blog/content/posts/18-build-your-own-kubernetes-cluster-part-9/index.md

title, date, description, tags, draft

title	date	description	tags	draft
Setup a HA Kubernetes cluster Part IX - Code metrics with SonarQube & load testing	2023-10-09	Follow this opinionated guide as starter-kit for your own Kubernetes platform...	kubernetes testing sonarqube load-testing k6	true

{{< lead >}} Be free from AWS/Azure/GCP by building a production grade On-Premise Kubernetes cluster on cheap VPS provider, fully GitOps managed, and with complete CI/CD tools 🎉 {{< /lead >}}

This is the Part IX of more global topic tutorial. [Back to first part]({{< ref "/posts/10-build-your-own-kubernetes-cluster" >}}) for intro.

Code Metrics

SonarQube is leading the code metrics industry for a long time, embracing full Open Core model, and the community edition it's completely free of charge even for commercial use. It covers advanced code analysis, code coverage, code duplication, code smells, security vulnerabilities, etc. It ensures high quality code and help to keep it that way.

SonarQube installation

SonarQube as its dedicated Helm chart which perfect for us. However, it's the most resource hungry component of our development stack so far (because Java project ? End of troll), so be sure to deploy it on almost empty free node, maybe a dedicated one. In fact, it's the last Helm chart for this tutorial, I promise!

Create dedicated database for SonarQube same as usual.

{{< highlight host="demo-kube-k3s" file="main.tf" >}}

variable "sonarqube_db_password" {
  type      = string
  sensitive = true
}

{{< /highlight >}}

{{< highlight host="demo-kube-k3s" file="terraform.tfvars" >}}

sonarqube_db_password = "xxx"

{{< /highlight >}}

{{< highlight host="demo-kube-k3s" file="sonarqube.tf" >}}

resource "kubernetes_namespace_v1" "sonarqube" {
  metadata {
    name = "sonarqube"
  }
}

resource "helm_release" "sonarqube" {
  chart      = "sonarqube"
  version    = "10.1.0+628"
  repository = "https://SonarSource.github.io/helm-chart-sonarqube"

  name      = "sonarqube"
  namespace = kubernetes_namespace_v1.sonarqube.metadata[0].name

  set {
    name  = "prometheusMonitoring.podMonitor.enabled"
    value = "true"
  }

  set {
    name  = "postgresql.enabled"
    value = "false"
  }

  set {
    name  = "jdbcOverwrite.enabled"
    value = "true"
  }

  set {
    name  = "jdbcOverwrite.jdbcUrl"
    value = "jdbc:postgresql://postgresql-primary.postgres/sonarqube"
  }

  set {
    name  = "jdbcOverwrite.jdbcUsername"
    value = "sonarqube"
  }

  set {
    name  = "jdbcOverwrite.jdbcPassword"
    value = var.sonarqube_db_password
  }
}

resource "kubernetes_manifest" "sonarqube_ingress" {
  manifest = {
    apiVersion = "traefik.io/v1alpha1"
    kind       = "IngressRoute"
    metadata = {
      name      = "sonarqube"
      namespace = kubernetes_namespace_v1.sonarqube.metadata[0].name
    }
    spec = {
      entryPoints = ["websecure"]
      routes = [
        {
          match = "Host(`sonarqube.${var.domain}`)"
          kind  = "Rule"
          services = [
            {
              name = "sonarqube-sonarqube"
              port = "http"
            }
          ]
        }
      ]
    }
  }
}

{{< /highlight >}}

Be sure to disable the PostgreSQL sub chart and use our self-hosted cluster with both postgresql.enabled and jdbcOverwrite.enabled. If needed, set proper tolerations and nodeSelector for deploying on a dedicated node.

The installation take many minutes, be patient. Once done, you can access SonarQube on https://sonarqube.kube.rocks and login with admin / admin.

Project configuration

Firstly create a new project and retain the project key which is his identifier. Then create a global analysis token named Concourse CI that will be used for CI integration from your user account under /account/security.

Now we need to create a Kubernetes secret which contains this token value for Concourse CI, for usage inside the pipeline. The token is the one generated above.

Add a new concourse terraform variable for the token:

{{< highlight host="demo-kube-k3s" file="main.tf" >}}

variable "concourse_analysis_token" {
  type      = string
  sensitive = true
}

{{< /highlight >}}

{{< highlight host="demo-kube-k3s" file="terraform.tfvars" >}}

concourse_analysis_token = "xxx"

{{< /highlight >}}

The secret:

{{< highlight host="demo-kube-k3s" file="concourse.tf" >}}

resource "kubernetes_secret_v1" "concourse_sonarqube" {
  metadata {
    name      = "sonarqube"
    namespace = "concourse-main"
  }

  data = {
    url            = "https://sonarqube.${var.domain}"
    analysis-token = var.concourse_analysis_token
  }

  depends_on = [
    helm_release.concourse
  ]
}

{{< /highlight >}}

We are ready to tackle the pipeline for integration.

SonarScanner for .NET

As we use a dotnet project, we will use the official SonarQube scanner for .net. But sadly, as it's only a .NET CLI wrapper, it requires a java runtime to run and there is no official SonarQube docker image which contains both .NET SDK and Java runtime. But we have a CI now, so we can build our own QA image on our own private registry.

Create a new Gitea repo dedicated for any custom docker images with this one single Dockerfile:

{{< highlight host="demo-kube-images" file="dotnet-qa.dockerfile" >}}

FROM mcr.microsoft.com/dotnet/sdk:7.0

RUN apt-get update && apt-get install -y ca-certificates-java && apt-get install -y \
    openjdk-17-jre-headless \
    unzip \
    && rm -rf /var/lib/apt/lists/*

RUN dotnet tool install --global dotnet-sonarscanner
RUN dotnet tool install --global dotnet-coverage

ENV PATH="${PATH}:/root/.dotnet/tools"

{{< /highlight >}}

Note as we add the dotnet-sonarscanner tool to the path, we can use it directly in the pipeline without any extra step. I'll also add dotnet-coverage global tool for code coverage generation that we'll use later.

Then the pipeline:

{{< highlight host="demo-kube-flux" file="pipelines/images.yaml" >}}

resources:
  - name: docker-images-git
    type: git
    icon: coffee
    source:
      uri: https://gitea.kube.rocks/kuberocks/docker-images
      branch: main
  - name: dotnet-qa-image
    type: registry-image
    icon: docker
    source:
      repository: ((registry.name))/kuberocks/dotnet-qa
      tag: "7.0"
      username: ((registry.username))
      password: ((registry.password))

jobs:
  - name: dotnet-qa
    plan:
      - get: docker-images-git
      - task: build-image
        privileged: true
        config:
          platform: linux
          image_resource:
            type: registry-image
            source:
              repository: concourse/oci-build-task
          inputs:
            - name: docker-images-git
          outputs:
            - name: image
          params:
            DOCKERFILE: docker-images-git/dotnet-qa.dockerfile
          run:
            path: build
      - put: dotnet-qa-image
        params:
          image: image/image.tar

{{< /highlight >}}

Update the main.yaml pipeline to add the new job, then trigger it manually from Concourse UI to add the new above pipeline:

{{< highlight host="demo-kube-flux" file="pipelines/main.yaml" >}}

#...

jobs:
  - name: configure-pipelines
    plan:
      #...
      - set_pipeline: images
        file: ci/pipelines/images.yaml

{{< /highlight >}}

The pipeline should now start and build the image, trigger it manually if needed on Concourse UI. Once done, you can check it on your Gitea container packages that the new image gitea.kube.rocks/kuberocks/dotnet-qa is here.

Concourse pipeline integration

It's finally time to reuse this QA image in our Concourse demo project pipeline. Update it accordingly:

{{< highlight host="demo-kube-flux" file="pipelines/demo.yaml" >}}

#...

jobs:
  - name: build
    plan:
      - get: source-code
        trigger: true

      - task: build-source
        config:
          platform: linux
          image_resource:
            type: registry-image
            source:
              repository: ((registry.name))/kuberocks/dotnet-qa
              tag: "7.0"
              username: ((registry.username))
              password: ((registry.password))
          #...
          run:
            path: /bin/sh
            args:
              - -ec
              - |
                dotnet format --verify-no-changes

                dotnet sonarscanner begin /k:"KubeRocks-Demo" /d:sonar.host.url="((sonarqube.url))"  /d:sonar.token="((sonarqube.analysis-token))"
                dotnet build -c Release
                dotnet sonarscanner end /d:sonar.token="((sonarqube.analysis-token))"

                dotnet publish src/KubeRocks.WebApi -c Release -o publish --no-restore --no-build

      #...

{{< /highlight >}}

Note as we now use the dotnet-qa image and surround the build step by dotnet sonarscanner begin and dotnet sonarscanner end commands with appropriate credentials allowing Sonar CLI to send report to our SonarQube instance. Trigger the pipeline manually, all should pass, and the result will be pushed to SonarQube.

Feature testing

Let's cover the feature testing by calling the API against a real database. This is the opportunity to cover the code coverage as well.

xUnit

First add a dedicated database for test in the docker compose file as we won't interfere with the development database:

{{< highlight host="kuberocks-demo" file="docker-compose.yml" >}}

version: "3"

services:
  #...

  db_test:
    image: postgres:15
    environment:
      POSTGRES_USER: main
      POSTGRES_PASSWORD: main
      POSTGRES_DB: main
    ports:
      - 54320:5432

{{< /highlight >}}

Expose the startup service of minimal API:

{{< highlight host="kuberocks-demo" file="src/KubeRocks.WebApi/Program.cs" >}}

#...
public partial class Program
{
    protected Program() { }
}

{{< /highlight >}}

Then add a testing JSON environment file for accessing our database db_test from the docker-compose.yml:

{{< highlight host="kuberocks-demo" file="src/KubeRocks.WebApi/appsettings.Testing.json" >}}

{
  "ConnectionStrings": {
    "DefaultConnection": "Server=localhost;Port=54320;User Id=main;Password=main;Database=main;"
  }
}

{{< /highlight >}}

Now the test project:

dotnet new xunit -o tests/KubeRocks.FeatureTests
dotnet sln add tests/KubeRocks.FeatureTests
dotnet add tests/KubeRocks.FeatureTests reference src/KubeRocks.WebApi
dotnet add tests/KubeRocks.FeatureTests package Microsoft.AspNetCore.Mvc.Testing
dotnet add tests/KubeRocks.FeatureTests package Respawn
dotnet add tests/KubeRocks.FeatureTests package FluentAssertions

The WebApplicationFactory that will use our testing environment:

{{< highlight host="kuberocks-demo" file="tests/KubeRocks.FeatureTests/KubeRocksApiFactory.cs" >}}

using Microsoft.AspNetCore.Mvc.Testing;
using Microsoft.Extensions.Hosting;

namespace KubeRocks.FeatureTests;

public class KubeRocksApiFactory : WebApplicationFactory<Program>
{
    protected override IHost CreateHost(IHostBuilder builder)
    {
        builder.UseEnvironment("Testing");

        return base.CreateHost(builder);
    }
}

{{< /highlight >}}

The base test class for all test classes that manages database cleanup thanks to Respawn:

{{< highlight host="kuberocks-demo" file="tests/KubeRocks.FeatureTests/TestBase.cs" >}}

using KubeRocks.Application.Contexts;

using Microsoft.EntityFrameworkCore;
using Microsoft.Extensions.DependencyInjection;

using Npgsql;

using Respawn;
using Respawn.Graph;

namespace KubeRocks.FeatureTests;

[Collection("Sequencial")]
public class TestBase : IClassFixture<KubeRocksApiFactory>, IAsyncLifetime
{
    protected KubeRocksApiFactory Factory { get; private set; }

    protected TestBase(KubeRocksApiFactory factory)
    {
        Factory = factory;
    }

    public async Task RefreshDatabase()
    {
        using var scope = Factory.Services.CreateScope();

        using var conn = new NpgsqlConnection(
            scope.ServiceProvider.GetRequiredService<AppDbContext>().Database.GetConnectionString()
        );

        await conn.OpenAsync();

        var respawner = await Respawner.CreateAsync(conn, new RespawnerOptions
        {
            TablesToIgnore = new Table[] { "__EFMigrationsHistory" },
            DbAdapter = DbAdapter.Postgres
        });

        await respawner.ResetAsync(conn);
    }

    public Task InitializeAsync()
    {
        return RefreshDatabase();
    }

    public Task DisposeAsync()
    {
        return Task.CompletedTask;
    }
}

{{< /highlight >}}

Note the Collection attribute that will force the test classes to run sequentially, required as we will use the same database for all tests.

Finally, the tests for the 2 endpoints of our articles controller:

{{< highlight host="kuberocks-demo" file="tests/KubeRocks.FeatureTests/Articles/ArticlesListTests.cs" >}}

using System.Net.Http.Json;

using FluentAssertions;

using KubeRocks.Application.Contexts;
using KubeRocks.Application.Entities;
using KubeRocks.WebApi.Models;

using Microsoft.Extensions.DependencyInjection;

using static KubeRocks.WebApi.Controllers.ArticlesController;

namespace KubeRocks.FeatureTests.Articles;

public class ArticlesListTests : TestBase
{
    public ArticlesListTests(KubeRocksApiFactory factory) : base(factory) { }

    [Fact]
    public async Task Can_Paginate_Articles()
    {
        using (var scope = Factory.Services.CreateScope())
        {
            var db = scope.ServiceProvider.GetRequiredService<AppDbContext>();

            var user = db.Users.Add(new User
            {
                Name = "John Doe",
                Email = "john.doe@email.com"
            });

            db.Articles.AddRange(Enumerable.Range(1, 50).Select(i => new Article
            {
                Title = $"Test Title {i}",
                Slug = $"test-title-{i}",
                Description = "Test Description",
                Body = "Test Body",
                Author = user.Entity,
            }));

            await db.SaveChangesAsync();
        }

        var response = await Factory.CreateClient().GetAsync("/api/Articles?page=1&size=20");

        response.EnsureSuccessStatusCode();

        var body = (await response.Content.ReadFromJsonAsync<ArticlesResponse>())!;

        body.Articles.Count().Should().Be(20);
        body.ArticlesCount.Should().Be(50);

        body.Articles.First().Should().BeEquivalentTo(new
        {
            Title = "Test Title 50",
            Description = "Test Description",
            Body = "Test Body",
            Author = new
            {
                Name = "John Doe"
            },
        });
    }

    [Fact]
    public async Task Can_Get_Article()
    {
        using (var scope = Factory.Services.CreateScope())
        {
            var db = scope.ServiceProvider.GetRequiredService<AppDbContext>();

            db.Articles.Add(new Article
            {
                Title = $"Test Title",
                Slug = $"test-title",
                Description = "Test Description",
                Body = "Test Body",
                Author = new User
                {
                    Name = "John Doe",
                    Email = "john.doe@email.com"
                }
            });

            await db.SaveChangesAsync();
        }

        var response = await Factory.CreateClient().GetAsync($"/api/Articles/test-title");

        response.EnsureSuccessStatusCode();

        var body = (await response.Content.ReadFromJsonAsync<ArticleDto>())!;

        body.Should().BeEquivalentTo(new
        {
            Title = "Test Title",
            Description = "Test Description",
            Body = "Test Body",
            Author = new
            {
                Name = "John Doe"
            },
        });
    }
}

{{< /highlight >}}

Ensure all tests passes with dotnet test.

CI tests & code coverage

Now we need to integrate the tests in our CI pipeline. As we testing with a real database, create a new demo_test database through pgAdmin with basic test / test credentials.

{{< alert >}} In real world scenario, you should use a dedicated database for testing, and not the same as production. {{< /alert >}}

Let's edit the pipeline accordingly for tests:

{{< highlight host="demo-kube-flux" file="pipelines/demo.yaml" >}}

#...

jobs:
  - name: build
    plan:
      #...

      - task: build-source
        config:
          #...
          params:
            ConnectionStrings__DefaultConnection: "Server=postgres-primary.postgres; Port=5432; User Id=test; Password=test; Database=demo_test"
          run:
            path: /bin/sh
            args:
              - -ec
              - |
                dotnet format --verify-no-changes

                dotnet sonarscanner begin /k:"KubeRocks-Demo" /d:sonar.host.url="((sonarqube.url))"  /d:sonar.token="((sonarqube.analysis-token))" /d:sonar.cs.vscoveragexml.reportsPaths=coverage.xml
                dotnet build -c Release
                dotnet-coverage collect 'dotnet test -c Release --no-restore --no-build --verbosity=normal' -f xml -o 'coverage.xml'
                dotnet sonarscanner end /d:sonar.token="((sonarqube.analysis-token))"

                dotnet publish src/KubeRocks.WebApi -c Release -o publish --no-restore --no-build

#...

{{< /highlight >}}

Note as we already include code coverage by using dotnet-coverage tool. Don't forget to precise the path of coverage.xml to sonarscanner CLI too. It's time to push our code with tests or trigger the pipeline manually to test our integration tests.

If all goes well, you should see the tests results on SonarQube with some coverage done:

Coverage detail:

You may exclude some files from analysis by adding some project properties:

{{< highlight host="kuberocks-demo" file="src/KubeRocks.Application/KubeRocks.Application.csproj" >}}

<Project Sdk="Microsoft.NET.Sdk">
  <!-- ... -->

  <ItemGroup>
    <SonarQubeSetting Include="sonar.exclusions">
      <Value>appsettings.Testing.json</Value>
    </SonarQubeSetting>
  </ItemGroup>
</Project>

{{< /highlight >}}

Same for coverage:

{{< highlight host="kuberocks-demo" file="src/KubeRocks.Application/KubeRocks.Application.csproj" >}}

<Project Sdk="Microsoft.NET.Sdk">
  <!-- ... -->

  <ItemGroup>
    <SonarQubeSetting Include="sonar.coverage.exclusions">
      <Value>Migrations/**/*</Value>
    </SonarQubeSetting>
  </ItemGroup>
</Project>

{{< /highlight >}}

Sonar Analyzer

You can enforce many default sonar rules by using Sonar Analyzer directly locally before any code push.

Create this file at the root of your solution for enabling Sonar Analyzer globally:

{{< highlight host="kuberocks-demo" file="Directory.Build.props" >}}

<Project>
  <PropertyGroup>
    <AnalysisLevel>latest-Recommended</AnalysisLevel>
    <TreatWarningsAsErrors>true</TreatWarningsAsErrors>
    <CodeAnalysisTreatWarningsAsErrors>true</CodeAnalysisTreatWarningsAsErrors>
  </PropertyGroup>
  <ItemGroup>
    <PackageReference
      Include="SonarAnalyzer.CSharp"
      Version="9.8.0.76515"
      PrivateAssets="all"
      Condition="$(MSBuildProjectExtension) == '.csproj'"
    />
  </ItemGroup>
</Project>

{{< /highlight >}}

Any rule violation is treated as error at project building, which block the CI before execution of tests. Use latest-All as AnalysisLevel for psychopath mode.

At this stage as soon this file is added, you should see some errors at building. If you use VSCode with correct C# extension, these errors will be highlighted directly in the editor. Here are some fixes:

{{< highlight host="kuberocks-demo" file="src/KubeRocks.WebApi/Program.cs" >}}

#...

builder.Host.UseSerilog((ctx, cfg) => cfg
    .ReadFrom.Configuration(ctx.Configuration)
    .Enrich.WithSpan()
    .WriteTo.Console(
        outputTemplate: "[{Timestamp:HH:mm:ss} {Level:u3}] |{TraceId}| {Message:lj}{NewLine}{Exception}",
        // Enforce culture
        formatProvider: CultureInfo.InvariantCulture
    )
);

#...

{{< /highlight >}}

Delete WeatherForecastController.cs.

{{< highlight host="kuberocks-demo" file="tests/KubeRocks.FeatureTests.csproj" >}}

<Project Sdk="Microsoft.NET.Sdk">

  <PropertyGroup>
    <!-- ... -->

    <NoWarn>CA1707</NoWarn>
  </PropertyGroup>

  <!-- ... -->
</Project>

{{< /highlight >}}

Load testing

When it comes load testing, k6 is a perfect tool for this job and integrate with many real time series database integration like Prometheus or InfluxDB. As we already have Prometheus, let's use it and avoid us a separate InfluxDB installation. First be sure to allow remote write by enable enableRemoteWriteReceiver in the Prometheus Helm chart. It should be already done if you follow this tutorial.

K6

We'll reuse our flux repo and add some manifests for defining the load testing scenario. Firstly describe the scenario inside ConfigMap that scrape all articles and then each article:

{{< highlight host="demo-kube-flux" file="jobs/demo-k6.yaml" >}}

apiVersion: v1
kind: ConfigMap
metadata:
  name: scenario
  namespace: kuberocks
data:
  script.js: |
    import http from "k6/http";
    import { check } from "k6";

    export default function () {
      const size = 10;
      let page = 1;

      let articles = []

      do {
        const res = http.get(`${__ENV.API_URL}/Articles?page=${page}&size=${size}`);
        check(res, {
          "status is 200": (r) => r.status == 200,
        });

        articles = res.json().articles;
        page++;

        articles.forEach((article) => {
          const res = http.get(`${__ENV.API_URL}/Articles/${article.slug}`);
          check(res, {
            "status is 200": (r) => r.status == 200,
          });
        });
      }
      while (articles.length > 0);
    }

{{< /highlight >}}

And add the k6 Job in the same file and configure it for Prometheus usage and mounting above scenario:

{{< highlight host="demo-kube-flux" file="jobs/demo-k6.yaml" >}}

#...
---
apiVersion: batch/v1
kind: Job
metadata:
  name: k6
  namespace: kuberocks
spec:
  ttlSecondsAfterFinished: 0
  template:
    spec:
      restartPolicy: Never
      containers:
        - name: run
          image: grafana/k6
          env:
            - name: API_URL
              value: https://demo.kube.rocks/api
            - name: K6_VUS
              value: "30"
            - name: K6_DURATION
              value: 1m
            - name: K6_PROMETHEUS_RW_SERVER_URL
              value: http://prometheus-operated.monitoring:9090/api/v1/write
          command:
            ["k6", "run", "-o", "experimental-prometheus-rw", "script.js"]
          volumeMounts:
            - name: scenario
              mountPath: /home/k6
      tolerations:
        - key: node-role.kubernetes.io/runner
          operator: Exists
          effect: NoSchedule
      nodeSelector:
        node-role.kubernetes.io/runner: "true"
      volumes:
        - name: scenario
          configMap:
            name: scenario

{{< /highlight >}}

Use appropriate tolerations and nodeSelector for running the load testing in a node which have free CPU resource. You can play with K6_VUS and K6_DURATION environment variables in order to change the level of load testing.

Then you can launch the job with ka jobs/demo-k6.yaml. Check quickly that the job is running via klo -n kuberocks job/k6:

        /\      |‾‾| /‾‾/   /‾‾/
   /\  /  \     |  |/  /   /  /
  /  \/    \    |     (   /   ‾‾\
 /          \   |  |\  \ |  (‾)  |
/ __________ \  |__| \__\ \_____/ .io

execution: local
   script: script.js
   output: Prometheus remote write (http://prometheus-operated.monitoring:9090/api/v1/write)

scenarios: (100.00%) 1 scenario, 30 max VUs, 1m30s max duration (incl. graceful stop):
         * default: 30 looping VUs for 1m0s (gracefulStop: 30s)

After 1 minute of run, job should finish and show some raw result:

✓ status is 200

checks.........................: 100.00% ✓ 17748     ✗ 0
data_received..................: 404 MB  6.3 MB/s
data_sent......................: 1.7 MB  26 kB/s
http_req_blocked...............: avg=242.43µs min=223ns   med=728ns   max=191.27ms p(90)=1.39µs   p(95)=1.62µs
http_req_connecting............: avg=13.13µs  min=0s      med=0s      max=9.48ms   p(90)=0s       p(95)=0s
http_req_duration..............: avg=104.22ms min=28.9ms  med=93.45ms max=609.86ms p(90)=162.04ms p(95)=198.93ms
  { expected_response:true }...: avg=104.22ms min=28.9ms  med=93.45ms max=609.86ms p(90)=162.04ms p(95)=198.93ms
http_req_failed................: 0.00%   ✓ 0         ✗ 17748
http_req_receiving.............: avg=13.76ms  min=32.71µs med=6.49ms  max=353.13ms p(90)=36.04ms  p(95)=51.36ms
http_req_sending...............: avg=230.04µs min=29.79µs med=93.16µs max=25.75ms  p(90)=201.92µs p(95)=353.61µs
http_req_tls_handshaking.......: avg=200.57µs min=0s      med=0s      max=166.91ms p(90)=0s       p(95)=0s
http_req_waiting...............: avg=90.22ms  min=14.91ms med=80.76ms max=609.39ms p(90)=138.3ms  p(95)=169.24ms
http_reqs......................: 17748   276.81409/s
iteration_duration.............: avg=5.39s    min=3.97s   med=5.35s   max=7.44s    p(90)=5.94s    p(95)=6.84s
iterations.....................: 348     5.427727/s
vus............................: 7       min=7       max=30
vus_max........................: 30      min=30      max=30

As we use Prometheus for outputting the result, we can visualize it easily with Grafana. You just have to import this dashboard:

As we use Kubernetes, increase the loading performance horizontally is dead easy. Go to the deployment configuration of demo app for increasing replicas count, as well as Traefik, and compare the results.

Load balancing database

So far, we only load balanced the stateless API, but what about the database part ? We have set up a replicated PostgreSQL cluster, however we have no use of the replica that stay sadly idle. But for that we have to distinguish write queries from scalable read queries.

We can make use of the Bitnami PostgreSQL HA instead of simple one. It adds the new component Pgpool-II as main load balancer and detect failover. It's able to separate in real time write queries from read queries and send them to the master or the replica. The advantage: works natively for all apps without any changes. The cons: it consumes far more resources and add a new component to maintain.

A 2nd solution is to separate RW queries from where it counts: from the app. It requires some code changes, but it's clearly a far more efficient solution. Let's do this way.

PostgreSQL RO SVC

We have firstly to set up a service that will be dedicated for read queries. There is already postgresql-read, but it uses only replica, so no advantage for a simple 1 primary + 1 replica setup. So create a new one by going back to terraform project and let's apply following resource:

{{< highlight host="demo-kube-k3s" file="postgresql.tf" >}}

resource "kubernetes_service_v1" "postgresql_lb" {
  metadata {
    name      = "postgresql-lb"
    namespace = kubernetes_namespace_v1.postgres.metadata[0].name
  }
  spec {
    selector = {
      "app.kubernetes.io/instance" = "postgresql"
      "app.kubernetes.io/name"     = "postgresql"
    }
    port {
      name        = "tcp-postgresql"
      port        = 5432
      protocol    = "TCP"
      target_port = "tcp-postgresql"
    }
  }
}

{{< /highlight >}}

On the app side

Final check 🎊🏁🎊

Congratulation if you're getting that far !!!

You have a complete CI/CD flexible solution, HA ready and not that expensive.