Apache Spark in HGrid247-3.5
Apache Spark is an open-source distributed computing framework used to process large-scale data at high speed.
Spark provides:
- parallel data processing
- fault tolerance
- in-memory computation
Currently Spark is one of the main components in the Big Data ecosystem along with Apache Hadoop .
Spark Architecture
Spark architecture consists of several main components.
Driver Program
↓
Spark Context
↓
Cluster Manager (YARN / Standalone / Mesos)
↓
Worker Nodes
↓
Executors
These components allow data processing to be performed in parallel on multiple nodes .
Resilient Distributed Datasets (RDD)
RDD (Resilient Distributed Dataset) is the main data structure in Spark.
RDD is:
- immutable collection
- distributed dataset
- fault tolerant
RDDs are divided into logical partitions that can be processed in parallel on cluster nodes.
Characteristics of RDD
Some important characteristics of RDD:
Feature Description
-------------------------------------------------------
Immutable Data cannot be changed once created
Distributed Data is spread across many nodes
Fault Tolerant Spark can reconstruct data if a node fails.
Lazy Evaluation Operations are only executed when the action is called
Spark Workflow Generated by HGrid247
HGrid247 can automatically generate RDD-based Spark code from workflows created in the GUI.
The output file is usually:
projectname_spark2.jar
Running Spark-RDD Job on YARN Cluster
To run a Spark job on a Hadoop cluster use the command:
spark-submit [options] <jar-app> [app arguments]
Parameter explanation:
Parameter Meaning
---------------------------------------------
spark-submit command to run Spark job
options Spark configuration
jar-app Spark application JAR file
app arguments parameter input and output
Example Spark Execution
Example of running a Spark job generated by HGrid247:
spark-submit --class workshop.Lab01 --master yarn workshop_spark2.jar /user/hadoop/input /user/hadoop/output
Explanation:
Parameter Description
--class workshop.Lab01 the main class being run
--master yarn using YARN cluster
workshop_spark2.jar Spark application files
/user/hadoop/input input folder in HDFS
/user/hadoop/output output folder HDFS
Running in Client Mode
Another alternative is using client deploy mode .
spark-submit --class workshop.Lab01 --deploy-mode client workshop_spark2.jar /user/hadoop/input /user/hadoop/output
Mode differences:
Mode Description
----------------------------------------
cluster drivers running in clusters
client driver running on client machine
Advantages of Apache Spark
Some of Spark's advantages over MapReduce:
Faster Processing
Spark uses in-memory computing so it is much faster.
List of options
--master MASTER_URL spark://host:port, mesos://host:port, yarn, or 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).
--class CLASS_NAME Your application's main class (for Java / Scala apps).
--name NAME A name of your application.
--jars JARS Comma-separated list of local 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.
--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.
--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.
Spark standalone with cluster deploy mode only:
--driver-cores NUM Cores for driver (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:
--driver-cores NUM Number of cores used by the driver, only in cluster mode
(Default: 1).
--queue QUEUE_NAME The YARN queue to submit to (Default: "default").
--num-executors NUM Number of executors to launch (Default: 2).
If dynamic allocation is enabled, the initial number of
executors will be at least NUM.
--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.