fix(deps): update dependency io.delta:delta-core_2.12 to v2

[renovate]

This PR contains the following updates:

Package	Change	Age	Adoption	Passing	Confidence
io.delta:delta-core_2.12 (source)	1.1.0-nessie -> 2.1.0

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Release Notes

delta-io/delta

v2.1.0

We are excited to announce the release of Delta Lake 2.1.0 on Apache Spark 3.3. Similar to Apache Spark™, we have released Maven artifacts for both Scala 2.12 and Scala 2.13.

• 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
  • Added a config to use repartition(1) instead of coalesce(1) in Optimize for better performance when compacting many small files.
  • Improve Optimize performance by using a queue-based approach to parallelize the compaction jobs.
• 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 DeltaTableBuilder to 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 replaceWhere option.
  • 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.

Credits

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

v2.0.0

We are excited to announce the release of Delta Lake 2.0.0 on Apache Spark 3.2.

• Quick start guide on how to try the Delta Lake 2.0.0: https://docs.delta.io/2.0.0/quick-start.html
• Documentation: https://docs.delta.io/2.0.0/index.html
• Maven artifacts. Similar to Apache Spark™, we have released Maven artifacts for both Scala 2.12 and Scala 2.13.
  • 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/2.0.0/

The key features in this release are as follows.

• Support Change Data Feed on Delta tables. Change Data Feed represents the row level changes between different versions of the table. When enabled, additional information is recorded regarding row level changes for every write operation on the table. See the documentation for more details.

• Support Z-Order clustering of data to reduce the amount of data read. Z-Ordering is a technique to colocate related information in the same set of files. This data clustering allows column stats (released in Delta 1.2) to be more effective in skipping data based on filters in a query. See the documentation for more details.

• Support for idempotent writes to Delta tables to enable fault-tolerant retry of Delta table writing jobs without writing the data multiple times to the table. See the documentation for more details.

• Support for dropping columns in a Delta table as a metadata change operation. This command drops the column from metadata and not the column data in underlying files. See documentation for more details.

• Support for dynamic partition overwrite. Overwrite only the partitions with data written into them at runtime. See documentation for details.

• Experimental support for multi-part checkpoints to split the Delta Lake checkpoint into multiple parts to speed up writing the checkpoints and reading. See documentation for more details.

• Python and Scala API support for OPTIMIZE file compaction and Z-order by.

• Other notable changes

  • Improve the generated column data skipping by adding the support for skipping by nested column generated column
  • Improve the table schema validation by blocking the unsupported data types in Delta Lake.
  • Support creating a Delta Lake table with an empty schema.
  • Change the behavior of DROP CONSTRAINT to throw an error when the constraint does not exist. Before this version the command used to return silently.
  • Fix the symlink manifest generation when partition values contain space in them.
  • Fix an issue where incorrect commit stats are collected.
  • Support for SimpleAWSCredentialsProvider or TemporaryAWSCredentialsProvider in S3 multi-cluster write supported LogStore.
  • Fix an issue in generated columns that would not allow null columns in the insert DataFrame to be written even if the column was nullable.

Benchmark Framework Update

Independent of this release, we have improved the framework for writing large scala performance benchmarks (initial version added in version 1.2.0), we have added support for running benchmarks on Google Compute Platform using Google Dataproc (in addition to the existing support for EMR on AWS)

Credits

Adam Binford, Alkis Evlogimenos, Allison Portis, Ankur Dave, Bingkun Pan, Burak Yilmaz, Chang Yong Lik, Chen Qingzhi, Denny Lee, Eric Chang, Felipe Pessoto, Fred Liu, Fu Chen, Gaurav Rupnar, Grzegorz Kołakowski, Hussein Nagree, Jacek Laskowski, Jackie Zhang, Jiaan Geng, Jintao Shen, Jintian Liang, John O'Dwyer, Junyong Lee, Kam Cheung Ting, Karen Feng, Koert Kuipers, Lars Kroll, Liwen Sun, Lukas Rupprecht, Max Gekk, Michael Mengarelli, Min Yang, Naga Raju Bhanoori, Nick Grigoriev, Nick Karpov, Ole Sasse, Patrick Grandjean, Peng Zhong, Prakhar Jain, Rahul Shivu Mahadev, Rajesh Parangi, Ruslan Dautkhanov, Sabir Akhadov, Scott Sandre, Serge Rielau, Shixiong Zhu, Shoumik Palkar, Tathagata Das, Terry Kim, Tyson Condie, Venki Korukanti, Vini Jaiswal, Wenchen Fan, Xinyi, Yijia Cui, Yousry Mohamed

v1.2.1

We are excited to announce the release of Delta Lake 1.2.1 on Apache Spark 3.2. Similar to Apache Spark™, we have released Maven artifacts for both Scala 2.12 and Scala 2.13.

• Documentation: https://docs.delta.io/1.2.1/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/1.2.1/

Key features in this release

• Fix an issue with loading error messages in --packages mode. Previous release had a bug that resulted in user getting NullPointerException instead of proper error message when using Delta Lake with --packages mode either in pyspark or spark-shell (Fix, Test)
• Fix incorrect exception type thrown in some Python APIs. A bug caused pyspark to throw incorrect type of exceptions instead of expected AnalysisException. This issue is fixed. See issue #​1086 for more details.
• Fix for S3 multi-cluster mode configuration. A bug in the S3 multi-cluster mode caused --conf to not work for certain configuration parameters. This issue is fixed by having these configuration parameters begin with spark. See the updated documentation.
• Make the GCS LogStore configuration simpler by automatically deriving the LogStore implementation class config spark.delta.logStore.gs.impl from the scheme in the table path. See the updated documentation.
• Make SetAccumulator thread safe. SetAccumulator used by Merge was not thread safe and might cause executor heartbeat failures in rare cases. This was fixed by using a synchronized set.

Credits

Allison Portis, Chang Yong Lik, Kam Cheung Ting, Rahul Mahadev, Scott Sandre, Venki Korukanti

v1.2.0

We are excited to announce the release of Delta Lake 1.2.0 on Apache Spark 3.2. Similar to Apache Spark™, we have released Maven artifacts for both Scala 2.12 and Scala 2.13.

• Documentation: https://docs.delta.io/1.2.0/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/1.2.0/

Key features in this release

• Support multi-cluster write in Delta Lake tables stored in S3. Users now have the option of specifying a new and experimental LogStore implementation that supports concurrent reads and writes to a single Delta Lake table in S3 from multiple Spark drivers. See the documentation for more details.

• Support for compacting small files (optimize) into larger files in a Delta Lake table. Reduced number of data files improves read latency due to reduced metadata size and per-file overheads such as file-open overhead and file-close overhead. See the documentation for more details.

• Support for data skipping using column statistics. Column statistics are collected for each file as part of the Delta Lake table writes. These statistics can be used during the reading of a Delta Lake table to skip reading files not matching the filters in the query. See the documentation for more details.

• Support for restoring a Delta table to an earlier version. Restoring to an earlier version number or a version of a specific timestamp is supported using the SQL command, Scala APIs or Python APIs. See the documentation for more details.

• Support for column renaming in a Delta Lake table without the need to rewrite the underlying Parquet data files. See the documentation for more details.

• Support for arbitrary characters in column names in Delta tables. Before, the supported list of characters was limited by the support of the same in Parquet data format. Column names containing special characters such space, tab, ,, {, ( etc. are supported now. See the documentation for more details.

• Support for automatic data skipping using generated columns. For any partition column that is a generated column, partition filters will be automatically generated from any data filters on its generating column(s), when possible.

• Support for Google Cloud Storage is now generally available. See the documentation on how to read and write Delta Lake tables in Google Cloud Storage.

• Other notable changes

  • Create a new module delta-storage. This extracts out the LogStore interface and implementations in a separate module which is published as its own jar. This enables new implementations of LogStore without depending upon the complete Delta jars. See the migration guide here for more details.
  • Improve the error messages and exceptions to be better organized and queryable.
  • Support for gettimestamp expression in generated columns.
  • Snapshot/Checkpoint management improvements
    • Make loading snapshots resilient to corrupt checkpoints in Delta. When reading a checkpoint fails, we try to search for an alternative checkpoint and use it to construct a snapshot.
    • Fix to snapshot writing to not fail the write when a checkpoint fails due to non-fatal errors.
    • Optimization to reduce the number of list calls to storage
  • Improved output metrics for DELETE table command.
  • Improved output metrics for UPDATE table command.
  • Optimize merge operation in a Delta table with a large number of columns.
  • Fix a NullPointerException when trying to reference a DeltaLog created with a SparkContext that has stopped.
  • Fix an issue in handling null partition column values in the change data capture feature.
  • Fix an issue in adding a new column to the Delta table when the preceding column is of type Array.
  • Fix an issue where we are not closing the file list iterator when reading large log files in the Delta Streaming source.
  • Throw proper exceptions when searching for a Delta table in the catalog.
  • Fix a schema evolution issue when the column type is an array of structs.
  • Better handling of FileNotFoundException when reading Delta log files to distinguish between the corrupt log files and no files found.

Benchmark Framework

Independent of this release, we have also built a framework for writing large scale performance benchmarks on Delta tables using a real cluster. Currently, the framework provides a TPC-DS inspired benchmark to measure the ingestion time (e.g. time taken to create TPC-DS tables) and query times. But we encourage the community to contribute more benchmarks to measure performance of different real-world workloads on Delta tables.

Credits

Adam Binford, Alex Liu, Allison Portis, Anton Okolnychyi, Bart Samwel, Carmen Kwan, Chang Yong Lik, Christian Williams, Christos Stavrakakis, David Lewis, Denny Lee, Fabio Badalì, Fred Liu, Gengliang Wang, Hoang Pham, Hussein Nagree, Hyukjin Kwon, Jackie Zhang, Jan Paw, John ODwyer, Junlin Zeng, Jackie Zhang, Junyong Lee, Kam Cheung Ting, Kapil Sreedharan, Lars Kroll, Liwen Sun, Maksym Dovhal, Mariusz Krynski, Meng Tong, Peng Zhong, Prakhar Jain, Pranav, Ryan Johnson, Sabir Akhadov, Scott Sandre, Shixiong Zhu, Sri Tikkireddy, Tathagata Das, Tyson Condie, Vegard Stikbakke, Venkata Sai Akhil Gudesa, Venki Korukanti, Vini Jaiswal, Wenchen Fan, Will Jones, Xinyi Yu, Yann Byron, Yaohua Zhao, Yijia Cui

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