{"ok":true,"cached":true,"plan":{"sql":"WITH active_actors AS (\n  SELECT\n    id, full_name, title, total_users_30d,\n    CASE WHEN pricing_model = 'FREE' OR COALESCE(price_usd, 0) <= 0 THEN 0.0 WHEN pricing_model = 'FLAT_PRICE_PER_MONTH' THEN COALESCE(total_users_30d, 0) * 0.15 * COALESCE(price_usd, 0) WHEN pricing_model = 'PRICE_PER_DATASET_ITEM' THEN (COALESCE(runs30_total, 0) * (1 - LEAST(GREATEST(COALESCE(hard_failure_rate, 0), 0), 1))) * 50 * COALESCE(price_usd, 0) * 0.80 WHEN pricing_model = 'PAY_PER_EVENT' THEN CASE WHEN CASE WHEN COALESCE(price_usd, 0) >= 10 THEN COALESCE(total_users_30d, 0) * 1 * COALESCE(price_usd, 0) * 0.80 WHEN COALESCE(price_usd, 0) >= 1 THEN COALESCE(total_users_30d, 0) * 10 * COALESCE(price_usd, 0) * 0.80 ELSE NULL END IS NULL THEN ((COALESCE(runs30_total, 0) * (1 - LEAST(GREATEST(COALESCE(hard_failure_rate, 0), 0), 1))) * CASE WHEN COALESCE(price_usd, 0) >= 1 THEN 1 WHEN COALESCE(price_usd, 0) >= 0.1 THEN 2 WHEN COALESCE(price_usd, 0) >= 0.01 THEN 10 ELSE 50 END * COALESCE(price_usd, 0) * 0.80) ELSE LEAST(((COALESCE(runs30_total, 0) * (1 - LEAST(GREATEST(COALESCE(hard_failure_rate, 0), 0), 1))) * CASE WHEN COALESCE(price_usd, 0) >= 1 THEN 1 WHEN COALESCE(price_usd, 0) >= 0.1 THEN 2 WHEN COALESCE(price_usd, 0) >= 0.01 THEN 10 ELSE 50 END * COALESCE(price_usd, 0) * 0.80), CASE WHEN COALESCE(price_usd, 0) >= 10 THEN COALESCE(total_users_30d, 0) * 1 * COALESCE(price_usd, 0) * 0.80 WHEN COALESCE(price_usd, 0) >= 1 THEN COALESCE(total_users_30d, 0) * 10 * COALESCE(price_usd, 0) * 0.80 ELSE NULL END) END ELSE 0.0 END AS est_monthly_revenue\n  FROM apify.actor\n  WHERE COALESCE(is_deprecated, false) = false\n),\nsummary AS (\n  SELECT\n    CAST(COUNT(*) AS int) AS total_actors,\n    CAST(100.0 AS double precision) AS revenue_threshold_usd,\n    CAST(COUNT(*) FILTER (WHERE COALESCE(est_monthly_revenue, 0) >= 100.0) AS int) AS over_threshold_actors,\n    CAST(COUNT(*) FILTER (WHERE COALESCE(est_monthly_revenue, 0) < 100.0) AS int) AS under_threshold_actors,\n    CAST(ROUND(100.0 * COUNT(*) FILTER (WHERE COALESCE(est_monthly_revenue, 0) >= 100.0) / NULLIF(COUNT(*), 0), 2) AS double precision) AS over_threshold_pct,\n    CAST(ROUND(100.0 * COUNT(*) FILTER (WHERE COALESCE(est_monthly_revenue, 0) < 100.0) / NULLIF(COUNT(*), 0), 2) AS double precision) AS under_threshold_pct,\n    CAST(SUM(COALESCE(est_monthly_revenue, 0)) AS double precision) AS total_estimated_monthly_revenue,\n    CAST(SUM(COALESCE(est_monthly_revenue, 0)) FILTER (WHERE COALESCE(est_monthly_revenue, 0) >= 100.0) AS double precision) AS over_threshold_estimated_monthly_revenue,\n    CAST(SUM(COALESCE(est_monthly_revenue, 0)) FILTER (WHERE COALESCE(est_monthly_revenue, 0) < 100.0) AS double precision) AS under_threshold_estimated_monthly_revenue\n  FROM active_actors\n),\nranked AS (\n  SELECT\n    full_name,\n    CAST(COALESCE(est_monthly_revenue, 0) AS double precision) AS est_monthly_revenue,\n    ROW_NUMBER() OVER (ORDER BY COALESCE(est_monthly_revenue, 0) DESC NULLS LAST, total_users_30d DESC NULLS LAST) AS rn\n  FROM active_actors\n  WHERE COALESCE(est_monthly_revenue, 0) >= 100.0\n)\nSELECT\n  'Revenue threshold summary' AS title,\n  total_actors,\n  revenue_threshold_usd,\n  over_threshold_actors,\n  under_threshold_actors,\n  over_threshold_pct,\n  under_threshold_pct,\n  total_estimated_monthly_revenue,\n  over_threshold_estimated_monthly_revenue,\n  under_threshold_estimated_monthly_revenue,\n  CAST(ROUND(CAST(100.0 * over_threshold_estimated_monthly_revenue / NULLIF(total_estimated_monthly_revenue, 0) AS numeric), 2) AS double precision) AS over_threshold_revenue_pct,\n  CAST(ROUND(CAST(100.0 * under_threshold_estimated_monthly_revenue / NULLIF(total_estimated_monthly_revenue, 0) AS numeric), 2) AS double precision) AS under_threshold_revenue_pct,\n  (SELECT full_name FROM ranked WHERE rn = 1) AS top_actor,\n  (SELECT est_monthly_revenue FROM ranked WHERE rn = 1) AS top_estimated_monthly_revenue\nFROM summary\nLIMIT 1","mode":"sql","notes":[],"rationale":"pg sql path (Text-to-SQL / vector over pgvector)"},"results":[{"key":"Revenue threshold summary","url":null,"score":null,"title":"Revenue threshold summary","top_actor":"apify/instagram-scraper","total_actors":14980,"over_threshold_pct":8.65,"under_threshold_pct":91.35,"over_threshold_actors":1296,"revenue_threshold_usd":100,"under_threshold_actors":13684,"over_threshold_revenue_pct":99.27,"under_threshold_revenue_pct":0.73,"top_estimated_monthly_revenue":1626991.96223719,"total_estimated_monthly_revenue":15894533.5124196,"over_threshold_estimated_monthly_revenue":15778589.9896447,"under_threshold_estimated_monthly_revenue":115943.52277493}],"answer":"当前 playbook 快照中，非废弃 Apify Actor 共 14,980 个；保守估算月收入达到 US$100+ 的有 1,296 个，数量占比 8.65%。这些达标 Actor 的估算月收入合计约 US$15,778,589.99，占全部非废弃 Actor 估算月收入 US$15,894,533.51 的 99.27%。 达标组里当前头部是 apify/instagram-scraper，保守估算月收入约 US$1,626,991.96。","meta":{"q":"月收入高于100美金的actor有多少。占比怎么样（总收入占比和数量占比）","mode":"query","view":"auto","page":1,"limit":10}}