4.2 Preparing and Creating Scripts for KPI Management

Owned by Jenni Wallin
Last updated: Dec 13, 2023 by Joakim Österberg

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. Zookeeper and Kafka should be up and running as well. For more information about this, see 4.3 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


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 extract the KPI app after building it 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.


$ cd release/packages

$ java -jar kpi_spark_8.1.1.0-SNAPSHOT.mzp install


4. You will find the new directory mz_kpiapp that contain 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 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. 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 2.1.4 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.


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.


8. Startup Spark cluster:

$ start_master_workers.sh ...


9. Submit the app:

$ submit.sh kpiapp ...


10. 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
Next:

4.3 KPI Management - External Software