The document discusses a compatibility matching system designed to enhance relationships by evaluating personality, values, and beliefs. It outlines the technical architecture utilizing various data storage solutions and processing layers to handle a vast number of user interactions and match events. Additionally, it mentions the importance of scalability, low latency, and system performance in managing substantial match records.

2. V I J A Y V A N G A P A N D U
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3. W H O A R E W E ?

4. E H A R M O N Y C R E A T E S
T H E H A P P I E S T ,
M O S T P A S S I O N A T E
A N D M O S T F U L F I L L I N G
R E L A T I O N S H I P S *
* A C C O R D I N G T O A R E C E N T S T U D Y

5. M A R R I A G E S P E R D A Y

7. 150
questions
Personality
Values
Attributes
Beliefs

8. M A T C H I N G S Y S T E M
Compatibility Matching System®
C O M P A T I B I L I T Y
M A T C H I N G
A F F I N I T Y M A T C H I N G
M A T C H
D I S T R I B U T I O N

9. Intellect
Energy
Sociability
Ambition
Kindness
Curiosity
Humor
Spirituality

11. U S E R U P D A T E S

12. M A T C H D E L I V E R Y ( V 1 )
M A P - S I D E J O I N S
( T B ) S C O R I N G
V O L D E M O R T
M A T C H D A T A S E R V I C E
M A T C H I N G S Y S T E M
3 0 + M I L L I O N E V E N T S
6 5 + M I L L I O N U S E R S
3 0 + B I L L I O N R E C O R D S

13. V O L D E M O R T ?
T H A T N A M E
T H A T N A M E
S O U N D S F A M I L I A R

14. V O L D E M O R T
A U T O
P A R T I T I O N I N G
P L U G G A B L E
S E R I A L I Z A T I O N
A U T O
R E P L I C A T I O N
K E Y - V A L U E D Y N A M O
G O S S I P

15. U S E R U P D A T E S O ( N ^ 2 )
* U S E R U P D A T E D T H E A D D R E S S

16. N E E D F O R S C A L A B I L I T Y
V O L D E M O R T
30+ Million Match Events / Day
30+ Billion Match Records
Millions of user generated Events / Day
Low latency user requests
1 . 4 G B / M I N ( 1 4 )

17. N E E D F O R S C A L A B I L I T Y
G E T M A T C H E S R E S P O N S E
T I M E S

18. DATA STORE NEEDS
Q U E R I E S
L O W
L A T E N C Y
C R U D
O P E R A T I O N S
F I L T E R I N G
T H R O U G H P U T
4 0 + M I L L I O N
W R I T E S
3 0 + B I L L I O N
R E C O R D S

19. DATA STORE NEEDS
E A S Y T O
M A I N T A I N
C O N S I S T E N C Y
A V A I L A B L E
P A R T I T I O N
T O L E R A N C E

20. BREAKING CAP ?
Consistency
Availability
Partition
Tolerance
CA CP
AP
MongoDB
HBase
Redis
Cassandra
DynamoDB
Riak
RDBMS
KAFKA

21. L A M B D
• Robust and fault-tolerant system
• Serves a wide range of workloads and use cases
• linearly scalable
• Layered Architecture
Batch Layer
Query Layer
Speed Layer
- Nathan Marz

22. L A M B D
B A T C H L A Y E R
QUERYLAYER
S P E E D / S A V E L A Y E R
M A P - S I D E J O I N S
( T B ) S C O R I N G
M A T C H I N G
S Y S T E M
M E S S A G E
B R O K E R
B A T C H
S T O R A G E
S P E E D
S T O R A G E
MERGE

23. DATA STORE EVALUATION

24. C R U D
O P E R A T I O N S
T H R O U G H P U T
4 0 + M I L L I O N
W R I T E S
3 0 + B I L L I O N
R E C O R D S
E A S Y T O
M A I N T A I N
C O N S I S T E N C
Y
P A R T I T I O N
T O L E R A N C E
A V A I L A B I L I T Y
HBASE AS BATCH
STORE

25. T H R O U G H P U T
4 0 + M I L L I O N
W R I T E S
C O N S I S T E N C Y
A V A I L A B L E
P A R T I T I O N
T O L E R A N C E
KAFKA AS BROKER

26. C O N S I S T E N C Y
P A R T I T I O N
T O L E R A N C E
A V A I L A B L E
REDIS AS SPEED STORAGE
L O W L A T E N C Y

27. C R U D
O P E R A T I O N S
E A S Y T O
M A I N T A I N
AS SQL LAYER
Q U E R I E S
I N D E X I N G
T R A N S A C T I O N
S
M U L T I T E N A N C Y

28. C R U D
O P E R A T I O N SE A S Y T O
M A I N T A I N
PHO LIBRARY
Q U E R I E S
F I L T E R I N G

29. PHO LIBRARY
CONFIGURATION
ANNOTATE THE ENTITY BEAN
@Entity(value="user_matches")
public class MatchDataFeedItemDto implements Serializable {
@Embedded private MatchCommunicationElement communication;
@Embedded private MatchElement match;
@Property(value = "UID") private long storeUserIdKey;
@Property(value = "MID") private long matchId;
}
REGISTER THE BEAN
<util:list id="entityPropertiesMappings">
<value>com.eharmony.datastore.model.MatchDataFeedItemDto</value>
</util:list>
<bean id="entityPropertiesMappingContext" class="com.eharmony.datastore.mapper.EntityPropertiesMappingContext">
<constructor-arg ref="entityPropertiesMappings"/>
</bean>
<bean id="entityPropertiesResolver" class="com.eharmony.datastore.mapper.EntityPropertiesResolver">
<constructor-arg ref="entityPropertiesMappingContext"/>
</bean>
<bean id="phoenixHBaseQueryTranslator" class="com.eharmony.datastore.hbase.translator.PhoenixHBaseQueryTranslator">
<constructor-arg name="propertyResolver" ref="entityPropertiesResolver" />
</bean>
<bean id="phoenixHBaseQueryExecutor" class="com.eharmony.datastore.hbase.query.executor.PhoenixHBaseQueryExecutor">
<constructor-arg name="queryTranslator" ref="phoenixHBaseQueryTranslator"/>
<constructor-arg name="resultMapper" ref="phoenixProjectedResultMapper" />
</bean>

30. PHO LIBRARY
QUERY BUILDING
Disjunction disjunction = new Disjunction();
for (int statusFilter : statusFilters) {
disjunction.add(Restrictions.eq("status", statusFilter));
}
QueryBuilder.builderFor(FeedItemDto.class).select()
.add(Restrictions.eq("userId", userId))
.add(Restrictions.gte("spotlightEnd", spotlightEndDate))
.add(disjunction)
.setReturnFields(projection)
.addOrder(orderings)
.setMaxResults(maxResults)
.build();

31. http://eharmony.github.io/

32. L A M B D
K A F K A
M A P - S I D E J O I N S
( T B ) S C O R I N G
B A T C H L A Y E R
Q U E R Y L A Y E R
S P E E D / S A V E L A Y E R
M A T C H I N G S Y S T E M

33. P E R F O R M A N C E
H B A S E C U T O V E R
S A V E M A T C H R E S P O N S E
T I M E S
5 0 % 1 0 0 %
G E T M A T C H E S R E S P O N S E
T I M E S
H B A S E C U T O V E R 1 0 0 %

34. S O M E I N S I G H T

35. C H A L L E N G E S
H O T
R E G I O N M I G R A T I O N

36. metrics.codahale.com
M O N I T O R I N G

37. http://www.eharmony.com/about/careers/

38. T H A N K Y O U
Q U E S T I O N S ?
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