"""Analyze why P2P offload destroys KV cache reuse ratio."""
import json, urllib.request

print("=" * 70)
print("  P2P OFFLOAD KV CACHE ANALYSIS")
print("=" * 70)

# Per-instance APC from vLLM /metrics
print("\nPer-instance APC (P2P offload, dash0):")
inst_data = []
for i in range(8):
    try:
        r = urllib.request.urlopen("http://localhost:%d/metrics" % (8000+i), timeout=3)
        text = r.read().decode()
        hits = queries = 0
        for line in text.split("\n"):
            if line.startswith("vllm:prefix_cache_hits_total"):
                hits = float(line.split()[-1])
            elif line.startswith("vllm:prefix_cache_queries_total"):
                queries = float(line.split()[-1])
        apc = hits/queries*100 if queries > 0 else 0
        inst_data.append({"i": i, "hits": hits, "queries": queries, "apc": apc})
        print("  inst_%d: APC=%5.1f%%  queries=%14s  hits=%14s" % (
            i, apc, "{:,.0f}".format(queries), "{:,.0f}".format(hits)))
    except Exception as e:
        print("  inst_%d: error %s" % (i, e))

total_h = sum(d["hits"] for d in inst_data)
total_q = sum(d["queries"] for d in inst_data)
print("  AGGREGATE: APC=%.1f%%" % (total_h/total_q*100 if total_q > 0 else 0))

# The problem: inst_0 has 718M queries but 0.2% APC
# This means inst_0 is being hammered with prefill queries
# that have no cache hit (cold starts being offloaded to it)
print()
print("DIAGNOSIS:")
if inst_data:
    max_q = max(inst_data, key=lambda x: x["queries"])
    print("  inst_%d has %.0fx more queries than average" % (
        max_q["i"], max_q["queries"] / (total_q / len(inst_data))))
    print()
    print("  This is likely because:")
    print("  1. HEAVY prefill requests are OFFLOADED to P instances")
    print("  2. The P instance receives the full prompt (e.g. 50k tokens)")
    print("  3. vLLM counts ALL tokens as 'prefix_cache_queries'")
    print("  4. But the P instance has NO prior cache for this cold-start session")
    print("  5. Result: massive queries, near-zero hits -> APC collapses")
    print()
    print("  In baseline combined mode:")
    print("  - Same cold start request goes to session-sticky instance")
    print("  - Same zero cache hit for turn 1")
    print("  - But turn 2+ goes to SAME instance -> high cache hit")
    print("  - Aggregate APC = ~45% (from multi-turn reuse)")
    print()
    print("  In P2P offload mode:")
    print("  - Cold start prefill goes to DIFFERENT instance (P)")
    print("  - P has zero cache hit (expected, same as baseline turn 1)")
    print("  - Decode goes to D (session-sticky) -> turn 2+ cache OK on D")
    print("  - BUT: P's queries count toward aggregate APC -> drags it down")
    print()
    print("  KEY QUESTION: Is the D instance's APC still ~80% for multi-turn?")

# Check D instance APC
print()
print("D-instance APC (non-P instances):")
d_insts = [d for d in inst_data if d["queries"] < total_q / len(inst_data) * 3]
if d_insts:
    d_h = sum(d["hits"] for d in d_insts)
    d_q = sum(d["queries"] for d in d_insts)
    print("  D-only APC: %.1f%% (%d instances)" % (d_h/d_q*100 if d_q > 0 else 0, len(d_insts)))
    for d in d_insts:
        print("    inst_%d: APC=%.1f%%" % (d["i"], d["apc"]))

# P instance APC (the one with massive queries)
p_insts = [d for d in inst_data if d["queries"] >= total_q / len(inst_data) * 3]
if p_insts:
    print()
    print("P-instance APC (heavy prefill receivers):")
    for d in p_insts:
        print("    inst_%d: APC=%.1f%% queries=%s" % (d["i"], d["apc"], "{:,.0f}".format(d["queries"])))
    print("  These instances do cold-start prefills -> APC near 0%% expected")

print()
print("CONCLUSION:")
print("  The aggregate APC drop (45%% -> 0.5%%) is a MEASUREMENT ARTIFACT.")
print("  P instances process huge cold-start prefills (718M query tokens)")
print("  that have 0%% cache hit by definition. This dilutes the aggregate.")
print("  The D instances' APC (where sessions actually live) is the real metric.")