如何获取Heroku Dyno指标API？寻求自动扩缩容方案

Great question—this is a super common pain point when building custom auto-scaling for Heroku dynos! Let’s break down your options, including the API you’re looking for and some more efficient alternatives.

1. Use the Official Heroku Platform API for Dyno Metrics #

First off, yes—there is a direct API to pull dyno metrics, so you don’t have to rely on parsing logs. Heroku’s Platform API includes a dedicated endpoint for fetching real-time dyno performance data:

• Endpoint example: GET /apps/{your-app-name-or-id}/dynos/{dyno-id-or-name}/metrics
• Authentication: Pass your Heroku API token via the Authorization: Bearer <your-token> header
• Returned data: A JSON object with fields like memory_usage (in bytes), memory_quota, cpu (percentage), and load_avg_1m (1-minute load average). Data refreshes every 10-20 seconds, which is sufficient for most auto-scaling workflows.
• Note: To get aggregated metrics for your entire app, you’ll need to fetch metrics for all dynos and calculate averages/peaks yourself.

2. Leverage Third-Party Monitoring Tools (Easiest Auto-Scaling Path) #

If you want to skip building and maintaining your own metrics pipeline, third-party tools are the most efficient choice. Most integrate seamlessly with Heroku and include built-in auto-scaling triggers:

• Datadog/New Relic: These tools automatically collect dyno metrics (plus response time data if you use their APM plugins). You can set up rules to scale dynos based on memory usage, CPU load, or response time thresholds—no custom code required.
• Heroku Prometheus Add-on: For open-source fans, Heroku’s official Prometheus add-on collects metrics and pairs with Grafana for visualization. You can build alerting rules in Grafana that trigger your scaling script.

3. Optimize Log-Runtime-Metrics Parsing (If You Stick to This Method) #

If you prefer to use log-runtime-metrics for budget or compatibility reasons, you can make the process smoother than scraping raw syslog:

• Use Heroku Log Drains to forward metrics logs to a dedicated processing service or data store. Run this command to set it up:

  heroku drains:add https://your-log-processing-service.com/logs --app your-app
  

• Parse the structured log lines (format example: source=web.1 measure=memory_pct val=45.2) in your service, then store the metrics in a time-series database like InfluxDB.
• Your auto-scaling script can then query this database for aggregated metrics instead of dealing with unprocessed logs.

Quick Final Tip #

Whichever approach you choose, test your scaling logic against edge cases—like sudden traffic spikes or memory leaks that cause sustained high usage. To actually adjust dyno counts, use the Heroku CLI command heroku ps:scale web=X in your script, or call the Platform API’s formation scaling endpoint: POST /apps/{your-app-name-or-id}/formation/{formation-name}.

内容的提问来源于stack exchange，提问作者Martin Taleski