Service Decomposition Anti-Patterns#

Splitting a monolith into microservices is a common architectural goal. But bad decomposition creates systems that are harder to operate than the monolith they replaced. These anti-patterns are disturbingly common and often unrecognized until the team is deep in operational pain.

The Distributed Monolith#

The distributed monolith looks like microservices from the outside – separate repositories, separate deployments, separate CI pipelines – but behaves like a monolith at runtime. Services cannot be deployed independently because they are tightly coupled.

Trace, Span, and Context#

A trace represents a single request flowing through a distributed system. It is identified by a 128-bit trace ID. A span represents one unit of work within that trace – an HTTP handler, a database query, a message publish. Each span has a name, start time, duration, status, attributes (key-value pairs), and events (timestamped annotations). Spans form a tree: every span except the root has a parent span ID.

What OpenTelemetry Is#

OpenTelemetry (OTel) is a vendor-neutral framework for generating, collecting, and exporting telemetry data: traces, metrics, and logs. It provides APIs, SDKs, and the Collector – a standalone binary that receives, processes, and exports telemetry. OTel replaces the fragmented landscape of Jaeger client libraries, Zipkin instrumentation, Prometheus client libraries, and proprietary agents with a single standard.

The three signal types:

• Traces: Record the path of a request through distributed services as a tree of spans. Each span has a name, duration, attributes, and parent reference.
• Metrics: Numeric measurements (counters, gauges, histograms) emitted by applications and infrastructure. OTel metrics can be exported to Prometheus.
• Logs: Structured log records correlated with trace context. OTel log support bridges existing logging libraries with trace correlation.

The OTel Collector Pipeline#

The Collector is the central hub. It has three pipeline stages: