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Spark meetup v2.0.5

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In this talk, we’ll discuss technical designs of support of HBase as a “native” data source to Spark SQL to achieve both query and load performance and scalability: near-precise execution locality of query and loading, fine-tuned partition pruning, predicate pushdown, plan execution through coprocessor, and optimized and fully parallelized bulk loader. Point and range queries on dimensional attributes will benefit particularly well from the techniques. Preliminary test results vs. established SQL-on-HBase technologies will be provided. The speaker will also share the future plan and real-world use cases, particularly in the telecom industry.

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Spark meetup v2.0.5

  1. 1. HUAWEI TECHNOLOGIES CO., LTD. Spark SQL on HBASE Spark Meet-up Tech Talks Yan Zhou/Bing Xiao March 25, 2015
  2. 2. HUAWEI TECHNOLOGIES Co., Ltd.  Lead architect of Huawei Big Data Platform  Apache Pig committer  ex-Hadooper @ Yahoo!  15+ years of experience in DB, OLAP, distributed computing fields. About me Page ‹2›
  3. 3. HUAWEI TECHNOLOGIES Co., Ltd.  A Fortune Global 500, Private Company  Annual growth at 15% (revenue up to $46 Billion in 2014)  More than $6.5 Billion investment on R&D  Transition from telecom-equipment manufacturer to a leader of ICT (information and communications technology)  Big data and open source are part of company-wide strategies About Huawei Page ‹3›
  4. 4. HUAWEI TECHNOLOGIES Co., Ltd.  Spark SQL on HBase o Motivations o Data Model o SQL Semantics o Performance o Value Proposition  Demo  Roadmap  Q/A Agenda Page ‹4›
  5. 5. HUAWEI TECHNOLOGIES Co., Ltd.  Driven by use cases in verticals including telco  Telco Data is unique & complex  Flexible data organization for various types of queries: range, ad-hoc, interactive, DW  Stay in tune: planned sessions in future events Customer & Billing Data • Well Structured • TB Session Signaling Data: • Multi-Device generated • Hundreds TB-PB • Real-Time Biz Oriented MR/CHR Data: • Semi-structured & Nested • PB • Location-centered Data xDR Data: • 1~10PB each month Network Raw Data • Unstructured • TB/Sec • Linear Growth with Biz CRM Billing Signaling Data MR/CHR Type Data xDR Data Network Raw Data Note:In a typical network of ~30M subscribers and the data flow around 1TB/Sec. Page ‹5› Motivations
  6. 6. HUAWEI TECHNOLOGIES Co., Ltd.  NOSQL Key-Value data store on Hadoop  Following Google BigTable model  Emerging platform for scale-out relational data stores on Hadoop: o Splice Machines o Trafodion (HP) o Apache Phoenix (Salesforce) o Kylin (eBay)  M/R and API-based data access interfaces only Page ‹6› What is HBase?
  7. 7. HUAWEI TECHNOLOGIES Co., Ltd. Spark Core Spark SQL Existing HBase Access Path HadoopRDD TableInput/OutputFormat Features: Hadoop M/R plug-in Inflexible/Hard to use Limited pushdown capabilities High Latency HiveContext metastore Page ‹7› HBase
  8. 8. HUAWEI TECHNOLOGIES Co., Ltd. New Data Access Path metadata Featuring: • Fully distributed processing engine for scalability and fault tolerance • Scala/Java/Python APIs • Pluggable data source to Spark SQL through Spark SQL API • Enable systematic and powerful handling of pushdowns (key range, filters, coprocessor) • More SQL capabilities made possible (Primary Key, Update, INSERT INTO … VALUES, 2nd index, Bloom Filter, …) Page ‹8› Spark SQL Spark Core HBase
  9. 9. HUAWEI TECHNOLOGIES Co., Ltd.  Logical Data Model the same as Spark SQL: relational and type system.  Physical Data Model: • Support of composite primary keys • HBase rowkey of byte representation of composite primary keys • Logical non-key columns mapped onto <column family, column qualifier> • Persistent metadata on a special HBase table • Presplit tables supported Data Models Page ‹9›
  10. 10. HUAWEI TECHNOLOGIES Co., Ltd.  Based on Spark SQL syntax, plus …  DDL: • CREATE TABLE table_name (col1 TYPE1, col2 TYPE2, …, PRIMARY KEY (col7, col1, col3)) MAPPED BY (hbase_tablename, COLS=[col2=cf1.cq11, col4=cf1.cq12, col5=cf2.cq21, col6=cf2.cq22]) •ALTER TABLE table_name ADD/DROP column …  DML: • INSERT … INTO VALUES …  Bulk Loading: • LOAD DATA [PARALLEL] INPATH filePath [OVERWRITE] INTO TABLE tableName [FIELDS TERMINATED BY char] SQL Semantics Page ‹10›
  11. 11. HUAWEI TECHNOLOGIES Co., Ltd.  Precise partition pruning and partition-specific multidimensional predicate pushdowns based on partial evaluation of filter boolean expressions for queries Query Optimization Approach => Itemid > 300 AND amount < 30 => customer=‘John’ AND itemid < 100 AND amount > 200 E.g. a sales table with <customer, itemid> as a 2-dimensional primary key SELECT * from sales WHERE ((customer=‘Joe’ AND itemid > 300 AND amount < 30) OR (customer=‘John’ AND itemid < 100) AND amount > 200) The existing partitions/regions are: 1. (, ‘Ashley’) 2. [‘Ashley’, “Iris”) 3. [(‘Joe’, 10), (‘Joe’, 100)), 4. [(‘Joe’, 200), (‘Joe’, 1000)) 5. [‘John’, ‘York’) 6. [‘York’, )  The algorithms are generic and applicable to other organized data sets like hash-partitioned Hive tables as well.  Suitable for interactive ad hoc queries Page ‹11› for scan range for filtering
  12. 12. HUAWEI TECHNOLOGIES Co., Ltd.  Queries (TPC-DS, 10M records): Query Performance Page ‹12› SQL Query SparkSQL on HBase (Seconds) Phoenix (Seconds) 1-key-range select count(1) from store_sales where (ss_item_sk = 99 and ss_ticket_number > 1000); 0.18 0.03 2-key-range select count(1) from store_sales where (ss_item_sk = 99 and ss_ticket_number > 1000) or (ss_item_sk = 5000 and ss_ticket_number < 20000); 0.22 4.29 3-key-range select count(1) from store_sales where (ss_item_sk = 99 and ss_ticket_number > 1000) or (ss_item_sk = 5000 and ss_ticket_number < 20000) or (ss_item_sk = 28000 and ss_ticket_number <= 10000); 0.27 4.44 Aggregate on the secondary key select count(1) from store_sales group by ss_ticket_number; 37 79 • Cluster: o 1 master + 6 slaves with 48GB/node o Xeon 2.4G 16 cores
  13. 13. HUAWEI TECHNOLOGIES Co., Ltd.  Query performance (TPC-DS, 10M records): Query Performance 0.18 0.22 0.27 37 0.03 4.29 4.44 79 0 10 20 30 40 50 60 70 80 90 1-key-range 2-key-range 3-key-range aggregate on secondary key Seconds Spark SQL on HBase Phoenix Page ‹13›
  14. 14. HUAWEI TECHNOLOGIES Co., Ltd.  Performance optimization for tabular data bulk loading • late materialization of KeyValue cells  reduction of shuffle data volume • removal of sorting by reducers  lightweight reducer  more scalable • best effort to colocate reducers with the region servers  Optional parallel incremental loading after M/R in the bulk loader Bulk Load Optimization Page ‹14›
  15. 15. HUAWEI TECHNOLOGIES Co., Ltd.  Loading performance (TPC-DS, 10M records): Bulk Load Performance 557 185 1093 762 0 200 400 600 800 1000 1200 Load (no presplit) Load (6 presplit regions) Seconds Spark SQL on HBase Phoenix Page ‹15› • Cluster: o 1 master + 6 slaves with 48GB/node o Xeon 2.4G 16 cores
  16. 16. HUAWEI TECHNOLOGIES Co., Ltd.  Combined capabilities of Spark, SparkSQL and HBase o Spark Dataframe supported  More traditional DBMS capabilities made possible on HBase  Basis to build a highly performing and concurrent distributed big data SQL system  Optimized bulk loader for tabular data sets  Performance excellence Value Proposition Page ‹16›
  17. 17. HUAWEI TECHNOLOGIES Co., Ltd.  Source repo: https://github.com/Huawei-Spark/hbase/  emails: yan.zhou.sc@huawei.com, bing.xiao@huawei.com  Team members: Bo Meng, Xinyun Huang, Wang Fei, Stanley Burnitt, Shijun(Ken) Ma, Jacky Li, Stephen Boesch Plus our Big Data Teams in India and Hangzhou, China Project Info  Join us … o Comments, tryouts, and contributions are more than welcome o Open for Joint development in next phase project(s) o We are hiring: Big Data Engineers/Spark Fans Page ‹17›
  18. 18. HUAWEI TECHNOLOGIES Co., Ltd.  Environment  Hardware: 9-node (1 master + 8 slaves) blades  Software: Linux Enterprise Server 11.1  Data set: TPC-DS 10M records   Indexed-range query vs. full scan query through use of the DataFrame  Join between a Hbase table with an in-memory side table through use of Dataframe  Partial Evaluation • Schema = (id : Int, age: Int) • The row to be partially evaluated on is: ([1,5), null) • Predicate 1: (id < 1) OR (age > 30) => (age > 30) • Predicate 2: (id < 6) OR (age > 30) => True Demo Page ‹18›
  19. 19. HUAWEI TECHNOLOGIES Co., Ltd.  Targeting Spark 1.4  Coprocessor/Custom Filter  Filter/Partitions from CAST values in predicates  Latency reduction: Spark-3306 for external resource management  Optimizations of Sorting/Aggregation/Join on primary keys  Support of salting, timestamp, dynamic columns, nested data type, …  Views and materialized views Future Plan Page ‹19›
  20. 20. HUAWEI TECHNOLOGIES Co., Ltd.  Earliest Corporate sponsor of AMP Lab & its projects including Spark  One of Leading contributors to Spark: 10 & 11 contributors in Spark 1.2 & 1.3 releases  Highlighted contributions of New features : Power Iteration Clustering represents the first use of GraphX routines within MLLIB; ORCFiles Support; FP-Growth  Bring Spark & Apps on top of Spark into leading telcos globally as Spark is cornerstone of Huawei big data vertical solution Huawei’s Long Term Commitment to Spark & Ecosystem Page ‹20›
  21. 21. HUAWEI TECHNOLOGIES Co., Ltd. Huawei Planned Spark Roadmap Spark SQL & Core 1H 2015  Co-Processor, optimization  Spark SQL on Hbase  OrcFiles Support  Vectorized Processing ML & Streaming 2H 2015  Nested Data Spark on Yarn  Power iteration Clustering  PAM K-Medoids Streaming Analysis Algorithms New requirements from Spark implementation  Materialized View  Spark R GraphX in Telco Data Modeling  SQL99’2003 Compliance features Page ‹21›
  22. 22. HUAWEI TECHNOLOGIES Co., Ltd.  Reynold Xin, Michael Armbrust at Databricks reviewed Spark SQL on HBase design document and the code, provided feedback and helped improve the design  Xiangrui Meng at Databricks provided the guidance, reviewed and modified the code MLLib Power Iteration Clustering algorithm (Spark 1.3 release) & FP-Growth algorithm (Spark 1.3 release)  Huawei Big Data team in India & Hangzhou, China provided the performance testing/tuning and participated in code development Acknowledgements Page ‹22›
  23. 23. HUAWEI TECHNOLOGIES Co., Ltd. Page ‹23›
  24. 24. HUAWEI TECHNOLOGIES Co., Ltd. Phoenix Architecture and Data Access Path Phoenix Coprocessor Phoenix Coprocessor Phoenix Coprocessor Phoenix as HBase Client Page ‹24›
  25. 25. HUAWEI TECHNOLOGIES Co., Ltd. HBase vs. Cassandra vs. RDBMS HBase Cassandra RDBMS Special Nodes Master Seed Coordinator Synchronization Mechanism Zookeeper Gossip protocol … CAP Properties CP AP CA Data Access Shell, REST, Java/Thrift API CQL, Shell, Thrift SQL/JDBC/ODBC Data Size PBs PBs TBs Coprocessor/In-DB processing Yes No Yes Origins Google BigTable Amazon Dynamo + Google BigTable IBM System R Native to Hadoop (inclusion in Hadoop distributions, Hadoop/HDFS specific optimizations, …) Yes No No Index-Organization Single row index Single row index On any columns Dominant Vendor Backing/Lock-in No Datastax Oracle, MS, IBM Popular use scenarios Range queries; consistency; fast reads: Facebook Messenger Geographical distributed cluster; large deployments: Twitter Transactions, DW/DM
  26. 26. HUAWEI TECHNOLOGIES Co., Ltd. Spark SQL on HBase Spark SQL Spark Master HBase Master Spark Slave Spark Slave HBase Region Server HBase Region Server Architecture and Data Access Paths Zookeeper Quorum

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