62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
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"""Actor workload read-models — tenant-scoped aggregation."""
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from __future__ import annotations
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from typing import Any
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from psycopg2.extras import RealDictCursor
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from db import get_connection
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from tenant_context import TenantContext
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def get_actor_workload(ctx: TenantContext) -> list[dict[str, Any]]:
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"""Open/blocked/in_progress action counts per active actor in tenant."""
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conn = get_connection()
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try:
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with conn.cursor(cursor_factory=RealDictCursor) as cur:
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cur.execute(
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"""
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SELECT
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a.id AS actor_id,
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a.name AS display_name,
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a.actor_type,
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COUNT(*) FILTER (WHERE act.status = 'open') AS open_actions,
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COUNT(*) FILTER (WHERE act.status = 'blocked') AS blocked_actions,
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COUNT(*) FILTER (WHERE act.status = 'in_progress') AS in_progress_actions
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FROM actors a
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LEFT JOIN action_assignments aa
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ON aa.actor_id = a.id AND aa.tenant_id = a.tenant_id
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LEFT JOIN actions act
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ON act.id = aa.action_id
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AND act.tenant_id = aa.tenant_id
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AND act.status IN ('open', 'in_progress', 'blocked')
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WHERE a.tenant_id = %s AND a.is_active = TRUE
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GROUP BY a.id, a.name, a.actor_type
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ORDER BY
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CASE a.actor_type
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WHEN 'human' THEN 0
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WHEN 'working_group' THEN 1
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WHEN 'agent' THEN 2
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ELSE 3
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END,
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a.name
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""",
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(ctx.tenant_id,),
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)
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result = []
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for row in cur.fetchall():
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result.append(
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{
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"actor_id": str(row["actor_id"]),
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"display_name": row["display_name"],
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"actor_type": row["actor_type"],
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"open_actions": int(row["open_actions"] or 0),
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"blocked_actions": int(row["blocked_actions"] or 0),
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"in_progress_actions": int(row["in_progress_actions"] or 0),
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}
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)
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return result
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finally:
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conn.close()
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