Architecture · Application Performance
Read Telemetry Pipeline and Storage first.
Design Intent
APM should not be a burden for ops teams — minimal architecture, full features, works out of the box.
Three Core Components
| Component | Responsibility | Why this design |
|---|---|---|
| Ingest | Receive traces and metrics from applications | Data entry only — lightweight and efficient |
| Storage | Unified storage for all observability data | One engine — no multiple databases |
| Platform | Query, visualization, alerting, AI | All capabilities in one service |
vs traditional APM: Often Elasticsearch + Kafka + many microservices + complex config. DataBuff runs everything with 3 containers.
Full Data Pipeline
Key Design Choices
| Design | Value |
|---|---|
| OTel standard ingestion | Not tied to a specific agent; ecosystem-friendly |
| Automatic trace assembly | Distributed fragments become full chains — no app changes |
| Metrics derived from traces | One data source, two uses — lower collection cost |
| Minute-level pre-aggregation | Fast queries, efficient storage, efficient alert evaluation |
Value of Minimal Architecture
| Traditional APM | DataBuff | |
|---|---|---|
| Components | 10+ | 3 |
| Minimum resources | 16GB+ RAM | 8GB workable |
| Time to value | Days | Minutes |
| Ops staff | Dedicated team | Dev self-service |
Minimal does not mean bare-bones — troubleshooting, tracing, service metrics, topology, and alert evaluation are all covered in Phase 1.
Relationship with AI
Another benefit of minimal APM architecture: AI's data path is minimal too.
No multi-source stitching or cross-system queries — AI experts reach metrics, traces, topology, and alerts through one storage entry. That is the engineering foundation of AI-native OpenTelemetry APM.