Cloud Monitoring custom metrics allow you to extend Google Cloud's monitoring capabilities beyond built-in metrics to track application-specific data points that matter to your business.
Custom metrics are user-defined metrics that you create to monitor specific aspects of your applications, servi…Cloud Monitoring custom metrics allow you to extend Google Cloud's monitoring capabilities beyond built-in metrics to track application-specific data points that matter to your business.
Custom metrics are user-defined metrics that you create to monitor specific aspects of your applications, services, or infrastructure that aren't covered by default Google Cloud metrics. They follow the format 'custom.googleapis.com/[metric_name]' or 'workload.googleapis.com/[metric_name]'.
To create custom metrics, you have several approaches:
1. **Using the Monitoring API**: You can write time series data to Cloud Monitoring using the timeSeries.create method. This requires defining a metric descriptor that specifies the metric type, labels, value type, and metric kind (gauge, delta, or cumulative).
2. **Using Client Libraries**: Google provides client libraries for Python, Java, Go, Node.js, and other languages. You initialize a MetricServiceClient, create metric descriptors, and write data points programmatically.
3. **Using OpenTelemetry**: This is the recommended approach for new implementations. OpenTelemetry provides a vendor-neutral way to collect and export metrics to Cloud Monitoring.
4. **Using the Monitoring Agent**: The ops-agent can collect custom metrics from applications using protocols like StatsD or Prometheus format.
Key components when defining custom metrics include:
- **Metric Type**: A unique identifier for your metric
- **Labels**: Key-value pairs for filtering and grouping data
- **Value Type**: INT64, DOUBLE, BOOL, STRING, or DISTRIBUTION
- **Metric Kind**: GAUGE (point-in-time), DELTA (change since last reading), or CUMULATIVE (cumulative value)
Best practices include using meaningful naming conventions, adding descriptive labels for filtering, setting appropriate sampling intervals, and considering costs since custom metrics incur charges based on ingested data volume.
Once created, custom metrics integrate with Cloud Monitoring dashboards and alerting policies, enabling comprehensive observability for your cloud solutions.
Creating Cloud Monitoring Custom Metrics
Why It Is Important
Custom metrics in Google Cloud Monitoring allow you to track application-specific data that isn't captured by built-in metrics. This is essential for monitoring business logic, application performance indicators, and domain-specific measurements that are unique to your workloads. Understanding custom metrics is crucial for the GCP Associate Cloud Engineer exam as it demonstrates your ability to implement comprehensive monitoring solutions.
What Are Custom Metrics?
Custom metrics are user-defined metrics that extend Cloud Monitoring's capabilities beyond the default metrics collected from GCP services. They enable you to:
• Track application-specific performance data • Monitor business KPIs and custom events • Create metrics from log-based data • Measure any quantifiable aspect of your application
Custom metrics follow the naming convention: custom.googleapis.com/[metric_name]
How Custom Metrics Work
There are several methods to create custom metrics:
1. Using the Monitoring API You can write time series data to Cloud Monitoring using the timeSeries.create method. This requires creating a metric descriptor first, then writing data points.
2. Using OpenCensus/OpenTelemetry These libraries provide standardized ways to instrument your code and export metrics to Cloud Monitoring.
3. Log-Based Metrics You can create metrics from Cloud Logging entries using counter metrics (count log entries) or distribution metrics (extract numeric values from logs).
4. Using the Monitoring Agent The legacy monitoring agent can be configured to collect custom metrics from applications.
Key Components of Custom Metrics:
• Metric Descriptor: Defines the metric type, labels, and value type • Time Series: The actual data points with timestamps • Labels: Key-value pairs for filtering and grouping data • Metric Kind: GAUGE, DELTA, or CUMULATIVE • Value Type: INT64, DOUBLE, BOOL, STRING, or DISTRIBUTION
Exam Tips: Answering Questions on Creating Cloud Monitoring Custom Metrics
Tip 1: Remember that custom metrics have a prefix of custom.googleapis.com/ while log-based metrics use logging.googleapis.com/user/.
Tip 2: Know the difference between metric kinds - GAUGE represents a value at a point in time, DELTA represents change since last measurement, and CUMULATIVE represents a running total.
Tip 3: When questions mention tracking specific application behavior or business metrics, custom metrics are typically the correct answer.
Tip 4: Log-based metrics are the preferred solution when you need to create metrics from existing log data rather than instrumenting code.
Tip 5: Be aware of quotas and limits - there are limits on the number of custom metric descriptors and time series per project.
Tip 6: For questions about real-time application monitoring with minimal code changes, consider log-based metrics as the simpler approach.
Tip 7: Remember that custom metrics can be used in alerting policies and dashboards just like built-in metrics.
Tip 8: When the exam presents scenarios requiring instrumentation of code, look for answers mentioning OpenTelemetry or the Monitoring API client libraries.
Common Exam Scenarios:
• Monitoring application-specific queue depths or processing times • Creating alerts based on business metrics • Converting log entries into measurable metrics • Tracking custom SLIs for SLO monitoring