What is Jaeger? Meaning, Examples, Use Cases & Complete Guide?

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Quick Definition

Jaeger is an open-source distributed tracing system that helps developers and operators monitor and troubleshoot transactions across microservices and cloud-native systems.

Analogy: Jaeger is like a flight tracker for requests — it follows each request’s journey across services, showing stops, delays, and handoffs.

Formal technical line: Jaeger collects, stores, and visualizes span-based trace data using instrumentation that emits spans and context propagation, supporting sampling, storage backends, and query APIs.

Other meanings (less common):

  • A given person’s name or company name — context dependent.
  • Historical reference to garments or garments industry — context dependent.

What is Jaeger?

What it is / what it is NOT

  • Jaeger is a distributed tracing platform designed to collect and analyze span-based telemetry across services.
  • Jaeger is NOT an application performance profiler for single-process deep CPU sampling.
  • Jaeger is NOT a full replacement for metrics or logs; it complements them.

Key properties and constraints

  • Open-source, typically deployed on Kubernetes or VMs.
  • Supports OpenTelemetry and legacy OpenTracing SDKs.
  • Sampling controls required to limit high-cardinality data and cost.
  • Storage backends vary: in-memory, Elasticsearch, Cassandra, or remote write to managed tracing stores.
  • Network and agent placement matter for low overhead and reliability.
  • Security: supports TLS and authentication options, but defaults vary by deployment.

Where it fits in modern cloud/SRE workflows

  • Incident triage: link traces to errors and latency spikes.
  • Root-cause analysis: follow spans across services to find bottlenecks.
  • Performance engineering: measure tail latency, hotspots, and service dependencies.
  • Release validation: compare traces between versions and canaries.
  • Supports SLIs that require request-level context.

Text-only diagram description

  • Client request enters API gateway -> gateway creates root span -> request flows to service A -> service A creates child spans and calls service B and DB -> Jaeger clients send spans to local agent -> agent batches to collector -> collector stores spans in backend -> query service serves UI and APIs -> engineers query traces to inspect spans and timings.

Jaeger in one sentence

Jaeger is a distributed tracing system that captures and visualizes request flows across microservices to enable troubleshooting, latency analysis, and root-cause identification.

Jaeger vs related terms (TABLE REQUIRED)

ID Term How it differs from Jaeger Common confusion
T1 OpenTelemetry A collection of SDKs and protocols for telemetry; Jaeger is a backend and UI People confuse instrumenting vs storing
T2 Zipkin Another tracing system with similar goals but different storage and UI options Users assume identical feature parity
T3 Prometheus Metric collection and alerting system; not span-based tracing Confusion over metrics vs traces
T4 APM commercial Full commercial APM adds profiling and session replay; Jaeger is trace-centric Expectation of bundled features
T5 Logging Textual event data per request; tracing shows timing and causality Assuming logs replace traces

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

  • None

Why does Jaeger matter?

Business impact

  • Revenue protection: Faster triage reduces user downtime and conversion loss.
  • Trust: Reliable service and faster debugging improves customer confidence.
  • Risk mitigation: Traces reveal fragile dependencies that could cause outages.

Engineering impact

  • Incident reduction: Quick root-cause discovery reduces mean time to resolution.
  • Faster deployments: Trace-based validation shortens feedback loops for releases.
  • Better code changes: Developers can see cross-service latency effects of changes.

SRE framing

  • SLIs/SLOs: Use tail latency and error fraction per request traced via Jaeger.
  • Error budgets: Tracing helps attribute errors to particular services or releases.
  • Toil reduction: Automate common trace queries; reduce manual hunt for causes.
  • On-call: Traces reduce cognitive load and improve signal during incidents.

What commonly breaks in production (realistic examples)

  1. A downstream cache misconfiguration causes 95th percentile latency to spike for a subset of routes.
  2. A service version introduces synchronous blocking calls that increase tail latency.
  3. Network MTU or proxy timeouts create partial request failures and retries.
  4. Gradual resource exhaustion causes intermittent high latency under load.
  5. Mistaken sampling settings produce overwhelming storage costs or missing traces.

Where is Jaeger used? (TABLE REQUIRED)

ID Layer/Area How Jaeger appears Typical telemetry Common tools
L1 Edge and API gateway Traces root requests and routing delays HTTP spans, latency, response codes Ingress proxies, API gateways
L2 Microservices / App Instrumented spans inside services RPC spans, DB calls, cache calls Framework SDKs, OpenTelemetry
L3 Data and storage Traces DB queries and batch jobs Query duration, retries, locks Databases, queues
L4 Platform / Kubernetes Traces service-to-service networking Pod-to-pod latency, sidecar spans CNI, service mesh
L5 Serverless / PaaS Integrated traces across managed functions Invocation time, cold start spans Function platforms, managed tracing
L6 CI/CD and releases Traces validate rollouts and canaries Latency per revision, error traces CI pipelines, release managers
L7 Incident response Trace links in incident pages and runbooks Error traces, root cause spans Alerting and incident tools
L8 Security and auditing Trace metadata for request lineage Traceable request path and auth spans SIEM and audit tools

Row Details (only if needed)

  • None

When should you use Jaeger?

When it’s necessary

  • You have distributed services where a single request touches multiple processes or hosts.
  • You need to diagnose latency or failure cascades that metrics alone cannot explain.
  • You require request-level context to tie logs and metrics together.

When it’s optional

  • Monolithic apps where process-local profiling suffices.
  • Small services with limited traffic and simple failure modes.
  • Early-stage prototypes where the overhead of instrumentation is unnecessary.

When NOT to use / overuse it

  • For high-cardinality debug traces without sampling; storage and cost will explode.
  • Using tracing as the only observability source; it should complement logs and metrics.
  • Instrumenting every function call in high-frequency loops without aggregation.

Decision checklist

  • If requests cross process boundaries AND you need causal timing -> deploy Jaeger.
  • If only per-host resource metrics needed -> prefer metrics and local profilers.
  • If you need full session replay or synthetic tracing -> consider additional APM tools.

Maturity ladder

  • Beginner: Instrument critical endpoints, basic sampling, Jaeger backend with local storage, simple dashboards.
  • Intermediate: Full service coverage, adaptive sampling, storage in Elasticsearch/Cassandra, SLOs, runbooks.
  • Advanced: Distributed sampling, integrated CI/CD checks, automated anomaly detection with AI-assisted triage, secure multi-tenant deployments.

Example decisions

  • Small team: Instrument 3 user-facing routes and backend DB calls, deploy Jaeger on single-node Kubernetes cluster with minimal sampling.
  • Large enterprise: Full OpenTelemetry instrumentation, dedicated tracing cluster, controlled sampling and cost allocation, RBAC and TLS enforced.

How does Jaeger work?

Components and workflow

  • Instrumentation libraries emit spans with trace context within app code.
  • Local agent (daemon) collects spans from SDKs using UDP or HTTP.
  • Collector receives batches from agents, performs processing, and writes to storage backend.
  • Storage (Elasticsearch, Cassandra, or other) persists spans for query and retention.
  • Query service retrieves traces from storage and serves UI and APIs.
  • UI allows developers to search traces, visualize spans, and inspect logs and tags.

Data flow and lifecycle

  1. Request starts: instrumentation creates root span.
  2. Spans propagate context across services via headers.
  3. Each service emits spans for internal operations.
  4. SDK sends spans to local agent.
  5. Agent forwards to collector.
  6. Collector processes and stores spans.
  7. Query service indexes and returns traces to UI.
  8. Retention policy deletes old spans as configured.

Edge cases and failure modes

  • Agent unavailable: SDK caches or drops spans per policy; sampling decisions still matter.
  • Collector overload: batching delays, increased memory usage, partial drops.
  • Storage saturation: write failures or long query times.
  • Context loss: missing propagation causes broken traces.
  • High-cardinality tags: storage bloat and query performance issues.

Short practical example (pseudocode)

  • Instrument a request handler to start a span, add tags, and finish span after database call.
  • Pseudocode example omitted actual language specifics to avoid table misuse.

Typical architecture patterns for Jaeger

  • Sidecar agent per pod pattern: Deploy agent as a sidecar or DaemonSet for low-latency local collection. Use when you need minimal SDK network overhead.
  • Centralized agent with proxy: Single agent cluster sidecar forwards to central collectors. Use when you prefer fewer agents and can tolerate network hops.
  • Service mesh integrated: Let the service mesh inject trace headers and generate spans at the proxy layer. Use when mesh is in place and you want automatic tracing without code changes.
  • Serverless integrated tracing: Use platform-provided tracing headers and exporters to send traces to Jaeger or compatible endpoints. Use for functions and event-driven architectures.
  • Hybrid cloud: On-prem services send traces to a centralized SaaS or managed collector with secure tunneling. Use when compliance requires local processing and centralized analysis.

Failure modes & mitigation (TABLE REQUIRED)

ID Failure mode Symptom Likely cause Mitigation Observability signal
F1 Missing traces Partial or absent traces Context propagation lost Enforce header propagation and test Low trace rate vs requests
F2 High latency in UI Slow trace queries Storage overloaded or misindexed Increase resources or reindex High query latency metric
F3 Agent memory spikes Agent OOMs or restarts Large batch sizes or backlog Tune batch sizes and CPU limits Agent restart count
F4 Excessive storage cost Unexpected high storage use High cardinality tags or no sampling Apply sampling and tag controls Storage ingestion rate
F5 Collector errors 5xx errors in collector logs Overload or bad data Autoscale collector and validate payloads Collector error rate
F6 Incomplete spans Traces cut off mid-request SDK shutdown or timeout Ensure flush on shutdown and longer timeouts Traces with missing end timestamps
F7 Security exposure Sensitive data in spans Unredacted tags or logs Implement tag scrubbing and RBAC Presence of sensitive tags

Row Details (only if needed)

  • None

Key Concepts, Keywords & Terminology for Jaeger

  • Trace — A collection of spans representing one request journey — Shows causality — Pitfall: incomplete context propagation.
  • Span — A timed operation in a trace — Unit of work — Pitfall: too granular leads to noise.
  • Span context — Metadata that links spans — Enables trace correlation — Pitfall: missing when headers dropped.
  • Root span — The first span for a trace — Represents top-level request — Pitfall: misidentified when instrumentation inconsistent.
  • Child span — A descendant span — Represents sub-operation — Pitfall: dropped on async boundaries.
  • TraceID — Unique identifier for a trace — Used to fetch a trace — Pitfall: collisions in custom ID schemes.
  • SpanID — Unique identifier for a span — For linking spans — Pitfall: mis-assigned IDs break trees.
  • ParentID — Reference to parent span — Maintains hierarchy — Pitfall: parentless spans fragment trace.
  • Sampling — Strategy for selecting traces to store — Controls cost — Pitfall: sampling bias hides issues.
  • Head-based sampling — Decide at request start — Simple and common — Pitfall: misses downstream errors.
  • Tail-based sampling — Decide after request completion — Captures rare errors — Pitfall: more complex to implement.
  • Probabilistic sampling — Random sampling by rate — Easy to implement — Pitfall: misses rare slow traces.
  • Adaptive sampling — Adjust rate by traffic or error — Reduces noise — Pitfall: adds complexity.
  • Agent — Local process that buffers and forwards spans — Lowers SDK overhead — Pitfall: single point if misconfigured.
  • Collector — Central receiver that processes spans — Writes to storage — Pitfall: scaling omission causes drops.
  • Query service — API that retrieves traces — Feeds UI — Pitfall: slow when indexes are poor.
  • UI — Visualization for traces — For triage and analysis — Pitfall: inexperienced use leads to misinterpretation.
  • Storage backend — DB for spans (ES, Cassandra) — Persists traces — Pitfall: storage choice affects query speed.
  • Indexing — Metadata indexing of spans for search — Enables fast queries — Pitfall: heavy indexes increase cost.
  • Retention — How long traces are kept — Balances compliance and cost — Pitfall: too short hides regression history.
  • TTL — Time to live for stored spans — Automates deletion — Pitfall: too aggressive retention leads to missing data.
  • Tag — Key-value metadata on spans — Adds context — Pitfall: high cardinality tags bloat storage.
  • Log fields — Event logs inside spans — Useful for errors — Pitfall: verbose logs duplicate log storage.
  • Context propagation — Passing trace headers across services — Essential for full traces — Pitfall: some transports drop headers.
  • Instrumentation — Code to generate spans — Required for detailed traces — Pitfall: inconsistent instrumentation across services.
  • OpenTelemetry — Unified observability SDK and protocol — Interoperable with Jaeger — Pitfall: version mismatches.
  • OpenTracing — Legacy tracing API — Supported but mostly superseded — Pitfall: mixing APIs without mapping.
  • Exporter — Component that sends spans to backends — Configured per environment — Pitfall: misconfigured endpoints.
  • Sampling priority — Weight for trace retention — Helps keep important traces — Pitfall: mis-set priorities cause loss.
  • Batching — Group spans to reduce overhead — Improves throughput — Pitfall: large batches increase latency flush.
  • Flush on shutdown — Ensure spans sent before exit — Prevents data loss — Pitfall: short shutdown hooks drop spans.
  • Sidecar — Agent colocated with service container — Reduces network hops — Pitfall: increased pod resources.
  • DaemonSet agent — One agent per node — Simpler at scale — Pitfall: node-local failures affect many pods.
  • Service mesh tracing — Mesh proxies generate spans — Provides network-level traces — Pitfall: duplicate spans if app also instruments.
  • Correlation ID — Business identifier attached to spans — Connects traces to business context — Pitfall: exposing PII.
  • Tag scrubbing — Removing sensitive tags before storage — Protects data — Pitfall: lost useful debugging info if over-scrubbed.
  • Trace sampling rate — Percent of traces stored — Balances fidelity and cost — Pitfall: inconsistent rates per service.
  • Trace enrichment — Adding metadata on ingest — Helps search and grouping — Pitfall: enrichment overhead adds latency.
  • Multi-tenant tracing — Isolating traces by tenant — Needed for SaaS — Pitfall: noisy tenants affecting others.
  • Trace exporter pipeline — Sequence of processing stages — Enables processors and filters — Pitfall: misordering processors breaks expectations.
  • Correlated logs — Logs linked to spans via TraceID — Speeds debugging — Pitfall: log volume explosion.
  • Tail latency — High percentile latency for requests — Key SLI for user experience — Pitfall: missing trace coverage on tails.
  • Root-cause analysis — Using traces to find origin of error — Critical for incident work — Pitfall: partial traces complicate analysis.
  • Anomaly detection — Automated detection of unusual trace patterns — Augments SRE work — Pitfall: false positives without tuning.

How to Measure Jaeger (Metrics, SLIs, SLOs) (TABLE REQUIRED)

ID Metric/SLI What it tells you How to measure Starting target Gotchas
M1 Trace ingestion rate Volume of traces stored Count spans per second from collector Baseline traffic rate See details below: M1
M2 Trace success rate Fraction of requests with stored traces Traces stored divided by requests sampled 95% of sampled requests Sampling affects numerator
M3 Trace latency distribution Request latency percentiles Measure span durations per route 95th <= app SLO Incomplete traces skew tails
M4 Tail trace capture Capture of high-latency traces Tail-based sampling capture rate Capture most 99th percentile Implementation complexity
M5 Collector error rate Collector processing failures 5xx or error logs per minute Near zero after scaling Hidden errors in logs
M6 Agent drop rate Spans dropped at agent Dropped spans divided by received As close to 0 as possible Network loss can hide drops
M7 Storage write latency Time to persist spans Write time metrics from storage Under acceptable threshold Backend dependent
M8 Query latency Time to respond to trace queries API response time percentiles 95th under 1s for common queries Complex queries are slower
M9 Sampling ratio Percentage of requests sampled Samples out / total requests Start 1-5% for high traffic Low rates miss rare failures
M10 High-cardinality tag rate Frequency of new tag values Count distinct tag values Keep minimal distinct tags Can explode storage size

Row Details (only if needed)

  • M1: Measure using collector exported metrics or ingestion counters; correlate with request traffic metrics.

Best tools to measure Jaeger

Tool — Prometheus

  • What it measures for Jaeger: Collector and agent metrics, ingestion and error rates.
  • Best-fit environment: Kubernetes, cloud-native stacks.
  • Setup outline:
  • Scrape Jaeger collector and agent metrics endpoints.
  • Expose exporter metrics if using OpenTelemetry.
  • Create recording rules for high-level metrics.
  • Strengths:
  • Native for metrics and alerting.
  • Wide ecosystem and dashboard integrations.
  • Limitations:
  • Not for tracing query latency at deep granularity.
  • Long-term storage requires remote write.

Tool — Grafana

  • What it measures for Jaeger: Visualize Prometheus metrics and traces alongside each other.
  • Best-fit environment: Organizations using metrics dashboards.
  • Setup outline:
  • Add data sources for Prometheus and Jaeger.
  • Build dashboards combining SLO metrics and traces.
  • Configure access controls.
  • Strengths:
  • Flexible dashboards.
  • Integrated alerting and panels for traces.
  • Limitations:
  • Requires effort to design meaningful dashboards.
  • UI performance with many panels.

Tool — OpenTelemetry Collector

  • What it measures for Jaeger: Aggregates and forwards span metrics and traces.
  • Best-fit environment: Multi-cloud and heterogeneous instrumentations.
  • Setup outline:
  • Deploy as collector agent or gateway.
  • Configure receivers, processors, exporters to Jaeger and metrics stores.
  • Tune batching and sampling processors.
  • Strengths:
  • Central processing pipeline and transformations.
  • Supports exporting to multiple backends.
  • Limitations:
  • Configuration complexity increases with processors.
  • Resource usage under heavy load.

Tool — Elasticsearch monitoring

  • What it measures for Jaeger: Storage performance and indexing metrics for trace data.
  • Best-fit environment: When Jaeger uses Elasticsearch for storage.
  • Setup outline:
  • Enable Elasticsearch monitoring metrics.
  • Track indexing rate and query latency.
  • Tune shard allocations and index lifecycle policies.
  • Strengths:
  • Deep storage instrumentation.
  • Helpful for diagnosing search slowdowns.
  • Limitations:
  • Requires Elasticsearch expertise to tune.
  • Costs scale with index volume.

Tool — AI-assisted anomaly detector

  • What it measures for Jaeger: Detects unusual trace patterns and latency shifts.
  • Best-fit environment: Large, high-volume systems needing automation.
  • Setup outline:
  • Feed trace-derived metrics into detector.
  • Configure baselining windows and alerting thresholds.
  • Integrate with incident tooling for automated triage.
  • Strengths:
  • Faster anomaly detection and prioritization.
  • Can reduce manual on-call work.
  • Limitations:
  • Needs tuning to reduce false positives.
  • May require labeled incidents for supervised models.

Recommended dashboards & alerts for Jaeger

Executive dashboard

  • Panels:
  • Overall request volume and trend: shows traffic shifts.
  • 95th and 99th percentile latency per customer-facing service: business impact.
  • Error rate and SLO burn rate: executive SLI view.
  • Cost trend for traces stored: business visibility.
  • Why: High-level view for decision makers and resource allocation.

On-call dashboard

  • Panels:
  • Recent error traces for top services: immediate triage.
  • Live tail traces for impacted routes: root-cause work.
  • Collector and agent health metrics: operational status.
  • Recent deploys and associated trace anomalies: correlate changes.
  • Why: Focused for rapid investigation and remediation.

Debug dashboard

  • Panels:
  • Trace query latency distribution and top slow traces.
  • High-cardinality tag counts and top tag values.
  • Span duration heatmap per dependency call.
  • Sample trace list with links to UI.
  • Why: Detailed debugging and performance tuning.

Alerting guidance

  • What should page vs ticket:
  • Page: Collector down, agent OOMs, major SLO burn spikes, sampling completely failing.
  • Ticket: Slow query trends that are not urgent, storage nearing quota but not immediate.
  • Burn-rate guidance:
  • Alert when burn rate exceeds 2x expected for a short window, escalate if sustained.
  • Noise reduction tactics:
  • Deduplicate alerts by grouping by root cause tag.
  • Use suppression windows for planned deployments.
  • Implement rate-limited alerting for transient flaps.

Implementation Guide (Step-by-step)

1) Prerequisites – Inventory services to instrument. – Decide storage backend and retention policy. – Identify security and compliance requirements. – Ensure CI/CD can deploy instrumentation and collectors.

2) Instrumentation plan – Start with entry points and critical paths. – Use OpenTelemetry SDKs for forward compatibility. – Define standard tag names, and avoid PII tags. – Decide sampling strategy per service.

3) Data collection – Deploy agent as sidecar or DaemonSet based on architecture. – Configure OpenTelemetry Collector for batching, sampling, and export. – Secure collectors with TLS and authentication. – Monitor agent and collector metrics and logs.

4) SLO design – Define SLIs on tail latency and error fraction per critical route. – Choose SLO windows and error budget. – Map traces to SLI incidents for postmortem attribution.

5) Dashboards – Build executive, on-call, and debug dashboards. – Correlate traces with logs and metrics panels. – Use trace links to go directly into Jaeger UI.

6) Alerts & routing – Create alerts for trace ingestion drops, collector errors, and SLO burn. – Route pages to SREs and tickets to service owners as appropriate. – Add contextual links (trace example, recent deploy) in alerts.

7) Runbooks & automation – Create runbooks for common diagnostics: missing traces, high latency traces, storage full. – Automate common queries and triage steps with scripts or bots. – Provide pre-cooked trace queries for on-call.

8) Validation (load/chaos/game days) – Run load tests to validate sampling and storage scaling. – Include tracing checks in chaos experiments to see system behavior. – Run game days focused on tracing loss and recovery.

9) Continuous improvement – Review trace coverage monthly and expand instrumentation. – Tweak sampling based on traffic and SLO needs. – Automate tagging and enrichment where useful.

Checklists

Pre-production checklist

  • Instrumented critical endpoints present.
  • Agent and collector deployed in staging.
  • Sampling configured and validated.
  • Basic dashboards created and accessible.
  • Security and data scrubbing rules applied.

Production readiness checklist

  • Storage sizing verified for expected retention.
  • Collector autoscaling and HPA rules in place.
  • Alerting configured for ingestion and SLOs.
  • Access controls and RBAC set on UI and APIs.
  • Runbooks created and linked in incident tool.

Incident checklist specific to Jaeger

  • Verify collector and agent health metrics.
  • Check sampling rate and drop metrics.
  • Retrieve recent traces for affected routes.
  • Correlate trace timestamps with deploys and metrics.
  • Escalate to platform team if collector or storage issues.

Example Kubernetes steps

  • Deploy Jaeger operator or Helm chart into cluster.
  • Instrument services with OpenTelemetry SDK and set OTLP endpoint to cluster collector service.
  • Deploy OpenTelemetry Collector as DaemonSet or Gateway with appropriate processors.
  • Validate by sending sample traces and checking UI.

Example managed cloud service steps

  • Configure cloud function to attach trace headers and export to managed tracing endpoint.
  • Use cloud-managed collector or exporter in pipeline settings.
  • Validate by invoking functions and viewing traces in Jaeger or managed UI.

What to verify and what “good” looks like

  • Good: Traces are present for 95% of sampled requests, collector error rate near zero, query latency low for recent traces.

Use Cases of Jaeger

1) Cross-service latency spike – Context: Shopping cart checkout slows intermittently. – Problem: Hard to attribute to frontend, payment gateway, or DB. – Why Jaeger helps: Shows timing breakouts and which downstream call adds latency. – What to measure: 95th latency per call, DB query durations. – Typical tools: Jaeger, DB tracing, dashboards.

2) Regression after deploy – Context: New microservice release increases error rate. – Problem: Which service version caused the issue? – Why Jaeger helps: Traces carry deployment tags to link errors to versions. – What to measure: Error traces count per deployment tag. – Typical tools: Jaeger, CI/CD metadata enrichments.

3) Cache misconfiguration – Context: Certain user segments see cache misses causing DB load. – Problem: Identify which routes and headers cause misses. – Why Jaeger helps: Span tags show cache hit/miss for each request. – What to measure: Cache miss rate and downstream DB calls. – Typical tools: Jaeger, cache metrics.

4) Thundering herd on background job – Context: Batch job causes spike across services. – Problem: Hard to see batch root cause across services. – Why Jaeger helps: Trace of batch orchestration shows parallel call patterns. – What to measure: Request fan-out and downstream latency. – Typical tools: Jaeger, job scheduler metrics.

5) Serverless cold start troubleshooting – Context: Function cold starts cause high tail latency. – Problem: Need to measure cold-start duration vs warm. – Why Jaeger helps: Span tags indicate cold start vs warm invocation. – What to measure: Cold start latency percentiles. – Typical tools: Jaeger, serverless platform traces.

6) Payment failure chain – Context: Some payments fail after a sequence of dependent calls. – Problem: Pinpoint the failing dependency in a chain. – Why Jaeger helps: Shows exact failing span and error tag. – What to measure: Error fraction per dependency call. – Typical tools: Jaeger, payment gateway logs.

7) Security audit of request lineage – Context: Need to trace actions performed by a user across systems. – Problem: Reconstruct request path for compliance. – Why Jaeger helps: Traces record request flow and auth spans. – What to measure: Trace coverage and presence of auth tags. – Typical tools: Jaeger, audit logs.

8) Capacity planning – Context: Estimate storage and processing needs for traces. – Problem: Predict costs and scale. – Why Jaeger helps: Trace ingestion metrics inform storage sizing. – What to measure: Span ingestion rate and retention sizes. – Typical tools: Jaeger, storage monitoring.

9) Third-party dependency monitoring – Context: External API calls occasionally time out. – Problem: Which external call and when? – Why Jaeger helps: Spans record external call durations and outcomes. – What to measure: External call latency and error rate. – Typical tools: Jaeger, external dependency health checks.

10) A/B test performance validation – Context: Compare performance of two variants. – Problem: Need to isolate per-variant latency differences. – Why Jaeger helps: Traces enriched with A/B variant tags. – What to measure: Latency distribution per variant. – Typical tools: Jaeger, experiment platform.

Scenario Examples (Realistic, End-to-End)

Scenario #1 — Kubernetes: Slow API after rollout

Context: After a blue/green rollout, users report slow API responses at peak times.
Goal: Identify which pod version or dependency causes increased tail latency.
Why Jaeger matters here: Traces map requests to service versions, showing which spans add latency.
Architecture / workflow: Kubernetes services behind a service mesh; collectors run as DaemonSet; apps instrumented with OpenTelemetry.
Step-by-step implementation:

  • Ensure apps emit deployment tag with commit SHA.
  • Enable sampling at 5% and tail-based capture for top 1% latency.
  • Deploy Jaeger collector with HPA and storage to Elasticsearch.
  • Trigger a canary and run load tests.
  • Query traces by deployment tag and 99th percentile latency. What to measure: 95th and 99th latency per deployment tag; error traces per version.
    Tools to use and why: Jaeger UI, Grafana dashboards, OpenTelemetry.
    Common pitfalls: Missing deployment tag, sampling too low.
    Validation: High-latency traces show new version spans consuming time; rollback improves metrics.
    Outcome: Identify a blocking DB call introduced in new version and revert.

Scenario #2 — Serverless/Managed-PaaS: Cold start spike

Context: Periodic users see high latency due to cold starts in serverless functions.
Goal: Quantify cold start impact and validate mitigation.
Why Jaeger matters here: Traces have cold start tag and durations for init vs execution.
Architecture / workflow: Managed PaaS functions export spans to a collector endpoint.
Step-by-step implementation:

  • Instrument functions to add cold-start tag.
  • Configure exporter to send spans to collector.
  • Collect traces over peak times and separate cold vs warm.
  • Implement warmers or provisioned concurrency and re-measure. What to measure: Cold start percent of invocations and added latency.
    Tools to use and why: Jaeger, platform monitoring, OpenTelemetry.
    Common pitfalls: Platform missing propagation for async calls.
    Validation: Reduction in cold start traces and improved 95th latency.
    Outcome: Provisioned concurrency reduces cold-start impact within cost constraint.

Scenario #3 — Incident response / postmortem

Context: Intermittent errors for a payment flow lead to customer complaints.
Goal: Determine root cause and prevent recurrence.
Why Jaeger matters here: Traces link failed payment attempts to specific downstream errors.
Architecture / workflow: Payment microservice calls payment gateway and logging includes trace IDs.
Step-by-step implementation:

  • Collect error traces and sort by frequency and time.
  • Identify common failing spans and correlate with deploy timeline.
  • Reproduce sequence in staging and run focused tests.
  • Implement retry logic and add alerting on error signature. What to measure: Error count per dependency and traces per error type.
    Tools to use and why: Jaeger, logs correlated by TraceID, incident tracker.
    Common pitfalls: Low sampling rate hides errors.
    Validation: No recurrence in post-fix monitoring; SLO restored.
    Outcome: Root cause found in upstream dependency version mismatch; fix applied.

Scenario #4 — Cost/performance trade-off

Context: Trace storage costs are growing with traffic and enriched tags.
Goal: Reduce cost while keeping high-fidelity for critical requests.
Why Jaeger matters here: You can adapt sampling and tag policies to control volumes.
Architecture / workflow: High-traffic API with full-span tagging and long retention.
Step-by-step implementation:

  • Measure current ingestion rates and cost per GB.
  • Introduce adaptive sampling: retain more traces for error or high-latency, fewer for normal traffic.
  • Remove or hash high-cardinality tags.
  • Set tiered retention for critical traces only. What to measure: Span ingestion rate and retention size before and after.
    Tools to use and why: Jaeger, billing metrics, OpenTelemetry processors.
    Common pitfalls: Over-sampling for debug traces and losing business context.
    Validation: Maintain SLO coverage while cost drops.
    Outcome: 40% reduction in storage costs with preserved error trace capture.

Common Mistakes, Anti-patterns, and Troubleshooting

  1. Symptom: No traces for certain requests -> Root cause: Missing context propagation -> Fix: Ensure headers are forwarded in HTTP clients and middleware.
  2. Symptom: High storage growth -> Root cause: High-cardinality tags -> Fix: Remove or hash dynamic tags and limit tag cardinality.
  3. Symptom: Collector memory exhaustion -> Root cause: Large unbounded batches -> Fix: Reduce batch size and enable backpressure or autoscaling.
  4. Symptom: Too many irrelevant traces -> Root cause: Over-instrumentation with every function call -> Fix: Instrument only meaningful operations and aggregate loops.
  5. Symptom: Incomplete traces -> Root cause: Process shutdown without flush -> Fix: Implement SDK flush on shutdown and extend shutdown timeout.
  6. Symptom: Slow query responses -> Root cause: Poor indexing strategy in storage -> Fix: Reindex and tune index lifecycle policies.
  7. Symptom: Traces missing error details -> Root cause: Not recording exception logs into spans -> Fix: Add log events for exceptions in spans.
  8. Symptom: Duplicate spans from mesh and app -> Root cause: Both mesh and app generate spans -> Fix: Deduplicate or disable one source.
  9. Symptom: Alert noise -> Root cause: Alerts over-sensitive to small variance -> Fix: Adjust thresholds, group alerts, and use suppression.
  10. Symptom: Sensitive data stored in span — Root cause: Tags contain PII -> Fix: Add tag scrubbing processors before storage.
  11. Symptom: Sampling bias hides issues -> Root cause: Static probabilistic sampling -> Fix: Implement tail-based or adaptive sampling for rare events.
  12. Symptom: Broken traces after async queue -> Root cause: Trace context not propagated across messages -> Fix: Inject and extract context into message metadata.
  13. Symptom: Inconsistent span naming -> Root cause: No naming conventions -> Fix: Standardize span names and document guidelines.
  14. Symptom: Missing deploy metadata -> Root cause: Deploy tags not attached -> Fix: Add deployment tags from CI/CD pipeline.
  15. Symptom: Agent unreachable in certain nodes -> Root cause: Network policy blocking UDP/HTTP -> Fix: Adjust network policies and ensure service discovery.
  16. Symptom: High CPU in agents -> Root cause: Synchronous serialization on hot path -> Fix: Use asynchronous batching and tune worker threads.
  17. Symptom: Traces differ between staging and prod -> Root cause: Different sampling or instrumentation levels -> Fix: Align configs and test in staging.
  18. Symptom: Retention exceeded unexpectedly -> Root cause: Old indices not deleted -> Fix: Implement index lifecycle and monitor TTL metrics.
  19. Symptom: Lost trace IDs in logs -> Root cause: Logging framework not including TraceID -> Fix: Configure log processors to include TraceID context.
  20. Symptom: Cross-tenant trace bleed -> Root cause: No tenant isolation -> Fix: Implement tenant ID tags and RBAC with isolation.
  21. Symptom: Long-term query failures -> Root cause: Storage compaction or corruption -> Fix: Rebuild indices or restore from backups.
  22. Symptom: SLO measurement mismatch -> Root cause: Different datasets for SLI and trace counts -> Fix: Align measurement sources and sampling assumptions.
  23. Symptom: Slow node recovery -> Root cause: Collector backlog large -> Fix: Scale collectors and drain backlogs with throttling.
  24. Symptom: Tracing SDK version mismatch -> Root cause: Breaking API changes across libraries -> Fix: Standardize SDK versions and test upgrades.
  25. Symptom: Poor adoption by dev teams -> Root cause: Instrumentation friction -> Fix: Provide templates, CI checks, and code examples.

Observability pitfalls included: missing context propagation, over-instrumentation, high cardinality tags, duplicate spans, and sampling bias.

Best Practices & Operating Model

Ownership and on-call

  • Platform team owns collector and storage infrastructure.
  • Service teams own instrumentation and SLIs for their services.
  • On-call rotations should include a tracing responder on platform shifts during major incidents.

Runbooks vs playbooks

  • Runbooks: Step-by-step operational steps for common failures (collector down, ingestion drop).
  • Playbooks: Strategic responses for complex incidents including communication, mitigation, and rollback plans.

Safe deployments

  • Use canary and progressive rollouts with tracing checks before full deployment.
  • Rollback when trace-based performance regressions cross SLO thresholds.

Toil reduction and automation

  • Automate sampling adjustments and index lifecycle management.
  • Auto-generate common trace queries and dashboards from service metadata.

Security basics

  • Scrub PII before storage.
  • Secure transport with TLS between SDKs, agents, and collectors.
  • Enforce RBAC on user access to traces in multi-tenant environments.

Weekly/monthly routines

  • Weekly: Review high-error traces and top slow traces.
  • Monthly: Audit tag cardinality and adjust instrumentation.
  • Quarterly: Cost review for storage and retention.

What to review in postmortems related to Jaeger

  • Trace coverage for incident requests.
  • Sampling settings during incident.
  • Any tracing system outages or limitations that hindered triage.

What to automate first

  • Automatic tagging of service and deployment metadata in traces.
  • Sampling rules for error and latency retention.
  • Alerts for collector and agent health.

Tooling & Integration Map for Jaeger (TABLE REQUIRED)

ID Category What it does Key integrations Notes
I1 OpenTelemetry SDK and collector pipeline Jaeger exporter, metrics stores Core instrumentation standard
I2 Prometheus Metrics collection and alerting Jaeger collector metrics Use for operational metrics
I3 Grafana Dashboards and visualization Prometheus and Jaeger Link traces from panels
I4 Elasticsearch Storage and indexing for traces Jaeger storage backend Tunable but costly at scale
I5 Cassandra Storage for high throughput Jaeger storage backend Good write performance for large volumes
I6 Service mesh Automatic trace header propagation Envoy, Istio spans Can generate network-level spans
I7 CI/CD Enrich traces with deploy metadata Git/CI variables to tags Automate deployment tags
I8 Incident tooling PagerDuty, ops tools Include trace links in incidents Speeds triage
I9 Log pipeline Correlate logs with traces Logging solutions with TraceID Enables root-cause correlation
I10 AI anomaly tools Detect trace anomalies Input trace metrics and histograms Useful for automation

Row Details (only if needed)

  • None

Frequently Asked Questions (FAQs)

H3: What is Jaeger used for?

Jaeger is used to trace requests across distributed systems to diagnose latency, errors, and dependency relationships.

H3: How do I instrument my application for Jaeger?

Use OpenTelemetry or Jaeger SDKs in your application to start and finish spans and propagate context via headers.

H3: How does Jaeger differ from OpenTelemetry?

OpenTelemetry is an instrumentation and collection standard; Jaeger is a backend and UI that consumes traces from that standard.

H3: How much does tracing cost?

Varies / depends on retention, sampling rates, storage backend, and traffic volume.

H3: How do I avoid high storage costs with Jaeger?

Use sampling strategies, limit tag cardinality, and tier retention for critical traces.

H3: What’s the difference between traces and logs?

Traces show causal timing across services; logs are event records. Both complement each other for troubleshooting.

H3: How do I ensure trace context across async systems?

Inject and extract trace context into message metadata and queue attributes.

H3: How to measure Jaeger reliability?

Track collector and agent error rates, ingestion metrics, and query latency SLIs.

H3: How to set sampling rates?

Start low for high-traffic services (1–5%), use tail-based or error-prioritized sampling for rare events.

H3: How to secure traces with sensitive data?

Implement tag scrubbing processors, encrypt transport, and enforce RBAC on trace access.

H3: How to correlate logs and traces?

Add TraceID to logs via logging context and configure your logging pipeline to index by TraceID.

H3: What’s the difference between Jaeger and a commercial APM?

Commercial APMs often include profiling, user session tracking, and deeper integrations; Jaeger focuses on traces.

H3: How to scale Jaeger in Kubernetes?

Scale collectors with HPA, use durable storage, and deploy agents as DaemonSets or sidecars.

H3: How do I do tail-based sampling with Jaeger?

Implement a tail-based sampling processor in the collection pipeline to make decisions after seeing spans.

H3: How to validate trace coverage?

Use sampling metrics and cross-check with request metrics to ensure a high ratio of sampled requests for critical routes.

H3: How do I handle multi-tenant tracing?

Add tenant tags, isolate storage, and enforce RBAC to prevent data leakage.

H3: What’s the easiest way to get started with Jaeger?

Instrument a single service, deploy a collector and agent in a test environment, and view traces for a few endpoints.

Conclusion

Jaeger provides critical visibility into request flows across distributed systems, enabling faster incident response, performance tuning, and release validation. It complements metrics and logs and should be integrated thoughtfully with sampling, storage, and security considerations.

Next 7 days plan

  • Day 1: Inventory critical services and identify top 5 endpoints for instrumentation.
  • Day 2: Deploy a staging Jaeger collector and agent with basic dashboards.
  • Day 3: Instrument one service with OpenTelemetry and validate trace flow.
  • Day 4: Configure sampling and retention policy aligned with cost targets.
  • Day 5: Build on-call and debug dashboards and create initial runbooks.
  • Day 6: Run a load test and validate collector scaling and sampling behavior.
  • Day 7: Review metrics, adjust sampling, and schedule a game day for tracing failure modes.

Appendix — Jaeger Keyword Cluster (SEO)

Primary keywords

  • Jaeger
  • Jaeger tracing
  • distributed tracing Jaeger
  • Jaeger tutorial
  • Jaeger guide
  • Jaeger vs Zipkin
  • Jaeger OpenTelemetry
  • Jaeger architecture
  • Jaeger deployment
  • Jaeger Kubernetes

Related terminology

  • distributed tracing
  • tracing system
  • span and trace
  • trace sampling
  • tail-based sampling
  • head-based sampling
  • trace context propagation
  • OpenTelemetry instrumentation
  • Jaeger collector
  • Jaeger agent
  • Jaeger query service
  • Jaeger UI
  • trace storage
  • Elasticsearch tracing
  • Cassandra tracing
  • Jaeger retention policy
  • trace ingestion
  • trace indexing
  • span tags
  • high cardinality tags
  • tag scrubbing
  • trace correlation ID
  • TraceID
  • SpanID
  • parent span
  • root span
  • child span
  • instrumentation libraries
  • automatic tracing
  • manual instrumentation
  • sidecar agent
  • DaemonSet agent
  • service mesh tracing
  • Istio tracing
  • Envoy tracing
  • OpenTracing legacy
  • Jaeger exporter
  • OpenTelemetry Collector
  • OTLP protocol
  • trace batching
  • flush on shutdown
  • collector autoscaling
  • sampling rules
  • adaptive sampling
  • probabilistic sampling
  • trace enrichment
  • trace-based SLOs
  • trace SLIs
  • 95th percentile latency
  • 99th percentile latency
  • tail latency traces
  • trace anomaly detection
  • Jaeger metrics
  • Prometheus Jaeger
  • Grafana Jaeger
  • trace dashboards
  • on-call traces
  • incident triage traces
  • root-cause analysis traces
  • trace-based cost control
  • trace storage optimization
  • multi-tenant tracing
  • RBAC traces
  • trace security
  • PII scrubbing in traces
  • trace retention tuning
  • index lifecycle policies
  • trace query latency
  • trace debugging
  • correlated logs
  • TraceID in logs
  • trace enrichment from CI
  • deployment tags in traces
  • canary tracing
  • tracing for serverless
  • function tracing cold start
  • tracing for microservices
  • tracing for monolith migration
  • tracing for payment flows
  • tracing for API gateways
  • tracing for cache misses
  • tracing for background jobs
  • tracing for batch processing
  • tracing for A/B testing
  • trace-driven development
  • observability pipeline
  • trace processors
  • trace exporters
  • trace filtering
  • trace deduplication
  • tracing best practices
  • tracing anti-patterns
  • tracing runbooks
  • tracing playbooks
  • tracing game days
  • tracing chaos engineering
  • trace health checks
  • trace ingestion monitoring
  • collector error metrics
  • agent drop metrics
  • storage write latency
  • query service metrics
  • Jaeger performance tuning
  • Jaeger troubleshooting
  • Jaeger common mistakes
  • Jaeger implementation guide
  • Jaeger use cases
  • Jaeger scenarios
  • Jaeger incident response
  • Jaeger cost reduction
  • Jaeger sampling strategies
  • Jaeger deployment checklist
  • Jaeger production readiness
  • Jaeger pre-production checklist
  • Jaeger validation tests
  • Jaeger load tests
  • Jaeger chaos tests
  • trace-based alerting
  • trace alerting best practices
  • trace alert dedupe
  • trace alert grouping
  • trace alert suppression
  • trace burn rate alerts
  • trace SLA tracking
  • trace SLO design
  • trace error budget
  • trace dashboards templates
  • Jaeger integration map
  • Jaeger toolchain
  • Jaeger ecosystem
  • Jaeger community
  • Jaeger operator
  • Jaeger Helm chart
  • Jaeger managed service
  • Jaeger SaaS vs self-hosted
  • Jaeger compliance considerations
  • Jaeger telemetry pipeline
  • Jaeger logging integration
  • Jaeger log correlation
  • Jaeger cost planning
  • Jaeger storage sizing
  • Jaeger index tuning
  • Jaeger retention policy examples
  • Jaeger security best practices
  • Jaeger TLS configuration
  • Jaeger RBAC configuration
  • Jaeger multi-cluster tracing
  • Jaeger hybrid cloud tracing
  • Jaeger remote write
  • Jaeger exporters list
  • Jaeger performance benchmarks
  • Jaeger troubleshooting steps
  • Jaeger observability pitfalls
  • Jaeger instrumentation examples
  • Jaeger pseudocode examples
  • Jaeger command-line examples
  • Jaeger UI walkthrough
  • Jaeger trace search
  • Jaeger trace filters
  • Jaeger trace grouping
  • Jaeger trace tags
  • Jaeger tag naming conventions
  • Jaeger span naming conventions
  • Jaeger trace enrichment CI
  • Jaeger trace provenance
  • Jaeger tenant isolation
  • Jaeger tenant tagging
  • Jaeger automated sampling
  • Jaeger retention tiers
  • Jaeger storage backends comparison
  • Jaeger elasticsearch tuning
  • Jaeger cassandra tuning
  • Jaeger query service scaling
  • Jaeger collector scaling
  • Jaeger agent configuration
  • Jaeger OpenTelemetry mapping
  • Jaeger SDK configuration
  • Jaeger language SDKs
  • Jaeger Java SDK
  • Jaeger Python SDK
  • Jaeger Go SDK
  • Jaeger Node SDK
  • Jaeger .NET SDK
  • Jaeger instrumentation checklist
  • Jaeger readme for developers
  • Jaeger runbook examples
  • Jaeger incident checklist
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