⚡ StrikeDB

One engine. Every data model. The fastest path for AI agents, RAG, and real-time apps.

StrikeDB is a single, unified data engine where relational tables, key-value, vectors, time-series, pub/sub, AI agent memory, and RAG are all views over one storage substrate — not five bolted-together systems with five licenses, five release cadences, and five places for drift to hide.

"Development at the speed of light" — but with the gaps fixed that every system you've been stitching together leaves open.

📚 Docs · 🚀 Live Benchmarks · 🧠 Agent Memory · 🔍 MITM Cache Debugger

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🏷️ Status

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🚀 Why StrikeDB

	Redis	pgvector	Qdrant	Milvus	Mem0	Zep	SpacetimeDB	StrikeDB
KV / counters	✅	❌	❌	❌	❌	❌	✅	✅
Relational tables	❌	✅	❌	❌	❌	❌	✅	✅
Vector / ANN	❌	⚠️ bolt-on	✅	✅	⚠️	⚠️	❌	✅ INT8 + f32 rerank
Time-series	❌	⚠️	❌	⚠️	❌	❌	❌	✅ native
Pub/sub push over wire	✅	❌	❌	❌	❌	❌	✅	✅ SUBSCRIBE/PUBLISH
RAG as a planned query	❌	❌	⚠️	⚠️	❌	❌	❌	✅ cost-based
Graph agent memory	❌	❌	❌	❌	✅	✅	❌	✅ typed edges + traversal
Bi-temporal facts (as-of)	❌	❌	❌	❌	❌	✅	❌	✅ native primitive
Procedural memory	❌	❌	❌	❌	✅	❌	❌	✅ per-agent namespace
MITM cache debugging	❌	❌	❌	❌	❌	❌	❌	✅ built-in
Logic in the DB	❌	❌	❌	❌	❌	❌	✅	✅ fuel-metered
ACL + password auth	⚠️ 6.x+	❌	✅	❌	❌	❌	❌	✅ SHA-256 + salt
One consistency model	❌	✅	✅	⚠️	❌	❌	⚠️	✅ MVCC
One license	BSD	PostgreSQL	Apache-2.0	Apache-2.0	MIT	Apache-2.0	BSL	✅ Apache-2.0

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📦 Install & Run

# build the release binary (zero external crates — pure stdlib Rust)
cargo build --release

# RAISE THE FD LIMIT FIRST — without it deep pipelines cap at ~4.5M/s instead
# of the documented ~16M/s (100 clients starve the 1024-fd default ceiling).
ulimit -n 100000

# start the server on the RESP wire (talk to it with any Redis client)
DBSTRIKE_WAL=./dbstrike.wal ./target/release/dbstrike 127.0.0.1:6380

# optional: write all stderr output to a log file
DBSTRIKE_WAL=./dbstrike.wal ./target/release/dbstrike 127.0.0.1:6380 --log dbstrike.log

# optional: enable password auth (no auth by default)
DBSTRIKE_PASS=mypassword DBSTRIKE_WAL=./dbstrike.wal ./target/release/dbstrike 127.0.0.1:6380

Talk to it with redis-cli, nc, or the bundled harnesses:

redis-cli -p 6380 PING          # -> PONG
redis-cli -p 6380 SET user:1 ada
redis-cli -p 6380 VADD 1 1.0 0.0 0.0
redis-cli -p 6380 VSEARCH 5 1.0 0.0 0.0
redis-cli -p 6380 VDEL 1          # -> :1  tombstone a vector (no longer searchable)
redis-cli -p 6380 SUBSCRIBE trades &
redis-cli -p 6380 PUBLISH trades "hello"
redis-cli -p 6380 CHECKPOINT   # snapshot current state + truncate WAL

# with password auth enabled:
redis-cli -a mypassword -p 6380 PING
redis-benchmark -h 127.0.0.1 -p 6380 -a mypassword -P 64 -c 100 -n 200000 -t set,get

# native Rust bench harness — in-process, 29 sections in ~22s
cargo run --release -p bench

# add --tcp to also exercise SUBSCRIBE / PUBLISH end-to-end against
# a running server on 127.0.0.1:6380
./target/release/dbstrike-bench --tcp 127.0.0.1:6380

# 21-section Python integration + fuzz + crash-recovery suite
python3 tests/test_dbstrike.py

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🎬 Real-world demos (native Rust, in crates/demos)

Three self-contained CLI apps that exercise the full engine over the RESP wire — proof the primitives compose into actual products, not just microbenchmarks. Zero external crates. Every app has a --bench (or --latency) mode that prints p50 / p99 / throughput + client RSS, so "fast" is objective, not a claim.

Measured on this machine (2000 stored memories, single connection)

App	Op	p50	p99	Throughput	Client RSS
agent-cli	MEM.REMEMBER (LTM + graph + BM25)	170 µs	247 µs	5,619 ops/s	2 MB
agent-cli	MEM.RECALL (hybrid dense + sparse)	311 µs	848 µs	3,022 ops/s	2 MB
tsdash	TSADD	26 µs	44 µs	36,361 ops/s	2 MB
tsdash	TSRANGE (1000-sample window, single-shard)	166 µs	252 µs	5,734 ops/s	3 MB
tsdash	TSRANGE.LATEST 100 (dashboard primitive)	52 µs	93 µs	17,035 ops/s	—
rtchat	PUBLISH→SUBSCRIBE round-trip	31 µs	120 µs	—	—

MEM.RECALL was 4.6× faster after this round's salience-cache optimization (651 → 3,022 ops/s; p50 2,337 → 311 µs). See the roadmap salience_cache entry below.

# start the server first
./target/release/dbstrike 127.0.0.1:6380 &

# 1) agent-cli — interactive AI agent (WM + LTM + graph + bi-temporal + RAG)
./target/release/agent-cli 127.0.0.1:6380 alice
#   :remember Alice is a senior engineer at Acme
#   :recall engineer               → semantic + keyword hybrid recall
#   :link 1 works_at 2             → typed edge in the memory graph
#   :fact CEO of Acme at 3000 for alice   → bi-temporal fact
#   :asof 5000 title               → as-of recall (Zep-style)

# 2) tsdash — live time-series dashboard (TSADD + TSRANGE + sparklines)
./target/release/tsdash 127.0.0.1:6380 30
#   renders cpu/mem/rps sparklines updated at 2 Hz for 30 seconds
#   dashboards should prefer TSRANGE.LATEST — 52µs p50 vs 166µs for a full range:
redis-cli -p 6380 TSRANGE.LATEST cpu 60

# 3) rtchat — realtime chat CLI (WebRTC-signaling shape: rooms + presence +
#    typing + push-latency measurement).  Note: video encoding needs
#    external media libs (ffmpeg/opencv) which we don't bundle; this
#    exercises the low-latency signalling substrate a real video app runs on.
./target/release/rtchat --self alice --room lobby --addr 127.0.0.1:6380
./target/release/rtchat --self bob   --room lobby --addr 127.0.0.1:6380
./target/release/rtchat --latency  --addr 127.0.0.1:6380   # PUB→SUB probe
#   measured: p50 = 30 µs, p99 = 89 µs round-trip over the wire

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⚡ The "speed of light" path

SpacetimeDB's whole pitch is logic inside the database, so the network hop between app and DB disappears. StrikeDB does the same — and fixes the one flaw that makes SpacetimeDB dangerous: the reducer-stall problem (one bad reducer serializes the entire DB).

Three in-house fixes:

1. Bulkhead by shard — a reducer only locks its key-range, not the world.
2. Fuel-metered WASM — every instruction is charged against a budget; overrun aborts and rolls back cleanly instead of trusting the author.
3. Circuit breaker — a reducer erroring above a threshold is auto-quarantined.

Measured: REDUCE p99 = 45.5µs over RESP, and zero lost updates under 32-thread × 500-op in-process contention (16,000/16,000 in 0.15s).

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📡 Realtime push over the wire — SUBSCRIBE / PUBLISH

Redis-compatible pub/sub sitting on top of the internal reactive/CDC layer:

# terminal A — subscriber
redis-cli -p 6380 SUBSCRIBE trades

# terminal B — publisher
redis-cli -p 6380 PUBLISH trades "AAPL 42"
# subscriber prints:  1) "message"  2) "trades"  3) "AAPL 42"

PUBLISH is a durable write on chan:<name> — every subscribed prefix receives the pushed event via the same MVCC+WAL substrate. Verified end-to-end in the native Rust bench in --tcp mode.

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🧠 AI Agent Memory — built to beat Mem0 / Letta / Zep

Every category leader's core primitive, on one substrate, in one process.

Memory type	Primitive from	StrikeDB API	Key format
Working memory (STM)	Redis	wm_set/wm_get (TTL)	mem:wm:<agent>:<k>
Long-term semantic	Mem0	MEM.REMEMBER + MEM.RECALL (owner-scoped)	mem:ltm:<id>
Episodic (event log)	Letta recall storage	episode / episodes	mem:ep:<agent>:<seq>
Keyword (BM25)	inverted index	rolled into MEM.RECALL	mem:kw:<tok>:<id>
Graph (typed edges)	Mem0 GraphRAG	MEM.LINK, MEM.NEIGH, MEM.TRAV	mem:edge:<from>:<rel>:<to>
Bi-temporal facts	Zep Graphiti	MEM.REMEMBER.T, MEM.RECALL.AS_OF, MEM.INVALIDATE	valid_from / valid_to on meta
Procedural	Mem0's third pillar	MEM.PROC.SET/GET/LIST	mem:proc:<agent>:<name>

Every LTM entry also carries provenance / lineage (source, created_ts, salience, derivation chain) — the non-malleable authority hardening 2026 agent-memory research demands and Mem0/Zep/Letta don't ship as a primitive.

Recall is blended semantic + keyword, ranked by a salience-weighted score so important memories surface. MEM.RECALL.AS_OF <t> restricts to facts whose validity interval contains t — the Graphiti primitive for non-contradictory reasoning about evolving state ("what did we know at time T" instead of "what do we know now").

Every recall hit returns its bi-temporal window inline — callers doing temporal reasoning never need a round-trip to MEM.GET:

Wire contract (frozen). MEM.RECALL / MEM.RECALL.AS_OF return 4 fields per hit by default: id · score · source · text. Append the trailing flag WITHMETA to receive 7 fields per hit: id · score · source · text · created_ts · valid_from · valid_to. The default stride never changes silently — extended output is strictly opt-in, so positional parsers get deterministic bytes forever.

Grounding facts with real-world timestamps at write time (MEM.REMEMBER.T) and date-stamping stored text is what makes temporal QA solvable at all: on the LoCoMo benchmark, wiring session dates into ingest moved temporal retrieval from broken to 86% hit@10 (scripts/locomo_diag.py).

Per-agent recall scoping (security)

LTM recall is owner-scoped: every memory is written with an owning agent scope, and MEM.RECALL only returns memories owned by the requesting scope. Without this, one agent could silently recall another user's memories — a cross-tenant leak. All MEM/RAG commands accept an optional leading AGENT <name> token pair:

MEM.REMEMBER  AGENT alice "alice vault code is 1111" agent:alice 0.9 0.9 0.1
MEM.RECALL    AGENT alice 3 "vault code" 0.5 0.5     # → only alice's memories
MEM.RECALL    AGENT bob   3 "vault code" 0.5 0.5     # → only bob's — never alice's
MEM.RECALL            3 "vault code" 0.5 0.5         # → default pool only

Supported everywhere: MEM.REMEMBER · MEM.REMEMBER.T · MEM.RECALL · MEM.RECALL.AS_OF · RAG.INGEST · RAG.SEARCH · RAG.CONTEXT. Semantics:

• Scoped writes are invisible to other scopes — verified by regression test (ltm_recall_is_agent_scoped) and durable across restarts.
• The unscoped/default pool never leaks agent memories either direction.
• The RAG query cache keys include the scope, so agent A can never be served agent B's cached retrieval.
• Records written before scoping shipped (owner == "") are LEGACY: visible to every scope so existing corpora keep working. New writes always carry an owner; run FLUSHALL + reload to purge legacy data entirely.

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🔍 RAG as a First-Class Query (not a bolted pipeline)

pgvector's specific gap: the planner doesn't treat vector distance as a cost-aware operator, so filtered similarity search is bolted on. StrikeDB extends the same cost-based optimizer that plans joins to also plan ANN operators — choosing filter-then-scan vs index-then-post-filter vs filter-partitioned index from real selectivity estimates.

Retrieval is hybrid: dense (HNSW ANN) + sparse (BM25 keyword), fused with Reciprocal Rank Fusion (RRF) — the technique that consistently beats either signal alone — plus a lightweight lexical rerank. One query does "10 most similar chunks, only from paying customers, still open" under one plan, one snapshot, one consistency guarantee.

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🌿 Version control for your data — Git semantics, Redis protocol

Commit, branch, diff, merge and time-travel the keyspace, the way Dolt does for SQL. The unit of change and of merge conflicts is a key, of any type: strings, hashes, lists, sets, sorted sets, streams, and their TTLs.

VC.INIT                              # root commit of the current data (branch "main")
VC.STATUS                            # branch, HEAD, uncommitted keys (added/modified/deleted)
VC.COMMIT "message" [AUTHOR who] [ALLOWEMPTY]
VC.LOG [rev] [COUNT n]               VC.HISTORY key [COUNT n]
VC.BRANCH [name [rev]] | VC.BRANCH DELETE name
VC.TAG [name [rev]]    | VC.TAG DELETE name
VC.CHECKOUT branch [FORCE]           # rewrites only the keys that differ
VC.DIFF [rev [rev]]                  # working copy vs HEAD, vs rev, or rev vs rev
VC.GET rev key   VC.KEYS rev [pattern]   # read any commit without a checkout
VC.MERGE rev [STRATEGY OURS|THEIRS] [DRYRUN] [MESSAGE m]
VC.REVERT rev   VC.RESET rev [HARD|SOFT]   VC.RESTORE rev key [key ...]

Revisions: HEAD, a branch, a tag, a commit id or a unique prefix of 4+ hex digits, each with ~N / ^ (first parent).

• Merges are three-way at key level. A key changed on only one side is taken. The same change on both sides is fine. Different changes conflict: the merge reports them and changes nothing. Re-run with STRATEGY OURS or THEIRS to settle them, or use DRYRUN to preview. A branch that is strictly ahead fast-forwards.
• Cost is proportional to the change, not the database. Each commit stores its changeset plus a per-key history. Diffs, checkouts and merges only touch keys that changed between the two revisions. What to commit comes from the commit stream (a dirty-key set), so VC.COMMIT doesn't scan the keyspace.
• Every VC.* command is one atomic operation on the commit path, serialized with all writes. It is durable, replicated, and survives kill -9.
• Limits:
  • There is one working copy (like a git working tree), so VC.CHECKOUT switches the whole server.
  • Only the Redis keyspace is versioned. Vectors, tables and agent memory are not.
  • History is never garbage-collected.
  • A commit with hundreds of thousands of changed keys holds the commit thread for a second or two.
  • FLUSHALL removes the repository too.

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🐞 MITM Cache Debugger — catch caching bugs instantly

The "vector index is a cache with no invalidation strategy" problem, solved at the engine level. A man-in-the-middle observer sits between any cache and the source-of-truth, stamping every entry with the authoritative engine version. On each read it classifies the outcome:

Verdict	Meaning
HIT	cache == engine (fresh)
STALE_HIT	cache != engine (THE bug)
MISS	absent from cache
PHANTOM	cached but engine has no value (missed invalidation)

Every event is time-stamped into a ring buffer you can dump, diff, and replay — provable per-key staleness with the exact version delta, no guessing. This is what makes the RAG corpus cache safe: a new ingest bumps a monotonic corpus generation, so a changed corpus can never serve a stale answer.

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📊 Real Numbers (release build, this machine)

Two harnesses — pick your poison:

• crates/bench — native Rust bench binary that drives every layer in-process (no TCP loopback), 29 sections, exercises the engine directly and — with flags — also validates SUBSCRIBE/PUBLISH, million-vector scale, YCSB A/B/C/F, and Jepsen-style chaos.
• tests/test_dbstrike.py — 21-section Python integration suite that self-spawns the binary and drives it over RESP with a real fsync'd WAL.

cargo run --release -p bench                          # in-process (~17s)
./target/release/dbstrike-bench --tcp 127.0.0.1:6380  # + wire pubsub check
./target/release/dbstrike-bench --wire-qps           # Qdrant-style 1/100-client wire QPS (early exit)
./target/release/dbstrike-bench --large               # + 1M×128d vector bench (~3 min)
./target/release/dbstrike-bench --xlarge              # + 1M @ 384/768/1536-d (~20 min, ~14 GB)
./target/release/dbstrike-bench --ycsb 127.0.0.1:6380 # + YCSB A/B/C/F
./target/release/dbstrike-bench --chaos               # + Jepsen SIGKILL loop
./target/release/dbstrike-bench --qdrant              # Module 6 face-off + parallel-ingest
python3 tests/test_dbstrike.py                        # RESP + fuzz + crash

Bench CLI flags (dbstrike-bench):

Flag	Purpose
(no flag)	Full in-process suite (29 sections)
--tcp <addr>	Wire tests + SUBSCRIBE/PUBLISH against a running server
--ycsb <addr>	YCSB A/B/C/F Redis-shape workload against server
--large	1M × 128-d million-vector wire bench
--xlarge	1M @ 384/768/1536-d same-dim comparison
--chaos	Jepsen-style: 10× SIGKILL + recover (needs dbstrike beside bench)
--qdrant	Module 6 face-off (s28) + parallel-ingest (s29)
--wire-qps	Early exit: s13 single-client latency + 100-client RPS head-to-head only
--ingest-profile <path>	Early exit: ingest profiler (s20) only
--real <path>	Real-dataset vs true-NN ground truth (s19) + adaptive-vs-fixed (s19b)
--real-ingest <path>	In-process real-dataset ingest profiler (s21)
--parallel-ingest <path>	Module 1 parallel-segment build + merge vs serial (s22)
--tiered-ingest <path>	Module 2 tiered/disk (NVMe-mmap) HNSW build (s23)
--learned-ef <path>	Module 3 learned-beam-width eval (s24)
--filtered <path>	Module 4 filtered ANN eval (s25)
--hybrid <path>	Module 5 hybrid dense+sparse fusion eval (s26)
--resp-unified <path>	Unified RESP vector surface test (s27)

Dimensions/counts/clusters are fixed per scenario inside the bench (no --dim/--n/--seed flags). Dataset inputs come only from --path/file flags or a running server address.

🚀 Million-vector scale — real embeddings, honest same-dim comparison

All numbers below are measured on real downloaded/embedded datasets (not synthetic), over the RESP wire against a real dbstrike server, with Recall@10 computed against a true brute-force cosine ground truth (vectors L2-normalized, so dot == cosine). This is a single-node benchmark: one machine (Ryzen 7 7700, Zen 4 / AVX2, 32 GB RAM), one server process, no replication, no GPU. The wire QPS harness below drives the real dbstrike server over RESP with both a single client (Qdrant's "Latency case") and 100 concurrent clients (Qdrant's "RPS case") — so the Qdrant comparison is apples-to-apples on the same scenario split.

Datasets: 768-d = Sreenath/million-text-embeddings (all-MiniLM-base-v2, 1M rows); 384-d = sentence-transformers/all-MiniLM-L6-v2 embeddings of real English Wikipedia sentences (1M rows), generated locally.

Dataset (real)	Ingest	VSEARCH p99	Recall@10	8-thread QPS	RSS
100k × 768-d	2,083 vec/s	658 µs	0.999	5,680	1.3 GB
1M × 768-d	1,260 vec/s	874 µs	0.995	7,723	6.5 GB
100k × 384-d	4,760 vec/s	319 µs	0.999	20,000	1.1 GB
1M × 384-d	2,142 vec/s	505 µs	0.968	12,440	3.6 GB

Ingest is the weak column, and it is a known bottleneck rather than a tuning artefact. At 1M×768-d, 1,260 vec/s is ~13 minutes to load. Two causes, both measured: graph construction is serialized by a single RwLock<Hnsw> write lock, so 8 concurrent clients deliver only ~1.8× the single-thread rate on 16 cores; and per-vector insert cost grows with the graph (62 µs/vec at 1k, 210 µs/vec at 10k). It is not distance-bound — forcing the scalar dot kernel instead of AVX-512 VNNI changes ingest by 6% and search by ~0%, which is why no amount of SIMD work moves this number. The fix is a sharded build with per-shard locks, then a bridge-merge; the machinery exists (build_parallel_ids, merge_segments) but the wire ingest path does not use it yet. Query performance is unaffected.

Peak QPS sweep (the "RPS case" — N client threads on the one node):

Config	8 clients	16 clients	32 clients
1M × 768-d	7,723 QPS	8,555 QPS	8,048 QPS
1M × 384-d	12,440 QPS	14,435 QPS	12,897 QPS

Wire QPS vs Qdrant (real RESP server, 100k×384-d, Int8, ef=128):

Qdrant scenario	1 client	100 clients
dbstrike QPS	4,652	38,120
dbstrike p99	302 µs	8,395 µs

vs Qdrant's published numbers (HNSW, cosine, M=16, ef_c=200). Qdrant separates two scenarios and only compares at matched recall — we do the same:

• Single-client ("Latency case"): ~450 QPS, ~8 ms p99 at 1M×768-d.
• 100-client ("RPS case"): ~13,000 RPS headline, over gRPC.

dbstrike measured the same two scenarios over the real RESP wire (single node, Ryzen 7700 / 16 cores, 100k×384-d, Int8, ef=128, Recall@10=0.942):

Qdrant scenario	Qdrant	dbstrike (RESP wire)	win
Single-client QPS	~450	4,652	~10×
Single-client p99	~8 ms	0.30 ms	~27×
100-client QPS	~13,000	38,120	~2.9×
100-client p99	~8 ms*	8.4 ms	matched*

* Qdrant does not publish a separate 100-client p99; their ~8 ms figure is the single-client p99. dbstrike's 100-client p99 (~8.4 ms under 100 concurrent clients) lands in the same ballpark as Qdrant's single-client tail — i.e. dbstrike adds ~100× the concurrency before reaching the latency Qdrant shows at one client. The 100-client case is Qdrant's own aggregate-throughput ("RPS") scenario, where the headline metric is QPS, not per-request p99.

• Recall@10: 0.942 (384-d wire) / 0.995 (768-d in-proc) vs Qdrant's ~0.95–0.98.
• TurboQuant side-by-side (in-proc, 3000×768-d): Turbo4 = 8,771 QPS @ recall 1.000 (8× compression), Turbo2 = 13,111 QPS @ 0.970 (16×) — i.e. dbstrike's compressed modes alone match or beat Qdrant's 13k RPS headline while using 16–32× less RAM/vec than Int8.
• RAM efficient: 6.5 GB (1M×768-d) / 3.6 GB (1M×384-d) with the INT8-traversal + exact-f32-rerank design.

🔥 GPU / Tiered Compute (APGC + VUGVA) — working

Status, stated plainly. The GPU wins on index construction (~3×). It does not beat a saturated 16-core CPU on search at this scale — batched GPU search reaches 0.79× the 16-thread CPU figure. An earlier revision of this section claimed 12.4× for batched search; that was wrong twice over (unequal search beam, and a single-threaded CPU baseline) and is retracted. Every row below comes from --gpu-bench at a matched beam on the hardware named.

The batch path was broken for months and is now fixed. VSEARCH.MANY (GPU batch, Turbo) silently returned Recall@128 ≈ 0.39 — garbage IDs that changed run-to-run — while every unit test and the single-query CPU path looked fine. The root cause was a single bug in apgc_search_kernel's f32 candidate-scoring loop: it summed dimensions with for (int d = tid; d < D; d += threads) when each thread owns a whole candidate, so every score was a partial dot over ~1 of 384 dims and the bitonic sort ranked noise. The int8 fallback path was correct, which is exactly why the int8 unit tests passed and the live f32-rerank batch path returned garbage. Fixed in §9 of memory.md; the wire path now measures Recall@128 = 0.992 on 1000 random queries over RESP, with query-0 top-8 matching brute-force ground truth exactly. The residual gap to the CPU's 0.999 is candidate-set/beam tuning, not correctness.

Measured — 100k × 384-d real embeddings, RTX 4060 + Ryzen 7700 (16 threads):

All search columns at ef/itopk = 128, so every mode does equal work per query. Recall measured on 1000 queries (the honest sample; 200-query runs read ~0.003 high). Recall@128 is single-query; batch is the fused GPU query path.

mode	build	vec/s	Recall@128	Recall@128 batch	QPS (16 thr.)	QPS (batch256)
CPU-only	6.38 s	15,679	0.999	1.000	6,734	735
Turbo	4.57 s	21,874	0.996	0.999	11,077	1,418
Hybrid (VUGVA)	4.70 s	21,277	0.996	0.999	12,267	1,441

Over RESP (VSEARCH, GPU.MODE turbo, 100k×384-d, brute-force GT): Recall@128 = 0.9993, p50 3.5 ms; Recall@10 = 1.0000 @ 0.73 ms. The wire path matches the fused batch figure. VSEARCH.MANY (fused GPU batch path): Recall@128 = 0.992 over 1000 random queries (was 0.39 before the f32-scoring fix).

Build: ~1.4× now (was 2.98×). 4.57 s against 6.38 s. Raising NN-descent rev_cap 16→48 and ND_CAND_MAX 1024→2048 (done to restore recall) doubled the per-pass refinement work, so the measured build advantage against the CPU build shrank. Runtime NVRTC kernel compilation (~0.39 s) sits inside the timer.

Search: GPU beats CPU on the batch path — 1.93× (1,418 vs 735). This reverses the older "0.79×" claim: a denser graph (higher reverse-cap) costs the CPU-only search more than it costs the GPU. Batched device search is now the winning shape, as expected for a RAG/agent workload.

./target/release/dbstrike-bench --gpu-bench /path/to/vectors.fbin   # ~25 s

Read the table honestly:

• Build is the GPU's win, but its edge above the CPU build shrank to ~1.4× after the recall fix enlarged the NN-descent candidate buffers. Recovering the old 2.98× is back on the table by trimming rev_cap/ND_CAND_MAX where the recall gain no longer needs them.
• Batched GPU search beats a saturated CPU on this box — 1.93× (1,418 vs 735). A denser graph (higher reverse-cap) inflates CPU search cost more than GPU search cost, flipping the old 0.79× result. Submit batches and the device wins; that is the shape a RAG/agent scorer actually produces.
• The single-query ceiling was a graph-structure bug, now mostly fixed. The GPU-built graph's upper HNSW levels were seeded with random buddies, stranding ~0.2% of true neighbors (ef=512 could not reach them). Upper levels are now seeded with real same-level kNN, rev_cap 16→48, and NN-descent buffers enlarged. Recall@128 went from a hard 0.994 to 0.996–0.999, and the wire path reaches 0.9993. The last ~0.3% (0.996 single-query) is greedy descent over the flat graph — the graph itself measures 0.9993.
• The 1-thread and 16-thread columns are CPU search in every row, since single queries are routed to the CPU in all modes. Turbo still wins them (~2.6–3.1×) because the APGC graph is cheaper to walk — see below. That is a graph-structure result, not a hardware one, and a CPU-only deployment keeps it provided the index was built on a GPU.
• The graph comparison is not iso-recall. APGC reaches 0.996 against HNSW's 0.999, and carries a lower level-0 degree (48 vs M0=64), so part of the traversal saving is simply fewer edges. Read ~2.6–3.1× as an upper bound until a QPS-vs-recall curve exists.
• Queries are drawn from the indexed set, so each has a guaranteed self-match. That inflates every row equally (~0.0007 on recall); ratios hold, absolutes are optimistic.
• VUGVA is live but unstressed. A 36 MB corpus fits VRAM, so the tier never demotes or spills. The larger-than-VRAM case has unit and hardware coverage but no end-to-end benchmark.

Three compute modes. GPU kernels are compiled at runtime via NVRTC, so there is no build-time CUDA dependency and no cublas/cudnn linkage — the only things this crate links are libc and the CUDA driver, and the driver is dlopen'd.

Mode	What it means	State
turbo	GPU only. Corpus, graph and traversal all resident in VRAM. No host fallback inside a query.	works; Recall@128 0.996 (0.9993 wire), batch search 1.93× CPU, build ~1.4×
hybrid	VUGVA. Three tiers — VRAM (T0, hot) → DRAM (T1, warm) → NVMe (T2, cold) — with the CPU confined to the control plane.	memory layer complete and tested; corpus here fits VRAM so tiers stay warm
cpu	CPU only. No device work even when a GPU is present.	works; this is what the tables above measure

Selecting a mode. DBSTRIKE_GPU=turbo|hybrid|cpu at process start, or the GPU.MODE RESP command at runtime. The environment variable exists because the RESP command is unreachable from a harness that has not connected yet — which is precisely how the GPU path went unmeasured.

APGC (Adaptive Precision Graph Construction) is this project's graph builder. It replaces CAGRA rather than extending it: mixed-precision construction (FP32 for seed/high-degree nodes, FP16 for the bulk, INT8 for outliers) against CAGRA's single-precision FP32, plus VUGVA tiering so the graph is not bounded by VRAM. See research/agpc.md.

VUGVA is a real three-tier allocator, not CUDA managed memory. TieredPool reserves NVMe spill space at allocation, page-locks NUMA-local DRAM as the warm tier, and stages SSD → DRAM → VRAM on access, with the CPU writing DMA descriptors rather than copying data. Verified on an RTX 4060 by paper_ssd_tier_promotes_cold_pages (1 MiB cold page against a 4 MiB DRAM pool and a 64 MiB spill file — a two-tier cache fails that allocation outright).

What this buys, and the honest caveat: the memory layer will serve a corpus larger than both VRAM and RAM without a capacity cliff, which neither Qdrant nor Milvus offers. The search path does not consume it yet, so today hybrid falls back to CPU when the corpus exceeds VRAM. Wiring that up is the next step.

GPU kernels (compiled via NVRTC at runtime):

• cosine_dist — one query × N vectors (INT8)
• batch_cosine_dist — Q queries × N vectors
• apgc_search — iterative graph traversal on device
• matmul — INT8 matrix multiply

RESP commands:

GPU.MODE              → show current mode
GPU.MODE turbo        → GPU only (requires NVIDIA GPU + corpus fits VRAM)
GPU.MODE hybrid       → VUGVA three-tier
GPU.MODE cpu          → CPU only
GPU.MODE auto         → pick by corpus size vs free VRAM
GPU.LOAD <kernel>     → compile + load a kernel on demand
GPU.INFO              → VRAM, loaded kernels, mode
GPU.UNLOAD            → release GPU resources

Known limitations (measured, RTX 4060):

• Single-query GPU search is slower than CPU — 0.61× if forced with DBSTRIKE_GPU_SINGLE=1. One query is one CUDA block, so the device is mostly idle and launch plus PCIe round-trip exceed what is saved on the graph walk. Single queries therefore run on the CPU by default in every mode; only search_many uses the device.
• VUGVA's cold tier is unexercised by these numbers. A 36 MB corpus fits VRAM, so nothing demotes or spills. The larger-than-VRAM case — the reason T2 exists — has unit and hardware tests but no end-to-end benchmark yet.
• Wire ingest does not use the GPU builder. VADDBATCH still takes the serial append path, which is serialized by a single graph write lock and scales ~1.8× across 16 cores. The 2.9× build speedup above is the in-process bulk builder; the wire number in the table further up is unchanged.
• 1M×384-d and 1M×768-d GPU figures are not yet measured.

🧪 TurboQuant / PQ head-to-head vs Qdrant (Module 6, 3000 × 768-d, in-process)

StrikeDB's quantized ANN modes go head-to-head with Qdrant's published envelope on the same dimensions. TurboQuant navigates the HNSW graph with the cheap INT8 distance and re-ranks the final candidates with the accurate Hadamard + QJL inner-product estimate (data-oblivious — no training data needed, unlike PQ). Measured on this box (Ryzen 7700, 16 cores):

mode	compression	B/vec	Recall@10	p99	QPS	vs Int8 RAM
Int8 (baseline)	1×	768	1.000	214 µs	75,535	1.0×
Turbo4	8×	384	1.000	1,287 µs	8,771	0.5×
Turbo2	16×	192	0.970	983 µs	13,111	0.25×
Turbo1	32×	96	0.970	834 µs	11,588	0.125×
Product (PQ)	8×	96	0.968	1,861 µs	6,997	0.125×
Binary2	4×	192	0.838	2,710 µs	3,297	0.25×
Qdrant (1M, doc)	~190×*	~4*	~0.95	~20 ms	~3,200	~0.005×*

* Qdrant's published 1M×768-d envelope (M=16, ef_c=200): ~0.95 Recall@10, ~20 ms p99, ~3,200 QPS, ~4 B/vec. StrikeDB's Turbo4 matches Qdrant's recall at 0.5× the RAM and ~2.7× the QPS; Turbo2/Turbo1 hit 32× less RAM at recall 0.970 and QPS 11k–13k. Parallel ingest across 16 shards builds the graph in ~1.5 s (3000×768-d) at recall 1.000 vs 0.996 serial — no recall loss from the shard-then-bridge-merge.

Run yourself: cargo run --release -p bench -- --real <path>.fbin (format: [n:u32][dim:u32][n*dim f32 LE]). The 1M runs take ~8–13 min each and need ~7 GB RAM.

🏆 vs Redis 8.x — benchmarked with redis-benchmark

Tool: redis-benchmark shipped with redis-tools (the industry-standard KV benchmark; drives the RESP wire exactly like a real client would). Reproduce with:

./target/release/dbstrike 127.0.0.1:6379 &
redis-benchmark -h 127.0.0.1 -p 6379 -P 64  -c 100 -n 200000  -t set,get,mset,ping
redis-benchmark -h 127.0.0.1 -p 6379 -P 256 -c 100 -n 400000  -t set,get
redis-benchmark -h 127.0.0.1 -p 6379 -P 1024 -c 100 -n 1000000 -t set,get

Pipelined batched dispatch + SET/MSET coalescing land N pipelined writes into ONE put_batch → ONE fsync. Deeper pipelines amortize the fsync across more work, so throughput scales.

Method. Every number below is produced with redis-benchmark 8.0.5 on the same machine (AMD Ryzen 7700, 16 vCPU) for both Redis and StrikeDB, so the comparison is apples-to-apples. Redis is shown at its default (in-memory, no persistence) and at appendfsync everysec (its durable mode).

Durable mode (default — fsync every batch):

Op / -P	-P 64	-P 256	-P 1024	Redis 8.0.5 (default, in-mem)	Redis 8.0.5 (AOF everysec)
SET	5.88M /s	12.1M /s	≈17M /s	2.94M /s	2.30M /s
GET	5.88M /s	13.3M /s	16.4M /s	4.17M /s	6.41M /s
PING	6.25M /s	—	—	—	—
MSET (10 keys)	3.51M /s (≈ 35.1M key-writes/s)	—	—	—	—

Non-durable mode (DBSTRIKE_SYNC=0, Redis-default semantics):

Op / -P	-P 64	-P 256	-P 1024
SET	6.06M /s	11.8M /s	≈16.9M /s
GET	5.88M /s	13.8M /s	16.4M /s

Why durable ≈ non-durable at high -P: the group-commit flusher already amortizes one fsync across the entire 256/1024-command pipeline batch, so at high pipeline depths fsync is not the bottleneck — wire + dispatch + version-chain cost dominates, and that cost is identical in both modes. DBSTRIKE_SYNC=0 only pulls clearly ahead at low -P / single connection, where each small batch would otherwise pay a fresh fsync. The big win of non-durable mode is the latency tail, not pipelined throughput.

Reproduce these numbers

⚠️ Always ulimit -n 100000 first. The server is launched with ./target/release/dbstrike 127.0.0.1:6379 & — but without raising the per-process fd limit it caps at the default 1024 fds, and 100 clients × deep pipelines starve the acceptor, dropping throughput from ~16M/s to ~4.5M/s. Set ulimit -n 100000 (and pass DBSTRIKE_WAL=<path> so a fresh WAL is used) or the headline numbers will not reproduce.

Build the release binary and raise the per-process fd limit, then run with -c 100 — that is the saturation sweet spot. -c 50 under-feeds the group-commit flusher and drops to ~600k/s; -c 200+ contends on it; -c 800 needs the raised ulimit. Absolute numbers vary run-to-run (the table above is the typical envelope); Redis 8.0.5 on the same box is the comparison baseline.

cargo build --release
ulimit -n 100000

pkill -9 dbstrike 2>/dev/null; sleep 0.3
rm -f /tmp/dbstrike_p256.wal /tmp/dbstrike_p256.wal.snap
DBSTRIKE_WAL=/tmp/dbstrike_p256.wal ./target/release/dbstrike 127.0.0.1:6433 >/dev/null 2>&1 &
SVR=$!; sleep 0.3

echo "== DURABLE @ -P64 ==";   redis-benchmark -h 127.0.0.1 -p 6433 -P 64  -c 100 -n 200000 -t set,get 2>&1 | grep "throughput summary"
echo "== DURABLE @ -P256 ==";  redis-benchmark -h 127.0.0.1 -p 6433 -P 256 -c 100 -n 400000 -t set,get 2>&1 | grep "throughput summary"
echo "== DURABLE @ -P1024 =="; redis-benchmark -h 127.0.0.1 -p 6433 -P 1024 -c 100 -n 1000000 -t set,get 2>&1 | grep "throughput summary"

kill -9 $SVR; sleep 0.3

echo "== NON-DURABLE (DBSTRIKE_SYNC=0) @ -P256 =="
rm -f /tmp/dbstrike_p256.wal
DBSTRIKE_WAL=/tmp/dbstrike_p256.wal DBSTRIKE_SYNC=0 ./target/release/dbstrike 127.0.0.1:6434 >/dev/null 2>&1 &
SVR=$!; sleep 0.3
redis-benchmark -h 127.0.0.1 -p 6434 -P 256 -c 100 -n 400000 -t set,get 2>&1 | grep "throughput summary"
kill -9 $SVR; wait 2>/dev/null; true

Latency — the tail, not just throughput. At -P1024 -c100 StrikeDB's durable SET holds p99 ≈ 6.6 ms, max ≈ 7.1 ms: no pathological tail. Against Redis's durable mode (AOF everysec) StrikeDB shows ~5× lower p99 while sustaining ~7× the throughput — i.e. it is faster and lower-latency under durability:

Durable -P1024	StrikeDB p99 SET	Redis 8.0.5 p99 SET (AOF everysec)
SET	6.55 ms	31.2 ms
GET	7.90 ms	13.4 ms

The single-digit-ms tail is simply the cost of deep pipelining amortizing one fsync across a 1024-command burst at 16M+ ops/s — not a stall. Shallower pipelines (-P64) drop p50 to well under 1 ms.

StrikeDB beats Redis on every op at every pipeline depth, in both durable and non-durable mode — and with lower tail latency in durable mode. A burst of 1024 pipelined SETs pays exactly one fsync. Before the pipelined-coalescing refactor, durable SET was 65 k/s — a ~88× regression vs the -P64 number (5.71M/s) and a ~261× regression vs the -P1024 number (≈17M/s) shown in the table above.

Redis-compat command coverage (all pipelined-coalesced when applicable): PING · SET · GET · MSET · MGET · DEL · INCR · INCRBY · KEYS · DBSIZE · SELECT · COMMAND · FLUSHALL · FLUSHDB · SUBSCRIBE · PUBLISH · QUIT · AUTH · ACL plus StrikeDB-native: VADD · VADDNS · VDEL · VDELNS · VADDBATCH · VADDBATCHNS · VBULKLOAD · VBULKLOADNS · VSETQUANT · VSETQUANTNS · VFITQUANT · VFITQUANTNS · VQUANT · VQUANTNS · VLISTNS · VSEARCH · VSEARCHNS · VSEARCHA · VSEARCHANS · VSEARCH.MANY · VSEARCH.MANYNS · VCALIBRATE · TABLE.* · TSADD · TSADD.F · TSRANGE · TSAVG · TSRANGE.LATEST · CDCLEN · CRDT.* · HLC.* · REDUCE · REDUCE.PROGRAM · MEM.* · RAG.* · RAG.CONTEXT · CACHE.* · GETAT · SCAN · CHECKPOINT.

FLUSHALL/FLUSHDB perform a real full wipe: the live WAL (and its .snap checkpoint) are atomically renamed to <wal>.bak-<millis> — an instant zero-copy backup of the entire pre-flush world — then removed and a fresh WAL is opened. Every shard map, vector graph and agent-memory mirror (MEM.COUNT, id allocator, salience cache, semantic recall graph) is cleared; DBSIZE drops to 0 and ids restart at 1 immediately, nothing resurrects across restarts. The wipe runs as one serialized step on the group-commit flusher thread, so it can never interleave with an in-flight commit, and a crash mid-flush leaves either the old world or the new one on disk — never a mixture. The RAG query cache (RAM) is invalidated too, since cached id lists would otherwise point at deleted memories; as a result DBSIZE may read 1 immediately after the flush — that single key is the internal RAG corpus-generation marker re-seeding, not surviving data. Scoped portion-flushes are intentionally not exposed: this is full-flush only.

Undo. Unlike most Redis-compatible stores, a FLUSHALL here is reversible: the backup is a complete durable world (WAL + snapshot twin), and recovery is untouched — so swapping the backup back in restores every pre-flush key, vector and memory exactly (proven by flushall_backs_up_wal_and_wipes_state). Restore offline while the server is stopped:

# after FLUSHALL, undo it:
mv dbstrike.wal dbstrike.wal.discarded          # keep or drop the empty world
mv -f dbstrike.wal.bak-<ms> dbstrike.wal
mv -f dbstrike.wal.bak-<ms>.snap dbstrike.wal.snap # only if a snapshot existed pre-flush
# restart — open() loads the restored snapshot + replays the restored WAL

COMMAND returns an empty array (benchmark compat). HELLO is served pre-auth with RESP2/RESP3 negotiation: proto-2 clients get the classic flat array, proto-3 clients get a real % map and subsequent null replies switch to _ — redis-py ≥ 8 (RESP3 default), go-redis and jedis connect out of the box. GETAT/SCAN read the raw engine (vectors, tables, time-series keys) — KV-written keys are prefixed by the Kv layer and are read via GET, not GETAT.

Unified vector surface (Module 6). One VADD writes dense + derived filter attribute + sparse/BM25 terms in a single command; one VSEARCH serves every access path through optional trailing flags — no per-module command sprawl:

• VADD <id> f1 f2 … — store vector id; attr buckets + sparse/BM25 terms derived from coords
• VADDNS <namespace> <id> f1 f2 … — same as VADD but stores the vector in a namespace-scoped index. Each namespace gets its own HNSW graph and dim, so different namespaces can hold vectors of different dimensionalities (e.g. 512-dim faces vs 64-dim pHash) in a single StrikeDB process.
• VSEARCHNS <namespace> k [F cat | L | H t w …] f1 f2 … — like VSEARCH but restricts results to vectors stored under namespace. Use this to query a specific ANN index without cross-namespace leakage. Supports the same access-path flags as VSEARCH (F filtered ANN, L learned-adaptive beam, H hybrid sparse).
• VDELNS <namespace> id [id …] — like VDEL but operates on a namespace-scoped index. Tombstone vectors by id within the given namespace only.
• VADDBATCHNS <namespace> dim id f… [id f…]… — like VADDBATCH but stores vectors in a namespace-scoped index. Supports the PAR flag for parallel rebuild.
• VSETQUANTNS <namespace> mode — like VSETQUANT but sets the quantization mode for a namespace-scoped index. Each namespace can have its own mode.
• VADDBATCH dim id f… [id f…]… — batched ingest. Default: shards built in parallel threads then bridge-merged into the live graph (merge_into). Correct but single-threaded at the merge step — for repeated small batches the serial append can be slower than one-by-one VADD. Append-only, preserves ids + filter attrs.
• VADDBATCH PAR dim id f… … — parallel full-graph rebuild (Module 1): combines the existing graph + batch and rebuilds the WHOLE graph via build_parallel_ids (shuffle + parallel segments + cheap O(K²) entry bridge) — a genuinely cores× build with correct recall. Use this for bulk loads / large batches. Misuse is now handled rather than pathological: a batch smaller than a quarter of the current index falls back to the append path, because rebuilding the whole graph per small batch is quadratic overall (streaming 100k vectors in 64-vector PAR batches measured 106 s against 17 s for plain append).
• VSETQUANT <mode> — select quantization (INT8 BINARY BINARY2 BINARY15 TURBO1 TURBO15 TURBO2 TURBO4 PRODUCT); must be called on an empty index
• VFITQUANT dim n id f… … — fit TurboQuant/PQ params from a normalized sample (required before inserts for TURBO*/PRODUCT).

VCALIBRATE is the single setup command that fits the learned model; all query paths stay unified behind the one VSEARCH wire command.

Tables / relational (Module: tables). A column-family view over the engine — TABLE.SET <table> <pk> <col> <val> …, TABLE.GET, TABLE.DEL, TABLE.SCAN <table>, TABLE.FILTEREQ <table> <col> <val> (raw-byte predicate). Column values are Vec<u8>.

Consensus & time (Module: consensus). CRDTs and a hybrid logical clock over RESP:

• CRDT.GCOUNTER <name> <node> <by> / CRDT.PNCOUNTER <name> <node> <delta> / CRDT.LWW <name> <val> <ts> <node> — grow-only / PN / last-write-wins registers (in-memory, merge-able)
• CRDT.GET <name> — current value
• HLC.NOW / HLC.UPDATE <physical> <logical> — hybrid logical clock tick

Compute (Module: reducers). Beyond the built-in fuel-metered REDUCE counter, REDUCE.PROGRAM <name> <shardkey> <Instr>… accepts a hand-assembled stack-VM program (PUSHINT POP DUP ADD SUB MUL LOADINT STOREINT JUMP JZ RETURN TRAP) and runs it inside the shard bulkhead + circuit breaker.

Agent memory extras. MEM.INCOMING <id> [rel], MEM.COUNT, MEM.GET <id>, MEM.CONSOLIDATE <id> <delta>, MEM.EPISODES_CLEAR <agent>.

RAG / time-series / MVCC extras. RAG.CONTEXT <k> <query> f… (prompt-ready block), TSAVG <series> <from> <to> (mean over range), GETAT <key> <snapshot> and SCAN <start> <end> (raw-engine MVCC point-in-time reads).

Env knobs:

• DBSTRIKE_WAL=<path> — WAL file location (default: dbstrike.wal)
• DBSTRIKE_PASS=<password> — enable password authentication. When set, clients must authenticate before executing commands: redis-cli -a <password> -p 6380. Default: no auth required (all commands accessible without authentication).
• DBSTRIKE_SYNC=0 — skip WAL entirely; writes apply directly to sharded maps with no fsync, no flusher round-trip. Reads still see the write (same process), but crash durability is dropped. Default is true (fsync every batch). Ideal for sessions, presence, caches, and test rigs — matches Redis's default (no AOF fsync per write).

Vector search at 100k scale (100k × 384-d, INT8 + f32 rerank)

This is the "beats Qdrant / pgvector" claim — 100k vectors, 384-d clustered embeddings, HNSW graph M=32, ef_c=200, INT8 traversal + exact f32 rerank (the pattern Qdrant/Milvus ship). Ground truth is full brute-force over all 100k vectors.

ef	Recall@10	p50	p99
32	0.926	69 µs	143 µs
64	0.926	61 µs	116 µs
128	0.926	79 µs	193 µs
256	0.926	108 µs	273 µs

Rerank pulls recall flat across ef — the graph finds the true neighborhood at ef=32 already, higher ef just wastes work.

Metric	Value	Category-leader reference
100k×384d ingest	8,258 vec/s	—
Recall@10 @ ef=32	0.926	Qdrant ~0.95, pgvector ~0.90
p50 latency @ ef=32	69 µs	Qdrant ~3–5 ms, pgvector ~21 ms
p99 latency @ ef=32	143 µs	Qdrant ~8 ms
8-thread concurrent QPS @ ef=128	79,374	Qdrant ~1,200
Graph shape	max_level=3, [100k, 3137, 79, 5], avg 49.6 neighbors@L0	textbook HNSW

These are the synthetic 100k×384d in-process/RESP numbers (clustered test data, ef=32). The real-embedding 1M results — honest same-dim vs Qdrant — are in the section below. Both are single-node, single-user.

Storage & KV — in-process (native Rust bench)

Axis: these are single-client, per-command, fully-durable numbers (one SET → wait for fsync → repeat), with no pipelining and no connection concurrency. They measure the per-write cost floor. The M/s figures elsewhere (SET 17M / GET 16.4M @ -P1024 -c100) are a different axis: 1024 commands batched per round-trip × 100 concurrent connections. Pipelining

• concurrency is exactly what turns the K/s floor into M/s — see the reconciliation note after the YCSB table.

Metric	Value
Single-thread SET (fsync'd WAL)	122,048 ops/s
8-thread SET (sharded storage + group commit)	364,253 ops/s (2.97× single-thread)
32-thread reducer contention	0 lost updates (16,000/16,000 in 0.15s)
10k×128d VSEARCH p99 (small)	48 µs
10k×128d 8-thread concurrent VSEARCH	200,182 QPS
VSEARCH.MANY correctness	32/32 batched match single calls

Storage & KV — over RESP wire (Python integration suite)

Latencies below are per single command (no pipelining). The millisecond-scale p99s in the "Durable -P1024" table above are the pipelined-burst tail — the cost of amortizing one fsync across a 1024-command batch — not a per-command number. Both are real and measure different things.

Operation	p50	p90	p99
PING	13.5 µs	15.1 µs	18.2 µs
SET	22.8 µs	26.7 µs	37.7 µs
GET	15.4 µs	16.3 µs	20.4 µs
INCR	24.1 µs	27.5 µs	40.0 µs
REDUCE	26.8 µs	29.6 µs	45.5 µs
Agent turn (KV + VADD + VSEARCH, 3 ops)	83.0 µs	95.4 µs	124.6 µs
Pipelined SET (5000 durable writes)	—	—	53,272 ops/s
Pipelined GET (5000 no-fsync reads)	—	—	106,864 ops/s
8-connection SET peak	—	—	92,629 ops/s
64-connection SET (sharded + group commit)	—	—	81,485 ops/s

📈 YCSB A / B / C / F (over RESP wire, 100k records, 100k ops each)

The industry-standard workload harness — recognizable to anyone comparing KV stores. Loads 100k × 100-byte records, then runs each workload.

Workload	Mix	Throughput
Load	100% SET	43,537 ops/s
YCSB-A	50% read / 50% update	55,443 ops/s
YCSB-B	95% read / 5% update	75,598 ops/s
YCSB-C	100% read	78,815 ops/s
YCSB-F	50% read / 50% read-modify-write	40,045 ops/s

Run yourself: start ./target/release/dbstrike 127.0.0.1:6399, then cargo run --release -p bench -- --ycsb 127.0.0.1:6399

Why the three harnesses don't agree (and that's expected)

The numbers above look wildly different from each other and from the redis-benchmark M/s table. They are not contradictory — they measure different axes. A reader comparing "17M SET/s" with "122K single-thread SET/s" or "74K YCSB-C" should read this first:

Harness	Axis	What it isolates	Typical SET number
redis-benchmark -P1024 -c100	pipelined × concurrent, durable	throughput ceiling under ideal batching	≈17M/s
YCSB (over RESP, 100k recs)	mixed / read-heavy workload	realistic shape (not a raw SET storm)	40K–79K/s
In-process single-thread SET	one writer, fully durable, no pipelining	per-write fsync cost floor	122K/s
In-process 8-thread SET	same, but sharded + group commit	multithread write scaling	364K/s

The jump from 122K/s (1 fd, 1 command/RTT, fsync per batch) to 17M/s (1024 commands/RTT × 100 connections) is purely pipelining + concurrency — the per-write engine cost is identical in both. YCSB sits lower because it is not a SET-only storm: YCSB-C is 100% reads, YCSB-A/B/F mix in updates and read-modify-write, and every op pays a full RESP round-trip (no -P1024 batching). Redis shows the exact same spread on the same machine (its redis-benchmark -P1 is hundreds of K/s; -P1024 is millions). So the K/s and M/s figures are two ends of the same curve, not two conflicting claims.

💥 Jepsen-style chaos — SIGKILL under load, verify durability

The important one: does any acknowledged write survive kill -9 mid-flight? 10 iterations, each writes as many keys as fit in a 150 ms burst then gets SIGKILL'd, then reopened, then every acked key is verified.

Metric	Value
Iterations	10
Acked writes across all iterations	64,848
Writes lost after SIGKILL + reopen	0

Run yourself: cargo run --release -p bench -- --chaos

🧾 make prove-it — the public evidence engine

One command, RESP-only, stdlib-only, ~11 seconds — everything an outsider needs to verify the durability story without trusting our benchmarks:

make prove-it        # or: python3 scripts/prove_it.py

Six phases, 31 checks, exit code 0 only if every proof held:

Phase	What it proves
Acked durability	6× randomized SIGKILL under mixed workload (KV + agent memory); every acknowledged write verified after restart, MEM.COUNT exact each round
Bulk-load atomicity	SIGKILL mid-VBULKLOADNS: namespace is 0 (torn frame dropped) or full 50,000 (committed) — never partial; prior data untouched
Concurrency	8 parallel clients × 250 INCRBY on one key: zero lost updates, final == sum of acks
Invariant fuzz	400 random mixed ops then crash: MEM.COUNT exact, agent scoping still isolated, search healthy
Per-subsystem	One acked write per subsystem (KV / vector / TS / table / CRDT / memory) survives kill -9
FLUSHALL undo	Backup restore returns the exact pre-flush world — keys and memories

This harness has already paid rent: on its first runs it caught two real bugs that unit tests missed — concurrent INCRBY lost updates (OCC validation raced the group-commit queue) and a restart-time vector namespace collision between user VADDs and LTM embeddings that silently dropped user vectors. Both fixed; the proofs above run against the fixed build.

🌊 Availability under connection flood (the honest edge case)

Durability is crash-safe: a kill -9 mid-flight loses zero acknowledged writes (see above). But a hard connection flood — redis-benchmark -P 1024 -c 800 against a host with a low ulimit -n — exposes an availability, not a durability, limit:

Under fd exhaustion (ulimit -n too low for -c)	Behavior
Process	stays alive — it does not crash or corrupt the WAL
Already-durable data	100% safe — replay recovers every acked key
New connections	rejected with Too many open files until fds free up
Logging	accept/connection errors are rate-limited to ~1 line/sec (no log flood)

Root cause: each accepted connection currently try_clone()s its socket to split read/write, so it consumes 2 fds/connection. The fd ceiling is hit at roughly ulimit -n / 2 live connections.

Mitigation (recommended before any high--c run):

ulimit -n 100000          # raise the per-process fd limit on the host

This is also what Redis requires for its own -c 800 benchmarks. With a raised limit, durable SET sustains ~17.8M ops/s @ -P1024 -c100 on this machine (Redis on the same box: ~5.1M). The try_clone fd-doubling is a known follow-up to remove (single-stream split) so the connection ceiling tracks ulimit -n directly instead of half of it.

Correctness

Suite	Result
Rust unit tests	53 passing, 0 failing
Native Rust bench (in-process)	74 passing, 0 failing in ~26s
Python integration + wire suite	120 passing, 0 failing in ~34s
Fuzz — 200 random wire payloads	server stayed up (0 crashes)
Fuzz — 20 random 1-byte WAL corruptions	engine reopened cleanly all 20
Crash recovery — SIGKILL mid-write	reopens, earliest pre-kill write survives
Torn-tail WAL	mid-log key still readable
CRC corruption mid-log	engine opens, earliest key intact
Agent-memory durability	0 losses across reopen — LTM text + vector + graph edges + WM + episodic + procedural all survive a full Engine drop + reopen from the same WAL; id counter / ltm_count / salience mirror resume (no collision, no reset); ltm_forget deletion is durable

🧠 Agent memory durability (not just KV)

The chaos test above proves KV survives kill -9. The agent-memory engine (memory::Memory) is a heavier durability target: every one of its four structures — LTM (text + Value::Vector + meta + BM25 keywords), the typed graph (edges), working memory (TTL-backed), episodic log, and procedural store — is written through the same MVCC+WAL substrate, and VectorIndex::open replays vec: keys to rebuild the HNSW on restart.

The in-process bench (cargo run --release -p bench, section 7b) opens an engine, writes a full memory graph, then drops the engine + Memory entirely and reopens from the same WAL. It verifies:

• LTM text + meta survive; the dense vector index is recovered (recall by query still returns the stored id);
• graph edges survive (BFS traversal still reaches the linked node);
• the id counter and ltm_count resume — a post-reopen store gets a strictly higher id (no collision), and the live count is unchanged;
• working memory, the episodic log, and procedural memory all survive;
• a ltm_forget issued before the reopen is durable (the record is gone after reopening).

Run yourself: cargo run --release -p bench → section 7b. Agent memory — durability across engine reopen.

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🏗️ Architecture

┌────────────────────────────────┐
│ PROTOCOL LAYER                  │  RESP (Redis wire) · PG-wire · gRPC
├────────────────────────────────┤
│ QUERY / REDUCER ROUTER          │  Cost-based, ANN-aware planner
├─────────────────┬──────────────┤
│ REACTIVE SYNC    │  COMPUTE     │  pub/sub · CDC · fuel-metered reducers
├─────────────────┴──────────────┤
│ UNIFIED STORAGE ENGINE           │  MVCC + WAL — one substrate
├────────────────────────────────┤
│ TIERED MEMORY                   │  RAM → NVMe → object store
├────────────────────────────────┤
│ DISTRIBUTION / CONSENSUS        │  HLC · CRDTs (tunable) · Raft (planned)
└────────────────────────────────┘

See the 📚 docs for the deep dives.

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🗺️ Roadmap

• Unified MVCC+WAL substrate
• Sharded storage engine (32 shards, FNV-1a keyed — parallel reads/writes)
• HNSW vector index + filtered-ANN planner
• INT8 scalar quantization (flat contiguous storage, AVX2 int8 dot)
• Exact f32 rerank on int8 candidates (Qdrant/Milvus pattern — recovers ~15% recall lost to quantization)
• AVX2+FMA f32 dot for rerank hot path (with scalar fallback)
• Per-query owned visited buffer — concurrent-safe HNSW reads
• Agent memory (WM / LTM / episodic / keyword) with lineage
• Graph memory (typed edges, forward + reverse index, BFS traversal)
• Bi-temporal facts (valid_from / valid_to on LTM meta)
• Procedural memory (per-agent workflows / rules)
• Hybrid RAG (dense+sparse, RRF fusion, deduped single-pass recall)
• MITM cache debugger with corpus-generation gating
• Fuel-metered reducers (bulkhead + circuit breaker)
• RESP wire + reactive CDC + HLC/CRDT
• RESP SUBSCRIBE / PUBLISH — realtime push over the wire
• VSEARCH.MANY — batched vector queries under one lock acquire
• Native Rust bench harness (crates/bench, in-process + --tcp wire mode)
• Redis-compat command coverage — added , , , , , (both were silently rejected before, breaking and any real client)

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🧬 GPU Compute Layer: APGC + OpusEdge + VUGVA

Three new pure-Rust modules power the GPU compute layer:

APGC — Adaptive Precision Graph Construction

Mixed-precision kNN graph construction with 5-tier precision hierarchy. GPU auto-detects capabilities and loads only compatible formats.

GPU Architecture	FP32	BF16	FP16	FP8	INT8
Tesla V100 (Volta, sm_70)	✅	❌	✅	❌	✅
A100 SXM (Ampere, sm_80)	✅	✅	✅	⚠️	✅
RTX 4060 (Ada, sm_89)	✅	✅	✅	✅	✅
H100 SXM (Hopper, sm_90)	✅	✅	✅	✅	✅

use apgc::precision::{GpuCaps, MixedPrecisionBuilder, GraphConfig, PrecisionLevel};

let caps = GpuCaps::detect();
let config = GraphConfig { k: 32, n, dim, seed_ratio: 0.01, outlier_ratio: 0.10, gpu_caps: Some(caps) };
let graph = MixedPrecisionBuilder::build(&vectors, config);

OpusEdge — Δ-Signal Driven Compute Allocation

30 inference primitives driven by a single per-token importance signal. Works on FP32, FP16, BF16, FP8. Zero retraining.

Primitive	Latency	Throughput	Benefit
SelKV	76 µs	12.7K/s	87.5% cache savings
Delta-AR	20 µs	49.5K/s	O(S²)→O(S·K)
HeadDeactivate	10 µs	97K/s	87.5% heads off
MPSR	0.9 µs	1.16M/s	KV→SSM recycle
IPSS	4.2 µs	238K/s	O(S) linear fallback

use opusedge::signal::DeltaSignal;
use opusedge::primitives::{SelKV, DeltaAR, HeadDeactivate};

let delta = DeltaSignal::from_proxy_delta(&hidden_states);
let eviction = SelKV::evict(&delta, 0.875, seq_len);
let routing = DeltaAR::route(&delta, 64);

VUGVA — Virtual Unified GPU VRAM Architecture

Chunk-based memory tiering: GPU VRAM → System RAM → NVMe.

Operation	Latency	Throughput
Chunk insert	35.5 µs/batch	28B chunks/s
LRU eviction	1.2 µs/chunk	85K cycles/s
Prefetch	68.7 ns/predict	14.5M predicts/s

Benchmarks (RTX 4060)

Metric	Value
Proxy-Δ extraction	60 ns/token (16.5M tokens/s)
Full pipeline search	59.3 µs (16.9K QPS)
APGC memory savings	50% vs FP32-only
SelKV cache reduction	87.5% at 76 µs/evict

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📜 License

Apache-2.0. One license, one engine, no bolt-ons.

⚠️ Known issue: Firefox Android DoH caching bug with custom domains.

Use Brave, Chrome, or disable DoH in Firefox settings.