←

Engineering Blog

3 min read

Full-Link Monitoring Tools: OTLP Ingest and Unified APM in Practice

When evaluating full-link monitoring tools, teams often ask: Is Jaeger enough? Do we need SkyWalking? This guide maps trace-only to unified APM and demos OTLP ingest with a four-layer drill-down on DataBuff Demo.

APMOpenTelemetryTraceMonitoring

Full-Link Monitoring Tools: OTLP Ingest and Unified APM in Practice

Full-Link Monitoring · OTLP · OpenTelemetry · Distributed Tracing · Jaeger · SkyWalking · DataBuff

When evaluating full-link monitoring tools, teams often ask: Is Jaeger enough? Do we need SkyWalking? How do we assemble the LGTM stack? This guide maps the spectrum from trace-only backends to unified APM, using DataBuff Demo checkout traces — aggregate stats, span waterfalls, and service flow — to show the four-layer drill-down loop after OTLP 4317 ingest: topology → aggregates → single request → contribution.

1The monitoring tool spectrum

CategoryExamplesScope
Trace-onlyJaeger, ZipkinDistributed call chains; metrics/alerts usually external
Unified APMSkyWalking, DataBuffTrace + metrics + topology + alerting
Cloud-native assemblyTempo + Prometheus + Loki + GrafanaFlexible; ops and dashboards are DIY

If the requirement is full-link monitoring tools with explicit OpenTelemetry, prioritize an OTLP-native backend over systems that only support legacy proprietary agents.

◆ ◆ ◆

2OpenTelemetry ingest essentials

App (OTel SDK) → OTLP gRPC 4317 or HTTP 4318 → Ingest/Collector → Storage → Web topology & Trace UI

A dual-export Collector is a common migration pattern: one receiver can export to existing Jaeger and a new APM backend in parallel, then compare trace fields and topology consistency [1].

export OTEL_EXPORTER_OTLP_ENDPOINT=http://<ingest-host>:4318 export OTEL_SERVICE_NAME=demo-service

◆ ◆ ◆

3Tool comparison (2026)

ToolOTLPTopologySpan waterfallNotes
JaegerNativeYesYesLightweight trace-first choice
SkyWalkingSupportedStrongYesMature community APM
LGTMTempo nativeGrafanaYesK8s-standard assembly
DataBuffPrimary pathAutoMulti-protocol spansUnified + service-flow contribution

◆ ◆ ◆

4Demo acceptance: topology to a single trace

Figure 1 · Architecture-level full-link view

Figure 1 · Architecture-level full-link view

Global topology auto-draws the service-a → service-b call chain plus MySQL, Redis, Kafka, ES, and remote HTTP edges. Node colors reflect health/alert status.

Figure 2 · Trace aggregate statistics

Figure 2 · Trace aggregate statistics

Tracing page: Trace volume bars (~30 per 15 minutes), P50–P99 latency lines (P95 stable ~240 ms). Click a bar to drill into the trace list.

Figure 3 · Single-request span waterfall

Figure 3 · Single-request span waterfall

TraceID 4b2a0a4c…, GET /demo/checkout, 240 ms total — Redis, remote HTTP fraud check, service-b Dubbo/HTTP, MySQL SELECT, ES search, Kafka publish spans expanded.

Figure 4 · Service flow and response contribution

Figure 4 · Service flow and response contribution

Entry service-a (240 ms / 2.9k calls) with downstream contribution: service-b 58%, Elasticsearch and MySQL ~8% each.

◆ ◆ ◆

5Install and POC

curl -fsSL https://databuff.ai/databuff/ai-apm-install.sh | bash
  • Web console default port 27403
  • After ingest, validate §4 four views: topology → trace stats → waterfall → service flow

◆ ◆ ◆

6Quick selection guide

  • Trace only, minimal architecture → Jaeger
  • Java microservices footprint + mature community → SkyWalking
  • Team standardized on Grafana → LGTM
  • OTLP-native topology/waterfall/contribution in one stack → DataBuff

◆ ◆ ◆

7References

  1. https://opentelemetry.io/docs/collector/configuration/
  2. https://opentelemetry.io/docs/specs/otlp/
  3. https://www.jaegertracing.io/docs/
  4. https://github.com/databufflabs/databuff
  5. https://databuff.ai/databuff/ai-apm-install.sh

◆ ◆ ◆

Learn more: github.com/databufflabs/databuff