# Protocol Buffers Benchmarks This directory contains benchmarking schemas and data sets that you can use to test a variety of performance scenarios against your protobuf language runtime. If you are looking for performance numbers of officially support languages, see [here]( https://github.com/protocolbuffers/protobuf/blob/master/docs/performance.md) ## Prerequisite First, you need to follow the instruction in the root directory's README to build your language's protobuf, then: ### CPP You need to install [cmake](https://cmake.org/) before building the benchmark. We are using [google/benchmark](https://github.com/google/benchmark) as the benchmark tool for testing cpp. This will be automaticly made during build the cpp benchmark. The cpp protobuf performance can be improved by linking with [tcmalloc library]( https://gperftools.github.io/gperftools/tcmalloc.html). For using tcmalloc, you need to build [gpertools](https://github.com/gperftools/gperftools) to generate libtcmallc.so library. ### Java We're using maven to build the java benchmarks, which is the same as to build the Java protobuf. There're no other tools need to install. We're using [google/caliper](https://github.com/google/caliper) as benchmark tool, which can be automaticly included by maven. ### Python We're using python C++ API for testing the generated CPP proto version of python protobuf, which is also a prerequisite for Python protobuf cpp implementation. You need to install the correct version of Python C++ extension package before run generated CPP proto version of Python protobuf's benchmark. e.g. under Ubuntu, you need to ``` $ sudo apt-get install python-dev $ sudo apt-get install python3-dev ``` And you also need to make sure `pkg-config` is installed. ### Go Go protobufs are maintained at [github.com/golang/protobuf]( http://github.com/golang/protobuf). If not done already, you need to install the toolchain and the Go protoc-gen-go plugin for protoc. To install protoc-gen-go, run: ``` $ go get -u github.com/golang/protobuf/protoc-gen-go $ export PATH=$PATH:$(go env GOPATH)/bin ``` The first command installs `protoc-gen-go` into the `bin` directory in your local `GOPATH`. The second command adds the `bin` directory to your `PATH` so that `protoc` can locate the plugin later. ### PHP PHP benchmark's requirement is the same as PHP protobuf's requirements. The benchmark will automaticly include PHP protobuf's src and build the c extension if required. ### Node.js Node.js benchmark need [node](https://nodejs.org/en/)(higher than V6) and [npm](https://www.npmjs.com/) package manager installed. This benchmark is using the [benchmark](https://www.npmjs.com/package/benchmark) framework to test, which needn't to manually install. And another prerequisite is [protobuf js](https://github.com/protocolbuffers/protobuf/tree/master/js), which needn't to manually install either ### C# The C# benchmark code is built as part of the main Google.Protobuf solution. It requires the .NET Core SDK, and depends on [BenchmarkDotNet](https://github.com/dotnet/BenchmarkDotNet), which will be downloaded automatically. ### Big data There's some optional big testing data which is not included in the directory initially, you need to run the following command to download the testing data: ``` $ ./download_data.sh ``` After doing this the big data file will automatically generated in the benchmark directory. ## Run instructions To run all the benchmark dataset: ### Java: ``` $ make java ``` ### CPP: ``` $ make cpp ``` For linking with tcmalloc: ``` $ env LD_PRELOAD={directory to libtcmalloc.so} make cpp ``` ### Python: We have three versions of python protobuf implementation: pure python, cpp reflection and cpp generated code. To run these version benchmark, you need to: #### Pure Python: ``` $ make python-pure-python ``` #### CPP reflection: ``` $ make python-cpp-reflection ``` #### CPP generated code: ``` $ make python-cpp-generated-code ``` ### Go ``` $ make go ``` ### PHP We have two version of php protobuf implemention: pure php, php with c extension. To run these version benchmark, you need to: #### Pure PHP ``` $ make php ``` #### PHP with c extension ``` $ make php_c ``` ### Node.js ``` $ make js ``` To run a specific dataset or run with specific options: ### Java: ``` $ make java-benchmark $ ./java-benchmark $(specific generated dataset file name) [$(caliper options)] ``` ### CPP: ``` $ make cpp-benchmark $ ./cpp-benchmark $(specific generated dataset file name) [$(benchmark options)] ``` ### Python: For Python benchmark we have `--json` for outputing the json result #### Pure Python: ``` $ make python-pure-python-benchmark $ ./python-pure-python-benchmark [--json] $(specific generated dataset file name) ``` #### CPP reflection: ``` $ make python-cpp-reflection-benchmark $ ./python-cpp-reflection-benchmark [--json] $(specific generated dataset file name) ``` #### CPP generated code: ``` $ make python-cpp-generated-code-benchmark $ ./python-cpp-generated-code-benchmark [--json] $(specific generated dataset file name) ``` ### Go: ``` $ make go-benchmark $ ./go-benchmark $(specific generated dataset file name) [go testing options] ``` ### PHP #### Pure PHP ``` $ make php-benchmark $ ./php-benchmark $(specific generated dataset file name) ``` #### PHP with c extension ``` $ make php-c-benchmark $ ./php-c-benchmark $(specific generated dataset file name) ``` ### Node.js ``` $ make js-benchmark $ ./js-benchmark $(specific generated dataset file name) ``` ### C# From `csharp/src/Google.Protobuf.Benchmarks`, run: ``` $ dotnet run -c Release ``` We intend to add support for this within the makefile in due course. ## Benchmark datasets Each data set is in the format of benchmarks.proto: 1. name is the benchmark dataset's name. 2. message_name is the benchmark's message type full name (including package and message name) 3. payload is the list of raw data. The schema for the datasets is described in `benchmarks.proto`. Benchmark likely want to run several benchmarks against each data set (parse, serialize, possibly JSON, possibly using different APIs, etc). We would like to add more data sets. In general we will favor data sets that make the overall suite diverse without being too large or having too many similar tests. Ideally everyone can run through the entire suite without the test run getting too long.