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Delta Lake 4.3.1

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@seewishnew seewishnew released this 08 Jul 04:59
v4.3.1
54ce026
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Delta Lake 4.3.1

We are pleased to announce the release of Delta Lake 4.3.1, a patch release on top of 4.3.0 with targeted bug fixes for OAuth authentication in the Delta REST Catalog, S3A fast listing through FilterFileSystem wrappers, and UC managed-table metadata handling.

Highlights

  • [Spark] Fix OAuth case-sensitivity bug in Delta REST Catalog authentication: Fixes a bug where CaseInsensitiveStringMap.entrySet() lowercased camelCase OAuth config keys (e.g. oauth.clientId), breaking Delta REST Catalog authentication.

Delta Spark

Delta Spark 4.3.1 is built on Apache Spark 4.1.0 and Apache Spark 4.0.1. As with Apache Spark, we publish Maven artifacts for Scala 2.13.

Bug fixes in this release:

  • Support delta.enabledFastS3AListFrom with OSS UnityCatalog (>=0.4.1). Because the unitycatalog spark connector introduced CredScopedFileSystem, which relies on S3AFileSystem under the hood, this fix unwraps the internal filesystem of CredScopedFileSystem and handles the casting to enable fast S3A listing.
  • Don't persist is_managed_location into Delta table metadata: This reserved DSv2 catalog property was leaking into the committed Metadata.configuration at table creation. It is now filtered out at AbstractDeltaCatalog.createTable and synthesized at load for managed tables, matching the behaviour of Spark's V1Table.

Delta Kernel

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

No changes to Delta Kernel in this patch release.

Delta UniForm

Delta UniForm's delta-iceberg and delta-hudi modules automatically keep Apache Iceberg and Apache Hudi metadata in sync with Delta commits, so Iceberg and Hudi readers can query Delta tables without data duplication.

No changes to Delta UniForm in this patch release.

Delta Sharing

Delta Sharing is a Spark DataSource that lets clients run batch, streaming, CDF, and time-travel reads on tables shared via the Delta Sharing protocol. The 2.13 suffix indicates Scala 2.13.

No changes to Delta Sharing in this patch release.

Delta Flink

The Kernel-based delta-flink connector (experimental) is released as part of this patch.

No changes to Delta Flink in this patch release.

Credits

Daniel Wang, Murali Ramanujam, Zheng Hu, Rakesh Veeramacheneni, Tathagata Das, Timothy Wang, Vishnu Chandrashekhar, Xin Huang, Yi Li

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Delta Lake 4.3.0

Delta Lake 4.3.0

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@seewishnew seewishnew released this 18 Jun 04:26
v4.3.0
76c12a2
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We are excited to announce the release of Delta Lake 4.3.0, which delivers new features, performance improvements, and protocol updates across Delta Spark, Kernel, UniForm, Sharing, and Flink. See the highlights below for the marquee changes.

Highlights

  • [Spark] Unity Catalog Delta REST API integration: Spark now supports the UC Delta API, using Unity Catalog as the source of truth for managed Delta tables. With server-side commit validation, server-advertised table features, and intent-based metadata updates, this integration provides consistent, safe access for Spark today and sets the foundation for future support across Flink, Trino, and other engines.
  • [Spark] Selective data replacement with replaceOn and replaceUsing DataFrame APIs: Spark now supports selectively replacing table data with the result of a DataFrame. Use replaceUsing to replace rows that match on specified columns, or replaceOn to replace rows that satisfy a user-defined condition.
  • [UniForm] Atomic + incremental Iceberg conversion and Spark 4.1 support: UniForm now writes Iceberg metadata atomically with the Delta commit, incrementally converts only the changed log range.
  • [Sharing] Streaming and CDF Support: Delta Sharing in 4.3.0 improves Spark Structured Streaming and Batch CDF support with automatic Delta response resolution, Parquet-to-Delta streaming conversion, shared DeltaFormattable streaming CDF support, and Trigger.AvailableNow support for shared tables.

Delta Spark

Delta Spark 4.3.0 is built on Apache Spark 4.1.0 and Apache Spark 4.0.1. As with Apache Spark, we publish Maven artifacts for Scala 2.13.

The key features of this release are:

  • Unity Catalog Delta REST API integration : Delta Spark now uses the new UC Delta REST API by default for UC-managed Delta tables. Managed Delta operations, including table loads, CREATE / CTAS, REPLACE, and all other metadata-changing writes such as DML, schema evolution, auto-merge, and supported ALTER TABLE updates, are routed through the new API. Non-Delta tables and external tables, including name-based and path-based access, continue to use the legacy delegate.
  • Delta DSv2 Connector with Delta Kernel (Experimental) : adds new features like support for batch writes to Catalog-Managed tables, Streaming source support including all read options and Catalog-driven batch CDC (SELECT … CHANGES FROM VERSION/TIMESTAMP, DV-aware) - gated behind spark.databricks.delta.changelogV2.enabled.
  • V2 Checkpoint performance hardening for large tables: V2 checkpoints now default to 50,000 actions per sidecar, so sidecar files are automatically split into multiple parts and checkpoint writes parallelize better out of the box.
  • Selectively replace data with 'replaceOn' and 'replaceUsing' DataFrame APIs: Use these options to replace part of the table with the result of a DataFrame. ‘replaceOn’ replaces rows that match a user-defined condition. ‘replaceUsing’ replaces rows where specified columns are equal. See Delta Lake API doc.
  • Implicit casting for DataFrame by-name writes: DataFrame writes that match by column name, except save() and saveAsTable().mode(“overwrite”), now apply Spark's implicit casts to align source values with the target schema, matching SQL INSERT BY NAME behavior.
  • Variant column statistics on write: Delta Spark now collects min/max statistics for Variant columns at write time, enabling data skipping on Variant-shredded tables.
  • REPLACE TABLE / RTAS / DPO production hardening: concurrency, source-materialization, and operational-metric coverage now spans the full set of REPLACE-style DataFrame writes introduced in Delta 4.2.

Other notable features and bug-fixes include:

Delta Kernel

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

The key features of this release are:

  • Incremental version-checksum construction: tableSizeBytes and numFiles are updated incrementally instead of via full log replay, making table-health diagnostics fast on large tables.
  • [Open tables with a missing _last_checkpo...
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Delta Lake 4.2.0

Delta Lake 4.2.0

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@openinx openinx released this 16 Apr 05:00
v4.2.0
9f600c4

We are excited to announce the release of Delta Lake 4.2.0! This release includes significant new features, improved safety and compatibility, and important bug fixes.

Highlights

  • [Spark] Unity Catalog Managed Table enhancements (experimental): REPLACE TABLE / RTAS and Dynamic Partition Overwrite support, automatic table schema/properties sync to catalog on table creation.
  • [Spark] Delta Spark V2 connector - streaming read (experimental): enhance streaming read capabilities for catalog-managed table by supporting critical options like startingTimestamp and skipChangeCommits.
  • [Flink] New Kernel-based Flink connector (experimental): a brand-new Kernel-based delta-flink connector that enables Apache Flink to read, write, and interact with catalog-managed Delta tables.
  • [Kernel] Geospatial, Variant GA, and Collations table feature: Delta Kernel can now read and write tables using geometry/geography types with bounding-box data skipping, generally available Variant columns, and collated string types.
  • [Security] The Delta project has undergone a substantial hardening effort across multiple surface areas, including stronger validation and dependency security scanning to proactively reduce supply-chain risk.

Delta Spark

Delta Spark 4.2.0 is built on Apache Spark 4.1.0 and Apache Spark 4.0.1. Similar to Apache Spark, we have released Maven artifacts for Scala 2.13.

The key features of this release are:

  • Delta Spark V2 Streaming read: Enhance streaming read capabilities for catalog-managed tables. The Delta V2 Spark connector now supports key options including startingVersion, startingTimestamp, maxBytesPerTrigger, maxFilesPerTrigger, excludeRegex, skipChangeCommits, ignoreDeletes, ignoreChanges, and ignoreFileDeletion.
  • REPLACE TABLE/RTAS/DPO support: Previously, operations like REPLACE TABLE, RTAS, and Dynamic Partition Overwrite (DPO) were not supported for catalog-managed Delta tables. This change will enable these operations to be a single, atomic action, significantly improving the safety and reliability of users' catalog-managed tables.
  • Server-Side Planning: OAuth Support (preview): The server-side planning client now supports OAuth-based authentication when delegating scan planning to an external catalog server.
  • INSERT BY NAME with Schema Evolution: SQL INSERT ... BY NAME statements now support automatic schema evolution, adding missing columns to the target table when delta.schemaAutoMerge.enabled is set. This brings INSERT BY NAME behavior in line with INSERT SELECT with schema evolution.
  • Force Statistics Collection: A new table property delta.stats.skipping.forceOptimizeStatsCollection enables forcing file statistics collection during query optimization. This ensures accurate data skipping for tables where statistics may be absent or stale, without requiring a manual OPTIMIZE run.
  • Allow CDF Writes for Non-Data-Changing Operations: When Change Data Feed is enabled, write operations that produce no data changes — such as add-only or remove-only commits — are now permitted. This reduces unnecessary write failures in CDC pipelines that perform metadata-only or compaction operations.
  • Variant Type in Schema Conversion: Delta Spark now correctly handles Spark's VariantType during Delta schema conversion, enabling seamless schema operations on tables with Variant columns.

Other notable changes:

Delta Kernel

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.

The key features of this release are:

  • Geospatial table feature: Delta Kernel now supports reading and writing tables with geometry and geography columns, including bounding-box data skipping via the StGeometryBoxesIntersect predicate.
  • Variant GA table feature: The Variant data type is now generally available in Delta Kernel.
  • Lazy schema parsing: Tables with unsupported column types in kernel (e.g. VOID) can now be loaded without error. The schema is only parsed when explicitly accessed, so connectors that don't need the schema can still read metadata, configuration, and other table properties from these tables.
  • Improved CommitInfo compatibility: Tables whose commits were written by external engines that omit engineInfo, operation, or txnId fields can now be read without errors.
  • [Add vacuumProtocolCheck ...
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Delta Lake 4.1.0

Delta Lake 4.1.0

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@openinx openinx released this 26 Feb 02:42
v4.1.0
7d2762e

We are excited to announce the release of Delta Lake 4.1.0! This release includes significant new features, performance improvements, and important platform upgrades.

Highlights

  • [Spark] Apache Spark 4.1.0 Support. The default build of Delta 4.1.0 leverages Apache Spark 4.1.0; however, it retains compatibility with Apache Spark 4.0.1.
  • [Spark] Catalog managed table enhancements (preview): Support UC managed table creation, batch read/write, streaming read/write.
  • [Spark] Spark V2 connector based on Delta Kernel API : A new Spark DataSource V2 connector backed by Delta Kernel, supporting streaming reads for catalog-managed tables.
  • [Spark] Server-Side Planning (preview): Delegate scan planning to catalog servers following the Apache Iceberg REST Catalog API. Supported filter, projection, and limits are pushed down to do the query planning.
  • [Spark] Conflict-free feature enablement: Enable Deletion Vectors and Column Mapping on existing tables without blocking or conflicting with concurrent writes.
  • [Kernel] Full support for catalog-managed tables, enabling Kernel-based connectors to interact with catalog-managed Delta tables (e.g., via Unity Catalog).

Delta Spark

Delta Spark 4.1.0 is built on Apache Spark 4.1.0 and Apache Spark 4.0.1. Similar to Apache Spark, we have released Maven artifacts for Scala 2.13.

Starting in Delta 4.1.0, Maven artifacts include a Spark version suffix (e.g., delta-spark_4.1_2.13 instead of delta-spark_2.13), with backward compatibility preserved in this release but dependency updates recommended. Separate artifacts are now published for Spark 4.1 and Spark 4.0 so users can choose the version matching their Spark runtime.

The key features of this release are:

  • Catalog-managed table enhancements: Delta Spark has added more support for Unity Catalog managed Delta tables via catalogManaged feature, enabling table creation, batch and streaming reads/writes (including time travel, and DML operations), history inspection, and OAuth-based authentication. This is still in preview and production usage is not recommended.
  • Delta v2 Spark Connector: A new Spark DataSource V2 connector backed by Delta Kernel supporting streaming read for catalog-managed table.
  • Server-Side Planning (preview): Delegate scan planning to an external catalog server, with filter, projection, and limit pushdown and multi-cloud credential support.
  • Atomic CTAS: CREATE TABLE AS SELECT for UC managed delta tables (MANAGED and EXTERNAL) is now fully atomic, working with UC 0.4.0. Other operations (including REPLACE TABLE, REPLACE TABLE AS SELECT, CREATE OR REPLACE TABLE, Dynamic Partition Overwrite) now fail fast instead of running in best-effort mode.
  • Conflict-free Deletion Vector enablement: Enable Deletion Vectors on existing tables without conflicting with concurrent transactions or requiring a maintenance window.
  • Conflict-free Column Mapping enablement: Enable Column Mapping on existing tables without conflicting with concurrent transactions or requiring a maintenance window.
  • Type widening enhancements: New decimal coercion modes during schema evolution (e.g., integer to decimal). The default automatic widening mode is now always.

Other notable changes:

  • Support writing AddFile stats as struct in checkpoints.
  • Fix multiple MERGE and INSERT struct null expansion bugs (#5612, #5658, #5759, #4759).
  • Fix timezone conversion for non-UTC timestamp partition values.
  • Fix metadata cleanup to retain files required for log reconstruction.
  • Fix DomainMetadata handling for RESTORE/CLONE and concurrent REPLACE.

Delta Kernel

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.

The key features of this release are:

  • Full catalog-managed tables support: enabling Kernel-based connectors to read from and write to Delta tables managed by a catalog such as Unity Catalog. This includes snapshot loading, time travel, change data feeds, commit publishing, and a reference Unity Catalog integration. Catalog-managed tables shift commit coordination from the filesystem to a managing catalog, unlocking capabilities like multi-table transactions.
  • Type widening support: Read tables with type widening enabled, including Iceberg compatibility checks for type changes.
  • Variant read/write support including variant stats.
  • Collation support: Writing collated tables, partition pruning, and data skipping with collations.
  • Checkpoint protection support to preserve table history during feature drops.

Delta Universal Format (UniForm)

Delta UniForm is enabled for Spark 4.0 in this release, restoring full Iceberg interoperability that was listed as a limitation in Delta 4.0.0. Both hudi and iceberg are currently not compatible with Spark 4.1, as support depends on upcoming releases providing Spark 4.1 compatible integratio...

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Delta Lake 4.0.1

Delta Lake 4.0.1

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@timothyw553 timothyw553 released this 16 Jan 00:40
v4.0.1
14460c1

We are excited to announce the release of Delta Lake 4.0.1! This release contains important bug fixes to 4.0.0 and it is recommended that users update to 4.0.1.

Highlights

  • [Spark] Breaking change: rename managed table feature from catalogOwned-preview to catalogManaged; legacy ucTableId has also transitioned to the new managed-table io.unitycatalog.tableId
  • [Spark] Unity Catalog OAuth: add OAuth authentication via catalog-scoped auth.* configs; tokens are acquired and refreshed automatically; static tokens remain supported.
  • [Spark] Unity Catalog managed table table creation: enable creating UC-managed Delta table and always send table properties to the UC server as source of truth.
  • [Spark] Spark compatibility fix: addressed NoSuchMethodError in REORG TABLE … APPLY (PURGE) when running with Spark 4.0.1.

Component-specific bug fixes are detailed below.

Delta Spark

Delta Spark 4.0.1 is built on Apache Spark™ 4.0.1. Similar to Apache Spark, we have released Maven artifacts for Scala 2.13.

The key features of this release are:

  • Catalog Managed Tables: managed table feature rename. The feature formerly named catalogOwned‑preview is standardized as catalogManaged. The associated Unity Catalog table ID property is updated accordingly (ucTableId → io.unitycatalog.tableId) ; calls that still send the legacy key are handled for compatibility during creation.
  • Fix Spark binary compatibility issue that caused REORG TABLE … APPLY (PURGE) to fail with NoSuchMethodError on Spark 4.0.1 by switching to the stable constructor and retrieving SQL configs via SparkSession.active.sessionState.conf in executors
  • Introduced end‑to‑end tests for Unity Catalog DML on the 4.0.x line to validate UC‑backed execution paths; scope is test‑only with no public API changes.

Unity Catalog Support

  • Official compatibility with UC 0.3.1. Delta 4.0.1 is officially tested with UC integration tests validating end‑to‑end behavior with UC 0.3.1. See the UC 0.3.1 release for the corresponding connector capabilities and APIs.

  • Support Unity Catalog OAuth authentication. Use catalog‑scoped auth.* configuration; tokens are automatically acquired and refreshed, avoiding embedded static tokens. Legacy static‑token configs continue to work for backward compatibility.

    Enable OAuth on a Spark catalog alias that points to UC:

    # Point a Spark catalog alias at Unity Catalog
spark.sql.catalog.mycatalog = "io.unitycatalog.connectors.spark.UCSingleCatalog"
spark.sql.catalog.mycatalog.uri = "https://<your-workspace-host>"

# OAuth (dynamic tokens) — supported keys
spark.sql.catalog.mycatalog.auth.type               = "oauth"
spark.sql.catalog.mycatalog.auth.oauth.uri          = "https://<auth-server-endpoint>"
spark.sql.catalog.mycatalog.auth.oauth.clientId     = "<client-id>"
spark.sql.catalog.mycatalog.auth.oauth.clientSecret = "<client-secret>"

# Static token (legacy-compatible)
spark.sql.catalog.mycatalog.auth.type  = "static"
spark.sql.catalog.mycatalog.auth.token = "<personal-access-token>"
# Legacy key also supported: spark.sql.catalog.mycatalog.token

    And run with Delta’s Spark extensions as usual:

    --conf "spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension" \
--conf "spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog"

  • Support Unity Catalog Managed Delta table creation. You can now create UC‑managed Delta tables via standard CREATE TABLE on a UC‑backed Spark catalog; at creation time, Delta sends table properties to the UC server so the server is the source of truth.

    Example:

    CREATE TABLE mycatalog.my_schema.events (
  id BIGINT,
  ts TIMESTAMP,
  data STRING
) 
USING delta 
TBLPROPERTIES (
  'delta.feature.catalogManaged' = 'supported'
);

Compatibility

  • Hadoop version: When using the Delta 4.0.1 release with Spark 4.0.1 please use Hadoop 3.4.x; Using Hadoop 3.3.x may lead to dependency conflicts and unsupported/runtime issues. Please update any 3.3.x references in commands and dependency coordinates to 3.4.x.

Credits

Allison Portis, Anudeep Konaboina, Dhruv Arya, Felipe Pessoto, Lukas Rupprecht, Oussama Saoudi, Tathagata Das, Timothy Wang, Yi Li, Hao Jiang, Zheng Hu

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Delta Lake 4.0.0

Delta Lake 4.0.0

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@allisonport-db allisonport-db released this 09 Jun 16:22
v4.0.0
6d055c5

We are excited to announce the final release of Delta Lake 4.0.0! This release includes several exciting new features.

Highlights

  • [Spark] Preview support for catalog-managed tables, a new table feature that transforms Delta Lake into a catalog-oriented lakehouse table format. This feature is still in the RFC stage, and as such, the protocol is still under development and is subject to change.
  • [Spark] 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.
  • [Spark] Support for the Variant data type to enable semi-structured storage and data processing, for flexibility and performance.
  • [Spark] Support a new DROP FEATURE implementation that allows dropping table features instantly without truncating history.
  • [Kernel] Support for reading and writing version checksum.
  • [Kernel] Support reading log compaction files for better performance during snapshot construction, and support writing log compaction files as a post commit hook.
  • [Kernel] Support for the Clustered Table feature which enables defining and updating the clustering columns on a table.
  • [Kernel] Support for writing to row tracking enabled tables.
  • [Kernel] Support for writing file statistics to the Delta log when they are provided by the engine. This enables data skipping using query filters at read time.

Details by each component.

Sunset of Delta Standalone and dependent connectors

Currently, Delta Standalone and its dependent connectors, including Delta Flink and Delta Hive, are no longer under active development. Starting in Delta 4.0 we will not be releasing these projects as part of the 4.x Delta releases. These connectors are in maintenance mode and, going forward, will only receive critical security fixes and high-severity bug patches in the 3.x series. We are committed to a full transition from Delta Standalone to Delta Kernel and a future Kernel-based Flink connector.

Delta Spark

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

The key features of this release are:

  • Delta Connect adds Spark Connect support to Scala and Python APIs of Delta Lake for Apache Spark. Spark Connect is a new project released in Apache Spark 4.0 that adds a decoupled client-server infrastructure which allows remote connectivity from Spark from everywhere. Delta Connect makes the DeltaTable interfaces compatible with the new Spark Connect protocol. For more information on how to use Delta Connect, see the Delta Connect documentation. Delta Connect is currently in preview.
  • Preview support for catalog-managed tables: Delta Spark now supports reading from and writing to tables that have the catalogOwned-preview feature enabled. This feature allows a catalog to broker all commits to the table it manages, giving the catalog the control and visibility it needs to prevent invalid operations (e.g. commits that violate foreign key constraints), enforce security and access controls, and opens the door for future performance optimizations. Currently write support includes INSERT, MERGE INTO, UPDATE, and DELETE operations.
    • Note: this feature is still in the RFC stage, and as such, the protocol is still under development and is subject to change. The catalogOwned-preview feature should not be enabled for production tables and tables created with this preview feature enabled may not be compatible with future Delta Spark releases.
  • 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. This feature was originally released in preview as part of Delta 4.0.0 Preview, as of 4.0.0 this feature is no longer in preview. Please see the documentation and the example for more details.
  • Preview support for shredded variants: Shredded variants are a storage optimization which allow for efficient sub-field extraction at the cost of higher write overhead, showing up to 20x read performance improvement. Shredded Variant data is stored according to the Parquet Variant Shredding specification. See the variantShredding RFC for more details.
    • 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.
  • Type Widening now supports a broader set of type changes and is no longer in preview. This feature allows you to change the data type of a column in your Delta table without rewriting the underlying data files. See the type widening documentation for a list of all supported type changes and additional information. Delta 3.3 or above is required to read tables with type widening enabled.
  • Support dropping table features without truncating history: The current drop feature implementation requires the execution of the command twice with a 24 hour waiting time in between. In addition, it also results in the truncation of the history of the Delta table to the last 24 hours. The new DROP FEATURE implementation allows dropping features instantly without truncating history. Dropping a feature introduces a new writer feature to the table, the checkpointProtection feature.
    • Dropping a feature with the new behaviour can be achieved as follows:
    ALTER TABLE table_name DROP FEATURE feature_name
    • We can still drop a feature with the old behavior as follows:
    ALTER TABLE table_name DROP FEATURE feature_name TRUNCATE HISTORY
    • The checkpointProtection feature can be dropped with history truncation.

Other notable changes include:

  • Support dropping table features using the DeltaTable Scala/Python APIs with deltaTable.dropFeatureSupport.
  • Support dropping the deletionVector table feature.
  • Support DataFrameReader options to unblock non-additive schema changes when streaming.
  • Invariant checks for DML commands to detect potential bugs in Delta or Spark earlier during execution and prevent committing the transaction in these cases.
  • Support the timestampdiff and timestampadd expressions for generated columns.
  • Support sorting within partitions when Z-ordering. This can be e...
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Delta Lake 3.3.2

Delta Lake 3.3.2

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@raveeram-db raveeram-db released this 31 May 00:20
v3.3.2
051aac5

We are excited to announce the release of Delta Lake 3.3.2! This release contains several important bug fixes and improvements to the 3.3.1 release and it is recommended that users upgrade to 3.3.2.

Component specific bug fixes are detailed below.

Delta Spark

Delta Spark 3.3.2 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 fixes in this release are:

  • Fix to clean up stale checksum files during Metadata cleanup to improve table maintenance

Delta Kernel

The key fixes in this release are:

  • Kernel improvement to eliminate dependencies on package-private Parquet classes for better compatibility with JVM environments with multiple class loaders.

Other projects

Delta Flink (Delta-Standalone based)

The key fixes in this release are:

  • Flink fix to correct mapping between Delta's BinaryType and Flink's data types for improved type compatibility.

Credits

Dhruv Arya, Prakhar Jain, Venkateshwar Korukanti, Scott Sandre

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Delta Lake 3.3.1

Delta Lake 3.3.1

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@raveeram-db raveeram-db released this 05 May 03:21
v3.3.1
bee74a2

We are excited to announce the release of Delta Lake 3.3.1! This release contains a few bug fixes to the 3.3.0 release and it is recommended that users upgrade to 3.3.1.

Component specific bug fixes are detailed below.

Delta Spark

Delta Spark 3.3.1 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 fixes in this release are:

  • Fix to allow user-specified on read if consistent with the table schema
  • Documentation update for Row Tracking to include Row Tracking Backfill introduced in Delta 3.3

Delta Kernel

The key fixes in this release are:

  • Kernel fix to handle non-uniform value types in map[string, string] in delta commit files

Other projects

No fixes or changes were made in the components below in this release but the corresponding artifacts are listed.

Delta Universal Format (UniForm)

Delta Sharing Spark

Credits

Wenchen Fan, Thang Long Vu

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Delta Lake 3.3.0

Delta Lake 3.3.0

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@allisonport-db allisonport-db released this 06 Jan 22:04
v3.3.0
f405c3f

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

Delta Lake 3.2.1

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@vkorukanti vkorukanti released this 26 Sep 21:00
v3.2.1
c8697c5

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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