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Distributed, fault-tolerant, transactional
Real-Time Integration: MongoDB and SQL Databases
Eugene Dvorkin
Architect, WebMD

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WebMD: A lot of data; a lot of traffic
~900 millions page view a month
~100 million unique visitors a month

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How We Use MongoDB
User Activity

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Why Move Data to RDBMS?
Preserve existing investment in BI
and data warehouse
To use analytical database such as
Vertica
To use SQL

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Why Move Data In Real-time?
Batch process is slow
No ad-hoc queries
No real-time reports

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Challenge in moving data
Transform Document to Relational Structure
Insert into RDBMS at high rate

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Challenge in moving data
Scale easily as data volume and velocity
increase

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Our Solution to move data in Real-time: Storm
tem.Storm – open source distributed real-
time computation system.
Developed by Nathan Marz - acquired
by Twitter

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Hadoop Storm
Our Solution to move data in Real-time: Storm

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Why STORM?
JVM-based framework
Guaranteed data processing
Supports development in multiple languages
Scalable and transactional

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Overview of Storm cluster
Master Node
Cluster Coordination
run worker processes

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Storm Abstractions
Tuples, Streams, Spouts, Bolts and Topologies

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Tuples
(“ns:events”,”email:edvorkin@gmail.com”)
Ordered list of elements

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Stream
Unbounded sequence of tuples
Example: Stream of messages from
message queue

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Spout
Read from stream of data – Queues, web
logs, API calls, mongoDB oplog
Emit documents as tuples
Source of Streams

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Bolts
Process tuples and create new streams

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Bolts
Apply functions /transforms
Calculate and aggregate
data (word count!)
Access DB, API , etc.
Filter data
Map/Reduce
Process tuples and create new streams

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Topology

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Topology
Storm is transforming and moving data

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MongoDB
How To Read All Incoming Data
from MongoDB?

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MongoDB
How To Read All Incoming Data
from MongoDB?
Use MongoDB OpLog

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What is OpLog?
Replication
mechanism in
MongoDB
It is a Capped
Collection

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Spout: reading from OpLog
Located at local database, oplog.rs collection

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Spout: reading from OpLog
Operations: Insert, Update, Delete

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Spout: reading from OpLog
Name space: Table – Collection name

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Spout: reading from OpLog
Data object:

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Sharded cluster

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Automatic discovery of sharded cluster

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Example: Shard vs Replica set discovery

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Example: Shard discovery

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Spout: Reading data from OpLog
How to Read data continuously
from OpLog?

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Spout: Reading data from OpLog
How to Read data continuously
from OpLog?
Use Tailable Cursor

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Example: Tailable cursor - like tail –f

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Manage timestamps
Use ts (timestamp in oplog entry) field to
track processed records
If system restart, start from recorded ts

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Spout: reading from OpLog

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SPOUT – Code Example

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TOPOLOGY

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Working With Embedded Arrays
Array represents One-to-Many relationship in
RDBMS

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Example: Working with embedded arrays

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Example: Working with embedded arrays
{_id: 1,
ns: “person_awards”,
o: { award: 'National Medal of Science',
year: 1975,
by: 'National Science Foundation' }
}
{ _id: 1,
ns: “person_awards”,
o: {award: 'Turing Award',
year: 1977,
by: 'ACM' }
}

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Example: Working with embedded arrays
public void execute(Tuple tuple) {
.........
if (field instanceof BasicDBList) {
BasicDBObject arrayElement=processArray(field)
......
outputCollector.emit("documents", tuple, arrayElement);

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Parse documents with Bolt

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{"ns": "people", "op":"i",
o : {
_id: 1,
name: { first: 'John', last:
'Backus' },
birth: 'Dec 03, 1924’
}
["ns": "people", "op":"i",
[“id”:1,
"name_first": "John",
"name_last":"Backus",
"birth": "DEc 03, 1924"
]
]
Parse documents with Bolt

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@Override
public void execute(Tuple tuple) {
......
final BasicDBObject oplogObject =
(BasicDBObject)tuple.getValueByField("document");
final BasicDBObject document = (BasicDBObject)oplogObject.get("o");
......
outputValues.add(flattenDocument(document));
outputCollector.emit(tuple,outputValues);
Parse documents with Bolt

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Write to SQL with SQLWriter Bolt

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Write to SQL with SQLWriter Bolt
["ns": "people", "op":"i",
[“id”:1,
"name_first": "John",
"name_last":"Backus",
"birth": "Dec 03, 1924"
]
]
insert into people (_id,name_first,name_last,birth) values
(1,'John','Backus','Dec 03,1924') ,
insert into people_awards (_id,awards_award,awards_award,awards_by)
values (1,'Turing Award',1977,'ACM'),
insert into people_awards (_id,awards_award,awards_award,awards_by)
values (1,'National Medal of Science',1975,'National Science Foundation')

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@Override
public void prepare(.....) {
....
Class.forName("com.vertica.jdbc.Driver");
con = DriverManager.getConnection(dBUrl, username,password);
@Override
public void execute(Tuple tuple) {
String insertStatement=createInsertStatement(tuple);
try {
Statement stmt = con.createStatement();
stmt.execute(insertStatement);
stmt.close();
Write to SQL with SQLWriter Bolt

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Topology Definition
TopologyBuilder builder = new TopologyBuilder();
// define our spout
builder.setSpout(spoutId, new MongoOpLogSpout("mongodb://",
opslog_progress)
builder.setBolt(arrayExtractorId ,new
ArrayFieldExtractorBolt(),5).shuffleGrouping(spoutId)
builder.setBolt(mongoDocParserId, new
MongoDocumentParserBolt()).shuffleGrouping(arrayExtractorId,
documentsStreamId)
builder.setBolt(sqlWriterId, new
SQLWriterBolt(rdbmsUrl,rdbmsUserName,rdbmsPassword)).shuffle
Grouping(mongoDocParserId)
LocalCluster cluster = new LocalCluster();
cluster.submitTopology("test", conf,
builder.createTopology());

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Topology Definition
TopologyBuilder builder = new TopologyBuilder();
// define our spout
builder.setSpout(spoutId, new MongoOpLogSpout("mongodb://",
opslog_progress)
builder.setBolt(arrayExtractorId ,new
ArrayFieldExtractorBolt(),5).shuffleGrouping(spoutId)
builder.setBolt(mongoDocParserId, new
MongoDocumentParserBolt()).shuffleGrouping(arrayExtractorId
,documentsStreamId)
builder.setBolt(sqlWriterId, new
SQLWriterBolt(rdbmsUrl,rdbmsUserName,rdbmsPassword)).shuffl
eGrouping(mongoDocParserId)
LocalCluster cluster = new LocalCluster();
cluster.submitTopology("test", conf,
builder.createTopology());

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Topology Definition
TopologyBuilder builder = new TopologyBuilder();
// define our spout
builder.setSpout(spoutId, new MongoOpLogSpout("mongodb://",
opslog_progress)
builder.setBolt(arrayExtractorId ,new
ArrayFieldExtractorBolt(),5).shuffleGrouping(spoutId)
builder.setBolt(mongoDocParserId, new
MongoDocumentParserBolt()).shuffleGrouping(arrayExtractorId,
documentsStreamId)
builder.setBolt(sqlWriterId, new
SQLWriterBolt(rdbmsUrl,rdbmsUserName,rdbmsPassword)).shuffle
Grouping(mongoDocParserId)
LocalCluster cluster = new LocalCluster();
cluster.submitTopology("test", conf,
builder.createTopology());

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Topology Definition
TopologyBuilder builder = new TopologyBuilder();
// define our spout
builder.setSpout(spoutId, new MongoOpLogSpout("mongodb://",
opslog_progress)
builder.setBolt(arrayExtractorId ,new
ArrayFieldExtractorBolt(),5).shuffleGrouping(spoutId)
builder.setBolt(mongoDocParserId, new
MongoDocumentParserBolt()).shuffleGrouping(arrayExtractorId,
documentsStreamId)
builder.setBolt(sqlWriterId, new
SQLWriterBolt(rdbmsUrl,rdbmsUserName,rdbmsPassword)).shuffle
Grouping(mongoDocParserId)
StormSubmitter.submitTopology("OfflineEventProcess",
conf,builder.createTopology())

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Lesson learned
By leveraging MongoDB Oplog or other
capped collection, tailable cursor and Storm
framework, you can build fast, scalable,
real-time data processing pipeline.

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Resources
Book: Getting started with Storm
Storm Project wiki
Storm starter project
Storm contributions project
Running a Multi-Node Storm cluster tutorial
Implementing real-time trending topic
A Hadoop Alternative: Building a real-time
data pipeline with Storm
Storm Use cases

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Resources (cont’d)
Understanding the Parallelism of a Storm
Topology
Trident – high level Storm abstraction
A practical Storm’s Trident API
Storm online forum
Mongo connector from 10gen Labs
MoSQL streaming Translator in Ruby
Project source code
New York City Storm Meetup

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Questions
Eugene Dvorkin, Architect, WebMD edvorkin@webmd.net
Twitter: @edvorkin LinkedIn: eugenedvorkin

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Next Sessions at 2:50
5th Floor:
WestSideBallroom3&4:DataModelingExamplesfromtheRealWorld
WestSideBallroom1&2: GrowingUpMongoDB
JuilliardComplex:BusinessTrack:MetLifeLeapfrogsInsuranceIndustry
withMongoDB-PoweredBigDataApplication
LyceumComplex: AsktheExperts:MongoDBMonitoringandBackup
ServiceSession
7th Floor:
EmpireComplex:HowWeFixedOurMongoDBProblems
SoHoComplex:HighPerformance,HighScaleMongoDBonAWS:AHands
OnGuide