关于通过Stackdriver自定义指标监控多GCP服务及实现方式的技术咨询

Hey there! Let's break down your questions one by one since you're looking to leverage Cloud Monitoring (formerly Stackdriver) custom metrics across various GCP services, plus monitor the tool itself and explore alternative approaches.

Each service has tailored ways to implement custom metrics:

• GCE: You’ve got two solid options. Install the Cloud Monitoring Agent on your VMs to collect system-level custom metrics (like custom app latency or disk usage beyond default thresholds), or use the Cloud Monitoring API directly from your application code. For example, a Python app on GCE can use the google-cloud-monitoring client library to send time-series data for things like per-endpoint request success rates.
• CloudSQL: Since you can’t install agents directly on CloudSQL instances, work with its logs or exported data. Set up a Cloud Function that triggers on CloudSQL log entries (like slow queries), parses the log data, and sends a custom metric (e.g., count of slow queries per minute) to Cloud Monitoring. Alternatively, use the CloudSQL Admin API to fetch non-default metrics and wrap that logic in a script to push custom metrics.
• GCS: Tie Cloud Functions to GCS event triggers (object creation/deletion, for example) to generate custom metrics. Track things like the number of large objects uploaded hourly or average processing time for new objects—your function can send these metrics straight to Cloud Monitoring via the API. You can also export GCS access logs to BigQuery, run aggregation queries, then push the results as custom metrics.
• GAE: Both standard and flexible environments support using Cloud Monitoring client libraries in your app code. For a Node.js GAE app, use @google-cloud/monitoring to report custom metrics like user sign-up rates or app-specific error rates. GAE flexible also works with the Cloud Monitoring Agent if you need system-level custom metrics.

Cloud Monitoring provides built-in metrics to track its own performance, like:

• Cloud Monitoring API request counts and latency
• Alert policy trigger rates and notification success/failure statuses
• Uptime check pass/fail rates

For custom metrics specific to Cloud Monitoring itself:

• Export Cloud Monitoring’s own logs to Cloud Logging, then use a Cloud Function to parse logs (like failed metric upload requests) and send a custom metric (e.g., daily count of failed metric submissions) back to Cloud Monitoring.
• Use the Cloud Monitoring API to query its internal metrics, process the data (like calculating average active alert policies per project), and push that as a custom metric.

Absolutely! Cloud Monitoring is designed to be programmable via its REST API and official client libraries (Python, Go, Java, etc.). You can treat it as a tool to:

• Automatically create custom metric descriptors for new services or apps
• Batch-upload custom time-series data from internal systems or scripts
• Build custom dashboards programmatically by fetching and visualizing custom metrics
• Set up alert policies based on custom metric thresholds—all via API calls

For example, you could write a Python script that runs hourly, fetches usage data from an internal tool, formats it into the required time-series format, and sends it to Cloud Monitoring using google-cloud-monitoring. This integrates seamlessly with existing DevOps workflows.

If you don’t want to use the Cloud Monitoring API directly, try these options:

• Cloud Functions + Logging: Tie Cloud Functions to Cloud Logging sinks for your GCP services. Parse log entries to extract meaningful data and convert it into custom metrics—great for event-driven use cases without heavy app code.
• Prometheus Integration: If you already use Prometheus, set up the Cloud Monitoring Exporter to send Prometheus custom metrics to Cloud Monitoring. This lets you leverage your existing Prometheus setup while centralizing metrics in GCP.
• Dataflow Processing: For high-volume logs or event streams, use Dataflow to process and aggregate data, then send the resulting custom metrics to Cloud Monitoring. Ideal for complex transformations or real-time analytics.
• BigQuery + Scheduled Queries: Export service data to BigQuery, write scheduled queries to calculate custom metrics (like monthly CloudSQL query volume), then use a Cloud Function to push those results to Cloud Monitoring as custom metrics.

内容的提问来源于stack exchange，提问作者KMi