Accesing Hdfs from Spark gives TokenCache error Can't get Master Kerberos principal for use as renewer

Question

I'm trying to run a test Spark script in order to connect Spark to hadoop. The script is the following

from pyspark import SparkContext

sc = SparkContext("local", "Simple App")
file = sc.textFile("hdfs://hadoop_node.place:9000/errs.txt")
errors = file.filter(lambda line: "ERROR" in line)
errors.count()

When I run it with pyspark I get

py4j.protocol.Py4JJavaError: An error occurred while calling o21.collect. : java.io.IOException: Can't get Master Kerberos principal for use as renewer at org.apache.hadoop.mapreduce.security.TokenCache.obtainTokensForNamenodesInternal(TokenCache.java:116) at org.apache.hadoop.mapreduce.security.TokenCache.obtainTokensForNamenodesInternal(TokenCache.java:100) at org.apache.hadoop.mapreduce.security.TokenCache.obtainTokensForNamenodes(TokenCache.java:80) at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:187) at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:251) at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:140) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:207) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:205) at scala.Option.getOrElse(Option.scala:120) at org.apache.spark.rdd.RDD.partitions(RDD.scala:205) at org.apache.spark.rdd.MappedRDD.getPartitions(MappedRDD.scala:28) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:207) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:205) at scala.Option.getOrElse(Option.scala:120) at org.apache.spark.rdd.RDD.partitions(RDD.scala:205) at org.apache.spark.api.python.PythonRDD.getPartitions(PythonRDD.scala:46) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:207) at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:205) at scala.Option.getOrElse(Option.scala:120) at org.apache.spark.rdd.RDD.partitions(RDD.scala:205) at org.apache.spark.SparkContext.runJob(SparkContext.scala:898) at org.apache.spark.rdd.RDD.collect(RDD.scala:608) at org.apache.spark.api.java.JavaRDDLike$class.collect(JavaRDDLike.scala:243) at org.apache.spark.api.java.JavaRDD.collect(JavaRDD.scala:27) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:606) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379) at py4j.Gateway.invoke(Gateway.java:259) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:207) at java.lang.Thread.run(Thread.java:744)

This happens despite the facts that

• I've done a kinit and a klist shows I have the correct tokens
• when I issue a ./bin/hadoop fs -ls hdfs://hadoop_node.place:9000/errs.txt it shows the file
• Both the local hadoop client and spark have the same configuration file

The core-site.xml in the spark/conf and hadoop/conf folders is the following (got it from one of the hadoop nodes)

<configuration>
    <property>

        <name>hadoop.security.auth_to_local</name>
        <value>
            RULE:[1:$1](.*@place)s/@place//
            RULE:[2:$1/$2@$0](.*/node1.place@place)s/^([a-zA-Z]*).*/$1/
            RULE:[2:$1/$2@$0](.*/node2.place@place)s/^([a-zA-Z]*).*/$1/
            RULE:[2:$1/$2@$0](.*/node3.place@place)s/^([a-zA-Z]*).*/$1/
            RULE:[2:$1/$2@$0](.*/node4.place@place)s/^([a-zA-Z]*).*/$1/
            RULE:[2:$1/$2@$0](.*/node5.place@place)s/^([a-zA-Z]*).*/$1/
            RULE:[2:$1/$2@$0](.*/node6.place@place)s/^([a-zA-Z]*).*/$1/
            RULE:[2:$1/$2@$0](.*/node7.place@place)s/^([a-zA-Z]*).*/$1/
            RULE:[2:nobody]
            DEFAULT
        </value>
    </property>
    <property>
        <name>net.topology.node.switch.mapping.impl</name>
        <value>org.apache.hadoop.net.TableMapping</value>
    </property>
    <property>
        <name>net.topology.table.file.name</name>
        <value>/etc/hadoop/conf/topology.table.file</value>
    </property>
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://server.place:9000/</value>
    </property>
    <property>
      <name>hadoop.security.authentication</name>
      <value>kerberos</value>
    </property>

    <property>
      <name>hadoop.security.authorization</name>
      <value>true</value>
    </property>

    <property>
      <name>hadoop.proxyuser.hive.hosts</name>
      <value>*</value>
    </property>

    <property>
      <name>hadoop.proxyuser.hive.groups</name>
      <value>*</value>
    </property>

</configuration>

Can someone point out what am I missing?

Answer 1

After creating my own hadoop cluster in order to better understand how hadoop works. I fixed it.

You have to provide Spark with a valid .keytab file which has been generated for an account which has at least read access to the hadoop cluster.

Also, you have to provide spark with the hdfs-site.xml of your hdfs cluster.

So for my case I had to create a keytab file which when you run

klist -k -e -t

on it you get entries like the following

host/fully.qualified.domain.name@REALM.COM

In my case the host was the literal word host and not a variable. Also in your hdfs-site.xml you have to provide the path of the keytab file and say that

host/_HOST@REALM.COM

will be your account.

Cloudera has a pretty detailed writeup on how to do it.

Edit after playing a little bit with different configurations I think the following should be noted. You have to provide spark with the exact hdfs-site.xml and core-site.xml of your hadoop cluster. Otherwise it wont work