Issue querying Google Postmaster’s v2beta API for Delivery Error

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Summary

The investigation focuses on a 400 INVALID_ARGUMENT error encountered when querying Google Postmaster’s v2beta DELIVERY_ERROR_RATE metric. Unlike other metrics (e.g., AUTH_SUCCESS_RATE), explicit and valid filtering by delivery_error_type is required. The root cause was insufficiently restrictive filtering, resulting in invalid API requests.

Root Cause

The DELIVERY_ERROR_RATE metric mandates structured filters specifying an allowed delivery error category. The error occurred because:

  • The API rejected requests lacking a filter parameter for this metric.
  • Filters used incorrect syntax or undocumented values for delivery_error_type.
  • Ambiguous documentation failed to explicitly list valid delivery_error_type values (e.g., RATE_LIMIT_EXCEEDED).

Why This Happens in Real Systems

  • Partial documentation: Enterprise APIs frequently omit exhaustive parameter specifications, assuming internal tribal knowledge.
  • Strict schema validation: API servers reject requests with unrecognized enum values or missing mandatory parameters (e.g., filters).
  • Inconsistent requirements: Metrics exhibit different validation rules (AUTH_SUCCESS_RATE explicitly defines allowed filters; DELIVERY_ERROR_RATE does not).

Real-World Impact

  • Blocked diagnostics: Engineers cannot monitor critical email delivery failures (e.g., spam flags, rate limits).
  • Delayed incident response: Lack of visibility prolongs resolution of deliverability issues.
  • Operational overhead: Manual workarounds divert resources from proactive optimizations.

Example or Code

{
  "metricDefinitions": [
    {
      "name": "delivery_error_rate",
      "baseMetric": {
        "standardMetric": "DELIVERY_ERROR_RATE"
      },
      "filter": "delivery_error_type = \"GMAIL_SERVICE_TOO_MANY_RECIPIENTS\""
    }
  ],
  "timeQuery": {
    "dateRanges": {
      "dateRanges": [
        {
          "start": { "year": 2025, "month": 11, "day": 1 },
          "end": { "year": 2025, "month": 11, "day": 30 }
        }
      ]
    }
  },
  "pageSize": 200
}

How Senior Engineers Fix It

  1. Reverse-engineer requirements: Use API discovery tools or testing tools to infer valid parameters.
  2. Verify documentation gaps: Cross-reference internal sources (e.g., API release notes, issue trackers).
  3. Iterative validation: Test with exhaustive enum values (e.g., INVALID_RECIPIENT, SPAM_BLOCKED).
  4. Enable logging: Inspect raw HTTP requests/responses to pinpoint malformed payloads via Google Cloud Logging.
  5. Escalate strategically: File prioritized bug reports using Google’s API issue tracker when documentation is inadequate.

Why Juniors Miss It

  • Implicit trust in documentation: Assume public docs are comprehensive, missing subtle discrepancies between metrics.
  • Limited debugging scope: Focus on syntax errors without exploring API-specific constraints (e.g., mandatory filters).
  • Underuse of tools: Neglect protocol-level inspection (e.g., Google APIs Explorer, network tracing).
  • Confirmation bias: Stop troubleshooting after initial failed attempts instead of systematically varying parameters.

Key takeaway: Always validate undocumented API constraints via adversarial testing and leverage platform-specific diagnostics.