"""Bounded, privacy preserving performance telemetry.

Metrics are deliberately process local.  RAG workers return one small envelope
to the API process, rather than attempting to share an in-memory deque.
"""
from __future__ import annotations
from contextvars import ContextVar
from collections import deque
import hashlib, threading, time

_current: ContextVar[object | None] = ContextVar("perf_metrics", default=None)
_lock = threading.Lock()
_http = deque(maxlen=512)
_sql = deque(maxlen=512)
_rag = deque(maxlen=512)

def _pct(values, p):
    if not values: return 0.0
    vals = sorted(float(v) for v in values)
    return round(vals[min(len(vals)-1, int((len(vals)-1)*p))], 2)

class RequestMetrics:
    def __init__(self, trace_id=""):
        self.trace_id = str(trace_id or "")[:64]; self.started = time.perf_counter()
        self.sql_count = self.sql_ms = self.sql_lock_errors = 0
    def sql(self, elapsed, error=None):
        self.sql_count += 1; self.sql_ms += float(elapsed)
        if error and "locked" in str(error).lower(): self.sql_lock_errors += 1
    def finish(self, method, path, status):
        elapsed = (time.perf_counter()-self.started)*1000
        # route templates are supplied by the caller; raw paths are never stored
        row={"endpoint": f"{method} {path}", "status": int(status),
             "elapsed_ms": round(elapsed,2), "sql_count": self.sql_count,
             "sql_ms": round(self.sql_ms,2), "sql_lock_errors": self.sql_lock_errors,
             "at": time.time()}
        with _lock: _http.append(row)
        return row

class request_scope:
    def __init__(self, trace_id=""): self.metrics=RequestMetrics(trace_id); self.token=None
    def __enter__(self): self.token=_current.set(self.metrics); return self.metrics
    def __exit__(self, *exc): _current.reset(self.token)

def current(): return _current.get()
def record_sql(elapsed, error=None):
    m=current()
    if m: m.sql(elapsed,error)

def record_rag(row):
    if not isinstance(row,dict): return
    # Keep phase-level embedding diagnostics while excluding query text and
    # filesystem details.  These fields let operators distinguish cold model
    # load, tokenizer, inference, serialization and IPC overhead.
    allowed = ("total_ms","validation_ms","embedding_ms","recall_ms","postprocess_ms",
               "cache_hit","result_count","error","embedding_total_ms",
               "embedding_process_start_ms","embedding_ipc_ms","embedding_model_load_ms",
               "embedding_tokenizer_ms","embedding_encode_ms","embedding_postprocess_ms",
               "embedding_source","embedding_count","embedding_model","embedding_version",
               "embedding_dim","embedding_dimension","embedding_pid","embedding_cache_hit",
               "embedding_cache_size","embedding_cache_hits","embedding_cache_misses","embedding_cache_hit_rate","worker_pid",
               "worker_reused","worker_start_ms","serialization_ms","merge_ms","dedup_ms","rrf_ms",
               "retrieval_scopes","warm_state","queue_wait_ms","transport_total_ms")
    clean={k: row.get(k) for k in allowed if k in row}
    with _lock: _rag.append(clean)

def _summary(rows, rag=False):
    if not rows: return {"samples":0,"p50_ms":0.0,"p95_ms":0.0,"errors":0}
    total=[r.get("total_ms",r.get("elapsed_ms",0)) for r in rows]
    out={"samples":len(rows),"p50_ms":_pct(total,.5),"p95_ms":_pct(total,.95),
         "errors":sum(1 for r in rows if int(r.get("status",200))>=400 or r.get("error"))}
    if rag: out["cache_hit_rate"]=round(sum(bool(r.get("cache_hit")) for r in rows)/len(rows),4)
    return out

def snapshot():
    with _lock: h=list(_http); s=list(_sql); r=list(_rag)
    sql_ms=[x.get("sql_ms",0) for x in h]
    slowest = sorted(h, key=lambda row: float(row.get("elapsed_ms", 0)), reverse=True)[:10]
    status_counts = {}
    for row in h:
        group = f"{int(row.get('status', 200)) // 100}xx"
        status_counts[group] = status_counts.get(group, 0) + 1
    return {"scope":"current_process","window_samples":len(h),"api":{**_summary(h),"p99_ms":_pct([x.get("elapsed_ms",0) for x in h],.99),"sql_count":sum(x.get("sql_count",0) for x in h),"sql_ms":round(sum(sql_ms),2),"sql_lock_errors":sum(x.get("sql_lock_errors",0) for x in h),"slowest":slowest,"status_counts":status_counts},"rag":{**_summary(r,True),"phases":{}},"resets_on_restart":True}

def rag_snapshot():
    with _lock: rows=list(_rag)
    out=_summary(rows,True)
    for phase in ("validation_ms","embedding_ms","recall_ms","postprocess_ms"):
        vals=[r.get(phase,0) for r in rows]; out.setdefault("phases",{})[phase]={"p50_ms":_pct(vals,.5),"p95_ms":_pct(vals,.95)}
    for phase in ("serialization_ms", "merge_ms", "dedup_ms", "rrf_ms", "queue_wait_ms", "transport_total_ms"):
        if any(phase in r for r in rows):
            vals = [r.get(phase, 0) for r in rows]
            out.setdefault("phases", {})[phase] = {
                "p50_ms": _pct(vals, .5), "p95_ms": _pct(vals, .95)}
    embedding_phases = ("embedding_total_ms", "embedding_process_start_ms", "embedding_ipc_ms",
                        "embedding_model_load_ms", "embedding_tokenizer_ms",
                        "embedding_encode_ms", "embedding_postprocess_ms")
    out["embedding"] = {
        phase: {"p50_ms": _pct([r.get(phase, 0) for r in rows], .5),
                "p95_ms": _pct([r.get(phase, 0) for r in rows], .95)}
        for phase in embedding_phases if any(phase in r for r in rows)
    }
    if rows:
        scoped = [r.get("retrieval_scopes") for r in rows if isinstance(r.get("retrieval_scopes"), list)]
        if scoped:
            latest = scoped[-1]
            out["scopes"] = latest[:12]
        warm_states = [r.get("warm_state") for r in rows if isinstance(r.get("warm_state"), dict)]
        if warm_states:
            out["warm_state"] = warm_states[-1]
        out["embedding"]["cache_hit_rate"] = round(
            sum(bool(r.get("embedding_cache_hit")) for r in rows if "embedding_cache_hit" in r)
            / max(1, sum(1 for r in rows if "embedding_cache_hit" in r)), 4)
        out["embedding"]["sources"] = sorted({str(r.get("embedding_source")) for r in rows if r.get("embedding_source")})
        out["embedding"]["models"] = sorted({str(r.get("embedding_model")) for r in rows if r.get("embedding_model")})
        out["embedding"]["pids"] = sorted({int(r["embedding_pid"]) for r in rows if str(r.get("embedding_pid", "")).isdigit()})
        counts = [int(r.get("embedding_count", 0) or 0) for r in rows if "embedding_count" in r]
        if counts:
            out["embedding"]["count_p50"] = _pct(counts, .5)
            out["embedding"]["count_max"] = max(counts)
            out["embedding"]["batch_warning"] = max(counts) > 1
    return out

def ingest_http(row):
    if isinstance(row,dict):
        with _lock: _http.append(dict(row))
def ingest_rag(row): record_rag(row)

def install_sql_hooks(engine):
    try:
        from sqlalchemy import event
        from sqlalchemy.engine import Engine
        if getattr(engine,"_perf_hooks_installed",False): return
        @event.listens_for(engine,"before_cursor_execute")
        def before(conn,cursor,statement,parameters,context,executemany): context._perf_started=time.perf_counter()
        @event.listens_for(engine,"after_cursor_execute")
        def after(conn,cursor,statement,parameters,context,executemany): record_sql((time.perf_counter()-getattr(context,"_perf_started",time.perf_counter()))*1000)
        @event.listens_for(engine,"handle_error")
        def error(exception_context): record_sql(0,exception_context.original_exception)
        engine._perf_hooks_installed=True
    except Exception: pass