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Building a real time analytics engine in JRuby
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David Dahl
March 02, 2013
Programming
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Building a real time analytics engine in JRuby
David Dahl
March 02, 2013
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Transcript
Building a real time analytics engine in JRuby David Dahl
@effata
whoami ‣ Senior developer at Burt ‣ Analytics for online
advertising ‣ Ruby lovers since 2009 ‣ AWS
None
None
None
Getting started ‣ Writing everything to mysql, querying for every
report - Broke down on first major campaign ‣ Precalculate all the things! ‣ Every operation in one application - Extremely scary to deploy ‣ Still sticking to MRI
None
Stuck ‣ Separate and buffer with RabbitMQ - Eventmachine ‣
Store stuff with MongoDB - Blocking operations ‣ Bad things
Java? ‣ Threading ‣ “Enterprise” ‣ Lots of libraries Think
about creating something Java ecosystem Discover someone has made it for you already Profit!
Moving to JRuby ‣ Threads! ‣ A real GC ‣
JIT ‣ Every Java, Scala, Ruby lib ever made ‣ Wrapping java libraries is fun! ‣ Bonus: Not hating yourself
Challenges
“100%” uptime ‣ We can “never” be down! ‣ But
we can pause ‣ Don’t want to fail on errors ‣ But it’s ok to die
Buffering ‣ Split into isolated services ‣ Add a buffering
pipeline between - We LOVE RabbitMQ ‣ Ack and persist in a “transaction” ‣ Figure out if you want - at most once - at least once
Databases ‣ Pick the right tool for the job ‣
MongoDB everywhere = bad ‣ Cassandra ‣ Redis ‣ NoDB - keep it streaming!
Java.util.concurrent
Shortcut
Executors Better than doing Thread.new
thread_pool = ! Executors.new_fixed_thread_pool(16) stuff.each do |item| thread_pool.submit do crunch_stuff(item)
end end
Blocking queues Producer/consumer pattern made easy Don’t forget back pressure!
queue = ! JavaConcurrent::LinkedBlockingQueue.new # With timeout queue.offer(data, 60, Java::TimeUnit::SECONDS)
queue.poll(60, Java::TimeUnit::SECONDS) # Blocking queue.put(data) queue.take
Back pressure Storage Timer Data processing Queue State
queue = ! JavaConcurrent::ArrayBlockingQueue.new(100) # With timeout queue.offer(data, 60, Java::TimeUnit::SECONDS)
queue.poll(60, Java::TimeUnit::SECONDS) # Blocking queue.put(data) queue.take
More awesomeness ‣ Java.util.concurrent - Atomic(Boolean/Integer/Long) - ConcurrentHashMap - CountDownLatch
/ Semaphore ‣ Google Guava ‣ LMAX Disruptor
Easy mode ‣ Thread safety is hard ‣ Use j.u.c
‣ Avoid shared mutual state if possible ‣ Back pressure
Actors Another layer of abstractions
Akka Concurrency library in Scala Most famous for its actor
implementation
Mikka Small ruby wrapper around Akka
class SomeActor < Mikka::Actor def receive(message) # do the thing
end end
Storm github.com/colinsurprenant/redstorm
We broke it But YOU should definitely try it out!
Hadoop github.com/iconara/rubydoop
module WordCount class Mapper def map(key, value, context) # ...
end end class Reducer def reduce(key, value, context) # ... end end end
Rubydoop.configure do |input_path, output_path| job 'word_count' do input input_path output
output_path mapper WordCount::Mapper reducer WordCount::Reducer output_key Hadoop::Io::Text output_value Hadoop::Io::IntWritable end end
Other cool stuff ‣ Hotbunnies ‣ Eurydice ‣ Bundesstrasse ‣
Multimeter
Thank you @effata
[email protected]