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/*"   ... 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