ml-engine-data.json

{"query_result": {"id": 22177529, "query_hash": "f4bcb2963c5114eadb5a28067f9f9e0b", "query": "WITH success AS (\n  WITH combined AS (\n    -- 1) Engine creation success metrics\n    SELECT\n      DATE(submission_timestamp) AS date,\n      labeled_metrics.key       AS engine_id,\n      'engine_creation'         AS metric_type,\n      CAST(values.key AS INT64) AS ms\n    FROM firefox_desktop.metrics\n    CROSS JOIN UNNEST(metrics.metrics.labeled_timing_distribution.firefox_ai_runtime_engine_creation_success) AS labeled_metrics\n    CROSS JOIN UNNEST(labeled_metrics.value.values) AS values\n    CROSS JOIN UNNEST(GENERATE_ARRAY(1, values.value)) AS repeat_number\n    WHERE\n      DATE(submission_timestamp) >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY)\n      AND client_info.app_channel IN ('nightly', 'beta', 'release')\n\n    UNION ALL\n\n    -- 2) Inference success metrics\n    SELECT\n      DATE(submission_timestamp) AS date,\n      labeled_metrics.key       AS engine_id,\n      'inference'               AS metric_type,\n      CAST(values.key AS INT64) AS ms\n    FROM firefox_desktop.metrics\n    CROSS JOIN UNNEST(metrics.metrics.labeled_timing_distribution.firefox_ai_runtime_run_inference_success) AS labeled_metrics\n    CROSS JOIN UNNEST(labeled_metrics.value.values) AS values\n    CROSS JOIN UNNEST(GENERATE_ARRAY(1, values.value)) AS repeat_number\n    WHERE\n      DATE(submission_timestamp) >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY)\n      AND client_info.app_channel IN ('nightly', 'beta', 'release')\n  )\n  SELECT\n    date,\n    engine_id,\n\n    -- Engine creation quantiles\n    APPROX_QUANTILES(IF(metric_type = 'engine_creation', ms, NULL), 100)[OFFSET(5)] / 1e6 AS engine_creation_p5,\n    APPROX_QUANTILES(IF(metric_type = 'engine_creation', ms, NULL), 100)[OFFSET(50)] / 1e6 AS engine_creation_p50,\n    APPROX_QUANTILES(IF(metric_type = 'engine_creation', ms, NULL), 100)[OFFSET(75)] / 1e6 AS engine_creation_p75,\n    APPROX_QUANTILES(IF(metric_type = 'engine_creation', ms, NULL), 100)[OFFSET(99)] / 1e6 AS engine_creation_p99,\n\n    -- Inference quantiles\n    APPROX_QUANTILES(IF(metric_type = 'inference', ms, NULL), 100)[OFFSET(5)] / 1e6   AS inference_p5,\n    APPROX_QUANTILES(IF(metric_type = 'inference', ms, NULL), 100)[OFFSET(50)] / 1e6  AS inference_p50,\n    APPROX_QUANTILES(IF(metric_type = 'inference', ms, NULL), 100)[OFFSET(75)] / 1e6  AS inference_p75,\n    APPROX_QUANTILES(IF(metric_type = 'inference', ms, NULL), 100)[OFFSET(99)] / 1e6  AS inference_p99,\n\n    -- Success counts\n    COUNTIF(metric_type = 'engine_creation') AS engine_creation_success_count,\n    COUNTIF(metric_type = 'inference')       AS inference_success_count\n\n  FROM combined\n  GROUP BY\n    date,\n    engine_id\n),\n\nfailures AS (\n  WITH combined AS (\n    -- Engine creation failures\n    SELECT\n      DATE(submission_timestamp)         AS date,\n      JSON_VALUE(event_extra.engineId)   AS engine_id,\n      'engine_creation'                  AS metric_type,\n      COUNT(*)                           AS failure_count\n    FROM `mozdata.firefox_desktop.events_stream`\n    WHERE\n      event = 'firefox.ai.runtime.engine_creation_failure'\n      AND DATE(submission_timestamp) >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY)\n      AND normalized_channel IN ('nightly', 'beta', 'release')\n    GROUP BY\n      date,\n      engine_id\n\n    UNION ALL\n\n    -- Inference failures\n    SELECT\n      DATE(submission_timestamp)         AS date,\n      JSON_VALUE(event_extra.engineId)   AS engine_id,\n      'inference'                        AS metric_type,\n      COUNT(*)                           AS failure_count\n    FROM `mozdata.firefox_desktop.events_stream`\n    WHERE\n      event = 'firefox.ai.runtime.run_inference_failure'\n      AND DATE(submission_timestamp) >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY)\n      AND normalized_channel IN ('nightly', 'beta', 'release')\n    GROUP BY\n      date,\n      engine_id\n  )\n  SELECT\n    date,\n    engine_id,\n    SUM(IF(metric_type = 'engine_creation', failure_count, 0)) AS engine_creation_failure_count,\n    SUM(IF(metric_type = 'inference',       failure_count, 0)) AS inference_failure_count\n  FROM combined\n  GROUP BY\n    date,\n    engine_id\n)\n\n-- Finally, join successes & failures into one result\nSELECT\n  COALESCE(s.date, f.date) AS date,\n  COALESCE(s.engine_id, f.engine_id) AS engine_id,\n\n  -- Success quantiles\n  s.engine_creation_p5,\n  s.engine_creation_p50,\n  s.engine_creation_p75,\n  s.engine_creation_p99,\n  s.inference_p5,\n  s.inference_p50,\n  s.inference_p75,\n  s.inference_p99,\n\n  -- Success counts\n  s.engine_creation_success_count,\n  s.inference_success_count,\n\n  -- Failure counts\n  f.engine_creation_failure_count,\n  f.inference_failure_count\n\nFROM success s\nFULL JOIN failures f\nUSING (date, engine_id)\nORDER BY\n  engine_id,\n  date;\n", "data": {"columns": [{"name": "date", "friendly_name": "date", "type": "string"}, {"name": "engine_id", "friendly_name": "engine_id", "type": "string"}, {"name": "engine_creation_p5", "friendly_name": "engine_creation_p5", "type": "float"}, {"name": "engine_creation_p50", "friendly_name": "engine_creation_p50", "type": "float"}, {"name": "engine_creation_p75", "friendly_name": "engine_creation_p75", "type": "float"}, {"name": "engine_creation_p99", "friendly_name": "engine_creation_p99", "type": "float"}, {"name": "inference_p5", "friendly_name": "inference_p5", "type": "float"}, {"name": "inference_p50", "friendly_name": "inference_p50", "type": "float"}, {"name": "inference_p75", "friendly_name": "inference_p75", "type": "float"}, {"name": "inference_p99", "friendly_name": "inference_p99", "type": "float"}, {"name": "engine_creation_success_count", "friendly_name": "engine_creation_success_count", "type": "integer"}, {"name": "inference_success_count", "friendly_name": "inference_success_count", "type": "integer"}, {"name": "engine_creation_failure_count", "friendly_name": "engine_creation_failure_count", "type": "integer"}, {"name": "inference_failure_count", "friendly_name": "inference_failure_count", "type": "integer"}], "rows": [{"date": "2026-03-07", "engine_id": null, "engine_creation_p5": null, "engine_creation_p50": null, "engine_creation_p75": null, "engine_creation_p99": null, "inference_p5": null, "inference_p50": null, "inference_p75": null, "inference_p99": null, "engine_creation_success_count": null, "inference_success_count": null, "engine_creation_failure_count": 0, "inference_failure_count": 3273}, {"date": "2026-03-08", "engine_id": null, "engine_creation_p5": null, "engine_creation_p50": null, "engine_creation_p75": null, "engine_creation_p99": null, "inference_p5": null, "inference_p50": null, "inference_p75": null, "inference_p99": null, "engine_creation_success_count": null, "inference_success_count": null, "engine_creation_failure_count": 0, "inference_failure_count": 3235}, {"date": "2026-03-09", "engine_id": null, "engine_creation_p5": null, "engine_creation_p50": null, "engine_creation_p75": null, "engine_creation_p99": null, "inference_p5": null, "inference_p50": null, "inference_p75": null, "inference_p99": null, "engine_creation_success_count": null, "inference_success_count": null, "engine_creation_failure_count": 0, "inference_failure_count": 4304}, {"date": "2026-03-10", "engine_id": null, "engine_creation_p5": null, "engine_creation_p50": null, "engine_creation_p75": null, "engine_creation_p99": null, "inference_p5": null, "inference_p50": null, "inference_p75": null, "inference_p99": null, "engine_creation_success_count": null, "inference_success_count": null, "engine_creation_failure_count": 0, "inference_failure_count": 4459}, {"date": "2026-03-11", "engine_id": null, "engine_creation_p5": null, "engine_creation_p50": null, "engine_creation_p75": null, "engine_creation_p99": null, "inference_p5": null, "inference_p50": null, "inference_p75": null, "inference_p99": null, 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