Integrating legacy systems into modern monitoring pipelines can be challenging, especially when those systems lack native support for metrics exposition. Prometheus, a powerful open-source monitoring and alerting toolkit, relies heavily on exporters to gather metrics from external systems. While many standard exporters exist, legacy infrastructure often requires custom-built exporters tailored to specific protocols, data formats, or APIs.

In this post, we will explore the technical process behind building custom Prometheus exporters that bridge the gap between legacy systems and modern observability. This guide targets intermediate to advanced users who want to deepen their monitoring stack capabilities and ensure comprehensive visibility across their entire infrastructure.

Why Build Custom Exporters for Legacy Systems

Legacy systems often rely on outdated protocols, proprietary data sources, or lack any native metrics endpoints. These systems might expose data via logs, SNMP, database queries, or bespoke APIs. Standard Prometheus exporters usually do not support these unique scenarios out-of-the-box.

Building custom exporters allows you to:

  • Extract meaningful metrics from unconventional data sources
  • Normalize and expose metrics in Prometheus’ /metrics HTTP format
  • Enable unified monitoring across heterogeneous technology stacks
  • Implement domain-specific metric transformations and filtering

This approach results in a more reliable and scalable monitoring solution tailored precisely to your environment.

Understanding Prometheus Exporter Architecture

Before diving into development, it’s crucial to understand the typical architecture of a Prometheus exporter:

  • Scrape Endpoint: An HTTP server exposes metrics in a plain text format at /metrics.
  • Data Collection Logic: The exporter gathers raw data from the legacy system, processes it, and converts it into Prometheus metrics.
  • Metric Types: Metrics are exposed as counters, gauges, histograms, or summaries depending on the nature of the data.
  • Concurrency and Performance: Exporters should efficiently handle multiple scrape requests and minimize latency.

Most custom exporters are implemented in Go due to its excellent concurrency model and official Prometheus client libraries, but other languages like Python or JavaScript can also be used depending on your stack and team expertise.

Step-by-Step Guide to Building a Custom Exporter

1. Define Metrics Requirements

Start by identifying the key metrics your legacy system can expose. These could include resource usage, error counts, throughput, or custom business KPIs. Define metric names, types, labels, and expected update intervals.

2. Choose Your Programming Language and Client Library

Select a language with a mature Prometheus client library. Go is recommended as it offers:

  • Official Prometheus client support (prometheus/client_golang)
  • Native HTTP server capabilities with net/http
  • Strong concurrency primitives for efficient scraping

Alternatively, Python’s prometheus_client or Node.js libraries are available for teams with different language preferences.

3. Implement Data Collection Logic

This is the core of your exporter. Connect to your legacy system through the appropriate interface:

  • Database Queries: Use SQL to extract counters or state variables.
  • Log Parsing: Tail and parse logs, transforming key events into metrics.
  • SNMP Polling: Use SNMP libraries to query device metrics.
  • Custom APIs or Protocols: Handle bespoke protocols with custom parsers.

Ensure this logic is efficient and handles failures gracefully. Cache data if necessary to reduce load on legacy systems.

4. Expose Metrics via HTTP Endpoint

Use the client library to define metrics and register them. Implement an HTTP server exposing the /metrics endpoint, returning metrics in Prometheus text exposition format.

Example in Go:

http.Handle("/metrics", promhttp.Handler())
log.Fatal(http.ListenAndServe(":9100", nil))
5. Add Labels and Metadata

Use labels to differentiate dimensions such as host, service, or instance. This enriches your metrics and enables more granular querying in Prometheus.

6. Handle Concurrency and Efficient Scraping

Ensure your exporter can serve concurrent scrape requests without blocking. Use mutexes or atomic operations to avoid race conditions when updating metrics.

7. Test and Validate Metrics

Before deploying, validate the metrics output with tools like promtool and verify scraping behavior in Prometheus. Monitor for correctness and performance under load.

Best Practices for Production-Ready Exporters

  • Graceful Error Handling: Exporters should never crash on data collection errors; instead, log and continue.
  • Metric Naming Conventions: Follow Prometheus guidelines to ensure consistency and avoid collisions.
  • Resource Efficiency: Limit CPU and memory usage to avoid impacting legacy systems.
  • Security: Restrict access to the /metrics endpoint if sensitive data is exposed.
  • Documentation: Provide clear README and usage instructions for your exporter.

Integrating Custom Exporters into Prometheus Monitoring Stack

Once your exporter is ready and deployed, add it to your Prometheus scrape configuration:

scrape_configs:
  - job_name: 'legacy-system'
    static_configs:
      - targets: ['legacy-exporter-host:9100']

Use Prometheus’ powerful querying language (PromQL) to create alerts and dashboards based on the newly exposed metrics. This integration brings legacy systems visibility into your modern monitoring and alerting workflows.

Conclusion

Building custom metrics exporters for Prometheus is a powerful technique to unlock observability for legacy systems that don’t natively support modern monitoring protocols. By carefully designing your exporter, leveraging appropriate client libraries, and following best practices, you can create scalable, reliable bridges between outdated technology and contemporary monitoring infrastructures.

This not only enhances operational awareness but also future-proofs your monitoring strategy as your infrastructure evolves. Start building your custom exporters today and transform your legacy systems into fully observable components of your DevOps ecosystem.