twitter stream processing
Post on 10-May-2015
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Twitterstream processing
Colin Surprenant@colinsurprenantLead ninja
Big Data MontrealApril 2012
Twitter - Spring 2012
● 350 million tweets/day● 140 million active users● >1 million applications using API
Daily Twitter @ Needium
50 000 000 processed tweets 5 000 opportunities 500 messages sent 100GB data
Anatomy of a tweet
What's in a tweet? Anything else?
avatar
usertimestamp
message
How to get the tweets?
● Streaming API
Subscribe to realtime feeds moving forward ● REST Search API
Search request on past data (1 week)
Streaming APIpublic statuses from all users
● status/filtertrack/location/follow○ 5000 follow user ids
○ 400 track keywords
○ 25 location boxes
○ rate limited
● status/sample○ 1% of all public statuses (message id mod 100)
○ two status/sample streams will result in same data
Streaming APIper user streams
● User Streams
○ all data required to update a user's display○ requires user's OAuth token○ statuses from followings, direct messages, mentions○ cannot open large number of user streams from same host
● Site Streams○ multiplexing of multiple User Streams
Streaming APIFirehose
need more/full data? only through partners ● gnip.com● datasift.com
● filtering/tracking
● partial to full Firehose
What's the catch? $$$lots of it
Search API
● REST API (http request/response)
○ search query○ geocode (lat, long, radius)○ result type (mixed/recent/popular)○ since id
● max 100 rpp and 1500 results● rate limited (~1 request/sec)
StormDistributed and fault-tolerant realtime computation
https://github.com/nathanmarz/storm
Storm
The promise ● Guaranteed data processing● Horizontal scalability● Fault-tolerance● No intermediate message brokers● Higher level abstraction than message passing● Just work
RedStorm
Storm: Java + Closure
RedStorm: JRuby integration & DSL for Storm
Ruby + Storm on JVM
https://github.com/colinsurprenant/redstorm
+
StormTypical use cases
Streamprocessing
Continuouscomputation
DistributedRPC
StormConcepts
Streams
Unbounded sequence of tuples
Tuple Tuple Tuple Tuple Tuple
StormConcepts
Spouts
Source of streams
TupleTuple
TupleTuple
Tuple
TupleTuple
TupleTuple
Tuple
StormConcepts
Bolts
Processes input streams and produce new streams
Tuple Tuple Tuple Tuple Tuple
Tuple Tuple Tuple Tuple Tuple
Tuple Tuple Tuple Tuple Tuple
StormConcepts
Topology
Network of spouts and bolts
StormConcepts
bolt A bolt B
bolt A bolt B
field X
field Y
bolt A bolt B
bolt A bolt B
random+fair distribution across tasks
replication to all tasks
partition on specified field entire stream to single task
Shuffle
GlobalFields
All
Grouping
Storm
What Storm does ● Distributes code● Robust process management● Monitors topologies and reassigns failed tasks● Provides reliability by tracking tuple trees● Routing and partitioning of stream
TweitgeistLive top 10 trending hashtags on Twitter
DEMO
https://github.com/colinsurprenant/tweitgeistLive demo: http://tweitgeist.needium.com
TweitgeistArchitecture
Twitterstream
Queue
Parallel extraction
Partitionedcounting
Partitionedcounting
Partitionedranking
Partitionedranking
Merging
Queue
Frontend
N partitions N partitions
TweitgeistArchitecture
● Parallel computation across partitions of the stream● Scalable architecture● Work for full Twitter firehose with very little mods
TweitgeistStorm Topology
TwitterStreamSpout
ExtractMessageBolt
ExtractHashtagBolt
RollingCountBolt
shuf
fle
shuf
fle
field
hash
tag
RankBolt
field
hash
tag
MergeBolt
glob
al
Shuffle grouping: Tuples are randomly distributed across the bolt's tasksFields grouping: The stream is partitioned by the fields specified in the groupingGlobal grouping: The entire stream goes to a single one of the bolt's tasks
Redisqueue
Redisqueue
Twitter U
I
streamreader
messageextract
hashtagextract
rollingcounter
ranking merging
TweitgeistTopology definition
TweitgeistWhere
Codehttps://github.com/colinsurprenant/tweitgeist
Live demohttp://tweitgeist.needium.com
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