Learn how customers are using MongoDB to aggregate risk data from multiple front office sources to calculate exposure.

1. Webinar : Managing Real Time Risk Analytics with MongoDB
will begin at 14:00 GMT / 7:00 AM PDT / 2:00 PM UTC
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2. Easy to Start, Easy to Develop, Easy to Scale
Managing Real Time Risk Analytics with
MongoDB
10gen, Inc.
November 2012

3. @dmroberts
daniel.roberts@10gen.com
Solution Architect
Based in London
http://www.10gen.com/

4. Last Time
•Document-Oriented
•High Volume Data Feeds
•Dynamic schema •Tick Data capture
•Agile •Risk Analytics
•Flexible •Product Catalogs & Trade
•High Performance Capture
•Highly Available •P&L Reporting
•Horizontal Scale Out •Portfolio Management
•Reference Data Management
•Quantitative Analysis
•Automated Trading

5. Key Features for reporting /
analytics
•Dynamic Query Language
•Aggregation Framework
•Dynamic & Flexible Schemas
•Atomic Updates to documents
•Upserts
•Horizonal Scale Out
•Map Reduce
•Hadoop Integration

6. Sharded Architecture

7. Risk Analytics & Reporting
Use Case:
•Collect and aggregate risk data
•Calculate risk / exposures
•Potentially real time
Why MongoDB?
•Collect data from a single or multiple sources
•Different formats
•Documents used to create ‘pre-aggregated’ reports
•Real Time
•Aggregation Framework for reporting
•e.g. exposure for a counter party
•Internal MR or Hadoop connector
•Batch process risk data

8. Portfolio / Position reporting
Use Case:
•Store positions or portfolio information
•Query to find current positions/portfolios
•Query by client or trader
Why MongoDB?
•Customer/client my have many different products
•Aggregation Framework to calculate values and views
•Work on extremely large data sets
•Current and historic data

9. Reporting / Analytics requirements
•How quickly do you need answers?
•How often do you need updates?
•Requirements will drive which methods to utilise.
•Generally the high the latency tolerance the greater the insight.
•Choices
•Batch calculations - large complex data volumes
•Pre-Aggregated - specific and very fast
•Real-time calculations - As needed reports and calculations

10. Batch Processing
•MongoDB internal Map Reduce
•Hadoop Map Reduce with MongoDB connector
raw
•Insight after batch run
hourly
•For instance every hour or day
•Output to documents/collection daily
•Fast read once data produced
monthly
•Results not up to last millisecond
•Can generate insight from huge datasets
•Rolled up stats
•Source collections -> reporting collection

11. Sharded MongoDB + Hadoop
Shard 1 Shard 2 Shard 3 Shard 4 Shard 5
c z t f v w y
a s u g e h d b x
Hadoop Hadoop Hadoop Hadoop Hadoop
Node Node Node Node Node
Hadoop Hadoop
Hadoop Hadoop
Node Node
Node Node

12. Use Query Language
•Query across documents using MongoDB JSON query language
•Infer results in the application code.
•Dynamic - but what happens when we have 1 billion documents.
•Indexing strategy key
•var data = db.pl.find({ positionId: 1234 })[0]
{
"_id" : ObjectId("50990a10fd421cb025407cb1"),
"positionId" : 1234,
"security" : "ORCL",
"quantity" : 1000,
"price" : 30.23,
"currency" : "USD"
}
data.price * data.quantity = 30230.00

13. Leverage schema design
•Group useful data together into documents
•Utilise upsert and $inc functionality of MongoDB
•Pre-aggregate reports
•$inc incrementing counters is light weight.
•Fast pre calculated data
•Low latency retrieval
•http://docs.mongodb.org/manual/use-cases/pre-aggregated-reports/

14. Pre-Aggregated Reports -
Daily trade volumes
// daily buckets, each hour a sub-document
{ _id: "2012-11-06-1231", security:‘ORCL’
ts: ISODate("2012-11-06T00:00:00.000Z")
daily: 67345234,
minute: { 0: { 0: 2034, 1: 735, ... 59: 2644 },
...
7: { 0: 15434, 1: 334, ... 59: 64234 }
}
}
// Increment counters in document. Automatically
insert new document (upsert : true)
> db.trades.update(
{ _id: "2012-11-06-1231", security:‘ORCL’ },
{ $inc: { "minute.4.09": 1034, “daily” : 1034 } },
true)

15. Aggregation Framework
•Much simpler and faster than MongoDB map reduce
•Replaces common MR use cases in MongoDB
•Native operators in the MongoDB core
db.pl.aggregate([
! {$match:{"clientId" : 4321}},
{ $project :
{ value :
{ $multiply:["$quantity", "$price"] }
}
}
]);

16. Aggregation FIX Execution report
Calculating average price for a fulfilled order:
db.ExecutionReport.aggregate(
{
$match : {"Instrument.Symbol":"MSFT"},
$group :
{
_id : "$ClOrdID",
reportsPerOrd : { $sum : 1 },
totalNumOrdered : { $sum : "$OrderQtyData.OrderQty"},
avgPrice : { $avg : "$AvgPx" }
}
});

17. Summary
•Number of choices for Aggregating data.
•MongoDB Map Reduce
•Pre-Compute - Schema Design
•Hadoop Connector
•Aggregation Framework

18. download at mongodb.org
@dmroberts
daniel.roberts@10gen.com
Free online training - http://education.10gen.com/
www.meetup.com/London-MongoDB-User-Group/
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http://bit.ly/mongodb @dmroberts http://linkd.in/joinmongo