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January 2015 HUG: Using HBase Co-Processors to Build a Distributed, Transactional RDBMS

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Monte Zweben Co-Founder and CEO of Splice Machine, will discuss how to use HBase co-processors to build an ANSI-99 SQL database with 1) parallelization of SQL execution plans, 2) ACID transactions with snapshot isolation and 3) consistent secondary indexing.

Transactions are critical in traditional RDBMSs because they ensure reliable updates across multiple rows and tables. Most operational applications require transactions, but even analytics systems use transactions to reliably update secondary indexes after a record insert or update.

In the Hadoop ecosystem, HBase is a key-value store with real-time updates, but it does not have multi-row, multi-table transactions, secondary indexes or a robust query language like SQL. Combining SQL with a full transactional model over HBase opens a whole new set of OLTP and OLAP use cases for Hadoop that was traditionally reserved for RDBMSs like MySQL or Oracle. However, a transactional HBase system has the advantage of scaling out with commodity servers, leading to a 5x-10x cost savings over traditional databases like MySQL or Oracle.

HBase co-processors, introduced in release 0.92, provide a flexible and high-performance framework to extend HBase. In this talk, we show how we used HBase co-processors to support a full ANSI SQL RDBMS without modifying the core HBase source. We will discuss how endpoint transactions are used to serialize SQL execution plans over to regions so that computation is local to where the data is stored. Additionally, we will show how observer co-processors simultaneously support both transactions and secondary indexing.

The talk will also discuss how Splice Machine extended the work of Google Percolator, Yahoo Labs’ OMID, and the University of Waterloo on distributed snapshot isolation for transactions. Lastly, performance benchmarks will be provided, including full TPC-C and TPC-H results that show how Hadoop/HBase can be a replacement of traditional RDBMS solutions.

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January 2015 HUG: Using HBase Co-Processors to Build a Distributed, Transactional RDBMS

  1. 1. Powering OLTP Apps on Hadoop Monte Zweben Co-Founder and CEO January 24, 2015
  2. 2. 2 Who are We? THE ONLY HADOOP RDBMS Replace your old RDBMS with a scale-out SQL database Affordable, Scale-Out ACID Transactions No Application Rewrites 10x Better Price/Perf
  3. 3. 3 Campaign Management: Harte-Hanks Overview Digital marketing services provider Unified Customer Profile Real-time campaign management Complex OLTP and OLAP environment Challenges Oracle RAC too expensive to scale Queries too slow – even up to ½ hour Getting worse – expect 30-50% data growth Looked for 9 months for a cost-effective solution Solution Diagram Initial Results ¼ cost with commodity scale out 3-7x faster through parallelized queries 10-20x price/perf with no application, BI or ETL rewrites Cross-Channel Campaigns Real-Time Personalization Real-Time Actions
  4. 4. 4 Reference Architecture: Operational Apps Provide affordable scale-out for applications with a high concurrency of real-time reads/writes 3rd Party Data Sources Operational App (e.g., CRM, Supply Chain, eCommerce, Unica Campaign Mgmt) Customers Operational Employees Operational Reports & Analytics
  5. 5. 5 Reference Architecture: Operational Data Lake Offload real-time reporting and analytics from expensive OLTP and DW systems OLTP Systems Ad Hoc Analytics Operational Data Lake Executive Business Reports Operational Reports & Analytics ERP CRM Supply Chain HR … Data Warehouse Datamart Stream or Batch Updates ETL Real-Time, Event-Driven Apps
  6. 6. 6 Reference Architecture: Unified Customer Profile Improve marketing ROI with deeper customer intelligence and better cross-channel coordination Unified Customer Profile (aka DMP) Operational Reports for Campaign Performance Social Feeds Web/eCommerc e Clickstreams WebsiteDatamart Stream or Batch Updates BI Tools Demand Side Platform (DSP) Ad Exchange 1st Party/ CRM Data 3rd Party Data (e.g., Axciom) Ad Perf. Data (e.g., Doubleclick) Email Mktg Data Call Center Data POS Data Email Marketing App Ad Hoc Audience Segmentation BI Tools
  7. 7. 7 Proven Building Blocks: Hadoop and Derby APACHE DERBY  ANSI SQL-99 RDBMS  Java-based  ODBC/JDBC Compliant APACHE HBASE/HDFS  Auto-sharding  Real-time updates  Fault-tolerance  Scalability to 100s of PBs  Data replication
  8. 8. Derby  100% JAVA ANSI SQL RDBMS – CLI, JDBC, embedded  Modular, Lightweight, Unicode  Authentication and Authorization  Concurrency  Project History  Started as Cloudscape in 1996  Acquired by Informix… then IBM…  IBM Contributed code to Apache project in 2004  An active Apache project with conservative development  DB2 influence. Many of the same limits/features  Has Oracle’s stamp of approval – Java DB and included in JDK6 8
  9. 9. Derby Advanced Features  Java Stored Procedures  Triggers  Two-phase commit (XA Support)  Updatable SQL Views  Full Transaction Isolation Support  Encryption  Custom Functions 9
  10. 10. Splice SQL Processing  PreparedStatement ps = conn.prepareStatement(“SELECT * FROM T WHERE ID = ?”); 1. Look up in cache using exact text match (skip to 6 if plan found in cache) 2. Parse using JavaCC generated parser 3. Bind to dictionary, acquire types 4. Optimize Plan 5. Generate code for plan 6. Create instance of plan 10
  11. 11. Splice Details  Parse Phase  Forms explicit tree of query nodes representing statement  Generate Phase  Generate Java byte code (an Activation) directly into an in-memory byte array  Loaded with special ClassLoader that loads from the byte array  Binds arguments to proper types  Optimize Phase  Determine feasible join strategies  Optimize based on cost estimates  Execute Phase  Instantiates arguments to represent specific statement state  Expressions are methods on Activation  Trees of ResultSets generated that represent the state of the query 11
  12. 12. Splice Modifications to Derby 12 Derby Component Derby Splice Version Store Block File-based HBase Tables Indexes B-Tree Dense index in HBase Table Concurrency Lock-based, Aries MVCC - Snapshot Isolation Project-Restrict Plan Predicates on centralized file scanner Predicates pushed to shards and locally applied Aggregation Plan Aggregation serially computed Aggregations pushed to shards and spliced together Join Plan Centralized Hash and NLJ chosen by optimizer Distributed Broadcast, Sort- Merge, Merge, NLJ, and Batch NLJ chosen by optimizer Resource Management Number of Connections and Memory Limitations Task Resource Queues and Write Governor
  13. 13. 13 HBase: Proven Scale-Out  Auto-sharding  Scales with commodity hardware  Cost-effective from GBs to PBs  High availability thru failover and replication  LSM-trees
  14. 14. 14 Distributed, Parallelized Query Execution Parallelized computation across cluster Moves computation to the data Utilizes HBase co-processors No MapReduce
  15. 15. Splice HBase Extensions  Asynchronous Write Pipeline  Non-blocking, flushable writes  Writes data, indexes, and constraints (index) concurrently  Batches writes in chunks for bulk WAL Edits vs. single WAL Edits  Synchronization free internal scanner vs. synchronized external scanner  Linux Scheduler Modeled Resource Manager  Resource Queues that handle DDL, DML, Dictionary, and Maintenance Operations  Sparse Data Support  Efficiently store sparse data  Does not store nulls 15
  16. 16. Schema Advantages  Non-Blocking Schema Changes  Add columns in a DDL transaction  No read/write locks while adding columns  Sparse Data Support  Efficiently store sparse data  Does not store nulls 16
  17. 17. ANSI SQL-99 Coverage 17  Data types – e.g., INTEGER, REAL, CHARACTER, DATE, BOOLEAN, BIGINT  DDL – e.g., CREATE TABLE, CREATE SCHEMA, ALTER TABLE, DELETE, UPDATE  Predicates – e.g., IN, BETWEEN, LIKE, EXISTS  DML – e.g., INSERT, DELETE, UPDATE, SELECT  Query specification – e.g., SELECT DISTINCT, GROUP BY, HAVING  SET functions – e.g., UNION, ABS, MOD, ALL, CHECK  Aggregation functions – e.g., AVG, MAX, COUNT  String functions – e.g., SUBSTRING, concatenation, UPPER, LOWER, POSITION, TRIM, LENGTH  Conditional functions – e.g., CASE, searched CASE  Privileges – e.g., privileges for SELECT, DELETE, INSERT, EXECUTE  Cursors – e.g., updatable, read-only, positioned DELETE/UPDATE  Joins – e.g., INNER JOIN, LEFT OUTER JOIN  Transactions – e.g., COMMIT, ROLLBACK, READ COMMITTED, REPEATABLE READ, READ UNCOMMITTED, Snapshot Isolation  Sub-queries  Triggers  User-defined functions (UDFs)  Views – including grouped views
  18. 18. 18 Lockless, ACID transactions State-of-the-Art Snapshot Isolation 18 Adds multi-row, multi-table transactions to HBase with rollback Fast, lockless, high concurrency ZooKeeper coordination Extends research from Google Percolator, Yahoo Labs, U of Waterloo Transaction A Transaction B Transaction C Ts Tc
  19. 19. 19 BI and SQL tool support via ODBC No application rewrites needed 19
  20. 20. SQL Database Ecosystem 20 Ad-hoc Analytics Operational (OLTP + OLAP) New SQL IN-MEMORY RDBMSMPP New SQL Proprietary HW Lower Cost Higher Cost Hadoop RDBMS SQL-on-Hadoop Phoenix SQL-on-HBase
  21. 21. What People are Saying… 21 Recognized as a key innovator in databases Scaling out on Splice Machine presented some major benefits over Oracle ...automatic balancing between clusters...avoiding the costly licensing issues. Quotes Awards An alternative to today’s RDBMSes, Splice Machine effectively combines traditional relational database technology with the scale-out capabilities of Hadoop. The unique claim of … Splice Machine is that it can run transactional applications as well as support analytics on top of Hadoop.
  22. 22. 22 Summary THE ONLY HADOOP RDBMS Replace your old RDBMS with a scale-out SQL database Affordable, Scale-Out ACID Transactions No Application Rewrites 10x Better Price/Perf
  23. 23. Questions? Monte Zweben mzweben@splicemachine.com

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