Configure Service Model

The service model describes your data, which KPIs to generate and how to calculate them. A JSON representation is used to describe the model, which includes the following top-level objects:

dimension
tree
metric
kpi
threshold (optional)

Configure the service model in a KPI profile. To create a new KPI profile profile, click the New Configuration button in the Build view, and then select KPI Profile.

Dimension and Tree Objects

Start with the dimension and tree objects. The dimensions describe the fields of your data that are used for grouping and the tree the relation between them. The identifying fields in the input data are region and country. A region has one or more countries. The data type is sales. In the dimension object we specify each of our identifying fields as separate objects, with the datatype and field.

Configuring Dimensions and Tree

JSON

xmlDimensions

Metrics

Define the metrics using the amount field in the input data:

totalSales - For total sales, sum up the amount for each record by using the sum function on the expression expr, which contains the amount field.
avgSales - For average sales use the avg function instead of sum.
numSales - To count the number of records, use the conditional function isSet in the expression. This function evaluates to 1 if there is a value in amount or 0 if there is no value. Use the function sum to sum up the 1s and 0s.

Configuring Metrics

JSON

EclipseMetrics

KPIs

Define the KPIs. The expected output is the total sales, average sales, and number of sales per region and country in 60 second periods.

Use the property node to describe where in the topology the KPI should be calculated and windowSize to set the period length. Use the names of the metrics defined above in the expr property.

Configuring KPIs

JSON

KPIs

Completing and Saving the Profile

Select the Aggregated Output checkbox and set the Window Size to 60, and Save the profile with the name "SalesModel" in the folder "kpisales".

JSON - Complete Model

Complete Model

Create the Workflow

Create the real-time workflow. In this guide we will use Pulse agents to simulate sales data coming from three different sources, EMEA, AMERICAS, and APAC.

Add Pulse Agents and Analysis agent

Add three Pulse agents and an Analysis agent.

Workflow - Pulse Agents

Configure the Pulse agents as follows:

AMERICAS will send 1000 TPS - Set Time Unit to MILLISECONDS and Interval to 100
EMEA will send 500 TPS - Set Time Unit to MILLISECONDS and Interval to 200
APAC will send 250 TPS - Set Time Unit to MILLISECONDS and Interval to 400

To be able to identify the data, set the data to the region name.

Pulse agent configuration

The Pulse agents only sends us a simple event containing the name of the region, the other data that will be used in the KPI calculations are generated in the connected Analysis agent.

In the Analysis agent, click on the Set to Input button beneath UDR Types and paste the APL code below to create the input to KPI Management.

APL code

americas = listCreate(string, "US", "Canada", "Mexico", "Brazil", "Argentina", "Cuba", "Colombia"); list emea = listCreate(string, "Sweden", "UK", "Portugal", "Italy", "France", "Germany", "Norway", "Spain", "Finland", "Denmark"); list apac = listCreate(string, "India", "China", "Japan", "Thailand", "Australia", "Indonesia", "Malaysia","South Korea"); consume { // create KDR - the input for the KPI CLusterIn agent kpimanagement.KDR kdr = udrCreate(kpimanagement.KDR); // The KDR has a type field - we set this to the value we had for our data type in the model kdr.type = "sales"; // It also has a timestamp field - lets populate that from the current time but using seconds. kdr.timestamp = dateCreateNowMilliseconds() / 1000; string region = baToStr(input.Data); // the data in our use case (country, city, amount) we will put in the values field of the kdr. map sales = mapCreate(string,any); mapSet(sales, "region", region); // lets create a random amount between 1 and 1000 int amount = randomInt(1000); // set amount and city depending on the region if (region == "AMERICAS") { mapSet(sales, "amount", amount * 1.25d); mapSet(sales, "country", randomCountry(americas)); } else if (region == "EMEA") { mapSet(sales, "amount", amount * 1.0d); mapSet(sales, "country", randomCountry(emea)); } else if (region == "APAC") { mapSet(sales, "amount", amount * 0.65d); mapSet(sales, "country", randomCountry(apac)); } else { mapSet(sales, "amount", 0.0d); mapSet(sales, "country", "UNKNOWN"); debug("Unknown region:" + region); } kdr.values = sales; udrRoute(kdr); } // pick a random country from a list string randomCountry(list countries) { int index = randomInt(listSize(countries)); return listGet(countries, index); }]]>

Adding KPI Agent

Add a KPI agent and configure it to use the KPI profile that you created in step 1. Set Delay to 0.

Workflow - KPI agent

KPI agent configuration

Adding an Analysis Agent for Debug

Add another Analysis agent for debugging of the KPIs.

Final workflow configuration

In the Analysis agent, click on the Set to Input button beneath UDR Types and paste the APL code below to the Analysis agent.

APL code

kpis = input.kpiOutputUDRs; // loop the KPIs and debug string dateStr = ""; for (int i = 0; i < listSize(kpis); i++) { kpimanagement.KPIOutput kpi = listGet(kpis, i); dateToString(dateStr, dateCreateFromMilliseconds(kpi.periodStart * 1000), format); debug("Period start: " + dateStr + ", instance: " + kpi.instancePath + ", KPI: " + kpi.kpi + ", Value:" + kpi.value + ", Samples: " + kpi.sampleCount); } }]]>

Save the workflow.

Start the Workflow

Open Workflow in Monitor

Open the workflow configuration in the Workflow Monitor. Enable debugging and select events for the KPI agent and the Analysis agent that produces the debug output.

Starting the Workflow

Ensure to have an ec running and click on the Start button.

Viewing Result

The calculated KPIs will be displayed in the debug output in the Workflow Monitor.

Note!

It will take a minute before the output is displayed due to the configuration of the windowSize property in the service model.

Debug output

Step-by-Step Instructions