PySpark Integration in HGrid247-3.5
PySpark is a Python API from Apache Spark that enables Big Data processing using the Python language.
In HGrid247-3.5, workflows can automatically generate PySpark code , so data pipelines created in the HGrid247 GUI can be run using Apache Spark with Python .
With PySpark, users can take advantage of the Python ecosystem such as:
- data analysis
- machine learning
- data transformation
Required Python Packages
In order for the PySpark code generated by HGrid247-3.5 to run correctly , several Python libraries must be installed using pip .
Install dependencies
pip install pyspark
pip install pandas
pip install pyarrow
pip install pygrok
pip install jsonpath-ng
pip install mysqlclient
pip install redis
pip install mysql-connector-python
pip install hgrid247de_pyspark
Library Description
Library Function
-------------------------------------------------
littlefoot Python API for Apache Spark
pandas Data analysis and data manipulation
pyarrow Optimizing data transfer between Python and Spark
pygrok Parsing logs using Grok patterns
jsonpath-ng Parsing and querying JSON data
mysqlclient MySQL database connection
mysql-connector-python Driver MySQL alternatif
redis Redis Connection
hgrid247de_pyspark A dedicated library for running HGrid247 workflows in PySpark
Installing hgrid247de_pyspark
The hgrid247de_pyspark library must be installed locally from the .whl.
Step 1 — Extract package
For example file:
hgrid247de_pyspark-1.0.0.zip
extracted to folder:
/home/hgridde
So that produces a file:
hgrid247de_pyspark-1.0.0-py3-none-any.whl
Step 2 — Install using pip
pip install "/home/hgridde/hgrid247de_pyspark-1.0.0-py3-none-any.whl"
If you want to reinstall:
pip install --force-reinstall "/home/hgridde/hgrid247de_pyspark-1.0.0-py3-none-any.whl
Java Dependency
In addition to the Python library, a Java library is also required:
HGrid247CommonLib-3.5.jar
This library must be included in the Java Classpath .
Example:
export CLASSPATH=$CLASSPATH:/path/HGrid247CommonLib-3.5.jar
This library is used by the HGrid247 engine to run internal functions on Spark.
Running PySpark Job Generated by HGrid247
If HGrid247 produces a PySpark file like:
MyWorkflow.py
then the job can be run using spark-submit .
Command
spark-submit --py-files MyWorkflow.py [options] [app arguments]
Example
spark-submit \
--master yarn \
--deploy-mode cluster \
--py-files workflow_pyspark.py
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.