1. How to Make Hadoop
Easy, Dependable, and Fast
©MapR Technologies - Confidential 1

2. Agenda
 Quick MapR overview
 Typical and atypical use cases
– restaurant recommendation
– network security
– mega-scale fraud modeling
– log analysis through creative abuse of text retrieval
 Lessons Learned
– data import and export techniques
– zen integration
– accessing data from a variety of applications
– how to protect data from the most common cause of data loss
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3. MapR’s Complete Distribution for Apache Hadoop
 Apache Applications are MapR Control System
MapR
Integrated, tested, Heatmap™
LDAP, NIS
Integration
Quotas,
Alerts, Alarms
CLI,
REST APT
hardened and
Supported. Hive Pig Oozle Sqoop HBase Whirr
 100% Hadoop, HBase, Mahout Cascading Naglos Ganglia Flume Zoo-
Integration Integration keeper
HDFS API compatible
 Easy portability/
migration between Direct Real-Time Snap- Data
Access Streaming Volumes Mirrors shots Placement
distributions NFS
No NameNode High Performance Stateful Failover
 No changes required Architecture Direct Shuffle and Self Healing
to Hadoop applications
2.7
MapR’s Storage Services™
©MapR Technologies - Confidential 3

4. MapR: Lights Out Data Center Ready
Reliable Compute Dependable Storage
 Automated stateful failover  Business continuity with snapshots
 Automated re-replication and mirrors
 Self-healing from HW and SW failures  Recover to a point in time
 Load balancing  End-to-end check summing
 Rolling upgrades  Strong consistency
 No lost jobs or data  Data safe
 99999’s of uptime  Mirror across sites to meet
Recovery Time Objectives
©MapR Technologies - Confidential 4

5. Restaurant Recommendation
 Use transaction data to characterize users
 Determine restaurant affinities for transactors
 On demand, produce geo-local restaurant recommendation
 Web or mobile interface
©MapR Technologies - Confidential 5

6. Restaurant Recommendation
 Training goes-ins
– transaction data from purchases
– user feedback on recommendations
 Training goes-outs
– large recommendation data files
 Online goes-ins
– user id, current location, recent transaction history
– filters
 Online goes-outs
– restaurant recommendations
©MapR Technologies - Confidential 6

7. What is the Delivery Mechanism
 Database?
– export takes forever
– limited scalability
 Key value store?
– export takes forever
– YAWTM (yet another widget to maintain)
 Do we really need a mechanism at all?
©MapR Technologies - Confidential 7

8. Deploying Recommendations
Final recommendations
computed in browser/app
©MapR Technologies - Confidential 8

9. Summary
 With mirrors and NFS, no special “deployment” mechanism is
necessary
 User’s browser can do final assembly on the recommendations
 Recommendation components served as static files by web-server
©MapR Technologies - Confidential 9

10. Mega-scale Fraud Modeling
 Why not use the simplest modeling technology around?
– similar folk do similar things!
– just find tens of thousands of similar folk and see what they did
 Can we make it a million times faster than the prototype?
– well, yes … we can
 And can you deploy that into a live system?
 And can sequential and parallel versions co-exist?
©MapR Technologies - Confidential 10

11. Modeling with k-nearest Neighbors
a
b c
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12. Speeds and Feeds
 Single machine version can cluster at 20μs per point
– 1 million points in ~20s
– 100 million points in ~2000s = 40 minutes
 Parallel version can cluster at 20μs / nodes per point + 30 seconds
– 1 million points in 31 s on 20 nodes (ish)
– 100 million points in 150 s = 2.5 minutes (on 20 nodes)
 Really would like interchangeable versions
©MapR Technologies - Confidential 12

13. What About Deployment?
 Final matrix size is several GB
 Can’t have copy per thread
– can’t even wait to load many copies
 What about mmap?
– needs real files, can’t use HDFS
– NFS works great
 Need to deploy in map-reduce and real-time environments
– can’t depend on Hadoop features like distributed cache
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14. ©MapR Technologies - Confidential 14

15. Summary
 With mirrors and NFS, no special “deployment” mechanism is
necessary
 The modeling client can use NFS + mmap share memory between
threads or processes
 Mirrors can stage as many replicas as desired on whichever
machines are specified
©MapR Technologies - Confidential 15

16. Network Security
 Take an existing network security appliance
 Add magical parallel machine learning to find new attacks
 But don’t spend time copying data back and forth
 And don’t change the legacy code
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17. ©MapR Technologies - Confidential 17

18. Summary
 Legacy code “just works” with MapR’s NFS
 Map-reduce programs don’t care where the input comes from
 Exposing new control data requires no special mechanism
©MapR Technologies - Confidential 18

19. Log Analysis
 Receive 200K log lines per second or more
 Want to do multi-field search
 Want to search on log lines with < 30 second delay before search
©MapR Technologies - Confidential 19

20. Solr Based Flexible Analytics
 Solr/Lucene can index at 500K small documents per second
 Faceting provides simple aggregation
 Multiple index search is a given, not a special future enhancement
 Solr/Lucene has awesome record of stability
©MapR Technologies - Confidential 20

21. Data Ingestion and Indexing
SolR
SolR Solr
Incoming Indexer
Text
Kafka Indexer indexer
Data analysis
Real-time
Raw Live index
Older index shard
documents shards
Time sharded Solr indexes
©MapR Technologies - Confidential 21

22. Some Special Points
 Textual analysis is done in parallel outside of the indexer
 Raw documents are stored outside of Solr to minimize index size
 Index hot-spotting is a feature here because it gives time-based
sharding
 Indexing into NFS allows legacy code reuse
©MapR Technologies - Confidential 22

23. Basic Search
Solr
search
Query Web tier
SolR
SolR
Indexer
Solr
Indexer
search
Raw Live index
Older index shard
documents shards
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24. Additional Points
 The number of shards per core can be adjusted easily to match
load
 Near real-time indexing not really required
 No transaction logs need be kept by Solr for failure tolerance
– core failure requires other cores take on lost shards
– indexer failure requires indexer restart … Kafka retains unprocessed input
– indexing is idempotent
©MapR Technologies - Confidential 24

25. Secure Search
Auth
data
Solr
Security search
Query Web tier
filter
SolR
SolR
Indexer
Solr
Indexer
search
Raw Live index
Older index shard
documents shards
©MapR Technologies - Confidential 25

26. Conclusions
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27. Lessons Learned
 Import/export is often a non-issue
– NFS allows processing in place
 Legacy access via NFS provides high performance, minimal effort
 Interchangeable map-reduce and conventional programs are key
 Do simple tasks in simple ways. Save the effort for the big tasks
©MapR Technologies - Confidential 27

28. Zen Integration
 The student went to the master and asked how to integrate
multiple programs using different models
– The master said, “to do more, do less”
 The student went away and came back pointing out that HDFS
allows copying data in and out. He quoted Turing.
– The master said, “to do more, do less”
 The student thought about this for many days. In the
meantime, the master installed MapR and deleted all the
integration code.
 When the student returned and saw this, he asked where the
integration was.
 The master answered “ ” and the student was enlightened.
©MapR Technologies - Confidential 28

29. The Cause of Almost All Data Loss
©MapR Technologies - Confidential 29

30. The Cause of Almost All Data Loss
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31. The Cause of Almost All Data Loss
And snapshots are the cure (partially)
©MapR Technologies - Confidential 31

32. Time for Questions
 Download MapR to learn more
– http://mapr.com/download
 Send email with questions later
– tdunning@maprtech.com
 Tweet as the spirit moves
– @ted_dunning
 These slides and other resources
– http://www.mapr.com/company/events/speaking/tableau-11-8-12
©MapR Technologies - Confidential 32