Spark Release 1.2.0

Latest News

Spark 2.2.3 released (Jan 11, 2019)
Spark+AI Summit (April 23-25th, 2019, San Francisco) agenda posted (Dec 19, 2018)
Spark 2.4.0 released (Nov 02, 2018)
Spark 2.3.2 released (Sep 24, 2018)

Spark 1.2.0 is the third release on the 1.X line. This release brings performance and usability improvements in Spark’s core engine, a major new API for MLlib, expanded ML support in Python, a fully H/A mode in Spark Streaming, and much more. GraphX has seen major performance and API improvements and graduates from an alpha component. Spark 1.2 represents the work of 172 contributors from more than 60 institutions in more than 1000 individual patches.

To download Spark 1.2 visit the downloads page.

Spark Core

In 1.2 Spark core upgrades two major subsystems to improve the performance and stability of very large scale shuffles. The first is Spark’s communication manager used during bulk transfers, which upgrades to a netty-based implementation. The second is Spark’s shuffle mechanism, which upgrades to the “sort based” shuffle initially released in Spark 1.1. These both improve the performance and stability of very large scale shuffles. Spark also adds an elastic scaling mechanism designed to improve cluster utilization during long running ETL-style jobs. This is currently supported on YARN and will make its way to other cluster managers in future versions. Finally, Spark 1.2 adds support for Scala 2.11. For instructions on building for Scala 2.11 see the build documentation.

Spark Streaming

This release includes two major feature additions to Spark’s streaming library, a Python API and a write ahead log for full driver H/A. The Python API covers almost all the DStream transformations and output operations. Input sources based on text files and text over sockets are currently supported. Support for Kafka and Flume input streams in Python will be added in the next release. Second, Spark streaming now features H/A driver support through a write ahead log (WAL). In Spark 1.1 and earlier, some buffered (received but not yet processed) data can be lost during driver restarts. To prevent this Spark 1.2 adds an optional WAL, which buffers received data into a fault-tolerant file system (e.g. HDFS). See the streaming programming guide for more details.

MLLib

Spark 1.2 previews a new set of machine learning API’s in a package called spark.ml that supports learning pipelines, where multiple algorithms are run in sequence with varying parameters. This type of pipeline is common in practical machine learning deployments. The new ML package uses Spark’s SchemaRDD to represent ML datasets, providing direct interoperability with Spark SQL. In addition to the new API, Spark 1.2 extends decision trees with two tree ensemble methods: random forests and gradient-boosted trees, among the most successful tree-based models for classification and regression. Finally, MLlib’s Python implementation receives a major update in 1.2 to simplify the process of adding Python APIs, along with better Python API coverage.

Spark SQL

In this release Spark SQL adds a new API for external data sources. This API supports mounting external data sources as temporary tables, with support for optimizations such as predicate pushdown. Spark’s Parquet and JSON bindings have been re-written to use this API and we expect a variety of community projects to integrate with other systems and formats during the 1.2 lifecycle.

Hive integration has been improved with support for the fixed-precision decimal type and Hive 0.13. Spark SQL also adds dynamically partitioned inserts, a popular Hive feature. An internal re-architecting around caching improves the performance and semantics of caching SchemaRDD instances and adds support for statistics-based partition pruning for cached data.

GraphX

In 1.2 GraphX graduates from an alpha component and adds a stable API. This means applications written against GraphX are guaranteed to work with future Spark versions without code changes. A new core API, aggregateMessages, is introduced to replace the now deprecated mapReduceTriplet API. The new aggregateMessages API features a more imperative programming model and improves performance. Some early test users found 20% - 1X performance improvement by switching to the new API.

In addition, Spark now supports graph checkpointing and lineage truncation which are necessary to support large numbers of iterations in production jobs. Finally, a handful of performance improvements have been added for PageRank and graph loading.

Other Notes

PySpark’s sort operator now supports external spilling for large datasets.
PySpark now supports broadcast variables larger than 2GB and performs external spilling during sorts.
Spark adds a job-level progress page in the Spark UI, a stable API for progress reporting, and dynamic updating of output metrics as jobs complete.
Spark now has support for reading binary files for images and other binary formats.

Upgrading to Spark 1.2

Spark 1.2 is binary compatible with Spark 1.0 and 1.1, so no code changes are necessary. This excludes APIs marked explicitly as unstable. Spark changes default configuration in a handful of cases for improved performance. Users who want to preserve identical configurations to Spark 1.1 can roll back these changes.

spark.shuffle.blockTransferService has been changed from nio to netty
spark.shuffle.manager has been changed from hash to sort
In PySpark, the default batch size has been changed to 0, which means the batch size is chosen based on the size of object. Pre-1.2 behavior can be restored using SparkContext([... args... ], batchSize=1024).
Spark SQL has changed the following defaults:
- spark.sql.parquet.cacheMetadata: false -> true
- spark.sql.parquet.compression.codec: snappy -> gzip
- spark.sql.hive.convertMetastoreParquet: false -> true
- spark.sql.inMemoryColumnarStorage.compressed: false -> true
- spark.sql.inMemoryColumnarStorage.batchSize: 1000 -> 10000
- spark.sql.autoBroadcastJoinThreshold: 10000 -> 10485760 (10 MB)

Known Issues

A few smaller bugs did not make the release window. They will be fixed in Spark 1.2.1:

Netty shuffle does not respect secured port configuration. Work around - revert to nio shuffle: SPARK-4837
java.io.FileNotFound exceptions when creating EXTERNAL hive tables. Work around - set hive.stats.autogather = false. SPARK-4892.
Exception PySpark zip function on textfile inputs: SPARK-4841
MetricsServlet not properly initialized: SPARK-4595

Credits

Aaron Davidson – Improvements in Core; bug fixes in Core and Shuffle; improvement in Core and Shuffle
Aaron Staple – Improvements in Core, MLlib, and Streaming; new features in PySpark; bug fixes in SQL
Adam Pingel – Improvement in Core
Ahir Reddy – Improvements in Core
Akshat Aranya – Bug fixes in Core
Alex Liu – Bug fixes in SQL
Alexander Ulanov – New features in MLlib
Allan Douglas R. De Oliveira – Improvements in Core
Anand Avati – Improvement in Core
Anant Asthana – Improvement in Core, MLlib, and SQL
Andrew Ash – Documentation and bug fixes in Core
Andrew Bullen – Bug fixes in MLlib
Andrew Or – Improvements in Core and YARN; bug fixes in Windows, Core, and YARN; improvement in Core and YARN
Andy Konwinski – Documentation in Core
Aniket Bhatnagar – Bug fixes in Core and Streaming
Ankur Dave – Improvements and bug fixes in GraphX
Arun Ahuja – Documentation in Core
Benoy Antony – Bug fixes in Web UI and YARN
Bertrand Bossy – Bug fixes in Core
Bill Bejeck – Bug fixes in Core
Brenden Matthews – Bug fixes in Mesos
Burak Yavuz – New features in MLlib
Chao Chen – Improvements and documentation in Core
Cheng Hao – Test, improvements, new features, bug fixes, and improvement in SQL
Cheng Lian – Improvements in Core and SQL; test in SQL; new features in SQL; bug fixes in Core and SQL; documentation in Core
Chester Chen – Bug fixes in YARN
Chip Senkbeil – New features in Core
Chirag Aggarwal – Bug fixes in SQL
Chris Cope – Bug fixes in YARN
Christoph Sawade – Improvements in MLlib and PySpark
Cody Koeninger – Improvements in SQL
Colin Patrick Mccabe – Improvements in Core
DB Tsai – Improvements and improvement in MLlib
Dale Richardson – Improvements in Core
Dan McClary – New features in SQL
Dan Osipov – New features in EC2
Daoyuan Wang – Improvements in Core and SQL; new features in SQL; bug fixes in Core and SQL; documentation in Core
Davies Liu – Improvements in Core, SQL, MLlib, and PySpark; new features in Core, Streaming, PySpark, and MLlib, and PySpark; bug fixes in Streaming, Core, SQL, MLlib, and PySpark; documentation in Core
Derek Ma – Bug fixes in Core and Streaming
DoingDone9 – Bug fixes in SQL
Egor Pahomov – Bug fixes in Core
Eric Eijkelenboom – Bug fixes in Core
Eric Liang – Bug fixes in Core and SQL
Erik Erlandson – Improvements and improvement in Core
Eugen Cepoi – Improvements in Core
Fairiz Azizi – Improvements in Core
Felix Maximilian Moller – Documentation in Core
Gankun Luo – Bug fixes in SQL
Grega Kespret – Documentation in Core
GuoQiang Li – Improvements in Core and MLlib; bug fixes in Core, Web UI, MLlib, and PySpark; improvement in YARN
Hari Shreedharan – Bug fixes and improvement in Streaming
Henry Cook – Documentation in Core
Holden Karau – Documentation in Core; bug fixes in PySpark
Hong Shen – Improvements in Core
Hossein Falaki – Bug fixes in Web UI
Ian Hummel – Improvements in Core
Jacky Li – Bug fixes in Core
Jakub Dubovsky – Bug fixes in Core
Jascha Swisher – Bug fixes in Core
Jay Vyas – Documentation in Core
Jeremy Freeman – New features in Streaming and MLlib; bug fixes in Core and PySpark
Jey Kottalam – Bug fixes in Core
Jie Huang – Documentation and bug fixes in Core
Jim Carroll – Improvements and bug fixes in SQL
Jim Lim – Improvements in Core; bug fixes in YARN
Jongyoul Lee – Bug fixes in Core and Mesos
Joseph Bradley – Improvements in MLlib
Joseph E. Gonzalez – Documentation in Core; bug fixes in GraphX and MLlib
Joseph K. Bradley – Improvements in Core and MLlib; new features in MLlib and SQL; bug fixes in MLlib; documentation in Core and MLlib
Josh Rosen – Improvements in Java API, Core, Web UI, and Shuffle; new features in Java API, Core, and Web UI; bug fixes in Core, PySpark, and Streaming; documentation in Core
Kai Sasaki – Bug fixes in Core
Kay Ousterhout – Improvements in Core and Web UI; bug fixes in Core and Web UI
Ken Takagiwa – Documentation in Core
Kenichi Maehashi – Improvements in Core
Kevin Mader – Improvements in Java API and Core
Kousuke Saruta – Improvements in Project Infra, Core, PySpark, YARN, SQL, and Web UI; bug fixes in Core, PySpark, MLlib, YARN, SQL, and Web UI; documentation in Core
Larry Xiao – Improvements and bug fixes in GraphX
Li Zhihui – Improvements in Core
Liang-Chi Hsieh – Improvements in Core; bug fixes in Core and SQL
Lianhui Wang – Bug fixes in GraphX
Lijie Xu – Bug fixes in Core and GraphX
Liquan Pei – Documentation in Core; new features in MLlib and PySpark
Liu Hao – Bug fixes in Core
Lu Lu – Improvements in GraphX
Madhu Siddalingaiah – Documentation in Core
Manish Amde – Improvements and new features in MLlib
Marcelo Vanzin – Test in YARN; improvement in Core and YARN; new features in Core; bug fixes in Core and YARN; improvements in Core
Mario Pastorelli – Documentation in Core
Mark G. Whitney – Documentation in YARN
Mark Hamstra – Bug fixes in Core
Mark Mims – Improvements in Web UI
Martin Weindel – Documentation in Core and Mesos
Masayoshi TSUZUKI – Improvements in Windows, Core, and PySpark; bug fixes in Windows, Core, and PySpark
Matei Zaharia – Improvement in Core and SQL; bug fixes in Core and SQL
Matthew Cheah – Bug fixes in Core
Matthew Farrellee – Improvements in Core; new features in PySpark; bug fixes in Core and PySpark
Matthew Rocklin – Bug fixes in Core
Matthew Taylor – Bug fixes in SQL
Michael Armbrust – Improvements in SQL; new features in SQL; bug fixes in Core, PySpark, and SQL; documentation in Core
Michael Griffiths – Bug fixes in PySpark
Michelangelo D’Agostino – Improvements in MLlib and PySpark
Mike Timper – Bug fixes in SQL
Min Shen – Bug fixes in YARN
Mingfei Shi – Bug fixes in Core
Mubarak Seyed – Improvements in Streaming
NamelessAnalyst – Improvements in GraphX
Nan Zhu – Bug fixes and Improvements in Core
Nathan Artz – Documentation in Core
Nathan Howell – Bug fixes in SQL
Nicholas Chammas – Improvement in Core; improvements in Project Infra, Core, and EC2; bug fixes in Project Infra, EC2, and SQL; documentation in Core
Niklas Wilcke – Improvements in MLlib; bug fixes in Core
Nishkam Ravi – Bug fixes in Core
Oded Zimerman – Bug fixes in GraphX
Patrick Wendell – Improvements in Core; bug fixes in Project Infra, Core, and Mesos
Prashant Sharma – Improvements in Core; bug fixes in Streaming and Core; improvement in Core, YARN, and Streaming
Praveen Seluka - New feature in Core
Qiping Li – Improvements and new features in MLlib
RJ Nowling – Improvements in MLlib; bug fixes in GraphX; documentation in Core
Ravindra Pesala – Improvements, new features, and bug fixes in SQL
Raymond Liu – Improvement in Core and Shuffle
Renat Yusupov – Bug fixes in SQL
Reno Zhang – Improvements in YARN
Reynold Xin – Improvements in Core, Shuffle, EC2, and SQL; new features in Project Infra, Core, and EC2; bug fixes in Core and SQL; improvement in Core, Shuffle, and SQL
Reza Zadeh – Improvements in Core; new features in MLlib; documentation in Core
Rob O’Dwyer – Improvements in PySpark
Rong Gu – Improvements in Core
Rui Li – New features in Java API
Saisai Shao – Improvements in Streaming; bug fixes in Streaming and Shuffle
Sandy Ryza – Improvements in Core, MLlib, and YARN; new features in Core; bug fixes in Core and SQL
Santiago M. Mola – Documentation in Core
Sean Owen – Improvement in Streaming; improvements in Core and Streaming; new features in Core; bug fixes in Java API, Core, MLlib, and Streaming; documentation in Core
Shane Knapp – Bug fixes in Core
Shiti Saxena – Improvement in Core
Shivaram Venkataraman – Improvements in Core; bug fixes in Core and EC2
Shixiong Zhu – Test in Core; improvements in Core and Web UI; bug fixes in Core, Web UI, and YARN; documentation in Streaming and Core
Bai Shou – Improvements and bug fixes in SQL
Shuo Xiang – New features and bug fixes in MLlib
Su Yan – Bug fixes in Core
Sung Chung – Improvements in MLlib
Surong Quan – Improvements in Streaming
Takuya UESHIN – Test in SQL; documentation in Core; bug fixes in Core and SQL; improvements in SQL
Tal Sliwowicz – Bug fixes in Core
Tathagata Das – Improvements in Core and Streaming; bug fixes in Streaming and Core; improvement in Streaming
Ted Yu – Bug fixes and improvement in Core
Thomas Graves – Bug fixes in Core and YARN
Tianshuo Deng – Bug fixes in Core and Shuffle
Timothy Chen – Bug fixes in Mesos
Tingjun Xu – Bug fixes in YARN
Tomohiko K. – Bug fixes in Core and PySpark; improvement in PySpark
Uncle Gen – Improvements in GraphX
Uri Laserson – Improvements in PySpark
Varadharajan Mukundan – Improvements in Core
Venkata Ramana Gollamudi – New features and bug fixes in SQL
Victor Tso – Bug fixes in Core
Vida Ha – Improvements in SQL; bug fixes in EC2
Viper Kun – Documentation in Core
Wang Fei – Test in SQL; improvements in Core and SQL; bug fixes in Core and SQL; documentation in Core
Wang Tao – Improvements in Core, YARN, and SQL; bug fixes in Core and YARN
Ward Viaene – Bug fixes in PySpark
Wenchen Fan – Bug fixes in SQL
William Benton – Improvements and bug fixes in SQL
Xiangrui Meng – Improvements in Core, PySpark, MLlib, SQL, Java API, and Web UI; documentation in Core; new features in SQL, MLlib, and PySpark; bug fixes in Core, MLlib, and PySpark; improvement in PySpark, MLlib, and SQL
Xinyun Huang – Improvements in SQL
Yadong Qi – Test in Core; improvements and bug fixes in Streaming
Yanbo Liang – New features in MLlib
Yantang Zhai – Improvements in Core; bug fixes in Core, Web UI, and SQL
Yash Datta – Improvements in SQL
Ye Xianjin – Improvements in Core
Yin Huai – Documentation in Core; bug fixes in SQL
Zdenek Farana – Bug fixes in SQL
Zhan Zhang – Build fixes in SQL
Zhang, Liye – Improvements and bug fixes in Core

Thanks to everyone who contributed!

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