Can I run the program, listens at the port in the cluster?
I want to write an application that accepts http requests and performs some calculation using spark
Yes, you can run any code you want on driver node. You can use, for example, spray.io http server and connect to spark actor system:
import org.apache.spark.SparkEnv
implicit val system = SparkEnv.get.actorSystem
But there is no way to execute arbitrary code on workers. Workers run only code blocks inside RDD's map-reduce functions.
It is hard to understand your English, but, if I understood you correctly you are looking for something like Spark-JobServer
Related
The spark application uses some API calls which do not use spark-session. I believe when the piece of code doesn't use spark it is getting executed on the master node!
Why do I want to know this?
I am getting a java heap space error while I am trying to POST some files using API calls and I believe if I upgrade the master and increase driver mem it can be solved.
I want to understand how this type of application is executed on the Spark cluster?
Is my understanding right or am I missing something?
It depends - closures/functions passed to the built-in function transform or any code in udfs you create, code in forEachBatch (and maybe a few other places) will run on the workers. Other code runs on driver
BackGround:
Our project is build on PlayFrameWork.
Front-end language: JavaScript
Back-end language: Scala
we are develope a web application,the server is a cluster.
Want to achieve:
In the web UI, User first input some parameters which about query, and click the button such as "submit".Then these parameters will be sent to backend. (This is easy，obviously)
When backend get parameters, backend start reading and process the data which store in HDFS. Data processing include data-cleaning,filtering and other operations such as clustering algorithms,not just a spark-sql query. All These operations need to run on spark cluster
We needn't manually pack a fat jar and submit it to cluster and send the result to front-end（These are what bothering me!）
What we have done:
We build a spark-project separately in IDEA. When we get parameters, we manually assign these parameters to variables in spark-project.
Then "Build Artifacts"->"Bulid" to get a fat jar.
Then submit by two approaches:
"spark-submit --class main.scala.Test --master yarn /path.jar"
run scala code directly in IDEA on local mode (if change to Yarn, will throw Exceptions).
When program execution finished, we get the processed_data and store it.
Then read the processed_data's path and pass it to front-end.
All are not user interactively submit. Very stupid!
So if I am a user, I want to query or process data on cluster and get feedback on front-end conveniently.
What should i do?
Which tools or lib could use?
Thanks!
Here is multiple ways to submit a spark job:
using spark-submit command on terminal.
using spark built-in rest API. you can click to find out how to use it.
providing a rest API in yourself in your program and set the api as the Main-Class to run the jar on your spark cluster master. By doing so, your api should dispatch the input job submit requests to the certain action you want. At your api you should instantiate the class where your SparkContext is instantiated. This action is the equivalent of the spark-submit action. It means that when rest api receives the job submission request and do as mentioned above you can see the job progression on the master web ui and then your job termination your api is up and waits for your next request.
**The 3rd solution is my own experience to run different types of algorithms in a web crawler. **
So generally you have two approaches:
Create Spark application that will also be a web service
Create Spark application that will be called by a web service
First approach - spark app is a web service, is not good approach, because for as long as your web service will be running you will also use resources on a cluster (except if you run spark on mesos with specific configuration) - read more about cluster managers here.
Second approach - service and spark app separated is better. In this approach you can create one or multiple spark applications that will be launched by calling spark submit from web service. There are also two options - create single spark app that will be called with parameters that will specify what to do, or create one spark app for one query. The result of the queries in this approach could be just saved to a file or sent to a web server via network or any using any other inter process communication approach.
I use livy to use Spark as a service. My application send some commands to livy as code, however, spark needs to initialize some variables(read some files, make some map&reduce operations etc.) and this take time. This initializing part is common for all sessions. After the construction, different statements may be sent to these sessions.
What i wonder is when livy creates a session, is it possible to copy an old session line an image or should it start everything from scratch?
Thank you in advance.
After some amount of researches, it is not possible with Livy server. The only responsibility of Livy is serving a rest service for applications to reach the Spark framework in the Hadoop cluster. For each request (whether batch or session), it opens a seperate spark-shell. Therefore, it is not possible to clone an existing session.
Also one more addition, I really didn't like the way livy server handles the external dependencies. Generating a fat jar is not an appropriate way for hadoop environment, since there are a lot of them. However, if you implement a spark application with command-line arguments it is an easy way to communicate with the Hadoop environment via HTTP with an interactive manner.
I am building a service which takes in a string of Spark code to execute on a cluster. Is there any way for me to set the Spark context to the cluster and execute without building a jar and submitting it?
Indeed, you can use the spark shell, or look at something like the IBM Spark Kernel, Zeppelin, etc. to have a long running Spark Context you can submit code to and have it run. As you are almost certainly already aware of, be very careful with accepting strings and executing them on the cluster (e.g. only from a trusted source).
I have a spark job, which I normally run with spark-submit with the input file name as the argument. Now I want to make the job available for the team, so people can submit an input file (probably through some web-API), then the spark job will be trigger, and it will return user the result file (probably also through web-API). (I am using Java/Scala)
What do I need to build in order to trigger the spark job in such scenario? Is there some tutorial somewhere? Should I use spark-streaming for such case? Thanks!
One way to go is have a web server listening for jobs, and each web request potentially triggering an execution of a spark-submit.
You can execute this using Java's ProcessBuilder.
To the best of my knowledge, there is no good way of invoking spark jobs other than through spark-submit.
You can use Livy.
Livy is an open source REST interface for using Spark from anywhere.
Livy is a new open source Spark REST Server for submitting and interacting with your Spark jobs from anywhere. Livy is conceptually based on the incredibly popular IPython/Jupyter, but implemented to better integrate into the Hadoop ecosystem with multi users. Spark can now be offered as a service to anyone in a simple way: Spark shells in Python or Scala can be ran by Livy in the cluster while the end user is manipulating them at his own convenience through a REST api. Regular non-interactive applications can also be submitted. The output of the jobs can be introspected and returned in a tabular format, which makes it visualizable in charts. Livy can point to a unique Spark cluster and create several contexts by users. With YARN impersonation, jobs will be executed with the actual permissions of the users submitting them.
Please check this url for info.
https://github.com/cloudera/livy
You can use SparkLauncher class to do this. You will need to have a REST API that will take file from the user and after that trigger the spark job using SparkLauncher.
Process spark = new SparkLauncher()
.setAppResource(job.getJarPath())
.setMainClass(job.getMainClass())
.setMaster("master spark://"+this.serverHost + ":" + this.port)
.launch();