Benchmarking

Overview

gRPC is designed for both high-performance and high-productivity design of distributed applications. Continuous performance benchmarking is a critical part of the gRPC development workflow. Multi-language performance tests run every few hours against the master branch, and these numbers are reported to a dashboard for visualization.

• Multi-language performance dashboard @master (latest dev version)
• Legacy dashboard (same data as above)

Performance testing design

Each language implements a performance testing worker that implements a gRPC WorkerService. This service directs the worker to act as either a client or a server for the actual benchmark test, represented as BenchmarkService. That service has two methods:

• UnaryCall – a unary RPC of a simple request that specifies the number of bytes to return in the response.
• StreamingCall – a streaming RPC that allows repeated ping-pongs of request and response messages akin to the UnaryCall.

These workers are controlled by a driver that takes as input a scenario description (in JSON format) and an environment variable specifying the host:port of each worker process.

Languages under test

The following languages have continuous performance testing as both clients and servers at master:

• C++
• Java
• Go
• C#
• Node.js
• Python
• Ruby

In addition to running as both the client-side and server-side of performance tests, all languages are tested as clients against a C++ server, and as servers against a C++ client. This test aims to provide the current upper bound of performance for a given language’s client or server implementation without testing the other side.

Although PHP or mobile environments do not support a gRPC server (which is needed for our performance tests), their client-side performance can be benchmarked using a proxy WorkerService written in another language. This code is implemented for PHP but is not yet in continuous testing mode.

Scenarios under test

There are several important scenarios under test and displayed in the dashboards above, including the following:

• Contentionless latency – the median and tail response latencies seen with only 1 client sending a single message at a time using StreamingCall.
• QPS – the messages/second rate when there are 2 clients and a total of 64 channels, each of which has 100 outstanding messages at a time sent using StreamingCall.
• Scalability (for selected languages) – the number of messages/second per server core.

Most performance testing is using secure communication and protobufs. Some C++ tests additionally use insecure communication and the generic (non-protobuf) API to display peak performance. Additional scenarios may be added in the future.

Testing infrastructure

All performance benchmarks are run in our dedicated GKE cluster, where each benchmark worker (a client or a server) gets scheduled to different GKE node (and each GKE node is a separate GCE VM) in one of our worker pools. The source code for the benchmarking framework we use is publicly available in the test-infra github repository.

Most test instances are 8-core systems, and these are used for both latency and QPS measurement. For C++ and Java, we additionally support QPS testing on 32-core systems. All QPS tests use 2 identical client machines for each server, to make sure that QPS measurement is not client-limited.