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For Book Discount: @ellen_friedman
Contact:
– tdunning@maprtech.com
– @ted_dunning
Twitter for this talk
– #mapr_uk
Slides and such:
– http://info.mapr.com/ted-uk-05-2012
3. 3©MapR Technologies - Confidential
Company Background
MapR provides the industry’s best Hadoop Distribution
– Combines the best of the Hadoop community
contributions with significant internally
financed infrastructure development
Background of Team
– Deep management bench with extensive analytic,
storage, virtualization, and open source experience
– Google, EMC, Cisco, VMWare, Network Appliance, IBM,
Microsoft, Apache Foundation, Aster Data, Brio, ParAccel
Proven
– MapR used across industries (Financial Services, Media,
Telcom, Health Care, Internet Services, Government)
– Strategic OEM relationship with EMC and Cisco
– Over 1,000 installs
4. 4©MapR Technologies - Confidential
We Also Do …
Open source development
– Zookeeper
– Hadoop
– Mahout
– Stuff
Partner workshops
– Machine learning
– Information architecture
– Cluster design
5. 5©MapR Technologies - Confidential
We Also Do …
Open source development
– Zookeeper
– Hadoop
– Mahout
– Stuff
Partner workshops
– Machine learning
– Information architecture
– Cluster design
6. 6©MapR Technologies - Confidential
The Problem
A certain bank
– had lots of customers
– had lots of prospective customers
– had a non-trivial number of fraudulent customers
– had a non-trivial number of fraudulent merchants
They also
– collected data
– built models
– collected more data
– built more models
7. 7©MapR Technologies - Confidential
But …
These models were arduous to build
And hard to test
So people suggested something simpler
Like k-nearest neighbor
8. 8©MapR Technologies - Confidential
What’s that?
Find the k nearest training examples
Use the average value of the target variable from them
This is easy … but hard
– easy because it is so conceptually simple and you don’t have knobs to turn
or models to build
– hard because of the stunning amount of math
– also hard because we need top 50,000 results
Initial prototype was massively too slow
– 3K queries x 200K examples takes hours
– needed 20M x 25M in the same time
9. 9©MapR Technologies - Confidential
What We Did
Mechanism for extending Mahout Vectors
– DelegatingVector, WeightedVector, Centroid
Searcher interface
– ProjectionSearch, KmeansSearch, LshSearch, Brute
Super-fast clustering
– Kmeans, StreamingKmeans
12. 12©MapR Technologies - Confidential
But These Require k-means!
Need a new k-means algorithm to get speed
Streaming k-means is
– One pass (through the original data)
– Very fast (20 us per data point with threads)
– Very parallelizable
13. 13©MapR Technologies - Confidential
How It Works
For each point
– Find approximately nearest centroid (distance = d)
– If d > threshold, new centroid
– Else possibly new cluster
– Else add to nearest centroid
If centroids > K ~ C log N
– Recursively cluster centroids with higher threshold
Result is large set of centroids
– these provide approximation of original distribution
– we can cluster centroids to get a close approximation of clustering original
– or we can just use the result directly
14. 14©MapR Technologies - Confidential
Parallel Speedup?
1 2 3 4 5 20
10
100
20
30
40
50
200
Threads
Timeperpoint(μs)
2
3
4
5
6
8
10
12
14
16
Threaded version
Non- threaded
Perfect Scaling
✓
15. 15©MapR Technologies - Confidential
Warning, Recursive Descent
Inner loop requires finding nearest centroid
With lots of centroids, this is slow
But wait, we have classes to accelerate that!
16. 16©MapR Technologies - Confidential
Warning, Recursive Descent
Inner loop requires finding nearest centroid
With lots of centroids, this is slow
But wait, we have classes to accelerate that!
(Let’s not use k-means searcher, though)
17. 17©MapR Technologies - Confidential
Contact:
– tdunning@maprtech.com
– @ted_dunning
Slides and such:
– http://info.mapr.com/ted-uk-05-2012
Hinweis der Redaktion MapR combines the best of the open source technology with our own deep innovations to provide the most advanced distribution for Apache Hadoop.MapR’s team has a deep bench of enterprise software experience with proven success across storage, networking, virtualization, analytics, and open source technologies.Our CEO has driven multiple companies to successful outcomes in the analytic, storage, and virtualization spaces.Our CTO and co-founder M.C. Srivas was most recently at Google in BigTable. He understands the challenges of MapReduce at huge scale. Srivas was also the chief software architect at Spinnaker Networks which came out of stealth with the fastest NAS storage on the market and was acquired quickly by NetAppThe team includes experience with enterprise storage at Cisco, VmWare, IBM and EMC. Our VP of Engineering led emerging technologies and a 600 person for EMC’s NAS engineering team. We also have experience in Business Intelligence and Analytic companies and open source committers in Hadoop, Zookeeper and Mahout including PMC members.MapR is proven technology with installs by leading Hadoop installations across industries and OEM by EMC and Cisco.