How the controller-runtime Cache Actually Works, and Why Your Controller Does Not Crash the API Server

If you take only one idea from this article, take this:

r.Get() and r.List() inside a reconciler typically do not read from the API server. They
read from a local in-memory cache, which the manager warms up with list and then keeps
current through watch.

Almost every other property of the system follows from that one fact:

• Reads are cheap, but not strongly consistent immediately after a write.
• Writes go straight to the API server, not through the cache.
• The size of the local cache and the set of indexes directly drive memory consumption.
• An incorrectly written List() can silently turn into a linear scan over tens of thousands
  of objects.
• APIReader is rarely needed — but in some places you really cannot do without it.

The rest of the article unpacks why this is so and how the model is wired underneath.

To avoid arguments about terminology, start with the basic model.

A few terms collected up front, so you do not have to jump back and forth later. Skim or skip
if any of them are already familiar.

The Reflector is the only component that talks to the API server directly. It has exactly two
jobs: do a single list at startup, then keep a watch open from there on.

This is where the resourceVersion earns its keep. Along with the list of objects, the API
server returns the version at which the snapshot was produced. The Reflector then says to the
API server, “open a watch from version X”, and receives a stream of events for everything
that happened after that version. That is the basis of consistency: there is no risk of
missing an event between list and watch, because watch resumes exactly at the point
where list ended.

If the connection drops, the Reflector reconnects with the last known resourceVersion. If
the API server replies with 410 Gone (“that version is no longer in the history, you are
too far behind”), the Reflector performs a fresh list and starts over. This is called a
relist, and it does not happen on a schedule — only in those failure scenarios.

This piece is worth pausing on. DeltaFIFO is the buffer between the Reflector and the rest of
the informer. Its input is a stream of events from the API server; its output is the same
events, but grouped by key and in strict order.

More precisely, DeltaFIFO solves three problems:

1. It preserves order. Whatever stream of changes flows in for default/my-deploy, the
   consumer sees the same ordering the API server delivered.
2. It groups by key. All deltas for a single namespace/name accumulate in one slot.
   Pop() returns not a single delta but a slice of every delta accumulated under that
   key — the consumer sees, in one shot, everything that has happened to the object since the
   last call.
3. It deduplicates selectively. The built-in dedupDeltas function collapses
   consecutive Deleted deltas for the same key, so two delete events do not turn into
   two separate processing rounds.

An important caveat: DeltaFIFO does not merge consecutive Added or consecutive Updated
deltas. Collapsing every intermediate state into a single final one is, in general, not its
job.

A worked example. Suppose three events for object default/my-deploy arrive in quick
succession:

1. Added — the Deployment is created (say, with spec.replicas=1).
2. Updated — somebody bumps spec.replicas to 2.
3. Updated — and immediately to 3.

DeltaFIFO places all three deltas into the slot keyed by default/my-deploy. Pop() returns
them as a single slice, and sharedIndexInformer.HandleDeltas walks through them in order:
first OnAdd, then two OnUpdate calls (one for the intermediate 1→2 transition and one
for the final 2→3). The event handler runs three times, no shortcuts.

There is per-object deduplication, but not in DeltaFIFO — it lives one layer up, in the
controller’s workqueue. The mechanic is straightforward: for each delta from DeltaFIFO, the
controller’s event handler extracts the namespace/name key from the object and enqueues
it. Re-inserting the same key silently coalesces with the existing entry; the workqueue does
not care about the object itself.

A concrete picture: you create a Pod. Within a second or two a flurry of Updated deltas
arrives — the scheduler assigns a node, the kubelet sets Pending, then ContainerCreating,
Running, Ready. Five deltas in a row, and the event handler fires on every one of them —
but throughout this window the workqueue holds a single entry with the key default/my-pod.
By the time Reconcile pops it, the cache already holds the final state, and Reconcile
runs once.

So you get two layers with cleanly separated responsibilities:

• DeltaFIFO — an ordered queue of deltas, grouped by key, with deduplication only for
  consecutive Deleted events. Its job is to deliver change facts to consumers in the right
  order.
• workqueue — a queue of keys with proper deduplication and rate limiting. This is
  the layer that collapses “ten updates in a row → one reconcile”.

If you keep that two-layer picture in your head, it becomes clear why a flood of events
against a single object barely affects controller throughput — the workqueue absorbs them.

The Indexer (also known as ThreadSafeStore) is the local copy of the cluster. Underneath
it is a plain map[string]interface{} keyed by namespace/name, plus a mutex, plus a
dictionary of registered indexes (covered in their own section below).

Yes — at heart it is a map in memory. No B-trees, no LSMs. That is precisely why a cache-hit
r.Get costs microseconds: it is a map lookup followed by a copy of a Go struct.

A SharedIndexInformer fuses Reflector, DeltaFIFO, and Indexer together and exposes two
interfaces to the rest of the world:

• Read objects directly from the indexer.
• Register a ResourceEventHandler and receive notifications for every event coming out of
  DeltaFIFO — OnAdd, OnUpdate, OnDelete. The store is updated in lockstep with the
  handler call, so by the time your handler runs, the indexer already reflects the new state.

“Outside” here means your controllers. When a controller registers Watches(...), under the
hood it asks the informer: “add a handler that, on every change, enqueues the key into my
workqueue”. The controller’s workers then pop keys one at a time and call your
Reconcile(ctx, ctrl.Request{NamespacedName: ...}).

The keyword in the name is Shared. The manager creates one informer per GVK, and
every controller, webhook, and event source within that manager subscribes to it:

In other words: an informer is the thing that subscribed to Pods once, holds them locally,
and serves every interested party in the process. From the API server’s perspective, that is
one list and one watch per GVK, regardless of how many reconcilers live inside your
process.

Step by step, here is what happens between the moment the manager starts and the first
r.Get inside your reconciler:

1. The manager’s mgr.Start(ctx) brings up every registered informer.
2. For each GVK, the Reflector performs a full list of every object that falls within your
   scope.
3. The list response is loaded into the informer’s store, registered indexes are rebuilt,
   and the informer’s HasSynced() flag flips to true.
4. After that, a watch is opened starting from the resourceVersion returned by list.
5. Only then does the controller start invoking Reconcile — specifically, once
   cache.WaitForCacheSync has returned true for every source it owns. Until that point,
   workers do not drain the workqueue, even if events have already started piling up.

So in controller-runtime, “the reconciler is running but the cache is still empty” is
not a state you can ever observe by construction. The warm-up always happens up front,
never lazily.

What happens during the first r.Get? Suppose your reconciler contains:

var obj appsv1.Deployment
err := r.Get(ctx, req.NamespacedName, &obj)

Under the hood it boils down to roughly this:

item, exists, err := indexer.GetByKey("default/my-deploy")
if !exists {
    return apierrors.NewNotFound(...)
}
// DeepCopy into obj

No HTTP, no TLS, no protobuf serialization, no etcd. A map lookup, a struct copy, return.
Microseconds.

To repeat, because it matters: even the very first Get in the controller’s lifetime reads
from a fully warmed-up, fully indexed snapshot. There is no “first time slow, then fast”.

Note: This applies specifically to mgr.GetClient(). If for some reason you need to
read objects before mgr.Start() (for example, during initialization), use
mgr.GetAPIReader(), which goes straight to the API server. More on this later.

Another point that often gets lost. client.Client in controller-runtime is a composite
object:

• Reads (Get, List) go through the cache.
• Writes (Create, Update, Patch, Delete, DeleteAllOf) go straight to the API
  server.

This is not a hack — it is a deliberate design choice:

• Reads are frequent; they should be cheap.
• Writes are rare; they should be exact.
• Writing through the cache would invite split-brain — the local copy thinks the change went
  through, while the API server has already rejected it.

It is worth dwelling on “should be exact”. This is where resourceVersion shows up again.

When you read an object from the cache, you do not get its current state in etcd — you get
the state as the Reflector last observed it. That state carries a resourceVersion. You then
mutate the object and call r.Update(ctx, &obj). The request goes to the API server right
now, and the API server checks:

• Does the resourceVersion in your PUT match the resourceVersion in etcd? Yes — write
  it.
• No, etcd already has a newer one? Reply with 409 Conflict — somebody beat you to it.

This is optimistic concurrency control. No real locks are taken; everybody writes in
parallel; but only one of the racing Update calls wins — the one that arrives with the
current version. Everyone else gets a 409 and is expected to re-read and try again.

A familiar pattern:

obj.Spec.Replicas = ptr.To(int32(5))
if err := r.Update(ctx, &obj); err != nil {
    return ctrl.Result{}, err
}

// re-read and confirm it is now 5
var fresh appsv1.Deployment
_ = r.Get(ctx, key, &fresh)
fmt.Println(*fresh.Spec.Replicas) // surprise: 3

This is not a controller-runtime bug. It is a property of an eventually consistent system:
the cache catches up asynchronously, through the watch.

The right pattern is to never rely on instant freshness. Reconcile must be idempotent and
must always look at the current state. If it does not match the desired state, the next
reconcile fixes it. You do not need to “wait 100ms” or “re-trigger”. You need to write the
logic so that one or two extra invocations break nothing.

If you genuinely need guaranteed freshness — for example, in a validating webhook where you
cannot afford to act on stale state — that is what APIReader is for. More on this
shortly.

To make sense of this, a quick word on event mechanics inside a controller. When you register
a source via Watches(...), two layers sit between the indexer and your Reconcile:

• Predicate — the filter. It looks at an event (CreateEvent, UpdateEvent,
  DeleteEvent, GenericEvent) and decides whether to pass it through.
• EventHandler — the transformer. It receives the object and turns it into one or more
  ctrl.Request values that go into the workqueue. The classic
  EnqueueRequestForObject enqueues the namespace/name of the current object.

Here is the critical part. Predicates and handlers receive the same objects that live in
the informer’s shared store. The same *corev1.Pod is seen by every controller subscribed
to Pods.

Because Go has no immutable structs, nothing prevents you from doing
pod.Labels["foo"] = "bar" directly inside a handler. Historically, Get and List
returned a pointer into the store as well, with predictable consequences: somebody patched a
status “for convenience” in one controller and broke the world view of an unrelated
controller next door.

Today, controller-runtime performs a DeepCopy on Get and List by default. The simple
rule:

• Anything you receive from r.Get / r.List is yours; mutate freely.
• Anything you receive in a Predicate or an EventHandler is shared, not yours. If you
  must mutate it, call obj.DeepCopy() first; otherwise you are silently corrupting the
  cache for every other controller subscribed to the same type.

A concrete review heuristic: if predicate.Funcs{UpdateFunc: ...} or
handler.EnqueueRequestsFromMapFunc(...) contains expressions like
e.ObjectNew.SetLabels(...) or obj.Status.X = Y, stop and ask whether a DeepCopy is
missing before that mutation.

An informer has a resyncPeriod parameter (10 hours by default in controller-runtime), and
many people read it as “rebuild the cache from the API server every N hours”.

It does not. A resync does not perform a list. It re-emits everything currently in the
indexer back through DeltaFIFO as Sync deltas, and the informer processes them as usual,
calling OnUpdate(old, old) for each object. This gives a controller that has somehow
missed its reconcile window (a stuck worker, a dropped handler) a chance to see the world
again. It generates no traffic to the API server.

A real relist happens only in two cases: when the watch died with 410 Gone, and when
you explicitly recreate the informer.

A small note that often saves time. Sometimes you want to wait inside a reconciler — “we
just called the provider’s API; if it is not ready yet, retry in a minute”. The temptation is
to spin up time.Sleep or your own goroutine.

Resist it. controller-runtime already provides a built-in mechanism:

return ctrl.Result{RequeueAfter: 30 * time.Second}, nil

The controller puts your req back into the workqueue with a delayed trigger 30 seconds out.
If a real event for the same object arrives within that window, the reconcile fires
immediately, without waiting for the timer (the key is deduplicated in the queue). This is
both cheaper and more correct than a hand-rolled timer: you do not hold a worker, and you do
not risk missing a real event.

There is also ctrl.Result{Requeue: true} — enqueue immediately, subject to the rate
limiter.

Now you get to what is, arguably, the most useful capability of the cache — and the one most
controllers leave unused.

By default, a List from the cache looks like this:

var pods corev1.PodList
_ = r.List(ctx, &pods)
for _, p := range pods.Items {
    if p.Spec.NodeName == "node-1" {
        // do something
    }
}

It works — until the cluster has 50,000 Pods and reconciles run hundreds of times per
second, at which point the controller is shuffling the same half-gigabyte of pointers back
and forth on every trigger. O(n) per reconcile.

The Indexer in client-go can do much better. You declare up front which field you want to
index on:

// Index by spec.nodeName for Pods
if err := mgr.GetFieldIndexer().IndexField(
    ctx,
    &corev1.Pod{},
    "spec.nodeName",
    func(obj client.Object) []string {
        pod := obj.(*corev1.Pod)
        if pod.Spec.NodeName == "" {
            return nil
        }
        return []string{pod.Spec.NodeName}
    },
); err != nil {
    return err
}

Two things about that call are worth making explicit, because the tidy example hides
them behind a convention.

The index name is arbitrary. That second argument, "spec.nodeName", is only a
string key the index is registered under. controller-runtime does not parse it as
JSONPath and does not check it against the object’s schema — you could write "by-node"
or "xyzzy" and it would behave identically. The only rule is that the exact same
string comes back in MatchingFields at query time. Naming the index after the field it
happens to read is a readability convention, nothing more.

The indexed value is computed, not read. The function returns whatever strings you
build; they need not be the verbatim contents of any single field. You can lowercase a
value, join several fields into one composite key, bucket a timestamp (the time-bucket
trick below does exactly this), or emit a string that appears nowhere in the object
literally. Whatever the function returns becomes a key in the inverted dictionary, and a
MatchingFields lookup for that exact key is what finds the objects again. The only
constraint is that the value has to be derivable from the object you are indexing.

What is an inverted index? The term comes from search engines. Normally you have
documents and each document has a list of words in it. “Inverted” means the relationship is
flipped: a dictionary in which the key is a word and the value is the list of documents that
contain it. Same idea here: the key is the value of a field (for example, node-1), and the
value is the list of object keys whose field has that value:

map["node-1"] = {"default/pod-a", "kube-system/pod-b", ...}
map["node-2"] = {"default/pod-c", ...}

What the indexer does:

• On every incoming event (ADDED, MODIFIED, DELETED), the indexer runs the object
  through your indexing function, gets back the set of index keys, and updates the inverted
  dictionary. If a Pod migrates from node-1 to node-2, the node-1 key loses its
  reference to it and the node-2 key gains one.
• By the time you call List, the index is already current. You do not pay for a
  rebuild at query time — no scan over all objects, no dictionary reconstruction. All the
  work was done up front, at the moment the object changed.

And now you can write:

var pods corev1.PodList
_ = r.List(ctx, &pods,
    client.MatchingFields{"spec.nodeName": "node-1"},
)

This is not “fetch the full list, then filter”. It is a lookup in the inverted index → a
ready set of keys → a fetch of the corresponding objects. A different code path entirely.

The comparison to SQL is more accurate than it might look at first:

Note the last row: MatchingFields does not make magic out of thin air. For every field
you want to look up via MatchingFields you need a corresponding IndexField registered
during manager setup. Without one, controller-runtime rejects the query and returns
an error.

A few things worth keeping in mind:

• Equality only. No range queries, no LIKE, no sorts, no aggregates. If you need
  “everything older than five minutes”, either do a regular List and filter in code, or
  use a time-bucket trick: instead of indexing the precise time.Time, index a rounded
  value (for example, now.Truncate(5*time.Minute).Format(...)). You can then select objects
  by a specific window.

• MatchingLabels is not an index. Many people assume that since label-based lookups are
  so common, there must be an optimization for them. There is not — ThreadSafeStore keeps
  no separate label dictionary.

  When you call List(..., MatchingLabels{...}), the controller honestly walks every
  cached object of the given type and checks each one against the selector. That is O(n),
  exactly what IndexField is supposed to save you from.

  The API server itself supports filtering the event stream by a specific label selector. To
  make that effective in your controller, you have to optimize at the cache population
  stage, via cache.ByObject{Label: ...}, not at the read stage. This is covered in the
  next section on selective caches.

  And if you need a fast lookup by a specific label across already cached objects, register
  an IndexField for that label by hand. That works.

• An index costs memory. Every index is an extra dictionary keyed by every object. Do
  not index everything in sight speculatively.

• You can only index data that is in the object itself. You cannot index a Pod by “has a
  related PVC with such-and-such flag”. Either store that bit in the Pod itself, or index
  the PVC, not the Pod.

Note: An index is built at registration time and is populated as part of the initial
list. By the time the first Reconcile runs, both Get and List with MatchingFields
work correctly — the index is not built lazily.

By default, an informer pulls every object of its type from every namespace. For Pod,
Secret, ConfigMap, and Event in a large cluster, that is a multi-gigabyte surprise
delivered on the first list at startup.

It hurts especially with:

• Secrets, because Helm stores release state in them (helm.sh/release.v1.*), and those
  secrets are often a hundred kilobytes each.
• v1.Node objects, whose status.images field carries a list of every image that has
  ever landed on the node — tens of kilobytes per node in busy clusters.
• Events, which can be very numerous and which you almost certainly do not need cached at
  all.

In controller-runtime, caching policy lives in cache.Options, passed when constructing
the manager:

mgr, err := ctrl.NewManager(cfg, ctrl.Options{
    Cache: cache.Options{
        ByObject: map[client.Object]cache.ByObject{
            // Cache Secrets only from your own namespace, and only by label
            &corev1.Secret{}: {
                Namespaces: map[string]cache.Config{
                    "my-controller": {},
                },
                Label: labels.SelectorFromSet(labels.Set{
                    "app.kubernetes.io/managed-by": "my-controller",
                }),
            },
            // Cache all Pods, but trim noise on the way into the store
            &corev1.Pod{}: {
                Transform: func(obj any) (any, error) {
                    pod := obj.(*corev1.Pod)
                    pod.ManagedFields = nil
                    return pod, nil
                },
            },
        },
    },
})

A subtle point: this is a manager-level setting and it affects every controller in the
process that reads the corresponding type. If you narrow the cache for Secrets to a single
namespace and another controller in the same binary needs all secrets in the cluster, that
controller will not see them. Before you tighten the scope, audit who else is reading
the type.

A short tour of the options:

• Namespaces restricts the visible scope. If your controller only manages its own
  namespace, there is no reason to keep other people’s objects in memory.
• Label / Field become parameters of the watch itself. The API server only sends
  matching objects, saving network and memory.
• Transform is invoked before the object lands in the store. It is the perfect place to
  drop managedFields, oversized annotations, or the binary data of ConfigMaps that
  you do not need.
• DefaultLabelSelector / DefaultNamespaces apply the same restriction globally, when
  every type needs the same scope.

Caveat: A selector limits what is cached, not what exists. If an object does
not match your selector, then as far as your controller is concerned, it does not exist in
either Get or List. This bites people: somebody mislabels a single Secret and then
spends half a day figuring out why their controller “cannot see it”.

A separate pattern: you need to know that an object exists, but you do not need its spec or
data. Typical examples: a controller that waits for a Secret with a particular name to
appear but never reads it; one that counts PersistentVolume objects by the
topology.kubernetes.io/zone label; one that reacts to ConfigMap objects in a namespace
by name and does not care about contents.

Caveat: PartialObjectMetadata by definition gives you nothing from spec or
status — only ObjectMeta. So you cannot filter through it on spec fields (such as
a PersistentVolume’s storageClassName or a Pod’s nodeName); those fields do not
exist in the local copy. Everything covered by metadata-only is labels, annotations,
ownerReferences, finalizers, creationTimestamp, and the rest of metadata.

For this case there is PartialObjectMetadata:

var list metav1.PartialObjectMetadataList
// Note: Kind is the singular ("Secret"), not "SecretList".
// controller-runtime infers the list shape from the variable type.
list.SetGroupVersionKind(schema.GroupVersionKind{
    Group:   "",
    Version: "v1",
    Kind:    "Secret",
})
if err := r.List(ctx, &list, client.InNamespace("my-ns")); err != nil {
    return err
}

Under the hood this is a separate watch that asks the API server for metadata only. The
store keeps such objects without Data, Spec, or Status — only ObjectMeta. For
Secrets the memory difference can reach an order of magnitude.

mgr.GetAPIReader() returns a client.Reader that goes straight to the API server, around
the cache. When you actually need it:

• Validating webhooks, where the freshness of the object is critical. The cache in
  another process may be lagging at that very moment, and you would block a legitimate
  Update.
• A one-off read of a resource for which you do not maintain an informer. Spinning up a watch
  for a single operation is expensive.
• Reads before mgr.Start(), for instance during initialization. The regular
  mgr.GetClient() returns nothing useful at that point.
• Paginated traversal of large result sets through client.Limit / client.Continue.
  The cache-backed client ignores those parameters and always returns the full result set
  from the in-memory store; to actually page through the API server, you need APIReader (or
  a direct client of your own).

The price is a real network request. One thing to avoid: do not build “look in the cache, and
if missing, fall back to the API” logic. That is exactly the split-brain pattern the cache
is meant to protect you from.

If you do not need a local cache for a given type at all — say, the type is “fat”, read
rarely, and the list + watch overhead is not worth paying — you can tell the manager not to
cache it. This is configured through client.Options.Cache.DisableFor:

mgr, err := ctrl.NewManager(cfg, ctrl.Options{
    Client: client.Options{
        Cache: &client.CacheOptions{
            DisableFor: []client.Object{
                &corev1.Secret{},
            },
        },
    },
})

With this configuration, mgr.GetClient().Get(...) and List(...) for Secret go straight
to the API server, bypassing the cache. No informer is started for that type, which means no
list at startup and no permanent memory pressure from a store. This is a more radical
alternative to APIReader: where APIReader is reached for ad hoc, individual requests,
DisableFor turns the cache off for the type wholesale.

Real-world projects use this. Several established CNCF operators disable caching on Secrets,
both to save memory and to avoid hammering the API server with a large list at startup.

Aside: If you want to avoid a watch on the API server entirely, you can feed the
controller events from a source of your own design, bypassing list + watch. In
controller-runtime this is done with WatchesRawSource / source.Channel: you can wire
the controller to events from any place — an internal queue, a kubelet, a custom watch.
Niche, but a perfectly valid pattern when the API server should not be touched.

A short checklist worth running through before you ship a controller into a live cluster:

• Constrain cache scope (Namespaces, Label, Field selectors), especially for “fat”
  types: Secret, ConfigMap, Event, Pod, Node.
• Add a Transform for objects whose heavy fields you do not need —
  ManagedFields alone consume a noticeable share of memory.
• Add an IndexField for every List that uses MatchingFields. No index means a
  hidden O(n) scan on every reconcile.
• Do not mutate objects you receive in an EventHandler or a Predicate without a prior
  DeepCopy. Mutations to the store break neighboring controllers silently and persistently.
• Make Reconcile idempotent. It must behave correctly even if it is invoked five times
  in a row with no real change.
• Do not expect read-after-write from the cache immediately after Update. The cache
  lags during that window.
• When you need freshness (webhooks, initialization, one-off reads), use APIReader,
  not the regular client.
• Use PartialObjectMetadata for types where you only need metadata. It can save
  gigabytes.
• Do not call mgr.GetClient() before mgr.Start(). The informer is not yet warm, the
  store is empty, and you will get either NotFound or an empty List and then spend half
  a day investigating why an object “disappeared”.
• For deferred actions, use RequeueAfter, not time.Sleep and not your own goroutines.

In one breath:

• The cache in controller-runtime is not an optimization, it is the operating model. Under
  the hood it is Reflector + DeltaFIFO + Indexer — exactly the same primitives that
  power Kubernetes itself.
• r.Get and r.List go to memory; Create, Update, Patch, and Delete go straight
  to the API server. Feedback flows in through the watch.
• IndexField plus MatchingFields turn the cache into a near-complete query engine with
  inverted indexes.
• Namespaces, selectors, PartialObjectMetadata, and Transform are the levers that
  control how much memory and traffic you actually consume.
• APIReader is the emergency exit for cases where you genuinely need the freshest version
  of an object.

And the single sentence to remember: r.Get inside a reconciler does not call the API
server. Ever. Not even the first time. Once that becomes a reflex, half the questions on
controller code reviews answer themselves.