Hadoop: Beyond MapReduce
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© Hortonworks Inc. 2013 Hadoop: Beyond MapReduce Steve Loughran, Hortonworks [email protected] @steveloughran Big Data workshop, June 2013
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Overview of the above and beyond MapReduce, for the HPC/science community. Key point: move up the stack, reuse what is there. But: some of these people are capable of writing their own YARN apps, so they should be encouraged to do so if they see a need.
Transcript of Hadoop: Beyond MapReduce
© Hortonworks Inc. 2013
Hadoop: Beyond MapReduce
Steve Loughran, [email protected]@steveloughran
Big Data workshop, June 2013
© Hortonworks Inc.
Hadoop MapReduce
1. Map: events <k,v>* pairs2. Reduce: <k,[v1, v2,.. vn]> <k,v'>
•Map trivially parallelisable on blocks in a file•Reduce parallelise on keys•MapReduce engine can execute Map and Reduce sequences against data
•HDFS provides data location for work placement
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MapReduce democratised big data
•Conceptual model easy to grasp•Can write and test locally, superlinear scaleup
•Tools and stack
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You don't need to understand parallel coding to run appsacross 1000 machines
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The stack is key to use
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Kafka
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Example: Pig
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generated = LOAD '$src/$srcfile' USING PigStorage(',' , '-noschema') AS (line: int, gaussian: double, b: boolean, c:chararray );sorted = ORDER generated BY c ASC;result = FILTER sorted BY gaussian >= 0;
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Example: Apache Giraph
•Graph nodes in RAM•exchange data with peers at barriers•use cases: PageRank, Friend-of-Friend
•But also: modelling cells in a heart
Bulk-Synchronous-Parallel -read Pregel paper
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But there is a lot more we can do
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New Algorithms and runtimes
•Giraph for graph work•Stream processing: Storm• Iterative and chained processing: Dryad-style•Long-lived processes
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Production-side issues
•Scale to 10K nodes•Eliminate SPOFs & Bottlenecks• Improve versioning by moving MR engine user-side
•Avoid having dedicated servers for other roles
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YARN: Yet Another Resource Negotiator
App Master manages the
app
AM can request
containers and run code in
them
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YARN vs Other Resource Negotiators
•MapReduce #1 initial use case•Failures:AM handles worker failures, YARN handles AM failures
•Scheduling Locality: sources of data, destinations. AM gets provides location requests along with (CPU, RAM
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Pig/Hive-MR versus Pig/Hive-Tez
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I/O Synchronization
BarrierI/O Pipelining
Pig/Hive - Tez Pig/Hive - Tez
SELECT a.state, COUNT(*)
FROM a JOIN b ON (a.id = b.id)
GROUP BY a.state
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FastQuery: Beyond Batch with YARN
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Tez Generalizes Map-Reduce
Simplified execution plans processdata more efficiently
Always-On Tez Service
Low latency processing forall Hadoop data processing
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You too can write a distributed execution framework -if you need to
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Start with the work in progress
•Hamster: MPI•Storm-YARN from Yahoo!•Hoya: HBase on YARN me
And start with other people's code•Continuuity Weave -looks best place to start
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What are the services and algorithms we are going to need?
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http://hortonworks.com/careers/Page 17
P.S: we are hiring
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