Real-time observability for timing-sensitive systems: Query patterns with RocqStat + feature flags

Hook: When a flag flip can break your timing budget — and you need to know instantly

Feature flags are a powerful release control. In safety-critical, timing-sensitive systems (automotive ECUs, avionics, industrial controllers), a single toggle flip can push a control path past its worst-case execution time (WCET) and create a hazardous timing regression. The problem isn’t just detecting higher latency — it’s proving correlation: did the flag flip cause the WCET violation, or was it noise from system load or interrupts?

Executive summary: What this article gives you (2026 update)

• Concrete telemetry schema to capture per-invocation timing plus flag state and platform context.
• Query patterns (PromQL / SQL-time-series) and RocqStat-friendly pipelines to correlate flag flips with WCET regressions.
• Dashboard and alerting blueprints that trigger when WCET budgets are violated after a flag flip.
• Runbook actions and advanced strategies (automated rollback, CI preflight, static+dynamic correlation) aligned with 2026 trends — including the recent Vector & RocqStat consolidation.

Why this matters in 2026

Late 2025 and early 2026 saw the industry double down on timing verification. Vector’s acquisition of RocqStat (January 2026) signaled consolidation: timing analysis and WCET estimation are being integrated directly into mainstream verification toolchains (VectorCAST and similar). That accelerates tie-ins between static WCET estimates and runtime telemetry. For teams shipping software-defined vehicles, industrial robots, or medical devices, this means you can — and should — build observability patterns that immediately expose timing regressions and their relationship to feature toggles.

“Timing safety is becoming a critical” — Eric Barton, Senior VP of Code Testing Tools, Vector (about the RocqStat acquisition, January 2026)

High-level approach

1. Instrument every relevant execution path with high-resolution timing and flag-state snapshots.
2. Stream those events to a time-series system (Prometheus, ClickHouse, or a RocqStat analytics backend).
3. Create recording rules that compute path-level percentiles and maximums (WCET proxies) and link them to flag flip events.
4. Alert when a WCET budget is violated in the window following a flip; include evidence (before/after distributions, spike traces) in the alert payload.
5. Automate rollback or gating in CI/CD when correlation exceeds a defined confidence threshold.

Telemetry schema: what to record (schema examples)

Telemetry should be compact, high-cardinality where needed, and consistent across builds. Use OpenTelemetry as the transport; store metrics in a long-retention TSDB and traces in a distributed tracing store. Below is a recommended JSON event schema for per-invocation telemetry.

{
  "timestamp": "2026-01-18T12:34:56.789Z",
  "component": "control_loop",
  "path_id": "control.update.pid_step",
  "invocation_id": "uuid-1234",
  "duration_us": 2350,
  "duration_us_wall": 2400,        // wall clock vs CPU
  "wcet_budget_us": 2000,
  "code_version": "v1.42-rc3",
  "flag_states": {
    "enable_new_pid": "on",
    "use_fast_math": "off"
  },
  "flag_metadata": {
    "enable_new_pid.version": "2026-01-18T12:30:01Z"
  },
  "cpu_core": 2,
  "irq_count_delta": 3,
  "heap_free_bytes": 524288,
  "trace_id": "trace-abc",
  "sampling_hint": "full"
}

Key fields explained:

• path_id: unique identifier for the execution path or basic block group (stable across builds).
• duration_us: high-resolution measured time (use hardware timers where possible).
• wcet_budget_us: the asserted safety budget for the path.
• flag_states: snapshot of relevant toggles at invocation time. Keep this limited to flags that affect timing.
• flag_metadata: optional mapping of last flip times or release IDs for traceability.

Instrumentation patterns (example code)

Below are pragmatic patterns for C/C++ embedded code and a server-side example (Node.js) that shows how to capture flag state and timing.

Embedded C pattern (RTOS/hardware timer)

// pseudo-code: capture timing and flag state
uint64_t t0 = hw_timer_read();
execute_control_path();
uint64_t t1 = hw_timer_read();
uint32_t duration_us = timer_delta_us(t1, t0);

// build a compact protobuf or binary event and send via telemetry link
telemetry_event_t ev = {
  .path_id = PATH_CONTROL_PID,
  .duration_us = duration_us,
  .wcet_budget_us = 2000,
  .flags_bitmap = read_flag_bitmap()  // small bitmask for known toggles
};
telemetry_send(&ev);

Server / Edge-side Node.js example (OpenTelemetry + Prometheus)

const { meter } = require('@opentelemetry/api-metrics');
const featureFlags = require('./flags');
const timer = meter.createHistogram('path.duration_us', { unit: 'us' });

async function handleControl(req) {
  const start = process.hrtime.bigint();
  await controlPath();
  const end = process.hrtime.bigint();
  const durationUs = Number((end - start) / 1000n);

  timer.record(durationUs, {
    path_id: 'control.update.pid_step',
    code_version: process.env.CODE_VER,
    flag_enable_new_pid: featureFlags.isOn('enable_new_pid') ? 'on' : 'off'
  });
}

How to represent flag flips as metrics

To correlate flag flips with timing, emit a flip event as a monotonically increasing counter or timestamp metric. Example names:

• feature_flag_flip_total{flag="enable_new_pid"} (counter increments on every flip)
• feature_flag_last_flip_ts{flag="enable_new_pid"} (gauge updated to Unix epoch when flip happens)

This allows you to write queries that detect “a flip happened in the last X minutes” and combine that with execution-time statistics.

Query patterns: PromQL and time-series SQL

Below are example queries to help you rapidly detect correlation between a flag flip and a WCET violation.

PromQL: Detect a WCET violation within 5 minutes after a flip

# Assumes: path_duration_ms metric and feature_flag_flip_total counter

# 1) Condition A: did a flip occur in the last 5m?
flip_happened = increase(feature_flag_flip_total{flag="enable_new_pid"}[5m]) > 0

# 2) Condition B: did any invocation exceed the budget in the same 5m window?
max_duration = max_over_time(path_duration_ms{path_id="control.update.pid_step"}[5m])

# Alert expression (concept):
flip_happened and (max_duration > 2000)

In a Prometheus alerting rule you would write this as a single boolean expression, or create recording rules for the two pieces and then combine them.

SQL / ClickHouse: quantify before/after distributions

-- compute P95 before and after the latest flip for the flag
WITH latest_flip AS (
  SELECT max(timestamp) AS flip_ts
  FROM flag_flips
  WHERE flag = 'enable_new_pid'
)
SELECT
  'before' AS window,
  quantile(0.95)(duration_us) AS p95_us
FROM invocations, latest_flip
WHERE invocations.path_id = 'control.update.pid_step'
  AND invocations.timestamp BETWEEN flip_ts - INTERVAL 30 MINUTE AND flip_ts

UNION ALL

SELECT
  'after' AS window,
  quantile(0.95)(duration_us) AS p95_us
FROM invocations, latest_flip
WHERE invocations.path_id = 'control.update.pid_step'
  AND invocations.timestamp BETWEEN flip_ts AND flip_ts + INTERVAL 30 MINUTE;

This gives you a statistical comparison and can be extended to bootstrap confidence intervals or perform a two-sample test for significance.

Dashboard design: what to show for quick triage

Design dashboards with quick triage in mind. Each incident should give you the cause-and-effect evidence in under a minute.

• Top row (incident banner): Active alerts, flag flip timeline, affected code versions, candidate culprit flag(s).
• Timing heatmap: heatmap of duration_us per path_id over time (color intensity = count above budget).
• Before/After comparison panel: p50/p90/p95/p99 and max for the selected path, windowed around last flip.
• Trace/sample panel: list of slow traces with flag snapshot, CPU and IRQ deltas; link to distributed trace view.
• Correlation score: computed metric (0–100) combining temporal proximity, magnitude of WCET delta, and reproducibility (how many invocations exceeded the budget).

Implement these panels in Grafana or a custom UI. Example Grafana queries: max_over_time(path_duration_ms{path_id=~"..."}[5m]), increase(feature_flag_flip_total[5m]), and a table panel populated by ClickHouse SQL shown earlier.

Alerting blueprints: when to fire and what to include

Alerting must be specific and actionable. A basic alert when a flip correlates with a WCET violation should include:

• Alert title: "WCET budget violated after flip: enable_new_pid"
• Severity and affected paths
• Evidence: before/after percentiles, number of violating invocations, sample traces (links)
• Context: code_version, deployment id, node id, time since flip
• Suggested action: rollback flag, escalate to safety engineer, trigger CI rollback

Sample Prometheus-style alert rule (conceptual):

groups:
- name: timing-flip-alerts
  rules:
  - alert: WCETBudgetViolatedAfterFlip
    expr: (
      increase(feature_flag_flip_total{flag="enable_new_pid"}[10m]) > 0
      and
      max_over_time(path_duration_ms{path_id="control.update.pid_step"}[10m]) > 2000
    )
    for: 1m
    labels:
      severity: critical
    annotations:
      summary: "WCET budget violated after flip for enable_new_pid"
      description: "P95/P99 and sample traces attached. Code version: {{ $labels.code_version }}"

Runbook: step-by-step triage when the alert fires

1. Confirm flip timestamp and code version from the alert payload.
2. Open the dashboard's before/after panel and check p95/p99 deltas.
3. Inspect sample traces to determine whether the slow path matches the code change introduced by the flag.
4. Check platform noise signals (CPU load, IRQs, thermal throttling). If multiple nodes show the same regression coincident with the flip, treat as high confidence.
5. If confidence threshold met, flip the flag back (via safe rollback procedure) and monitor for recovery within configured grace period (e.g., 5–10 minutes).
6. File a verification ticket: attach RocqStat static WCET analysis results and runtime traces for root cause analysis.

Advanced strategies and 2026 best practices

1) Combine static WCET (RocqStat) with runtime telemetry

Static timing analysis (what RocqStat provides) gives you formal upper bounds. Runtime telemetry gives you observed distributions. In 2026, integrated toolchains are becoming the norm: run RocqStat during CI to produce expected WCETs for each path and ingest those estimates as wcet_budget_us in runtime telemetry. That closes the loop between verification and observability.

2) Canary toggles with timing gates

Use staged rollouts where the feature flag platform integrates with the telemetry pipeline. Only promote a flag to broader population if dynamic timing metrics remain within budget for the canary cohort for a set period. This pattern pairs naturally with edge-first rollout and canary design for low-latency cohorts.

3) Automated rollback on high-confidence correlation

If your pipeline can demonstrate high confidence (e.g., >95% of invocations exceed budget in two independent nodes within N minutes), automate the flag rollback and create a traceable incident ticket. Treat the rollback as a safety action with audit logs. Integrate this with CI/CD gates (see guidance on CI preflight and safe rollback patterns from operational playbooks).

4) Use ML-based anomaly detection for subtle regressions

In 2026, solutions increasingly include lightweight ML detectors that learn normal execution time distributions per path and trigger alarms on distributional shifts. Combine ML alerts with the explicit flip-detection logic for faster, more accurate triage. For examples of predictive detection patterns outside security, see work on predictive AI detection.

5) Keep flag-state cardinality manageable

High-cardinality flag combinations explode observability cost. Only snapshot flags that affect timing-sensitive code paths and use bitmaps or hashed representations to minimize telemetry volume.

Case study (condensed): detecting a regression after a toggle

Scenario: An automotive torque-control update is gated behind feature flag enable_fast_control. After enabling the flag on a test fleet, alerts indicated WCET exceeded the 2ms budget on control.update.path in 12/50 heads within 7 minutes.

What the team did:

• Used PromQL to confirm increase(feature_flag_flip_total{flag="enable_fast_control"}[10m]) > 0 and max_over_time(...) > 2000.
• Compared p95/p99 before/after using ClickHouse and found p99 rose from 1.6ms to 3.1ms.
• Opened RocqStat static WCET report for the new control routine; static analysis showed a worst-case path that, when enabled, could exceed the budget in rare scheduler states.
• Rolled back the flag, observed recovery, and assigned devs to optimize the code path and add a preflight WCET check in the CI pipeline.

Implementation checklist

• Instrument all timing-sensitive code paths with path_id and duration_us.
• Emit feature flag flip counters and last-flip timestamps.
• Ingest RocqStat/Vector WCET estimates into telemetry as budgets (tie static analysis output into runtime budgets).
• Build recording rules for p95/p99/max and an alert rule that cross-references flips with violations.
• Create dashboards with before/after comparisons and trace links (see dashboard design playbook).
• Automate rollback gating and add a CI static+dynamic timing gate; consider integrating with your CI/CD and preflight checks (see operational CI patterns).

Final recommendations

In 2026, timing observability is shifting from after-the-fact telemetry to integrated verification: static WCET estimates, runtime metrics, and feature flag state must be first-class citizens in your observability pipeline. Treat each flag flip as a potentially hazardous operation in timing-critical systems: emit clear, queryable flip metrics, capture high-resolution timing and platform context, and use correlation queries that combine flip windows with distributional checks.

Call to action

If you run timing-sensitive systems and use feature flags, start by implementing the telemetry schema above for a single high-risk path and wire it to your TSDB and alerting system. Measure before-and-after for a controlled flip, iterate on thresholds, and then expand to other paths. If you want a practical workshop or an integration blueprint that includes RocqStat/VectorCAST CI checks plus Prometheus/Grafana alerting templates, contact the toggle.top observability practice — we’ll help you map static WCET results to runtime telemetry and build safe rollback automation.

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