What is the Application Metrics pattern and what is the difference between push and pull metric collection?

The Application Metrics pattern instruments each service to emit numeric measurements — counters, gauges, histograms, and summaries — that describe its runtime behaviour. These metrics feed dashboards, alerting rules, and capacity-planning models that plain logs cannot efficiently support (logs are for discrete events; metrics are for continuous numerical trends).

Common metric types:

• Counter — monotonically increasing (e.g., total HTTP requests served, total errors). Never decremented except on process restart.
• Gauge — a value that goes up and down (e.g., current active connections, JVM heap used, queue depth).
• Histogram — distributes observations into configurable buckets (e.g., request latency distribution, enabling P50/P95/P99 calculations).

Pull model (Prometheus): The Prometheus server periodically scrapes a /metrics HTTP endpoint on each service instance. The service maintains in-memory metric state; Prometheus pulls it on its own schedule.

// Micrometer / Prometheus metric registration (Java)
Counter httpRequests = Counter.builder("http_requests_total")
    .tag("method", "GET").tag("status", "200")
    .register(Metrics.globalRegistry);
httpRequests.increment();

// Prometheus scrapes GET /actuator/prometheus every 15s

Push model (StatsD, Prometheus Pushgateway): The service actively sends metric updates to a collection agent or gateway. Used when services are short-lived (batch jobs, serverless functions) that do not run long enough to be scraped.