Prometheus has become the de facto standard for monitoring cloud-native applications and infrastructure. However, many databases and applications expose metrics in formats that Prometheus does not natively support. This is where custom Prometheus exporters come in — acting as bridges to convert proprietary or specialized metrics into the Prometheus exposition format.

For intermediate and advanced users, building custom exporters allows granular control over what and how metrics are scraped, enabling more precise monitoring and alerting. In this post, we’ll deep dive into the architecture, best practices, and practical steps to develop exporters tailored for specific databases and applications.

Why Build Custom Exporters

While many exporters exist for popular systems, unique or legacy databases and bespoke applications often lack ready-made exporters. Here are some reasons to go custom:

  • Proprietary protocols or metrics not supported by standard exporters
  • Complex queries or aggregations that require transformation before exposure
  • Enhancing performance by filtering unnecessary metrics before scraping
  • Integrating business-specific logic or contextual metadata into metrics
  • Supporting security or compliance needs by controlling metric exposure

Understanding Prometheus Exporter Architecture

A Prometheus exporter essentially acts as an HTTP server exposing a /metrics endpoint that Prometheus scrapes periodically. The exporter’s job is to:

  1. Collect raw metrics from the target system (database or app) using its native APIs, logs, or command-line tools.
  2. Transform and format these metrics into the Prometheus text exposition format.
  3. Serve these metrics over HTTP in a lightweight, performant manner.

Most custom exporters are implemented in Go, leveraging the official Prometheus client libraries that handle metric registration, exposition, and concurrency.

Choosing the Right Metrics for Your Exporter

Not all metrics are equally valuable. Carefully select metrics that provide actionable insights and avoid noise. Consider:

  • System-level metrics such as CPU, memory, and disk usage from the database host
  • Database-specific metrics like query latency, cache hits, connection pool status
  • Application-level metrics such as request counts, error rates, or transaction durations
  • Custom business KPIs that can be extracted or derived from logs or APIs

Use labels effectively to slice and dice metrics by dimensions such as query type, user, or region.

Step-by-Step Guide to Building a Custom Exporter

1. Set Up Your Development Environment

Install Go and the Prometheus client libraries:

go get github.com/prometheus/client_golang/prometheus
go get github.com/prometheus/client_golang/prometheus/promhttp
2. Define Your Metrics

Use prometheus.NewGaugeVec, prometheus.NewCounterVec, or prometheus.NewHistogramVec to create metric collectors. For example:

var queryLatency = prometheus.NewHistogramVec(
  prometheus.HistogramOpts{
    Name: "db_query_latency_seconds",
    Help: "Latency distributions for database queries",
  },
  []string{"query_type"},
)
3. Implement Data Collection Logic

Write functions to query your database or parse application outputs. This might involve:

  • Executing SQL queries to fetch metrics tables
  • Accessing REST APIs or management endpoints
  • Parsing log files or status commands

Ensure this logic is efficient and handles errors gracefully.

4. Update Metrics and Expose HTTP Handler

Periodically update your metric collectors with new data. Then expose the /metrics endpoint using Prometheus’s HTTP handler:

http.Handle("/metrics", promhttp.Handler())
log.Fatal(http.ListenAndServe(":9100", nil))
5. Packaging and Deployment

Containerize your exporter for easy deployment with Docker or Kubernetes. Ensure proper configuration for scraping intervals and resource limits.

Best Practices and Optimization Tips

  • Batch data retrieval to minimize overhead and latency
  • Use context timeouts to avoid hanging queries during metric collection
  • Leverage labels sparingly to prevent high cardinality issues that degrade Prometheus performance
  • Implement caching layers if metrics are expensive to compute and don’t need to be updated every scrape
  • Add health and readiness endpoints to integrate with Kubernetes probes
  • Follow security best practices, such as restricting access to the exporter endpoint

Real-World Example: Exporter for a NoSQL Database

Suppose you want to monitor a NoSQL database that exposes metrics only via its REST API. Your exporter would:

  • Periodically query the REST API for raw JSON stats
  • Parse the JSON to extract relevant metrics like read/write throughput and error counts
  • Convert these stats into Prometheus counters and gauges with meaningful labels
  • Expose the /metrics endpoint for Prometheus scraping

This approach allows you to monitor otherwise unsupported systems seamlessly.

Conclusion

Building custom Prometheus exporters empowers you to extend observability to any database or application — no matter how unique or proprietary. With a solid understanding of Prometheus’s data model, client libraries, and good software practices, you can create efficient exporters tailored to your environment’s needs.

By carefully selecting metrics, optimizing data collection, and following best practices, your custom exporters will provide invaluable insights that drive performance tuning, troubleshooting, and capacity planning. Start building your tailored exporters today to unlock the full potential of your monitoring stack.