I have pseudo distributed hadoop 2.2.0 Environment setup in my laptop.I can run mapreduce applications(including Pig and Hive jobs) and the status of the applications can be seen from the Web UI http://localhost:8088
I have downloaded the Spark library and just used the file system-HDFS for the spark applications.when I launch a spark application,it is getting launched and the execution also gets completed successfully as expected.
But the Web UI http://localhost:8088 is not listing the Spark application completed/launched.
Please suggest if there is any other additional configuration is required for seeing Spark applications in the Web UI.
(Note: http://localhost:50070this Web UI shows the files correctly,when tried writing files to HDFS via Spark applications)
You might have figured it out but for others who are starting with Spark.You can see all the spark jobs on
http://localhost:4040
after your spark context is initiated(port can be different eg 4041). Based on Standalone installation you can see the master and slave status on
http://localhost:8080
(for slave port is usually 8081 onward). you need to Spark-submit jobs to yarn-cluster or client to see the same on hadoop webservices.
Related
I am trying to implement a Rest client for Flink to send jobs via Restful Flink services. And also I want to integrate Flink and Kubernetes natively. I have decided to use “Application Mode” as deployment mode according to Flink documentation .
I have already implemented a job and packaged it as jar. And I have tested it on Standalone Flink. But my aim is to move on Kubernetes and deploy my application in Application mode via Rest API of Flink.
I have already investigated the samples at Flink documentation - Native Kubernetes. But I cannot find a sample for executing same samples via Restful services (esp. how to set --target kubernetes-application/kubernetes-session or other parameters).
In addition to samples, I checked out the Flink sources from GitHub and tried to find some sample implementation or get some clue.
I think the below ones are related with my case.
org.apache.flink.client.program.rest. RestClusterClient
org.apache.flink.kubernetes. KubernetesClusterDescriptorTest. testDeployApplicationCluster
But they are all so complicated for me to understand below points.
For application mode, are there any need to initialize a container to serve Flink Rest services before submitting job? If so, is it JobManager?
For application mode, how can I set the same command line parameters via Rest services?
For session mode, in command line samples, kubernetes-session.sh is executed before job submission to initialize a JobManager container. How sould I do this step via Rest client?
For session mode, how can I set the same command line parameters via Rest services? Although the command line samples send .jar job as parameter, should I upload jar before submitting job?
Could you please provide me some clue/sample to continue my implementation?
Best regards,
Burcu
I suspect that if you study the implementation of the Apache Flink Kubernetes Operator you'll find some clues.
I wrote code with Scala to run a Cluster in EMR. Also, I have a Spark application written in Scala. I want to run this Spark application on EMR Cluster. But is it possible for me to do this in the first script (that launch EMR Cluster)? I want to do all of them with the SDK, not through the console or CLI. It has to be a kind of automatization, not a single manual job (or minimize manual job).
Basically;
Launch EMR Cluster -> Run Spark Job on EMR -> Terminate after job finished
How do I do it if possible?
Thanks.
HadoopJarStepConfig sparkStepConf = new HadoopJarStepConfig()
.withJar("command-runner.jar")
.withArgs(params);
final StepConfig sparkStep = new StepConfig()
.withName("Spark Step")
.withActionOnFailure("CONTINUE")
.withHadoopJarStep(sparkStepConf);
AddJobFlowStepsRequest request = new AddJobFlowStepsRequest(clusterId)
.withSteps(new ArrayList<StepConfig>(){{add(sparkStep);}});
AddJobFlowStepsResult result = emr.addJobFlowSteps(request);
return result.getStepIds().get(0);
If you are looking just for automation you should read about Pipeline Orchestration-
EMR is the AWS service which allows you to run distributed applications
AWS DataPipeline is an Orchestration tool that allows you to run jobs (activities) on resources (EMR or even EC2)
If you'd just like to run a spark job consistently, I would suggest creating a data pipeline, and configuring your pipeline to have one step, which is to run the Scala spark jar on the master node using a "shellcommandactivity". Another benefit is that the jar you are running can be stored in AWS S3 (object storage service) and you'd just provide the s3 path to your DataPipeline and it will pick up that jar, log onto the EMR service it has brought up (with the configurations you've provided)- clone that jar on the master node, run the jar with the configuration provided in the "shellcommandactivity", and once the the job exits (successfully or with an error) it will kill the EMR cluster so you aren't paying for it and log the output
Please read more into it: https://aws.amazon.com/datapipeline/ & https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/what-is-datapipeline.html
And if you'd like you can trigger this pipeline via the AWS SDK or even set the pipeline to run on a schedule
I have an Apache Spark service instance on IBM cloud(light plan). After I submit a Spark job I want to see its progress, it would be perfect to see it the Spark way - get the Spark progress UI with number of partitions and everything. I would also like to get a connection to the history server.
I saw that I can run ./spark-submit.sh ... --status <app id> but I would like to get something more informative.
I saw the comment
You can track the current execution of your running application and see the details of previously run jobs on the Spark job history UI by clicking Job History on the Analytics for Apache Spark service console.
here, but fail to understand where exactly I get this console/history thing.
As a side note, is there any detailed technical documentation of this service, e.g. number of concurrent jobs which can run, technology stack etc..?
As per spark Documentation:
Every SparkContext launches a web UI, by default on port 4040, that displays useful information about the application. This includes:
A list of scheduler stages and tasks
A summary of RDD sizes and memory usage
Environmental information.
Information about the running executors
You can access this interface by simply opening http://{driver-node}:4040 in a web browser. If multiple SparkContexts are running on the same host, they will bind to successive ports beginning with 4040 (4041, 4042, etc).
Bottom Line : http://{driver-node}:4040 (replace driver-node with the node where spark job invoked) and you should be good to go
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 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).