Self-Awareness: Strengths and Weaknesses Questions

Situation / requirement: Users expect instant feedback for micro-interactions (e.g., press "like" and UI toggles immediately). Backend can be eventual-consistent (async write, fan-out, dedupe). SLOs must reflect perceived UX (frontend) and eventual backend correctness.

Proposed SLIs (what to measure)

• PerceivedLatency SLI: fraction of client interactions that produce an immediate UI response < 100ms (measured by client telemetry: time from input to UI update).
• Success-Visible SLI: fraction of interactions where the final visible state converges to the expected state within T_conv (e.g., 5s) — measured by client “state-check” telemetry or browser re-sync events.
• Backend-Processing SLI: fraction of server-side canonical writes that succeed within T_backend (e.g., 30s) — measured from server write completion metrics / durable ack.
• ReconciliationLag SLI: p95 time for background reconciliation (eventual consistency fix) to correct divergence.

Error definitions (what counts as error)

• PerceivedLatency error: UI did not update within 100ms.
• Visible-consistency error: within T_conv the user’s view does not match the canonical state (false positive/negative like).
• Backend error: write failed or lost (after retries) or took > T_backend.
• Reconciliation error: reconciliation not completed within acceptable window.

Example SLOs (align UX and backend)

• PerceivedLatency: 99.9% of interactions <100ms.
• Visible-consistency: 99.5% converge within 5s.
• Backend-Processing: 99.9% writes succeed within 30s.
• ReconciliationLag: p95 < 10s.

Measurement approach

• Client-side telemetry (SDK/event): report timestamps for user action, UI update, and subsequent syncs (with sampling + privacy).
• Server metrics & traces: write-ahead log timestamps, queue lengths, worker success/failure counts.
• Correlate client and server traces via request IDs for debugging.

Error-budget handling & runbook

• If PerceivedLatency SLO breached: immediate mitigation focus on frontend (degraded animation, local optimistic update kept), roll back recent deployments affecting client path, increase capacity for front-end endpoints, enable feature-conservative rollout.
• If Visible-consistency or Backend SLO breached: prioritize backend queue processing, increase worker concurrency, apply backpressure to writers, temporarily disable non-critical downstream fan-out, enable “read-repair” priority for affected objects.
• If ReconciliationLag grows: escalate to on-call, run targeted reconciliation jobs, pause feature rollouts that increase write amplification.
• Use error budget automation: when budget < X%, throttle experimental features, halt rollout, and open a reliability incident for >Y% breaches.

Design trade-offs and rationale

• Separate perceived UX SLOs from backend correctness SLOs so optimistic UI doesn’t mask reliability problems.
• Keep T_conv and T_backend explicit; choose values based on human perception (sub-second for UI, seconds for eventual consistency).
• Use client telemetry for UX alignment; cross-check with server-side authoritative metrics to avoid false positives.

This approach ensures users get instant feedback while the SRE team enforces backend correctness via measurable, actionable SLOs and automated error-budget responses.

Goal: alert separately on client-side rendering errors (JS exceptions, high CLS) vs network/backend issues (504/timeout) and correlate to reduce false positives.

Approach summary:

• Create independent signal sources: RUM (browser telemetry: JS errors, CLS, page load times), network logs / edge logs (HTTP 5xx, timeouts), backend traces (latency, errors).
• Alert on anomalies in each signal, then run a correlation check before firing high-severity alerts (e.g., if many JS errors but no server errors or trace errors, treat as client/regression; if 504 spikes coincide with backend trace errors, treat as backend outage).
• Use rate thresholds + relative-change (percentile, anomaly) to avoid noisy alerts.

Example PromQL (backend / edge metrics):

# 504 rate per minute from edge proxy
sum(rate(edge_http_responses_total{status="504"}[5m])) by (service)
/
sum(rate(edge_http_requests_total[5m])) by (service)
> 0.01

Example Elasticsearch-style (RUM) queries:

# Count JS exceptions in last 5m grouped by release
POST /rum-events/_search
{
  "query":{"bool":{"must":[{"term":{"event.type":"js_error"}},{"range":{"@timestamp":{"gte":"now-5m"}}}]}},
  "aggs":{"by_release":{"terms":{"field":"app.release"},"aggs":{"count":{"value_count":{"field":"_id"}}}}}
}

High CLS anomaly (RUM):

# Pseudocode: percent of page loads with CLS > 0.1
SELECT count(*) FILTER (WHERE cls > 0.1) / count(*) FROM rum_pageviews WHERE timestamp > now()-5m

Correlation logic to reduce false positives:

• When JS error rate > threshold, check:
  • edge 5xx/timeout rate in same region / CDN POP in last 5m — if high, tag alert as “possible network-induced client errors”.
  • trace spans: sample traces for affected requests — if backend span error or increased latency present, escalate as backend incident.
  • user-agent / browser breakdown: if errors concentrated to one browser version, likely regression — create page-owner/rollout alert.
• When 504 rate spikes, check:
  • RUM: are affected page loads showing increased load time / resource errors? If no RUM users affected (low real-user impact), suppress high-severity paging and create P1 only if backend traces also show errors.

Practical implementation:

• Implement multi-step alerting in alert manager / Kibana watches:
  1. Fire low-severity immediate alert per-signal (JS error surge OR 504 surge).
  2. Run enrichment webhook that queries other data sources (RUM ↔ traces ↔ edge logs) within same time window and client geographic/pop.
  3. If correlation indicates common root cause (e.g., 504s + backend errors), escalate to page on-call; if isolated to client-side (JS errors concentrated by release/browser), notify frontend team without global paging.

Key thresholds and heuristics:

• Use both absolute counts and relative change (e.g., >200 errors AND >3x baseline) to avoid noise.
• Use grouping keys: release, browser, region, edge POP, route.
• Sample traces (synchronous) for failed requests to verify backend error codes and latencies.

Why this reduces false positives:

• Independent detection avoids one noisy source triggering global paging.
• Correlation confirms causality (client vs network vs backend).
• Grouping by release/browser isolates rollouts.
• Using both rate and delta prevents alerts from seasonal traffic spikes.

Edge cases:

• Intermittent CDN issues causing asymmetric signals — include POP-level correlation.
• Synthetic monitoring to supplement sparse RUM coverage.
• Ensure alert suppression during known deploy windows via maintenance windows.

Approach:

• Stream the CSV line-by-line, updating a compact streaming quantile summary per country.
• Use t-digest (accurate for extreme quantiles, small memory) to estimate the 95th percentile. This fits SRE needs: low memory, mergeable, and fast.

Code (requires tdigest: pip install tdigest):

import csv
import sys
from tdigest import TDigest
from collections import defaultdict

def compute_95th_lcp_per_country(csv_path):
    # mapping country -> TDigest object
    digests = defaultdict(TDigest)

    with open(csv_path, newline='') as f:
        reader = csv.DictReader(f)
        for row in reader:
            try:
                country = row['country']
                lcp = int(row['lcp_ms'])
            except (KeyError, ValueError):
                continue  # skip malformed rows
            digests[country].update(lcp)

    # compute 95th percentile per country
    result = {}
    for country, td in digests.items():
        result[country] = td.quantile(0.95)
    return result

if __name__ == '__main__':
    path = sys.argv[1]
    out = compute_95th_lcp_per_country(path)
    for country, p95 in sorted(out.items()):
        print(f"{country}\t{p95:.1f} ms")

Why t-digest:

• Designed for streaming quantiles, excellent accuracy for high percentiles.
• Small, mergeable summaries — good for sharded processing or MapReduce.

Complexity:

• Time: O(n) to process n rows; each update amortized O(1)–O(log k) where k is digest centroid count.
• Space: O(c * k) where c = number of countries with data and k = centroids per digest (usually small).

Edge cases:

• Missing/invalid lcp_ms or country — skip or log.
• Very high cardinality of countries could increase memory; consider eviction/aggregation (e.g., roll up rare countries into "other").
• If external libs disallowed, implement GK or P² algorithm (more code, more complexity) or use fixed histograms if value range known.

Requirements:

• Correlate per-device/network-layer events (RSSI, RSRP/RSRQ, SINR, carrier MCC/MNC, cell tower ID, carrier outage events) with RUM app UX metrics (cold start time, time-to-interactive (TTI), screen render time, API latency, error rate).
• Support aggregation by geography, carrier, device model, OS version, app version, and time window.
• Fast drill-down from carrier-wide trends to affected devices/sessions.

Dashboard layout:

1. Overview (top): carrier-level heatmap showing avg TTI and avg RSRP by carrier and region; trend lines for global TTI and carrier signal quality.
2. Signal vs UX correlation: scatterplot of RSRP (or SINR) vs TTI with density shading; binned boxplots of TTI by RSRP buckets.
3. Latency & errors by carrier: stacked timeseries of API p50/p95, error rate, and packet loss.
4. Session sampler: list of recent sessions with signal timeline + UX timeline for root-cause replay.
5. Drill-down controls: carrier (MCC/MNC), region, device model, OS, app version, customer segment.

Critical dimensions:

• Carrier (MCC/MNC), Cell tower ID
• Signal metrics: RSSI/RSRP/RSRQ/SINR, network type (3G/4G/5G), carrier status
• Device: model, OS version, modem firmware
• App: version, release, build, feature flags
• Geography: country/region/city, metro
• Time window & session id

Example queries:

• Correlate avg TTI by RSRP bucket (SQL-style):

SELECT rrp_bucket, AVG(tti_ms) AS avg_tti, PERCENTILE(tti_ms, 95) AS p95_tti
FROM rum_sessions
JOIN network_metrics USING(session_id)
WHERE timestamp >= now() - interval '1 hour'
GROUP BY rrp_bucket
ORDER BY rrp_bucket;

• PromQL-style alertable series (carrier p95 TTI):

p95_tti{carrier="310-260"} > 2000

• Count of sessions with severe signal drop and high TTI:

SELECT COUNT(*) FROM sessions s
WHERE EXISTS (
  SELECT 1 FROM network_metrics n
  WHERE n.session_id = s.id AND n.rsrp < -110 AND n.timestamp BETWEEN s.start AND s.end
)
AND s.tti_ms > 1500
AND s.timestamp >= now() - interval '15 minutes';

Alerting on carrier-wide degradation:

• Multi-signal rule: alert when p95 TTI for a carrier increases >50% vs baseline AND median RSRP drops >6 dB in same 5m window, sustained for 3 consecutive windows.
• Statistical anomaly detection: use rolling baseline (7-day, same hour) and alert on significant deviation (z-score >3) for carrier-level p95 TTI, error rate, or packet loss.
• Breadth threshold: require impact >X% of sessions (e.g., >5% of sessions in region/carrier exceed TTI SLO) to avoid noisy single-device alerts.
• Auto-runbook fields in alert: affected carrier, region, % sessions impacted, top device models, top APNs, time window, sample session ids.

Reasoning and best practices:

• Combine absolute thresholds (SLO breaches) with relative and breadth checks to avoid false positives.
• Always include context fields (session id, device, cell id) in alerts for fast triage.
• Store signal metrics at high cardinality for short-term forensic (5–15 min), aggregate for long-term trends.
• Instrument sampling to capture full signal+UX timelines for a subset of sessions to keep costs manageable.

Largest Contentful Paint (LCP)

• What: Time until the largest visible element (hero image/text) is rendered.
• Why UX: Users perceive the page as usable when main content appears; long LCP feels slow and increases bounce.
• Collection: Prefer RUM (real users) for distribution across devices, networks; supplement with synthetic labs for regression testing and CI.
• Interpretation: Inspect percentiles (p50/p75/p95) by user segment (geography, device, connection). Track trends and correlate with deployments or CDN/asset changes.
• Alerting: Alert on sustained p75/p95 degradation (e.g., p95 > 4s for 15m) or increase vs baseline (relative +30% for 30m). Use severity tiers and require corroborating signals (increased errors or request latency) to reduce noise.

Interaction to Next Paint (INP / previously FID)

• What: Measures responsiveness — delay from user interaction to next frame paint. INP is lifecycle-focused; FID was first input only.
• Why UX: High INP feels unresponsive and blocks user tasks (clicks, typing).
• Collection: RUM is essential since responsiveness depends on real device CPU and input patterns. Synthetic can emulate but may miss real-thread contention.
• Interpretation: Look at p95/p99 INP; isolate long tasks, main-thread CPU, and third-party scripts.
• Alerting: Alert when p99 INP exceeds threshold (e.g., >250–500ms) for sustained windows or when a deployment introduces a step-change. Suppress alerts if root-cause is client-side (e.g., slow third-party) and ticketed.

Cumulative Layout Shift (CLS)

• What: Sum of unexpected layout shifts during page lifecycle (visual instability).
• Why UX: Shifting elements cause misclicks and irritation; harms perceived quality.
• Collection: RUM captures real shifts across viewports; synthetic can help reproduce with deterministic interactions.
• Interpretation: Use session-scoped CLS and session counts with shifts >0.1. Correlate with late-loading images, webfonts, or injected ads.
• Alerting: Alert on rising fraction of sessions with CLS >0.1 (e.g., >1% of sessions over 30m) or large spike in average CLS. Combine with tagging by page/route to reduce noise.

General SRE guidance

• Use percentiles (p50/p75/p95/p99) and user-segmentation, not just averages.
• Alert on sustained, significant deviations from baseline and on high-percentile tail, not transient blips.
• Require multiple corroborating signals (errors, backend latency, deployments) and use noise-reduction tactics: dedupe, rate-limits, maintenance windows, and runbooks linking metric to common causes (CDN, assets, third-party).
• Integrate into SLOs: set user-impacting thresholds (e.g., 95% of page loads with LCP <2.5s) and tie alerts to error-budget burn to prioritize incidents.