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Nathan Marz Twitter Distributed and fault-tolerant realtime computation Storm
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Storm Distributed and fault-tolerant realtime computation
Nathan Marz Twitter
Basic info • Open sourced September 19th • Implementation is 15,000 lines of code • Used by over 25 companies • >2400 watchers on Github (most watched JVM project)
• Very active mailing list • >1800 messages • >560 members
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
Scaling 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 processing
Distributed RPC
Continuous 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
Unbounded sequence of tuples
Tuple
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
Task execution
Tasks are spread across the cluster
Task execution
Tasks are spread 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: mod 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
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 Tweeter
Follower Follower
URL
Tweeter
Follower Follower
Tweeter Follower
Distinct follower Distinct follower Distinct follower
Count
Reach
Reach topology
Reach topology
Reach topology
Reach topology
Keep set of followers for each request id in memory
Reach topology
Update followers set when receive a new follower
Reach topology
Emit partial count after receiving all followers for a request id
Demo
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
Transactional topologies
How do you do idempotent counting with an at least once delivery guarantee?
Transactional topologies
Won’t you overcount?
Transactional topologies
Transactional topologies solve this problem
Transactional topologies
Built completely on top of Storm’s primitives of streams, spouts, and bolts
Transactional topologies
Batch 1
Batch 2
Batch 3
Process small batches of tuples
Transactional topologies
Batch 1
Batch 2
Batch 3
If a batch fails, replay the whole batch
Transactional topologies
Batch 1
Batch 2
Batch 3
Once a batch is completed, commit the batch
Transactional topologies
Batch 1
Batch 2
Batch 3
Bolts can optionally be “committers”
Transactional topologies Commit 1
Commit 1
Commit 2
Commit 3
Commit 4
Commit 4
Commits are ordered. If there’s a failure during commit, the whole batch + commit is retried
Example
Example New instance of this object for every transaction attempt
Example
Aggregate the count for this batch
Example
Only update database if transaction ids differ
Example
This enables idempotency since commits are ordered
Example
(Credit goes to Kafka devs for this trick)
Transactional topologies
Multiple batches can be processed in parallel, but commits are guaranteed to be ordered
Transactional topologies • Will be available in next version of Storm (0.7.0)
• Requires a source queue that can replay identical batches of messages
• storm-kafka has a transactional spout implementation for Kafka
Storm UI
Storm on EC2
https://github.com/nathanmarz/storm-deploy
One-click deploy tool
Starter code
https://github.com/nathanmarz/storm-starter
Example topologies
Documentation
Ecosystem • Scala, JRuby, and Clojure DSL’s • Kestrel, AMQP, JMS, and other spout adapters • Serializers • Multilang adapters • Cassandra, MongoDB integration
Questions?
http://github.com/nathanmarz/storm
Future work • State spout • Storm on Mesos • “Swapping” • Auto-scaling • Higher level abstractions
Implementation
KafkaTransactionalSpout
Implementation all
all
all
Implementation all
all
all
TransactionalSpout is a subtopology consisting of a spout and a bolt
Implementation all
all
all
The spout consists of one task that coordinates the transactions
Implementation all
all
all
The bolt emits the batches of tuples
Implementation all
all
all
The coordinator emits a “batch” stream and a “commit stream”
Implementation all
all
all
Batch stream
Implementation all
all
all
Commit stream
Implementation all
all
all
Coordinator reuses tuple tree framework to detect success or failure of batches or commits and replays appropriately