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***This is not the official open source version. For that, please go to spark-jobserver/spark-jobserver. All issues and pull requests should start there.

spark-jobserver provides a RESTful interface for submitting and managing Apache Spark jobs, jars, and job contexts. This repo contains the complete Spark job server project, including unit tests and deploy scripts.

Features

Version Information

VersionSpark Version
0.3.10.9.1
0.4.01.0.2

Quick start / development mode

You need to have SBT installed.

From SBT shell, simply type "re-start". This uses a default configuration file. An optional argument is a path to an alternative config file. You can also specify JVM parameters after "---". Including all the options looks like this:

re-start /path/to/my.conf --- -Xmx8g

Note that re-start (SBT Revolver) forks the job server in a separate process. If you make a code change, simply type re-start again at the SBT shell prompt, it will compile your changes and restart the jobserver. It enables very fast turnaround cycles.

For example jobs see the job-server-tests/ project / folder.

When you use re-start, the log file goes to job-server/job-server-local.log. There is also an environment variable EXTRA_JAR for adding a jar to the classpath.

WordCountExample walk-through

First, to package the test jar containing the WordCountExample: sbt job-server-tests/package. Then go ahead and start the job server using the instructions above.

Let's upload the jar:

curl --data-binary @job-server-tests/target/job-server-tests-0.4.0.jar localhost:8090/jars/test
OK⏎

The above jar is uploaded as app test. Next, let's start an ad-hoc word count job, meaning that the job server will create its own SparkContext, and return a job ID for subsequent querying:

curl -d "input.string = a b c a b see" 'localhost:8090/jobs?appName=test&classPath=spark.jobserver.WordCountExample'
{
  "status": "STARTED",
  "result": {
    "jobId": "5453779a-f004-45fc-a11d-a39dae0f9bf4",
    "context": "b7ea0eb5-spark.jobserver.WordCountExample"
  }
}⏎

NOTE: If you want to feed in a text file config and POST using curl, you want the --data-binary option, otherwise curl will munge your line separator chars. Like:

curl --data-binary @my-job-config.json 'localhost:8090/jobs?appNam=...'

From this point, you could asynchronously query the status and results:

curl localhost:8090/jobs/5453779a-f004-45fc-a11d-a39dae0f9bf4
{
  "status": "OK",
  "result": {
    "a": 2,
    "b": 2,
    "c": 1,
    "see": 1
  }
}⏎

Note that you could append &sync=true when you POST to /jobs to get the results back in one request, but for real clusters and most jobs this may be too slow.

Another way of running this job is in a pre-created context. Start a new context:

curl -d "" 'localhost:8090/contexts/test-context?num-cpu-cores=4&mem-per-node=512m'
OK⏎

You can verify that the context has been created:

curl localhost:8090/contexts
["test-context"]⏎

Now let's run the job in the context and get the results back right away:

curl -d "input.string = a b c a b see" 'localhost:8090/jobs?appName=test&classPath=spark.jobserver.WordCountExample&context=test-context&sync=true'
{
  "status": "OK",
  "result": {
    "a": 2,
    "b": 2,
    "c": 1,
    "see": 1
  }
}⏎

Note the addition of context= and sync=true.

Create a Job Server Project

In your build.sbt, add this to use the job server jar:

resolvers += "Job Server Bintray" at "http://dl.bintray.com/spark-jobserver/maven"

libraryDependencies += "spark.jobserver" % "job-server-api" % "0.4.0" % "provided"

For most use cases it's better to have the dependencies be "provided" because you don't want SBT assembly to include the whole job server jar.

To create a job that can be submitted through the job server, the job must implement the SparkJob trait. Your job will look like:

object SampleJob  extends SparkJob {
    override def runJob(sc:SparkContext, jobConfig: Config): Any = ???
    override def validate(sc:SparkContext, config: Config): SparkJobValidation = ???
}

Let's try running our sample job with an invalid configuration:

curl -i -d "bad.input=abc" 'localhost:8090/jobs?appName=test&classPath=spark.jobserver.WordCountExample'

HTTP/1.1 400 Bad Request
Server: spray-can/1.2.0
Date: Tue, 10 Jun 2014 22:07:18 GMT
Content-Type: application/json; charset=UTF-8
Content-Length: 929

{
  "status": "VALIDATION FAILED",
  "result": {
    "message": "No input.string config param",
    "errorClass": "java.lang.Throwable",
    "stack": ["spark.jobserver.JobManagerActor$$anonfun$spark$jobserver$JobManagerActor$$getJobFuture$4.apply(JobManagerActor.scala:212)", 
    "scala.concurrent.impl.Future$PromiseCompletingRunnable.liftedTree1$1(Future.scala:24)", 
    "scala.concurrent.impl.Future$PromiseCompletingRunnable.run(Future.scala:24)", 
    "akka.dispatch.TaskInvocation.run(AbstractDispatcher.scala:42)",
    "akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)", 
    "scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)", 
    "scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)", 
    "scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)", 
    "scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)"]
  }
}

Using Named RDDs

Named RDDs are a way to easily share RDDs among job. Using this facility, computed RDDs can be cached with a given name and later on retrieved. To use this feature, the SparkJob needs to mixin NamedRddSupport:

object SampleNamedRDDJob  extends SparkJob with NamedRddSupport {
    override def runJob(sc:SparkContext, jobConfig: Config): Any = ???
    override def validate(sc:SparkContext, config: Contig): SparkJobValidation = ???
}

Then in the implementation of the job, RDDs can be stored with a given name:

this.namedRdds.update("french_dictionary", frenchDictionaryRDD)

Other job running in the same context can retrieve and use this RDD later on:

val rdd = this.namedRdds.get[(String, String)]("french_dictionary").get

(note the explicit type provided to get. This will allow to cast the retrieved RDD that otherwise is of type RDD[_])

For jobs that depends on a named RDDs it's a good practice to check for the existence of the NamedRDD in the validate method as explained earlier:

def validate(sc:SparkContext, config: Contig): SparkJobValidation = {
  ...
  val rdd = this.namedRdds.get[(Long, scala.Seq[String])]("dictionary")
  if (rdd.isDefined) SparkJobValid else SparkJobInvalid(s"Missing named RDD [dictionary]")
}

Deployment

  1. Copy config/local.sh.template to <environment>.sh and edit as appropriate.
  2. bin/server_deploy.sh <environment> -- this packages the job server along with config files and pushes it to the remotes you have configured in <environment>.sh
  3. On the remote server, start it in the deployed directory with server_start.sh and stop it with server_stop.sh

Note: to test out the deploy to a local staging dir, or package the job server for Mesos, use bin/server_package.sh <environment>.

Architecture

The job server is intended to be run as one or more independent processes, separate from the Spark cluster (though it very well may be colocated with say the Master).

At first glance, it seems many of these functions (eg job management) could be integrated into the Spark standalone master. While this is true, we believe there are many significant reasons to keep it separate:

Flow diagrams are checked in in the doc/ subdirectory. .diagram files are for websequencediagrams.com... check them out, they really will help you understand the flow of messages between actors.

API

Jars

GET /jars            - lists all the jars and the last upload timestamp
POST /jars/<appName> - uploads a new jar under <appName>

Contexts

GET /contexts         - lists all current contexts
POST /contexts/<name> - creates a new context
DELETE /contexts/<name> - stops a context and all jobs running in it

Jobs

Jobs submitted to the job server must implement a SparkJob trait. It has a main runJob method which is passed a SparkContext and a typesafe Config object. Results returned by the method are made available through the REST API.

GET /jobs                - Lists the last N jobs
POST /jobs               - Starts a new job, use ?sync=true to wait for results
GET /jobs/<jobId>        - Gets the result or status of a specific job
GET /jobs/<jobId>/config - Gets the job configuration

Context configuration

A number of context-specific settings can be controlled when creating a context (POST /contexts) or running an ad-hoc job (which creates a context on the spot).

When creating a context via POST /contexts, the query params are used to override the default configuration in spark.context-settings. For example,

POST /contexts/my-new-context?num-cpu-cores=10

would override the default spark.context-settings.num-cpu-cores setting.

When starting a job, and the context= query param is not specified, then an ad-hoc context is created. Any settings specified in spark.context-settings will override the defaults in the job server config when it is started up.

Any spark configuration param can be overridden either in POST /contexts query params, or through spark .context-settings job configuration. In addition, num-cpu-cores maps to spark.cores.max, and mem-per- node maps to spark.executor.memory. Therefore the following are all equivalent:

POST /contexts/my-new-context?num-cpu-cores=10

POST /contexts/my-new-context?spark.cores.max=10

or in the job config when using POST /jobs,

spark.context-settings {
    spark.cores.max = 10
}

For the exact context configuration parameters, see JobManagerActor docs as well as application.conf.

Job Result Serialization

The result returned by the SparkJob runJob method is serialized by the job server into JSON for routes that return the result (GET /jobs with sync=true, GET /jobs/<jobId>). Currently the following types can be serialized properly:

If we encounter a data type that is not supported, then the entire result will be serialized to a string.

Contribution and Development

Contributions via Github Pull Request are welcome. See the TODO for some ideas.

Publishing packages

Contact

For user/dev questions, we are using google group for discussions: https://groups.google.com/forum/#!forum/spark-jobserver

Please report bugs/problems to: https://github.com/ooyala/spark-jobserver/issues

License

Apache 2.0, see LICENSE.md

Copyright(c) 2014, Ooyala, Inc.

TODO