Building large scale, job processing systems with Scala Akka Actor framework
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The document discusses building massive scale, fault tolerant job processing systems using the Scala Akka framework. It describes implementing a master-slave architecture with actors where an agent runs on each storage node to process jobs locally, achieving high throughput. It also covers controlling system load by dynamically adjusting parallelism, and implementing fine-grained fault tolerance through actor supervision strategies.
![Unit testing
• Scalatra test framework: very easy to read!
TaskActorTest.receive(BadFileMsg) must throw
FileNotFoundException
• Mocks for network and database calls
val mockHttp = mock[HttpExecutor]
TaskActorTest ! doHttpPost
there was atLeastOne(mockHttp).POST
• Extensive testing of failure injection scenarios](https://image.slidesharecdn.com/svcc2012-121121024435-phpapp01/85/Building-large-scale-job-processing-systems-with-Scala-Akka-Actor-framework-24-320.jpg)
Building large scale, job processing systems with Scala Akka Actor framework
- 1. Building massive scale, fault tolerant, job processing systems with Scala Akka framework Vignesh Sukumar SVCC 2012
- 2. About me • Storage group, Backend Engineering at Box • Love enterprise software! • Interested in Big Data and building distributed systems in the cloud
- 3. About Box • Leader in enterprise cloud collaboration and storage • Cutting-edge work in backend, frontend, platform and engineering services • A really fun place to work – we have a long slide!
- 4. Talk outline • Job processing requirements • Traditional & new models for job processing • Akka actors framework • Achieving and controlling high IO throughput • Fine-grained fault tolerance
- 5. Typical architecture in a cloud storage environment
- 6. Practical realities •Storage nodes are usually of varying configurations (OS, processing power, storage capacity, etc) mainly because of rapid evolution in provisioning operations •Some nodes are more over-worked than the others (for ex, accepting live uploads) •Billions of files; petabytes
- 7. Job processing requirements • Iterate over all files (billions, petabyte scale): for ex, check consistency of all files • High throughput • Fault tolerant • Secure
- 8. Traditional job processing model
- 9. Why traditional models fail in cloud storage environments • Not scalable: petabyte scale, billions of files • Insecure: cannot move files out of storage nodes • No performance control: easy to overwhelm any storage node • No fine grained fault tolerance
- 10. Compute on Storage • Move job computation directly to storage nodes • Utilize abundant CPU on storage nodes • Metadata store still stays in a highly available system like a RDBMS • Results from operations on a file are completely independent
- 11. Master – slave architecture
- 12. Benefits • High IO throughput: Direct access; no transfer of files over a network • Secure: files do not leave storage nodes • Better performance control: compute can easily monitor system load and back off • Better fault tolerance handling: finer grained handling of errors
- 13. Master node • Responsible for accepting job submissions and splitting them to tasks for slave nodes • Stateful: keeps durable copy of jobs and tasks in Zookeeper • Horizontally scalable: service can be run on multiple nodes
- 14. Agent • Runs directly on the storage nodes on a machine-independent JVM container • Stateless: no task state is maintained • Monitors system load with back-off • Reports results directly to master without synchronizing with other agents
- 15. Implementation with the the Scala Akka Actor framework
- 16. Actors • Concurrent threads abstraction with no shared state • Exchange messages • Asynchronous, non-blocking • Multiple actors can map to a single OS thread • Parent-children hierarchical relationship
- 17. Actors and messages • Class MyActor extends Actor { def receive = { case MsgType1 => // do something } } // instantiation and sending messages val actorRef = system.actorOf(Props(new MyActor)) actorRef ! MsgType1
- 19. Achieving high IO throughput • Parallel, asynchronous IO through “Futures” val fileIOResult = Future { // issue high latency tasks like file IO } val networkIOResult = Future { // read from network } Futures.awaitAll(<wait time>, fileIOResult, networkIOResult) fileIOResult onSuccess { // do something } networkIOResult onFailure { // retry }
- 20. Controlling system throughput • The problem: agents need to throttle themselves as storage nodes serve live traffic • Adjust number of parallel workers dynamically through a monitoring service
- 21. Controlling throughput: Examples •Parallelism parameters can be gotten from a separate configuration service on a per node basis •Some machines can be speeded up and others slowed down this way •The configuration can be updated on a cron schedule to speed up during weekends
- 22. Fine grained fault tolerance with Supervisors • Parents of child actors can define specific fault-handling strategies for each failure scenario in their children • Components can fail gracefully without affecting the entire system
- 23. Supervision strategy: Examples Class TaskActor extends Actor { // create child workers override val supervisorStrategy = OneForOneStrategy(maxNrOrRetries = 3) { case SqlException => Resume // retry the same file case FileCorruptionException => Stop // don’t clobber it! case IOException => Restart // report and move on }
- 24. Unit testing • Scalatra test framework: very easy to read! TaskActorTest.receive(BadFileMsg) must throw FileNotFoundException • Mocks for network and database calls val mockHttp = mock[HttpExecutor] TaskActorTest ! doHttpPost there was atLeastOne(mockHttp).POST • Extensive testing of failure injection scenarios
- 25. Takeaways • Keep your architecture simple by modeling actor message flow along the same paths as parent-child actor hierarchy (i.e., no message exchange between peer child actors) • Design and implement for component failures • Write unit tests extensively: we did not have any fundamental level functionality breakage • Box Engineering is awesome!
Editor's Notes
- #8: 1. Example of a job is to check consistency of all the files: this will involve iterating over every file on all storage nodes, reading file and verifying content integrity.
- #10: Scalability: non-performant because of the IO bottleneck in getting files to the application cluster Insecure: application clusters can store the files locally. It’s easy to melt a single a storage node by reading or writing a lot to it Cannot perform fine grained fault tolerance