Hadoop World Vertica

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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Hadoop World Vertica

by otrajman

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