grpc/tools/run_tests/performance

Overview of performance test suite, with steps for manual runs:

For design of the tests, see http://www.grpc.io/docs/guides/benchmarking.html.

Pre-reqs for running these manually:

In general the benchmark workers and driver build scripts expect linux_performance_worker_init.sh to have been ran already.

To run benchmarks locally:

• From the grpc repo root, start the run_performance_tests.py runner script.

On remote machines, to start the driver and workers manually:

The run_performance_test.py top-level runner script can also be used with remote machines, but for e.g., profiling the server, it might be useful to run workers manually.

1. You'll need a "driver" and separate "worker" machines. For example, you might use one GCE "driver" machine and 3 other GCE "worker" machines that are in the same zone.

2. Connect to each worker machine and start up a benchmark worker with a "driver_port".

• For example, to start the grpc-go benchmark worker: grpc-go worker main.go --driver_port <driver_port>

Comands to start workers in different languages:

• Note that these commands are what the top-level run_performance_test.py script uses to build and run different workers through the build_performance.sh script and "run worker" scripts (such as the run_worker_java.sh).

Running benchmark workers for C-core wrapped languages (C++, Python, C#, Node, Ruby):

• These are more simple since they all live in the main grpc repo.

$ cd <grpc_repo_root>
$ tools/run_tests/performance/build_performance.sh
$ tools/run_tests/performance/run_worker_<language>.sh

• Note that there is one "run_worker" script per language, e.g., run_worker_csharp.sh for c#.

Running benchmark workers for gRPC-Java:

• You'll need the grpc-java repo.

$ cd <grpc-java-repo>
$ ./gradlew -PskipCodegen=true :grpc-benchmarks:installDist
$ benchmarks/build/install/grpc-benchmarks/bin/benchmark_worker --driver_port <driver_port>

Running benchmark workers for gRPC-Go:

• You'll need the grpc-go repo

$ cd <grpc-go-repo>/benchmark/worker && go install
$ # if profiling, it might be helpful to turn off inlining by building with "-gcflags=-l"
$ $GOPATH/bin/worker --driver_port <driver_port>

Build the driver:

• Connect to the driver machine (if using a remote driver) and from the grpc repo root:

$ tools/run_tests/performance/build_performance.sh

Run the driver:

1. Get the 'scenario_json' relevant for the scenario to run. Note that "scenario json" configs are generated from scenario_config.py. The driver takes a list of these configs as a json string of the form: {scenario: <json_list_of_scenarios> } in its --scenarios_json command argument. One quick way to get a valid json string to pass to the driver is by running the run_performance_tests.py locally and copying the logged scenario json command arg.

2. From the grpc repo root:

• Set QPS_WORKERS environment variable to a comma separated list of worker machines. Note that the driver will start the "benchmark server" on the first entry in the list, and the rest will be told to run as clients against the benchmark server.

Example running and profiling of go benchmark server:

$ export QPS_WORKERS=<host1>:<10000>,<host2>,10000,<host3>:10000
$ bins/opt/qps_json_driver --scenario_json='<scenario_json_scenario_config_string>'

Example profiling commands

While running the benchmark, a profiler can be attached to the server.

Example to count syscalls in grpc-go server during a benchmark:

• Connect to server machine and run:

$ netstat -tulpn | grep <driver_port> # to get pid of worker
$ perf stat -p <worker_pid> -e syscalls:sys_enter_write # stop after test complete

Example memory profile of grpc-go server, with go tools pprof:

• After a run is done on the server, see its alloc profile with:

$ go tool pprof --text --alloc_space http://localhost:<pprof_port>/debug/heap