Hi,
Can somebody help me out with this error ? I have exhausted all possible options to overcome this problem but just can't get it to work!!
I am running this from Jupyter notebook:
from pyspark.sql import SQLContext
And this is the error I am getting:
ImportError Traceback (most recent call last)
<ipython-input-3-88c7df6faf6a> in <module>()
----> 1 from pyspark.sql import SQLContext
ImportError: cannot import name 'SQLContext'
I am using Python3. I have also converted all the python library scripts within /usr/lib/spark/python/pyspark and /usr/lib/spark/python/pyspark/sql directories to be compatible with Python3.
And I am using Cloudera VM 5.4.2. Here's the copy of /etc/spark/conf/spark-env.sh contents:
#!/usr/bin/env bash
# This file is sourced when running various Spark programs.
# Copy it as spark-env.sh and edit that to configure Spark for your site.
# 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_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 workers to start (Default: 2)
# - SPARK_EXECUTOR_CORES, Number of cores for the workers (Default: 1).
# - SPARK_EXECUTOR_MEMORY, Memory per Worker (e.g. 1000M, 2G) (Default: 1G)
# - SPARK_DRIVER_MEMORY, Memory for Master (e.g. 1000M, 2G) (Default: 512 Mb)
# - 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_HISTORY_OPTS, to set config properties only for the history server (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: $USER)
# - SPARK_NICENESS The scheduling priority for daemons. (Default: 0)
###
### === IMPORTANT ===
### Change the following to specify a real cluster's Master host
###
export STANDALONE_SPARK_MASTER_HOST=`hostname`
export SPARK_MASTER_IP=$STANDALONE_SPARK_MASTER_HOST
### Let's run everything with JVM runtime, instead of Scala
export SPARK_LAUNCH_WITH_SCALA=0
export SPARK_LIBRARY_PATH=${SPARK_HOME}/lib
export SPARK_MASTER_WEBUI_PORT=18080
export SPARK_MASTER_PORT=7077
export SPARK_WORKER_PORT=7078
export SPARK_WORKER_WEBUI_PORT=18081
export SPARK_WORKER_DIR=/var/run/spark/work
export SPARK_LOG_DIR=/var/log/spark
export SPARK_PID_DIR='/var/run/spark/'
if [ -n "$HADOOP_HOME" ]; then
export LD_LIBRARY_PATH=:/usr/lib/hadoop/lib/native
fi
export HADOOP_CONF_DIR=${HADOOP_CONF_DIR:-/etc/hadoop/conf}
if [[ -d $SPARK_HOME/python ]]
then
for i in
do
SPARK_DIST_CLASSPATH=${SPARK_DIST_CLASSPATH}:$i
done
PYLIB="$SPARK_HOME/python/lib"
if [ -f "$PYLIB/pyspark.zip" ]; then
PYSPARK_ARCHIVES_PATH=
for lib in "$PYLIB"/*.zip; do
if [ -n "$PYSPARK_ARCHIVES_PATH" ]; then
PYSPARK_ARCHIVES_PATH="$PYSPARK_ARCHIVES_PATH,local:$lib"
else
PYSPARK_ARCHIVES_PATH="local:$lib"
fi
done
export PYSPARK_ARCHIVES_PATH
fi
fi
SPARK_DIST_CLASSPATH="$SPARK_DIST_CLASSPATH:$SPARK_LIBRARY_PATH/spark-assembly.jar"
SPARK_DIST_CLASSPATH="$SPARK_DIST_CLASSPATH:"
SPARK_DIST_CLASSPATH="$SPARK_DIST_CLASSPATH:/usr/lib/hadoop/lib/*"
SPARK_DIST_CLASSPATH="$SPARK_DIST_CLASSPATH:/usr/lib/hadoop/*"
SPARK_DIST_CLASSPATH="$SPARK_DIST_CLASSPATH:/usr/lib/hadoop-hdfs/lib/*"
SPARK_DIST_CLASSPATH="$SPARK_DIST_CLASSPATH:/usr/lib/hadoop-hdfs/*"
SPARK_DIST_CLASSPATH="$SPARK_DIST_CLASSPATH:/usr/lib/hadoop-mapreduce/lib/*"
SPARK_DIST_CLASSPATH="$SPARK_DIST_CLASSPATH:/usr/lib/hadoop-mapreduce/*"
SPARK_DIST_CLASSPATH="$SPARK_DIST_CLASSPATH:/usr/lib/hadoop-yarn/lib/*"
SPARK_DIST_CLASSPATH="$SPARK_DIST_CLASSPATH:/usr/lib/hadoop-yarn/*"
SPARK_DIST_CLASSPATH="$SPARK_DIST_CLASSPATH:/usr/lib/hive/lib/*"
SPARK_DIST_CLASSPATH="$SPARK_DIST_CLASSPATH:/usr/lib/flume-ng/lib/*"
SPARK_DIST_CLASSPATH="$SPARK_DIST_CLASSPATH:/usr/lib/paquet/lib/*"
SPARK_DIST_CLASSPATH="$SPARK_DIST_CLASSPATH:/usr/lib/avro/lib/*"
