i've just registrated a free instance of "Analitycs for Apache Spark" and followed this tutorial to use spark submit ibm ad hoc designed script to run an app from my local machine on bluemix cloud cluster. The issue is the following: i've made everithing that was described in the tutorial and lunched this script
./spark-submit.sh --vcap ./vcap.json --deploy-mode cluster --master
https://spark.eu-gb.bluemix.net --files /home/vito/vinorosso2.csv
--conf spark.service.spark_version=2.2.0
/home/vito/workspace_2/sbt-esempi/target/scala-2.11/isolationF3.jar
--class progettoSisDis.MasterNode
everything proceed fine (dataset vinorosso2.csv and my fatJar are correctly uploaded) until the terminal sais :" submission complete" at this point when i go to the log file created by the script there was this error message :
Submit job result: Invalid plan and spark version combination in HTTP request (ibm.SparkService.PayGoPersonal, 2.0.0)
Submission ID:
ERROR: Problem submitting job. Exit
So, it wasn't enough to register a free instance of Analitycs for apache spark to submit a spark job? Hope someone can help. By the way, if it helps, on my local machine i'm using spark with intellij idea (scala). Byyye
From https://console.bluemix.net/docs/services/AnalyticsforApacheSpark/index-gentopic2.html#using_spark-submit you need to be using Spark version 1.6.x or 2.0.x. Your submit job is set to version 2.2.0. Try submitting using spark.service.spark_version=2.0.0 (assuming your code will work with this version of Spark).
Can any one please let me know how to submit spark Job from locally and connect to Cassandra cluster.
Currently I am submitting the Spark job after I login to Cassandra node through putty and submit the below dse-spark-submit Job command.
Command:
dse spark-submit --class ***** --total-executor-cores 6 --executor-memory 2G **/**/**.jar --config-file build/job.conf --args
With the above command, my spark Job able to connect to cluster and its executing, but sometimes facing issues.
So I want to submit spark job from my local machine. Can any one please guide me how to do this.
There are several things you could mean by "run my job locally"
Here are a few of my interpretations
Run the Spark Driver on a Local Machine but access a remote Cluster's resources
I would not recommend this for a few reasons, the biggest being that all of your job management will still be handled between your remote machine and the executors in the cluster. This would be equivalent of having a Hadoop Job Tracker running in a different cluster than the rest of the Hadoop distribution.
To accomplish this though you need to run a spark submit with a specific master uri. Additionally you would need to specify a Cassandra node via spark.cassandra.connection.host
dse spark-submit --master spark://sparkmasterip:7077 --conf spark.cassandra.connection.host aCassandraNode --flags jar
It is important that you keep the jar LAST. All arguments after the jar are interpreted as arguments for the application and not spark-submit parameters.
Run Spark Submit on a local Machine but have the Driver run in the Cluster (Cluster Mode)
Cluster mode means your local machine sends the jar and environment string over to the Spark Master. The Spark Master then chooses a worker to actually run the driver and the driver is started as a separate JVM by the worker. This is triggered using the --deploy-mode cluster flag. In addition to specifying the Master and Cassandra Connection Host.
dse spark-submit --master spark://sparkmasterip:7077 --deploy-mode cluster --conf spark.cassandra.connection.host aCassandraNode --flags jar
Run the Spark Driver in Local Mode
Finally there exists a Local mode for Spark which starts the entire Spark Framework in a single JVM. This is mainly used for testing. Local mode is activated by passing `--master local``
For more information check out the Spark Documentation on submitting applications
http://spark.apache.org/docs/latest/submitting-applications.html
I have submitted my spark job as mentioned here bin/spark-submit --class DataSet BasicSparkJob-assembly-1.0.jar without mentioning the --master parameter or spark.master parameter. Instead of that job gets submitted to my 3 node spark cluster. But i was wondering where it submitted the job because it is not showing any information in the Running Applications
If you do not set the master in --master nor spark.master Spark will run locally.
You could still view the progress of your job. By default the UI will be availalbe during the running of your spark job on http://localhost:4040.
When your job finishes, this UI will be killed and you could not view the history of your application unless you configured Spark history server
It's likely that Spark is running your in local mode on your development machine.
I use Jenkins to run a spark-submit every hour with --deploy-mode cluster set. Is approach recommended? Because some things don't seem to work.
When I go to my spark server, and press the Application name, it tries to go to my computer on that port (which is firewalled) instead of the server. With --deploy-mode cluster I'd expect to be able to see this on the spark master since the spark-submit returns immediately in this mode.
Also, I can't seem to open the DAG. Is that related?
The full command I use is spark-submit --master spark://mysparkserver:6066 --deploy-mode cluster --class someClass --conf spark.driver.userClassPathFirst=true http://theJarUrl
Found it! Despite the fact that you can enable event logging on spark master, you also need to specify it in spark-submit using --conf spark.eventLog.enabled=true --conf spark.eventLog.dir=/opt/spark/spark-events. After you can watch the events on the spark-master by clicking the application name. It will also enable inspecting finished applications.
Despite that the DAG visualisation still doesn't work for me. If anyone has a suggestion for that, please leave a comment.
I'm trying to submit a Spark app from local machine Terminal to my Cluster.
I'm using --master yarn-cluster. I need to run the driver program on my Cluster too, not on the machine I do submit the application i.e my local machine
When I provide the path to application jar which is in my local machine, would spark-submit automatically upload it to my Cluster?
I'm using
bin/spark-submit
--class com.my.application.XApp
--master yarn-cluster --executor-memory 100m
--num-executors 50 /Users/nish1013/proj1/target/x-service-1.0.0-201512141101-assembly.jar
1000
and getting error
Diagnostics: java.io.FileNotFoundException: File file:/Users/nish1013/proj1/target/x-service-1.0.0-201512141101- does not exist
In Documentation ,http://spark.apache.org/docs/latest/submitting-applications.html#launching-applications-with-spark-submit
Advanced Dependency Management When using spark-submit, the
application jar along with any jars included with the --jars option
will be automatically transferred to the cluster.
But seems like it does not !
I see you are quoting the spark-submit page from Spark Docs but I would spend a lot more time on the Running Spark on YARN page. Bottom-line, look at:
There are two deploy modes that can be used to launch Spark
applications on YARN. In yarn-cluster mode, the Spark driver runs
inside an application master process which is managed by YARN on the
cluster, and the client can go away after initiating the application.
In yarn-client mode, the driver runs in the client process, and the
application master is only used for requesting resources from YARN.
Further you note, "I need to run the driver program on my Cluster too, not on the machine I do submit the application i.e my local machine"
So I agree with you that you are right to run --master yarn-cluster instead of --master yarn-client
(and one comment notes what might just be a syntax error where you dropped "assembly.jar" but I think this will apply as well...)
Some of the basic assumptions about non-YARN implementations change a lot when YARN is introduced, mostly related to Classpaths and the need to push jars to the workers.
From an email on the Apache Spark User list:
YARN cluster mode. Spark submit does upload your jars to the cluster.
In particular, it puts the jars in HDFS so your driver can just read
from there. As in other deployments, the executors pull the jars from
the driver.
So finally, from the Apache Spark YARN doc:
Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory
which contains the (client side) configuration files for the Hadoop
cluster. These configs are used to write to HDFS and connect to the
YARN ResourceManager.
NOTE: I only see you adding a single JAR, if there's a need to add other JARs there's a special note about doing that with YARN:
In yarn-cluster mode, the driver runs on a different machine than the
client, so SparkContext.addJar won’t work out of the box with files
that are local to the client. To make files on the client available to
SparkContext.addJar, include them with the --jars option in the launch
command.
That page in the link has some examples.
And of course you downloaded or built the YARN-specific version of Spark.
Background, in a standalone cluster deployment using spark-submit and the option --deploy-mode cluster, yes you do need to make sure every worker node has access to all the dependencies, Spark will not push them to the cluster. This is because in "standalone cluster" mode with Spark as the job manager, you don't know which node the driver will run on! But that doesn't apply to your case.
But if I could, depending on the size of the jars you are uploading, I would still explicitly put the jars on each node, or "globally available" via HDFS, for another reason from the docs:
From Advanced Dependency Management, it seems to present the best of both worlds, but also a great reason for manually pushing your jars out to all nodes:
local: - a URI starting with local:/ is expected to exist as a local
file on each worker node. This means that no network IO will be
incurred, and works well for large files/JARs that are pushed to each
worker, or shared via NFS, GlusterFS, etc.
But I assume that local:/... would change to hdfs:/ ... not sure on that one.
Yes and no. It depends on what you mean. Spark deploys the .jar to the nodes in the cluster. However, it won't upload your .jar file from your local machine to the cluster.
You can find more info in the Submitting Applications page. As you can see, in the arguments you pass to spark-submit, there is one that needs to be globally visible: the application-jar.
application-jar: Path to a bundled jar including your application and
all dependencies. The URL must be globally visible inside of your
cluster, for instance, an hdfs:// path or a file:// path that is
present on all nodes.
As far as I understand, what you want is to use yarn-client, not yarn-cluster. This will run the driver in the client (e.g., the machine which you are trying to call spark-submit on, for example your laptop), without the need of copying the .jar file on the cluster. More about this here.
Try adding --jars option before your /path/to/jar/file
spark-submit --jars /tmp/test.jar