In this session we explore a case study of a large-scale government fraud detection program that prevents billions of dollars in fraudulent payments each year leveraging the beta release of the GemFire+Greenplum Connector, which is planned for release in GemFire 9. Topics will include an overview of the system architecture and a review of the new GemFire+Greenplum Connector features that simplify use cases requiring a blend of massively parallel database capabilities and accelerated in-memory data processing.

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Large Scale Fraud Analytics
GemFire Greenplum Connector (G2C)

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Background
Ÿ Government fraud revenue retention program
Ÿ Detecting & retaining ~$5B annually
– Primary focus on identity theft
– Processes up to 8 million cases per day
– Current & historic data size ~60 TB (compressed)
Ÿ Modifying architecture to integrate GemFire for scalable
Java-based business logic, web service integration, and
event driven design

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Fraud Systems Simplified
Prepare
• Ingest
• Restructure (ETL)
Score
• Model Evaluation
Disposition
• Business Logic
• Prioritization
Respond
• Investigation
• Stop Payments
Business Logic Engine
ETL
Reporting
In-db Analytics
Application Services

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Case Study Architecture – Scaling Up
GemFire
Greenplum
Spring Boot App Services
Informatica w/ PWX (ETL)
Business Objects
(Reporting)
Legacy Logic
Implementation
Logic Engine
In-db Analytics
Greenplum
Prepare
• Ingest
• Restructure (ETL)
Score
• Model Evaluation
Disposition
• Business Logic
• Prioritization
Respond
• Investigation
• Stop Payments

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Pivotal Greenplum (GPDB)
Ÿ Postgres Community OSS
– Original fork of 8.2.15
– Massively parallel processing
database
Ÿ Master coordinates queries
across segments databases
Ÿ Supports in-database model
evaluation
– MadLib, PL/R, SAS
GPDB
Logical
GPDB
Physical
GPDB
Software
Master
Segments

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Initial Implementation
Ÿ Fraud model results evaluated
by business logic engine
Ÿ Flat file data extraction
– Significant custom code to
construct required object model
– Table à CSV à POJO
Ÿ Shared element in an otherwise
distributed system
– Performance considerations
GPDB
Legacy Logic
Implementation

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Architecture Adjustments
Ÿ New requirements introduced
external integrations
– Drives desire for web-services
Ÿ Desire to improve performance
& simplify codebase
Ÿ Expanding business logic
– Logic engine run as a GemFire
function
GemFire
GPDB
Legacy Logic
Implementation
Spring Boot (App Services)

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GemFire Greenplum Connector

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Context
Greenplum!
ANSI
SQL
Analytical
Parallel
Configurable Data
Load
GemFire!App 1App 1App 1
App 1App 1App 2
Native API
Rest /
HTTP
Transactional
Custom Apps
Transactional
data write
behind
Data Science,
Analytics & ML

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GemFire Greenplum Connector (G2C)
Ÿ Extension package for GemFire
Ÿ Provides simple import and export of data between GemFire
regions & Greenplum tables
– Parallel data motion leveraging Greenplum’s external table interface
Ÿ Simple mapping between table rows and PdxInstance
– Flat object relational mapping
– Set of predefined type conversions
– Configurable GemFire data collocation

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Greenplum
Master
Segments GemFire
G2C Data Interfaces
JDBC /
ODBC
Data
Node
Data
Node
Control Logic

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GpdbService is the primary entry
point for explicitly invoked data
motion
1. Import - loads the full table
contents from Greenplum
2. Export - sends region
contents to Greenplum
Sample Data Import / Export
Cache cache = CacheFactory.getAnyInstance();
GpdbService gpdb = GpdbService.getInstance(cache);
long count;
count = gpdb.importRegion(region);
count = gpdb.exportRegion(region);
1
2

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Basic Cache Configuration
Configured via GemFire extension
framework
• 1) Each region maps to a jndi data
source back by Greenplum
• 2) Link an entity type and table
• 3) Declare a field to be used as the key
• Compound keys supported
• 4) Define a mapping between the table
columns
• Default auto-configuration
• Optional name and column attributes for
naming convention changes
• Class used to control type conversion
• Set of built in types
<region name="Parent">
<region-attributes refid="PARTITION">
<partition-attributes/>
</region-attributes>
<gpdb:store datasource="datasource">
<gpdb:types>
<gpdb:pdx name="io.pivotal...entity.Parent"
table="parent">
<gpdb:id field="id" />
<gpdb:fields>
<gpdb:field name="name" />
<gpdb:field name="id" column="id" />
<gpdb:field name="income"
class="java.math.BigDecimal" />
</gpdb:fields>
</gpdb:pdx>
</gpdb:types>
</gpdb:store>
</region>
2
1
3
4

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Configuring Collocation
Parent-child foreign key relationships
supported through collocation
1. Compound keys configurations
result in a HashMap based key in
GemFire
2. Provided partition resolver works
with compound keys
<region name="Child">
<...>
<partition-resolver>
<class-name>
io.pivotal.gemfire.gpdb.IdPartitionResolver
</class-name>
<parameter name="field">
<string>parentId</string>
</parameter>
</...>
<gpdb:id>
<gpdb:field ref="parentId" />
<gpdb:field ref="id" />
</gpdb:id>
<gpdb:fields>
<gpdb:field name="parentId"/>
<gpdb:field name="id" />
</...>
1
2

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Configuring Automatic Synchronization
● Data exported to Greenplum via
asynchronous eventing
○ Time and batch size triggers
available
● Causes each GemFire member to
independently interact with Greenplum
○ Configure GPDB resource queues
accordingly
<region name="Child">
<...>
<gpdb:store datasource="datasource">
<gpdb:synchronize mode="automatic"
time-interval="3000"
persistent="false" />
<gpdb:types>
<...>

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Case Study G2C Configuration Details
Ÿ Existing required domain objects
– Multiple many-to-one groupings
Ÿ Wide tables / objects (500+ fields)
Ÿ Data Collocation configured on
caseId
Ÿ Source tables wrapped in views
CaseWrapper
- caseId
- …
ModelScores
- caseId
- …
Documents
- caseId
- …
PriorHistory
- caseId
- …
OtherData…
- caseId
- …
* *
* *
1
LogicResults
- caseId
- …

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Simple Loading – Single Table per Object
:LoadTrigger :GPDBService :Region :AsyncEventLister :LogicEngine results:Region
Import()
put()
processEvents()
process()
put()

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Complex Loading – Multiple Tables per Object
:MergeLoader :GPDBService :Region :LogicEngine results:Region
Import()
put()
process()
put()
par
assemble()
:LoadTrigger
executeFunction()

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Impacts & Results
Ÿ Simplified implementation & code reduction
Ÿ Maintained or improved data motion rates
– Case study CPU bound
– Additional improvements in the backlog
Ÿ Improved system throughput

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Questions?

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