In the space of Big Data, Pivotal offers two powerful data processing tools namely HAWQ and GemFire. HAWQ is a scalable OLAP SQL-on-Hadoop system, while GemFire is OLTP like, in-memory data grid and event processing system. This presentation will show different integration approaches that allow integration and data exchange between HAWQ and GemFire. The practical experience in applying Spring Boot and Spring XD for some of the use cases will be shared while walking you through the implementation of the different Integration strategies. Amongst other we will show an integration path that leverages SpringXD to ingest GemFire data and store it in HDFS as well as the benefits of using Spring Boot to implement REStful proxy for the HAWQ Web Table integration scenario.

1. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/
SPRINGONE2GX
WASHINGTON, DC
Federated Queries with HAWQ
SQL on Hadoop and Beyond
By Christian Tzolov
@christzolov

2. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/
Safe Harbor Statement
The following is intended to outline the general direction of Pivotal's offerings. It is
intended for information purposes only and may not be incorporated into any
contract. Any information regarding pre-release of Pivotal offerings, future updates
or other planned modifications is subject to ongoing evaluation by Pivotal and is
subject to change. This information is provided without warranty or any kind,
express or implied, and is not a commitment to deliver any material, code, or
functionality, and should not be relied upon in making purchasing decisions
regarding Pivotal's offerings. These purchasing decisions should only be based on
features currently available. The development, release, and timing of any features
or functionality described for Pivotal's offerings in this presentation remain at the
sole discretion of Pivotal. Pivotal has no obligation to update forward looking
information in this presentation.
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3. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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Bio
Christian Tzolov
Pivotal Technical Architect,
BigData, Hadoop, SpringXD
Apache Committer, Crunch PMC member
ctzolov@pivotal.io
@christzolov
blog.tzolov.net

4. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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Contents
• Data System Concepts
• Data System Reference Architectures.
• Real Life Use Case: OLTP and OLAP Data Systems Integration
• Passive Data Synchronization (Demo)
• Federated Queries With HAWQ
• HAWQ Web Tables
• HAWQ PXF Architecture
• Geode PXF (Demo)
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5. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/
How Compute
Arbitrary Functions
on Arbitrary Data
Query = Function (All Data)
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6. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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Data System Concepts
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7. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/
Data
What is Data?
• Data is Information
• Derived vs. Fact Information
• Not derived information will be called Data
• Data hold to be true simply because it exists
• All other information is derived from the Data
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8. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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Data System
System that computes arbitrary functions on arbitrary data.
• Low Latency
• High Throughput
• Scalability
• Ad hoc queries
• Provenance
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9. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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Data System Principles
The 6-elements Application = Data-Native System
• Fact Data
• Immutable Data
• Deterministic Functions
• Data-Lineage
• Data Locality - space or temporal
• All Data vs. Working Set
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10. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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Data System – Key Properties
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11. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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Data System Architectures
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12. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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Data System Architectural Patterns
How to build Data Systems?
• Data Lake
• Lambda
• Kappa / Samza
• Tachyon
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13. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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Data Lake (From ETL to ELT)
Single Store For All Data
• Store Raw Data
• No Data Schema Limitations
• No Data Volume Limitations
• Late evaluation
• Extract-Transform-Load (ETL) vs. Extract-Load-Transform (ELT)
• Data Lineage
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• Batch Layer
• Serving Layer
• Speed Layer
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Lambda Architecture

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• Durable Queues (Kafka)
• Replaying History
• Versioned Outputs
• Dynamic Output Switch
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Kappa Architecture

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Tachyon
No write speed constrains
• Data-Lineage Based Fault Tolerance
• Asynchronous Checkpoints
• Unbounded Writes
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17. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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Real Life Use Case
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18. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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Real Life Use Case
• Integrate an In-Memory Data Grid - Geode (e.g. GemFire) with SQL-On-Hadoop
OLAP system - HAWQ
• Provide an unified data view across both systems
• Use Geode as Slowly Changing Dimensions (SCDs) store for HAWQ
• Keep the Operational and Historical data in Sync
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• Cache - Performance / Consistency / Resiliency
• Region
• Distributed j.u.Map on steroids
• Highly available, redundant
• Distributed
• Locator, Server, Client
Apache Geode & GemFire (OLTP)
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Geode use cases…
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China Railway
Corporation
5,700 train stations
4.5 million tickets per day
20 million daily users
1.4 billion page views per day
40,000 visits per second
* http://pivotal.io/big-data/pivotal-gemfire
Indian Railways
7,000 stations
72,000 miles of track
23 million passengers daily
120,000 concurrent users
10,000 transactions per minute

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HAWQ (SQL-on-HADOOP)
• Enterprise SQL on Hadoop analytic engine built around a high-performance
Greenplum MPP Database
• HAWQ runs natively on Apache Hadoop, HDFS and YARN.
• Supports multiple Hadoop file formats: Parquet, HDFS and Avro
• 100% ANSI SQL compliant (ANSI SQL-92, SQL-99, and SQL-2003…)
• ODBC and JDBC connectivity
• Implemented in C and C++
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22. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/
HAWQ (SQL-on-HADOOP)
• Runs TPC-DS benchmark in half the wall clock time that Impala
• Beats Impala by overall 454% in performance
• Compared to Hive, HAWQ provides an additional of 344% of performance
improvement on complex queries
• Impala and Apache Hive™ do not support all standard TPC-DS queries
• HAWQ runs 100% of them natively.
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23. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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Test Environment
• HDFS – Distributed file system. PHD or HDP Hadoop distributions.
• HAWQ - SQL-on-Hadoop.
• Geode/GemFire - In-memory data grid.
• SpringXD – runtime integration and streaming platform.
• Apache Ambari – Manages all Clusters.
• Apache Zeppelin – Web interface for interacting with different Data Systems.
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GEODE
Locator,
Servers
HAWQ
Master,
Segments
HDFS
NameNode, DataNodes
SPRING
XD
Admin,
Containers
Zeppelin Notebook
Ambari

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Test Environment - Ambari Management
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25. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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Passive Data Synchronization
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26. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/
Passive Synchronization (Case 1)
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Passive Synchronization (Case 1) - Demo
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28. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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Passive Synchronization (Case 2) - gpfdist
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29. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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Passive Synchronization (Case 2): Demo
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Federated Queries With HAWQ
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31. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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Access dynamic data sources on a web server or by executing OS scripts.
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HAWQ Web Table – Architecture

32. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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HAWQ Web Table
• HAWQ Web Table - access dynamic data sources on a web server or by
executing OS scripts.
• Leverage Geode REST API and OQL.
• RESTful SpringBoot app to convert JSON into TSV.
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CREATE EXTERNAL WEB TABLE EMPLOYEE_WEB_TABLE (...)
EXECUTE E'curl http://<adapter proxy>/gemfire-api/v1/
queries/adhoc?q=<URLencoded OQL statement>'
ON MASTER FORMAT 'text' (delimiter '|' null 'null' escape E'');

33. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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HAWQ Web Table – Limitations
• Not Scalable
• No “Filter Push Down”
• Inflexibility
• No Compression
• Requires Additional Components
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34. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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HAWQ PXF/Geode External Table
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Pivotal eXtension Framework (PXF)
• Java-Based
• Parallel, High Throughput Data Access
• Heterogeneous Data Sources.
• ANSI-compliant SQL On Any Dataset
• Wide variety of PXF plugins
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36. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/
PXF Architecture
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Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/
PXF Data Model
• Data Source is modeled as a collection of one or more Fragments.
• Each Fragment consists of many Rows that in turn are split into typed Fields.
• Analyzer (optional) provides PXF statistical data for the HAWQ query optimizer
• Metadata about the data source locations, access attributes, table schemas
formats, SQL queries filters, etc
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38. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/
PXF Processors
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39. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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PXF Runtime Model
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CREATE EXTERNAL TABLE ext_table_name
<attr list, ...>
LOCATION('pxf://<host>:<port>/path/to/data?
FRAGMENTER=package.name.FragmenterForX&
ACCESSOR=package.name.AccessorForX&
RESOLVER=package.name.ResolverForX&
<Other custom user options>=<value>')
FORMAT ‘custom'(formatter='pxfwritable_import');
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PXF External Tables Definition

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Available PXF Plugins
• HdfsTextSimple
• HdfsTextMulti
• Hive
• HiveRC
• HiveText
• Hbase
• Avro
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• Accumulo
• Casandra
• JSON
• Redis
• Geode/Gemfire
• Pipes

42. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/
Build Geode PXF Plugin
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43. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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<profile>
<name>GEMFIRE</name>
<description>A profile for reading Gemfire data</description>
<plugins>
<fragmenter>io.pivotal.pxf.plugins.gemfire.GemfireFragmenter</
fragmenter>
<accessor>io.pivotal.pxf.plugins.gemfire.GemfireAccessor</accessor>
<resolver>io.pivotal.pxf.plugins.gemfire.GemfireResolver</resolver>
</plugins>
</profile>
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PXF Geode Profile

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Geode External Table Definition
CREATE EXTERNAL TABLE <GEMFIRE_TABLE_NAME> (...)
LOCATION('pxf://<namenode>/<path>?
PROFILE=GEMFIRE &
LOCATORS=<gemfire-server:port> &
REGION=<region-name>')
FORMAT 'CUSTOM' (FORMATTER='pxfwritable_import');
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45. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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public class GemfireFragmenter extends Fragmenter {
public GemfireFragmenter(InputData inputData) throws Exception {
super(inputData);
}
public List<Fragment> getFragments() throws Exception {
return Arrays.asList (new Fragment(
inputData.getDataSource(),
new String[] { inputData.getUserProperty("URL-HOST") },
new byte[0]));
}
}
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Geode Fragmenter

46. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/
public class GemfireAccessor extends Plugin implements ReadAccessor {
public GemfireAccessor(InputData input) throws Exception {
super(input);
}
public boolean openForRead() throws Exception { … }
public OneRow readNextObject() throws Exception {…}
public void closeForRead() throws Exception {…}
}
46
Geode Accessor

47. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/
public class GemfireResolver extends Plugin implements ReadResolver {
public GemfireResolver(InputData input) {
super(input);
}
public List<OneField> getFields(OneRow paramOneRow) throws Exception {
}
}
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Geode Resolver

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Demo 3: Federated queries with PXF/Geode
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49. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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Demo 3: Federated queries with PXF/Geode
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50. Unless otherwise indicated, these slides are © 2013-2015 Pivotal Software, Inc. and licensed under a
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By Christian Tzolov
@christzolov
HADOOP WORKFLOWS AND DISTRIBUTED YARN APPS USING SPRING
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