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Quick Definition

Synthetic Checks are automated, scripted tests that simulate user or system interactions with an application, API, or infrastructure component to validate availability, performance, and correctness from outside the system.

Analogy: Synthetic Checks are like scheduled test drives of a delivery route to confirm roads, traffic, and loading docks are usable before real deliveries start.

Formal technical line: Synthetic Checks are externally executed, deterministic probes that produce measurable telemetry (latency, success rate, content validation) used as SLIs for availability and performance monitoring.

Other meanings (less common):

• Synthetic monitoring: often used interchangeably with Synthetic Checks in observability platforms.
• Canary tests: short, targeted synthetics run as pre-deploy verifications.
• Service-level probes: a term used in some organizations for synthetics focused solely on SLIs.

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What is Synthetic Checks?

• What it is / what it is NOT
• It is: scripted, repeatable external tests executed on a schedule or on-demand to validate end-to-end behavior.
• It is NOT: real user monitoring (RUM) which captures actual user traffic, nor unit tests which validate internal code paths only.

• It is NOT a replacement for load testing; synthetics typically validate correctness and SLIs, not peak scalability.

• Key properties and constraints

• External perspective: executes outside the application runtime to emulate real clients.
• Deterministic scripts: repeatable actions with predictable results for baseline comparisons.
• Observable output: yields telemetry such as response codes, latencies, content matches, and simulated user flows.
• Frequency vs cost trade-off: higher frequency gives finer resolution but increases cost and potential probe-induced load.
• Geographic diversity: checks should run from multiple regions to catch edge network problems and CDN issues.
• Security considerations: synthetic credentials and secrets must be rotated and stored securely.

• Limitations: cannot fully reproduce complex user behavior, heavy stateful interactions, or extremely high-load scenarios.

• Where it fits in modern cloud/SRE workflows

• Pre-deploy gating: run light synthetics as part of CI/CD pipelines or pre-production promotion.
• Post-deploy verification: smoke checks immediately after rollout to detect regressions.
• Continuous availability monitoring: synthetic SLIs feed SLO calculations and error budgets.
• Incident detection and validation: synthetics can triage whether an alert reflects external user impact.
• Chaos and resilience validation: combined with fault injection to validate graceful degradation.

• Security posture: can validate WAF rules, authentication flows, and certificate expiry.

• A text-only “diagram description” readers can visualize

• External probe agents in multiple regions -> network -> DNS/CDN -> edge -> load balancer -> service mesh -> application tier -> downstream APIs/databases -> synthetic validation checks return telemetry to monitoring platform -> alerting pipeline -> SLO calculator -> on-call routing.

Synthetic Checks in one sentence

Synthetic Checks are automated external probes that emulate user or system interactions to continuously validate application availability, correctness, and performance from real-world vantage points.

Synthetic Checks vs related terms (TABLE REQUIRED)

ID	Term	How it differs from Synthetic Checks	Common confusion
T1	Real User Monitoring	Observes actual user traffic rather than simulated probes	People expect RUM to cover synthetic coverage
T2	Health Check	Often local and coarse; synthetics are external and detailed	Health checks sometimes mistaken as full synthetic tests
T3	Canary Deployment	Canary is a release strategy; synthetics can validate canaries	Teams mix up canary rollout with canary test types
T4	Load Testing	Focuses on capacity and stress rather than functional correctness	Load tests used to detect availability like synthetics
T5	Integration Test	Runs inside CI environments; synthetics validate externally	Integration tests are internal and not geographically distributed
T6	Heartbeat Probe	Very lightweight availability ping; synthetics perform workflows	Heartbeats may miss content and flow regressions
T7	API Contract Test	Validates schema and contract; synthetics validate live behavior	Contract tests do not measure network or infra impact

Row Details (only if any cell says “See details below”)

• None

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Why does Synthetic Checks matter?

• Business impact (revenue, trust, risk)
• Often detects external failures before customers complain, protecting revenue streams for e-commerce and SaaS billing flows.
• Helps preserve brand trust by ensuring public endpoints and key user journeys remain functional.

• Reduces risk of long-duration outages by enabling rapid detection and automated responses to degradations.

• Engineering impact (incident reduction, velocity)

• Provides deterministic alerting signals that reduce noisy alerts derived from internal metrics alone.
• Enables quicker rollback or mitigation actions when combined with deployment automation, improving release velocity.

• Facilitates reproducible incident validation by capturing exact synthetic inputs and responses.

• SRE framing (SLIs/SLOs/error budgets/toil/on-call)

• Synthetic Checks commonly provide SLIs for availability and latency of critical customer journeys.
• SREs use synthetic-derived SLIs to define SLOs and manage error budgets tied to release policies.
• Synthetic automation reduces toil by automating routine verification tasks and on-call runbook validation.

• On-call rotation benefits from clear, externally observable signals that align with customer impact.

• 3–5 realistic “what breaks in production” examples

• DNS misconfiguration causes site unreachable for specific regions while internal health checks pass.
• CDN mis-routing or cache miss leads to 500s for static assets for certain geographies.
• OAuth provider integration regression causes login failures for new sessions but not refresh tokens.
• Rate-limiting change in a downstream API results in sporadic 429s for checkout workflows.
• Certificate expiry on a subdomain breaks embedded widgets while main site remains functional.

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Where is Synthetic Checks used? (TABLE REQUIRED)

ID	Layer/Area	How Synthetic Checks appears	Typical telemetry	Common tools
L1	Edge / CDN	Probes request routing, cache hits, TLS handshake	status code latency cert info	Synthetic platforms
L2	Network / DNS	DNS resolution and routing checks from regions	resolution time error type	DNS probe services
L3	Service / API	API endpoint calls with payloads and response validation	response code body match latency	API monitoring tools
L4	Application / UI	Browser-level scripted flows and element checks	load time render errors screenshots	Browser synthetics
L5	Data / DB	Query validation through API endpoints or read replicas	query latency consistency errors	DB-aware probes
L6	Kubernetes	Synthetic probes hitting services through Ingress, service mesh	pod routing failures latency	K8s integrated checks
L7	Serverless / PaaS	Invocation of functions and managed endpoints	cold start time error rates	Function monitoring tools
L8	CI/CD	Pre-merge or pre-deploy smoke checks	pass/fail logs latency	CI runners w/ hooks
L9	Security / WAF	Probes to validate rules and auth flows	blocked attempts status codes	Security test runners

Row Details (only if needed)

• None

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When should you use Synthetic Checks?

• When it’s necessary
• Customer-facing endpoints that directly impact revenue (checkout, login, billing).
• External APIs where third-party SLA expectations exist.
• Post-deploy verification after automated rollouts and canary promotions.

• Regulatory or compliance scenarios where periodic verification of functionality is required.

• When it’s optional

• Internal admin-only tools with low customer impact.
• Highly volatile experimental endpoints where synthetics would produce high noise until stabilized.

• Very high-frequency checks on low-priority endpoints without SLO justification.

• When NOT to use / overuse it

• Avoid creating synthetics for every minor endpoint; this increases cost and alert noise.
• Do not substitute synthetics for comprehensive performance and load testing.

• Avoid using synthetics to test extremely stateful workflows that synthetics cannot faithfully represent.

• Decision checklist

• If endpoint affects revenue and user flow -> implement synthetic checks with SLIs.
• If endpoint is internal and low-impact -> consider occasional smoke checks instead.

• If the system has frequent false positives from synthetics -> reduce frequency, add retry logic, and add circuit-breaker awareness.

• Maturity ladder

• Beginner: Single-region availability checks with status codes and latency alerts.
• Intermediate: Multi-region browser and API synthetic flows linked to SLOs and CI/CD gates.

• Advanced: Geo-distributed, network-aware synthetics with credential rotation, chaos integration, and ML-driven anomaly detection.

• Example decision for small teams

• Small e-commerce team: implement a single-region API checkout synthetic + login check run every 5 minutes; alert to on-call Slack channel and require immediate review.

• Example decision for large enterprises

• Global SaaS: implement multi-region synthetics for onboarding, billing, and admin APIs; integrate with SLOs and automated rollback; multi-tier alerts and runbooks.

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How does Synthetic Checks work?

• Components and workflow 1. Script/Playbook: defines actions, requests, validation rules, authentication, and pacing. 2. Probe agents: execution environment (cloud regions, hosted agents, private probes). 3. Scheduler: triggers checks at configured intervals or on-demand. 4. Telemetry pipeline: collects metrics, logs, screenshots, and traces from executions. 5. Analyzer/SLO calculator: computes SLIs, compares against SLOs, and computes burn rates. 6. Alerting and automation: routes incidents to paging, creates tickets, or triggers remediation. 7. Secret manager: stores credentials used by synthetics securely with rotation. 8. Visualization: dashboards and runbooks for debugging.

• Data flow and lifecycle

• Author script -> schedule and distribute to probes -> probe executes -> collects response + artifacts -> telemetry forwarded to observability backend -> analysis transforms into SLIs -> SLO engine updates error budget -> alerts fire if thresholds exceeded -> human or automated remediation -> iterations on script.

• Edge cases and failure modes

• Flaky synthetic due to transient network jitter -> causes false positives.
• Probe environment divergence: probe IPs blocked by security rules -> creates false outage.
• Credential expiration causing mass failures -> synthetic alerts flood but actual user sessions may persist.
• Time-of-day dependencies: validation that depends on data state (e.g., promotions) may fail in off-hours.

• Resource exhaustion: probes at high frequency might overload a staging service.

• Short practical examples (pseudocode)

• Pseudocode for API check:
  • Prepare auth token from secret manager
  • POST /checkout with test cart
  • Assert response.status == 200
  • Assert body contains orderId
  • Report latency and status
• Pseudocode for browser check:
  • Open homepage
  • Wait for login button visible
  • Click login, submit test credentials
  • Verify dashboard element present and capture screenshot

Typical architecture patterns for Synthetic Checks

• Simple Interval Ping
• Use-case: basic availability on single endpoint
• When to use: early stage services or single-team owned endpoints
• Multi-region Health Probes
• Use-case: detect regional network or CDN issues
• When to use: global services and multi-tenant SaaS
• Browser Flow Synthetics
• Use-case: complex UI flows with JS rendering
• When to use: customer-facing web apps and onboarding funnels
• API Contract + Content Validation
• Use-case: verify payload correctness and business logic
• When to use: microservices with strict contract requirements
• Canary Gate Integration
• Use-case: run targeted synthetics during staged rollouts
• When to use: automated deployments with canary strategies
• Private Endpoint Probing
• Use-case: internal services behind VPN or on private VPC
• When to use: internal tooling and restricted admin APIs

Failure modes & mitigation (TABLE REQUIRED)

ID	Failure mode	Symptom	Likely cause	Mitigation	Observability signal
F1	Flaky probes	Intermittent alerts with no customer reports	Network jitter or transient failures	Add retry, increase sampling, compare RUM	increased variance in latency
F2	Blocked probe IPs	All synthetics from agent fail	Security rules or WAF blocks	Use diverse probes and adjust allowlists	uniform 403 or connection reset
F3	Credential expiry	Sudden auth failures across checks	Secrets not rotated or expired	Integrate secret manager rotation	auth error codes 401 403
F4	Environment drift	Script passes locally but fails remote	Missing headers or different data sets	Centralize environment config in tests	mismatch in content checks
F5	Probe overload	Synthetic-induced high load on service	Too many probes or heavy flows	Throttle frequency and use lightweight checks	backend CPU and 5xx increase
F6	Time-dependent data failures	Checks fail at certain times	Synthetic assumptions about data state	Use sandbox test data and isolation	pattern of failures at same time
F7	False positives from caching	Responses vary due to cache state	Cache invalidation or TTL issues	Vary cache keys or use cache-busting	inconsistent content hash
F8	Obfuscated UI changes	Browser synthetics fail after minor UI tweak	DOM changes or CSS selectors broken	Use resilient selectors and content checks	increased UI assertion failures

Row Details (only if needed)

• None

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Key Concepts, Keywords & Terminology for Synthetic Checks

• Synthetic Check: scripted external probe that validates endpoint behavior.
• Synthetic Monitoring: continuous execution of synthetic checks for availability.
• Real User Monitoring (RUM): telemetry from actual users, complementary to synthetics.
• SLI: Service Level Indicator; metric representing user-facing quality.
• SLO: Service Level Objective; target for an SLI over a time window.
• Error Budget: allowable SLO violations used to guide releases.
• Canary: staged deployment used to validate new versions before full rollout.
• Smoke Test: lightweight checks to verify basic functionality post-deploy.
• Health Check: local or internal probe meant for orchestration, not full validation.
• Heartbeat: minimal probe that verifies service is reachable.
• Probe Agent: the runtime executing a synthetic check.
• Scheduler: component that triggers probe execution at intervals.
• Private Probe: synthetic agent running in customer VPC or behind firewall.
• Public Probe: externally hosted agent running from cloud regions.
• Playbook / Script: the definition of steps for a synthetic check.
• Headless Browser: browser running without UI used for browser-level synthetics.
• DOM Selector: element locator for browser synthetic assertions.
• Content Validation: assert expected texts or JSON keys in responses.
• Latency SLI: measurement of response time percentiles.
• Uptime SLI: measurement of successful responses over total checks.
• Availability: proportion of time service responds correctly to synthetics.
• Timeout: maximum allowed response time before test considered failed.
• Retry Policy: rules for reattempts before marking test failed.
• Secret Manager: system to store credentials used in synthetics.
• Screenshot Artifact: visual capture used for debugging UI failures.
• Trace Context: distributed tracing metadata captured during synthetic execution.
• Observability Pipeline: system ingesting synthetic telemetry into dashboards.
• Alerting Policy: conditions and routing for paging and ticketing.
• Burn Rate: speed at which error budget is consumed.
• Flakiness: inconsistent failures not caused by application regressions.
• Load Impact: resource consumption on backend caused by synthetics.
• Private VPC Probe: probe running inside a private network for internal checks.
• Geo-coverage: geographic distribution of probe agents.
• Synthetic SLA: externally communicated SLA often linked to commercial contracts.
• Chaos Testing: deliberate fault injection combined with synthetics to assert resilience.
• Canary Gate: automated decision point using synthetics to approve rollout.
• Test Data Isolation: using dedicated data to avoid polluting production.
• Regression Detection: using historical synthetic baselines to spot regressions.
• Runbook: documented remediation steps for incidents triggered by synthetics.
• Playbook Automation: automated remediation triggered by alerts (runbooks as code).

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How to Measure Synthetic Checks (Metrics, SLIs, SLOs) (TABLE REQUIRED)

ID	Metric/SLI	What it tells you	How to measure	Starting target	Gotchas
M1	Availability (success rate)	External success of critical flow	successful checks divided by total	99.9% for critical flows	synthetic frequency affects sensitivity
M2	Latency P95	User experience for most users	95th percentile of response times	Depends on app; 500ms for APIs start	outliers and probes skew percentiles
M3	Latency P99	Tail latency issues	99th percentile latency	1.5x P95 as baseline	sparse sampling yields noisy P99
M4	Time to Detect (TTD)	How quickly issues get noticed	mean time between failure occurrence and alert	<2 minutes for critical	depends on check interval
M5	Time to Recover (TTR)	Time to restore service	mean time from alert to resolution	<30 minutes for ops-run services	runbook gaps increase TTR
M6	Content Validation Rate	Correctness of responses	fraction of checks where content matched	100% target for transactional flows	brittle assertions cause false alarms
M7	Geographical Failure Rate	Regional degradation detection	failures by region divided by total checks	region parity with global	insufficient regions miss issues
M8	Error Budget Burn Rate	Speed SLO is being consumed	errors per unit time vs budget	alert at burn rate >5x	noisy metrics can inflate burn rate
M9	Synthetic Noise Rate	False positives ratio	number of false alerts divided by alerts	aim <10% of alerts	lack of dedupe and retries inflate it
M10	Probe Health	Probe agent availability	agent heartbeats and execution success	99% agent uptime	single-agent dependency is risky

Row Details (only if needed)

• None

Best tools to measure Synthetic Checks

Tool — Synthetic Platform A

• What it measures for Synthetic Checks: availability, latency, content checks, screenshots.
• Best-fit environment: multi-region SaaS monitoring for web and APIs.
• Setup outline:
• Define check scripts in platform UI or YAML.
• Configure probe locations and frequency.
• Store credentials in integrated secret store.
• Hook telemetry to observability backend.
• Create dashboards and SLOs.
• Strengths:
• Easy setup and global probes.
• Integrated SLO and alerting features.
• Limitations:
• Cost scaling with frequency.
• Less control over private probe environments.

Tool — Browser Headless Runner B

• What it measures for Synthetic Checks: full browser rendering and UI element checks.
• Best-fit environment: complex single-page apps and interactive flows.
• Setup outline:
• Author scripts using browser automation APIs.
• Run on headless agents in CI or dedicated probe infra.
• Capture screenshots and DOM snapshots on failures.
• Integrate traces for each run.
• Strengths:
• Accurate simulation of user behavior.
• Visual artifacts for debugging.
• Limitations:
• Resource intensive and slower than API probes.
• More prone to flakiness from UI changes.

Tool — CI/CD Runner + Smoke Scripts C

• What it measures for Synthetic Checks: pre-deploy verification of core endpoints.
• Best-fit environment: teams with fast CI pipelines.
• Setup outline:
• Package synthetic tests as part of pipeline jobs.
• Run short smoke suite after deployment stage.
• Fail pipeline on critical failures.
• Strengths:
• Tight integration with deployment workflow.
• Keeps checks close to code changes.
• Limitations:
• Limited geographic coverage.
• Cannot run continuously post-deploy.

Tool — Private VPC Probe D

• What it measures for Synthetic Checks: internal services behind firewalls.
• Best-fit environment: internal admin tooling and private APIs.
• Setup outline:
• Deploy probe agent into VPC or cluster.
• Secure agent using IAM and secret rotation.
• Schedule checks centrally and report back to observability endpoint.
• Strengths:
• Access to internal-only endpoints.
• Low network variability from public internet.
• Limitations:
• Operational overhead to maintain agents.
• Potential single-point-of-failure if agent host has issues.

Tool — Tracing-integrated Runner E

• What it measures for Synthetic Checks: end-to-end traces and latency breakdowns.
• Best-fit environment: microservices architectures with tracing support.
• Setup outline:
• Inject trace context in synthetic requests.
• Capture spans across services during check runs.
• Correlate synthetic errors with service-level traces.
• Strengths:
• Fast root-cause identification across services.
• Correlation of synthetic metrics with real backend traces.
• Limitations:
• Requires tracing enabled across services.
• Higher complexity in setup and data volume.

Recommended dashboards & alerts for Synthetic Checks

• Executive dashboard
• Panels:
  • Global availability SLI (rolling 7d)
  • Error budget consumption (dailies)
  • Top impacted regions and flows
  • Business KPIs correlated with synthetic failures

• Why: provides C-suite and product leaders high-level health and risk.

• On-call dashboard

• Panels:
  • Current failing synthetics with recent history
  • Alert count and burn rate
  • Probe health and agent locations
  • Recent screenshots/log snippets for failed runs

• Why: allows on-call to triage quickly and determine user impact.

• Debug dashboard

• Panels:
  • Per-flow latency percentiles P50/P95/P99
  • Request/response payload samples
  • Traces for failed runs by correlation ID
  • Recent deployment tags and canary status
• Why: supports engineers in fast RCA and regression detection.

Alerting guidance:

• What should page vs ticket
• Page: critical user-impacting flows failing across multiple regions or sustained burn rate exceeding thresholds.
• Ticket: single-region blip or non-critical internal endpoint issues.
• Burn-rate guidance
• Page when burn rate >5x for critical SLO with sustained window (e.g., 30 minutes).
• Create incident when error budget consumption threatens near-term release windows.
• Noise reduction tactics
• Deduplicate similar alerts by grouping by flow, region, and failure type.
• Suppress alerts during scheduled maintenance windows.
• Add short retry logic to ignore single transient failures, then escalate on repeated failures.

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Implementation Guide (Step-by-step)

1) Prerequisites – Inventory of critical flows and endpoints. – Secret management solution for synthetic credentials. – Observability backend accepting custom metrics and traces. – CI/CD integration points for pre-deploy and post-deploy hooks. – Defined SLOs or targets for critical flows.

2) Instrumentation plan – Identify top 3-10 critical user journeys. – Define SLIs per journey (availability and latency). – Decide probe types (API vs browser vs private). – Determine frequency and geographic coverage.

3) Data collection – Configure probes to send metrics, traces, and artifacts. – Ensure timestamps, IDs, and metadata for correlation. – Store artifacts for a retention period that supports RCA.

4) SLO design – Choose SLI windows (rolling 7d/28d). – Set realistic starting SLOs based on historical RUM + synthetics. – Define error budget policies and escalation thresholds.

5) Dashboards – Build executive, on-call, and debug dashboards. – Include context: deployment tags, config changes, and maintenance windows.

6) Alerts & routing – Create tiered alerting policies based on severity and burn-rate. – Route critical pages to on-call and create tickets for advisory alerts.

7) Runbooks & automation – Document runbooks for each critical synthetic failure mode. – Automate common remediations (e.g., flush cache, restart service, rotate secret).

8) Validation (load/chaos/game days) – Run game days to validate synthetic alerts trigger expected mechanisms. – Use chaos experiments to ensure synthetics surface degradations.

9) Continuous improvement – Periodically review synthetic flakiness and refine scripts. – Tune SLOs based on business impact and historical data.

Checklists:

• Pre-production checklist
• Verify probe scripts against staging endpoints.
• Ensure secrets used in tests are test-scoped.
• Validate telemetry ingestion and dashboards.

• Run manual verification of probes before enabling schedules.

• Production readiness checklist

• Confirm multi-region probes are enabled.
• Set appropriate alert routing and escalation.
• Ensure runbooks exist and on-call has read access.

• Validate probe agent health and patch levels.

• Incident checklist specific to Synthetic Checks

• Record failing check ID and recent run artifacts.
• Check probe health and network reachability.
• Correlate with RUM and backend metrics.
• Follow runbook steps: verify secrets, check WAF logs, check DNS.
• Escalate to dev team if evidence indicates application regression.

Examples:

• Kubernetes example
• Deploy private probe as a Deployment with 3 replicas in the cluster.
• Mount service account with least privilege to fetch secrets.
• Schedule probes hitting Ingress endpoints.

• Verify “good”: probes report success across replicas and include traces.

• Managed cloud service example

• Use SaaS synthetic platform with private agent in VPC to hit managed DB endpoints.
• Store service credentials in cloud provider secret store.
• Verify “good”: multi-region results aligned and error budget healthy.

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Use Cases of Synthetic Checks

1) Login flow monitoring for a SaaS product – Context: Customers must sign in to access paid features. – Problem: OAuth provider integration changes cause login failures. – Why synthetics help: Validate end-to-end login from multiple regions before user impact. – What to measure: login success rate, total time to dashboard render. – Typical tools: browser headless, API probes.

2) Checkout and payment processing for e-commerce – Context: Checkout integrates payment gateway and basket service. – Problem: Payment provider transient errors or tokenization regressions. – Why synthetics help: Detect payment path issues impacting revenue immediately. – What to measure: checkout success rate, payment provider response codes. – Typical tools: API checkers and CI pre-deploy gating.

3) DNS and CDN propagation checks – Context: Global CDN and DNS changes during maintenance. – Problem: Misconfiguration causing routing errors in specific regions. – Why synthetics help: Geo probes reveal regional reachability issues. – What to measure: DNS resolution time, 200 vs 4xx/5xx ratio by region. – Typical tools: DNS probes and synthetic platforms.

4) Feature flag rollout validation – Context: Feature flags gradually enabled for subsets of users. – Problem: New feature causes regression in user flows for flagged users. – Why synthetics help: Targeted checks validate behavior with and without the flag. – What to measure: flow success rate for flagged vs unflagged. – Typical tools: CI pipeline checks and canary gates.

5) API contract validation for third-party integrators – Context: Partners depend on stable API contracts. – Problem: Schema drift or unexpected changes lead to partner breakage. – Why synthetics help: Scheduled contract checks catch changes early. – What to measure: schema mismatch rate, response structure validation. – Typical tools: API contract testers.

6) Internal admin tool availability behind VPN – Context: Internal dashboards are critical for ops. – Problem: Unexpected network ACL prevents access to internal tooling. – Why synthetics help: Private probes inside VPC validate internal reachability. – What to measure: availability and latency from within the VPC. – Typical tools: Private probes deployed to cluster.

7) Serverless cold start detection – Context: Frequent cold starts degrade user experience. – Problem: New deployment increases cold start times. – Why synthetics help: Probes measure invocation latency including cold starts. – What to measure: invocation latency distribution and cold start frequency. – Typical tools: function invocation probes and tracing.

8) Certificate expiry monitoring for embedded widgets – Context: Widgets served from separate domains need valid certs. – Problem: Expired certs break widget loads even when main site is fine. – Why synthetics help: Periodic TLS checks detect expiry and chain issues. – What to measure: cert expiry days, handshake errors. – Typical tools: TLS probe checks.

9) Data integrity verification for reporting pipelines – Context: ETL pipelines feed dashboards. – Problem: Upstream schema change breaks report queries. – Why synthetics help: Queries run by synthetic checks validate expected rows or counts. – What to measure: row counts, query latency, schema presence. – Typical tools: scheduled DB query probes.

10) Auto-remediation verification post-incident – Context: Automated script attempts restart on 503s. – Problem: Auto-remediation fails to restore service. – Why synthetics help: Verify that remediation restored external behavior. – What to measure: post-remediation availability and latency. – Typical tools: automation hooks and synthetic rechecks.

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Scenario Examples (Realistic, End-to-End)

Scenario #1 — Kubernetes Ingress Regression

Context: Production cluster updated ingress controller causing some 502s. Goal: Detect and pinpoint ingress-related failures rapidly using synthetics. Why Synthetic Checks matters here: External probes validate real user traffic paths through Ingress, catching routing regressions invisible to internal health probes. Architecture / workflow: Multi-region public probes -> CDN -> Ingress -> Service -> Pod -> backend. Step-by-step implementation:

• Deploy browser and API synthetics targeting Ingress hostnames.
• Run synthetics from multiple regions every minute.
• Capture traces and correlate with ingress controller logs and pod restarts.
• Create alert when availability drops below SLO for >5 minutes. What to measure: availability, P95 latency, 502 rate. Tools to use and why: Private probes in cluster for internal visibility and public probes for user perspective. Common pitfalls: Missing correlation IDs between probes and ingress logs. Validation: Run simulated ingress failure in staging and confirm synthetic alerts trigger runbook. Outcome: Faster detection and automated rollback to previous ingress release.

Scenario #2 — Serverless Checkout Cold Start (Serverless/PaaS)

Context: Checkout uses serverless functions; after deploy cold starts increased. Goal: Monitor cold start impact on checkout latency and revenue conversions. Why Synthetic Checks matters here: Synthetic invocations capture cold-start latencies in controlled tests, enabling rollbacks or optimization. Architecture / workflow: Public probes -> API Gateway -> Function -> Payment gateway. Step-by-step implementation:

• Create synthetic that invokes functionless endpoint repeatedly with varying intervals to simulate warm and cold starts.
• Record cold start times and success rates.
• Integrate with SLO to trigger alert on P99 increase. What to measure: cold start frequency, invocation latency, error rate. Tools to use and why: Function-invocation probes and tracing-integrated runners. Common pitfalls: Using synthetic patterns that keep function warm and miss cold starts. Validation: Force scaling to zero then run synthetics to ensure cold start observed. Outcome: Identification of a dependency causing initialization slowdown and subsequent optimization.

Scenario #3 — Postmortem Verification (Incident-response)

Context: Outage caused by expired API token for third-party billing provider. Goal: Verify remediation and prevent recurrence. Why Synthetic Checks matters here: Synthetic checks would have alerted token expiration earlier and validated remediation after rotation. Architecture / workflow: Scheduled API synthetic for billing provider -> fail on 401 -> alert -> secret rotation -> synthetic validate success. Step-by-step implementation:

• Add synthetic check that authenticates with billing provider nightly.
• Alert to on-call if 401 occurs.
• After postmortem, add secret rotation policy and synthetic post-rotation validation. What to measure: auth success rate and times to rotate. Tools to use and why: CI smoke tests and scheduled API probes. Common pitfalls: Storing production tokens in non-rotated test harness. Validation: Revoke a test token in staging to verify alerting and rotation hooks. Outcome: Reduced recurrence risk and faster detection.

Scenario #4 — Cost vs Performance Trade-off (Cost/Performance)

Context: Team considering increasing synthetic frequency to every 10s to capture short-lived issues. Goal: Balance detection fidelity against cost and backend load. Why Synthetic Checks matters here: High-frequency synthetics can detect transient failures but may add significant cost and load. Architecture / workflow: Public probes scheduled at configurable intervals -> metrics ingestion. Step-by-step implementation:

• Baseline current failure patterns with 1-min frequency.
• Run 10s frequency for a controlled experiment in non-peak window.
• Measure synthetic-induced load and cost delta.
• Decide target frequency per critical flow based on ROI. What to measure: detection improvement rate, cost per detection, backend CPU and error rate from probes. Tools to use and why: Synthetic platform with metered usage and backend telemetry. Common pitfalls: Probes interacting with rate-limited downstream systems. Validation: Monitor backend metrics and compare detection of short-lived incidents. Outcome: Adopt hybrid model: high-frequency checks for critical short-lived flows during business hours, lower-frequency otherwise.

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Common Mistakes, Anti-patterns, and Troubleshooting

(List 20 items)

1) Symptom: Repeated false alerts from a synthetic every 5 minutes -> Root cause: probe flakiness due to single-agent network instability -> Fix: add multi-region probes, implement retries, and mark single-agent failures as advisory.

2) Symptom: Synthetic tests failing after deployment only from certain regions -> Root cause: WAF rules blocking new probe IP ranges -> Fix: update WAF allowlists, use official probe IP list, and adopt private probes for internal flows.

3) Symptom: High synthetic-induced load on backend -> Root cause: too many heavy browser checks -> Fix: reduce frequency, replace with lightweight API checks for health, and sample browser checks.

4) Symptom: Alerts triggered for expected maintenance windows -> Root cause: no maintenance suppression configured -> Fix: add scheduled maintenance windows and alert suppression rules.

5) Symptom: SLO burning quickly without clear cause -> Root cause: brittle content assertions causing false failures -> Fix: relax assertions, use tolerant checks, and cross-validate with RUM.

6) Symptom: Synthetic checks fail due to expired credentials -> Root cause: manual secrets not rotated -> Fix: integrate secret manager and automated rotation with synthetic validation.

7) Symptom: No correlation between synthetic alerts and backend logs -> Root cause: missing trace context in synthetic requests -> Fix: inject trace headers and correlate trace IDs.

8) Symptom: On-call overwhelmed by synthetic alerts during deploys -> Root cause: synthetics not integrated with deployment gating -> Fix: suspend non-critical checks during deploys or use canary gates.

9) Symptom: Browser synthetics break after UI redesign -> Root cause: DOM selector brittleness -> Fix: use semantic selectors, text asserts, or accessibility IDs.

10) Symptom: Synthetics report failures but RUM shows no user impact -> Root cause: probes run from unusual network vantage points -> Fix: align probe geography and simulate realistic client paths.

11) Symptom: Missing internal endpoint coverage -> Root cause: dependence on public probes only -> Fix: deploy private probes or run probes inside VPC.

12) Symptom: High P99 noise -> Root cause: sparse sampling frequency -> Fix: increase frequency selectively for critical flows or use aggregation windows.

13) Symptom: Synthetic artifacts not retained -> Root cause: short artifact retention policy -> Fix: extend retention for failed runs to support RCA.

14) Symptom: Alert storms after probe agent upgrade -> Root cause: agent version incompatibility -> Fix: roll back agent, validate compatibility matrix, and stage agent upgrades.

15) Symptom: Synthetic tests bypass feature flags -> Root cause: test accounts not configured with same flag evaluation -> Fix: ensure tests use appropriate targeting contexts.

16) Symptom: Inconsistent TLS results -> Root cause: probe CA bundle mismatch -> Fix: standardize CA bundles or use managed TLS checks.

17) Symptom: Synthetic tests slow to detect outages -> Root cause: long intervals on critical flows -> Fix: increase frequency or add immediate post-deploy smoke checks.

18) Symptom: Alerts lack actionable data -> Root cause: missing logs and screenshots attached to alerts -> Fix: include run artifacts in alert payloads.

19) Symptom: Synthetic platform cost unexpectedly high -> Root cause: unbounded growth of checks and high-frequency browser runs -> Fix: audit checks, rationalize frequency, and tier checks by criticality.

20) Symptom: Synthetics show different results in staging vs production -> Root cause: environment differences and test data state -> Fix: create identical test data provisioning and segregate environments.

Observability pitfalls (at least five integrated above):

• Missing trace context, insufficient artifact retention, poorly correlated telemetry, over-aggregation hiding regional issues, and sparse sampling skewing P99.

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Best Practices & Operating Model

• Ownership and on-call
• Assign ownership of synthetics to service/product teams that own the customer flow.
• On-call rotations handle synthetic alerts for the owned services; platform team supports probe infra.
• Runbooks vs playbooks
• Runbooks: step-by-step fixes for known synthetic failure modes.
• Playbooks: higher-level escalation and cross-team coordination steps.
• Safe deployments (canary/rollback)
• Use synthetic canary gates that must pass before full rollout.
• Automate rollback when critical synthetic SLOs breach during canary.
• Toil reduction and automation
• Automate provisioning of probes and secret rotation.
• Auto-remediate common errors (cache flush, service restart) with synthetic verification post-remediation.
• Security basics
• Store all synthetic credentials in a secret manager with rotation policies.
• Limit probe agent permissions by least privilege.
• Encrypt artifacts in transit and at rest.

Weekly/monthly routines

• Weekly: review failing synthetics and flakiness trends.
• Monthly: audit checks for relevance, prune obsolete ones, and review costs.
• Quarterly: review SLOs and adjust based on business and telemetry.

What to review in postmortems related to Synthetic Checks

• Did synthetics detect the issue and when?
• Were the synthetic artifacts sufficient for RCA?
• Did synthetic alerting align with actual customer impact?
• Were runbooks effective and followed?
• Actions: refine checks, add probes, or update runbooks.

What to automate first

• Secret rotation and synthetic validation after rotation.
• Probe agent health monitoring and auto-restart.
• Post-deploy smoke checks automatically run and report.

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Tooling & Integration Map for Synthetic Checks (TABLE REQUIRED)

ID	Category	What it does	Key integrations	Notes
I1	Synthetic SaaS	Hosts global probe agents and runs checks	Observability backends alerting CI	Good for public endpoints
I2	Headless Browser	Executes UI flows and captures screenshots	Tracing and artifact storage	Resource intensive
I3	CI Runner	Runs pre/post deploy smoke synthetics	CI/CD and repo hooks	Best for deployment gating
I4	Private Agent	Runs checks inside VPC or cluster	Secret manager and logging	Required for internal endpoints
I5	Tracing System	Correlates synthetic runs with traces	Distributed tracing and APM	Enables fast RCA
I6	Secret Manager	Stores credentials for synthetics	Probe agents and CI	Rotate and audit access
I7	Alerting Platform	Routes alerts to pages and tickets	On-call systems and chatops	Deduplication features important
I8	DNS Probe	Validates DNS resolution and TTLs	CDN and DNS management	Geo coverage important
I9	Load Testing	Simulates high throughput for capacity	Synthetic scripts as user flows	Not a substitute for load tests
I10	Chaos Engine	Injects faults while synthetics run	Orchestration and fault injection	Validates resilience

Row Details (only if needed)

• None

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Frequently Asked Questions (FAQs)

How do I decide which flows to synthetic-check?

Prioritize flows that impact revenue, onboarding, and admin operations. Use product impact and historical incidents to rank.

How frequently should synthetics run?

Start with 1–5 minutes for critical flows, 5–15 minutes for medium, and hourly for low-impact endpoints; adjust based on cost and sensitivity.

How do I avoid synthetic-induced load on production?

Use low-frequency checks, lightweight API calls where possible, and private probes for heavy or internal flows; throttle and stagger runs.

How is Synthetic Checks different from RUM?

Synthetics simulate traffic externally on a schedule; RUM captures telemetry from real users continuously.

What’s the difference between health checks and synthetics?

Health checks are often lightweight and for orchestration; synthetics emulate real user interactions end-to-end.

What’s the difference between synthetics and canary tests?

Canary is a deployment strategy; synthetics are verification checks that can be used during canary phases.

How do I secure credentials used in synthetic checks?

Store them in a secret manager, grant minimal access to probe agents, and automate rotation with synthetic validation.

How do I measure if a synthetic check is flaky?

Track false positive rate, variance in latency, and correlated probe health issues; aim to keep synthetic noise low.

How do I integrate synthetics with CI/CD?

Run smoke tests in post-deploy stages and use synthetic success as a gate for promotion or rollback.

How do I set SLOs for synthetic checks?

Use historical synthetic and RUM data to set realistic SLOs; document decision rationale and error budget policies.

How do I test internal-only endpoints using synthetics?

Deploy private probe agents inside the VPC or Kubernetes cluster and report metrics to centralized observability.

How do I debug synthetic failures faster?

Capture screenshots, response bodies, traces, and include deployment metadata; automate artifact collection.

How do I handle maintenance windows?

Configure suppression windows in alerting platform and annotate SLO dashboards with maintenance periods.

How do I detect probe agent failures?

Monitor probe heartbeats, execution success rate, and agent logs; alert when agent health drops.

How do I instrument traces for synthetic checks?

Inject standard trace headers into requests and ensure services propagate traces for synthetic runs.

How do I keep synthetic checks cost-effective?

Tier checks by criticality, balance browser/API probes, limit geographic coverage to business-relevant regions.

What’s the best way to measure end-user impact using synthetics?

Combine synthetics with RUM and backend metrics to triangulate impact and prioritize alerts.

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Conclusion

Synthetic Checks are a practical, externally-observable method to continuously validate application availability, correctness, and performance. They are most powerful when integrated with SLOs, deployment gates, and incident automation, and when combined with RUM and tracing for full context.

Next 7 days plan:

• Day 1: Inventory critical flows and set initial SLIs.
• Day 2: Implement one API synthetic for the highest-priority flow.
• Day 3: Configure alerts and dashboard for that synthetic.
• Day 5: Add multi-region probes and secret manager integration.
• Day 7: Run a small game day to validate detection and runbooks.

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Appendix — Synthetic Checks Keyword Cluster (SEO)

• Primary keywords
• Synthetic checks
• Synthetic monitoring
• Synthetic tests
• Synthetic checks for APIs
• Synthetic website checks
• Synthetic monitoring SLOs
• Synthetic availability checks
• Synthetic latency monitoring
• Synthetic health checks

• Synthetic monitoring best practices

• Related terminology

• Real user monitoring
• SLI SLO error budget
• Canary verification
• Browser synthetic flows
• Headless browser testing
• Probe agents
• Private VPC probe
• Multi-region synthetics
• Synthetic runbook
• Synthetic artifact capture
• Synthetic test frequency
• Synthetic monitoring cost
• Synthetic-induced load
• Synthetic flakiness detection
• Synthetic alerting strategy
• Synthetic dashboards
• Synthetic post-deploy smoke tests
• Synthetic integration CI CD
• Tracing for synthetics
• Secret rotation for probes
• DNS synthetic checks
• CDN synthetic monitoring
• TLS certificate checks synthetic
• API contract synthetic tests
• Synthetic content validation
• Synthetic error budget management
• Synthetic noise reduction
• Synthetic multi-cloud probes
• Synthetic private agent deployment
• Synthetic canary gate
• Synthetic automation remediation
• Synthetic game day validation
• Synthetic chaos testing
• Synthetic monitoring tools
• Synthetic monitoring comparison
• Synthetic monitoring checklist
• Synthetic test design
• Synthetic data isolation
• Synthetic monitoring runbooks
• Synthetic monitoring architecture
• Synthetic monitoring for Kubernetes
• Synthetic monitoring for serverless
• Synthetic monitoring for PaaS
• Synthetic monitoring for SaaS
• Synthetic monitoring for e-commerce
• Synthetic monitoring for login flows
• Synthetic monitoring for payment systems
• Synthetic monitoring metrics
• Synthetic monitoring SLIs
• Synthetic monitoring SLO guidance
• Synthetic monitoring P99 latency
• Synthetic monitoring availability targets
• Synthetic monitoring verification
• Synthetic monitoring troubleshooting