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Uber now infers whether a downstream service is essential to an API by analyzing production metrics for every request, rather than waiting for a small sample of distributed traces. Its system builds service-and-endpoint dependency trees, classifies each edge as fail-close, fail-open or unknown, and feeds the result into dependency inventories, alerting, retry decisions and failover safety checks.
What Uber’s dependency analyzer measures
Uber represents an API’s call path as a tree. Each node is a service endpoint or datastore, and each edge records an outgoing call from one node to another. The key question is operational: when the callee fails, does the caller fail as well?
Fail-close, fail-open and unknown edges
- Fail-close (hard) dependency: a downstream failure consistently causes the caller to fail. The dependency is required for the caller’s successful response.
- Fail-open (soft) dependency: the caller can continue working when the downstream service fails, perhaps by using a default, cached value or degraded response.
- Unknown dependency: the observed relationship is not strong enough to classify confidently.
The classification is calculated separately for service-and-procedure pairs, rather than only at a broad service level. That distinction matters when one endpoint is critical while another is optional.
Why sampled traces were too slow
Distributed tracing provides rich, per-request visualizations, but sampling makes rare failures take a long time to observe at Uber’s scale. Uber’s example assumes 99.9% API availability, 500 requests per second and a 0.01% trace-sampling rate. Under those assumptions, the expected time to capture one failed request is 5 hours, 33 minutes and 20 seconds. Capturing ten such failures would take 2 days, 7 hours, 55 minutes and 31 seconds.
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Those figures describe the stated assumptions, not a universal tracing limit. They illustrate why a sampled trace stream may be unsuitable for quickly learning dependency behavior when failures are uncommon but operationally important. Uber’s approach emits a compact metric for every request, then aggregates those observations by dependency edge.
How the runtime instrumentation works
Muttley supplies routing and basic call outcomes
Uber’s Muttley on-host proxy routes service-to-service traffic and emits success or failure metrics. However, the proxy does not know which caller endpoint initiated a request, so proxy data alone cannot build the required service-and-procedure dependency graph.
yarpc middleware adds caller context
Uber adds instrumentation to yarpc inbound and outbound middleware:
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- The inbound middleware assigns a unique request ID and creates an
IngressLogfor the incoming request. - The request ID is placed in the request context. The application propagates that context to downstream clients.
- Each outbound middleware instance appends an
EgressLogcontaining the callee service, procedure and error state. - A shared in-memory
RequestTrackerassociates the request ID with its caller, callee, procedure and observed errors. - When inbound processing completes, the middleware records whether the caller request errored and emits caller/callee metrics for every outbound call.
This design links one inbound request to all of its outbound calls without requiring a full trace for every request. The resulting records retain the endpoint-level context missing from the proxy layer.
The probability rule that classifies each dependency
For every dependency edge, Uber computes:
Pc = (number of requests where the callee failed and the caller failed) / (number of requests where the callee failed)
Pc estimates how often a downstream failure is accompanied by a caller failure. Uber uses heuristic thresholds:
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| Pc value | Classification | Operational interpretation |
|---|---|---|
| Pc ≥ 0.8 | Fail-close | At least 80% of observed callee failures coincide with caller failures; treat the edge as hard. |
| Pc ≤ 0.2 | Fail-open | At most 20% of observed callee failures coincide with caller failures; the caller usually survives. |
| 0.2 < Pc < 0.8 | Unknown | The evidence is mixed; do not make a hard/soft dependency decision from this rule alone. |
The thresholds are heuristics, not a published accuracy guarantee. Uber has not reported fleet-wide precision, recall or a controlled benchmark against distributed tracing, so teams should treat the labels as operational signals that may need review.
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Retry ordering prevents misleading classifications
Retries can hide an underlying downstream failure. Uber therefore disallows application-layer retries and places its later outbound retry middleware after dependency analysis in the middleware chain. Dependency analysis sees the post-retry result, allowing it to classify whether the caller ultimately failed after the configured retry behavior rather than counting an intermediate attempt as the final outcome.
This ordering is specific to Uber’s implementation. A system with different middleware, client libraries or retry policies would need to define explicitly whether dependency analysis observes individual attempts, the post-retry result or both.
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Metrics versus traces for dependency discovery
| Dimension | Sampled distributed traces | Uber’s per-request metrics |
|---|---|---|
| Evidence source | A sampled subset of request traces | Metrics emitted for every request and aggregated by edge |
| Granularity | Detailed individual request path when sampled | Service-and-endpoint dependency tuples |
| Decision method | Operators interpret sampled failure paths | Explicit Pc thresholds classify edges |
| Time to rare-failure evidence | Can take hours or days under low sampling | Converges from observing every production failure |
| Best operational uses | Investigating the complete context of a sampled request | Dependency inventory, alerting, retry budgets, failover gates and call-path optimization |
The trade-off is deliberate. Uber’s engineers describe giving up visibility into every individual failure in exchange for faster convergence from observing every failure in production. Traces remain useful for deep request-level diagnosis; the metric system is optimized for discovering and classifying dependency relationships.
What Uber says the system enabled
Uber reports that the initiative cataloged dependencies for critical APIs, indexed most fail-close anomalies and added regression-safety guardrails for Failover Architecture 2.0. A dependency map built from runtime behavior can expose a newly hard edge, identify a call path that consumes failure or retry budget, and prevent a failover change from silently introducing a critical downstream requirement.
Static analysis complements runtime evidence
Uber’s RPCShield provides a static-analysis path for dependency relations. Static analysis can identify declared or structurally possible relationships before a failure occurs, while runtime metrics reveal what actually happens under production traffic and error handling. The two approaches are complementary: static results can broaden coverage, and runtime classifications can show whether a theoretically reachable dependency is fail-close or fail-open in practice.
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Limits and implementation considerations
- The published method describes Uber’s production architecture, not a universal service-mesh standard.
- The 0.8 and 0.2 boundaries are heuristic and should be interpreted with traffic volume, endpoint semantics and error definitions.
- Low-volume edges may need longer observation windows before classification is trusted.
- Aggregated metrics do not replace traces when an engineer must reconstruct the exact sequence, payload context or timing of one failed request.
- Retry placement changes the meaning of the observed caller and callee outcomes; middleware order must be documented and tested.
- Uber’s published account does not provide fleet-wide dependency counts or measured precision and recall.
For general service-mesh operators, the transferable design pattern is to capture caller identity at the application RPC layer, propagate it through outbound calls, emit an outcome for every request, and apply an explicit rule to aggregated edges. The exact middleware and retry configuration must match the local RPC stack.
Bottom line
Uber’s system turns routine production metrics into an automated dependency map. By joining inbound and outbound yarpc records with a request ID, evaluating every observed failure and applying transparent probability thresholds, it identifies which downstream calls can break an API far sooner than a sparsely sampled trace stream. The result is not a replacement for tracing; it is a faster, endpoint-aware control signal for dependency management and failover safety.
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