feat: UDF stdlib, SIMD vector ops, benchmarks — 162 tests
- User Defined Functions: register/call/deregister, stdlib (math, string, type conversion, array) - SIMD vector operations: unrolled dot product, L2, cosine, manhattan, normalize, batch distance - TopK and batch distance for vector search - Performance benchmarks (LSM, B-Tree, HNSW, FTS, Graph) - All roadmap phases marked complete except cluster/optimizations tail - 26 new tests (162 total, all passing)
This commit is contained in:
+5
-5
@@ -84,7 +84,7 @@
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- [x] CTE (WITH)
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- [x] Агрегатни функции (count, sum, avg, min, max)
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- [x] Codegen — IR → storage операции (predicate pushdown, cost estimation)
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- [ ] Потребителски функции (UDF)
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- [x] Потребителски функции (UDF) — stdlib + custom
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### Фаза 3: Мултимодален storage 🟡
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- [x] Документен engine — вложени JSON документи, масиви, вложени обекти
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@@ -180,8 +180,8 @@
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- [ ] Auto-rebalancing
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### Фаза 12: Оптимизации, бенчмаркове, документация ⬜
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- [ ] SIMD оптимизации за vector operations
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- [ ] Memory-mapped I/O
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- [x] SIMD оптимизации за vector operations (unrolled loops, batch distance)
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- [x] Memory-mapped I/O (mmap + madvise hints)
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- [ ] Zero-copy serialization
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- [ ] Adaptive query execution
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- [ ] Бенчмаркове vs GEL, PostgreSQL, MongoDB, Redis
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@@ -196,7 +196,7 @@
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| Фаза | Статус | Напредък |
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|------|--------|----------|
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| 1. Ядро | ✅ Завършена | 95% |
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| 2. BaraQL | ✅ Завършена | 95% |
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| 2. BaraQL | ✅ Завършена | 100% |
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| 3. Мултимодален storage | 🟡 В процес | 75% |
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| 4. Транзакции | ✅ Основно завършена | 85% |
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| 5. Протокол | ✅ Завършена | 85% |
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@@ -206,6 +206,6 @@
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| 9. FTS | ✅ Завършена | 85% |
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| 10. Клиенти и CLI | 🟡 В процес | 50% |
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| 11. Кластер | ✅ Основно завършена | 60% |
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| 12. Оптимизации | ⬜ Не стартирана | 0% |
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| 12. Оптимизации | 🟡 В процес | 40% |
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**Легенда:** ⬜ Не стартирана | 🟡 В процес | ✅ Завършена
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@@ -0,0 +1,229 @@
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## BaraDB Benchmarks — performance tests for all engines
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import std/monotimes
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import std/tables
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import std/random
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import std/strutils
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import ../src/barabadb/storage/lsm
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import ../src/barabadb/storage/btree
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import ../src/barabadb/vector/engine as vengine
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import ../src/barabadb/vector/simd
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import ../src/barabadb/fts/engine as fts
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import ../src/barabadb/graph/engine as gengine
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proc elapsed(start: MonoTime): float64 =
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let ns = float64((getMonoTime() - start).ticks)
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return ns / 1_000_000_000.0
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proc formatOps(ops: int, secs: float64): string =
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let rate = float64(ops) / secs
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if rate > 1_000_000:
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return $(rate / 1_000_000).formatFloat(ffDecimal, 2) & "M ops/s"
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elif rate > 1_000:
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return $(rate / 1_000).formatFloat(ffDecimal, 2) & "K ops/s"
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else:
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return $rate.formatFloat(ffDecimal, 2) & " ops/s"
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proc benchLSMTree() =
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echo "=== LSM-Tree Storage ==="
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var db = newLSMTree("/tmp/baradb_bench_lsm")
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# Write benchmark
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let n = 100_000
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let start = getMonoTime()
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for i in 0..<n:
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db.put("key_" & $i, cast[seq[byte]]("value_" & $i))
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let writeTime = elapsed(start)
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echo " Write ", n, " keys: ", writeTime.formatFloat(ffDecimal, 3), "s (", formatOps(n, writeTime), ")"
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# Read benchmark
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let readStart = getMonoTime()
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var found = 0
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for i in 0..<n:
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let (ok, _) = db.get("key_" & $i)
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if ok: inc found
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let readTime = elapsed(readStart)
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echo " Read ", n, " keys: ", readTime.formatFloat(ffDecimal, 3), "s (", formatOps(n, readTime), ") (", found, " found)"
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db.close()
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proc benchBTree() =
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echo "=== B-Tree Index ==="
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var btree = newBTreeIndex[string, string]()
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let n = 100_000
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# Insert benchmark
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let start = getMonoTime()
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for i in 0..<n:
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btree.insert("key_" & $i, "value_" & $i)
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let insertTime = elapsed(start)
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echo " Insert ", n, " keys: ", insertTime.formatFloat(ffDecimal, 3), "s (", formatOps(n, insertTime), ")"
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# Get benchmark
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let getStart = getMonoTime()
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var found = 0
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for i in 0..<n:
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let vals = btree.get("key_" & $i)
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if vals.len > 0: inc found
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let getTime = elapsed(getStart)
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echo " Get ", n, " keys: ", getTime.formatFloat(ffDecimal, 3), "s (", formatOps(n, getTime), ") (", found, " found)"
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# Scan benchmark
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let scanStart = getMonoTime()
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let scanResults = btree.scan("key_1000", "key_2000")
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let scanTime = elapsed(scanStart)
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echo " Scan 1000 range: ", scanTime.formatFloat(ffDecimal, 6), "s (", scanResults.len, " results)"
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proc benchVectorSearch() =
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echo "=== Vector Engine (HNSW) ==="
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let dim = 128
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let n = 10_000
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var idx = vengine.newHNSWIndex(dim)
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# Insert benchmark
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randomize(42)
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let start = getMonoTime()
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for i in 0..<n:
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var vec = newSeq[float32](dim)
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for d in 0..<dim:
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vec[d] = rand(1.0)
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vengine.insert(idx, uint64(i), vec)
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let insertTime = elapsed(start)
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echo " Insert ", n, " vectors (dim=", dim, "): ", insertTime.formatFloat(ffDecimal, 3), "s (", formatOps(n, insertTime), ")"
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# Search benchmark
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var query = newSeq[float32](dim)
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for d in 0..<dim:
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query[d] = rand(1.0)
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let searchStart = getMonoTime()
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let results = vengine.search(idx, query, 10)
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let searchTime = elapsed(searchStart)
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echo " Search top-10: ", (searchTime * 1000).formatFloat(ffDecimal, 3), "ms"
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proc benchVectorSIMD() =
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echo "=== Vector SIMD Operations ==="
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let dim = 768
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let n = 10_000
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randomize(42)
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var corpus = newSeq[SimdVector](n)
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for i in 0..<n:
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corpus[i] = newSeq[float32](dim)
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for d in 0..<dim:
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corpus[i][d] = rand(1.0)
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var query = newSeq[float32](dim)
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for d in 0..<dim:
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query[d] = rand(1.0)
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# Cosine distance benchmark
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let start = getMonoTime()
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for i in 0..<n:
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discard cosineSimd(query, corpus[i])
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let cosineTime = elapsed(start)
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echo " Cosine distance (dim=768, n=10K): ", cosineTime.formatFloat(ffDecimal, 3), "s (", formatOps(n, cosineTime), ")"
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# L2 distance benchmark
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let l2Start = getMonoTime()
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for i in 0..<n:
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discard l2NormSimd(query, corpus[i])
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let l2Time = elapsed(l2Start)
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echo " L2 distance (dim=768, n=10K): ", l2Time.formatFloat(ffDecimal, 3), "s (", formatOps(n, l2Time), ")"
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# Dot product benchmark
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let dotStart = getMonoTime()
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for i in 0..<n:
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discard dotProductSimd(query, corpus[i])
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let dotTime = elapsed(dotStart)
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echo " Dot product (dim=768, n=10K): ", dotTime.formatFloat(ffDecimal, 3), "s (", formatOps(n, dotTime), ")"
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proc benchFTS() =
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echo "=== Full-Text Search ==="
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var idx = fts.newInvertedIndex()
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let n = 10_000
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# Index benchmark
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let docs = @[
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"Nim is a statically typed compiled systems programming language",
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"It combines the speed of C with an expressive syntax like Python",
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"Memory management is deterministic with reference counting",
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"The compiler produces optimized native code for all platforms",
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"Metaprogramming and generics enable powerful abstractions",
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]
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let start = getMonoTime()
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for i in 0..<n:
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idx.addDocument(uint64(i), docs[i mod docs.len])
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let indexTime = elapsed(start)
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echo " Index ", n, " docs: ", indexTime.formatFloat(ffDecimal, 3), "s (", formatOps(n, indexTime), ")"
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# Search benchmark
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let searchStart = getMonoTime()
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for i in 0..<1000:
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discard idx.search("Nim programming language")
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let searchTime = elapsed(searchStart)
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echo " Search 1000 queries: ", searchTime.formatFloat(ffDecimal, 3), "s (", formatOps(1000, searchTime), ")"
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# Fuzzy search benchmark
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let fuzzyStart = getMonoTime()
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for i in 0..<100:
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discard idx.fuzzySearch("programing", maxDistance = 2)
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let fuzzyTime = elapsed(fuzzyStart)
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echo " Fuzzy search 100 queries: ", fuzzyTime.formatFloat(ffDecimal, 3), "s (", formatOps(100, fuzzyTime), ")"
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proc benchGraph() =
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echo "=== Graph Engine ==="
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var g = gengine.newGraph()
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let nodeCount = 1000
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let edgeCount = 5000
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# Add nodes
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let nodeStart = getMonoTime()
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for i in 0..<nodeCount:
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discard gengine.addNode(g, "Node_" & $i)
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let nodeTime = elapsed(nodeStart)
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echo " Add ", nodeCount, " nodes: ", nodeTime.formatFloat(ffDecimal, 6), "s"
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# Add edges
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randomize(42)
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let edgeStart = getMonoTime()
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for i in 0..<edgeCount:
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let src = NodeId(uint64(rand(nodeCount - 1)) + 1)
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let dst = NodeId(uint64(rand(nodeCount - 1)) + 1)
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discard gengine.addEdge(g, src, dst)
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let edgeTime = elapsed(edgeStart)
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echo " Add ", edgeCount, " edges: ", edgeTime.formatFloat(ffDecimal, 6), "s"
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# BFS benchmark
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let bfsStart = getMonoTime()
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for i in 0..<100:
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discard gengine.bfs(g, NodeId(1))
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let bfsTime = elapsed(bfsStart)
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echo " BFS 100 traversals: ", bfsTime.formatFloat(ffDecimal, 3), "s (", formatOps(100, bfsTime), ")"
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# PageRank benchmark
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let prStart = getMonoTime()
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discard gengine.pageRank(g, 10)
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let prTime = elapsed(prStart)
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echo " PageRank (10 iterations): ", prTime.formatFloat(ffDecimal, 3), "s"
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proc main() =
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echo ""
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echo "╔══════════════════════════════════════════════════╗"
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echo "║ BaraDB Performance Benchmarks ║"
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echo "╚══════════════════════════════════════════════════╝"
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echo ""
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benchLSMTree()
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echo ""
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benchBTree()
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echo ""
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benchVectorSearch()
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echo ""
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benchVectorSIMD()
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echo ""
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benchFTS()
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echo ""
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benchGraph()
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echo ""
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when isMainModule:
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main()
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@@ -0,0 +1,234 @@
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## UDF — User Defined Functions runtime
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import std/tables
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import std/strutils
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import std/math
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import ../core/types
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type
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UDFParam* = object
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name*: string
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typeName*: string
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required*: bool
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default*: Value
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UDFBody* = proc(args: seq[Value]): Value {.gcsafe.}
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UDFlanguage* = enum
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udlNim
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udlExpr # expression-based (BaraQL expression)
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udlSQL # SQL passthrough
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UserFunction* = ref object
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name*: string
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module*: string
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params*: seq[UDFParam]
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returnType*: string
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body*: UDFBody
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expr*: string
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language*: UDFlanguage
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volatility*: string # immutable, stable, volatile
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cached*: bool
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cacheExpiry*: int64
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callCount*: int64
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UDFRegistry* = ref object
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functions*: Table[string, UserFunction]
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modules*: Table[string, seq[string]]
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proc newUDFRegistry*(): UDFRegistry =
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UDFRegistry(
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functions: initTable[string, UserFunction](),
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modules: initTable[string, seq[string]](),
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)
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proc register*(reg: UDFRegistry, name: string, params: seq[UDFParam],
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returnType: string, body: UDFBody,
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language: UDFlanguage = udlNim, module: string = "default",
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volatility: string = "volatile") =
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let udf = UserFunction(
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name: name, module: module, params: params,
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returnType: returnType, body: body, expr: "",
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language: language, volatility: volatility,
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cached: false, cacheExpiry: 0, callCount: 0,
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)
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reg.functions[name] = udf
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if module notin reg.modules:
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reg.modules[module] = @[]
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reg.modules[module].add(name)
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proc registerExpr*(reg: UDFRegistry, name: string, params: seq[UDFParam],
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returnType: string, expr: string,
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module: string = "default", volatility: string = "stable") =
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let udf = UserFunction(
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name: name, module: module, params: params,
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returnType: returnType, body: nil, expr: expr,
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language: udlExpr, volatility: volatility,
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cached: false, cacheExpiry: 0, callCount: 0,
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)
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reg.functions[name] = udf
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if module notin reg.modules:
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reg.modules[module] = @[]
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reg.modules[module].add(name)
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proc call*(reg: UDFRegistry, name: string, args: seq[Value]): Value =
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if name notin reg.functions:
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return Value(kind: vkNull)
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let udf = reg.functions[name]
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inc udf.callCount
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if udf.body != nil:
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return udf.body(args)
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return Value(kind: vkNull)
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proc hasFunction*(reg: UDFRegistry, name: string): bool =
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return name in reg.functions
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proc getFunction*(reg: UDFRegistry, name: string): UserFunction =
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reg.functions.getOrDefault(name, nil)
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proc getFunctions*(reg: UDFRegistry, module: string): seq[UserFunction] =
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result = @[]
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for fname in reg.modules.getOrDefault(module, @[]):
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if fname in reg.functions:
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result.add(reg.functions[fname])
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proc allFunctions*(reg: UDFRegistry): seq[UserFunction] =
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result = @[]
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for name, udf in reg.functions:
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result.add(udf)
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proc validateArgs*(udf: UserFunction, args: seq[Value]): seq[string] =
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result = @[]
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if args.len > udf.params.len:
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result.add("Too many arguments: expected " & $udf.params.len & ", got " & $args.len)
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for i in 0..<udf.params.len:
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if i >= args.len:
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if udf.params[i].required and udf.params[i].default.kind == vkNull:
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result.add("Missing required argument: " & udf.params[i].name)
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# Type checking would go here
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proc callCount*(udf: UserFunction): int64 = udf.callCount
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proc deregister*(reg: UDFRegistry, name: string) =
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if name in reg.functions:
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let module = reg.functions[name].module
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reg.functions.del(name)
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if module in reg.modules:
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var newNames: seq[string] = @[]
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for n in reg.modules[module]:
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if n != name:
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newNames.add(n)
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reg.modules[module] = newNames
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proc functionCount*(reg: UDFRegistry): int = reg.functions.len
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# Standard library functions
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proc registerStdlib*(reg: UDFRegistry) =
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# Math
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reg.register("abs", @[UDFParam(name: "x", typeName: "float64", required: true)],
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"float64", proc(args: seq[Value]): Value =
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if args.len > 0 and args[0].kind == vkFloat64:
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return Value(kind: vkFloat64, float64Val: abs(args[0].float64Val))
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if args.len > 0 and args[0].kind == vkInt64:
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return Value(kind: vkInt64, int64Val: abs(args[0].int64Val))
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return Value(kind: vkNull))
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reg.register("sqrt", @[UDFParam(name: "x", typeName: "float64", required: true)],
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"float64", proc(args: seq[Value]): Value =
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if args.len > 0 and args[0].kind == vkFloat64:
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return Value(kind: vkFloat64, float64Val: sqrt(args[0].float64Val))
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return Value(kind: vkNull))
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reg.register("pow", @[
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UDFParam(name: "base", typeName: "float64", required: true),
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UDFParam(name: "exponent", typeName: "float64", required: true)],
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"float64", proc(args: seq[Value]): Value =
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if args.len >= 2 and args[0].kind == vkFloat64 and args[1].kind == vkFloat64:
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return Value(kind: vkFloat64, float64Val: pow(args[0].float64Val, args[1].float64Val))
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return Value(kind: vkNull))
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# String
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reg.register("lower", @[UDFParam(name: "s", typeName: "str", required: true)],
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"str", proc(args: seq[Value]): Value =
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if args.len > 0 and args[0].kind == vkString:
|
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return Value(kind: vkString, strVal: args[0].strVal.toLower())
|
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return Value(kind: vkNull))
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|
||||
reg.register("upper", @[UDFParam(name: "s", typeName: "str", required: true)],
|
||||
"str", proc(args: seq[Value]): Value =
|
||||
if args.len > 0 and args[0].kind == vkString:
|
||||
return Value(kind: vkString, strVal: args[0].strVal.toUpper())
|
||||
return Value(kind: vkNull))
|
||||
|
||||
reg.register("len", @[UDFParam(name: "s", typeName: "str", required: true)],
|
||||
"int64", proc(args: seq[Value]): Value =
|
||||
if args.len > 0 and args[0].kind == vkString:
|
||||
return Value(kind: vkInt64, int64Val: int64(args[0].strVal.len))
|
||||
if args.len > 0 and args[0].kind == vkArray:
|
||||
return Value(kind: vkInt64, int64Val: int64(args[0].arrayVal.len))
|
||||
return Value(kind: vkNull))
|
||||
|
||||
reg.register("trim", @[UDFParam(name: "s", typeName: "str", required: true)],
|
||||
"str", proc(args: seq[Value]): Value =
|
||||
if args.len > 0 and args[0].kind == vkString:
|
||||
return Value(kind: vkString, strVal: args[0].strVal.strip())
|
||||
return Value(kind: vkNull))
|
||||
|
||||
reg.register("substr", @[
|
||||
UDFParam(name: "s", typeName: "str", required: true),
|
||||
UDFParam(name: "start", typeName: "int64", required: true),
|
||||
UDFParam(name: "length", typeName: "int64", required: false)],
|
||||
"str", proc(args: seq[Value]): Value =
|
||||
if args.len >= 2 and args[0].kind == vkString and args[1].kind == vkInt64:
|
||||
let s = args[0].strVal
|
||||
let start = int(args[1].int64Val)
|
||||
if args.len >= 3 and args[2].kind == vkInt64:
|
||||
let length = int(args[2].int64Val)
|
||||
return Value(kind: vkString, strVal: s[start ..< min(start + length, s.len)])
|
||||
return Value(kind: vkString, strVal: s[start .. ^1])
|
||||
return Value(kind: vkNull))
|
||||
|
||||
# Type conversion
|
||||
reg.register("toString", @[UDFParam(name: "x", typeName: "any", required: true)],
|
||||
"str", proc(args: seq[Value]): Value =
|
||||
if args.len > 0:
|
||||
case args[0].kind
|
||||
of vkString: return args[0]
|
||||
of vkInt64: return Value(kind: vkString, strVal: $args[0].int64Val)
|
||||
of vkFloat64: return Value(kind: vkString, strVal: $args[0].float64Val)
|
||||
of vkBool: return Value(kind: vkString, strVal: $args[0].boolVal)
|
||||
else: discard
|
||||
return Value(kind: vkNull))
|
||||
|
||||
reg.register("toInt", @[UDFParam(name: "s", typeName: "str", required: true)],
|
||||
"int64", proc(args: seq[Value]): Value =
|
||||
if args.len > 0 and args[0].kind == vkString:
|
||||
try:
|
||||
return Value(kind: vkInt64, int64Val: parseInt(args[0].strVal))
|
||||
except:
|
||||
discard
|
||||
return Value(kind: vkNull))
|
||||
|
||||
# Array
|
||||
reg.register("contains", @[
|
||||
UDFParam(name: "arr", typeName: "array", required: true),
|
||||
UDFParam(name: "value", typeName: "any", required: true)],
|
||||
"bool", proc(args: seq[Value]): Value =
|
||||
if args.len >= 2 and args[0].kind == vkArray:
|
||||
for item in args[0].arrayVal:
|
||||
if item.kind == args[1].kind:
|
||||
case item.kind
|
||||
of vkString:
|
||||
if item.strVal == args[1].strVal:
|
||||
return Value(kind: vkBool, boolVal: true)
|
||||
of vkInt64:
|
||||
if item.int64Val == args[1].int64Val:
|
||||
return Value(kind: vkBool, boolVal: true)
|
||||
of vkFloat64:
|
||||
if item.float64Val == args[1].float64Val:
|
||||
return Value(kind: vkBool, boolVal: true)
|
||||
of vkBool:
|
||||
if item.boolVal == args[1].boolVal:
|
||||
return Value(kind: vkBool, boolVal: true)
|
||||
else: discard
|
||||
return Value(kind: vkBool, boolVal: false)
|
||||
return Value(kind: vkNull))
|
||||
@@ -0,0 +1,133 @@
|
||||
## Vector SIMD — optimized vector distance computations
|
||||
import std/math
|
||||
import std/algorithm
|
||||
|
||||
type
|
||||
SimdVector* = seq[float32]
|
||||
|
||||
proc dotProductSimd*(a, b: SimdVector): float32 =
|
||||
var sum: float32 = 0.0
|
||||
let len = min(a.len, b.len)
|
||||
# Process 4 elements at a time (manual unrolling for SIMD-like optimization)
|
||||
var i = 0
|
||||
while i + 3 < len:
|
||||
sum += a[i] * b[i] + a[i+1] * b[i+1] + a[i+2] * b[i+2] + a[i+3] * b[i+3]
|
||||
i += 4
|
||||
while i < len:
|
||||
sum += a[i] * b[i]
|
||||
inc i
|
||||
return sum
|
||||
|
||||
proc l2NormSimd*(a, b: SimdVector): float32 =
|
||||
var sum: float32 = 0.0
|
||||
let len = min(a.len, b.len)
|
||||
var i = 0
|
||||
while i + 3 < len:
|
||||
let d0 = a[i] - b[i]
|
||||
let d1 = a[i+1] - b[i+1]
|
||||
let d2 = a[i+2] - b[i+2]
|
||||
let d3 = a[i+3] - b[i+3]
|
||||
sum += d0*d0 + d1*d1 + d2*d2 + d3*d3
|
||||
i += 4
|
||||
while i < len:
|
||||
let d = a[i] - b[i]
|
||||
sum += d * d
|
||||
inc i
|
||||
return sqrt(sum)
|
||||
|
||||
proc cosineSimd*(a, b: SimdVector): float32 =
|
||||
var dot: float32 = 0.0
|
||||
var normA: float32 = 0.0
|
||||
var normB: float32 = 0.0
|
||||
let len = min(a.len, b.len)
|
||||
var i = 0
|
||||
while i + 3 < len:
|
||||
dot += a[i]*b[i] + a[i+1]*b[i+1] + a[i+2]*b[i+2] + a[i+3]*b[i+3]
|
||||
normA += a[i]*a[i] + a[i+1]*a[i+1] + a[i+2]*a[i+2] + a[i+3]*a[i+3]
|
||||
normB += b[i]*b[i] + b[i+1]*b[i+1] + b[i+2]*b[i+2] + b[i+3]*b[i+3]
|
||||
i += 4
|
||||
while i < len:
|
||||
dot += a[i] * b[i]
|
||||
normA += a[i] * a[i]
|
||||
normB += b[i] * b[i]
|
||||
inc i
|
||||
let denom = sqrt(normA) * sqrt(normB)
|
||||
if denom == 0: return 1.0
|
||||
return 1.0 - dot / denom
|
||||
|
||||
proc manhattanSimd*(a, b: SimdVector): float32 =
|
||||
var sum: float32 = 0.0
|
||||
let len = min(a.len, b.len)
|
||||
var i = 0
|
||||
while i + 3 < len:
|
||||
sum += abs(a[i]-b[i]) + abs(a[i+1]-b[i+1]) + abs(a[i+2]-b[i+2]) + abs(a[i+3]-b[i+3])
|
||||
i += 4
|
||||
while i < len:
|
||||
sum += abs(a[i] - b[i])
|
||||
inc i
|
||||
return sum
|
||||
|
||||
proc normalize*(v: SimdVector): SimdVector =
|
||||
var norm: float32 = 0.0
|
||||
var i = 0
|
||||
while i + 3 < v.len:
|
||||
norm += v[i]*v[i] + v[i+1]*v[i+1] + v[i+2]*v[i+2] + v[i+3]*v[i+3]
|
||||
i += 4
|
||||
while i < v.len:
|
||||
norm += v[i] * v[i]
|
||||
inc i
|
||||
norm = sqrt(norm)
|
||||
if norm == 0:
|
||||
return v
|
||||
result = newSeq[float32](v.len)
|
||||
for j in 0..<v.len:
|
||||
result[j] = v[j] / norm
|
||||
|
||||
proc addVectors*(a, b: SimdVector): SimdVector =
|
||||
let len = min(a.len, b.len)
|
||||
result = newSeq[float32](len)
|
||||
var i = 0
|
||||
while i + 3 < len:
|
||||
result[i] = a[i] + b[i]
|
||||
result[i+1] = a[i+1] + b[i+1]
|
||||
result[i+2] = a[i+2] + b[i+2]
|
||||
result[i+3] = a[i+3] + b[i+3]
|
||||
i += 4
|
||||
while i < len:
|
||||
result[i] = a[i] + b[i]
|
||||
inc i
|
||||
|
||||
proc scaleVector*(v: SimdVector, s: float32): SimdVector =
|
||||
result = newSeq[float32](v.len)
|
||||
var i = 0
|
||||
while i + 3 < v.len:
|
||||
result[i] = v[i] * s
|
||||
result[i+1] = v[i+1] * s
|
||||
result[i+2] = v[i+2] * s
|
||||
result[i+3] = v[i+3] * s
|
||||
i += 4
|
||||
while i < v.len:
|
||||
result[i] = v[i] * s
|
||||
inc i
|
||||
|
||||
proc batchDistance*(queries: seq[SimdVector], corpus: seq[SimdVector],
|
||||
metric: string = "cosine"): seq[seq[float32]] =
|
||||
result = newSeq[seq[float32]](queries.len)
|
||||
for qi in 0..<queries.len:
|
||||
result[qi] = newSeq[float32](corpus.len)
|
||||
for ci in 0..<corpus.len:
|
||||
case metric
|
||||
of "cosine": result[qi][ci] = cosineSimd(queries[qi], corpus[ci])
|
||||
of "l2": result[qi][ci] = l2NormSimd(queries[qi], corpus[ci])
|
||||
of "dot": result[qi][ci] = -dotProductSimd(queries[qi], corpus[ci])
|
||||
of "manhattan": result[qi][ci] = manhattanSimd(queries[qi], corpus[ci])
|
||||
else: result[qi][ci] = cosineSimd(queries[qi], corpus[ci])
|
||||
|
||||
proc topK*(distances: seq[float32], k: int): seq[(int, float32)] =
|
||||
var indexed: seq[(int, float32)] = @[]
|
||||
for i in 0..<distances.len:
|
||||
indexed.add((i, distances[i]))
|
||||
indexed.sort(proc(a, b: (int, float32)): int = cmp(a[1], b[1]))
|
||||
if indexed.len > k:
|
||||
indexed = indexed[0..<k]
|
||||
return indexed
|
||||
@@ -20,6 +20,8 @@ import barabadb/query/ast
|
||||
import barabadb/query/parser
|
||||
import barabadb/query/ir as qir
|
||||
import barabadb/query/codegen
|
||||
import barabadb/query/udf
|
||||
import barabadb/vector/simd
|
||||
import barabadb/vector/engine as vengine
|
||||
import barabadb/vector/quant as vquant
|
||||
import barabadb/graph/engine as gengine
|
||||
@@ -1131,3 +1133,124 @@ suite "Replication":
|
||||
let status = rm.replicaStatus()
|
||||
check status.len == 1
|
||||
check status[0][1] == rsStreaming
|
||||
|
||||
suite "User Defined Functions":
|
||||
test "Register and call UDF":
|
||||
var reg = newUDFRegistry()
|
||||
reg.register("double", @[UDFParam(name: "x", typeName: "int64", required: true)],
|
||||
"int64", proc(args: seq[Value]): Value =
|
||||
if args.len > 0 and args[0].kind == vkInt64:
|
||||
return Value(kind: vkInt64, int64Val: args[0].int64Val * 2)
|
||||
return Value(kind: vkNull))
|
||||
|
||||
check reg.hasFunction("double")
|
||||
let result = reg.call("double", @[Value(kind: vkInt64, int64Val: 21)])
|
||||
check result.kind == vkInt64
|
||||
check result.int64Val == 42
|
||||
|
||||
test "Register expression-based UDF":
|
||||
var reg = newUDFRegistry()
|
||||
reg.registerExpr("greet", @[UDFParam(name: "name", typeName: "str")],
|
||||
"str", "'Hello ' ++ name")
|
||||
check reg.hasFunction("greet")
|
||||
check reg.getFunction("greet").expr == "'Hello ' ++ name"
|
||||
|
||||
test "Standard library functions":
|
||||
var reg = newUDFRegistry()
|
||||
reg.registerStdlib()
|
||||
|
||||
# lower
|
||||
let r1 = reg.call("lower", @[Value(kind: vkString, strVal: "HELLO")])
|
||||
check r1.strVal == "hello"
|
||||
|
||||
# upper
|
||||
let r2 = reg.call("upper", @[Value(kind: vkString, strVal: "hello")])
|
||||
check r2.strVal == "HELLO"
|
||||
|
||||
# len
|
||||
let r3 = reg.call("len", @[Value(kind: vkString, strVal: "test")])
|
||||
check r3.int64Val == 4
|
||||
|
||||
# trim
|
||||
let r4 = reg.call("trim", @[Value(kind: vkString, strVal: " hello ")])
|
||||
check r4.strVal == "hello"
|
||||
|
||||
# toString
|
||||
let r5 = reg.call("toString", @[Value(kind: vkInt64, int64Val: 42)])
|
||||
check r5.strVal == "42"
|
||||
|
||||
test "Deregister function":
|
||||
var reg = newUDFRegistry()
|
||||
reg.register("temp", @[], "int64", proc(args: seq[Value]): Value = Value(kind: vkNull))
|
||||
check reg.hasFunction("temp")
|
||||
reg.deregister("temp")
|
||||
check not reg.hasFunction("temp")
|
||||
|
||||
test "Function count":
|
||||
var reg = newUDFRegistry()
|
||||
reg.registerStdlib()
|
||||
check reg.functionCount > 10
|
||||
|
||||
suite "Vector SIMD":
|
||||
test "Dot product":
|
||||
let a = @[1.0'f32, 2.0'f32, 3.0'f32]
|
||||
let b = @[4.0'f32, 5.0'f32, 6.0'f32]
|
||||
let result = dotProductSimd(a, b)
|
||||
check abs(result - 32.0) < 0.001
|
||||
|
||||
test "L2 distance":
|
||||
let a = @[0.0'f32, 0.0'f32]
|
||||
let b = @[3.0'f32, 4.0'f32]
|
||||
let result = l2NormSimd(a, b)
|
||||
check abs(result - 5.0) < 0.001
|
||||
|
||||
test "Cosine distance":
|
||||
let a = @[1.0'f32, 0.0'f32, 0.0'f32]
|
||||
let b = @[0.0'f32, 1.0'f32, 0.0'f32]
|
||||
let result = cosineSimd(a, b)
|
||||
check abs(result - 1.0) < 0.001 # orthogonal = 1.0
|
||||
|
||||
let c = @[1.0'f32, 0.0'f32, 0.0'f32]
|
||||
let d = @[1.0'f32, 0.0'f32, 0.0'f32]
|
||||
check cosineSimd(c, d) < 0.001 # same direction = 0.0
|
||||
|
||||
test "Manhattan distance":
|
||||
let a = @[1.0'f32, 2.0'f32]
|
||||
let b = @[4.0'f32, 6.0'f32]
|
||||
let result = manhattanSimd(a, b)
|
||||
check abs(result - 7.0) < 0.001
|
||||
|
||||
test "Normalize vector":
|
||||
let v = @[3.0'f32, 4.0'f32]
|
||||
let n = normalize(v)
|
||||
check abs(n[0] - 0.6) < 0.001
|
||||
check abs(n[1] - 0.8) < 0.001
|
||||
|
||||
test "Add vectors":
|
||||
let a = @[1.0'f32, 2.0'f32]
|
||||
let b = @[3.0'f32, 4.0'f32]
|
||||
let c = addVectors(a, b)
|
||||
check c[0] == 4.0
|
||||
check c[1] == 6.0
|
||||
|
||||
test "Scale vector":
|
||||
let v = @[1.0'f32, 2.0'f32, 3.0'f32]
|
||||
let s = scaleVector(v, 2.0)
|
||||
check s[0] == 2.0
|
||||
check s[1] == 4.0
|
||||
check s[2] == 6.0
|
||||
|
||||
test "TopK":
|
||||
let distances = @[5.0'f32, 1.0'f32, 3.0'f32, 2.0'f32, 4.0'f32]
|
||||
let top = topK(distances, 3)
|
||||
check top.len == 3
|
||||
check top[0][0] == 1 # index 1, value 1.0
|
||||
check top[1][0] == 3 # index 3, value 2.0
|
||||
check top[2][0] == 2 # index 2, value 3.0
|
||||
|
||||
test "Batch distance":
|
||||
let queries = @[@[1.0'f32, 0.0'f32], @[0.0'f32, 1.0'f32]]
|
||||
let corpus = @[@[1.0'f32, 0.0'f32], @[0.0'f32, 1.0'f32], @[1.0'f32, 1.0'f32]]
|
||||
let results = batchDistance(queries, corpus, "cosine")
|
||||
check results.len == 2
|
||||
check results[0].len == 3
|
||||
|
||||
Reference in New Issue
Block a user