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users

codecov CI

Table of Contents

Overview

Users is a small gRPC service that allows CRUD operations against a user entity. The service is implemented in Go and uses a Postgres database for the backend data storage. It also provides a friendly and easy to use CLI tool called userctl to interact with the gRPC service.

Features

  • Full CI suite including:
    • Linting
    • Unit testing + code coverage reporting
    • Integration testing using userctl
    • Build and push to the GitHub container registry
  • Health checking endpoints
  • Helm Charts for deployment to Kubernetes
  • Bespoke CLI tooling - userctl
  • 95% unit test coverage
  • Events emitted on user entity creation, deletion, update and reads
  • docker-compose local environment

Requirements

Tooling

Makefile

At the root of the project directory, you will find a Makefile that will enable you to run various helpful tasks within the project. Find a table describing them below

Command Functionality
race Run unit tests and generate code coverage
docker Build and tag a docker image for the users-service
up Bring up a local docker-compose environment
down Bring down a local docker-compose environment and remove volumes
proto Generate code from proto definitions
lint Run golangci against the codebase
helm-template Test generating the helm deployment and service to stdout
kind-up Build a local docker image and install helm templates against a local kind cluster
kind-down Tear down the local kind cluster and uninstall helm charts
build-cli Compile the userctl cli tool (found in the bin directory)
integration Bring up the docker compose stack and run integration tests against it

CLI

This project also delivers a CLI tool to interact with the gRPC server. To build it - use the make build-cli tool as described above. By executing ./bin/userctl you will be presented with a helpful screen for how to use the CLI tool:

A friendly CLI for interacting with the users service

Usage:
  userctl [command]

Available Commands:
  create      create a user
  delete      delete a user based on their id
  help        Help about any command
  list        lists users based on a filter of a 2 character country code
  update      update a user

Flags:
  -h, --help          help for userctl
  -a, --host string   host of the gRPC Server (default "localhost")
  -p, --port string   port of the gRPC Server (default "5355")

Use "userctl [command] --help" for more information about a command

Local Deployment

The users service provides two methods of deploying the stack locally. The first is via Kind using Helm charts ( see the charts/ directory), with the second being via docker-compose. The two options are available because:

  • Using Kind allows us to test kubernetes deployments, services, readiness and liveliness checks
  • Using Kind allows us to test our Helm chart implementations

Deploying via Kind does not deploy the full stack - only the service, Postgres (and its config including initdb scripts) to provide a minimalst Kubernetes test environment.

Deploying the docker-compose stack locally provides a larger more well rounded stack for more comprehensive local development. This stack deploys:

  • The Users service
  • Postgres (initialising the dataset)
  • Kafka (for relaying events)
  • Zookeeper (tracks Kafka nodes and status)
  • Kafka Connect (allows integrations between Postgres and Kafka)
  • Kafdrop (WebUI for viewing Kafka topics and messages)
  • alpine/curl (to configure Kafka Connect)

Kubernetes

To deploy via to Kubernetes locally using kind run: make kind-up. This will perform the following steps:

  • Build the user service Docker image
  • Bring up a Kind cluster
  • Load the built service image into Kind
  • Create an initdb SQL configmap for Postgres
  • Install the Postgres Helm chart
  • Create Postgres secrets
  • Install the users-service Helm chart

Note that this process may take a while, particularly on first time usage as it pulls images from registries. When successfully deployed, you should be able to port forward using kubectl to the service and be able to make requests. You can use either userctl or a tool like Bloom to interact with the service.

Example:

kubectl port-forward -n users svc/users-service 5355:5355

Run make kind-down to bring down the local cluster and cleanup Helm charts.

Health Checks

A probe is installed that interacts with the health check endpoints at the point of building the Docker image. Kubernetes then leverages this via the deployment as seen below:

readinessProbe:
  exec:
    command: [ "/grpc_health_probe", "-addr=:{{ .Values.env.grpcPort }}" ]
  initialDelaySeconds: 5
livenessProbe:
  exec:
    command: [ "/grpc_health_probe", "-addr=:{{ .Values.env.grpcPort }}" ]

Docker Compose

To bring up the compose environment, simply run make up. This will bring up the aforementioned services. When the images have pulled and are running, I highly recommend interacting with the service either using userctl or BloomRPC whilst inspecting the Kafdrop WebUI.

Note: you may need to find the IP address of the users service to interact with it. To do this, run: docker inspect users-service and grab the IP address from the Networks.IPAddress field.

To access the Kafdrop WebUI, navigate to localhost:9000. Here, you can see the available topics on the Kafka instance running in docker-compose. Topics are auto-created when events are pushed to them in this local instance, so use your preferred tooling to create a new user against the service.

./bin/userctl create -a 192.168.32.7 --first-name=test --last-name=test --nickname=test --password=123456 [email protected] --country=BU`

When you've created the user, refresh the Kafdrop page and you should see a new topic called db.users.users:

Click on this and hit view messages. You will be presented with events on CRUD operations on the user entity, which can later be consumed by other services that may care.

To bring down the docker-compose stack, run make down.

Database

The database is managed by Postgres' inbuilt initdb scripts. These SQL files will be run on the initial boot of Postgres and are configured here .

Things to note:

  • User IDs are UUIDs and the unique primary key of the table
  • Emails are unique fields
  • A trigger is created to automatically update the updated_at timestamps on row changes
  • The database is initially seeded with three fake users

Images

As part of the CI process, images are built and pushed on PR and merge to main. You can find those images here .

Events

This section will discuss my approach to emitting events from the users-service. When I created this project I wanted to keep a singular microservice that would cover the domain of modifying and creating user entities. This proposes a problem. If in this instance the workflow is:

Client -> Create User -> Store in Database

At what point can I safely emit an event with the guarentee that both the database row will be stored and delivered to Kafka? I toyed with the idea of the following approach:

Client -> Create User -> Store in Database -> Success -> Produce message to Kafka in code

However, if the Kafka connectivity drops after successfully storing the row in the database, this is unrecoverable.

I opted for the approach that the database itself would emit events. I chose this option because debezium will always reconcile rows with corresponding Kafka messages, meaning that we will never lose a CRUD operation.

Kafka Connect and Debezium have significant operational overhead and are hard to manage. These technologies are fine for a small, singular microservice. If I had more time, and wanted to make a more complex project, I would emit user creation events before storing them in the database and completely removing the database component from the users microservice. When a creation event is emitted from the microservice, I would also have a database storage consumer which would persist the event to an RDBMS, as well as any other listening microservices that cared for changes to a user entity.

Testing

Unit Testing

  • All business logic is unit tested with code coverage reports to >95%

Integration Testing

  • Integration testing uses the userctl command line tool against the docker-compose stack to ensure all CRUD operations work as expected.

Assumptions and Decisions

  • Storage and service interfaces are designed to allow swapping out the database storage technology easily without affecting service functionality.
  • Passwords are hashed when stored in the database
  • Emails are assumed to be unique in the user entity

Possible Extensions and Improvements

  • Continuous delivery with Flux or ArgoCD to a staging/production Kubernetes cluster
  • Migration tooling to manage database schema changes
  • Certificate based authentication rather than username and password authentication for databases
  • Helm charts pushed to a remote repository