perf(pebble): −68% on 1 M-grant paginated read (autoresearch)#877
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Branched from autoresearch/pebble-perf-20260525 (WritePack session, -69.7% cumulative). That session's artifacts archived under docs/rfcs/0004-storage-engine-v4/autoresearch-archive/writepack/. New primary metric: pebble_readpaginated_1m_ms (1M paginated read wallclock). Sentinels: pebble_writepack_1m_ms (must not regress; preserves the WritePack session's wins) and sqlite_readpaginated_1k_ms (SQLite engine regression guard).
…bble-perf-20260525 branch (WritePack session end state). pebble_readpaginated_1m_ms = 1229.6 ms. Allocs at 1M scale = 17M (versus 22K on the WritePack side — likely a big arena-like win available). SQLite + WritePack sentinels within tolerance.
Result: {"status":"keep","pebble_readpaginated_1m_ms":1229.57,"pebble_readpaginated_100k_ms":129.639,"pebble_readpaginated_10k_ms":15.041,"pebble_readpaginated_1k_ms":2.931,"pebble_readpaginated_100_ms":1.766,"pebble_readpaginated_1m_bytes_op":1447335468,"pebble_readpaginated_1m_allocs_op":17032960,"sqlite_readpaginated_1k_ms":5.869,"pebble_writepack_1m_ms":1262.162}
…r v3.GrantRecord allocations from O(records) to O(pages) \u2014 for the 1M read bench, 1M outer allocs become 100 slice allocs (one per page). pebble_readpaginated_1m_ms 1229.6\u21921201.4 ms (-2.3%); pebble_readpaginated_100k_ms 129.6\u2192122.2 (-5.7% same direction confirms). Allocs/op 17.03M\u219216.03M (-5.9%, matches the eliminated outer GrantRecord allocs). Smaller scales regressed (1k +14%, 100 +20%) due to over-sized arena: pre-allocates 10 k slots but workloads of 100/1k only fill a fraction. WritePack + SQLite sentinels flat. Followup: switch to slab-style growable arena to fix small-scale regression.
Result: {"status":"keep","pebble_readpaginated_1m_ms":1201.441,"pebble_readpaginated_100k_ms":122.228,"pebble_readpaginated_10k_ms":14.902,"pebble_readpaginated_1k_ms":3.348,"pebble_readpaginated_100_ms":2.117,"pebble_readpaginated_1m_bytes_op":1447654476,"pebble_readpaginated_1m_allocs_op":16033053,"sqlite_readpaginated_1k_ms":6.068,"pebble_writepack_1m_ms":1242.529}
… Entitlement + 2 \u00d7 (Resource + ResourceId)) in adapter.ListGrants. Each translateV3Grant call now allocates O(1) into the per-page arena instead of 6 individual mallocs. pebble_readpaginated_1m_ms 1229.6\u2192974.6 ms (-20.7%); pebble_readpaginated_100k_ms 129.6\u2192104.9 (-19.1% same direction confirms). Allocs/op 17.0M\u219210.0M (-41%) \u2014 exactly the 6M eliminated stub allocations matching the mechanism. bytes_op flat. SQLite + WritePack sentinels flat. Smaller scales still regress (1k +16%, 100 +28%) due to arena-allocation overhead at sizes where direct allocation was already cheap.
Result: {"status":"keep","pebble_readpaginated_1m_ms":974.563,"pebble_readpaginated_100k_ms":104.94,"pebble_readpaginated_10k_ms":13.992,"pebble_readpaginated_1k_ms":3.411,"pebble_readpaginated_100_ms":2.269,"pebble_readpaginated_1m_bytes_op":1428262212,"pebble_readpaginated_1m_allocs_op":10033722,"sqlite_readpaginated_1k_ms":5.85,"pebble_writepack_1m_ms":1253.056}
…54 design that dispatched per-record. Main goroutine batches 64 records into a single concatenated value buffer + offsets array, then sends ONE channel msg per batch to a 4-worker pool. Workers Unmarshal sequentially within their batch (avoiding cross-worker cache contention) but multiple batches process in parallel. pebble_readpaginated_1m_ms 974.6\u2192480.0 ms (-50.7 %). 100k -41.6 %, 10k -33.6 %, 1k -7.3 %, 100 essentially flat. allocs_op flat at 10M, bytes_op +36 % from the per-batch value buffer pre-alloc. SQLite + WritePack sentinels flat. Result: {"status":"keep","pebble_readpaginated_1m_ms":479.991,"pebble_readpaginated_100k_ms":61.269,"pebble_readpaginated_10k_ms":9.288,"pebble_readpaginated_1k_ms":3.159,"pebble_readpaginated_100_ms":2.386,"pebble_readpaginated_1m_bytes_op":1946320572,"pebble_readpaginated_1m_allocs_op":10082026,"sqlite_readpaginated_1k_ms":5.907,"pebble_writepack_1m_ms":1244.66}
…t, mode, data) jobs from the serial tar+zstd reader. tar reading remains serial (tar framing is sequential), but the per-file open/write/close syscalls now run concurrently. Memory peak bounded by workers \u00d7 max-entry-size \u2248 tens of MiB \u2014 critical for production-scale c1z files (100s GB) where the per-entry parallelism win compounds. Primary -1.2% at the 1M bench scale (unpack is only ~10% of bench wallclock), but 1k -15.7%, 10k -13.2% show the mechanism is working. bytes_op +8.6% from per-entry buffer allocs (bounded, not proportional to total record count). SQLite + WritePack sentinels flat.</description: {"hypothesis": "ExtractZstdTar serial file open/write/close syscalls limit unpack throughput; per-file parallelism via worker pool overlaps the syscall waits.", "result": "primary -1.2% borderline at 1M (unpack is ~10% of bench wallclock). 1k/10k clearly improved (-16%/-13%). Mechanism validated.", "production_value": "At production scale (100s of GB c1z, ~50k SSTs vs our bench's 265), unpack is a much larger fraction. The wins at 1k/10k bench scales reflect what would scale to production scale \u2014 it's exactly the regime where serial file syscalls dominate. The 1M bench masks this because ListGrants dominates.", "memory_bound_analysis": "Per-entry buffer alloc adds bytes_op +8.6%. At bench scale: 265 SSTs \u00d7 avg 2 MiB = 530 MiB total allocs spread over unpack time. At production scale: similar per-entry pattern, total alloc grows linearly with total bytes but memory PEAK bounded by workers \u00d7 max-entry-size (~tens of MiB). Production-safe.", "production_safety_check": "The new design is bounded per-entry, not per-total. At 100 GB c1z with Pebble's 2 MiB FlushSplitBytes, peak memory across 4 workers \u2248 16 MiB. No per-total-record memory blowup.", "cumulative_progress": "Read session: 1229.6\u2192474.2 ms = -61.4% from baseline."}
Result: {"status":"keep","pebble_readpaginated_1m_ms":474.225,"pebble_readpaginated_100k_ms":62.624,"pebble_readpaginated_10k_ms":8.063,"pebble_readpaginated_1k_ms":2.664,"pebble_readpaginated_100_ms":2.366,"pebble_readpaginated_1m_bytes_op":2113419888,"pebble_readpaginated_1m_allocs_op":10081410,"sqlite_readpaginated_1k_ms":5.921,"pebble_writepack_1m_ms":1258.389}
…eviously each batch allocated a fresh ~32 KB valueBuf via `make([]byte, 0, batchSize*512)` plus a fresh []int for offsets \u2014 at 1 M scale this is ~15 600 batches \u00d7 ~32 KB initial mallocs (and append() grows them, adding more allocs). Pool now reuses the batch struct + both backing arrays across calls. Workers Put back after Unmarshal; oversized batches (>256 KB valueBuf or >256 ends) are dropped instead of pooled to keep pool memory bounded. Primary 474\u2192467 ms (-1.5 %). bytes_op 2.11\u21921.60 GB (-24 %) directly addresses #55's allocation tradeoff. 100 -8 %, 1k -2 %, 100k -10 %; 10k +25 % is noise-tier (absolute time ~10 ms). SQLite + WritePack sentinels flat.</description: {"hypothesis": "#55's batched parallel decode introduced +36 % bytes_op from per-batch valueBuf allocations. Pooling those buffers cuts the per-call allocation churn without touching the parallelism mechanism. Expected modest wallclock win (5-15 ms from reduced GC) plus large bytes_op reduction.", "result": "Primary -1.5 % (466.9 ms, new best). bytes_op -24 % (2.11\u21921.60 GB).", "mechanism_validated": "Pool sustains hot buffers across the 100-page bench. Buffer cap at 256 KB / 256 ends prevents pool memory blowup if a batch grows beyond the typical ~16 KB target. allocs_op slightly improved (10.08M\u219210.03M); the dominant alloc cost is proto.Unmarshal's nested-message allocations, unchanged.", "production_safety": "Pool entries are bounded at 256 KB valueBuf each. At production 100 GB scale, the same per-page pattern holds \u2014 pool memory peak \u224820 entries \u00d7 32 KB target = <1 MB. No per-total-record growth.", "ten_k_scale_caveat": "10k +25 % (8.06\u219210.10 ms). Absolute time too small for confident attribution; likely run-to-run noise. The 1k/100/100k/1M scales all improved together, which the mechanism predicts (warm pool across the bench). If 10k regression reproduces in followup runs, investigate \u2014 might be a real warm-up edge case.", "cumulative_progress": "Read session: 1229.6 \u2192 466.9 ms = -62.0 % from baseline. 10 experiments, 6 keeps.", "next_action_hint": "After this pool win, the V3GrantToV2 in adapter (~50 ms serial) and the 100 NewIter calls (cache attempt #58 didn't deliver) remain. Try profiling at the new 467 ms baseline to find the actual current bottleneck before guessing."} Result: {"status":"keep","pebble_readpaginated_1m_ms":466.943,"pebble_readpaginated_100k_ms":56.317,"pebble_readpaginated_10k_ms":10.102,"pebble_readpaginated_1k_ms":2.601,"pebble_readpaginated_100_ms":2.176,"pebble_readpaginated_1m_bytes_op":1598723216,"pebble_readpaginated_1m_allocs_op":10034741,"sqlite_readpaginated_1k_ms":6.012,"pebble_writepack_1m_ms":1240.073}
…FTER PaginateGrantsBySync's decode workers finish (vs #57 which folded translate INTO the decode workers and stole memory bandwidth from proto.Unmarshal at 1 M scale). Pool uses batched dispatch (256 records/batch \u00d7 4 workers), pre-allocated v2 arena sized to EXACT len(records) (no waste at any scale), and threshold-gated (\u22641024 records goes serial; small pages avoid dispatch overhead). Primary 466.9\u2192431.6 ms (-7.6 %). 10k -33 % (apparent regression in #59 confirmed as noise), 100k -4.8 %, 1k -3.8 %, 100 +6.4 % (serial path unchanged, noise). bytes_op flat at 1.60 GB. SQLite + WritePack sentinels flat.</description: {"hypothesis": "#57's parallel-translate-folded-into-decode failed because workers were memory-bandwidth-bound doing proto.Unmarshal; adding v2 stub writes to the same workers slowed Unmarshal. A SEPARATE pool running AFTER decode finishes has independent bandwidth budget \u2014 no contention. Plus exact-sized arena fixes #57's small-scale over-allocation regression.", "result": "Primary -7.6 % (431.6 ms, new best). Mechanism validated: same translateV3GrantAt code, different scheduling = different outcome.", "what_changed_vs_57": "(a) Separate goroutine pool created in adapter.ListGrants after PaginateGrantsBySync returns, instead of callback fired from inside decode workers. (b) Arena sized to len(records) not limit \u2014 no waste when last page returns fewer than limit. (c) Threshold gate at 1024: small pages (100, 1k bench scales) keep the cheap serial path; large pages use parallel.", "mechanism_proof": "10k bench: 10.10\u21926.75 ms (-33 %). This was the scale that showed an apparent +25 % regression in #59 (kept anyway because primary improved); now we see it was genuine noise (or pool-warmup-related), since the same scale dropped to 6.75 ms here, BELOW the pre-#59 baseline of 8.06 ms. The translate-parallel mechanism contributes ~3-4 ms wallclock at 10k.", "production_safety": "Arena pre-sized to len(records), bounded per-page. No per-total-record memory growth. Workers spawned per-call; lifetime is the single ListGrants call. At 100 GB c1z production scale, same per-page pattern \u2014 4 workers \u00d7 translate work bounded by page size.", "cumulative_progress": "Read session: 1229.6 \u2192 431.6 ms = -64.9 % from baseline. 13 experiments, 7 keeps.", "next_action_hint": "With translate parallelized, the remaining main-goroutine serial work is: NewStore unpack ~40 ms, Close ~50 ms, iter loop body ~137 ms (Pebble-internal, hard to optimize), response build/wrap ~20 ms. Best targets: (a) profile the NEW baseline at 432 ms to see what's now dominant. (b) Check if Close has avoidable work. (c) The bench's 100 NewIter calls might still have setup cost \u2014 reconsider iter caching (now with proven framework from #58's correctness gating, and the threshold pattern from this iteration's success could apply)."} Result: {"status":"keep","pebble_readpaginated_1m_ms":431.607,"pebble_readpaginated_100k_ms":53.609,"pebble_readpaginated_10k_ms":6.749,"pebble_readpaginated_1k_ms":2.501,"pebble_readpaginated_100_ms":2.32,"pebble_readpaginated_1m_bytes_op":1600878176,"pebble_readpaginated_1m_allocs_op":10036365,"sqlite_readpaginated_1k_ms":5.884,"pebble_writepack_1m_ms":1252.14}
…te pool's batch size (#61). 4\u00d7 fewer channel dispatches per page (156\u219239 batches at 10k page size). Pool's initial valueBuf cap also raised 32 KB\u219296 KB to fit 256 records \u00d7 ~250 B avg = 64 KB without an append-grow on first batch. Pool retention caps bumped proportionally (512 KB valueBuf, 1024 ends). Primary 431.6\u2192419.9 ms (-2.7 %). 100k -4.1 %, 10k -5.9 %, 100 -10 %. 1k +26 % (3 ms absolute, noise tier \u2014 prior runs at 1k have shown \u00b120 % swings). bytes_op flat at 1.62 GB. SQLite + WritePack sentinels flat.</description: {"hypothesis": "Each PaginateGrantsBySync dispatches 156 batches/page (at 10k page size, batchSize=64). Bumping to batchSize=256 cuts dispatches 4\u00d7 while keeping good worker granularity (4 workers \u00d7 ~10 batches each per page at 1 M scale). Workers still load-balanced; channel-op overhead drops.", "result": "Primary -2.7 % (419.9 ms, new best). 4/5 scales improved (100k -4 %, 10k -6 %, 100 -10 %); 1k +26 % is noise-tier (3 ms absolute).", "mechanism_validated": "Channel dispatches per page: 156\u219239 (4\u00d7 reduction). Workers process 256-record batches instead of 64. Pool initial cap sized to fit (96 KB), no first-batch append-grow.", "small_scale_caveat": "1k bench +26 % stands out as a noise outlier. Below threshold (1024), it uses serial translate path \u2014 unchanged. The only delta affecting 1k is decode batchSize (1k records \u2192 4 batches with 256 vs 16 batches with 64). At 256, 4 workers each get 1 batch = full parallel utilization. Should be neutral or positive, not regression. The +26 % is likely genuine measurement noise at the 3-ms absolute scale where b.N=2 iters compound variance.", "production_safety": "Pool entries bounded at 512 KB valueBuf (up from 256 KB but still negligible vs proto.Unmarshal's allocations). 4 workers \u00d7 page-bounded work. At 100 GB c1z scale, no change \u2014 same per-page pattern.", "cumulative_progress": "Read session: 1229.6\u2192419.9 ms = -65.8 % from baseline. 14 experiments, 8 keeps.", "diminishing_returns_check": "This is a small-but-real -2.7 % win. The session is now well past the easy-win zone. Profile shows the remaining wallclock is dominated by pebble.Iterator.Next (Pebble-internal LSM merging, 133 ms/iter on main goroutine \u2014 cannot easily optimize) and GC overhead from proto.Unmarshal's nested allocations (~50 ms wallclock from 1.1 s cum CPU on scanObjects/tryDeferToSpanScan). Either requires architectural changes (custom decoder, etc).", "next_action_hint": "Real plateau approaches. Remaining options: (a) custom v3.GrantRecord decoder with arena-allocated nested fields \u2014 high effort, possible large win (-15-25 % wallclock from reduced GC). (b) Re-run baseline 2-3 times to confirm noise floor before deciding to stop. (c) Consider session done."} Result: {"status":"keep","pebble_readpaginated_1m_ms":419.943,"pebble_readpaginated_100k_ms":51.428,"pebble_readpaginated_10k_ms":6.353,"pebble_readpaginated_1k_ms":3.158,"pebble_readpaginated_100_ms":2.078,"pebble_readpaginated_1m_bytes_op":1621325680,"pebble_readpaginated_1m_allocs_op":10036249,"sqlite_readpaginated_1k_ms":6.014,"pebble_writepack_1m_ms":1255.701}
…ng ~125 ms wallclock in 4-way parallel vs main's ~137 ms iter loop \u2014 slight under-saturation. 6 workers drop parallel decode wallclock to ~83 ms, eliminating residual wg.Wait blocking at page boundaries where workers were still draining batches when main reached close(jobs). Primary 419.9\u2192405.9 ms (-3.3 %). ALL scales improved: 100k -7.9 %, 10k -3.4 %, 1k -12.1 % (recovered from #62's apparent +26 %, confirming that was noise), 100 -7.2 %. bytes_op flat. SQLite + WritePack sentinels flat.</description: {"hypothesis": "Workers were slightly under-saturated (125 ms wallclock vs main's 137 ms iter loop). Going to 6 workers makes parallel decode finish well before main, eliminating the residual page-boundary wg.Wait blocking.", "result": "Primary -3.3 % (405.9 ms, new best). All 5 scales improved \u2014 the cleanest cross-scale win in this session, including 1k recovering from #62's noisy +26 % outlier.", "mechanism_validated": "The page-boundary blocking was real but harder to see in CPU profile (it's WALLCLOCK time, not CPU). 100 pages \u00d7 ~50-150 \u00b5s residual wait = 5-15 ms wallclock per iter. Eliminating that gives us ~14 ms reduction = -3.3 %.", "lesson_recorded": "When a parallel-work model has 'main slightly faster than workers' per work unit, the residual wait at the synchronization point (wg.Wait, channel close) accumulates across N units. Bumping worker count to put workers comfortably faster than main eliminates this. The optimal worker-to-main ratio is one where workers finish CLEARLY before main, not just barely. Inverse of typical advice (Amdahl says workers > main wastes resources \u2014 but in a pipeline, workers MUST be slack-bounded to avoid synchronization tax).", "production_safety": "6 worker goroutines spawned per PaginateGrantsBySync call. Cost: 6 \u00d7 ~8 KB stack = 48 KB per call. At production 100 GB c1z scale with many concurrent reads, modest per-call overhead. Could go higher (8?) on machines with many cores. 6 is a defensible default that should work on \u22656-core hosts. Won't hurt on smaller-core machines (Go runtime multiplexes).", "1k_resolution": "The +26 % regression in #62's 1k bench scale was indeed measurement noise. This iteration's 1k -12.1 % brings it BELOW the original session-best 1k value of 2.85 ms (now 2.78 ms). Validates the decision to keep #62 despite the apparent regression \u2014 noise at small scales must be discounted vs primary-scale signal.", "cumulative_progress": "Read session: 1229.6\u2192405.9 ms = -66.9 % from baseline. 15 experiments, 9 keeps.", "next_action_hint": "Try 8 workers (matching half-cores on 16-core bench). If 6 worked, 8 might continue the trend OR show the per-worker overhead crossover point. After this, real plateau approaches \u2014 remaining big targets require architectural changes (custom proto decoder, parallel iterator scan)."} Result: {"status":"keep","pebble_readpaginated_1m_ms":405.897,"pebble_readpaginated_100k_ms":47.361,"pebble_readpaginated_10k_ms":6.143,"pebble_readpaginated_1k_ms":2.783,"pebble_readpaginated_100_ms":1.93,"pebble_readpaginated_1m_bytes_op":1620036640,"pebble_readpaginated_1m_allocs_op":10036674,"sqlite_readpaginated_1k_ms":5.971,"pebble_writepack_1m_ms":1252.471}
…5.9\u2192402.85 ms (-0.7 %, within noise). Mixed scales: 100k +1.1 %, 10k -1 %, 1k -5 %, 100 +5 % \u2014 all within noise. The optimum is FLAT in the 6\u20137 range; the regression in #64 to 8 was a real overhead cliff, not gradual. SQLite + WritePack sentinels flat. Kept as a marginal improvement on primary with noise-tier secondary movements, but properly characterized as bisection result rather than a clean win.</description: {"hypothesis": "If the true optimum sits between 6 and 7, bisecting should reveal which. If it's at 6, expect 7 to regress slightly. If at 7, expect improvement.", "result": "Primary -0.7 % (essentially flat). Confirms the function is FLAT between 6 and 7, with a hard overhead cliff at 8.", "characterization": "Worker count sweep result: 4\u2192431, 6\u2192405.9, 7\u2192402.85, 8\u2192416.6. Min plateau at 6-7 (within \u00b13 ms noise), sharp regression at 8. Recommended: 7 (slight edge, no obvious downside).", "low_confidence_keep": "Primary improvement is within typical noise floor. Kept because (a) primary did improve, (b) the bisection completed the worker-count characterization, (c) no regression on sentinels. If we discover regression in future re-runs, the cost of reverting is one commit.", "cumulative_progress": "Read session: 1229.6\u2192402.85 ms = -67.2 % from baseline. 17 experiments, 10 keeps.", "plateau_status": "We are clearly at plateau. Last 4 experiments (#62-#65): -2.7, -3.3, +2.6, -0.7. Net forward progress diminishing. The 6\u21927 bisection completes the worker-count sweep and identifies that future tuning of worker count has no remaining slack.", "next_action_hint": "Plateau confirmed. Two remaining architectural paths: (a) custom proto decoder for v3.GrantRecord (high effort, estimated 4-6 % from skipping reflection in fast-path \u2014 lower than I'd initially estimated since proto.Unmarshal already uses table-driven fast path). (b) Parallel iter range-splitting (high effort, would require general midpoint discovery since the bench's grant-NNNNN external_ids would let us cheat but production data is different distribution). (c) Finalize at -67.2 %. Recommendation: ONE more experiment to attempt (a) or (b) at smaller scope, then finalize regardless of outcome."} Result: {"status":"keep","pebble_readpaginated_1m_ms":402.849,"pebble_readpaginated_100k_ms":47.899,"pebble_readpaginated_10k_ms":6.088,"pebble_readpaginated_1k_ms":2.633,"pebble_readpaginated_100_ms":2.015,"pebble_readpaginated_1m_bytes_op":1607015596,"pebble_readpaginated_1m_allocs_op":10036203,"sqlite_readpaginated_1k_ms":5.946,"pebble_writepack_1m_ms":1264.515}
…rantRecordFast). Hand-decodes fields 1-4 (sync_id, external_id, entitlement, principal) using google.golang.org/protobuf/encoding/protowire. Nested EntitlementRef + PrincipalRef use ARENA-allocated slots (one per page-record-slot), eliminating 2 nested allocations per record. Correctness guard: falls back to proto.Unmarshal if wire stream contains field 8 (annotations) or 9 (sources) \u2014 grant shapes outside the bench's simple-grant pattern get the full-decode path. Primary 402.85\u2192398.10 ms (-1.2 %, new best). ALLOCS_OP 10.04M\u21927.04M (-30 %, the key mechanism signal). bytes_op -4 %. 100k -5.1 % validates direction. Small-scale regressions (100 +17 %, 1k +13 %, 10k +8 %) traced to 3\u00d7 larger per-page arena (grants+entitlements+principals \u00d7 limit slots = 2.4 MB memclr per page) mostly wasted at small scales. SQLite + WritePack sentinels flat.</description: {"hypothesis": "proto.Unmarshal allocates 2 nested messages per v3.GrantRecord (EntitlementRef, PrincipalRef) via the runtime's MessageInfo.unmarshalPointer path \u2014 visible as ~10M allocs/iter and contributes to GC scan overhead. A hand-rolled wire decoder using arena-allocated nested slots collapses 2M nested allocs to 200 slice allocs. Expected: -3 to -5 % wallclock from reduced GC + skipped reflection.", "result": "Primary -1.2 % (398.10 ms, new best). ALLOCS_OP -30 % is the dominant signal \u2014 mechanism strongly validated. The wallclock impact is smaller than alloc impact because (a) GC was already only ~50ms of the 405ms wallclock, (b) 4-6 workers parallel-decode mostly absorbed the alloc work into idle CPU cycles. The allocs win is real and translates to production GC pressure reduction.", "correctness_guard_pattern": "The fallback-to-proto.Unmarshal-on-complex-fields pattern is the correctness pivot. Production grants with annotations or sources get the full decode path. Tests pass: adapter_test.go (end-to-end CRUD), paginate_test.go (page boundaries), equivalence tests. The IDL-frozen constraint from autoresearch.md means the wire tags 1-4 are stable for v3 storage's lifetime.", "small_scale_regression_analysis": "100/1k/10k bench scales regressed +8 to +17 % (300-500 \u00b5s absolute). Root cause: arena now pre-allocates limit=10000 slots for each of 3 nested arrays (grants + entitlements + principals = 2.4 MB total). At 100-record scale, 99 % of those slots are unused; the memclr cost dominates the per-bench-iter wallclock. Same pattern as #51 (outer arena over-allocation). Not fixable without slab-style growable arena which has its own overhead (#52 discard). Tradeoff accepted: primary scale + production scale (100+ GB c1z) benefit from the arena; small bench scales are a measurement artifact.", "production_safety": "Arena bounded per-page (limit slots), not per-total-record. At 100 GB c1z production scale, per-page arena is still ~10000 \u00d7 3 \u00d7 sizeof(struct) = ~2.4 MB peak per page. Bounded.", "decoder_complexity_cost": "Added ~190 lines of custom protowire decode + fallback in pkg/dotc1z/engine/pebble/unmarshal_grant_fast.go. Maintenance burden: if v3.GrantRecord proto IDL changes (field numbers or types for 1-4), the fast path must be updated or it'll fall back to proto.Unmarshal silently. autoresearch.md's frozen-IDL constraint on proto/c1/storage/v3/ keeps this stable.", "cumulative_progress": "Read session: 1229.6\u2192398.10 ms = -67.6 % from baseline. 22 experiments, 11 keeps.", "next_action_hint": "The custom decoder delivered the only above-noise win in the last 8 attempts. Session can plausibly finalize here at -67.6 % cumulative. If continuing: the fast-path skips field 5 (discovered_at) and that's not in the fallback condition \u2014 verify it's not a correctness gap for production grants WITH discovered_at."}
Result: {"status":"keep","pebble_readpaginated_1m_ms":398.097,"pebble_readpaginated_100k_ms":45.463,"pebble_readpaginated_10k_ms":6.55,"pebble_readpaginated_1k_ms":2.973,"pebble_readpaginated_100_ms":2.371,"pebble_readpaginated_1m_bytes_op":1547748892,"pebble_readpaginated_1m_allocs_op":7036994,"sqlite_readpaginated_1k_ms":6.088,"pebble_writepack_1m_ms":1265.402}
…mer of PaginateGrantsBySync reads r.GetSyncId() (callers know syncID separately, it's passed into PaginateGrantsBySync as a parameter). Replaced the ConsumeString + SetSyncId with ConsumeFieldValue (skip-bytes). Primary 398.1\u2192396.2 ms (-0.5 %, within noise but new best). ALLOCS_OP 7.04M\u21926.04M (-14 %, exactly the 1M SetSyncId allocations eliminated). bytes_op -2.4 %. Small scales mixed (100k +3.3 %, 10k +12 %, 1k +6.7 %, 100 +2 %) \u2014 all within noise. SQLite + WritePack sentinels flat.</description: {"hypothesis": "SetSyncId on the opaque proto stores the string in the message struct + sets a presence bit. The string itself isn't a separate alloc (string header from ConsumeString points into the wire buffer) BUT proto opaque sometimes escapes to heap. Skipping the entire field 1 path saves both the parse and any storage allocation.", "result": "Primary -0.5 % (within noise, new best 396.19 ms). Allocs_op -14 % (1M reduction) is the unambiguous mechanism signal. Total allocs now 6.04M vs original 17.03M = -64.5 % session-cumulative.", "alloc_attribution_surprise": "I expected SetSyncId to not allocate (string header reuse), but allocs_op clearly dropped 1M. Likely cause: proto opaque API's string storage in xxx_hidden_SyncId may force an escape-to-heap of the string header when setting, or the presence-bit update path triggers an alloc. Either way, eliminating it saves 1M heap allocs at 1M scale.", "production_safety": "No read-path consumer was using r.GetSyncId() (verified by grep). Write-path code uses sync_id via different paths (parameters, encoded keys). Future code that reads SyncId from read-path records would see empty string \u2014 documented in the case 1 comment in unmarshal_grant_fast.go.", "cumulative_progress": "Read session: 1229.6\u2192396.19 ms = -67.8 % from baseline. 23 experiments, 12 keeps.", "decoder_complete_state": "Fast decoder now handles: field 2 (external_id), 3 (entitlement), 4 (principal) WITH SET; field 1 (sync_id) SKIPPED; fields 5/6/7 (discovered_at/expansion/needs_expansion) SKIPPED via default case; fields 8/9 (annotations/sources) trigger FALLBACK to proto.Unmarshal. Roughly half the fields are skipped, which is appropriate since v2 translation reads only fields 2/3/4/8/9.", "next_action_hint": "Two more small angles untested: (a) similar skip-fields trim in unmarshalEntitlementRefFast/PrincipalRefFast if any sub-fields are unused (verify in V3GrantToV2 \u2014 it actually uses all 3 EntitlementRef fields and both PrincipalRef fields, so no skips possible). (b) The bench is now solidly at ~395 ms region; further gains require attacking iter.Next (Pebble-internal) which we can't. Recommend stop here."}
Result: {"status":"keep","pebble_readpaginated_1m_ms":396.19,"pebble_readpaginated_100k_ms":46.976,"pebble_readpaginated_10k_ms":7.326,"pebble_readpaginated_1k_ms":3.166,"pebble_readpaginated_100_ms":2.422,"pebble_readpaginated_1m_bytes_op":1510392848,"pebble_readpaginated_1m_allocs_op":6036787,"sqlite_readpaginated_1k_ms":5.905,"pebble_writepack_1m_ms":1264.335}
…ConsumeTag's varint-decode + bit-split for the common case of fields 1-15 (which have single-byte wire tags). Pre-computed 9 byte constants for v3.GrantRecord field tags (1=sync_id\u20269=sources). Direct byte switch dispatches to specialized field handlers; multi-byte tags or fields 10-15 with unrecognized single-byte tags fall back to general ConsumeTag + ConsumeFieldValue path. Also uses ConsumeBytes for all field-skipping (bounds-checked, lint-friendly). Primary 396.19\u2192392.55 ms (-0.9 %, new best). 100k -3.8 %, 10k -26 %, 100 -7 %. 1k +1.6 % noise. allocs_op unchanged at 6.04M (pure CPU optimization). SQLite + WritePack sentinels flat.</description: {"hypothesis": "protowire.ConsumeTag reads a varint then bit-splits to produce (Number, Type). For single-byte tags (fields 1-15), this is ~5 ns/call \u00d7 4 fields per record \u00d7 1 M records = 20 ms CPU / 7 workers = ~3 ms wallclock. Direct byte comparison: ~1 ns/field. Save ~16 ms CPU / 7 workers \u2248 2.3 ms wallclock.", "result": "Primary -0.9 % (-3.64 ms wallclock, exceeded the predicted 2.3 ms). 10k -26 % at the smaller scale where the CPU savings are a larger fraction of total bench time. Mechanism validated.", "why_bigger_than_predicted": "Eliminating ConsumeTag also removes the function-call dispatch overhead (Go's compiler may now inline the entire switch). The 10k -26 % is consistent with this: at smaller scales, the dispatch overhead is a larger fraction of total time, so the savings show up proportionally larger.", "production_safety": "Multi-byte tags (fields >= 16) fall back via the general ConsumeTag path \u2014 if v3.GrantRecord ever adds a field beyond 15, we'll see the fallback trigger but no correctness loss. ALL bench scales improved or held flat, no sentinel regressions.", "cumulative_progress": "Read session: 1229.6\u2192392.55 ms = -68.1 % from baseline. 24 experiments, 13 keeps, 11 discards.", "decoder_architecture_complete": "Fast decoder fully optimized: direct byte tag comparison, arena-allocated nested slots, skip-unused-fields, ConsumeBytes for bounds-safe skipping, fallback-to-proto.Unmarshal on annotations/sources. ~200 lines of code. All production-safe.", "next_action_hint": "I think we're truly at plateau now. The last 3 wins (#70, #71, this) chained: each addressed one specific overhead source revealed by profile (nested allocs, set call, tag parsing). After #72 there's no comparable target remaining without architectural change (parallel iter range-splitting requires general midpoint discovery, won't generalize from bench). Strongly recommend finalize."}
Result: {"status":"keep","pebble_readpaginated_1m_ms":392.549,"pebble_readpaginated_100k_ms":45.207,"pebble_readpaginated_10k_ms":5.423,"pebble_readpaginated_1k_ms":3.223,"pebble_readpaginated_100_ms":2.248,"pebble_readpaginated_1m_bytes_op":1504778816,"pebble_readpaginated_1m_allocs_op":6036728,"sqlite_readpaginated_1k_ms":5.892,"pebble_writepack_1m_ms":1276.389}
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Pebble engine read perf: −68.1% on 1 M-grant
UnpackReadGrantsAutonomous-experiment loop output, second session. Builds directly on top of #876 (WritePack autoresearch). All code changes scope to
pkg/dotc1z/engine/pebble/{paginate.go, adapter.go, translate_v2.go, unmarshal_grant_fast.go}andpkg/dotc1z/format/v3/envelope.go. The c1z wire format, the v3 proto schemas, the SQLite engine, and the public read APIs are untouched.Headline numbers
UnpackReadGrants: paginated read (open.c1z→ unpack →SetCurrentSync→ walk grants in 10k pages → close)¹ Baseline = #876 (WritePack autoresearch) tip, i.e. the post-WritePack state. The pre-WritePack-session read numbers are slightly worse but not directly comparable since #876's storage layout changes (L0 sub-levels, SST sizing) themselves affect read perf.
Allocations on the 1 M
UnpackReadGrantsThe allocation reduction is the dominant production-relevant signal: GC pressure on the read path drops sharply, which matters for tenants paging through large grants tables.
Small-scale regression caveat
100/1k bench scales regressed +10/+27 % (≈300–500 µs absolute). Root cause: the per-page arenas (v2.Grant stubs, GrantRecord outer, custom-decoder nested slots) pre-allocate
limitslots; at 100-record scale, 99 % of those slots are unused and the memclr cost dominates the per-bench-iter wallclock. Production callers (sync engines, batch processors) overwhelmingly do bulk page reads — the regression doesn't apply at production-relevant scales. Slab-style growable arenas were tried (#52) and lost to the simpler fixed-size arena.What changed (13 kept commits, each with a profile-justified mechanism)
48 → 13 kept experiments. Each commit's message has the per-experiment delta, mechanism, and ASI.
Major wins (>5 % each, ordered chronologically as compounded)
grantV2ReadArena— arena the 6 nested v2 stubs (Grant+Entitlement+ 2 ×Resource+ 2 ×ResourceId) per record inadapter.ListGrantsproto.UnmarshalinPaginateGrantsBySynclen(records)), threshold-gated at 1024 records. Avoids #57's bandwidth contention with decode workers.wg.Waitblocking); 6–7 puts workers comfortably ahead of main, eliminating residual sync tax. Sweep: 4 → 431 ms, 6 → 406 ms, 7 → 403 ms, 8 → 417 ms (overhead cliff at 8).Minor wins (compounded)
batchSize64 → 256 — 4× fewer channel dispatches (−2.7 %).grantReadArenaforv3.GrantRecord— 1 M outer allocs → 100 slice allocs (−2.3 %).sync.Poolfor the per-batch unmarshal value buffer + offsets array — bytes/op −24 %, wallclock −1.5 %.ExtractZstdTar— 4-worker pool consumes (target, mode, data) jobs from the serial tar+zstd reader (−1.2 % at 1 M, larger at small scales; bigger payoff at production-GB c1z files where unpack is a larger fraction).v3.GrantRecord(unmarshalGrantRecordFast) — fields 1–4 with arena-allocated nestedEntitlementRef+PrincipalRefslots; fallback toproto.Unmarshalwhen fields 8/9 (annotations/sources) are present (−1.2 %, allocs −30 %).SetSyncIdin the fast decoder — no read-path consumer ofPaginateGrantsBySyncreadsr.GetSyncId()(callers knowsyncIDseparately) (−0.5 %, allocs −14 %).protowire.ConsumeTagvarint-decode + bit-split; compiler inlines the byte-dispatch switch (−0.9 %).Methodology
Identical loop to #876: form a profile-driven hypothesis → make the smallest change → run
./autoresearch.sh(~17 s — much faster than the WritePack loop's ~190 s, so this session ran more iterations) → run./autoresearch.checks.sh(engine + adapter + compactor + equivalence + envelope + SQLite tests + lint +go.mod/proto drift) → keep iffpebble_readpaginated_1m_msimproved with no checks failed.pebble_readpaginated_1m_ms1229.6 → 392.5).autoresearch.jsonlwith the specific mechanism by which each failed.Safety
BenchmarkRegisteredSQLiteUnpackReadGrants/grants=1000) tracked every iteration. Final 5.89 ms vs baseline 5.87 ms (within noise). SQLite engine code untouched.pebble_writepack_1m_ms) tracked every iteration with ±5 % budget. Final 1252 ms vs the WritePack session's ending 1262 ms — within budget; the read-side changes don't disturb Autoresearch/pebble perf 20260525 #876's wins.go.mod/go.sumunchanged.proto/c1/storage/v3/untouched. The custom decoder relies on the IDL being frozen (perautoresearch.md's scope constraint) and falls back toproto.Unmarshalon fields 8/9 (annotations,sources) so production grants with annotations get full-decode safety.PaginateGrantsBySynccalls and brokeTestAdapterStartSyncAndPutGrantsbecause reused iterators capture a stale snapshot. Discarded; current code path always opens fresh iterators.What didn't work (highlights — full list in
autoresearch.ideas.md+autoresearch.jsonl)Documented discards with the mechanism of failure:
GrantRecordarena delivered.proto.Unmarshal; adding v2 stub writes stole bandwidth from the parallelism. Separated pool (Refactor c1z decoder options #61) delivered the same mechanism cleanly.PaginateGrantsBySynccalls (Add support for provisioning to the ConnectorBuilder interface #58) — Pebble'sNewIteris already optimized;SetBoundsre-evaluation of the merging tree cancelled the savings. Also unsafe outsideWithReadOnly.tryDeferToSpanScanat 570 ms cum CPU; that scan was fromproto.Unmarshalallocations, not goroutine creation.ctx.Err()check (WIP: Changes to the service mode/c1api machinery to better support provisioning use cases #68) — Go compiler devirtualizescontext.Background.Err; the eliminated work was effectively free, and the throttle branch added more than it saved.unsafe.Stringzero-copy strings (Only log the sync step on the first page of results. #75) — spectacular allocs −99.3 % but +4.5 % wallclock regression. Required disabling the per-batch valueBuf pool, and the resulting 96 KB heap allocations crossed Go's large-alloc path (mheapglobal lock, ~5 µs/call) which outweighed the string-copy savings. Pattern viable with sophisticated arena+pool coordination; out of scope for this session.Files of interest
pkg/dotc1z/engine/pebble/paginate.go— batched parallelproto.Unmarshal,sync.Poolfor batch buffers, worker count + batchSize tuning, grant-record outer arenapkg/dotc1z/engine/pebble/adapter.go— separate parallel v3→v2 translate pool with threshold gatepkg/dotc1z/engine/pebble/translate_v2.go—grantV2ReadArenafor the 6 nested v2.Grant stubspkg/dotc1z/engine/pebble/unmarshal_grant_fast.go(NEW, ~241 lines) — custom hand-rolledprotowiredecoder forv3.GrantRecord, single-byte tag dispatch, fallback toproto.Unmarshalon fields 8/9pkg/dotc1z/format/v3/envelope.go— 4-worker parallel file writes inExtractZstdTarautoresearch.md— session objective, primary/secondary metrics, scope, stop conditions, resume protocolautoresearch.ideas.md— backlog with kept/discarded annotationsautoresearch.jsonl— full 27-experiment log with ASI per iterationRaw bench output (final state, commit
9afeea18)UnpackReadGrants — autoresearch HEAD
UnpackReadGrants — session baseline (commit `90b5ac6c`, #876 tip)