- Documentation: https://docs.delta.io/2.1.0/index.html
- Maven artifacts: delta-core_2.12, delta-core_2.13, delta-contribs_2.12 delta_contribs_2.13, delta-storage, delta-storage-s3-dynamodb
- Python artifacts:https://pypi.org/project/delta-spark/2.1.0/
The key features in this release are as follows
- Support for Apache Spark 3.3.
- Support for [TIMESTAMP | VERSION] AS OF in SQL. With Spark 3.3, Delta now supports time travel in SQL to query older data easily. With this update, time travel is now available both in SQL and through the DataFrame API.
- Support for Trigger.AvailableNow when streaming from a Delta table. Spark 3.3 introduces Trigger.AvailableNow for running streaming queries like Trigger.Once in multiple batches. This is now supported when using Delta tables as a streaming source.
- Support for SHOW COLUMNS to return the list of columns in a table.
- Support for DESCRIBE DETAIL in the Scala and Python DeltaTable API. Retrieve detailed information about a Delta table using the DeltaTable API and in SQL.
- Support for returning operation metrics from SQL Delete, Merge, and Update commands. Previously these SQL commands returned an empty DataFrame, now they return a DataFrame with useful metrics about the operation performed.
- Optimize performance improvements
- Other notable changes
- Support for using variables in the VACUUM and OPTIMIZE SQL commands.
- Improvements for CONVERT TO DELTA with catalog tables.
- Autofill the partition schema from the catalog when it’s not provided.
- Use partition information from the catalog to find the data files to commit instead of doing a full directory scan. Instead of committing all data files in the table directory, only data files under the directories of active partitions will be committed.
- Support for Change Data Feed (CDF) batch reads on column mapping enabled tables when DROP COLUMN and RENAME COLUMN have not been used. See the documentation for more details.
- Improve Update performance by enabling schema pruning in the first pass.
- Fix for
DeltaTableBuilderto preserve table property case of non-delta properties when setting properties.
- Fix for duplicate CDF row output for delete-when-matched merges with multiple matches.
- Fix for consistent timestamps in a MERGE command.
- Fix for incorrect operation metrics for DataFrame writes with a
- Fix for a bug in Merge that sometimes caused empty files to be committed to the table.
- Change in log4j properties file format. Apache Spark upgraded the log4j version from 1.x to 2.x which has a different format for the log4j file. Refer to the Spark upgrade notes.
Benchmark framework update
Improvements to the benchmark framework (initial version added in version 1.2.0) including support for benchmarking arbitrary functions and not just SQL queries. We’ve also added Terraform scripts to automatically generate the infrastructure to run benchmarks on AWS and GCP.
Adam Binford, Allison Portis, Andreas Chatzistergiou, Andrew Vine, Andy Lam, Carlos Peña, Chang Yong Lik, Christos Stavrakakis, David Lewis, Denis Krivenko, Denny Lee, EJ Song, Edmondo Porcu, Felipe Pessoto, Fred Liu, Fu Chen, Grzegorz Kołakowski, Hedi Bejaoui, Hussein Nagree, Ionut Boicu, Ivan Sadikov, Jackie Zhang, Jiawei Bao, Jintao Shen, Jintian Liang, Jonas Irgens Kylling, Juliusz Sompolski, Junlin Zeng, KaiFei Yi, Kam Cheung Ting, Karen Feng, Koert Kuipers, Lars Kroll, Lin Zhou, Lukas Rupprecht, Max Gekk, Min Yang, Ming DAI, Nick, Ole Sasse, Prakhar Jain, Rahul Shivu Mahadev, Rajesh Parangi, Rui Wang, Ryan Johnson, Sabir Akhadov, Scott Sandre, Serge Rielau, Shixiong Zhu, Tathagata Das, Terry Kim, Thomas Newton, Tom van Bussel, Tyson Condie, Venki Korukanti, Vini Jaiswal, Will Jones, Xi Liang, Yijia Cui, Yousry Mohamed, Zach Schuermann, sherlockbeard, yikf