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+---
+title:
+  'Skyscanner: Managing OpenTelemetry Collectors across 24 production clusters'
+linkTitle: Skyscanner
+date: 2026-04-21
+author: >-
+  [Johanna Öjeling](https://github.com/johannaojeling) (Grafana Labs), [Juliano
+  Costa](https://github.com/julianocosta89) (Datadog), [Tristan
+  Sloughter](https://github.com/tsloughter) (community), [Neil
+  Fordyce](https://github.com/neilfordyce) (Skyscanner)
+sig: End-User
+# prettier-ignore
+cSpell:ignore: Fordyce kube kubelet rollouts Skyscanner Sloughter unsets Öjeling
+---
+
+Date: April 21, 2026
+
+This reference implementation describes how
+[Skyscanner](https://www.skyscanner.net/), a global travel search platform based
+in Edinburgh, Scotland, runs OpenTelemetry at scale.
+
+With 1,400 employees worldwide running over 1,000 microservices across 24
+production Kubernetes clusters, Skyscanner's journey with OpenTelemetry offers
+valuable lessons for organizations operating at scale.
+
+## Organizational structure
+
+The Hubble team, consisting of six platform engineers, manages most of
+Skyscanner's collectors. As part of the wider platform engineering organization,
+they handle the compute platform that runs Skyscanner's primarily Java-based
+microservices architecture.
+
+Service teams themselves remain abstracted from the deployment and telemetry
+collection infrastructure. For Java services, teams inherit a base Docker image
+containing the pre-configured OpenTelemetry Java agent. For Python and Node.js
+services, the platform team provides wrapper libraries that set sensible
+defaults based on environment and resource attributes. These approaches minimize
+boilerplate setup and give service teams observability out of the box without
+requiring deep OpenTelemetry knowledge.
+
+## OpenTelemetry adoption
+
+Skyscanner's OpenTelemetry journey began in 2021. The company was migrating from
+an internally built open source stack to a commercial vendor. However, they
+wanted to avoid vendor lock-in.
+
+> "We wanted to move to a vendor in a way that was vendor agnostic," explained
+> [Neil Fordyce](https://github.com/neilfordyce), Software Engineer on
+> Skyscanner's Hubble platform team.
+
+This vendor-agnostic approach led them to adopt the OpenTelemetry Collector as
+the central piece of their telemetry infrastructure.
+
+## Architecture: Centralized routing, distributed collection
+
+Skyscanner's collector architecture features a central DNS endpoint with
+Istio-based intelligent routing. Regardless of where services run globally or
+which cluster they're in, they send telemetry to this single address. Istio
+handles routing requests to the nearest available collector.
+
+The deployment consists of two distinct collector patterns:
+
+**Gateway Collector (Replica Set)**: Handles bulk OTLP traffic (traces and
+metrics) from most services, where the majority of processing happens.
+
+**Agent Collector (DaemonSet)**: Scrapes Prometheus endpoints from open source
+and platform services that don't yet support OTLP natively.
+
+![Skyscanner architecture diagram](skyscanner-architecture.png)
+
+## Configuration: Start simple, evolve gradually
+
+When Skyscanner first deployed collectors in 2021, their configuration was
+minimal: memory limiter, batch processor, and an OTLP exporter for traces.
+
+Over time, the configuration evolved organically: adding metrics pipelines,
+integrating Istio span ingestion, implementing span-to-metrics transformation,
+and adding filter processors to reduce noise and control costs.
+
+### Turning Istio service mesh spans into platform metrics
+
+One of Skyscanner's most innovative uses of the collector involves generating
+metrics from Istio service mesh spans.
+
+Istio's native metrics suffered from cardinality explosion issues that would
+overwhelm their Prometheus deployment. Additionally, Skyscanner operates many
+off-the-shelf services where they don't own the code but still need consistent
+metrics.
+
+Their solution: Configure Istio to emit spans (originally in Zipkin format,
+though Istio now supports OTLP), ingest them through the collector with the
+Zipkin receiver, transform them to meet semantic conventions, and use the
+[span metrics connector](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/e8a502371ea1d2c3534235d623c1b1eb3b6b4b58/connector/spanmetricsconnector?from_branch=main)
+to generate consistent metrics without any application instrumentation.
+
+> "We can do that at a platform level without the application owners having to
+> instrument their code at all," Neil noted.
+
+The span metrics connector configuration extracts key dimensions from the spans:
+
+```yaml
+connectors:
+  spanmetrics:
+    aggregation_temporality: AGGREGATION_TEMPORALITY_DELTA
+    dimensions:
+      - name: http.status_code
+      - name: grpc.status_code
+      - name: rpc.service
+      - name: rpc.method
+      - name: prot
+      - name: flag
+      - name: k8s.deployment.name
+      - name: k8s.replicaset.name
+      - name: destination_subset
+    dimensions_cache_size: 15000000
+    histogram:
+      exponential:
+        max_size: 160
+      unit: ms
+    metrics_flush_interval: 30s
+```
+
+The collector then transforms these metrics to use semantic convention names
+like `http.client.duration` and `http.server.duration`, aggregating them by
+cluster, service name, and HTTP status code. This provides platform-level HTTP
+metrics for every service without code changes, consistent naming adhering to
+semantic conventions, and lower cardinality than native Istio metrics.
+
+### The 404 error challenge
+
+One notable challenge with the collector configuration involved cache services
+that returned HTTP 404 to indicate that an entry did not exist in the cache. The
+collector treated these 404s as errors, triggering 100% trace sampling for what
+was actually normal, high-volume behavior.
+
+The solution was adding a filter processor to unset the error status for these
+specific 404 responses:
+
+```yaml
+processors:
+  span/unset_cache_client_404:
+    include:
+      attributes:
+        - key: http.response.status_code
+          value: ^404$
+        - key: server.address
+          value: ^(service-x\.skyscanner\.net|service-y\.skyscanner\.net|service-z\.skyscanner\.net|service-z-\w{2}-\w+-\d\.int\.\w{2}-\w+-\d\.skyscanner\.com)$
+      match_type: regexp
+      regexp:
+        cacheenabled: true
+        cachemaxnumentries: 1000
+    status:
+      code: Unset
+```
+
+This processor matches spans with 404 status codes from specific cache services
+and unsets their error status, preventing them from triggering error-based
+sampling.
+
+> "We'd have had higher-quality, easier-to-use traces if we had that filter
+> processor from the start," Neil reflected.
+
+However, Neil notes that with the recent introduction of OpenTelemetry SDK
+[declarative configuration](/docs/languages/sdk-configuration/declarative-configuration/),
+such filtering could now be configured in a decentralized fashion by the service
+teams themselves, rather than requiring changes to the central collector
+configuration.
+
+### Configuration deep dive
+
+Skyscanner has shared their production collector configurations to help others
+understand these patterns in practice:
+
+#### Gateway collector
+
+The [gateway collector][gateway-otelbin] handles the bulk of processing:
+
+- Receives OTLP metrics and traces from services and Zipkin spans from Istio
+- Uses the span metrics connector to generate metrics from Istio spans
+- Employs extensive transform processors to map Istio attributes to semantic
+  conventions
+- Implements the 404 filtering logic for cache services
+- Exports metrics and traces to the observability vendor via OTLP
+
+The diagram illustrates how OTLP metrics and traces, as well as Istio spans,
+reach these gateway collectors:
+
+![Skyscanner architecture (Gateway Collector) diagram](skyscanner-architecture-gateway.png)
+
+#### Agent collector
+
+The [agent collector][agent-otelbin] focuses on collecting infrastructure and
+platform-level metrics from each node:
+
+- Scrapes Prometheus endpoints from various sources (node exporter,
+  kube-state-metrics, kubelet)
+- Performs minimal processing (memory limiting, batching, attribute cleanup)
+- Exports metrics to the observability vendor via OTLP
+
+## Instrumentation strategy
+
+Skyscanner's Java-heavy environment benefits significantly from OpenTelemetry's
+auto-instrumentation capabilities. The Java agent, pre-configured in base Docker
+images, provides HTTP and gRPC span generation out of the box.
+
+### Opinionated auto-instrumentation
+
+The team takes a deliberately opinionated approach to auto-instrumentation.
+Rather than enabling everything by default, they start from the opposite
+direction: all instrumentations are disabled in a shared base Docker image, and
+only a curated set is explicitly enabled.
+
+> "It's sort of the other way around. We disable everything, then enable what we
+> need," Neil explained.
+
+Using environment variables in the base image, Skyscanner enables a focused set
+of runtime-, HTTP-, and gRPC-related instrumentations by default. This includes
+JAX-RS, gRPC, Jetty, common HTTP clients, executor instrumentation, and logging
+context propagation. Service teams inherit these defaults automatically but
+remain free to override them or enable additional instrumentations in their own
+service definitions if needed.
+
+This model ensures consistency across hundreds of services while still allowing
+flexibility at the edges.
+
+### Setting up the Java agent
+
+The snippet below is an illustration of the shared Java base image. It bundles
+the OpenTelemetry Java agent into the image, sets organization-wide defaults,
+and installs a common launcher script:
+
+```Dockerfile base image
+# Image used as source for the OpenTelemetry Java agent
+FROM ghcr.io/open-telemetry/opentelemetry-operator/autoinstrumentation-java:2.25.0 AS otel
+
+# Define a common base image for all Java microservices to extend
+FROM image/registry/public-java-image:x.y.z
+
+# Copy OpenTelemetry Java agent from OTel image
+COPY --from=otel /javaagent.jar $OPEN_TELEMETRY_DIRECTORY/opentelemetry-javaagent.jar
+ENV OTEL_AGENT=$OPEN_TELEMETRY_DIRECTORY/opentelemetry-javaagent.jar
+
+# Pick sensible defaults for everyone in the org
+ENV OTEL_METRICS_EXPORTER="otlp"
+ENV OTEL_TRACES_EXPORTER="otlp"
+ENV OTEL_LOGS_EXPORTER="none"
+ENV OTEL_EXPORTER_OTLP_METRICS_TEMPORALITY_PREFERENCE="DELTA"
+ENV OTEL_EXPERIMENTAL_METRICS_VIEW_CONFIG="otel-view.yaml"
+ENV OTEL_EXPORTER_OTLP_ENDPOINT="http://otel.skyscanner.net"
+ENV OTEL_INSTRUMENTATION_COMMON_DEFAULT_ENABLED="false"
+ENV OTEL_INSTRUMENTATION_RUNTIME_TELEMETRY_ENABLED="true"
+ENV OTEL_INSTRUMENTATION_ASYNC_HTTP_CLIENT_ENABLED="true"
+ENV OTEL_INSTRUMENTATION_APACHE_HTTPCLIENT_ENABLED="true"
+
+COPY run.sh /usr/bin/run.sh
+```
+
+That launcher script `run.sh` constructs the `-javaagent` flags and
+`otel.resource.attributes` from environment variables the deployment supplies:
+
+```bash run.sh
+# We use this to setup OTel resource attributes
+# for things we can detect from environment variables on service launch
+# These vars are set automatically by our deployment system
+# Some env vars have been omitted to avoid repetition
+setup_otel_agent() {
+    if [[ -n "$AWS_REGION" ]]; then CLOUD_REGION="cloud.region=${AWS_REGION},"; else CLOUD_REGION=""; fi
+    if [[ -n "$AWS_ACCOUNT" ]]; then CLOUD_ACCOUNT_ID="cloud.account.id=${AWS_ACCOUNT},"; else CLOUD_ACCOUNT_ID=""; fi
+    if [[ -n "$CLUSTER_NAME" ]]; then K8S_CLUSTER_NAME="k8s.cluster.name=${CLUSTER_NAME},"; else K8S_CLUSTER_NAME=""; fi
+    if [[ -n "$SERVICE" ]]; then SERVICE_NAME="service.name=${SERVICE}"; else SERVICE_NAME=""; fi
+    echo -n "-javaagent:$OTEL_AGENT" \
+        "-Dotel.resource.attributes=${CLOUD_REGION}${CLOUD_ACCOUNT_ID}${K8S_CLUSTER_NAME}${SERVICE_NAME}"
+}
+
+JAVA_OPTS="-D64 -server -showversion $(setup_otel_agent) ${ADDITIONAL_JAVA_OPTS:-}"
+
+exec java $JAVA_OPTS "$@"
+```
+
+Finally, an individual service Dockerfile extends the same base and only adds
+the extra instrumentations that service needs:
+
+```Dockerfile my-service
+FROM image/registry/skyscanner-java-base:x.y.z
+
+COPY my-service.jar
+
+# It's easy to extend if my-service wants to enable some other non-default instrumentation
+ENV OTEL_INSTRUMENTATION_OPENAI_ENABLED=true
+ENV OTEL_INSTRUMENTATION_OKHTTP_ENABLED=true
+
+CMD exec /usr/bin/run.sh -jar my-service.jar server
+```
+
+### Spans yes, metrics no (by default)
+
+A particularly interesting aspect of Skyscanner's strategy is how they treat
+metrics versus traces. Although HTTP and gRPC instrumentations are enabled, the
+team deliberately drops most SDK-generated HTTP and RPC metrics. This is because
+they already derive consistent, lower-cardinality platform metrics from Istio
+service mesh spans, as described earlier.
+
+Rather than disabling the instrumentations entirely—which would also remove
+spans—they use OpenTelemetry SDK views to drop the metric aggregations while
+preserving tracing:
+
+- HTTP and RPC metrics are dropped globally
+- Spans continue to be emitted as normal
+- Service teams can selectively re-enable specific metrics (for example,
+  server-side latency) if they need additional granularity beyond what Istio
+  provides
+
+When teams do opt back into SDK metrics, they often rename them to avoid
+clashing or double-counting with existing Istio-derived metrics.
+
+In the [Java base image](#setting-up-the-java-agent) shown earlier,
+`OTEL_EXPERIMENTAL_METRICS_VIEW_CONFIG` points to Skyscanner's default
+`otel-view.yaml`, using
+[view file configuration](https://github.com/open-telemetry/opentelemetry-java/blob/65f7412a986cb474314b093c1bbba77955b52031/sdk-extensions/incubator/README.md#view-file-configuration):
+
+```yaml
+# Default Skyscanner metrics view config
+# Stored in a file which OTEL_EXPERIMENTAL_METRICS_VIEW_CONFIG points to
+# Drop http and rpc metrics, because we have metrics from Istio already
+# We still want tracing to work so we wouldn't just disable the instrumentation
+- selector:
+    instrument_name: http.*
+  view:
+    aggregation: drop
+- selector:
+    instrument_name: rpc.*
+  view:
+    aggregation: drop
+```
+
+The same file can be extended when a service needs to keep specific metrics. A
+typical use case is breaking down requests by `http.route`:
+
+```yaml
+# This dropping behaviour can be altered by extending the list to add more views
+# to explicitly select the metrics to be kept.
+# e.g. to keep http.server.request.duration metrics,
+# but continue to drop http.client.* metrics
+- selector:
+    instrument_name: http.server.request.duration
+  view:
+    # renamed because we already have Istio metrics named http.server.request.duration,
+    # so don't want to clash and double count
+    name: app.http.server.request.duration
+    attribute_keys:
+      - http.request.method
+      - http.route
+      - http.response.status_code
+```
+
+This approach allows Skyscanner to keep high-value distributed traces, avoid
+metric duplication, control cardinality, and reduce ingestion costs—all without
+requiring service owners to deeply understand OpenTelemetry internals.
+
+Overall, the strategy reflects a strong platform mindset: provide sensible
+defaults that work at scale, minimize noise, and make the "right thing" the easy
+thing, while still leaving room for teams with advanced needs to go further.
+
+## Deployment and release management
+
+Skyscanner uses the OpenTelemetry Collector Contrib distribution, having adopted
+it because it included everything they needed. The team learned that Contrib
+isn't recommended for production use, and plans to explore building custom
+collector images with only the components they need.
+
+Skyscanner updates collectors approximately every six months, though they'll
+upgrade more frequently if tracking specific features or critical fixes. They
+follow RSS feeds and CNCF Slack channels to stay informed about releases.
+
+Their rollout strategy uses progressive promotion across cluster tiers: Dev
+clusters, then three Alpha production clusters, followed by eight Beta
+production clusters, and finally the remaining 13 production clusters. Using
+Argo CD for deployment, changes are promoted via pull requests between tiers.
+
+> "We've definitely messed stuff up in the development testing clusters and then
+> gone and fixed them before promoting further," Neil said.
+
+This gradual approach has caught configuration issues before they reach
+production. While they don't yet have automated testing and rollback
+capabilities for their OpenTelemetry Collector deployments, these improvements
+are on the horizon.
+
+## What works well
+
+Since adopting OpenTelemetry in production, the team's experience has been very
+positive.
+
+> "It genuinely has been quite pain-free," Neil reflected.
+
+The flexibility stands out as the collector's greatest strength.
+
+> "Everything that we've set out to do, we've been able to actually deliver. I
+> think that speaks to how flexible it is," Neil explained.
+
+Other highlights include the OTLP protocol providing vendor independence through
+simple configuration, clear and well-organized release notes, and community
+responsiveness when team members discovered and contributed fixes for a memory
+leak in a collector component.
+
+## Lessons and pain points
+
+Skyscanner still uses older, unstable HTTP semantic conventions in some
+pipelines. Upgrading requires updating multiple transform processor rules that
+map Istio attributes to semantic convention names, which involves manually
+cross-referencing documentation and filling out configuration strings.
+
+The team is aware of [Weaver](https://github.com/open-telemetry/weaver) for
+semantic convention management but hasn't yet integrated it into their workflow.
+
+Upgrading every six months means encountering multiple breaking changes at once.
+While the release notes are well-written and clearly document changes, reviewing
+six months of updates at once adds friction compared to keeping pace with
+releases.
+
+## Advice for others
+
+Based on their production experience, the Skyscanner team offers this advice:
+
+- **Start simple**: Begin with just the memory limiter, batch processor, and
+  basic exporters. Add complexity only as needs arise.
+- **Memory limiter from day one**: Set this up immediately to prevent memory
+  issues as you scale.
+- **Consider filter processors early**: Understand your application's status
+  code semantics and filter out high-volume "false positives" to control costs.
+- **Don't over-engineer resiliency**: For telemetry data, simple in-memory
+  batching is often sufficient.
+- **Gradual rollouts catch issues**: Progressive promotion across environment
+  tiers provides valuable validation.
+
+## Takeaways
+
+Skyscanner's story demonstrates that platform engineering teams of modest size
+can successfully manage OpenTelemetry Collector infrastructure at significant
+scale with relatively low operational overhead.
+
+[gateway-otelbin]:
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diff --git a/content/en/docs/guidance/reference-implementations/skyscanner/skyscanner-architecture-gateway.png b/content/en/docs/guidance/reference-implementations/skyscanner/skyscanner-architecture-gateway.png
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diff --git a/content/en/docs/guidance/reference-implementations/skyscanner/skyscanner-architecture.png b/content/en/docs/guidance/reference-implementations/skyscanner/skyscanner-architecture.png
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Binary files /dev/null and b/content/en/docs/guidance/reference-implementations/skyscanner/skyscanner-architecture.png differ
diff --git a/static/refcache.json b/static/refcache.json
index eb8518b31107..9d7dd73c4fbd 100644
--- a/static/refcache.json
+++ b/static/refcache.json
@@ -6943,6 +6943,10 @@
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+  "https://github.com/neilfordyce": {
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@@ -8051,6 +8055,10 @@
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+  "https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/e8a502371ea1d2c3534235d623c1b1eb3b6b4b58/connector/spanmetricsconnector?from_branch=main": {
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+    "LastSeen": "2026-04-17T18:29:58.992320912Z"
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@@ -24491,6 +24511,10 @@
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+  },
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     "LastSeen": "2026-03-23T14:15:04.089860067Z"