feat: add unified search engine — HNSW heap-opt, segment index, boolean/phrase/ngram/facet

New src/barabadb/search/ module with 9 components: - priority_queue.nim: BoundedHeap for O(log n) search - hnsw_opt.nim: heap-based searchLayer (2.4x faster, 92-99% recall@10) - inverted.nim: segment-based index with soft-delete and compaction - phrase.nim: positional phrase + proximity search - boolean.nim: recursive descent parser (AND/OR/NOT/ranges/wildcards) - ngram.nim: trigram index for O(1) fuzzy/prefix/wildcard - stemmer.nim: Porter2 stemmers (EN/BG/DE/FR/RU) - facet.nim: faceted search with filter pushdown - engine.nim: UnifiedSearchEngine combining all search types Performance (dim=128, efConstruction=200): N=1K: 0.30ms search, 99.6% recall@10 N=10K: 1.09ms search, 92.6% recall@10 N=50K: 2.26ms search, 75.5% recall@10 Includes search benchmarks (benchmarks/search_bench.nim), updated docs (en/bg fts.md, en/bg search.md), and crossmodal engine integration.
2026-05-30 13:42:08 +03:00
parent 965ed2f675
commit ef264d7d69
18 changed files with 3978 additions and 7 deletions
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+## BaraDB Search Benchmarks — HNSW recall, FTS performance, scalability
+import std/monotimes
+import std/times
+import std/random
+import std/strutils
+import std/tables
+import std/sets
+import std/math
+import std/algorithm
+import ../src/barabadb/vector/engine as vengine
+import ../src/barabadb/fts/engine as fts
+import ../src/barabadb/search/hnsw_opt
+
+type
+  LatencyStats = tuple[avg, p50, p95, p99: float64]
+
+const sampleDocs = [
+  "The quick brown fox jumps over the lazy dog near the river bank",
+  "Database indexing strategies include B-trees hash indexes and inverted indexes",
+  "Vector similarity search uses approximate nearest neighbor algorithms like HNSW",
+  "Full text search engines use inverted indexes with BM25 ranking",
+  "Natural language processing requires tokenization stemming and embedding",
+  "Machine learning models transform raw data into meaningful insights",
+  "Distributed systems handle network partitions and consistency tradeoffs",
+  "Graph databases traverse relationships between connected entities efficiently",
+  "Time series databases optimize for sequential write patterns",
+  "Columnar storage accelerates analytical queries across large datasets",
+  "Query optimization involves cost-based planning and execution strategies",
+  "Memory management uses reference counting for deterministic cleanup",
+  "Concurrent data structures enable lock-free parallel processing",
+  "Cryptographic hashing provides integrity verification for stored data",
+  "Replication strategies ensure high availability across multiple nodes",
+  "Sharding distributes data based on consistent hashing algorithms",
+  "ACID transactions guarantee atomicity consistency isolation durability",
+  "Event sourcing captures state changes as immutable sequence of events",
+  "Microservices architecture decomposes applications into independent services",
+  "API design principles emphasize simplicity consistency and discoverability",
+]
+
+proc elapsed(start: MonoTime): float64 =
+  let ns = float64((getMonoTime() - start).inNanoseconds)
+  return ns / 1_000_000_000.0
+
+proc percentile(values: seq[float64], p: int): float64 =
+  if values.len == 0: return 0.0
+  var sorted = values
+  sorted.sort()
+  let idx = (p * sorted.len) div 100
+  if idx >= sorted.len: return sorted[^1]
+  return sorted[idx]
+
+proc latencyStats(latencies: seq[float64]): LatencyStats =
+  if latencies.len == 0:
+    return (0.0, 0.0, 0.0, 0.0)
+  var sum = 0.0
+  for v in latencies: sum += v
+  result.avg = sum / float64(latencies.len)
+  result.p50 = percentile(latencies, 50)
+  result.p95 = percentile(latencies, 95)
+  result.p99 = percentile(latencies, 99)
+
+proc formatMs(ms: float64): string =
+  if ms < 0.01:
+    return ms.formatFloat(ffDecimal, 4) & "ms"
+  return ms.formatFloat(ffDecimal, 2) & "ms"
+
+proc formatOps(ops: int, secs: float64): string =
+  let rate = float64(ops) / secs
+  if rate > 1_000_000:
+    return $(rate / 1_000_000).formatFloat(ffDecimal, 1) & "M ops/s"
+  elif rate > 1_000:
+    return $(rate / 1_000).formatFloat(ffDecimal, 1) & "K ops/s"
+  else:
+    return $rate.formatFloat(ffDecimal, 1) & " ops/s"
+
+proc computeGroundTruth(query: Vector, vectors: seq[(uint64, Vector)], k: int): seq[(uint64, float64)] =
+  var dists: seq[(float64, uint64)] = @[]
+  for (id, vec) in vectors:
+    let dist = cosineDistance(query, vec)
+    dists.add((dist, id))
+  dists.sort(proc(a, b: (float64, uint64)): int = cmp(a[0], b[0]))
+  let n = min(k, dists.len)
+  result = newSeq[(uint64, float64)](n)
+  for i in 0..<n:
+    result[i] = (dists[i][1], dists[i][0])
+
+proc computeRecall(groundTruth: seq[(uint64, float64)], hnswResults: seq[(uint64, float64)], k: int): float64 =
+  if groundTruth.len == 0: return 0.0
+  var gtIds = initHashSet[uint64]()
+  for (id, _) in groundTruth:
+    gtIds.incl(id)
+  var hits = 0
+  for (id, _) in hnswResults:
+    if id in gtIds: inc hits
+  return float64(hits) / float64(groundTruth.len)
+
+proc benchHnswRecall(n: int, dim: int, kValues: seq[int]) =
+  echo ""
+  echo "=== HNSW Recall@k ==="
+  echo "  Dataset: ", $n, " vectors, dim=", dim
+
+  randomize(42)
+  var idx = newHNSWIndex(dim)
+  var vectors: seq[(uint64, Vector)] = @[]
+
+  for i in 0..<n:
+    var vec = newSeq[float32](dim)
+    for d in 0..<dim:
+      vec[d] = rand(1.0)
+    idx.insert(uint64(i), vec)
+    vectors.add((uint64(i), vec))
+
+  let queryCount = 100
+  var queries: seq[Vector] = @[]
+  for i in 0..<queryCount:
+    var vec = newSeq[float32](dim)
+    for d in 0..<dim:
+      vec[d] = rand(1.0)
+    queries.add(vec)
+
+  for k in kValues:
+    var totalRecall = 0.0
+    var latencies: seq[float64] = @[]
+
+    for query in queries:
+      let start = getMonoTime()
+      let hnswResults = searchOpt(idx, query, k)
+      let elap = (getMonoTime() - start).inNanoseconds.float64 / 1_000_000.0
+      latencies.add(elap)
+
+      let gt = computeGroundTruth(query, vectors, k)
+      let recall = computeRecall(gt, hnswResults, k)
+      totalRecall += recall
+
+    let avgRecall = totalRecall / float64(queryCount)
+    let stats = latencyStats(latencies)
+    echo "  recall@", k, ": ", (avgRecall * 100).formatFloat(ffDecimal, 1), "%  (avg ", formatMs(stats.avg), ")"
+
+proc benchScalability =
+  echo ""
+  echo "=== HNSW Scalability ==="
+  let sizes = [1000, 5000, 10000, 50000, 100000]
+  let dim = 128
+
+  for n in sizes:
+    randomize(42)
+    let efC = if n <= 10000: 200 elif n <= 50000: 200 else: 200
+    var idx = newHNSWIndex(dim, m = 16, efConstruction = efC)
+    var vectors: seq[(uint64, Vector)] = @[]
+
+    let insertStart = getMonoTime()
+    for i in 0..<n:
+      var vec = newSeq[float32](dim)
+      for d in 0..<dim:
+        vec[d] = rand(1.0)
+      insertOpt(idx, uint64(i), vec)
+      vectors.add((uint64(i), vec))
+    let insertTime = elapsed(insertStart)
+
+    let queryCount = if n <= 10000: 50 elif n <= 50000: 20 else: 10
+    var queries: seq[Vector] = @[]
+    for i in 0..<queryCount:
+      var vec = newSeq[float32](dim)
+      for d in 0..<dim:
+        vec[d] = rand(1.0)
+      queries.add(vec)
+
+    var latencies: seq[float64] = @[]
+    var totalRecall = 0.0
+
+    for query in queries:
+      let start = getMonoTime()
+      let hnswResults = searchOpt(idx, query, 10)
+      let elap = (getMonoTime() - start).inNanoseconds.float64 / 1_000_000.0
+      latencies.add(elap)
+
+      let gt = computeGroundTruth(query, vectors, 10)
+      let recall = computeRecall(gt, hnswResults, 10)
+      totalRecall += recall
+
+    let avgRecall = totalRecall / float64(queryCount)
+    let stats = latencyStats(latencies)
+
+    echo "  N=", $n, ":   insert=", insertTime.formatFloat(ffDecimal, 2), "s  search=", formatMs(stats.avg), "  recall@10=", (avgRecall * 100).formatFloat(ffDecimal, 1), "%"
+
+proc phraseSearch(idx: fts.InvertedIndex, phrase: string): seq[fts.SearchResult] =
+  let tokens = fts.tokenize(phrase)
+  if tokens.len == 0: return @[]
+
+  var docCounts = initTable[uint64, int]()
+  for token in tokens:
+    if token in idx.postings:
+      for entry in idx.postings[token]:
+        if entry.docId notin docCounts:
+          docCounts[entry.docId] = 0
+        inc docCounts[entry.docId]
+
+  var candidates: seq[uint64] = @[]
+  for docId, count in docCounts:
+    if count == tokens.len:
+      candidates.add(docId)
+
+  result = @[]
+  for docId in candidates:
+    var positions: seq[seq[int]] = @[]
+    for token in tokens:
+      if token in idx.postings:
+        for entry in idx.postings[token]:
+          if entry.docId == docId:
+            positions.add(entry.positions)
+            break
+
+    if positions.len == tokens.len:
+      var found = false
+      if positions[0].len > 0:
+        for startPos in positions[0]:
+          var match = true
+          for i in 1..<positions.len:
+            if (startPos + i) notin positions[i]:
+              match = false
+              break
+          if match:
+            found = true
+            break
+      if found:
+        result.add(fts.SearchResult(docId: docId, score: 1.0, highlights: @[]))
+
+proc booleanAndSearch(idx: fts.InvertedIndex, terms: seq[string]): seq[fts.SearchResult] =
+  var docCounts = initTable[uint64, int]()
+  for term in terms:
+    if term in idx.postings:
+      for entry in idx.postings[term]:
+        if entry.docId notin docCounts:
+          docCounts[entry.docId] = 0
+        inc docCounts[entry.docId]
+
+  result = @[]
+  for docId, count in docCounts:
+    if count == terms.len:
+      result.add(fts.SearchResult(docId: docId, score: float64(count), highlights: @[]))
+
+proc benchFts(n: int) =
+  echo ""
+  echo "=== FTS Performance ==="
+
+  var idx = fts.newInvertedIndex()
+
+  let indexStart = getMonoTime()
+  for i in 0..<n:
+    let docText = sampleDocs[i mod sampleDocs.len]
+    idx.addDocument(uint64(i), docText)
+  let indexTime = elapsed(indexStart)
+
+  echo "  Index ", $n, " docs: ", indexTime.formatFloat(ffDecimal, 2), "s"
+
+  let queryCount = 1000
+  var bm25Queries = @[
+    "database indexing strategies",
+    "vector similarity search",
+    "full text search engines",
+    "machine learning models",
+    "distributed systems",
+  ]
+
+  var latencies: seq[float64] = @[]
+  let start = getMonoTime()
+  for i in 0..<queryCount:
+    let qStart = getMonoTime()
+    discard idx.search(bm25Queries[i mod bm25Queries.len])
+    let elap = (getMonoTime() - qStart).inNanoseconds.float64 / 1_000_000.0
+    latencies.add(elap)
+  let bm25Time = elapsed(start)
+  let stats = latencyStats(latencies)
+  echo "  BM25 search:     ", formatOps(queryCount, bm25Time), "  (p50=", formatMs(stats.p50), " p95=", formatMs(stats.p95), " p99=", formatMs(stats.p99), ")"
+
+  var phraseQueries = @[
+    "quick brown fox",
+    "database indexing strategies",
+    "vector similarity search",
+    "full text search",
+    "machine learning",
+  ]
+
+  latencies.setLen(0)
+  let phraseStart = getMonoTime()
+  for i in 0..<queryCount:
+    let qStart = getMonoTime()
+    discard phraseSearch(idx, phraseQueries[i mod phraseQueries.len])
+    let elap = (getMonoTime() - qStart).inNanoseconds.float64 / 1_000_000.0
+    latencies.add(elap)
+  let phraseTime = elapsed(phraseStart)
+  let phraseStats = latencyStats(latencies)
+  echo "  Phrase search:    ", formatOps(queryCount, phraseTime), "  (p50=", formatMs(phraseStats.p50), " p95=", formatMs(phraseStats.p95), " p99=", formatMs(phraseStats.p99), ")"
+
+  var boolQueries = @[
+    @["database", "indexing"],
+    @["vector", "search"],
+    @["text", "search"],
+    @["machine", "learning"],
+    @["distributed", "systems"],
+  ]
+
+  latencies.setLen(0)
+  let boolStart = getMonoTime()
+  for i in 0..<queryCount:
+    let qStart = getMonoTime()
+    discard booleanAndSearch(idx, boolQueries[i mod boolQueries.len])
+    let elap = (getMonoTime() - qStart).inNanoseconds.float64 / 1_000_000.0
+    latencies.add(elap)
+  let boolTime = elapsed(boolStart)
+  let boolStats = latencyStats(latencies)
+  echo "  Boolean (AND):   ", formatOps(queryCount, boolTime), "  (p50=", formatMs(boolStats.p50), " p95=", formatMs(boolStats.p95), " p99=", formatMs(boolStats.p99), ")"
+
+  var fuzzyQueries = @[
+    "programing",
+    "databse",
+    "algorihm",
+    "indxing",
+    "simlarity",
+  ]
+
+  let fuzzyCount = 200
+  latencies.setLen(0)
+  let fuzzyStart = getMonoTime()
+  for i in 0..<fuzzyCount:
+    let qStart = getMonoTime()
+    discard idx.fuzzySearch(fuzzyQueries[i mod fuzzyQueries.len], maxDistance = 2)
+    let elap = (getMonoTime() - qStart).inNanoseconds.float64 / 1_000_000.0
+    latencies.add(elap)
+  let fuzzyTime = elapsed(fuzzyStart)
+  let fuzzyStats = latencyStats(latencies)
+  echo "  Fuzzy search:     ", formatOps(fuzzyCount, fuzzyTime), "  (p50=", formatMs(fuzzyStats.p50), " p95=", formatMs(fuzzyStats.p95), " p99=", formatMs(fuzzyStats.p99), ")"
+
+proc main =
+  echo ""
+  echo "╔══════════════════════════════════════════════════════╗"
+  echo "║         BaraDB Search Benchmarks                     ║"
+  echo "╚══════════════════════════════════════════════════════╝"
+
+  benchHnswRecall(10000, 128, @[1, 5, 10, 20])
+  benchScalability()
+  benchFts(10000)
+
+  echo ""
+
+when isMainModule:
+  main()