The Merchant Data team at Groupon uses MongoDB heavily in its mission to create the most comprehensive database of places and merchants in the world. We think of ourselves as a key part of Groupon's platform because our data is necessary for salesforce CRM and public merchant pages, among other things, and directly impacts the business. Our team uses a real-time data processing pipeline that includes automated matching and categorization in a Storm cluster and crowd sourcing for manual cleaning. MongoDB is a key piece of our infrastructure because we use a cluster to generate a set of candidates for matching new data coming into our system. In this talk we will discuss: A high level view of the Merchant Data processing pipeline, including the data collection, realtime processing, and serving layers, an overview of our data model, how we mapped our data model into infrastructure, and the dynamics of parallel querying in our storm cluster.

1. MongoDB at Groupon
Peter Bakkum
@pbbakkum

2. Merchant
Data
CRM
Merchant
Pages

4. Merchant
Data
CRM
Merchant
Pages
Self-Service Others

5. Arnold: Declarative Crowd-Machine
Data Integration
Shawn Jeffery, Liwen Sun, Matt DeLand,
Nick Pendar, Rick Barber, Andrew Galdi
CIDR 2013
cidrdb.org/cidr2013/Papers/CIDR13_Paper22.pdf

6. Concordance
{
name: “Joe’s Pizza”,
location: {
address: “1000 Market St.”,
postal_code: “94100-1001”
},
source: 1
}
{
name: “Joes Pizza”,
location: {
address: “1000 Market Street”,
postal_code: “94100”
},
source: 2
}
{
name: “Joes”,
location: {
address: “1000 Market”,
postal_code: “94100”
},
source: 3
}

7. Data Systems

8. Content Input
Data
Processing
Serving

9. Content Input
Data Sets
Input
Feeds
Normalization
Crowd
Sourcing
Web
Crawling

10. Content Input
Crawl Store
Web Crawler
Configuration
Normalized Data
Recent Feed History

11. Data
Processing
Storm Topology
Storm

12. Serving
HTTP Access Layer
Varnish

13. Data Processing

14. storm topology
bolts
parsing
normalization
concordance
geocoding
persistence

15. place model
record
tree

16. places.find({
_id: “013e4e2afc26”
})
placeCollection.find({
location.postcode: “94100”,
location.country: “US”
})
places.findAndModify(
{
_id: “013e4e2afc26”
persisted_at: “2013-02-01T0:00:00Z”
},
{ place model })
Concordance
Persistence

17. config cluster
4 arbiters
4 shards of 2 nodes
replica set failover
64 GB dedicated hardware
storm workers
mongos routers

18. ID Scheme

19. UUID v1
82d991c6-b098-11e2-8fc0-c82a14fffe86
82d9996e-b098-11e2-8fc0-c82a14fffe86
82d99f04-b098-11e2-8fc0-c82a14fffe86
82d9a40e-b098-11e2-8fc0-c82a14fffe86

21. UUID vB
wwwwwwww-xxxx-byyy-yyyy-zzzzzzzzzzzz
w: controllable counter
x: process id
b: literal 'b’
y: fragment of MAC address
z: milliseconds since epoch (UTC)

22. toggle
c8c9cef9-7a7f-bd53-7a50-013e4e2afbde
14951cfa-7a7f-bd53-7a50-013e4e2afbde
6f5169fb-7a7f-bd53-7a50-013e4e2afbde
ba2da6fc-7a7f-bd53-7a50-013e4e2afbde
f5166777-7a7f-bd53-7a50-013e4e2afc26
f5166778-7a7f-bd53-7a50-013e4e2afc26
f5166779-7a7f-bd53-7a50-013e4e2afc26
f516677a-7a7f-bd53-7a50-013e4e2afc26

24. github.com/groupon/locality-uuid.java
github.com/groupon/locality-uuid.rb

25. Backup and MapReduce

26. Hadoop Cluster

27. places.ns
places.0
places.1
places.2
…
char[128]
name
DiskLoc
firstExtent
DiskLoc
lastExtent
places.ns
places.0
places.1
places.2
…

28. places.ns
places.0
places.1
places.2
…
places.0 places.1
extent extent extent

29. places.ns
places.0
places.1
places.2
…
places.0 places.1
extent extent extent
MapReduce
Input Split
MapReduce
Input Split
MapReduce
Input Split

30. public void map(
Text key,
WritableBSONObject value,
Context context)
{
String id = (String) value.get(“_id”);
...
}

31. Mongo Cluster
Hadoop Cluster
MapReduce Job
Backs up Mongo data to
Hadoop
Much faster data export
Exploits our Hadoop cluster

32. Peter Bakkum
@pbbakkum
pbakkum@groupon.com