Apache spark error while running with pyspark in a cluster [duplicate] - apache-spark

Question

Here is what I am trying to do.
I have created two nodes of DataStax enterprise cluster,on top of which I have created a java program to get the count of one table (Cassandra database table).
This program was built in eclipse which is actually from a windows box.
At the time of running this program from windows it's failing with the following error at runtime:
Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory
The same code has been compiled & run successfully on those clusters without any issue. What could be the reason why am getting above error?
Code:
import org.apache.spark.SparkConf;
import org.apache.spark.SparkContext;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.SchemaRDD;
import org.apache.spark.sql.cassandra.CassandraSQLContext;
import com.datastax.bdp.spark.DseSparkConfHelper;
public class SparkProject {
public static void main(String[] args) {
SparkConf conf = DseSparkConfHelper.enrichSparkConf(new SparkConf()).setMaster("spark://10.63.24.14X:7077").setAppName("DatastaxTests").set("spark.cassandra.connection.host","10.63.24.14x").set("spark.executor.memory", "2048m").set("spark.driver.memory", "1024m").set("spark.local.ip","10.63.24.14X");
JavaSparkContext sc = new JavaSparkContext(conf);
CassandraSQLContext cassandraContext = new CassandraSQLContext(sc.sc());
SchemaRDD employees = cassandraContext.sql("SELECT * FROM portware_ants.orders");
//employees.registerTempTable("employees");
//SchemaRDD managers = cassandraContext.sql("SELECT symbol FROM employees");
System.out.println(employees.count());
sc.stop();
}
}

Answer 1

I faced similar issue and after some online research and trial-n-error, I narrowed down to 3 causes for this (except for the first the other two are not even close to the error message):
As indicated by the error, probably you are allocating the resources more than that is available. => This was not my issue
Hostname & IP Address mishaps: I took care of this by specifying the SPARK_MASTER_IP and SPARK_LOCAL_IP in spark-env.sh
Disable Firewall on the client : This was the solution that worked for me. Since I was working on a prototype in-house code, I disabled the firewall on the client node. For some reason the worker nodes, were not able to talk back to the client for me. For production purposes, you would want to open-up certain number of ports required.

Answer 2

My problem was that I was assigning too much memory than my slaves had available. Try reducing the memory size of the spark submit. Something like the following:
~/spark-1.5.0/bin/spark-submit --master spark://my-pc:7077 --total-executor-cores 2 --executor-memory 512m
with my ~/spark-1.5.0/conf/spark-env.sh being:
SPARK_WORKER_INSTANCES=4
SPARK_WORKER_MEMORY=1000m
SPARK_WORKER_CORES=2

Answer 3

Please look at Russ's post
Specifically this section:
This is by far the most common first error that a new Spark user will
see when attempting to run a new application. Our new and excited
Spark user will attempt to start the shell or run their own
application and be met with the following message
...
The short term solution to this problem is to make sure you aren’t
requesting more resources from your cluster than exist or to shut down
any apps that are unnecessarily using resources. If you need to run
multiple Spark apps simultaneously then you’ll need to adjust the
amount of cores being used by each app.

Answer 4

In my case, the problem was that I had the following line in $SPARK_HOME/conf/spark-env.sh:
SPARK_EXECUTOR_MEMORY=3g
of each worker,
and the following line in $SPARK_HOME/conf/spark-default.sh
spark.executor.memory 4g
in the "master" node.
The problem went away once I changed 4g to 3g. I hope that this will help someone with the same issue. The other answers helped me spot this.

Answer 5

I have faced this issue few times even though the resource allocation was correct.
The fix was to restart the mesos services.
sudo service mesos-slave restart
sudo service mesos-master restart