$ ${SPARK_HOME}/bin/spark-shell --help Usage: ./bin/spark-shell [options]

• Scala REPL options: -I <file> preload <file>, enforcing line-by-line interpretation
  
• Generic options: --name NAME A name of your application. --master MASTER_URL local, spark://host:port, mesos://host:port, yarn, or k8s://https://host:port. (Default: local[*]) --deploy-mode DEPLOY_MODE Whether to launch the driver program locally ("client") or on one of the worker machines inside the cluster ("cluster"). (Default: client) --conf PROP=VALUE Arbitrary Spark configuration property. --properties-file FILE Path to a file from which to load extra properties. If not specified, this will look for conf/spark-defaults.conf. --class CLASS_NAME Your application’s main class (for Java / Scala apps). --jars JARS Comma-separated list of jars to include on the driver and executor classpaths. --packages Comma-separated list of maven coordinates of jars to include on the driver and executor classpaths. Will search the local maven repo, then maven central and any additional remote repositories given by --repositories. The format for the coordinates should be groupId:artifactId:version. --exclude-packages Comma-separated list of groupId:artifactId, to exclude while resolving the dependencies provided in --packages to avoid dependency conflicts. --repositories Comma-separated list of additional remote repositories to search for the maven coordinates given with --packages. --py-files PY_FILES Comma-separated list of .zip, .egg, or .py files to place on the PYTHONPATH for Python apps. --files FILES Comma-separated list of files to be placed in the working directory of each executor. File paths of these files in executors can be accessed via SparkFiles.get(fileName). --driver-memory MEM Memory for driver (e.g. 1000M, 2G) (Default: 1024M). --driver-java-options Extra Java options to pass to the driver. --driver-library-path Extra library path entries to pass to the driver. --driver-class-path Extra class path entries to pass to the driver. Note that jars added with --jars are automatically included in the classpath. --executor-memory MEM Memory per executor (e.g. 1000M, 2G). (Default: 1G) --proxy-user NAME User to impersonate when submitting the application. This argument does not work with --principal / --keytab. --help, -h Show this help message and exit. --verbose, -v Print additional debug output. --version, Print the version of current Spark.
  
• Cluster deploy mode only: --driver-cores NUM Number of cores used by the driver, only in cluster mode. (Default: 1)
  
• Spark standalone or Mesos with cluster deploy mode only: --supervise If given, restarts the driver on failure. --kill SUBMISSION_ID If given, kills the driver specified. --status SUBMISSION_ID If given, requests the status of the driver specified.
  
• Spark standalone and Mesos only: --total-executor-cores NUM Total cores for all executors.
  
• Spark standalone and YARN only: --executor-cores NUM Number of cores per executor. (Default: 1 in YARN mode, or all available cores on the worker in standalone mode)
  
• YARN only: --queue QUEUE_NAME The YARN queue to submit to. (Default: "default") --num-executors NUM Number of executors to launch. If dynamic allocation is enabled, the initial number of executors will be at least NUM. (Default: 2) --archives ARCHIVES Comma separated list of archives to be extracted into the working directory of each executor. --principal PRINCIPAL Principal to be used to login to KDC, while running on secure HDFS. --keytab KEYTAB The full path to the file that contains the keytab for the principal specified above. This keytab will be copied to the node running the Application Master via the Secure Distributed Cache, for renewing the login tickets and the delegation tokens periodically.

Spark’s shell provides an interactive shell to learn the Spark API. It is available in either Scala (spark-shell) or Python (pyspark).

$ spark-shell Using Spark’s default log4j profile: org/apache/spark/log4j-defaults.properties Setting default log level to "WARN". To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel). Spark context Web UI available at http://localhost:4040 Spark context available as ’sc’ (master = local[*], app id = local-1558893967168). Spark session available as ’spark’. Welcome to ____ __ / __/__ ___ _____/ /__ _\ \/ _ \/ _ `/ __/ ’_/ /___/ .__/\_,_/_/ /_/\_\ version 2.4.3 /_/ Using Scala version 2.11.12 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_172) Type in expressions to have them evaluated. Type :help for more information. scala>
Default Spark Session configuration: scala> spark.conf.getAll.foreach(println(_)) (spark.app.name,Spark shell) (spark.app.id,local-1562026247680) (spark.master,local[*]) (spark.submit.deployMode,client) (spark.driver.host,192.168.2.33) (spark.driver.port,43485) (spark.executor.id,driver) (spark.sql.catalogImplementation,hive) (spark.home,/opt/spark-2.4.3-bin-hadoop2.7) (spark.repl.class.uri,spark://192.168.2.33:43485/classes) (spark.jars,) (spark.repl.class.outputDir,/tmp/spark-f9071428-76aa-49bc-84de-3163bd98e0b4/repl-e149d9f1-3060-4476-a11f-f2337c381a42) (spark.ui.showConsoleProgress,true)

scala> :help All commands can be abbreviated, e.g., :he instead of :help. :help [command] | print this summary or command-specific help :quit | exit the interpreter :type [-v] <expr> | display the type of an expression without evaluating it :kind [-v] <expr> | display the kind of expression’s type :imports [name name ...] | show import history, identifying sources of names :implicits [-v] | show the implicits in scope :history [num] | show the history (optional num is commands to show) :h? <string> | search the history :edit <id>|<line> | edit history :warnings | show the suppressed warnings from the most recent line which had any :javap <path|class> | disassemble a file or class name :line <id>|<line> | place line(s) at the end of history :load <path> | interpret lines in a file :paste [-raw] [path] | enter paste mode or paste a file :power | enable power user mode :replay [options] | reset the repl and replay all previous commands :require <path> | add a jar to the classpath :reset [options] | reset the repl to its initial state, forgetting all session entries :save <path> | save replayable session to a file :sh <command line> | run a shell command (result is implicitly => List[String]) :settings <options> | update compiler options, if possible; see reset :silent | disable/enable automatic printing of results

spark-shell will automatically import some packages.
To list the imported packages use the command :imports:
scala> :imports 1) import org.apache.spark.SparkContext._ (70 terms, 1 are implicit) 2) import spark.implicits._ (1 types, 67 terms, 37 are implicit) 3) import spark.sql (1 terms) 4) import org.apache.spark.sql.functions._ (385 terms)

spark-shell will create automatically an instance of Spark Session (accessible using spark variable) and an instance of Spark Context (accessible using sc variable).
► Spark Session available as "spark".
► Spark Context available as "sc".

Spark Session variable: scala> :type spark #org.apache.spark.sql.SparkSession
Spark Context variable: scala> :type sc #org.apache.spark.SparkContext
You can use auto-completion (tab key) to get available options: scala> spark.<tab> baseRelationToDataFrame conf emptyDataFrame implicits range sessionState sql streams udf catalog createDataFrame emptyDataset listenerManager read sharedState sqlContext table version close createDataset experimental newSession readStream sparkContext stop time
scala> sc.<tab> accumulable binaryRecords defaultParallelism getPersistentRDDs killExecutor objectFile setCallSite submitJob accumulableCollection broadcast deployMode getPoolForName killExecutors parallelize setCheckpointDir textFile accumulator cancelAllJobs doubleAccumulator getRDDStorageInfo killTaskAttempt range setJobDescription uiWebUrl addFile cancelJob emptyRDD getSchedulingMode listFiles register setJobGroup union addJar cancelJobGroup files hadoopConfiguration listJars removeSparkListener setLocalProperty version addSparkListener cancelStage getAllPools hadoopFile longAccumulator requestExecutors setLogLevel wholeTextFiles appName clearCallSite getCheckpointDir hadoopRDD makeRDD requestTotalExecutors sparkUser applicationAttemptId clearJobGroup getConf isLocal master runApproximateJob startTime applicationId collectionAccumulator getExecutorMemoryStatus isStopped newAPIHadoopFile runJob statusTracker binaryFiles defaultMinPartitions getLocalProperty jars newAPIHadoopRDD sequenceFile stop

First start by by importing the package sys.process._.
To run a Linux command, just wrap it in double quotes followed by a dot and exclamation characters .!

scala> import sys.process._ #import sys.process._ scala> "hdfs dfs -ls /".! drwxrwxr-x+ - hive hadoop /hive drwx-wx-wx - hive hadoop /tmp