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Building a Kafka based key-value store in Rust - Part 3: API

In the previous installment of this series we implemented the core of a key-value stored backed by Kafka and sled. Today we will finish the API for it. If you haven't read it already I suggest you go read part 2 of the series.

For an API let's use GRPC, there are many options, of course, but GRPC is is a good compromise between performance and flexibilty, perhaps at the cost of some complexity. GRPC is based on Protobuf and HTTP/2, both binary protocols with good serialization performance. To use GRPC in Rust we have to install some dependencies.

  • tonic - which implements the GRPC server (and clients)
  • prost - which generates rust code from .proto files
  • tonic-health - implements the GRPC healthcheck service useful for exposing a standardized healthcheck for use with kubernetes.

Now with that out of the way we need to specify our GRPC service using a the protobuf IDL. Let's place the file in proto/kvs2.proto

syntax = "proto3";
package kvs2;

service Kvs2 {
  rpc MultiGet(MultiGetRequest) returns (MultiGetReply);
  rpc Get(GetRequest) returns (GetReply);
  rpc Set(SetRequest) returns (SetReply);

message SetRequest {
  bytes key = 1;
  bytes value = 2;

enum SetStatus {

message SetReply {
  SetStatus status = 1;

message GetRequest {
  bytes key = 1;

enum ReadStatus {
  OK = 0;
  NOT_FOUND  = 1;

message GetReply {
  ReadStatus status = 1;
  bytes data = 2;

message MultiGetRequest {
  repeated bytes keys = 1;

message MultiGetResponsePart {
  bytes key = 1;
  ReadStatus status = 2;
  bytes data = 3;

message MultiGetReply {
  repeated MultiGetResponsePart parts = 1;

This defines exactly the same API as we discussed in the first part of the series. Now for this to work we need to do a couple of thing. First, we need to add a build.rs file, this file also known as a build script is used by to enable code generation. The below snippet ensures that code is generated based on the protobuf file we specified earlier.

fn main() -> Result<(), Box<dyn std::error::Error>> {

Pretty self-explanatory, if you ask me, but perhaps most impressively we're still only using the Cargo build chain to compile - no extra executables needed.

Now we can create a new file rpc.rs to implement our API.

use crate::config::Config;
use crate::service::{Error, Service};
use futures::FutureExt;
use kvs2::kvs2_server::{Kvs2, Kvs2Server};
use kvs2::{
    GetReply, GetRequest, MultiGetReply, MultiGetRequest, MultiGetResponsePart, ReadStatus,
    SetReply, SetRequest, SetStatus,
use std::time::Duration;
use tonic::{transport::Server, Request, Response, Status};

pub mod kvs2 {

Most important in this snippet is the include_proto which brings the protobuf-generated code into scope. We can the implement the service itself.

struct Kvs2Impl {
    svc: Service,

fn err_to_status(err: Error) -> Status {
    match err {
        Error::KafkaError { source: _ } => Status::unavailable("Kafka error"),
        Error::SledError { source: _ } => Status::unavailable("Sled error"),

impl Kvs2 for Kvs2Impl {
    async fn get(&self, request: Request<GetRequest>) -> Result<Response<GetReply>, Status> {
            .map(|res| match res {
                Some(x) => GetReply {
                    status: ReadStatus::Ok.into(),
                    data: x.to_vec(),
                None => GetReply {
                    status: ReadStatus::NotFound.into(),
                    data: (&[0u8; 0]).to_vec(),
            .map(|x| Response::new(x))
    async fn set(&self, request: Request<SetRequest>) -> Result<Response<SetReply>, Status> {
            .insert_globally(&request.get_ref().key, &request.get_ref().value)
            .map(|_| SetReply {
                status: SetStatus::Published.into(),
            .map(|x| Response::new(x))
    async fn multi_get(
        request: Request<MultiGetRequest>,
    ) -> Result<Response<MultiGetReply>, Status> {
        let keys = &request.get_ref().keys;
            .map(|x| {
                    .map(|(i, y)| match y {
                        Some(data) => MultiGetResponsePart {
                            key: keys[i].to_vec(),
                            data: data.to_vec(),
                            status: ReadStatus::Ok.into(),
                        None => MultiGetResponsePart {
                            key: keys[i].to_vec(),
                            data: (&[0u8; 0].to_vec()).to_vec(),
                            status: ReadStatus::NotFound.into(),
            .map(|parts| MultiGetReply { parts })
            .map(|x| Response::new(x))

The service is then simply translating between Protobuf and our internal service struct. We can then create a serve function like this:

pub async fn serve(_config: &Config, svc: Service) -> Result<(), Box<dyn std::error::Error>> {
    let svc = Kvs2Impl { svc };
    let addr = "".parse()?;

    let (mut health_reporter, health_service) = tonic_health::server::health_reporter();
        .set_service_status("", tonic_health::ServingStatus::Serving)

        .serve_with_shutdown(addr, tokio::signal::ctrl_c().map(|_| ()))

Which creates a GRPC server, on the standard port containing a healthcheck and a Kvs2Server. Which will keep on servering until it receives a SIGTERM or the equivalent.

We can now set up our main.rs to create both Kafka consumers as well as our GRPC endpoints.

mod config;
mod consumer;
mod producer;
mod rpc;
mod service;
mod storage;

#[tokio::main(worker_threads = 4)]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let config = config::load()?;
    let prod = producer::Producer::new(&config)?;
    let svc = service::Service::new(prod, &config).await?;
    let consumer = consumer::Consumer::new(&config, svc.clone())?;

    let cons_handle = tokio::spawn(async move {
    let svc_rpc = svc.clone();
    let serve_handle = tokio::spawn(async move {
        rpc::serve(&config, svc_rpc).await.unwrap_or(());

    let (a, b) = tokio::join!(cons_handle, serve_handle);



Done! And there you have it that's how you create a Kafka backed key-value store that is also accessible via GRPC. I hope you enjoyed this series

A picture of William Rudenmalm

William Rudenmalm

Technologist at Sobel Software Research

William Rudenmalm is a european technologist passionate about the next big thing and the people building it. William is particularly interested in scaling engineering organizations and navigating trade offs of architecture and velocity. In the field of computer science, his expertise lies in distributed systems, scalability and machine learning. Among the technologies William is particularly excited about are Kubernetes, Rust, nats, Kafka and neo4j.