Understanding Output Relay

What is Output Relay?

Output Relay is a mechanism that allows one test case to pass data to another test case within the same test pipeline. Think of it as a "data handoff" between tests.

Simple Analogy: Imagine a relay race where runners pass a baton. In Output Relay:

• The baton is a variable (like a token, ID, or URL)
• The first runner (Output Case) generates and passes the data
• The next runner (Input Case) receives and uses that data

Why Do You Need Output Relay?

The Problem: Hardcoded Dependencies

Without Output Relay, you might write tests like this:

PythonCopy
# Test A: Login and get token
def test_login():
    token = api.login("user", "pass")
    # Token is lost after this test ends!

# Test B: Get user profile (BROKEN - no token!)
def test_get_profile():
    token = "???"  # Where does this come from?
    api.get_profile(token)

Common workarounds (all problematic):

• Hardcode tokens → Breaks when tokens expire
• Combine into one giant test → Hard to maintain, slow to debug
• Use global variables → Tests become interdependent and fragile

The Solution: Output Relay

With Output Relay, Test A can pass the token to Test B:

Each test stays independent, but data flows through the pipeline.

Core Concepts

Output Case vs Input Case

Relay Variable

A Relay Variable is a named variable that:

1. Is declared in the Output Case's environment variables (on Testany Platform)
2. Is populated by the Output Case's code at runtime
3. Is consumed by the Input Case as an environment variable

Data Flow in a Pipeline

Here's how data flows through a typical pipeline with Output Relay:

In this example:

• Case A outputs ACCESS_TOKEN
• Case B inputs ACCESS_TOKEN, outputs USER_ID
• Case C inputs both ACCESS_TOKEN (from A) and USER_ID (from B)

End-to-End Setup Overview

Setting up Output Relay involves three steps across two systems:

Step 1: Write Code to Output Data

In your Output Case, use the TESTANY_OUTPUT_RELAY_SERVICE environment variable to POST data:

PythonCopy
# Python example
import os
import requests

relay_service = os.environ.get('TESTANY_OUTPUT_RELAY_SERVICE')
requests.post(relay_service, json={
    'ACCESS_TOKEN': 'eyJhbGciOiJIUzI1NiIs...'
})

Step 2: Mark Variable as Relay on Platform

On the Test Case detail page, add an environment variable and click the relay icon to mark it as a relay variable.

Step 3: Configure Pipeline YAML

In your pipeline definition, use relay to connect cases:

YAMLCopy
kind: rule/v1.3
spec:
  rules:
    - run: A1B2C3D4  # Login case
    - run: E5F6A7B8  # Profile case
      relay:
        - key: ACCESS_TOKEN
          refKey: A1B2C3D4/ACCESS_TOKEN  # Reference login case's output

Constraints and Limitations

Relay Only Works with Passed Cases

In rule/v1.3, this also means the test script that consumes relay data cannot be in a relationship where it might start at the same time as the test script that provides relay data. The platform requires the test script that provides relay data to finish before the test script that consumes relay data starts.

This means you cannot configure:

YAMLCopy
# INVALID - relay with whenFailed
- run: E5F6A7B8  # Input case
  whenFailed: A1B2C3D4  # Output case failed, no reliable data
  relay:
    - key: TOKEN
      refKey: A1B2C3D4/TOKEN

No Circular Dependencies

The platform automatically detects and rejects circular relay configurations:

YAMLCopy
# INVALID - circular dependency
- run: A1B2C3D4  # Case A
  relay:
    - key: X
      refKey: E5F6A7B8/Y
- run: E5F6A7B8  # Case B
  relay:
    - key: Y
      refKey: A1B2C3D4/X  # Error: circular dependency

Performance Testing Not Supported

Output Relay is designed for functional testing only. It is not available for performance/load testing scenarios.

Next Steps

Now that you understand the concepts, proceed to the implementation guides:

Quick Reference