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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 followingIn preparation for using KPI Management, you need to extract the following scripts:

The scripts are as follows:

• flush.sh

• kpi_params.sh

• spark_common_param.sh

• start_master_workers.sh

• stop.sh

• submit.sh

These scripts will be used by for different procedures that you find in the sections for KPI management Management - Distributed Processingsections.

Preparations before creating extracting scripts:

A Prerequisite prerequisite is that Spark, ZooKeeper, and Kafka are installed. Zookeeper and Kafka should be up and running as well.

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 For more information about this, see KPI Management - External Software.

Before running the command to extract the scripts, these parameters need to be set as environment variables as they will be entered into some scripts:

export KAFKA_BROKERS="127.0.0.1:9092"
export SPARK_UI_PORT=4040 
export MZ_PLATFORM_AUTH="mzadmin:DR-4-1D2E6A059AF8120841E62C87CFDB3FF4"
export MZ_KPI_PROFILE_NAME="kpi_common.SalesModel"
export MZ_PLATFORM_URL="http://127.0.0.1:9036"
export ZOOKEEPER_HOSTS="127.0.0.1:2181"
export SPARK_HOME=opt/spark-3.3.2-bin-hadoop3-scala2.13
export KAFKA_HOME=/opt/kafka_2.13-3.3.1
export $PATH=$SPARK_HOME/bin:$KAFKA_HOME/bin:$PATH

Extracting scripts and KPI app:

1. Set up your preferred KPI profile.

2. Find the kpi_spark*.mzp among the installation files

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1. and copy it to

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1. where you want to keep your

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1. KPI application files.

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1. To

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1. extract the KPI app after building it

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1. run the following command. It extracts the software needed by spark for the KPI app as well as the scripts needed for starting and configuring spark.

   Code Block
   $ cd

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1. release/

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1. packages $ java -jar kpi_spark_

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1. 9.1.

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1. 0.0

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1. .mzp install

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2. You will find the new directory mz_kpiapp that contains all app software.

   Code Block
   $ 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

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2. Move the mz_kpiapp folder

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1. and add it to the PATH

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1. .

   Code Block
   Example: $ mv mz_kpiapp ~/ $ export PATH=$PATH:/home/user/mz_kpiapp/bin

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2. Set the environment variable SPARK_HOME.

   Code Block
   $ export SPARK_HOME="your spark home"

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2. These extracted scripts, kpi_params.sh and spark_common_params.sh, are more of examples than a finished configuration so you need to modify the scripts under the bin folder according to your specifications and requirements.

   In kpi_params.sh, KAFKA_BROKERS need to be configured with the hosts and ports of the kafka brokers. For example:

   export KAFKA_BROKERS="192.168.1.100:9092,192.168.1.101:9092,192.168.1.102:9092"

   The username and password for a user with access to the profile is needed to be entered as the property MZ_PLATFORM_AUTH, unless the default username and password mzadmin/dr is used. The password is encrypted using the mzsh command encryptpassword. The memory settings may need to be altered depending on the expected load, as well as the UI port for the KPI App inside Spark (default 4040).
   In addition to the addresses and ports of the platform, kafka and zookeeper may need to be updated.

   In spark_common_params.sh, you may need to change the master host IP and ports if applicable. Edit the kpiapp/bin/spark_common_param.sh, so it has the SPARK_HOME path.

3. 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:

   Code Block
   language none
   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

4. Add this to the jvmargs section of the execution context definition for the ec that will run the KPI Management workflows. For example:
   You can open the configuration by running:
   mzsh mzadmin/<password> topo open kpi_ec
   

   Code Block
   jvmargs { args=[ "--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" ] }

Starting KPI

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1. Startup Spark cluster, here “kpiapp” is a configurable name:

   Code Block
   $ start_master_workers.sh $ submit.sh kpiapp

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1. Submit the app:

   Code Block
   $ submit.sh kpiapp ...

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1. You should now

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1. be able to see

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1. workers, and

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1. executors:

   Code Block
   $ jps Will give you something like: pid1 Worker pid2 Worker pid3 CoarseGrainedExecutorBackend pid4 CoarseGrainedExecutorBackend pid5 DriverWrapper pid6 CodeServerMain pid8 Master