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Storm
Distributed and fault-tolerant realtime computation




                                          Nathan Marz
                                            Twitter
Storm at Twitter




  Twitter Web Analytics
Before Storm



Queues        Workers
Example




 (simplified)
Example




Workers schemify tweets
 and append to Hadoop
Example




Workers update statistics on URLs by
incrementing counters in Cassandra
Example




Distribute tweets randomly
    on multiple queues
Example




Workers share the load of
  schemifying tweets
Example




Desire all updates for same
 URL go to same worker
Message locality

• Because:
 • No transactions in Cassandra (and no
    atomic increments at the time)
 • More effective batching of updates
Implementing message
        locality


• Have a queue for each consuming worker
• Choose queue for a URL using consistent hashing
Example




Workers choose queue to enqueue
   to using hash/mod of URL
Example




    All updates for same URL
guaranteed to go to same worker
Adding a worker
Adding a worker
                      Deploy




Reconfigure/redeploy
Problems

• Scaling is painful
• Poor fault-tolerance
• Coding is tedious
What we want
• Guaranteed data processing
• Horizontal scalability
• Fault-tolerance
• No intermediate message brokers!
• Higher level abstraction than message passing
• “Just works”
Storm
Guaranteed data processing
Horizontal scalability
Fault-tolerance
No intermediate message brokers!
Higher level abstraction than message passing
“Just works”
Use cases



  Stream      Distributed   Continuous
processing       RPC        computation
Storm Cluster
Storm Cluster




Master node (similar to Hadoop JobTracker)
Storm Cluster




Used for cluster coordination
Storm Cluster




 Run worker processes
Starting a topology
Killing a topology
Concepts

• Streams
• Spouts
• Bolts
• Topologies
Streams


Tuple   Tuple   Tuple   Tuple   Tuple   Tuple   Tuple




          Unbounded sequence of tuples
Spouts




Source of streams
Spout examples


• Read from Kestrel queue
• Read from Twitter streaming API
Bolts




Processes input streams and produces new streams
Bolts
• Functions
• Filters
• Aggregation
• Joins
• Talk to databases
Topology




Network of spouts and bolts
Tasks




Spouts and bolts execute as
many tasks across the cluster
Stream grouping




When a tuple is emitted, which task does it go to?
Stream grouping

• Shuffle grouping: pick a random task
• Fields grouping: consistent hashing on a
  subset of tuple fields
• All grouping: send to all tasks
• Global grouping: pick task with lowest id
Topology
shuffle      [“id1”, “id2”]




           shuffle
[“url”]


  shuffle

              all
Streaming word count




TopologyBuilder is used to construct topologies in Java
Streaming word count




Define a spout in the topology with parallelism of 5 tasks
Streaming word count




Split sentences into words with parallelism of 8 tasks
Streaming word count



Consumer decides what data it receives and how it gets grouped




Split sentences into words with parallelism of 8 tasks
Streaming word count




   Create a word count stream
Streaming word count




      splitsentence.py
Streaming word count
Streaming word count




  Submitting topology to a cluster
Streaming word count




  Running topology in local mode
Demo
Traditional data processing
Traditional data processing




   Intense processing (Hadoop, databases, etc.)
Traditional data processing




Light processing on a single machine to resolve queries
Distributed RPC




Distributed RPC lets you do intense processing at query-time
Game changer
Distributed RPC




Data flow for Distributed RPC
DRPC Example


Computing “reach” of a URL on the fly
Reach


Reach is the number of unique people
    exposed to a URL on Twitter
Computing reach
                Follower
                           Distinct
      Tweeter   Follower   follower

                Follower
                           Distinct
URL   Tweeter              follower   Count   Reach
                Follower

                Follower   Distinct
      Tweeter              follower
                Follower
Reach topology
Guaranteeing message
     processing




       “Tuple tree”
Guaranteeing message
     processing

• A spout tuple is not fully processed until all
  tuples in the tree have been completed
Guaranteeing message
     processing

• If the tuple tree is not completed within a
  specified timeout, the spout tuple is replayed
Guaranteeing message
     processing




      Reliability API
Guaranteeing message
     processing




“Anchoring” creates a new edge in the tuple tree
Guaranteeing message
     processing




 Marks a single node in the tree as complete
Guaranteeing message
     processing

• Storm tracks tuple trees for you in an
  extremely efficient way
Storm UI
Storm UI
Storm UI
Storm on EC2


https://github.com/nathanmarz/storm-deploy




          One-click deploy tool
Documentation
State spout (almost done)


       Synchronize a large amount of
  frequently changing state into a topology
State spout (almost done)




Optimizing reach topology by eliminating the database calls
State spout (almost done)




  Each GetFollowers task keeps a synchronous
     cache of a subset of the social graph
State spout (almost done)




This works because GetFollowers repartitions the social
 graph the same way it partitions GetTweeter’s stream
Future work

• Storm on Mesos
• “Swapping”
• Auto-scaling
• Higher level abstractions
Questions?


http://github.com/nathanmarz/storm
What Storm does
•   Distributes code and configurations

•   Robust process management

•   Monitors topologies and reassigns failed tasks

•   Provides reliability by tracking tuple trees

•   Routing and partitioning of streams

•   Serialization

•   Fine-grained performance stats of topologies

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