Building a service registry, a structure that tracks all people, services and systems that interact with your infrastructure, can be extremely powerful.

If you pick the right format, it can be the glue between totally distinct toolchains. Placing the registry at the heart of all your other tools means you no longer need to worry about keeping it up-to-date: the registry defines what is created, rather than describing it.

By distributing the registry so every developer, infrastructure component or one-off script can easily read it, you’ll find use cases for this data everywhere. You can even push this data into systems like your monitoring stack, allowing automated systems to make decisions on the ownership information it provides.

As part of a revamp of our infrastructure tooling, we’ve introduced a service registry into GoCardless. This post explains how we built the registry and some of the use cases we’ve found for it.

What is it?

The GoCardless service registry is a Jsonnet library, stored as a file inside the same Git repository that contains our infrastructure configuration. Jsonnet, for those not familiar, is an extension to JSON that aims to support flexible reuse and customisation of data structures.

Jsonnet files evaluate to JSON, and the service registry is no different:

$ jsonnet registry.jsonnet
  "clusters": [...],
  "services": [...],
  "teams": [...],
  "projects": [...],

Perhaps you thought a service registry was a webserver, maybe hooked up to a database, serving the data via a REST API? That wouldn’t be strange, and there are many systems that do just that, but I’d suggest the approach of building a registry out of a single JSON file (compiled from whatever templating language you choose, be it Jsonnet or otherwise) has several advantages:

  • JSON files are so universally compatible that you’ll be able to use this anywhere
  • If you’ve already adopted Git-ops workflows, tracking changes to the registry in Git should feel very natural
  • It’s just data, and your registry is only as good as the data you put in it. Removing the distraction of building an API means you encourage a focus on building the right data model, which is what really matters

From the output of the jsonnet registry.jsonnet command, you can see we’re tracking our Kubernetes clusters, any services that we run, organisation teams who interact with the services, and Google Cloud Platform projects.

You don’t need to start by tracking all these types, but the simplicity of a Jsonnet library means it costs very little to add a new type. We began with services, then wanted to ensure no service referenced an invalid team. It was a natural evolution to add teams, and this pattern has happened many times over.

Service entry (make-it-rain)

Our registry began as a list of services, where each service had a metadata.jsonnet that defined its service entry.

For the purpose of this post, imagine we have a (fake) service called make-it-rain, which has a service entry that looks like this:

// Example service called make-it-rain, powering a dashboard of falling
// gold coins whenever anyone takes a payment via GoCardless.
// Banking teams love money, which is why they created this dashboard.
// It's officially owned by banking-integrations, but core-banking
// sometimes optimise the React code.
// It consumes data about new payments from Google Pub/Sub, and has a
// separate Google Cloud Platform project for each of its environments,
// of which there are two: staging and production.'make-it-rain', 'gocardless/make-it-rain') +
service.mixin.withTeam('banking-integrations') +
service.mixin.withAlertsChannel('make-it-rain-alerts') +
]) +
    // By default, every environment should have banking-integrations as
    // admins, and core-banking as operators (they provide on-call cover
    // for the falling gold coins).
    environment.mixin.rbac.withAdmins('banking-integrations') +
    function(environment) ['staging') +
      environment.mixin.withGoogleProject('gc-prd-make-it-stag-833e') +
      environment.mixin.withTargets(['compute-staging-brava', namespace='make-it-rain'),
      // Unlike most services, the production environment should permit
      // a non-engineering team to open consoles. Sometimes we take a
      // manual payment outside of GoCardless, and banking-operations
      // open a make-it-rain console and run a script, so we don't miss
      // any gold coins.'production') +
      environment.mixin.rbac.withOperatorsMixin('banking-operations') +
      environment.mixin.withGoogleProject('gc-prd-make-it-prod-1eb1') +
      environment.mixin.withTargets(['compute-banking', namespace='make-it-rain'),

Take a moment to read the Jsonnet- this produces a JSON structure that you can see here. It includes a definition of the service and all its environments, with a list of deployment targets for each environment that defines where the deployment lives.

There’s some configuration of team permissions and Google Cloud Platform references- we’ll see how we can use them next.

Provisioning infrastructure

Once you have a list of service entries like make-it-rain, we can use it to tightly integrate with all the rest of our infrastructure tools.

Most infrastructure teams deal with many (in my mind, too many) tools. The GoCardless team provisions infrastructure with terraform, manages virtual machines with Chef, and Kubernetes resources with Jsonnet templating. Other teams may use far more.

Thankfully, our service registry is plain ol’ JSON, and easily consumed by all of these tools. Once imported, we can begin provisioning infrastructure in response to changes in the registry. This is a change from the registry describing the infrastructure at a point-in-time, to becoming the definition what really exists.

When you must update registry to create infrastructure, you guarantee the registry is up-to-date, and know it can no longer become stale. This allows you to trust the registry in use-cases that weren’t possible if it could fall out-of-date, a benefit we’ll see when we integrate it with our tools.

Let’s see how this works in practice.


When someone creates a service like make-it-rain, we’ll import their entry into the registry. Our CD pipelines will detect a registry change and begin provisioning core resources required for every service.

First, we have a Jsonnet templated cluster service that we use to create privileged Kubernetes cluster resources, such as namespaces. As the templating imports the registry as just another Jsonnet file, it will detect we’re missing a namespace (make-it-rain) in the compute-staging-brava and compute-banking clusters, and automatically create them.

After we have a namespace, we’ll create the supporting resources. Included in this are resource quotas, limiting the amount of cluster resource make-it-rain could consume- these limits can be tweaked or overriden in the cluster service Jsonnet:

// utopia/services/cluster/instances/compute-staging.jsonnet
cluster {
  spaces+: {
    'make-it-rain'+: {
      quota+: {
        spec+: {
          // These React apps are getting crazy...
          hard+: { cpu: '32', memory: '32Gi' },

Permissions are one of the more complicated things about managing Kubernetes clusters. Especially when aiming for a Devops workflow, with application engineers empowered to care for their own Kubernetes resources, you want to establish a consistent permission model up-front. Consistency means you can accurately describe your security stance for audits, and helps maintain productivity for engineers who work across multiple teams.

Your registry, being the authoritative definition of service RBAC, can be used to power your Kubernetes RBAC and enforce that consistency. Looking at our make-it-rain production environment, we can see the RBAC fields:

  "type": "Service",
  "spec": {
    "name": "make-it-rain",
    "repository": "gocardless/make-it-rain",
    "team": "banking-integrations",
    "environments": [
        "type": "Environment",
        "spec": {
          "name": "staging",
          "rbac": {
            "admins": [
            "operators": [
            "viewers": []

We made a decision to model just three roles for a service, viewer, operator and admin- from our experience, it seems this is flexible enough for almost all use cases. We thought it would be great if all permissions granted to humans were derived from these member lists, instead of scattering the membership across our infrastructure configuration (Kubernetes, terraform, Chef).

Now we have our registry, we can do just that. Using Kubernetes permissions as an example, it’s simple to:

  • Identify the list of teams who are viewers, operators, or admins for any services that exist within each cluster namespace
  • Use these lists to create RoleBindings in the service namespace, granting appropriate permissions to each member of the roles

We implement this in a single file, cluster/app/spaces-rbac.jsonnet, which allows us to map over all namespaces in a cluster and provision the RoleBinding Kubernetes resources. Jsonnet is great for this type of data manipulation, proving–yet again!–how using a static registry does not limit how flexibly you can query the data.

Google Cloud Platform

It’s not just Kubernetes resources in Jsonnet, though. GoCardless is a heavy user of Google Cloud Platform, and if this permission model is sound, we should be able to apply it to our Cloud estate too.

For this, we have a terraform project called registry which loads our registry.jsonnet using the Jsonnet terraform provider. Just as with Kubernetes resources, we query into the registry to derive the GCP permissions required for each of our service environments.

Looking at the make-it-rain staging environment, you’ll notice a googleProject field:

  "type": "Service",
  "spec": {
    "name": "make-it-rain",
    "repository": "gocardless/make-it-rain",
    "team": "banking-integrations",
    "environments": [
        "type": "Environment",
        "spec": {
          "name": "staging",
          "googleProject": "gc-prd-make-it-stag-833e"
          "rbac": { ... },

This field means our staging environment is linked against the Google project with project ID gc-prd-make-it-stag-833e. This means GCP resources, including the IAM memberships, must be provisioned in this Google project.

We also detect a linked Google project, and deploy an instance of the Config Connector for the make-it-rain namespace. This allows developers to provision Google Cloud Resources (like a CloudSQL instance, of BigQuery dataset) like any other Kubernetes resource, all automatically deployed through a registry change.

Returning to permissions, this means we know what Google project we need to create them in. And from our RBAC, we know who has which viewer, operator, or admin role.

The missing piece is what Google IAM roles to grant. For this, you may have noticed a mention of Google services in the original make-it-rain service entry:

]) +

This tells us that make-it-rain makes use of Google Pub/Sub. Using this, we can write some Jsonnet that maps Google services to appropriate IAM permissions for each role:

// Configure the Google IAM roles we want to bind people of different role
// (viewer/operator/admin) type to, depending on what Google service they are
// configured to have access to.
googleServiceIAMRoles: {
  '': {
    viewers: ['roles/pubsub.viewer'],
    operators: ['roles/pubsub.editor'],
    admins: ['roles/pubsub.admin'],

With this, we can implement the registry queries that aggregate these permissions into a separate Jsonnet file, registry-permissions.jsonnet, inside of the registry terraform project. Terraform is far less suited to manipulating data than Jsonnet, so we aim to produce whatever structure is easiest for the terraform to understand, leading to extremely simple terraform code.

We end up with a simple list of Google groups to Google Cloud Platform IAM roles:

    "member": "banking-integra[email protected]",
    "project": "gc-prd-make-it-stag-833e",
    "role": "roles/pubsub.admin"
    "member": "[email protected]",
    "project": "gc-prd-make-it-stag-833e",
    "role": "roles/pubsub.editor"

Finally, we import these permissions into our terraform, with almost no additional data processing required:

provider "jsonnet" {}

# [{member, project, role}]
data "jsonnet_file" "registry_permissions" {
  source = "registry-permissions.jsonnet"

# An IAM member per permission binding, sourced from our Jsonnet
resource "google_project_iam_member" "permissions" {
  for_each = {
    for permission in data.registry_permissions : join(".", [
      permission.project, permission.member, permission.role
    ]) => permission

  project = each.value.project
  member  = "group:${each.value.member}"
  role    = each.value.role

And just like that, we express our GCP permissions using the same data source that powers our Kubernetes RBAC, traced back to the service definition in our cannonical registry.

While this permission model may not fit your team, it should be clear you can encode whatever model you want into your registry. Once you establish a sound data model, you’ll be surprised by how easily you can use this to power your various tools.

If you do it right, it should help a small group of SREs manage an ever increasing number of services, using the automation to ensure consistency. That’s what we hope, at any rate!


So far we’ve covered provisioning of infrastructure, possibly the most useful way to leverage a service registry. But once you use it to create infrastructure, the registry becomes a trusted map of everything that exists, which can be a great help when creating user friendly developer tools.


Before anyone can use the registry, they need to access it. If we’re aiming to make the registry truly ubiquitous, we need to provide tools that can fetch a registry from anywhere, without requiring additional setup or authentication material.

For this, taking inspiration from Google Application Default Credentials, we implemented a discovery flow that should work from anywhere. We enable this by deploying the registry to several locations:

  • For developers, we upload a registry JSON blob to a GCS bucket. Every GoCardless developer is authenticated against Google Cloud Platform from their local machine, which we can use to grant them access. We’ll rely on registry access for several essential tools: we might have chosen to pull the file from our Github repo, but suspect GCS might be a bit more reliable 🙈
  • For infrastructure, we place the registry in a globally accessible ConfigMap in all of our Kubernetes clusters, and permit access from cluster service accounts

We want users to consume the registry from either of these locations transparently. For this, we implement the discovery flow in a Golang pkg/registry that can be vendored into any Golang application.

The interface is as simple as:

package registry

// Discover loads the service registry, falling back to a number of locations.
func Discover(context.Context, kitlog.Logger, DiscoverOptions) (*Registry, error) {

For those who don’t use Go or are writing shell scripts, we rely on a binary called utopia, a tool we vendor into developer and production runtimes along with several other GoCardless specific tools. The binary supports a utopia registry command, which calls the standard discovery flow and prints the JSON registry.

$ utopia registry | jq keys

So now it’s accessible everywhere, what can we do with it?

Improving UX

Like many teams, our maturing Kubernetes expertise encouraged us to break our large cluster into many smaller clusters. Where most services used to live in a single cluster, they are now spread across many, and might move depending on maintenance or business requirements.

Our developer tools used to default to the primary cluster, but this assumption was becoming less and less useful as we moved around our workloads. It wasn’t just what cluster you wanted either: developers needed to understand what Kubernetes namespace their service existed in, and often the value of their service release label.

This was beginning to complicate our developer tools:

$ anu consoles create \
    --context <cluster-name> \
    --namespace <namespace> \
    --release <release> \

Application engineers shouldn’t need to know our cluster topology from heart- that’s quite an ask for someone who infrequently touches that configuration. I suspect new joiners were encouraged to type magic values they didn’t really understand, a habit you want to discourage when talking about production applications. And whenever maintenance moved a service, it could potentially break several runbooks.

Developers at GoCardless think in terms of service and environment, not physical location. Our registry can help us here- if it’s easy to map service and environment to the cluster and namespace in which it’s deployed, then we can start offering interfaces that better align with how developers think:

# --service can be provided, or automatically inferred from the current repo
$ utopia consoles create --environment staging -- bash

And it’s not just finding services. Companies our size tend to have many tools that are interconnected in ways that aren’t obvious, and definitely not supported natively by the tools themselves. But with a registry like ours, it’s easy to encode those connections and provide a much more joined-up experience.

As an example, we run Kibana and Elasticsearch to provide centralised logging. Service logs are routed to specific indices, and you need to know what index stores your logs to find them via Kibana.

By adding a loggingIndex type to our registry, we can easily map a service environment to a Kibana index pattern. This provides all the information we need to implement a shortcut for jumping into a service’s logs:

# Open the browser with Kibana at the right index, with filters
# for this service environment
$ utopia logs --service=make-it-rain --environment=staging --since=1h

These improvements may seem small, but can reduce cognitive load in situations where it really pays, such as during incident response. The efforts to make developers lives easier are, I think, appreciated.

Monitoring and alerting

As the final case study, it’s worth demonstrating how easily a static registry can be translated into a totally different medium.

GoCardless has a lot of services, and a growing number of teams. As teams take more operational responsibility over their services, we’re seeing a noticeable increase in the number of developers writing Prometheus alerting rules for their service.

With increased usage came a pressing issue of alert routing. From the start, we’ve supported routing alerts to a specific Slack channel by providing a channel label in the alert rule.

For our make-it-rain service, we could direct our alerts to the #make-it-rain-alerts Slack channel like so:

# Example Prometheus recording rule for a make-it-rain service
  name: make-it-rain
    - alert: ItsStoppedRaining
      expr: rate(make_it_rain_coins_fallen_total[1m]) < 1
        channel: make-it-rain-alerts

This isn’t that great, as you may forget to change the alert rules if your channel changes. You also need to repeat the channel label across all your rules, or have your alert wind up in the catch-all #specialops alert graveyard- alerts that are silently dropped are never good news.

So while this was sub-par but we could manage, there were some things we couldn’t support with this system. One specific case were common alerts intended to cover more than a single service.

As an example, any applications that run in Kubernetes share a number of failure cases, from PodCrashLooping to PodPending and even PodOOMKilled. We have common definitions for these alerts in every cluster, but couldn’t add a channel label to the common definition as that would direct all these alerts to a single channel, when many teams run services in the same cluster.

Our solution was to create a new Prometheus recording rule, gocardless_service, for every service deployment target in our registry. This looks something like this:

# service-rules.yaml
  name: gocardless_service
    - record: gocardless_service
      expr: "1"
        service: make-it-rain
        team: banking-integrations
        channel: make-it-rain-alerts
        environment: staging
        namespace: make-it-rain
        release: make-it-rain
        cluster: compute-staging-brava

This effectively uploads our registry into Prometheus, which means we can use the rules to dynamically join our alerts onto team routing decisions. Our common Prometheus alerts now look like this:

// (kube_pod_status_phase{phase="Pending"} == 1)
// * on(namespace, release) group_left(team, channel) (
//   gocardless_service{location="local"}
// )
  alert: 'KubernetesPodPending',
  expr: withServiceLabels('kube_pod_status_phase{phase="Pending"} == 1'),

Where the withServiceLabels templates the PromQL query in the comment, which joins the alert expression onto our registry team and channel mappings, causing the alert to be sent directly to the #make-it-rain-alerts channel.

We’ve been happier with this than any of our other alert routing attempts, not least because it happens automatically. If you change the alerts channel for a service in the registry, Prometheus will immediately adjust and re-route the alert elsewhere.

This is just one of the ways we can use this data- we can even alert on any resources in our clusters that aren’t in the registry, and more besides.


At GoCardless, we’re about to release a total reimagining of our infrastructure tooling, and the service registry has been an essential piece of that puzzle.

Once you have a registry, you start seeing solutions to problems you didn’t even realise existed. When you start orienting teams around that data model, you can encourage consistency and benefit from a shared mental model of your infrastructure.

This post describes some of the benefits we’ve seen, and solutions to problems I think most engineering orgs of our size experience. I encourage people to give this a go- you might just like it, too.

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