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Faster Batch Processing with Cloudera 5.7: Hive-on-Spark is ready for production
The document discusses Hive-on-Spark, an integration that enables Apache Hive to utilize Apache Spark as its execution engine, providing significant performance improvements—averaging 3x faster than the traditional Hive-on-MapReduce. It outlines key features, benefits, configuration requirements, and optimization techniques for using Hive-on-Spark effectively, such as executor management and resource allocation. Future enhancements and support developments are also highlighted, indicating ongoing collaboration and improvements in the ecosystem.
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Faster Batch Processing with Cloudera 5.7: Hive-on-Spark is ready for production
- 1. 1© Cloudera, Inc.All rights reserved. Faster Batch Processing with Hive-on-Spark Santosh Kumar | Cloudera Rui Li | Intel
- 2. 2© Cloudera, Inc.All rights reserved. Agenda • What is Hive-on-Spark? • Using Hive-on-Spark • Performance Metrics • Configuration & Tuning • What’s Next? • Q&A
- 3. 3© Cloudera, Inc.All rights reserved. Apache Spark Flexible, in-memory data processing for Hadoop Easy Development Flexible Extensible API Fast Batch & Stream Processing • Rich APIs for Scala, Java, and Python • Interactive shell • APIs for different types of workloads: • Batch • Streaming • Machine Learning • Graph • In-Memory processing and caching
- 4. 4© Cloudera, Inc.All rights reserved. Spark Takes Advantage of Memory • Resilient Distributed Datasets (RDD) • In-memory data-structure partitioned across a set of machines • Can fall back to disk when data-set does not fit in memory • Created by parallel transformations on data in stable storage • Provides fault-tolerance through concept of lineage
- 5. 5© Cloudera, Inc.All rights reserved. Introduction • Enables Hive to use Spark as underlying execution engine • Motivations • Consolidation of Spark as execution engine • Better performance • Increased adoption of Hive (e.g. for Spark users) • Community effort by Cloudera, IBM, Intel, MapR, and others
- 6. 6© Cloudera, Inc.All rights reserved. Choosing the Right SQL Engine Know Your Audience, Know Your Use Case Batch Processing BI and SQL Analytics Procedural Development SQLOR Impala
- 7. 7© Cloudera, Inc.All rights reserved. Current State of Hive-on-Spark (HoS) • Fully supported production release in C5.7 • Functional parity with Hive-on-MapReduce (HoMR) • Average 3x performance improvement vs HoMR • Automatic configuration and optimizations via Cloudera Manager • Strong early user base • Early commitment for future collaboration from Intel and others
- 8. 8© Cloudera, Inc.All rights reserved. Design Principles • Minimize impact on existing code path • Minimizes functional and performance impact • Minimizes maintenance • Maximizes support for Hive features – current as well as future • Spark invoked only at execution layer • HoS produces similar logical operators plan as HoMR • Logical plan runs on low-level Spark primitives • Minimizes usage of advanced Spark primitives
- 9. 9© Cloudera, Inc.All rights reserved. Getting Started with Hive-on-Spark
- 10. 10© Cloudera, Inc.All rights reserved. Configuration • Minimal configurations needed • Via Cloudera Manager: Set “Spark on YARN Service” (internally sets spark.master=yarn-cluster) • Set hive.execution.engine=spark per service or query • Only yarn-cluster is supported • Cloudera Manager auto-configures most configurations • Configuration & Tuning Guide available on Docs
- 11. 11© Cloudera, Inc.All rights reserved. Performance Avg. ~3X faster than Hive-on-MapReduce More Suitable Less Suitable Complex workloads w/ multiple MR stages e.g. filter followed by JOIN followed by GROUP BY Simple workloads e.g. select * Disk-bound w/ multiple disk reads/writes CPU bound workloads e.g. complex UDFs Workloads requiring mins to hours for completion Workloads typically requiring <1 min
- 12. 12© Cloudera, Inc.All rights reserved. Query Execution: Background Input status_updates( userid int,status string,ds string) profiles(userid int,school string,gender int) Output school_summary(school string,cnt int,ds string) gender_summary(gender int,cnt int,ds string)
- 13. 13© Cloudera, Inc.All rights reserved. Query Execution: MapReduce BEGINS CONTINUES CONTINUES ENDS
- 14. 14© Cloudera, Inc.All rights reserved. Query Execution: MapReduce BEGINS CONTINUES CONTINUES ENDS
- 15. 15© Cloudera, Inc.All rights reserved. Query Execution: MapReduce BEGINS CONTINUES CONTINUES ENDS FileSinkOperator (disk write) and TableScanOperator (disk read) are very costly
- 16. 16© Cloudera, Inc.All rights reserved. Query Execution: Hive-on-Spark Costly Steps Removed BEGINS CONTINUES CONTINUES ENDS
- 17. 17© Cloudera, Inc.All rights reserved. Query Execution: Hive-on-Spark Costly Steps Removed BEGINS CONTINUES CONTINUES ENDS
- 18. 18© Cloudera, Inc.All rights reserved. Optimization for Resource Management: Long-Live Executors (LLE) • MR: Each query an independent YARN application • Spark: Each SQL session is a long-lived YARN application • First query of a session spawns a YARN app • Subsequent queries re-use same YARN app as well as containers • Session disconnect shuts down YARN app and releases container resources
- 19. 19© Cloudera, Inc.All rights reserved. Long-Lived Executors Details • Hive User Session will submit Spark Application to YARN • Spark YARN Application: • YARN container = Spark Executors live in YARN containers • YARN Application Master = RemoteDriver • Submits Spark ‘jobs’, aka Hive queries, to Spark executors • Connects back to HS2 to report job progress from Spark executors User1 User2 HiveServer2 Session1 Session2 YARN Cluster AM (RemoteDriver1) Containers (Executors) AM (RemoteDriver2) Containers (Executors)
- 20. 20© Cloudera, Inc.All rights reserved. Configuration and Tuning Hive-on-Spark
- 21. 21© Cloudera, Inc.All rights reserved. Spark Configuration • Size of executors • Bigger and fewer executors • Threads contention • GC pressure • Smaller and more executors • Less memory efficient • Bigger start-up overhead
- 22. 22© Cloudera, Inc.All rights reserved. Spark Configuration • CPU • Around 5-7 cores per executor • Memory • Leave 10% for OS cache • Executor memory overhead • Tune by case • Can be heavily used by Netty • Usually 15% - 20% • Around 3GB per core
- 23. 23© Cloudera, Inc.All rights reserved. Spark Configuration • Serialization • spark.serializer – kryo performs better and is REQUIRED by HoS • spark.kryo.referenceTracking – disable to avoid java performance issue • Shuffle • spark.shuffle.compress • spark.shuffle.spill.compress • Trade CPU for I/O • Increase number of reducers
- 24. 24© Cloudera, Inc.All rights reserved. Partitioning • Number of mappers • Inputformat • mapreduce.input.fileinputformat.split.maxsize • Number of reducers • hive.exec.reducers.bytes.per.reducer • mapreduce.job.reduces • HoS tends to launch more reducers • Merge small files • hive.merge.sparkfiles
- 25. 25© Cloudera, Inc.All rights reserved. Hive Configuration • General optimizations • Enable vectorization • Enable CBO • Map join auto convertion • Map side aggregation • Etc.
- 26. 26© Cloudera, Inc.All rights reserved. Hive Configuration • Map join • hive.auto.convert.join.noconditionaltask.size • HoS doesn’t support conditional map join yet • HoS uses raw data size as small table size – different from MR • hive.stats.collect.rawdatasize • Skew join • Compile time – same as MR • Runtime - HoS will split the original task at join
- 27. 27© Cloudera, Inc.All rights reserved. Resource Allocation • Static allocation • spark.executor.instances • Won’t release until session is closed • Recommended for benchmarking • Dynamic allocation • spark.dynamicAllocation.enabled • spark.executor.dynamicAllocation.initialExecutors • spark.executor.dynamicAllocation.minExecutors • spark.executor.dynamicAllocation.maxExecutors • Number of executors per Spark application scales up and down • Suited for multi-tenancy scenarios (multi-session)
- 28. 28© Cloudera, Inc.All rights reserved. Resource Allocation • Pre-warm containers • hive.prewarm.enabled • spark.scheduler.maxRegisteredResourcesWaitingTime • spark.scheduler.minRegisteredResourcesRatio • Attempt for better parallelism • Considerable delay for start-up job • Not recommended for short-lived sessions
- 29. 29© Cloudera, Inc.All rights reserved. Configuration and Tuning Summary • Number and size of executors most important determinants of performance • Resolve query performance/failures by allocating more executors with more CPU and RAM • spark.executor.instances, spark.executor.cores, spark.executor.memory, spark.yarn.executor.memoryOverhead • Cloudera Manager takes care of most of the optimizations • Most Hive config settings applicable to HoS, but few have different semantics • See Config and Tuning Guide for details
- 30. 30© Cloudera, Inc.All rights reserved. Roadmap • Additional Optimizations • Dynamic Partition Pruning • Vectorization support • Cost-Based Optimizer • Others – Caching RDDs across queries, Optimize self join/union etc. • Supportability Enhancements • Better support for debugging and logging • More informative stage description in WebUI • Others: Improve Hue integration, additional metrics specific to HoS etc. • Rebase to Spark 2.0 and Parquet 1.8
- 31. 31© Cloudera, Inc.All rights reserved. More Information & Next Steps Get Started • Download C5.7: www.cloudera.com/downloads Release Notes • www.cloudera.com/documentation/enterprise/latest/topics/rg_release_ notes.html Training Classes • university.cloudera.com
- 32. 32© Cloudera, Inc.All rights reserved. Questions?