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Python and Go : Part I - gRPC

Author image

Miki Tebeka

Introduction

Like tools, programming languages tend to solve problems they are designed to. You can use a knife to tighten a screw, but it’s better to use a screwdriver. Plus there is less chance of you getting hurt in the process.

The Go programming language shines when writing high throughput services, and Python shines when used for data science. In this series of blog posts, we’re going to explore how you can use each language to do the part it’s better and explore various methods of communication between Go & Python.

Note: Using more than one language in a project has its cost. If you can write everything in Go or Python only - by all means do that. However, there are cases where using the right language for the job reduces the overall engineering overhead for readability, maintenance and performance.

In this post, we’ll learn how Go and Python programs can communicate between each other using gRPC. This post assumes basic knowledge in both Go and Python.

gRPC Overview

gRPC is a remote procedure call (RPC) framework from Google. It uses Protocol Buffers as a serialization format and uses HTTP2 as the transport medium.

By using these two well established technologies, you gain access to a lot of knowledge and tooling that’s already available. Many companies I consult with use gRPC to connect internal services.

Another advantage of using protocol buffers is that you write the message definition once, and then generate bindings to every language from the same source. This means that various services can be written in different programming languages and all the applications agree on the format of messages.

Protocol buffers are also an efficient binary format: you gain both faster serialization times and less bytes over the wire, and this alone will save you money. Benchmarking on my machine, serialization time compared to JSON is about 7.5 faster and the data generated is about 4 times smaller.

Example: Outlier Detection

Outlier detection, also called anomaly detection, is a way of finding suspicious values in data. Modern systems collect a lot of metrics from their services, and it’s hard to come up with simple thresholds for finding malfunctioning services, which means waking on call developers at 2am too many times..

We’ll start by implementing a Go service that collects metrics. Then, using gRPC, we’ll send these metrics to a Python service, which will perform outlier detection on them.

Project Structure

In this project, we’re going with a simple approach of having Go as the main project and Python as a sub-project in the source tree.

Listing 1

.
├── client.go
├── gen.go
├── go.mod
├── go.sum
├── outliers.proto
├── pb
│   └── outliers.pb.go
└── py
    ├── Makefile
    ├── outliers_pb2_grpc.py
    ├── outliers_pb2.py
    ├── requirements.txt
    └── server.py

Listing1 shows the directory structure of our project. The project is using Go modules and the name for the module is defined in the go.mod file (see listing 2). We’re going to reference the module name ( github.com/ardanlabs/python-go/grpc) in several places.

Listing 2

01 module github.com/ardanlabs/python-go/grpc
02
03 go 1.14
04
05 require (
06     github.com/golang/protobuf v1.4.2
07     google.golang.org/grpc v1.29.1
08     google.golang.org/protobuf v1.24.0
09 )

Listing 2 shows what the go.mod file looks like for our project. You can see on line 01 where the name of the module is defined.

Defining Messages & Services

In gRPC, you start by writing a .proto file which defines the messages being sent and the RPC methods.

Listing 3

01 syntax = "proto3";
02 import "google/protobuf/timestamp.proto";
03 package pb;
04
05 option go_package = "github.com/ardanlabs/python-go/grpc/pb";
06
07 message Metric {
08    google.protobuf.Timestamp time = 1;
09    string name = 2;
10    double value = 3;
11 }
12
13 message OutliersRequest {
14    repeated Metric metrics = 1;
15 }
16
17 message OutliersResponse {
18    repeated int32 indices = 1;
19 }
20
21 service Outliers {
22    rpc Detect(OutliersRequest) returns (OutliersResponse) {}
23 }

Listing 3 shows what the outliers.proto looks like. Important things to mention are on line 02 in listing 3, where we import the protocol buffers definition of timestamp, and then on line 05, where we define the full Go package name - github.com/ardanlabs/python-go/grpc/pb

A metric is a measurement of resource usage that is used for monitoring and diagnostics your system. We define a Metric on line 07, which has a timestamp, a name (e.g. “CPU”), and a float value. For example we can say that at 2020-03-14T12:30:14 we measured 41.2% CPU utilization.

Every RPC method has an input type (or types) and an output type. Our method Detect (line 22) uses an OutliersRequest message type (line 13) as the input and an OutliersResponse message type (line 17) for the output. The OutliersRequest message type is a list/slice of Metric and the OutliersResponse message type is a list/slice of indices where the outlier values were found. For example, if we have the values of [1, 2, 100, 1, 3, 200, 1], the result will be [2, 5] which is the index of the outliers 100 and 200.

Python Service

In this section, we’ll go over the Python service code.

Listing 4

.
├── client.go
├── gen.go
├── go.mod
├── go.sum
├── outliers.proto
├── pb
│   └── outliers.pb.go
└── py
    ├── Makefile
    ├── outliers_pb2_grpc.py
    ├── outliers_pb2.py
    ├── requirements.txt
    └── server.py

Listing 4 shows how the code for the Python service is in the py directory off the root of the project.

To generate the Python bindings you’ll need to install the protoc compiler, which you can downloadhere. You can also install the compiler using your operating system package manager (e.g. apt-get, brew …)

Once you have the compiler installed, you’ll need to install the Python grpcio-tools package.

Note: I highly recommend using a virtual environment for all your Python projects. Read this to learn more.

Listing 5

$ cat requirements.txt

OUTPUT:
grpcio-tools==1.29.0
numpy==1.18.4

$ python -m pip install -r requirements.txt

Listing 5 shows how to inspect and install the external dependencies for our Python project. The requirements.txt specifies the external dependencies for the project, very much like how go.mod specifies dependencies for Go projects.

As you can see from the output of the cat command, we need two external dependencies: grpcio-tools and numpy . A good practice is to have this file in source control and always version your dependencies (e.g. numpy==1.18.4), similar to what you would do with your go.mod for your Go projects.

Once you have the dependencies installed, you can generate the Python bindings.

Listing 6

$ python -m grpc_tools.protoc \
    -I.. --python_out=. --grpc_python_out=. \
    ../outliers.proto

Listing 6 shows how to generate the Python binding for the gRPC support. Let’s break this long command in listing 5 down:

  • python -m grpc_tools.protoc runs the grpc_tools.protoc module as a script.
  • -I.. tells the tool where .proto files can be found.
  • --python_out=. tells the tool to generate the protocol buffers serialization code in the current directory.
  • --grpc_python_out=. tells the tool to generate the gRPC code in the current directory.
  • ../outliers.proto is the name of the protocol buffers + gRPC definitions file.

This Python command will run without any output, and at the end you’ll see two new files: outliers_pb2.py which is the protocol buffers code and outliers_pb2_grpc.py which is the gRPC client and server code.

Note: I usually use a Makefile to automate tasks in Python projects and create a make rule to run this command. I add the generated files to source control so that the deployment machine won’t have to install the protoc compiler.

To write the Python service, you need to inherit from the OutliersServicer defined in outliers_pb2_grpc.py and override the Detect method. We’re going to use the numpy package and use a simple method of picking all the values that are more than two standard deviations from the mean.

Listing 7

01 import logging
02 from concurrent.futures import ThreadPoolExecutor
03
04 import grpc
05 import numpy as np
06
07 from outliers_pb2 import OutliersResponse
08 from outliers_pb2_grpc import OutliersServicer, add_OutliersServicer_to_server
09
10
11 def find_outliers(data: np.ndarray):
12     """Return indices where values more than 2 standard deviations from mean"""
13     out = np.where(np.abs(data - data.mean()) > 2 * data.std())
14     # np.where returns a tuple for each dimension, we want the 1st element
15     return out[0]
16
17
18 class OutliersServer(OutliersServicer):
19     def Detect(self, request, context):
20          logging.info('detect request size: %d', len(request.metrics))
21          # Convert metrics to numpy array of values only
22          data = np.fromiter((m.value for m in request.metrics), dtype='float64')
23          indices = find_outliers(data)
24          logging.info('found %d outliers', len(indices))
25          resp = OutliersResponse(indices=indices)
26          return resp
27
28
29 if __name__ == '__main__':
30     logging.basicConfig(
31          level=logging.INFO,
32          format='%(asctime)s - %(levelname)s - %(message)s',
33	)
34     server = grpc.server(ThreadPoolExecutor())
35     add_OutliersServicer_to_server(OutliersServer(), server)
36     port = 9999
37     server.add_insecure_port(f'[::]:{port}')
38     server.start()
39     logging.info('server ready on port %r', port)
40     server.wait_for_termination()

Listing 7 shows the code in the server.py file. This is all the code we needed to write the Python service. In line 19 we override Detect from the generated OutlierServicer and write the actual outlier detection code. In line 34 we create a gRPC server that uses a ThreadPoolExecutor to run requests in parallel and in line 35 we register our OutliersServer to handle requests in the server.

Listing 8

$ python server.py

OUTPUT:
2020-05-23 13:45:12,578 - INFO - server ready on port 9999

Listing 8 shows how to run the service.

Go Client

Now that we have our Python service running, we can write the Go client that will communicate with it.

We’ll start by generating Go bindings for gRPC. To automate this process, I usually have a file called gen.go with a go:generate command to generate the bindings. You will need to download the github.com/golang/protobuf/protoc-gen-go module which is the gRPC plugin for Go.

Listing 9

01 package main
02
03 //go:generate mkdir -p pb
04 //go:generate protoc --go_out=plugins=grpc:pb --go_opt=paths=source_relative outliers.proto

Listing 9 shows the gen.go file and how go:generate is used to execute the gRPC plugin to generate the bindings.

Let’s break down the command on line 04 which generates the bindings:

  • protoc is the protocol buffer compiler.
  • --go-out=plugins=grpc:pb tells protoc to use the gRPC plugin and place the files in the pb directory.
  • --go_opt=source_relative tells protoc to generate the code in the pb directory relative to the current directory.
  • outliers.proto is the name of the protocol buffers + gRPC definitions file.

When you run go generate in the shell, you should see no output, but there will be a new file called outliers.pb.go in the pb directory.

Listing 10

.
├── client.go
├── gen.go
├── go.mod
├── go.sum
├── outliers.proto
├── pb
│   └── outliers.pb.go
└── py
    ├── Makefile
    ├── outliers_pb2_grpc.py
    ├── outliers_pb2.py
    ├── requirements.txt
    └── server.py

Listing 10 shows the pb directory and the new file outliers.pb.go that was generated by the go generate call. I add the pb directory to source control so if the project is cloned to a new machine the project will work without requiring protoc to be installed on that machine.

Now we can build and run the Go client.

Listing 11

01 package main
02
03 import (
04     "context"
05     "log"
06     "math/rand"
07     "time"
08
09     "github.com/ardanlabs/python-go/grpc/pb"
10     "google.golang.org/grpc"
11     pbtime "google.golang.org/protobuf/types/known/timestamppb"
12 )
13
14 func main() {
15     addr := "localhost:9999"
16     conn, err := grpc.Dial(addr, grpc.WithInsecure(), grpc.WithBlock())
17     if err != nil {
18          log.Fatal(err)
19     }
20     defer conn.Close()
21
22     client := pb.NewOutliersClient(conn)
23     req := pb.OutliersRequest{
24          Metrics: dummyData(),
25     }
26
27     resp, err := client.Detect(context.Background(), &req)
28     if err != nil {
29          log.Fatal(err)
30     }
31     log.Printf("outliers at: %v", resp.Indices)
32 }
33
34 func dummyData() []*pb.Metric {
35     const size = 1000
36     out := make([]*pb.Metric, size)
37     t := time.Date(2020, 5, 22, 14, 13, 11, 0, time.UTC)
38     for i := 0; i < size; i++ {
39          m := pb.Metric{
40               Time: Timestamp(t),
41               Name: "CPU",
42               // Normally we're below 40% CPU utilization
43               Value: rand.Float64() * 40,
44          }
45          out[i] = &m
46          t.Add(time.Second)
47     }
48     // Create some outliers
49     out[7].Value = 97.3
50     out[113].Value = 92.1
51     out[835].Value = 93.2
52     return out
53 }
54
55 // Timestamp converts time.Time to protobuf *Timestamp
56 func Timestamp(t time.Time) *pbtime.Timestamp {
57     return &pbtime.Timestamp {
58         Seconds: t.Unix(),
59         Nanos: int32(t.Nanosecond()),
60     }
61 }

Listing 11 shows the code from client.go. The code fills an OutliersRequest value on line 23 with some dummy data (generated by the dummyData function on line 34) and then on line 27 calls the Python service. The call to the Python service returns an OutliersResponse value.

Let’s break down the code a little more:

  • On line 16, we connect to the Python server, using the WithInsecure option since the Python server we wrote doesn’t support HTTPS.
  • On line 22, we create a new OutliersClient with the connection we made on line 16.
  • On line 23, we create the gPRC request.
  • On line 27, we perform the actual gRPC call. Every gRPC call has a context.Context as the first parameter, allowing you to control timeouts and cancellations.
  • gRPC has it’s own implementation of a Timestamp struct. On line 56, we have a utility function to convert from Go’s time.Time value to gRPC Timestamp value.

Listing 12

$ go run client.go

OUTPUT:
2020/05/23 14:07:18 outliers at: [7 113 835]

Listing 12 shows you how to run the Go client. This assumes the Python server is running on the same machine.

Conclusion

gRPC makes it easy and safe to pass messages from one service to another. You can maintain one place where all data types and methods are defined, and there is great tooling and best practices for the gRPC framework.

The whole code: outliers.proto, py/server.py and client.go is less than a 100 lines. You can view the project code here.

There is much more to gRPC like timeout, load balancing, TLS, and streaming. I highly recommend going over the official site read the documentation and play with the provided examples.

In the next post in this series, we’ll flip the roles and have Python call a Go service.

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