In a microservices architecture, each service or microservice is designed to perform a specific task or function, and these services communicate with each other through APIs (Application Programming Interfaces).

When two or more microservices need to communicate with each other, they do so through APIs that expose their functionality to other services. The services that provide data or functionality to other services are referred to as “upstream” services, while the services that consume data or functionality from other services are called “downstream” services.In API microservices architecture, the terms “upstream” and “downstream” refer to the direction of data flow between microservices.


Upstream microservices provide data to downstream microservices, which consume and process that data. This data flow can be thought of as a stream flowing downstream from the source (upstream) to the destination (downstream).

Upstream microservices are responsible for providing data, functionality, or services to downstream microservices. For example, a customer service may provide customer data to an order service or a product service may provide product data to a shopping cart service.


Downstream microservices consume data, functionality, or services from upstream microservices. For example, a shopping cart service may consume product data from a product service or an order service may consume customer data from a customer service.

Simple Example

For example, consider an e-commerce application with microservices for product catalog, inventory management, order processing, and payment processing. When a user places an order, the order processing microservice is downstream of the inventory management microservice, which is in turn downstream of the product catalog microservice.

In this scenario, the product catalog microservice, is an upstream microservice, providing data to the downstream inventory management microservice. Similarly, the inventory management microservice is an upstream microservice, providing data to the downstream order processing microservice. The payment processing microservice is downstream of the order processing microservice, as it receives data from the order processing microservice to process the payment.

Data Flow in Stream

In a typical microservices architecture, the communication between microservices happens asynchronously. When a downstream service needs to consume data or functionality from an upstream service, it sends an API request to the upstream service. The upstream service then processes the request and returns a response, which the downstream service can consume and use in its own processing.

The data flow in microservices architecture is unidirectional, with upstream microservices providing data and functionality to downstream microservices. However, a downstream microservice can also act as an upstream service to another service further downstream in the data flow.

Understanding the data flow between microservices is essential for designing, testing, and troubleshooting microservices architecture. It allows developers and QA team to identify the dependencies between services and ensure that each service has the necessary data and functionality to perform its task efficiently. It also helps in identifying and resolving issues that may arise due to changes in upstream services or changes in the data format or structure of the API response.

Now that we’ve got a bit of a premise in what these are, what are the challenges that as a QA you might face in testing upstream or downstream API’s?

Challenges in testing

When testing downstream APIs, there are several challenges that can arise, including:

  • Dependency on external systems: Downstream APIs are often dependent on external systems such as databases, servers, and other upstream APIs. Testing these systems requires coordination with external teams and may cause delays in the testing process.
  • Authentication/Authorisation: Mostly downstream API’s will depend on some kind of authorisation to fetch data from upstream services. Broken authentication or incorrectly configured authentication would result in downstream getting more or less data from the upstream services.
  • Changes in upstream APIs: Upstream APIs that provide data to the downstream APIs can change frequently, leading to compatibility issues and failures in the downstream API. This requires constant monitoring and updates to the downstream API.
  • Complexity: Downstream APIs can be complex, involving multiple data sources and complex data transformations. This complexity can make testing challenging and time-consuming.
  • Testing environment: Downstream APIs may require specific testing environments that mimic the production environment. Setting up and maintaining these environments can be difficult and time-consuming.
  • Security: Downstream APIs may be vulnerable to security threats such as unauthorized access, data breaches, and denial-of-service attacks. It is important to ensure that the API is secure and that sensitive data is protected.
  • Performance: Downstream APIs may be required to handle a large volume of requests, and performance can be a critical factor in their success. Testing for scalability and performance can be challenging and require specialized tools and expertise.