Spark on Mesos - MesosCon 2016

Update on Spark on Mesos state of art

Transcript

1. Spark on Mesos Tim Chen Mirantis tnachen@gmail.com Dean Wampler Lightbend dean.wampler@lightbend.com
2. Dean Wampler • Architect for Big Data Products at Lightbend • Early advocate for Spark on Mesos • O’Reilly author – Programming Scala, 2nd Edition – Programming Hive – Functional Programming for Java Developers Timothy Chen • Principal Engineer at Mirantis • Previously Lead engineer at Mesosphere • Apache Mesos PMC • Spark contributor, help maintain Spark on Mesos
3. What’s this all about, then? • Why Spark on Mesos? • What’s happened since last year? • Demo • What’s next for Spark and Mesos?
4. Why Spark on Mesos • Hadoop is great, but ... – … resource management with YARN is limited to compute engines like MapReduce and Spark. • What if your clustering system could run everything?
5. Why Spark on Mesos • Hadoop is great, but ... – … Big Data is moving to streaming (“Fast Data”) and Spark offers mini-batch streaming. • What if your cluster system offered dynamic and flexible resource scheduling able to meet the needs of evolving, long-running streams?
6. Why Spark on Mesos • Hadoop is great, but ... – … it doesn’t support other popular tools like Cassandra, Akka, web frameworks, ... • Maybe you need the SMACK stack: – Spark – Mesos – Akka – Cassandra – Kafka There’s a Scheduler for that!
7. What’s happened since last year? • What’s new in Mesos • What’s new in Spark on Mesos • Deprecating fine-grained mode
8. What’s new in Mesos? • Resource quotas • Dynamic reservation *Beta* • CNI network Support • GPU Support • Unified Containerizer • More..
9. What’s new in Spark on Mesos? • Integration test suite • New Coarse grained scheduler • Mesos framework authentication • Cluster mode now supports Python
10. Integration Test Suite • A recent release candidate for Spark broke Mesos integration completely. – Better integration testing clearly needed. – Lightbend and Mesosphere collaborated on an automated integration test suite. https://github.com/typesafehub/mesos-spark-integration-tests
11. Integration Test Suite • “mesos-docker” subproject: – Builds Docker image with Ubuntu, Mesos, Spark, and HDFS. – Scripts to run cluster with 1 master and N slaves, configurable #s of CPUs, memory, etc. • (Not needed if you already have a Mesos cluster ;^)
12. Integration Test Suite • “test-runner” subproject: – Executes a suite of tests on your Mesos or DC/OS cluster. – Currently exercises dynamic allocation, coarse-grain and fine-grain modes, etc.
13. New Coarse Grain Scheduler How the old Coarse grain scheduler works? Launch 1 Spark executor per agent - Rough steps: - Evaluate offers as it comes in from the master - Offers that meets min cpu (1) and min memory requirements - Use as much cores until meets spark.cores.max - Every executor requests fixed memory
14. New Coarse Grain Scheduler How the old Coarse grain scheduler works? Mesos Agent 1 CPU: 8 Memory: 8gb Mesos Agent 2 CPU: 8 Memory: 8gb Mesos Agent 3 CPU: 8 Memory: 8gb CoarseMesosSchedulerBackend spark.cores.max=12 spark.executor.memory=4gb Spark Executor CPU 8 Memory 4gb Spark Executor CPU 4 Memory 4gb
15. New Coarse Grain Scheduler How the old Coarse grain scheduler works? Mesos Agent 1 CPU: 8 Memory: 8gb Mesos Agent 2 CPU: 2 Memory: 8gb Mesos Agent 3 CPU: 2 Memory: 8gb CoarseMesosSchedulerBackend spark.cores.max=12 spark.executor.memory=4gb Spark Executor CPU 8 Memory 4gb Spark Executor CPU 2 Memory 4gb Spark Executor CPU 2 Memory 4gb
16. New Coarse Grain Scheduler How the old Coarse grain scheduler works? Mesos Agent CPU: 8 Memory: 64gb Mesos Agent CPU: 2 Memory: 64gb Mesos Agent CPU: 2 Memory: 64gb CoarseGrainedMesosScheduler Spark Executor CPU 8 Memory 64gb Spark Executor CPU 2 Memory 64gb Spark Executor CPU 2 Memory 64gb spark.cores.max=12 spark.executor.memory=64gb
17. New Coarse Grain Scheduler Problems with the old scheduler: - Only allow one executor per slave - Unpredictable executor performance - Unpredictable allocations
18. New Coarse Grain Scheduler Mesos Agent 1 CPU: 8 Memory: 8gb Mesos Agent 2 CPU: 8 Memory: 8gb Mesos Agent 3 CPU: 8 Memory: 8gb CoarseMesosSchedulerBackend spark.cores.max=12 spark.executor.memory=4gb spark.executor.cores=4 Spark Executor CPU 4 Memory 4gb Spark Executor CPU 4 Memory 4gb Spark Executor CPU 4 Memory 4gb
19. New Coarse Grain Scheduler - Allows multiple executors per slave - More predictable executor performance - (Soon) Better allocation
20. Mesos Framework Authentication • Mesos supports framework authentication. • Roles can be set per framework – Impacts the relative weight of resource allocation • Optional authentication information to allow the framework to be connected to the master.
21. Getting rid of fine-grained mode? Coarse-grained Mode Fine-grained Mode
22. Getting rid of fine-grained mode? • Why two modes? – FG uses resources more efficiently, because of start- on-demand and Spark executor+task are removed when no longer needed. – CG holds onto all allocated tasks until the job finishes. – But that makes CG faster to start tasks; nice for interactive jobs (e.g., SQL queries). – While FG has a longer start up time.
23. Getting rid of fine-grained mode? • Today: – Dynamic Allocation reclaims unused executors. • (Although running this service on every node is a disadvantage) • Hence, the advantages of FG are becoming less important.
24. Getting rid of fine-grained mode? • Spark has lots of redundant code to implement both modes. • So, to simplify the code base and operations, FG is now deprecated, but it can’t be removed yet.
25. GPUs Mesos Running _____________ on __________with _______ on top of ______ using _____ in the _____! Demo Deep Learning Tensorflow Spark Cloud
26. What’s Next for Mesos? • Pod support • Multiple roles support • Event Bus • Improved Container Security (capabilities, etc) • More….
27. What’s Next for Spark on Mesos? • GPU Support on Mesos • Multi-tenant cluster mode • Use revocable resources • Better scheduling – Strategies (e.g: Spread, Binpack) – Scheduling metrics • More integration test coverage: – More cluster and job configuration options. – Roles and authentication scenarios.
28. What’s Next for Spark on Mesos? • Make “production” easier: – Easier overriding of configuration with config files outside the jars. – Better documentation. – Easier access to Spark UIs and logs from Mesos UIs – Improved metrics and UI. – Smarter acceptance of resources offered.
29. What’s this all about, then? • Why Spark on Mesos? • What’s happened since last year? • Demo • What’s next for Spark and Mesos?
30. THANK YOU. tnachen@gmail.com @tnachen dean.wampler@lightbend.com @deanwampler