Performance testing of the gRPC service is pretty similar compared to testing any REST or GraphQL API.
1. Tool selection/protocol for gRPC Performance testing
The First question is always the tool selection. Without a proper tool for generating load and analyzing results, Performance testing is very difficult and requires a lot of time and special skills. Challenge is that Performance testing tools usually do not support gRPC/HTTP2 very well. Usual suspects Jmeter etc are not a good choice.
As SaaS testing services are more and more popular these days one needs to consider if the system under test is available to SaaS or not. If not, On-premise solutions are the only possibility. Obviously, it is good if the tool of your selection has both options available.
gRPC services can be unary or streaming methods. If you need to support them both, your testing tool should do that too. Obviously, support in the sales brochure is not enough and tools should be evaluated with real service.
2.Requirements and realistic test scenarios
For any Performance testing understanding how service is/will be used in real life. Usually, it is a good strategy to test each Method separately or with small use cases first and eventually concurrently, executing different use cases weighted with the right load. The usual approach in performance testing requirements is to find the biggest peak hour for the service and set the requirement to fit this peak hour load for example 1 million transactions per hour.
Test data in Performance testing is essential. In practice, this means realistic input data with a realistic variation.
Usually, development and the test environment for Functional tests do not have the capacity and scalability needed for the big performance. As the performance tests can be started in dev/functional test environment
5. Analysis and comparisons
It is likely that when you run performance tests for your service for the first time results /performance is not good enough. Also, regression is possible, meaning tests should be executed again after important releases or even continuously every day/week.
If your tool also supports trends and historical data it makes analysis a lot easier than manually with spreadsheets using a lot of energy and time.
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