I want to run a maven project in spark cluster mode. I have the application jar file. I also have one master and 6 workers in working condition. But when I execute the jar application, the work is not getting distributed among the workers. The following is the command I gave from the spark directory.
./bin/spark-submit --class org.deeplearning4j.mlp.MnistMLPExample --master spark://115.145.173.152:7077 --driver-memory 10g /home/hadoop/Niki/mnist/target/dl4j-spark-0.7-SNAPSHOT-bin.jar.
If I add another parameter --deploy-mode cluster, Then its throwing exception as follows:
Exception in thread "main" com.beust.jcommander.ParameterException: Unknown option: --deploy-mode
Can anyone help me out. Thanks a lot
Hi Nikitha yes you need jar file in all worker nodes because spark transformations and actions will execute on worker nodes and if they use this path they search file in there local path so distribute it on all worker nodes also Can you please tell why you use this jar file path in spark code.
You are running spark in standalone mode. There is no cluster/client mode in standalone. It is relvent in yarn only.
So I've been using sbt with assembly to package all my dependencies into a single jar for my spark jobs. I've got several jobs where I was using c3p0 to setup connection pool information, broadcast that out, and then use foreachPartition on the RDD to then grab a connection, and insert the data into the database. In my sbt build script, I include
"mysql" % "mysql-connector-java" % "5.1.33"
This makes sure the JDBC connector is packaged up with the job. Everything works great.
So recently I started playing around with SparkSQL and realized it's much easier to simply take a dataframe and save it to a jdbc source with the new features in 1.3.0
I'm getting the following exception :
java.sql.SQLException: No suitable driver found for
jdbc:mysql://some.domain.com/myschema?user=user&password=password at
java.sql.DriverManager.getConnection(DriverManager.java:596) at
java.sql.DriverManager.getConnection(DriverManager.java:233)
When I was running this locally I got around it by setting
SPARK_CLASSPATH=/path/where/mysql-connector-is.jar
Ultimately what I'm wanting to know is, why is the job not capable of finding the driver when it should be packaged up with it? My other jobs never had this problem. From what I can tell both c3p0 and the dataframe code both make use of the java.sql.DriverManager (which handles importing everything for you from what I can tell) so it should work just fine?? If there is something that prevents the assembly method from working, what do I need to do to make this work?
This person was having similar issue: http://apache-spark-user-list.1001560.n3.nabble.com/How-to-use-DataFrame-with-MySQL-td22178.html
Have you updated your connector drivers to the most recent version? Also did you specify the driver class when you called load()?
Map<String, String> options = new HashMap<String, String>();
options.put("url", "jdbc:mysql://localhost:3306/video_rcmd?user=root&password=123456");
options.put("dbtable", "video");
options.put("driver", "com.mysql.cj.jdbc.Driver"); //here
DataFrame jdbcDF = sqlContext.load("jdbc", options);
In spark/conf/spark-defaults.conf, you can also set spark.driver.extraClassPath and spark.executor.extraClassPath to the path of your MySql driver .jar
These options are clearly mentioned in spark docs: --driver-class-path postgresql-9.4.1207.jar --jars postgresql-9.4.1207.jar
The mistake I was doing was mentioning these options after my application's jar.
However the correct way is to specify these options immediately after spark-submit:
spark-submit --driver-class-path /somepath/project/mysql-connector-java-5.1.30-bin.jar --jars /somepath/project/mysql-connector-java-5.1.30-bin.jar --class com.package.MyClass target/scala-2.11/project_2.11-1.0.jar
Both spark driver and executor need mysql driver on class path so specify
spark.driver.extraClassPath = <path>/mysql-connector-java-5.1.36.jar
spark.executor.extraClassPath = <path>/mysql-connector-java-5.1.36.jar
With spark 2.2.0, problem was corrected for me by adding extra class path information for SparkSession session in python script :
spark = SparkSession \
.builder \
.appName("Python Spark SQL basic example") \
.config("spark.driver.extraClassPath", "/path/to/jdbc/driver/postgresql-42.1.4.jar") \
.getOrCreate()
See official documentation https://spark.apache.org/docs/latest/configuration.html
In my case, spark is not launched from cli command, but from django framework https://www.djangoproject.com/
spark.driver.extraClassPath does not work in client-mode:
Note: In client mode, this config must not be set through the SparkConf directly in your application, because the driver JVM has already started at that point. Instead, please set this through the --driver-class-path command line option or in your default properties file.
Env variable SPARK_CLASSPATH has been deprecated in Spark 1.0+.
You should first copy the jdbc driver jars into each executor under the same local filesystem path and then use the following options in you spark-submit:
--driver-class-path "driver_local_file_system_jdbc_driver1.jar:driver_local_file_system_jdbc_driver2.jar"
--class "spark.executor.extraClassPath=executors_local_file_system_jdbc_driver1.jar:executors_local_file_system_jdbc_driver2.jar"
For example in case of TeraData you need both terajdbc4.jar and tdgssconfig.jar .
Alternatively modify compute_classpath.sh on all worker nodes, Spark documentation says:
The JDBC driver class must be visible to the primordial class loader on the client session and on all executors. This is because Java’s DriverManager class does a security check that results in it ignoring all drivers not visible to the primordial class loader when one goes to open a connection. One convenient way to do this is to modify compute_classpath.sh on all worker nodes to include your driver JARs.
There exists a simple Java trick to solve your problem. You should specify Class.forName() instance. For example:
val customers: RDD[(Int, String)] = new JdbcRDD(sc, () => {
Class.forName("com.mysql.jdbc.Driver")
DriverManager.getConnection(jdbcUrl)
},
"SELECT id, name from customer WHERE ? < id and id <= ?" ,
0, range, partitions, r => (r.getInt(1), r.getString(2)))
Check the docs
Simple easy way is to copy "mysql-connector-java-5.1.47.jar" into "spark-2.4.3\jars\" directory
I had the same problem running jobs over a Mesos cluster in cluster mode.
To use a JDBC driver is necessary to add the dependency to the system classpath not to the framework classpath. I only found the way of doing it by adding the dependency in the file spark-defaults.conf in every instance of the cluster.
The properties to add are spark.driver.extraClassPath and spark.executor.extraClassPath and the path must be in the local file system.
I add the jar file to the SPARK_CLASSPATH in spark-env.sh, it works.
export SPARK_CLASSPATH=$SPARK_CLASSPATH:/local/spark-1.6.3-bin-hadoop2.6/lib/mysql-connector-java-5.1.40-bin.jar
I was facing the same issue when I was trying to run the spark-shell command from my windows machine. The path that you pass for the driver location as well as for the jar that you would be using should be in the double quotes otherwise it gets misinterpreted and you would not get the exact output that you want.
you also would have to install the JDBC driver for SQL server from the link : JDBC Driver
I have used the below command for this to work fine for me on my windows machine:
spark-shell --driver-class-path "C:\Program Files\Microsoft JDBC Driver 6.0 for SQL Server\sqljdbc_6.0\enu\jre8\sqljdbc42.jar" --jars "C:\Program Files\Microsoft JDBC Driver 6.0 for SQL Server\sqljdbc_6.0\enu\jre8\sqljdbc42.jar"
what's the difference between run a jar file with commands "hadoop jar " and "yarn -jar " ?
I've used the "hadoop jar" command on my MAC successfully but I want be sure that the execution is being correct and parallel on my four cores.
Thanks!!!
Short Answer
They are probably identical for you, but even if they aren't, they should both utilize your cluster to the best of its ability.
Longer Answer
The /usr/bin/yarn script sets up the execution environment so that all of the yarn commands can be run. The /usr/bin/hadoop script isn't quite as concerned about yarn specific functionality. However, if you have your cluster set up to use yarn as the default implementation of mapreduce (MRv2), then hadoop jar will probably act the same as yarn jar for a mapreduce job.
Either way you're probably fine, but you can always check the resource manager (or job tracker) web interface to see how your job is distributed across the cluster (whether it's a single node cluster or not)
I'm working on a hadoop cluster with CDH4.2.0 installed and ran into this error. It's been fixed in later versions of hadoop but I don't have access to update the cluster. Is there a way to tell hadoop to use this jar when running my job through the command line arguments like
hadoop jar MyJob.jar -D hadoop.mapreduce.client=hadoop-mapreduce-client-core-2.0.0-cdh4.2.0.jar
where the new mapreduce-client-core.jar file is the patched jar from the ticket. Or must hadoop be completely recompiled with this new jar? I'm new to hadoop so I don't know all the command line options that are possible.
I'm not sure how that would work as when you're executing the hadoop command you're actually executing code in the client jar.
Can you not use MR1? The issue says this issue only occurs when you're using MR2, so unless you really need Yarn you're probably better using the MR1 library to run your map/reduce.
I have a 6 node cloudera based hadoop cluster and I'm trying to connect to an oracle database from a sqoop action in oozie.
I have copied my ojdbc6.jar into the sqoop lib location (which for me happens to be at: /opt/cloudera/parcels/CDH-4.2.0-1.cdh4.2.0.p0.10/lib/sqoop/lib/ ) on all the nodes and have verified that I can run a simple 'sqoop eval' from all the 6 nodes.
Now when I run the same command using Oozie's sqoop action, I get "Could not load db driver class: oracle.jdbc.OracleDriver"
I have read this article about using shared libs and it makes sense to me when we're talking about my task/action/workflow specific dependencies. But I see a JDBC driver installation as an extention to sqoop and so I think it belongs in the sqoop installation lib.
Now the question is, while sqoop sees this ojdbc6 jar I have put into it's lib folder, how come my Oozie workflow doesn't see it?
Is this something expected or am I missing something?
As an aside, what do you guy think about where is the appropriate location for a JDBC driver jar?
Thanks in advance!
The JDBC driver jar (and any jars it depends on) should go in your Oozie sharelib folder on HDFS. I'm running Hortonworks Data Platform 1.2 instead of Cloudera 4.2 so the details may vary, but my JDBC driver is located in /user/oozie/share/lib/sqoop. This should allow you to run Sqoop with the JDBC via Oozie.
It is not necessary to put to the JDBC driver jar in the sqoop lib on the data nodes. In my setupt I can't run a simple sqoop eval from the command line on my data nodes. I understand the logic for why you thought this would work. The reason the JDBC driver jar needs to be on HDFS is so that all the data nodes have access to it. Your solution should accomplish the same goal. I'm not familiar enough with the inner workings of Oozie to say why using the sharelib works but your solution does not.
In CDH5, you should put the jar to '/user/oozie/share/lib/lib_${timestamp}/sqoop', and after that, you must update the sharelib or restart oozie.
update sharelib:
oozie admin -oozie http://localhost:11000/oozie -sharelibupdate
If you are using CDH-5 the JDBC driver jar (and any jars it depends on) should go in '/user/oozie/share/lib/lib_timestamp/sqoop' folder on HDFS.
I was facing the same issue it was not able to find the mysql jar. I am using cloudera 4.4 in this even oozie admin -oozie http://localhost:11000/oozie -sharelibupdate command will not work
To resolve the issue I had followed the below steps:
create a user in Hue with hdfs and provide the admin privileges
using Hue UI upload the jar into /user/oozie/share/lib/sqoop hdfs path
or you can use below command:
hadoop put /var/lib/sqoop2/mysql-connector-java.jar /user/oozie/share/lib/sqoop
Once the jar is placed run the oozie command.