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line 91: mapr: command not found? - Spark Shell

Question asked by brett_ on Nov 18, 2016
Latest reply on Nov 21, 2016 by brett_

Hi all, new user to mapr here. I'm not sure how to debug this error, any advice would help:

 

/opt/mapr/spark/spark-1.6.1/conf/spark-env.sh: line 91: mapr: command not found

 

shell prompt as root:

root@node00:/opt/mapr/spark/spark-1.6.1/bin# ./spark-shell

/opt/mapr/spark/spark-1.6.1/conf/spark-env.sh: line 91: mapr: command not found

Welcome to

      ____              __

     / __/__  ___ _____/ /__

    _\ \/ _ \/ _ `/ __/  '_/

   /___/ .__/\_,_/_/ /_/\_\   version 1.6.1

      /_/

 

Using Scala version 2.10.5 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_25)

Type in expressions to have them evaluated.

Type :help for more information.

Spark context available as sc.

SQL context available as sqlContext.

 

scala>

 

 

spark-env.sh file (line 91 in bold):

 

# Options read when launching programs locally with

# ./bin/run-example or ./bin/spark-submit

# - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files

# - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node

# - SPARK_PUBLIC_DNS, to set the public dns name of the driver program

# - SPARK_CLASSPATH, default classpath entries to append

 

# Options read by executors and drivers running inside the cluster

# - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node

# - SPARK_PUBLIC_DNS, to set the public DNS name of the driver program

# - SPARK_CLASSPATH, default classpath entries to append

# - SPARK_LOCAL_DIRS, storage directories to use on this node for shuffle and RDD data

# - MESOS_NATIVE_JAVA_LIBRARY, to point to your libmesos.so if you use Mesos

 

# Options read in YARN client mode

# - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files

# - SPARK_EXECUTOR_INSTANCES, Number of executors to start (Default: 2)

# - SPARK_EXECUTOR_CORES, Number of cores for the executors (Default: 1).

# - SPARK_EXECUTOR_MEMORY, Memory per Executor (e.g. 1000M, 2G) (Default: 1G)

# - SPARK_DRIVER_MEMORY, Memory for Driver (e.g. 1000M, 2G) (Default: 1G)

# - SPARK_YARN_APP_NAME, The name of your application (Default: Spark)

# - SPARK_YARN_QUEUE, The hadoop queue to use for allocation requests (Default: ‘default’)

# - SPARK_YARN_DIST_FILES, Comma separated list of files to be distributed with the job.

# - SPARK_YARN_DIST_ARCHIVES, Comma separated list of archives to be distributed with the job.

 

# Options for the daemons used in the standalone deploy mode

# - SPARK_MASTER_IP, to bind the master to a different IP address or hostname

# - SPARK_MASTER_PORT / SPARK_MASTER_WEBUI_PORT, to use non-default ports for the master

# - SPARK_MASTER_OPTS, to set config properties only for the master (e.g. "-Dx=y")

# - SPARK_WORKER_CORES, to set the number of cores to use on this machine

# - SPARK_WORKER_MEMORY, to set how much total memory workers have to give executors (e.g. 1000m, 2g)

# - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT, to use non-default ports for the worker

# - SPARK_WORKER_INSTANCES, to set the number of worker processes per node

# - SPARK_WORKER_DIR, to set the working directory of worker processes

# - SPARK_WORKER_OPTS, to set config properties only for the worker (e.g. "-Dx=y")

# - SPARK_DAEMON_MEMORY, to allocate to the master, worker and history server themselves (default: 1g).

# - SPARK_HISTORY_OPTS, to set config properties only for the history server (e.g. "-Dx=y")

# - SPARK_SHUFFLE_OPTS, to set config properties only for the external shuffle service (e.g. "-Dx=y")

# - SPARK_DAEMON_JAVA_OPTS, to set config properties for all daemons (e.g. "-Dx=y")

# - SPARK_PUBLIC_DNS, to set the public dns name of the master or workers

 

# Generic options for the daemons used in the standalone deploy mode

# - SPARK_CONF_DIR      Alternate conf dir. (Default: ${SPARK_HOME}/conf)

# - SPARK_LOG_DIR       Where log files are stored.  (Default: ${SPARK_HOME}/logs)

# - SPARK_PID_DIR       Where the pid file is stored. (Default: /tmp)

# - SPARK_IDENT_STRING  A string representing this instance of spark. (Default: $USER)

# - SPARK_NICENESS      The scheduling priority for daemons. (Default: 0)

 

 

#########################################################################################################

# Set MapR attributes and compute classpath

#########################################################################################################

 

# Set the spark attributes

export SPARK_HOME=/opt/mapr/spark/spark-1.6.1

 

# Load the hadoop version attributes

source /opt/mapr/spark/spark-1.6.1/mapr-util/hadoop-version-picker.sh

export HADOOP_HOME=$hadoop_home_dir

export HADOOP_CONF_DIR=$hadoop_conf_dir

export SPARK_LIBRARY_PATH=$MAPR_HADOOP_JNI_PATH

 

# Enable mapr impersonation

export MAPR_IMPERSONATION_ENABLED=1

 

MAPR_HADOOP_HBASE_VERSION=$(ls -1 /opt/mapr/hadoop/hadoop-0.20.2/lib/mapr-hbase-[0-9]*.jar 2> /dev/null | head -1)

MAPR_HADOOP_CLASSPATH=`hadoop classpath`

MAPR_HADOOP_JNI_PATH=`hadoop jnipath`

MAPR_SPARK_CLASSPATH="`mapr classpath`:$MAPR_HADOOP_CLASSPATH:$MAPR_HADOOP_HBASE_VERSION"

 

SPARK_MAPR_HOME=/opt/mapr

 

# Load the classpath generator script

source /opt/mapr/spark/spark-1.6.1/mapr-util/generate-classpath.sh

 

# Calculate hive jars to include in classpath

generate_compatible_classpath "spark" "1.6.1" "hive"

MAPR_HIVE_CLASSPATH=${generated_classpath}

if [ ! -z "$MAPR_HIVE_CLASSPATH" ]; then

  MAPR_SPARK_CLASSPATH="$MAPR_SPARK_CLASSPATH:$MAPR_HIVE_CLASSPATH"

fi

 

# Calculate hbase jars to include in classpath

generate_compatible_classpath "spark" "1.6.1" "hbase"

MAPR_HBASE_CLASSPATH=${generated_classpath}

if [ ! -z "$MAPR_HBASE_CLASSPATH" ]; then

  MAPR_SPARK_CLASSPATH="$MAPR_SPARK_CLASSPATH:$MAPR_HBASE_CLASSPATH"

  SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Dspark.executor.extraClassPath=$MAPR_HBASE_CLASSPATH -Dspark.driver.extraClassPath=$MAPR_HBASE_CLASSPATH"

fi

 

# Set SPARK_DIST_CLASSPATH

SPARK_DIST_CLASSPATH=$MAPR_SPARK_CLASSPATH

export SPARK_DIST_CLASSPATH

 

# Security status

source /opt/mapr/conf/env.sh

if [ "$MAPR_SECURITY_STATUS" = "true" ]; then

  SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Dhadoop.login=hybrid -Dmapr_sec_enabled=true"

fi

 

# scala

export SCALA_VERSION=2.10

export SPARK_SCALA_VERSION=$SCALA_VERSION

export SCALA_HOME=/opt/mapr/spark/spark-1.6.1/scala

export SCALA_LIBRARY_PATH=$SCALA_HOME/lib

 

# Use a fixed identifier for pid files

export SPARK_IDENT_STRING="mapr"

 

#########################################################################################################

#    :::CAUTION::: DO NOT EDIT ANYTHING ON OR ABOVE THIS LINE

#########################################################################################################

 

 

#

# MASTER HA SETTINGS

#

#export SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER  -Dspark.deploy.zookeeper.url=<zookeerper1:5181,zookeeper2:5181,..> -Djava.security.auth.login.config=/opt/mapr/conf/mapr.login.conf -Dzookeeper.sasl.client=false"

 

 

# MEMORY SETTINGS

export SPARK_DAEMON_MEMORY=1g

export SPARK_WORKER_MEMORY=16g

 

# Worker Directory

export SPARK_WORKER_DIR=$SPARK_HOME/tmp

 

# Environment variable for printing spark command everytime you run spark.Set to "1" to print.

# export SPARK_PRINT_LAUNCH_COMMAND=1

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