When I am running pyspark program interactive shell able to fetch the configuration file(config.ini) inside pyspark script,
But when I am trying to run same script using Spark submit command with master yarn and cluster deployment mode is cluster it giving me error as config file not exists, I have checked yarn log and able to see same, below is command for running the pyspark job.
spark2-submit --master yarn --deploy-mode cluster test.py /home/sys_user/ask/conf/config.ini
With spark2-sumbmit command there is parameter provided properties-file, you can use that to get this properties file available in spark-submit command.
e.g. spark2-submit --master yarn --deploy-mode cluster --properties-file $CONF_FILE_NAME pyspark_script.py
Pass the ini file in spark.files parameter
.config('spark.files', 'config/local/config.ini') \
Read in pyspark:
with open(SparkFiles.get('config.ini')) as config_file:
print(config_file.read())
It works for me.
Related
Normally, if I'm using Scala for Spark jobs I'll compile a jarfile and submit it with gcloud dataproc jobs submit spark, but sometimes for very lightweight jobs I might be using uncompiled Scala code in a notebook or using the spark-shell REPL, where I assume a SparkContext is already available.
For some of these lightweight use cases I can equivalently use PySpark and submit with gcloud dataproc jobs submit pyspark but sometimes I need easier access to Scala/Java libraries such as directly creating a org.apache.hadoop.fs.FileSystem object inside of map functions. Is there any easy way to submit such "spark-shell" equivalent jobs directly from a command-line using Dataproc Jobs APIs?
At the moment, there isn't a specialized top-level Dataproc Job type for uncompiled Spark Scala, but under the hood, spark-shell is just using the same mechanisms as spark-submit to run a specialized REPL driver: org.apache.spark.repl.Main. Thus, combining this with the --files flag available in gcloud dataproc jobs submit spark, you can just write snippets of Scala that you may have tested in a spark-shell or notebook session, and run that as your entire Dataproc job, assuming job.scala is a local file on your machine:
gcloud dataproc jobs submit spark --cluster ${CLUSTER} \
--class org.apache.spark.repl.Main \
--files job.scala \
-- -i job.scala
Just like any other file, you can also specify any Hadoop-compatible path in the --files argument as well, such as gs:// or even hdfs://, assuming you've already placed your job.scala file there:
gcloud dataproc jobs submit spark --cluster ${CLUSTER} \
--class org.apache.spark.repl.Main \
--files gs://${BUCKET}/job.scala \
-- -i job.scala
gcloud dataproc jobs submit spark --cluster ${CLUSTER} \
--class org.apache.spark.repl.Main \
--files hdfs:///tmp/job.scala \
-- -i job.scala
If you've staged your job file onto the Dataproc master node via an init action, you'd use file:/// to specify that the file is found on the cluster's local filesystem instead of your local filesystem where you're running gcloud:
gcloud dataproc jobs submit spark --cluster ${CLUSTER} \
--class org.apache.spark.repl.Main \
--files file:///tmp/job.scala \
-- -i job.scala
Note in all cases, the file becomes a local file in the working-directory of the main driver job, so the argument to "-i" can just be a relative path to the filename.
I am learning to develop spark applications using Scala. And I am in my very first steps.
I have my scala IDE on windows. configured and runs smoothly if reading files from local drive. However, I have access to a remote hdfs cluster and Hive database, and I want to develop, try, and test my applications against that Hadoop cluster... but I don't know how :(
If I try
val rdd=sc.textFile("hdfs://masternode:9000/user/hive/warehouse/dwh_db_jrtf.db/discipline")
I will get an error that contains:
Exception in thread "main" java.io.IOException: Failed on local exception: com.google.protobuf.InvalidProtocolBufferException: Protocol message end-group tag did not match expected tag.; Host Details : local host is: "MyLap/11.22.33.44"; destination host is: "masternode":9000;
Can anyone guide me please ?
You can use SBT to package your code in a .jar file. scp your file on your Node then try to submit it by doing a spark-submit.
spark-submit \
--class <main-class> \
--master <master-url> \
--deploy-mode <deploy-mode> \
--conf <key>=<value> \
... # other options
<application-jar> \
[application-arguments]
You can't access to your Cluster from your Windows Machine in that way.
My spark job is failing and it looks like the reason is that my configuration file is not found on the worker node.
My config file is currently in:
/src/main/resources/application.conf
I copied the file to the root folder where I run the spark-submit command and I did this:
spark-submit --class "com.path.to.main.MainClass" --master local[*] --files application.conf /path/to/jar.jar
That didn't seem to work either as I got the same error.
What am I doing wrong?
i need your help, i created 2 apps (one which using spray framework and the other one receive messages from kafka and send it to cassandra).
Both run all the time and should never stop.
I m in standalone on the server and my conf is :
- In spark_env.sh :
SPARK_MASTER_IP=MYIP
SPARK_EXECUTOR_CORES=2
SPARK_MASTER_PORT=7077
SPARK_EXECUTOR_MEMORY=4g
#SPARK_WORKER_PORT=65000
MASTER=spark://${SPARK_MASTER_IP}:${SPARK_MASTER_PORT}
SPARK_LOCAL_IP=MYIP
SPARK_MASTER_WEBUI_PORT=8080
- In spark_env.sh :
spark.master spark://MYIPMASTER:7077
spark.eventLog.enabled true
spark.eventLog.dir /opt/spark-1.6.1-bin-hadoop2.6/spark-events
spark.history.fs.logDirectory /opt/spark-1.6.1-bin-hadoop2.6/logs
spark.io.compression.codec lzf
spark.cassandra.connection.host MYIPMASTER
spark.cassandra.auth.username LOGIN
spark.cassandra.auth.password PASSWORD
I can access on both pages :
MYIP:8080/ and MYIP:4040/
But on http://MYIP:8080/, i see only my workers , i can t see my application which running.
When i submit i use this :
/opt/spark-1.6.1-bin-hadoop2.6/bin/spark-submit --class MYCLASS --verbose --conf spark.eventLog.enable=true --conf spark.master.ui.port=8080 --master local[2] /opt/spark-1.6.1-bin-hadoop2.6/jars/MYJAR.jar
Why ?
Could you help me?
Thanks a lot :)
In your spark-submit command you are using the --master as local[2] which is submitting the application in local mode. If you wants to run it on the standalone cluster that you are running then you should pass spark master URL in master option i.e. --master spark://MYIPMASTER:7077
In terms of the master, spark-submit will respect the setting by following orders,
The master URL in your application code, which is the
SparkSession.builder().master("...")
The --master parameter for the spark-submit command
The default configuration in your spark-defaults.conf
Mode: Standalone cluster
1> bin/spark-submit --class com.deepak.spark.App ../spark-0.0.2-SNAPSHOT.jar --master spark://172.29.44.63:7077, was not working because master was specified after the jar
2> bin/spark-submit --class com.deepak.spark.App --master spark://172.29.44.63:7077 ../spark-0.0.2-SNAPSHOT.jar, this worked
I am getting this error when is want to run SparkPi example.
beyhan#beyhan:~/spark-1.2.0-bin-hadoop2.4$ /home/beyhan/spark-1.2.0-bin-hadoop2.4/bin/spark-submit --master ego-client --class org.apache.spark.examples.SparkPi /home/beyhan/spark-1.2.0-bin-hadoop2.4/lib/spark-examples-1.jar
Spark assembly has been built with Hive, including Datanucleus jars on classpath
Error: Master must start with yarn, spark, mesos, or local
Run with --help for usage help or --verbose for debug output
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Also i already start my master via another terminal
>./sbin/start-master.sh
starting org.apache.spark.deploy.master.Master, logging to /home/beyhan/spark-1.2.0-bin-hadoop2.4/sbin/../logs/spark-beyhan-org.apache.spark.deploy.master.Master-1-beyhan.out
Any suggestion ?
Thanks.
Download and extract Spark:
$ cd ~/Downloads
$ wget -c http://archive.apache.org/dist/spark/spark-1.2.0/spark-1.2.0-bin-hadoop2.4.tgz
$ cd /tmp
$ tar zxf ~/Downloads/spark-1.2.0-bin-hadoop2.4.tgz
$ cd spark-1.2.0-bin-hadoop2.4/
Start master:
$ sbin/start-master.sh
Find master's URL from logs in the file that above command printed. Lets assume that master is: spark://ego-server:7077
In this case, you can also find your master url by visiting this URL: http://localhost:8080/
Start one slave, and connect it to master:
$ sbin/start-slave.sh --master spark://ego-server:7077
Another way to ensure that master up and running start a shell bound to that master:
$ bin/spark-submit --master "spark://ego-server:7077"
If you get a spark shell, then everything seems fine.
Now execute your job:
$ find . -name "spark-example*jar"
./lib/spark-examples-1.2.0-hadoop2.4.0.jar
$ bin/spark-submit --master "spark://ego-server:7077" --class org.apache.spark.examples.SparkPi ./lib/spark-examples-1.2.0-hadoop2.4.0.jar
The error you're getting
Error: Master must start with yarn, spark, mesos, or local
Means that --master ego-client is not recognized by spark.
Use
--master local
for a local execution of spark or
--master spark://your-spark-master-ip:7077