Table of Contents
Logs, Metrics, Traces & Events on a Single Lens
Why Unified Telemetry Matters
Gartner predicts that by 2026 more than 70 % of enterprises that apply observability will shorten decision-making latency—yet many teams still burn hours chasing scattered logs and silent metrics.
Elastic’s original goal was to make search feel like turning on a light. Today, the same spirit lets us unify every log, metric, trace, and event inside Elastic Stack, so correlation happens instantly and recovery starts sooner. Below is the exact playbook I use to move clients from reactive firefighting to predictive resilience.
The Business Benefits
- Single source of truth — one timeline, no swivel‑chair correlation.
- Cost leverage — hot / warm / cold tiers plus searchable snapshots slash TCO without losing depth.
- Faster root‑cause analysis — teams report dramatic MTTR cuts once traces auto‑link to the exact log line.
Quick Tip:
Multiply MTTR hours × revenue‑loss/hour; that figure usually dwarfs any license spend.
The Four Telemetry Signals Explained
Logs
Machine‑generated event records stored as JSON via Elastic Agent. (Variant keyword: Elastic Stack log unification.)
Metrics
Numerical time‑series from 400 + integrations—Kubernetes, AWS, JVM, Redis—complete with ready dashboards.
Traces
End‑to‑end transaction paths captured by Elastic APM or OpenTelemetry. Tail‑sampling vs head‑sampling: tail keeps finished traces that match rules, preserving anomalies while reducing storage.
Events & Alerts
Rule‑ or ML‑driven notifications that feed Slack, PagerDuty, or any webhook for real‑time action.
Quick Tip:
Keep alert documents in the same index pattern so post‑mortems share a common language.
Scalable Architecture Patterns
| Pattern | When to use | Key moves |
| Single Cluster, Multi‑Streams | < 5 TB/day, low latency | Separate logs-*, metrics-*, traces-*; ILM hot‑warm‑cold |
| Cross‑Cluster Search | Multi‑region estates | Ingest local, search global with CCS |
| Edge Ingest, Cloud Analyze | IIoT / retail branches | Elastic Agent → Fleet → Elastic Cloud |
Quick Tip:
Target ≈20 GB per shard and ≈1 GB JVM heap per hot‑tier shard to keep memory happy.
Ten‑Step Implementation Blueprint
- Map signals — list top‑five services and existing emitters.
- Install Elastic Agent with the Unified Observability policy; host metrics flow automatically.
- Enable APM Server (self‑managed) or Elastic Cloud APM.
- Instrument code — use native agents or OpenTelemetry SDK; set service.name.
- Configure data streams — logs-{service}-{env}, metrics-{service}, traces-{service}.
- Set ILM — 7 days hot, 21 days warm, 90 days cold + searchable snapshots.
- Activate ML jobs — latency anomaly, error‑rate spike.
- Create correlation rules with Kibana Detect Correlations.
- Dashboard — start from Elastic APM service view; pin KPIs to the SLO widget.
- Automate RCA — add a Watcher that posts correlated trace‑ID logs into the incident channel.
Five Pro Hacks
- Edge‑cache index templates to preload shards and dodge bootstrap spikes.
- Time‑series mode / roll‑ups save up to 70 % on metric storage without losing trendability.
- APM tail‑sampling — keep only “interesting” traces hot.
- Runtime field joins between kube‑pod UID and infra logs—no re‑index required.
- Universal Profiling (8.17+) adds CPU flamegraphs per trace for pinpoint tuning.
Common Pitfalls
- Cargo‑cult sharding — oversharding kills heap. Use ≈20 GB per shard and 1 GB JVM per hot shard.
- Siloed retention — logs 30 days and traces 3 days? Forget correlation. Harmonise ILM across streams.
- High cardinality — fields like session_id bloat storage; move them to span.attributes or limit length.
Real‑World Success Story
A payment switch drowning in 50K events/sec unified three clusters into one hot‑warm topology and enabled APM tail‑sampling. Indexing soared from 10K/sec to 50K/sec while search latency fell 80%.
Try‑This‑Tomorrow Checklist
- Install Elastic Agent on a non‑prod host.
- Enable System integration + APM for a demo app.
- Create a correlation rule in Kibana.
- Run stress-ng for five minutes; watch ML anomalies fire.
- Document findings in a runbook.
Frequently Asked Questions
What is unified telemetry in Elastic Stack?
Ingesting logs, metrics, traces, and alerts into one Elastic deployment so you query a single data model for end‑to‑end visibility.
How do I migrate Beats to Elastic Agent?
Deploy Elastic Agent in stand‑alone mode on the same host, disable the Beat, then switch policies in Fleet for full lifecycle management.
Does tail‑sampling lose data?
No—100% of spans for selected traces are retained, cutting storage while preserving detail where it matters.
Conclusion — One Lens, Infinite Clarity
Bringing logs, metrics, traces, and events under the Elastic Stack isn’t mere consolidation—it’s compounding insight. When every signal converges, anomalies surface faster, RCA accelerates, and engineers shift from firefighting to feature shipping.
Ready to slash MTTR and boost customer trust? Book a Telemetry Unification Diagnostic with Ashnik’s Elastic experts and sleep better knowing every packet, process, and span already has a story to tell.
