spark-jobserver/spark-jobserver. All issues and pull requests should start there.
***This is not the official open source version. For that, please go tospark-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
- "Spark as a Service": Simple REST interface for all aspects of job, context management
- Supports sub-second low-latency jobs via long-running job contexts
- Start and stop job contexts for RDD sharing and low-latency jobs; change resources on restart
- Kill running jobs via stop context
- Separate jar uploading step for faster job startup
- Asynchronous and synchronous job API. Synchronous API is great for low latency jobs!
- Works with Standalone Spark as well as Mesos
- Job and jar info is persisted via a pluggable DAO interface
- Named RDDs to cache and retrieve RDDs by name, improving RDD sharing and reuse among jobs.
Version Information
Version | Spark Version |
---|---|
0.3.1 | 0.9.1 |
0.4.0 | 1.0.2 |
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 = ???
}
runJob
contains the implementation of the Job. The SparkContext is managed by the JobServer and will be provided to the job through this method. This releaves the developer from the boiler-plate configuration management that comes with the creation of a Spark job and allows the Job Server to manage and re-use contexts.validate
allows for an initial validation of the context and any provided configuration. If the context and configuration are OK to run the job, returningspark.jobserver.SparkJobValid
will let the job execute, otherwise returningspark.jobserver.SparkJobInvalid(reason)
prevents the job from running and provides means to convey the reason of failure. In this case, the call immediatly returns anHTTP/1.1 400 Bad Request
status code.
validate
helps you preventing running jobs that will eventually fail due to missing or wrong configuration and save both time and resources.
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
- Copy
config/local.sh.template
to<environment>.sh
and edit as appropriate. 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
- On the remote server, start it in the deployed directory with
server_start.sh
and stop it withserver_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:
- We want the job server to work for Mesos and YARN as well
- Spark and Mesos masters are organized around "applications" or contexts, but the job server supports running many discrete "jobs" inside a single context
- We want it to support Shark functionality in the future
- Loose coupling allows for flexible HA arrangements (multiple job servers targeting same standalone master, or possibly multiple Spark clusters per job server)
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/). Currently the following types can be
serialized properly:
- String, Int, Long, Double, Float, Boolean
- Scala Map's with string key values (non-string keys may be converted to strings)
- Scala Seq's
- Array's
- Anything that implements Product (Option, case classes) -- they will be serialized as lists
- Maps and Seqs may contain nested values of any of the above
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.
- From the "master" project, please run "test" to ensure nothing is broken.
- You may need to set
SPARK_LOCAL_IP
tolocalhost
to ensure Akka port can bind successfully
- You may need to set
- Logging for tests goes to "job-server-test.log"
- Run
scoverage:test
to check the code coverage and improve it - Please run scalastyle to ensure your code changes don't break the style guide
- Do "re-start" from SBT for quick restarts of the job server process
- Please update the g8 template if you change the SparkJob API
Publishing packages
- Be sure you are in the master project
- Run
test
to ensure all tests pass - Now just run
publish
and package will be published to bintray
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
-
Add Swagger support. See the spray-swagger project.
-
Implement an interactive SQL window. See: spark-admin
-
Use
SparkContext.setJobGroup
with the job ID -
Support job cancellation via
cancelJobGroup
-
Stream the current job progress via a Listener
-
Add routes to return stage info for a job. Persist it via DAO so that we can always retrieve stage / performance info even for historical jobs. This would be pretty kickass.