基于Spark软件栈的下一代大数据分析


Next-Gen Big Data Analytics using the Spark stack Jason Dai Chief Architect of Big Data Technologies Software and Services Group, Intel 2 Agenda Overview • Apache Spark stack • Next-gen big data analytics • Our vision and efforts Highlights of our work on Spark stack • Reliability of Spark Streaming • SQL processing on Spark • Spark Stream-SQL • Tachyon hierarchical storage • Analytics & SparkR •… Next-Gen Big Data Analytics Volume • Massive scale & exponential growth Variety • Multi-structured, diverse sources & inconsistent schemas Value • Simple (SQL) – descriptive analytics • Complex (non-SQL) – predictive analytics Velocity • Interactive – the speed of thought • Streaming/online – drinking from the firehose The Spark stack 3 Project Overview Research & open source projects initiated by AMPLab in UC Berkeley Intel closely collaborating with AMPLab & community on open source development • Started in 2012 when still a research project • Intel among top 3 Spark contributors – Multiple Intel committers since the start of the project – Many critical contributions – Netty based shuffle, FairScheduler, metrics system, “yarn-client” mode, … Continuous collaborations with AMPLab on new innovations in BDAS • Major industry partners for Tachyon, GraphX & other machine learning efforts 4 BDAS: Berkeley Data Analytics Stack (Ref: https://amplab.cs.berkeley.edu/software/) 5 Next Generations of Big Data Analytics Next-gen big data analytics paradigm • Data captured & processed in a (semi-) real-time / streaming fashion • Data mined using SQL queries as well as large-scale advanced analytics (e.g., machine learning, graph analysis, etc.) • Interactive and iterative computations leveraging distributed in-memory data cache Messaging / Queue Stream Processing In-Memory Store Ad-hoc, interactive query & OLAP / BI Events / Logs Low Latency Processing Engine Persistent Storage NoSQL Data Warehouse Iterative machine learning and data mining Low Latency Query Engine 6 Next-Gen Big Data Analytics using the Spark stack Build next-gen Big Data analytics with the Spark stack • Analysis on streaming data: “real-time” decisions • Interactive analysis: faster decisions • Advanced analytics: “better” decisions Kafka Spark Streaming RDD Cache Ad-hoc, interactive query & OLAP / BI Events / Logs Spark, (MLlib, GraphX, etc.) HDFS NoSQL Data Warehouse Iterative machine learning and data mining Spark (Spark-SQL, Hive on Spark, etc.) For more details, see our talk “Real-Time Analytical Processing (RTAP) using Spark and Shark” at Strata Conference + Hadoop World NYC 2013 7 Agenda Overview • Apache Spark stack • Next-gen big data analytics • Our vision and efforts Highlights of our work on Spark stack • Reliability of Spark Streaming • SQL processing on Spark • Spark Stream-SQL • Tachyon hierarchical storage • Analytics & SparkR •… 8 Spark Streaming Spark Streaming: discrete stream (DStream) • Run streaming computation as a series of very small, deterministic (mini-batch) Spark jobs – As frequent as ~1/2 second • Better fault tolerance, straggler handling & state consistency High reliability of Spark Streaming • Collaborations between Databricks, Cloudera and Intel • Improving Spark driver reliability through WAL – https://issues.apache.org/jira/browse/SPARK-3129 • Improving Kafka receiver reliability by better managing Kafka offset tacking & WAL operations – https://issues.apache.org/jira/browse/SPARK-4062 Spark (mini-batch) job time = 1 - 2: input time = 0 - 1: input input stream state / output stream … … … state / output 9 SQL Processing on Spark: Hive on Spark Hive on Spark • Spark as a new execution engine for Hive • Combine the strength of Hive and Spark – Support full Hive feature set – Utilize Spark as the powerful execution engine, greatly boost SQL performance • Smooth migration for existing Hive users Community efforts • https://issues.apache.org/jira/browse/HIVE-7292 • Collaborations among Cloudera, Intel, MapR, Databricks, IBM, etc. • Current plan – Merge back to Hive trunk in February 2015 – Community Beta release in 1H’2015 Hive M/R TEZ Spark 10 SQL Processing on Spark: Spark SQL Spark SQL • Structured data analysis using SQL queries on Spark – Hive tables, Parquet files, etc. • Integration with analytics pipelines Intel’s efforts • Functionalities (e.g., HiveQL compatibility) – Data types (Timestamp, Date, Union, etc.) – GroupingSet/ROLLUP/CUBE (https://issues.apache.org/jira/browse/SPARK-2663) –… • Performance (esp. complex queries) – Join performance (https://issues.apache.org/jira/browse/SPARK-2211) – Aggregation performance (https://issues.apache.org/jira/browse/SPARK-4366) – Hive UDF performance (https://issues.apache.org/jira/browse/SPARK-4093) –… Spark Stream-SQL: SQL + Streaming Kafka Spark Streaming RDD Cache Ad-hoc, interactive query & OLAP / BI Events / Logs Spark, (MLLib, GraphX, etc.) HDFS NoSQL Data Warehouse Iterative machine learning and data mining Spark-SQL Spark Stream-SQL • Processing and analyzing input data stream (potentially combined with history/reference data) using SQL queries • Built on top of Spark Streaming and Spark SQL frameworks CREATE STREAM IF NOT EXISTS people_stream1 (name STRING, age INT) STORED AS LOCATION ‘kafka://…’; CREATE STREAM IF NOT EXISTS people_stream2 (name STRING, zipcode INT) STORED AS LOCATION ‘kafka://…’; SELECT zipcode, AVG(age) FROM people_stream1 JOIN people_stream2 ON people_stream1.name = people_stream2.name GROUPBY zipcode; 12 Spark Stream-SQL: SQL + Streaming Open sourced under Apache 2.0 license • https://github.com/intel-spark/stream-sql • Developer preview (based on Spark 0.7) available Currently under active development • An update based on latest Spark version will be available soon • Many more features & optimizations are being added • Plan to contribute back to the main Spark project Welcome Collaboration! 13 Tachyon Hierarchical Storage Tachyon • Distributed, reliable in-memory file system unifying different underlying storage systems – Memory across different servers organized as a cache pool for memory-speed data sharing Tachyon hierarchical storage • The cache pool manages multiple storage tiers for memory-speed data sharing – Efficient support for flash/SSD, cloud, and/ or HPC environment • Currently under active development – https://tachyon.atlassian.net/browse/TACHYON-33 • Will be available soon in Tachyon 0.6 release – https://github.com/amplab/tachyon Spark MapRe duce Spark SQL H2O GraphX Impala HDFS S3 Gluster FS Orange FS NFS Ceph …… …… (Source: http://www.slideshare.net/haoyuanli/tachyon20141121ampcamp5-41881671) Ramdisk Local SSD Local HDD Server Ramdisk Local SSD Local HDD Server Ramdisk Local SSD Local HDD Server ... Cache Pool HDFS T A C H Y O N Spark MapReduce Spark Streaming MLlib … 14 Analytics & SparkR SparkR • Distributed R frontend for analytics on Spark – RDD  Distributed list in R • Alpha developer release from UC Berkeley – https://github.com/amplab-extras/SparkR-pkg Our efforts/plans • Complete SparkR RDD APIs • Analytics performance improvements • Better integrations of SparkR and Spark analytics frameworks – MLlib, GraphX, etc. • Distributed DataFrame support – Leveraging Spark SQL (SchmeRDD, optimizer, etc.) 15 Summary Driving next generations of Big Data Analytics with Spark Stack • Analysis on streaming data: “real-time” decisions • Interactive analysis: faster decisions • Advanced analytics: “better” decisions Partnering with open source community • BDAS (Berkeley Data Analytics Stack) • Apache Spark project • Tachyon project • SparkR project •… Welcome Collaboration! 16 Notices and Disclaimers • Copyright © 2014 Intel Corporation. • Intel, the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries. *Other names and brands may be claimed as the property of others. See Trademarks on intel.com for full list of Intel trademarks. • All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest Intel product specifications and roadmaps • Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. • Performance tests are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. • For more complete information about performance and benchmark results, visit www.intel.com/benchmarks. • Intel does not control or audit third-party benchmark data or the web sites referenced in this document. You should visit the referenced web site and confirm whether referenced data are accurate. • Results have been estimated or simulated using internal Intel analysis or architecture simulation or modeling, and provided to you for informational purposes. Any differences in your system hardware, software or configuration may affect your actual performance. • Intel technologies may require enabled hardware, specific software, or services activation. Check with your system manufacturer or retailer. • No computer system can be absolutely secure. Intel does not assume any liability for lost or stolen data or systems or any damages resulting from such losses. • You may not use or facilitate the use of this document in connection with any infringement or other legal analysis concerning Intel products described herein. 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