This document discusses integrating Apache Hadoop with Vertica, an analytic database with MPP columnar architecture. It describes how Vertica can be used as a data source and target for Hadoop MapReduce jobs, with Vertica input and output formatters allowing data to be moved between the two systems. Examples are provided of using Vertica to serve as a structured data repository for Hadoop and running algorithms like tickstore with map pushdown to optimize queries.

1. Vertica Integration with Apache Hadoop Hadoop World NYC 2009 HDFS Hadoop Compute Cluster Map Map Map Reduce

2. Vertica ® Analytic Database MPP columnar architecture Second to sub-second queries 300GB/node load times Scales to hundreds of TBs Standard ETL & Reporting Tools www.vertica.com

3. What do people do with Hadoop? Transform data Archive data Look for Patterns Parse Logs

4. Big Data comes in Three Forms Unstructured Images, sound, video Semi-structured Logs, data feeds, event streams Fully Structured Relational tables

5. Availability, Scalability and Efficiency … how fast can you go from data to answers? Unstructured data needs to be analyzed to make sense. Semi-structure data parsed based on spec (or brute force). Structured data can be optimized for ad-hoc analysis.

6. Hadoop / Vertica Distributed processing framework (MapReduce) Distributed storage layer (HDFS) Vertica can be used as a data source and target for MapReduce Data can also be moved between Vertica and HDFS (sqoop) Hadoop talks to Vertica via custom Input and Output Formatters

7. Hadoop / Vertica Vertica serves as a structured data repository for hadoop Hadoop Compute Cluster Map Map Map Reduce

8. Hadoop / Vertica Vertica’s input formatter takes a parameterized query Relational Map operations can be pushed down to the database Vertica’s output formatter takes an existing table name or a description Vertica output tables can be optimized directly from hadoop

9. Hadoop / Vertica Federate multiple Vertica database clusters with hadoop Hadoop Compute Cluster Map Map Map Reduce Hadoop Compute Cluster Map Map Map Reduce Hadoop Compute Cluster Map Map Map Reduce Hadoop Compute Cluster Map Map Map Reduce

10. What is the Interface? Input Formatter Query specifies which data to read Query can be parameterizes (map push down) Each input split gets one parameter OR, input can be spliced with order by and limit (slower) Output Formatter Job specifies format for output table Vertica converts reduced output into trickle loads Vertica can optimize new tables

11. Some Hadoop / Vertica Applications Elastic Map Reduce parsing and loading CloudFront Logs Tickstore algorithm with map push down Analyze time series Sessionize click streams Parse and load logs

12. Basic Example Elastic Map Reduce parsing and loading CloudFront Logs Mapper reads from S3 CloudFront Logs Parses into records, transmits to reducer Reducer loads into Vertica All done with streaming API ~ 10 lines of python Limitless SQL

13. Advanced Example Tickstore algorithm with map push down Input formatter queries Vertica using map push down Identity Mapper passes through to reducer Reducer runs proprietary algorithm moving average, correlations, secret sauce Results are stored in a new table for further analysis Vertica optimizes the new table

14. How to get started Get a copy of hadoop from Apache or Cloudera Get vertica from www.vertica.com or via Amazon or RightScale or as a VM Grab the formatter and Vertica jdbc drivers from vetica.com/MapReduce Included in contrib from hadoop 0.21.0 (MR-775) Put the jars in hadoop/lib Run your Hadoop/Vertica job

15. Future Directions and Questions Archiving information lifecycle (sqoop) Invoking hadoop jobs from Vertica Joining Vertica data mid job Using Vertica for (structured) transient job data [email_address] Vertica.com/MapReduce