead of accessing it from the Delta table directory. See the documentation on how to enable this feature.
  • Auto compaction to address the small files problem during table writes. Auto compaction which runs at the end of the write query combines small files within partitions to large files to reduce the metadata size and improve query performance. See the documentation for details on how to enable this feature.
  • Optimized write is an optimization that repartitions and rebalances data before writing them out to a Delta table. Optimized writes improve file size and reduce the small file problem as data is written and benefit subsequent reads on the table. See the documentation for details on how to enable this feature.

Other notable changes include:

  • Peformance improvement by removing redundant jobs when performing DML operations with deletion vectors.
  • Update command now writes deletions vectors by default when the table has deletion vectors enabled.
  • Support for writing partition columns to data files.
  • Support for phaseout of v2 checkpoint table feature.
  • Fix an issue with case-sensitive column names in Merge.
  • Make VACCUM command to be Delta protocol aware so that it can only vacuum tables with protocol that it supports.

Delta Sharing Spark

This release of Delta adds a new module called delta-sharing-spark which enables reading Delta tables shared using the Delta Sharing protocol in Apache Spark™. It is migrated from https://github.com/delta-io/delta-sharing/tree/main/spark repository to https://github.com/delta-io/delta/tree/master/sharing repository. Last release version of delta-sharing-spark is 1.0.4 from the previous location. Next release of delta-sharing-spark is with the current release of Delta which is 3.1.0.

Supported read types are: read snapshot of the table, incrementally read the table using streaming or read the changes (Change Data Feed) between two versions of the table.

“Delta Format Sharing” is newly introduced since delta-sharing-spark 3.1, which supports reading shared Delta tables with advanced Delta features such as deletion vectors and column mapping.

Below is an example of reading a Delta table shared using the Delta Sharing protocol in a Spark environment. For more examples refer to the documentation.

import org.apache.spark.sql.SparkSession

val spark = SparkSession
  .builder()
  .appName("...")
  .master("...")
  .config(
     "spark.sql.extensions",
      "io.delta.sql.DeltaSparkSessionExtension"
  ).config(
     "spark.sql.catalog.spark_catalog",
      "org.apache.spark.sql.delta.catalog.DeltaCatalog"
  ).getOrCreate()

val tablePath = "<profile-file-path>#<share-name>.<schema-name>.<table-name>"

// Batch query
spark.read
  .format("deltaSharing")
  .option("responseFormat", "delta")
  .load(tablePath)
  .show(10)

Delta Universal Format (UniForm)

Delta Universal Format (UniForm) allows you to read Delta tables from Iceberg and Hudi (coming soon) clients. Delta 3.1.0 provided the following improvements:

  • Enhanced Iceberg support through IcebergCompatV2. IcebergCompatV2 adds support forLIST and MAP data types and improves compatibility with popular Iceberg reader clients.
  • Easier retrieval of the Iceberg metadata file location via familiar SQL syntax DESCRIBE EXTENDED TABLE.
  • A new SQL command to enable UniForm REORG TABLE table APPLY (UPGRADE UNIFORM(ICEBERG_COMPAT_VERSION=2)) on existing Delta tables. See the documentation for details.
  • Delta file statistics conversion to Iceberg including max/min/rowCount/nullCount which enables efficient data skipping when the tables are read as Iceberg in queries containing predicates.

Delta Kernel

The Delta Kernel project is a set of Java libraries (Rust will be coming soon!) for building Delta connectors that can read (and, soon, write to) Delta tables without the need to understand the [Delta protocol detai...

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Delta Lake 3.0.0

17 Oct 23:04
@scottsand-db scottsand-db
v3.0.0
6937c9d
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Delta Lake 3.0.0

We are excited to announce the final release of Delta Lake 3.0.0. This release includes several exciting new features and artifacts.

Highlights

Here are the most important aspects of 3.0.0:

Spark 3.5 Support

Unlike the initial preview release, Delta Spark is now built on top of Apache Spark™ 3.5. See the Delta Spark section below for more details.

Delta Universal Format (UniForm)

Delta Universal Format (UniForm) will allow you to read Delta tables with Hudi and Iceberg clients. Iceberg support is available with this release. UniForm takes advantage of the fact that all table storage formats, such as Delta, Iceberg, and Hudi, actually consist of Parquet data files and a metadata layer. In this release, UniForm automatically generates Iceberg metadata and commits to Hive metastore, allowing Iceberg clients to read Delta tables as if they were Iceberg tables. Create a UniForm-enabled table using the following command:

CREATE TABLE T (c1 INT) USING DELTA TBLPROPERTIES (
  'delta.universalFormat.enabledFormats' = 'iceberg');

Every write to this table will automatically keep Iceberg metadata updated. See the documentation here for more details, and the key implementations here and here.

Delta Kernel

The Delta Kernel project is a set of Java libraries (Rust will be coming soon!) for building Delta connectors that can read (and, soon, write to) Delta tables without the need to understand the Delta protocol details).

You can use this library to do the following:

  • Read data from Delta tables in a single thread in a single process.
  • Read data from Delta tables using multiple threads in a single process.
  • Build a complex connector for a distributed processing engine and read very large Delta tables.
  • [soon!] Write to Delta tables from multiple threads / processes / distributed engines.

Reading a Delta table with Kernel APIs is as follows.

TableClient myTableClient = DefaultTableClient.create() ;          // define a client
Table myTable = Table.forPath(myTableClient, "/delta/table/path"); // define what table to scan
Snapshot mySnapshot = myTable.getLatestSnapshot(myTableClient);    // define which version of table to scan
Predicate scanFilter = ...                                         // define the predicate
Scan myScan = mySnapshot.getScanBuilder(myTableClient)             // specify the scan details
        .withFilters(scanFilter)
        .build();
Scan.readData(...)                                                 // returns the table data

Full example code can be found here.

For more information, refer to:

  • User guide on step by step process of using Kernel in a standalone Java program or in a distributed processing connector.
  • Slides explaining the rationale behind Kernel and the API design.
  • Example Java programs that illustrate how to read Delta tables using the Kernel APIs.
  • Table and default TableClient API Java documentation

This release of Delta contains the Kernel Table API and default TableClient API definitions and implementation which allow:

  • Reading Delta tables with optional Deletion Vectors enabled or column mapping (name mode only) enabled.
  • Partition pruning optimization to reduce the number of data files to read.

Welcome Delta Connectors to the Delta repository!

All previous connectors from https://github.com/delta-io/connectors have been moved to this repository (https://github.com/delta-io/delta) as we aim to unify our Delta connector ecosystem structure. This includes Delta-Standalone, Delta-Flink, Delta-Hive, PowerBI, and SQL-Delta-Import. The repository https://github.com/delta-io/connectors is now deprecated.

Delta Spark

Delta Spark 3.0.0 is built on top of Apache Spark™ 3.5. Similar to Apache Spark, we have released Maven artifacts for both Scala 2.12 and Scala 2.13. Note that the Delta Spark maven artifact has been renamed from delta-core to delta-spark.

The key features of this release are:

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Delta Lake 2.4.0

25 May 22:10
@allisonport-db allisonport-db
v2.4.0
074ce17
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Delta Lake 2.4.0

We are excited to announce the release of Delta Lake 2.4.0 on Apache Spark 3.4. Similar to Apache Spark™, we have released Maven artifacts for both Scala 2.12 and Scala 2.13.

The key features in this release are as follows

  • Support for Apache Spark 3.4.
  • Support writing Deletion Vectors for the DELETE command. Previously, when deleting rows from a Delta table, any file with at least one matching row would be rewritten. With Deletion Vectors these expensive rewrites can be avoided. See What are deletion vectors? for more details.
  • Support for all write operations on tables with Deletion Vectors enabled.
  • Support PURGE to remove Deletion Vectors from the current version of a Delta table by rewriting any data files with deletion vectors. See the documentation for more details.
  • Support reading Change Data Feed for tables with Deletion Vectors enabled.
  • Support REPLACE WHERE expressions in SQL to selectively overwrite data. Previously “replaceWhere” options were only supported in the DataFrameWriter APIs.
  • Support WHEN NOT MATCHED BY SOURCE clauses in SQL for the Merge command.
  • Support omitting generated columns from the column list for SQL INSERT INTO queries. Delta will automatically generate the values for any unspecified generated columns.
  • Support the TimestampNTZ data type added in Spark 3.3. Using TimestampNTZ requires a Delta protocol upgrade; see the documentation for more information.
  • Other notable changes
    • Increased resiliency for S3 multi-cluster reads and writes.
      • Use a per-JVM lock to minimize the number of concurrent recovery attempts. Concurrent recoveries may cause concurrent readers to see a RemoteFileChangedException.
      • Catch any RemoteFileChangedException in the reader and retry reading.
    • Allow changing the column type of a char or varchar column to a compatible type in the ALTER TABLE command. The new behavior is the same as in Apache Spark and allows upcasting from char or varchar to varchar or string.
    • Block using overwriteSchema with dynamic partition overwrite. This can corrupt the table as not all the data may be removed, and the schema of the newly written partitions may not match the schema of the unchanged partitions.
    • Return an empty DataFrame for Change Data Feed reads when there are no commits within the timestamp range provided. Previously an error would be thrown.
    • Fix a bug in Change Data Feed reads for records created during the ambiguous hour when daylight savings occurs.
    • Fix a bug where querying an external Delta table at the root of an S3 bucket would throw an error.
    • Remove leaked internal Spark metadata from the Delta log to make any affected tables readable again.

Note: the Delta Lake 2.4.0 release does not include the Iceberg to Delta converter because iceberg-spark-runtime does not support Spark 3.4 yet. The Iceberg to Delta converter is still supported when using Delta 2.3 with Spark 3.3.

Credits

Alkis Evlogimenos, Allison Portis, Andreas Chatzistergiou, Anton Okolnychyi, Bart Samwel, Bo Gao, Carl Fu, Chaoqin Li, Christos Stavrakakis, David Lewis, Desmond Cheong, Dhruv Shah, Eric Maynard, Fred Liu, Fredrik Klauss, Haejoon Lee, Hussein Nagree, Jackie Zhang, Jintian Liang, Johan Lasperas, Lars Kroll, Lukas Rupprecht, Matthew Powers, Ming DAI, Ming Dai, Naga Raju Bhanoori, Paddy Xu, Prakhar Jain, Rahul Shivu Mahadev, Rui Wang, Ryan Johnson, Sabir Akhadov, Satya Valluri, Scott Sandre, Shixiong Zhu, Tom van Bussel, Venki Korukanti, Vitalii Li, Wenchen Fan, Xi Liang, Yaohua Zhao, Yuming Wang

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Delta Lake 2.3.0

05 Apr 18:42
@allisonport-db allisonport-db
v2.3.0
8a10d39
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Delta Lake 2.3.0

We are excited to announce the release of Delta Lake 2.3.0 on Apache Spark 3.3. Similar to Apache Spark™, we have released Maven artifacts for both Scala 2.12 and Scala 2.13.

The key features in this release are as follows

  • Zero-copy convert to Delta from Iceberg tables using CONVERT TO DELTA. This generates a Delta table in the same location and does not rewrite any parquet files. See the documentation for details.
  • Support SHALLOW CLONE for Delta, Parquet, and Iceberg tables to clone a source table without copying the data files. SHALLOW CLONE creates a copy of the source table’s definition but refers to the source table’s data files.
  • Support idempotent writes for DML operations. This feature adds idempotency to INSERT/DELETE/UPDATE/MERGE etc. operations using SQL configurations spark.databricks.delta.write.txnAppId and spark.databricks.delta.write.txnVersion.
  • Support “when not matched by source” clauses for the Merge command to update or delete rows in the chosen table that don’t have matches in the source table based on the merge condition. This clause is supported in the Python, Scala, and Java DeltaTable APIs. SQL Support will be added in Spark 3.4.
  • Support CREATE TABLE LIKE to create empty Delta tables using the definition and metadata of an existing table or view.
  • Support reading Change Data Feed (CDF) in SQL queries using the table_changes table-valued function.
  • Unblock Change Data Feed (CDF) batch reads on column mapping enabled tables when DROP COLUMN and RENAME COLUMN have been used. See the documentation for more details.
  • Improved read and write performance on S3 when writing from a single cluster. Efficient file listing decreases the metadata processing time when calculating a table snapshot. This is most impactful for tables with many commits. Set the Hadoop configuration delta.enableFastS3AListFrom to true to enable it.
  • Record VACUUM operations in the transaction log. With this feature, VACUUM operations and their associated metrics (e.g. numDeletedFiles) will now show up in table history.
  • Support reading Delta tables with deletion vectors.
  • Other notable changes
    • Support schema evolution in MERGE for UPDATE SET <assignments> and INSERT (...) VALUES (...) actions. Previously, schema evolution was only supported for UPDATE SET * and INSERT * actions.
    • Add .show() support for COUNT(*) aggregate pushdown.
    • Enforce idempotent writes for df.saveAsTable for overwrite and append mode.
    • Support Table Features to selectively add individual features when upgrading the table protocol version. This enables users to only add active features and will facilitate connectivity as downstream Delta connectors can selectively implement feature support.
    • Automatically generate partition filters for additional generation expressions.
    • Block protocol downgrades when replacing a Delta table to prevent any incorrect time-travel or CDF queries.
    • Fix replaceWhere with the DataFrame V2 overwrite API to correctly evaluate less than conditions.
    • Fix dynamic partition overwrite for tables with more than one partition data type.
    • Fix schema evolution for INSERT OVERWRITE with complex data types when the source schema is read incompatible.
    • Fix Delta streaming source to correctly detect read-incompatible schema changes during backfill when there is exactly one schema change in the versions read.
    • Fix a bug in VACUUM where sometimes the default retention period was used to remove files instead of the retention period specified in the table properties.
    • Include the table name in the DataFrame returned by the deltaTable.details() Python/Scala/Java API.
    • Improve the log message for VACUUM table_name DRY RUN.

Credits

Allison Portis, Andreas Chatzistergiou, Andrew Li, Bo Zhang, Brayan Jules, Burak Yavuz, Christos Stavrakakis, Daniel Tenedorio, Dhruv Shah, Felipe Pessoto, Fred Liu, Fredrik Klauss, Gengliang Wang, Haejoon Lee, Hussein Nagree, Jackie Zhang, Jiaheng Tang, Jintian Liang, Johan Lasperas, Jungtaek Lim, Kam Cheung Ting, Koki Otsuka, Lars Kroll, Lin Ma, Lukas Rupprecht, Ming DAI, Mitchell Riley, Ole Sasse, Paddy Xu, Prakhar Jain, Pranav, Rahul Shivu Mahadev, Rajesh Parangi, Ryan Johnson, Scott Sandre, Serge Rielau, Shixiong Zhu, Slim Ouertani, Tobias Fabritz, Tom van Bussel, Tushar Machavolu, Tyson Condie, Venki Korukanti, Vitalii Li, Wenchen Fan, Xinyi Yu, Yaohua Zhao, Yingyi Bu

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Delta Lake 2.0.2

13 Jan 22:44
@scottsand-db scottsand-db
v2.0.2
55d4bb7
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Delta Lake 2.0.2

We are excited to announce the release of Delta Lake 2.0.2 on Apache Spark 3.2. This release contains important bug fixes and a few high-demand usability improvements over 2.0.1 and it is recommended that users update to 2.0.2. Similar to Apache Spark™, we have released Maven artifacts for both Scala 2.12 and Scala 2.13.

This release includes the following bug fixes and improvements:

  • Record VACUUM operation in the transaction log. With this feature, VACUUM operations and their associated metrics (e.g. numDeletedFiles) will now show up in table history.
  • Support idempotent writes for DML operations. This feature adds idempotency to INSERTS/DELETE/UPDATE/MERGE etc. operations using SQL configurations spark.databricks.delta.write.txnAppId and spark.databricks.delta.write.txnVersion.
    Support passing Hadoop configurations via DeltaTable API 
    from delta.tables import DeltaTable
hadoop_config = {
  "fs.azure.account.auth.type": "OAuth",
  "fs.azure.account.oauth.provider.type": "...",
  "fs.azure.account.oauth2.client.id": "...",
  "fs.azure.account.oauth2.client.secret": "...",
  "fs.azure.account.oauth2.client.endpoint": "..."
}
delta_table = DeltaTable.forPath(spark, <table-path>, hadoop_config)
  • Minor convenience improvement to the DeltaTableBuilder:executeZOrderBy Java API which allows users to pass in varargs instead of a List.
  • Fail fast on malformed delta log JSON entries. Previously, Delta queries could return inaccurate results whenever JSON commits in the _delta_log were malformed. For example, an add action with a missing } would be skipped. Now, queries will fail fast, preventing inaccurate results.
  • Fix “Could not find active SparkSession” bug by passing in the SparkSession when resolving tables in the DeltaTableBuilder.

Credits:
Helge Brügner, Jiaheng Tang, Mitchell Riley, Ryan Johnson, Scott Sandre, Venki Korukanti, Jintao Shen, Yann Byron

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Delta Lake 2.2.0

05 Dec 23:01
@vkorukanti vkorukanti
v2.2.0
9bd46b4
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Delta Lake 2.2.0

We are excited to announce the release of Delta Lake 2.2.0 on Apache Spark 3.3. Similar to Apache Spark™, we have released Maven artifacts for both Scala 2.12 and Scala 2.13.

The key features in this release are as follows:

  • LIMIT pushdown into Delta scan. Improve the performance of queries containing LIMIT clauses by pushing down the LIMIT into Delta scan during query planning. Delta scan uses the LIMIT and the file-level row counts to reduce the number of files scanned which helps the queries read far less number of files and could make LIMIT queries faster by 10-100x depending upon the table size.

  • Aggregate pushdown into Delta scan for SELECT COUNT(*). Aggregation queries such as SELECT COUNT(*) on Delta tables are satisfied using file-level row counts in Delta table metadata rather than counting rows in the underlying data files. This significantly reduces the query time as the query just needs to read the table metadata and could make full table count queries faster by 10-100x.

  • Support for collecting file level statistics as part of the CONVERT TO DELTA command. These statistics potentially help speed up queries on the Delta table. By default the statistics are collected now as part of the CONVERT TO DELTA command. In order to disable statistics collection specify NO STATISTICS clause in the command. Example: CONVERT TO DELTA table_name NO STATISTICS

  • Improve performance of the DELETE command by pruning the columns to read when searching for files to rewrite.

  • Fix for a bug in the DynamoDB-based S3 multi-cluster mode configuration. The previous version wrote an incorrect timestamp which was used by DynamoDB’s TTL feature to cleanup expired items. This timestamp value has been fixed and the table attribute renamed from commitTime to expireTime. If you already have TTL enabled, please follow the migration steps here.

  • Fix non-deterministic behavior during MERGE when working with sources that are non-deterministic.

  • Remove the restrictions for using Delta tables with column mapping in certain Streaming + CDF cases. Earlier we used to block Streaming+CDF if the Delta table has column mapping enabled even though it doesn’t contain any RENAME or DROP columns.

  • Other notable changes

    • Improve the monitoring of the Delta state construction queries (additional queries run as part of planning) by making them visible in the Spark UI.
    • Support for multiple where() calls in Optimize scala/python API
    • Support for passing Hadoop configurations via DeltaTable API
    • Support partition column names starting with . or _ in CONVERT TO DELTA command.
    • Improvements to metrics in table history
    • Fix for accidental protocol downgrades with RESTORE command. Until now, RESTORE TABLE may downgrade the protocol version of the table, which could have resulted in inconsistent reads with time travel. With this fix, the protocol version is never downgraded from the current one.
    • Fix a bug in MERGE INTO when there are multiple UPDATE clauses and one of the UPDATEs is with a schema evolution.
    • Fix a bug where sometimes active SparkSession object is not found when using Delta APIs
    • Fix an issue where partition schema couldn’t be set during the initial commit.
    • Catch exceptions when writing last_checkpoint file fails.
    • Fix an issue when restarting a streaming query with AvailableNow trigger on a Delta table.
    • Fix an issue with CDF and Streaming where the offset is not correctly updated when there are no data changes.

Credits
Abhishek Somani, Adam Binford, Allison Portis, Amir Mor, Andreas Chatzistergiou, Anish Shrigondekar, Carl Fu, Carlos Peña ,Chen Shuai, Christos Stavrakakis, Eric Maynard, Fabian Paul, Felipe Pessoto, Fredrik Klauss, Ganesh Chand, Hedi Bejaoui, Helge Brügner, Hussein Nagree, Ionut Boicu, Jackie Zhang, Jiaheng Tang, Jintao Shen, Jintian Liang, Joe Harris, Johan Lasperas, Jonas Irgens Kylling, Josh Rosen, Juliusz Sompolski, Jungtaek Lim, Kam Cheung Ting, Karthik Subramanian, Kevin Neville, Lars Kroll, Lin Ma, Linhong Liu, Lukas Rupprecht, Max Gekk, Ming Dai, Mingliang Zhu, Nick Karpov, Ole Sasse, Paddy Xu, Patrick Marx, Prakhar Jain, Pranav, Rajesh Parangi, Ronald Zhang, Ryan Johnson, Sabir Akhadov, Scott Sandre, Serge Rielau, Shixiong Zhu, Supun Nakandala, Thang Long Vu, Tom van Bussel, Tyson Condie, Venki Korukanti, Vitalii Li, Weitao Wen, Wenchen Fan, Xinyi, Yuming Wang, Zach Schuermann, Zainab Lawal, sherlockbeard (github id)

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