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Event-Driven Retail System

In this tutorial, we will demonstrate how to compose an end-to-end Test Scenario for an event-driven system composing of multiple topics. This tutorial will demonstrate:

  • Testing the workflow to validate the integrations
    • Are messages correctly broadcast to Kafka topics from the external applications?
  • Testing the data integrity at each step:
    • Was our message enriched correctly based on the userId
    • Was a message received within an agreed SLA?

Architecture Reference

The general flow can be described as:

  • Message is produced into the pageviews topic
  • User Service consumes the message, enriches it with additional data and pushes a new message into the pageviews_enriched topic
  • Promotions Service consumes the enriched message, and assuming certain business rules are met, produces another message into the upsell_events topic

Test Scenario

Below demonstrates how to build a Test Scenario representing the above architecture.

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Remember, any dependent applications must be running to validate the scenario. In this case, the User and Promotions Services are considered external applications.

Note that:

  • The first task produces a message into pageviews topic
  • Once it's been processed by the User Service, the second task consumes the enriched event from enriched_pageviews
    • Note that it's chained onto the end port of the previous task
  • Lastly, the third task will consume any resultant events from upsell_events

Breaking it Down

Producer Task

This task produces a message into the pageviews topic. There are no Test Checks associated with it.

The cluster and topic are configured on the General tab. Then, a valid JSON message value is set on the Data tab.

The User Service will take care of enrichment based on the userId present in this message.

Consumer Task - Check Enriched

This Task is used to consume the enriched message and check the data within.

In the Data tab, the deserialisation formats for consuming the enriched message are set. In this case, we expect a JSON value format.

Only 1 enriched message is expected from the User Service, therefore the default lifecycle rules (stop after 1 message is consumed) are suitable.

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If messages were continuously being produced into this topic, we would need to use a filter to intercept the correct one.

Below shows the expected output for an enriched message.

// Consumed Message
{
"TIMESTAMP": 1652012999698,
"USERID": 12345,
"PAGEID": 200,
"PAGENAME": "Checkout",
"COUNTRY": "US",
"PLANTYPE": "Free",
"INTERESTS": "Gaming"
}

To validate the User Service is working correctly, we will create a Test Check on the consumed message.

  • Navigate to the Checks tab
  • The 'Plan Type' attribute is the result of message enrichment
  • It's accessible via JQ using .record.value.PLANTYPE
  • Set the type to string and Operator to equals
  • Assert that user 12345 is on a Free plan

Consumer Task - Check Upsell

The last task is used to ensure that Free members who visit the checkout page are upsold promotional products.

If the Promotions Service is working correctly, a message will be produced into the upsell_events topic.

From a business perspective, it's important this happens within an agreed SLA. For this example, the SLA is set at 500ms.

This is addressed via the Lifecycle section on the Data tab:

  • Set the fail condition to an elapsed time of 500ms
    • Meaning, if this process takes > 500ms, the test will be considered a fail

Execution

With the workflow and checks configured, the only remaining step is to execute the scenario.

Use Run to execute the test and observe the result.

In the above case, we can see the end-to-end test scenario has failed. Hovering over the events will provide more detail.

Consumer reached the Timeout fail condition

This means that the business SLA of 500ms was not met.

Navigating to the Checks tab will detail the result of any Test Checks. In this case, the check on the enriched data was successful.

userId 12345 was correctly attributed to a Free plan by the User Service

Great! We have our identified the components in our system that are passing, and those that are currently failing. Now we can start to address the reasons for failure!