To be able to handle the KPI management system in a Private Container Deployment, such as Kubernetes, you must prepare a number of scripts according to the instructions below. The scripts that you create are the following:
flush.sh
kpi_params.sh
spark_common_param.sh
start_master_workers.sh
stop.sh
submit.sh
These scripts will be used by different procedures that you find in the sections for KPI management - Distributed Processing.
Preparations before creating scripts:
A Prerequisite is that Spark, ZooKeeper, and Kafka are installed and up and running.
Creating scripts:
1. Set up your preferred KPI configuration or use the simplified example configuration, startup the platform. kpi_tst.zip
2. Find and copy the kpi_spark*.mzp among the installation files. Copy it to a place you want to keep your kpi application files.
3. To install the KPI app after building it, and extract the app installation:
$ cd build/libs $ java -jar kpi_spark_8.1.1.0-SNAPSHOT.mzp install
4. You will find the new directory mz_kpiapp that contains all app software.
$ ls -l mz_kpiapp/, will list: app # The MZ kpi app bin # Shell script to handle the app jars # Extra jar files for the app
5. Move the mz_kpiapp folder, to where you prefer to have it and add it to the PATH environment variable.
Example: $ mv mz_kpiapp ~/ $ export PATH=$PATH:/home/user/mz_kpiapp/bin
6. Set the environment variable SPARK_HOME.
$ export SPARK_HOME="your spark home"
7. Edit the kpiapp/bin/spark_common_param.sh, so it has the SPARK_HOME path.
Access the conf-folder of Apache Spark, the spark-defaults.conf.template file should be renamed to spark-defaults.conf and the following configuration variables and options added:
spark.driver.defaultJavaOptions --add-opens java.base/java.lang=ALL-UNNAMED \ --add-opens java.base/java.lang.invoke=ALL-UNNAMED \ --add-opens java.base/java.lang.reflect=ALL-UNNAMED \ --add-opens java.base/java.util=ALL-UNNAMED \ --add-opens java.base/java.util.concurrent=ALL-UNNAMED \ --add-opens java.base/java.util.concurrent.atomic=ALL-UNNAMED \ --add-opens java.base/java.io=ALL-UNNAMED \ --add-opens java.base/java.net=ALL-UNNAMED \ --add-opens java.base/java.nio=ALL-UNNAMED \ --add-opens java.base/sun.nio.ch=ALL-UNNAMED \ --add-opens java.base/sun.nio.cs=ALL-UNNAMED \ --add-opens java.base/sun.util.calendar=ALL-UNNAMED \ --add-opens java.base/sun.security.action=ALL-UNNAMED spark.executor.defaultJavaOptions --add-opens java.base/java.lang=ALL-UNNAMED \ --add-opens java.base/java.lang.invoke=ALL-UNNAMED \ --add-opens java.base/java.lang.reflect=ALL-UNNAMED \ --add-opens java.base/java.util=ALL-UNNAMED \ --add-opens java.base/java.util.concurrent=ALL-UNNAMED \ --add-opens java.base/java.util.concurrent.atomic=ALL-UNNAMED \ --add-opens java.base/java.io=ALL-UNNAMED \ --add-opens java.base/java.net=ALL-UNNAMED \ --add-opens java.base/java.nio=ALL-UNNAMED \ --add-opens java.base/sun.nio.ch=ALL-UNNAMED \ --add-opens java.base/sun.nio.cs=ALL-UNNAMED \ --add-opens java.base/sun.util.calendar=ALL-UNNAMED \ --add-opens java.base/sun.security.action=ALL-UNNAMED spark.master.rest.enabled true
Starting KPI
Prerequisite
Before you continue: Spark applications must be configured with a set of Kafka topics that are either shared between multiple applications or dedicated to specific applications. The assigned topics must be created before you submit an application to the Spark service. Before you can create the topics you must start the Kafka and Zookeeper services.
An example order of topics are the following:
kpi-input - For sending data to Spark
kpi-output - For spark to write the output to, and thus back to the workflow
kpi-alarm - For errors from Spark
9. Startup Spark cluster, here “kpiapp” is a configurable name:
$ start_master_workers.sh $ submit.sh kpiapp
10. Submit the app:
$ submit.sh kpiapp ...
11. You now can see 2 workers, and 2 executors:
$ jps Will give you something like: pid1 Worker pid2 Worker pid3 CoarseGrainedExecutorBackend pid4 CoarseGrainedExecutorBackend pid5 DriverWrapper pid6 CodeServerMain pid8 Master
Add Comment