Cloud Monitoring Sli Formulation Gke Dataproc

What Custom SLIs with DistributionCut and TimeSeriesRatio Are

In Google Cloud Monitoring, custom Service Level Indicators (SLIs) allow teams to define compliance criteria when standard pre-computed mesh or platform metrics are not automatically populated. Custom SLIs are constructed as ratios of valid positive (good) events to total valid events using either DistributionCut (for histogram/distribution metrics) or TimeSeriesRatio (for counter/gauge time-series metrics).

How It Meets the Requirements

• Latency Tracking for Workload 1 (GKE): GKE custom services do not have built-in default latency indicators. A DistributionCut SLI takes a latency distribution metric and establishes a numerical range (e.g., < 200 ms). The ratio of request samples falling within this acceptable range compared to all recorded samples defines the request-based latency SLI.
• Job Correctness for Workload 2 (Dataproc): Dataproc exports job metrics such as cluster/job/submitted_count and cluster/job/failed_count. Using a TimeSeriesRatio structure (goodTotalRatio), Cloud Monitoring computes the ratio of good executions against total valid executions by setting submitted_count as the total service filter and failed_count as the bad service filter.

Key Advantages

• Precise mathematical formulation: Measures true ratios of (good events) / (total valid events) across disparate metric formats.
• Native integration: Integrates directly with Cloud Monitoring SLO error-budget calculations and burn-rate alerting.
• Flexibility across workload types: Adapts cleanly to both continuous microservices and discrete batch processing pipelines.

Why This Is Optimal

This approach directly aligns with SRE best practices and Google Cloud's API structures for service monitoring, allowing both distribution-based latency and counter-based batch job correctness to be monitored within a single, unified SLO framework.