MELT stands for Metrics, Events, Logs, and Traces — the four signal types that modern software systems emit to support monitoring, debugging, and performance analysis. Each signal captures a distinct dimension of system behavior: metrics quantify what is happening numerically over time, events record discrete occurrences with semantic meaning, logs produce a granular append-only record of activity, and traces follow a request end-to-end across distributed services. Together these four data types form the foundational vocabulary of observability engineering.

Organizations care about MELT because no single signal type tells the whole story. A spike in an error-rate metric tells you something broke, but not where or why. Logs narrow the scope, traces pinpoint which service and which code path, and events answer whether a recent deployment or configuration change triggered the problem. As architectures shift toward microservices and cloud-native infrastructure, the ability to correlate all four signals quickly separates teams that resolve incidents in minutes from those that spend hours guessing.

Metrics are numeric measurements aggregated over time: CPU utilization, request throughput, latency percentiles. They are cheap to store, fast to query, and ideal for alerting, but they lose the detail needed for root-cause analysis. Events are timestamped records of something semantically significant: a deployment completed, a payment processed, a circuit breaker opened. In the OpenTelemetry data model, events are modeled as a specialized subtype of log records, distinguished by an event.name attribute, which means they travel through the same pipeline while remaining queryable as a distinct, lower-volume, higher-signal class of data.

Logs are the workhorse of the four. They are append-only, timestamped, and often semi-structured JSON or plain text. Every service, container, and network device writes logs, which makes them the highest-volume signal in most architectures and therefore the primary cost driver in observability budgets. Traces represent the end-to-end journey of a single request across service boundaries, composed of spans that carry timing data, context propagation headers, and metadata. Without traces, diagnosing latency in a system with dozens of microservices requires guesswork; with them, you can isolate a slow database call three hops deep in a call graph.

A practical on-call workflow illustrates why all four signals matter together. An alert fires on a metrics anomaly: p99 latency crosses a threshold. The engineer queries recent logs filtered to the affected service and time window, finds repeated timeout errors, grabs a trace ID from one of those log lines, and opens the trace to see exactly which downstream service call is responsible. That sequence — metric to log to trace — is only possible when all three signals share consistent timestamps, service identifiers, and ideally a shared trace context. Events add a fourth dimension: a deployment event timestamped two minutes before the latency spike closes the investigation with a clear cause.

OpenTelemetry is the open-source, vendor-neutral framework that standardizes how teams collect, process, and export MELT signals. It provides language SDKs for instrumenting application code, the OpenTelemetry Collector for pipeline processing, and OTLP (OpenTelemetry Protocol) as the wire format for transmitting data between collectors and backends. OpenTelemetry does not prescribe a storage backend, which means teams can route different signal types to purpose-built stores: time-series databases for metrics, columnar stores for logs, and trace-optimized backends such as Jaeger for distributed traces, all while sharing a single collection layer. As the second-largest CNCF project by contributor count after Kubernetes, OpenTelemetry has become the practical standard for MELT instrumentation.

Logs are the primary budget problem in MELT pipelines. A single busy service can emit millions of log lines per minute, and multiplying that across hundreds of services, containers, and infrastructure components produces data volumes that overwhelm observability platforms priced per ingested gigabyte. Traces compound the problem because every request across every service hop generates span data. Teams that default to storing everything in one tier often discover the cost is unsustainable before they have mature query patterns to justify it.

Practical mitigations include tiered storage strategies that keep only recent or indexed data in fast query tiers, head-based or tail-based sampling for traces, pre-aggregation for metrics to avoid storing raw time-series at full resolution indefinitely, and routing raw log data to a cost-efficient data lake while maintaining indexed summaries for interactive queries. Columnar storage formats handle MELT analytics queries well because these workloads typically scan specific fields across large time ranges rather than fetching full rows.

Getting all four signals into separate stores is straightforward. Getting them to correlate reliably is harder. Effective correlation requires consistent timestamp formats, shared service name conventions, and trace context propagation so that a trace ID present in a log line actually resolves to a trace in the tracing backend. Teams that instrument services independently, without enforcing a shared schema or adopting a framework like OpenTelemetry, often end up with four isolated data silos rather than an integrated observability pipeline.

Most teams adopt MELT signals incrementally rather than all at once. Metrics and logs are almost always already present: nearly every runtime, framework, and infrastructure component emits them by default. A practical starting sequence is to first ensure logs are structured (JSON rather than unformatted text), then instrument services with OpenTelemetry SDKs to emit traces, and finally define explicit events for the highest-value semantic occurrences in your system, such as deployments, errors, and key business actions.

For the collection layer, the OpenTelemetry Collector supports receivers, processors, and exporters for all four signal types and provides a single configuration surface for routing data to multiple backends. Prometheus handles the metrics side for most cloud-native environments. Once the collection pipeline is stable, focus on storage architecture: define retention tiers by signal type, instrument cardinality limits on metrics labels, and establish trace sampling policies before raw trace volume overwhelms your backend. For teams managing high-volume log analytics or CDN telemetry workloads specifically, the architecture decisions around the “L” in MELT deserve dedicated attention.