1.
MapReduce and NoSQLExploring the Solution Space

2.
Changing the Game

3.
Conventional MR + NoSQLCost Dollars Data Volume

4.
Conventional MR + NoSQLScale Dollars Data Volume

5.
Performance Profiles

6.
Performance Profiles GoodMapReduce Bad Throughput Bulk Update Latency Seek Good NoSQL Bad

7.
Performance Profiles MapReduce NoSQLThroughput Bulk Update Latency Seek

8.
Performance Profiles MapReduce NoSQLGoodBad Throughput Bulk Update Latency Seek

9.
Data GoalsCollectServeAnalyze

10.
Traditional Approach Collect Transactional AnalyticalUsers System System Apps OLTP OLAP Serve Analyze

11.
One Approach Collect Hadoop MapReduceUsers Apps NoSQL HDFS Serve Analyze

12.
Analysis ChallengesAnalytical Latency Data is always old Answers can take a long timeServing up analytical resultsHigher cost, complexityIncremental Updates

13.
Analysis ChallengesWord Countsa: 5342 New Document:aardvark: 13an: 4553 “The red aardvarksanteater: 27 live in holes.”...yellow: 302zebra:19

14.
Analysis ChallengesHDFS Log files:sources/routers MapReduce over datasources/apps from all sources for thesources/webservers week of Jan 13th

15.
One Approach Collect Hadoop MapReduceUsers Apps NoSQL HDFS Serve Analyze

16.
Possible Approach Collect Hadoop MapReduceUsers Apps NoSQL Serve Analyze

17.
Have to be careful to avoidcreating a system that’s badat everything

18.
What could go wrong?

19.
Performance Profiles MapReduce NoSQLGoodBad Throughput Bulk Update Latency Seek

20.
Performance Profiles MapReduce NoSQL MapReduce on NoSQLGoodBad Throughput Bulk Update Latency Seek

21.
Performance Profiles MapReduce NoSQL MapReduce on NoSQL Good BadThroughput Bulk Update Latency Seek

22.
Performance Profiles MapReduce on NoSQL Good BadThroughput Bulk Update Latency Seek

23.
Best PracticesUse a NoSQL db that has good throughput - it helps todo local communicationIsolate MapReduce workers to a subset of your NoSQLnodes so that some are available for fast queriesIf MR output is written back to NoSQL db, it isimmediately available for query

24.
THE IN T ER L L E C T I V E Concept-Based Search

25.
PatentsNews Articles PubMedClinical TrialsArXive Articles

26.
MongoDB PythonRuby on Rails Hadoop Thrift

27.
Feature VectorsUnstructured Data

28.
www.interllective.com

29.
MapReduce on MongoDBBuilt-in MapReduce - Javascriptmongo-hadoopMongoReduce

30.
MongoReduce

31.
Config Replicas App servers S P S PShards S P S P mongos

32.
Config Replicas App servers MR WorkersShards mongos

33.
Config Replicas App servers P P Single jobShards P P mongos

34.
MongoDBMappers read directlyfrom a single mongod mongodprocess, not throughmongos - tends to belocal Map HDFSBalancer can be turnedoff to avoid potential for mongosreading data twice

35.
MongoReduceOnly MongoDb mongodprimaries do writes.Schedule mappers onsecondaries Map HDFSIntermediate outputgoes to HDFS Reduce mongos

36.
MongoReduce mongodFinal output can go to HDFSHDFS or MongoDb Reduce mongos

37.
MongoReduce mongodMappers can just writeto global MongoDb Map HDFSthrough mongos mongos

38.
What’s Going On? Map Map Map Mapr1 r2 r3 r1 r2 r3 mongos mongos r1 r2 r3 P P P Identity Reducer

39.
MongoReduceInstead of specifying an HDFS directory for input, cansubmit MongoDb query and select statements:q = {article_source: {$in: [‘nytimes.com’, ‘wsj.com’]}s = {authors:true}Queries use indexes!

40.
MongoReduceIf outputting to MongoDb, new collections areautomatically sharded, pre-split, and balancedCan choose the shard keyReducers can choose to call update()

41.
MongoReduceIf writing output to MongoDb, specify an objectId toensure idempotent writes - i.e. not a random UUID

42.
https://github.com/acordova/MongoReduce

43.
DIY MapRed + NoSQLYourInputFormat YourInputSplit YourRecordReaderYourOutputFormat YourRecordWriter YourOutputCommitter

44.
Brisk

45.
Accumulo

46.
AccumuloBased on Googles BigTable designUses Apache Hadoop, Zookeeper, and ThriftFeatures a few novel improvements on the BigTabledesign cell-level access labels server-side programming mechanism called Iterators

47.
AccumuloBased on Googles BigTable designUses Apache Hadoop, Zookeeper, and ThriftFeatures a few novel improvements on the BigTabledesign cell-level access labels server-side programming mechanism called Iterators

48.
MapReduce and AccumuloCan do regular ol’ MapReduce just like w/ MongoDbBut can use Iterators to achieve a kind of ‘continualMapReduce’

49.
TabletServers App serversMaster Ingest clients

50.
TabletServer App server Reduce’ map() Ingest clientlive:142 WordCountin:2342holes:234 Table

51.
TabletServer App server Reduce’ map() Ingest clientlive:142in:2342 The redholes:234 aardvarks live in holes.

52.
TabletServer App server Reduce’ map() Ingest clientlive:142 aardvarks:1in:2342 live:1holes:234 in:1 holes:1

53.
TabletServer App server Reduce’ map() Ingest clientaardvarks:1live:143in:2343holes:235

54.
Accumulo Map Map map() map()r1 r2 r3 r1 r2 r3 client client r1 r2 r3 reduce’()

55.
Iterators row : column family : column qualifier : ts -> value can specify which key elements are unique, e.g. row : column family can specify a function to execute on values of identical key-portions, e.g. sum(), max(), min()

56.
Key to performanceWhen the functions are runRather than atomic increment: lock, read, +1, write SLOWWrite all values, sum at read time minor compaction time major compaction time

57.
TabletServer aardvark:1scan live:142 live:1 Reduce’ in:2342 in:1 live:143 sum() holes:234 holes:1 read

58.
TabletServer aardvark:1 live:142aardvark:1 live:1live:143 in:2342 Reduce’in:2343 sum() in:1holes:235 holes:234 holes:1 major compact

59.
Reduce’ (prime)Because a function has not seen all values for a givenkey - another may show upMore like writing a MapReduce combiner function

60.
‘Continuous’ MapReduceCan maintain huge result sets that are always availablefor queryUpdate graph edge weightsUpdate feature vector weightsStatistical countsnormalize after query to get probabilities

61.
Accumulo - latin to accumulate ...

62.
Accumulo - latin to accumulate ... awesomeness

63.
incubator.apache.org/accumulowiki.apache.org/incubator/AccumuloProposal

64.
Google PercolatorA system for incrementally processing updates to alarge data setUsed to create the Google web search index.Reduced the average age of documents in Googlesearch results by 50%.

65.
Google PercolatorA novel, proprietary system of Distributed Transactionsand Notifications built on top of BigTable

66.
Solution SpaceIncremental update, multi-row consistency: PercolatorResults can’t be broken down (sort): MapReduceNo multi-row updates: BigTableComputation is small: Traditional DBMS

67.
Questions