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![Map Hashtags in Python
#!/usr/bin/env python
import sys
sys.path.append(".")
from pymongo_hadoop import BSONMapper
def mapper(documents):
for doc in documents:
for hashtag in doc['entities']['hashtags']:
yield {'_id': hashtag['text'], 'count': 1}
BSONMapper(mapper)
print >> sys.stderr, "Done Mapping."](https://image.slidesharecdn.com/mongodbhadoopandhumongousmongosv2012-121205192507-phpapp01/75/MongoDB-Hadoop-and-humongous-data-MongoSV-2012-36-2048.jpg)
![Reduce hashtags in Python
#!/usr/bin/env python
import sys
sys.path.append(".")
from pymongo_hadoop import BSONReducer
def reducer(key, values):
print >> sys.stderr, "Hashtag %s" % key.encode('utf8')
_count = 0
for v in values:
_count += v['count']
return {'_id': key.encode('utf8'), 'count': _count}
BSONReducer(reducer)](https://image.slidesharecdn.com/mongodbhadoopandhumongousmongosv2012-121205192507-phpapp01/75/MongoDB-Hadoop-and-humongous-data-MongoSV-2012-37-2048.jpg)
![Popular Hash Tags
db.twit_hashtags.aggregate(a){
"result" : [
{ "_id" : "YouKnowYoureInLoveIf", "count" : 287 },
{ "_id" : "teamfollowback", "count" : 200 },
{ "_id" : "RT", "count" : 150 },
{ "_id" : "Arsenal", "count" : 148 },
{ "_id" : "milars", "count" : 145 },
{ "_id" : "sanremo","count" : 145 },
{ "_id" : "LoseMyNumberIf", "count" : 139 },
{ "_id" : "RelationshipsShould", "count" : 137 },
{ "_id" : "Bahrain", "count" : 129 },
{ "_id" : "bahrain", "count" : 125 }
],"ok" : 1
}](https://image.slidesharecdn.com/mongodbhadoopandhumongousmongosv2012-121205192507-phpapp01/75/MongoDB-Hadoop-and-humongous-data-MongoSV-2012-42-2048.jpg)
Uploaded bySteven Francia
MongoDB, Hadoop and humongous data - MongoSV 2012
The document discusses the concept of 'humongous data' and the exponential growth of data from 2000 to 2012, highlighting the advancements in data processing technologies such as MongoDB and Hadoop. It explains the applications of these technologies in handling large datasets, including methods like map-reduce and aggregation for processing data. The future of data is emphasized, with predictions of continued growth and the importance of tools like Hadoop and Storm in managing and analyzing big data.
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MongoDB, Hadoop and humongous data - MongoSV 2012
- 1.
- 2. Talking about What isHumongous Data Humongous Data & You MongoDB & Data processing Future of Humongous Data
- 3. @spf13 AKA Steve Francia 15+ years building the internet Father, husband, skateboarder Chief Solutions Architect @ responsible for drivers, integrations, web & docs
- 4. What is humongous data ?
- 5. 2000 Google Inc Today announcedit has released the largest search engine on the Internet. Google’s new index, comprising more than 1 billion URLs
- 6. 2008 Our indexing systemfor processing links indicates that we now count 1 trillion unique URLs (and the number of individual web pages out there is growing by several billion pages per day).
- 7. An unprecedented amount ofdata is being created and is accessible
- 8. Data Growth 1,000 1000 750 500 500 250 250 120 55 4 10 24 1 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 Millions of URLs
- 9. Truly Exponential Growth Is hard for people to grasp A BBC reporter recently: "Your current PC is more powerful than the computer they had on board the first flight to the moon".
- 10. Moore’s Law Applies tomore than just CPUs Boiled down it is that things double at regular intervals It’s exponential growth.. and applies to big data
- 11. How BIG isit?
- 12. How BIG isit? 2008
- 13. How BIG isit? 2007 2008 2005 2006 2003 2004 2001 2002
- 14. Why all this talkabout BIG Data now?
- 15. In the pastfew years open source software emerged enabling ‘us’ to handle BIG Data
- 16. The Big Data Story
- 17.
- 18. Doers & Tellerstalking about different things http://www.slideshare.net/siliconangle/trendconnect-big-data-report-september
- 19.
- 20.
- 21. Doers talk alot more about actual solutions
- 22. They know it’sa two sided story Storage Processing
- 23. Take aways MongoDB andHadoop MongoDB for storage & operations Hadoop for processing & analytics
- 24.
- 25. Applications have complex needs MongoDB ideal operational database MongoDB ideal for BIG data Not a data processing engine, but provides processing functionality
- 26. Many options for Processing Data •Process in MongoDB using Map Reduce •Process in MongoDB using Aggregation Framework •Process outside MongoDB (using Hadoop)
- 27. MongoDB Map Reduce Map() MongoDB Data Group(k) emit(k,v) map iterates on documents Document is $this Sort(k) 1 at time per shard Reduce(k,values) k,v Finalize(k,v) Input matches output k,v Can run multiple times
- 28. MongoDB Map Reduce MongoDBmap reduce quite capable... but with limits - Javascript not best language for processing map reduce - Javascript limited in external data processing libraries - Adds load to data store
- 29. MongoDB Aggregation Most uses of MongoDB Map Reduce were for aggregation Aggregation Framework optimized for aggregate queries Realtime aggregation similar to SQL GroupBy
- 30. MongoDB & Hadoop same as Mongo's Many map operations MongoDB shard chunks (64mb) 1 at time per input split Creates a list each split Map (k1,1v1,1ctx) Runs on same of Input Splits Map (k ,1v ,1ctx) thread as map each split Map (k , v , ctx) single server or sharded cluster (InputFormat) each split ctx.write(k2,v2)2 ctx.write(k2,v )2 Combiner(k2,values2)2 RecordReader ctx.write(k2,v ) Combiner(k2,values )2 Combiner(k2,values ) k2, 2v3 3 k , 2v 3 k ,v Partitioner(k2)2 Partitioner(k )2 Partitioner(k ) Sort(keys2) Sort(k2)2 Sort(k ) MongoDB Reducer threads Reduce(k2,values3) Output Format Runs once per key kf,vf
- 31.
- 32. DEMO Install Hadoop MongoDBPlugin Import tweets from twitter Write mapper in Python using Hadoop streaming Write reducer in Python using Hadoop streaming Call myself a data scientist
- 33. Installing Mongo-hadoop https://gist.github.com/1887726 hadoop_version '0.23' hadoop_path="/usr/local/Cellar/hadoop/ $hadoop_version.0/libexec/lib" git clone git://github.com/mongodb/mongo- hadoop.git cd mongo-hadoop sed -i '' "s/default/$hadoop_version/g" build.sbt cd streaming ./build.sh
- 34. Groking Twitter curl https://stream.twitter.com/1/ statuses/sample.json -u<login>:<password> | mongoimport -d test -c live ... let it run for about 2 hours
- 35.
- 36. Map Hashtags inPython #!/usr/bin/env python import sys sys.path.append(".") from pymongo_hadoop import BSONMapper def mapper(documents): for doc in documents: for hashtag in doc['entities']['hashtags']: yield {'_id': hashtag['text'], 'count': 1} BSONMapper(mapper) print >> sys.stderr, "Done Mapping."
- 37. Reduce hashtags inPython #!/usr/bin/env python import sys sys.path.append(".") from pymongo_hadoop import BSONReducer def reducer(key, values): print >> sys.stderr, "Hashtag %s" % key.encode('utf8') _count = 0 for v in values: _count += v['count'] return {'_id': key.encode('utf8'), 'count': _count} BSONReducer(reducer)
- 38. All together hadoop jartarget/mongo-hadoop-streaming-assembly-1.0.0-rc0.jar -mapper examples/twitter/twit_hashtag_map.py -reducer examples/twitter/twit_hashtag_reduce.py -inputURI mongodb://127.0.0.1/test.live -outputURI mongodb://127.0.0.1/test.twit_reduction -file examples/twitter/twit_hashtag_map.py -file examples/twitter/twit_hashtag_reduce.py
- 39. Popular Hash Tags db.twit_hashtags.find().sort({'count' : -1 }) { "_id" : "YouKnowYoureInLoveIf", "count" : 287 } { "_id" : "teamfollowback", "count" : 200 } { "_id" : "RT", "count" : 150 } { "_id" : "Arsenal", "count" : 148 } { "_id" : "milars", "count" : 145 } { "_id" : "sanremo", "count" : 145 } { "_id" : "LoseMyNumberIf", "count" : 139 } { "_id" : "RelationshipsShould", "count" : 137 } { "_id" : "Bahrain", "count" : 129 } { "_id" : "bahrain", "count" : 125 } { "_id" : "oomf", "count" : 117 } { "_id" : "BabyKillerOcalan", "count" : 106 } { "_id" : "TeamFollowBack", "count" : 105 } { "_id" : "WhyDoPeopleThink", "count" : 102 } { "_id" : "np", "count" : 100 }
- 40.
- 41. Aggregation in Mongo2.1 db.live.aggregate( { $unwind : "$entities.hashtags" } , { $match : { "entities.hashtags.text" : { $exists : true } } } , { $group : { _id : "$entities.hashtags.text", count : { $sum : 1 } } } , { $sort : { count : -1 } }, { $limit : 10 } )
- 42. Popular Hash Tags db.twit_hashtags.aggregate(a){ "result" : [ { "_id" : "YouKnowYoureInLoveIf", "count" : 287 }, { "_id" : "teamfollowback", "count" : 200 }, { "_id" : "RT", "count" : 150 }, { "_id" : "Arsenal", "count" : 148 }, { "_id" : "milars", "count" : 145 }, { "_id" : "sanremo","count" : 145 }, { "_id" : "LoseMyNumberIf", "count" : 139 }, { "_id" : "RelationshipsShould", "count" : 137 }, { "_id" : "Bahrain", "count" : 129 }, { "_id" : "bahrain", "count" : 125 } ],"ok" : 1 }
- 43. The Future of humongous data
- 44. What is BIG? BIG today is normal tomorrow
- 45. Data Growth 9,000 9000 6750 4,400 4500 2,150 2250 1,000 500 55 120 250 1 4 10 24 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Millions of URLs
- 46. Data Growth 9,000 9000 6750 4,400 4500 2,150 2250 1,000 500 55 120 250 1 4 10 24 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Millions of URLs
- 47. 2012 Generating over 250 Millionsof tweets per day
- 48. MongoDB enables usto scale with the redefinition of BIG. New processing tools like Hadoop & Storm are enabling us to process the new BIG.
- 49. Hadoop is our first step
- 50. MongoDB is committed toworking with best data tools including Hadoop, Storm, Disco, Spark & more
- 51. http://spf13.com http://github.com/spf13 @spf13 Questions? download at github.com/mongodb/mongo-hadoop