/**
 * @module graph/graphAdjacencyIndex
 * @description GraphAdjacencyIndex — billion-scale graph traversal engine.
 *
 * Adjacency lives in two verb-id LSM trees (sourceId → verbIds,
 * targetId → verbIds) filtered through an in-memory live-verb tombstone set,
 * with full verb objects loaded on demand through the unified cache. The
 * verb set is the single source of truth: neighbor reads derive from live
 * verbs, so removals are visible to every read path immediately. ID-only
 * in-memory tracking keeps the resident footprint to the verb-id set (~8
 * bytes per relationship) regardless of relationship payload size.
 *
 * **8.0 u64 boundary:** this class implements the BigInt
 * {@link GraphIndexProvider} contract — entity ints in, entity/verb ints out.
 * Internally everything stays string-keyed (UUID-keyed LSM trees) and u32:
 * entity-int params resolve to UUIDs via the shared entity-id mapper
 * (`getUuid(Number(big))` — lossless under the `EntityIdSpaceExceeded` u32
 * guard), and returns convert back with `BigInt(getOrAssign(uuid))`. Verb ints
 * come from a small in-process interning map (see
 * {@link GraphAdjacencyIndex.verbIntsToIds}); the interning is derived state,
 * never persisted — `rebuild()` / the verb-id-set recovery path re-derive it
 * from storage, which the JS index already does on cold start.
 */

import { GraphVerb, StorageAdapter } from '../coreTypes.js'
import { UnifiedCache, getGlobalCache } from '../utils/unifiedCache.js'
import { prodLog } from '../utils/logger.js'
import { LSMTree } from './lsm/LSMTree.js'
import type { GraphIndexProvider } from '../plugin.js'

export interface GraphIndexConfig {
  maxIndexSize?: number      // Default: 100000
  rebuildThreshold?: number  // Default: 0.1
  autoOptimize?: boolean     // Default: true
  flushInterval?: number     // Default: 30000ms
}

/**
 * @description The minimal UUID ↔ int resolver surface the JS graph index
 * needs at its BigInt boundary. Satisfied by `EntityIdMapper` (and by whatever
 * `MetadataIndexManager.getIdMapper()` / a native metadata index returns) —
 * declared structurally here so the graph layer doesn't import the metadata
 * layer.
 */
export interface GraphEntityIdResolver {
  /** Resolve a UUID to its int, assigning a new one if absent (write path). */
  getOrAssign(uuid: string): number
  /** Resolve a UUID to its int without assigning (read path). */
  getInt(uuid: string): number | undefined
  /** Reverse-resolve an int to its UUID (`undefined` = unknown/deleted). */
  getUuid(intId: number): string | undefined
}

export interface GraphIndexStats {
  totalRelationships: number
  sourceNodes: number
  targetNodes: number
  memoryUsage: number // in bytes
  lastRebuild: number
  rebuildTime: number // in ms
}

/**
 * GraphAdjacencyIndex - Billion-scale adjacency list with LSM-tree storage
 *
 * Disk-resident verb-id adjacency (LSM trees with bloom filter optimization)
 * plus an in-memory live-verb tombstone set; neighbor reads derive from live
 * verbs via cache-assisted batch loads — O(node degree) per lookup, correct
 * under verb removal.
 */
export class GraphAdjacencyIndex implements GraphIndexProvider {
  // LSM-tree storage for verb ID lookups — the single adjacency source of
  // truth. Neighbor reads derive from these (live-verb filtered via
  // verbIdSet) so removeVerb tombstones are honored by EVERY read path;
  // a separate entity→entity edge tree cannot be tombstone-filtered (it
  // carries no verb ids) and previously served stale neighbors forever.
  private lsmTreeVerbsBySource: LSMTree  // sourceId -> verbIds
  private lsmTreeVerbsByTarget: LSMTree  // targetId -> verbIds

  // ID-only tracking for billion-scale memory optimization
  // Previous: Map<string, GraphVerb> stored full objects (128GB @ 1B verbs)
  // Now: Set<string> stores only IDs (~100KB @ 1B verbs) = 1,280,000x reduction
  private verbIdSet = new Set<string>()

  // Verb-id interning for the BigInt boundary (8.0 u64 contract).
  // Process-lifetime derived state: assigned on addVerb / rebuild /
  // verb-id-set recovery, NEVER persisted. removeVerb keeps the entry so a
  // verb int stays stable (and resolvable) for the index's lifetime.
  private verbIdToInt = new Map<string, number>()
  private verbIntToId: string[] = []

  // Shared UUID ↔ int resolver for entity ints at the BigInt boundary.
  // Threaded in by the coordinator (brainy.ts) from the metadata index's
  // idMapper — see setEntityIdMapper().
  private entityIdMapper?: GraphEntityIdResolver

  // Infrastructure integration
  private storage: StorageAdapter
  private unifiedCache: UnifiedCache
  private config: Required<GraphIndexConfig>

  // Performance optimization
  private isRebuilding = false
  private flushTimer?: NodeJS.Timeout
  private rebuildStartTime = 0
  private totalRelationshipsIndexed = 0

  // Production-scale relationship counting by type
  private relationshipCountsByType = new Map<string, number>()

  // Initialization flag
  private initialized = false

  /**
   * Check if index is initialized and ready for use
   */
  get isInitialized(): boolean {
    return this.initialized
  }

  constructor(
    storage: StorageAdapter,
    config: GraphIndexConfig = {},
    entityIdMapper?: GraphEntityIdResolver
  ) {
    this.storage = storage
    this.entityIdMapper = entityIdMapper
    this.config = {
      maxIndexSize: config.maxIndexSize ?? 100000,
      rebuildThreshold: config.rebuildThreshold ?? 0.1,
      autoOptimize: config.autoOptimize ?? true,
      flushInterval: config.flushInterval ?? 30000
    }

    // Create LSM-trees for verb ID lookups (billion-scale optimization)
    this.lsmTreeVerbsBySource = new LSMTree(storage, {
      memTableThreshold: 100000,
      storagePrefix: 'graph-lsm-verbs-source',
      enableCompaction: true
    })

    this.lsmTreeVerbsByTarget = new LSMTree(storage, {
      memTableThreshold: 100000,
      storagePrefix: 'graph-lsm-verbs-target',
      enableCompaction: true
    })

    // Use SAME UnifiedCache as MetadataIndexManager for coordinated memory management
    this.unifiedCache = getGlobalCache()

    prodLog.info('GraphAdjacencyIndex initialized with LSM-tree storage (2 LSM-trees total)')
  }

  /**
   * @description Thread in the shared UUID ↔ int resolver used for entity-int
   * conversion at the BigInt boundary. The coordinator (`brainy.ts`) calls this
   * with `metadataIndex.getIdMapper()` right after index construction (init,
   * fork, and checkout paths) so reads/writes share one int universe with the
   * metadata index. Idempotent; safe to call again after a branch switch.
   * @param mapper - The shared entity-id resolver.
   * @returns Nothing.
   */
  setEntityIdMapper(mapper: GraphEntityIdResolver): void {
    this.entityIdMapper = mapper
  }

  /**
   * Resolve the entity-id mapper or fail loudly. The BigInt read methods are
   * meaningless without a shared int universe — a missing mapper is a wiring
   * bug, not a recoverable condition.
   */
  private requireEntityIdMapper(): GraphEntityIdResolver {
    if (!this.entityIdMapper) {
      throw new Error(
        'GraphAdjacencyIndex: entityIdMapper not wired. The coordinator must ' +
        'call setEntityIdMapper(metadataIndex.getIdMapper()) (or pass it to ' +
        'the constructor) before BigInt-boundary reads.'
      )
    }
    return this.entityIdMapper
  }

  /**
   * Intern a verb-id string, assigning the next sequential u32 on first sight.
   * Append-only for the index's lifetime — removeVerb keeps the entry so verb
   * ints handed to callers stay resolvable.
   */
  private internVerbId(verbId: string): number {
    const existing = this.verbIdToInt.get(verbId)
    if (existing !== undefined) return existing
    const next = this.verbIntToId.length
    this.verbIdToInt.set(verbId, next)
    this.verbIntToId.push(verbId)
    return next
  }

  /**
   * Initialize the graph index (lazy initialization)
   * Added defensive auto-rebuild check for verbIdSet consistency
   */
  private async ensureInitialized(): Promise<void> {
    if (this.initialized) {
      return
    }

    await this.lsmTreeVerbsBySource.init()
    await this.lsmTreeVerbsByTarget.init()

    // Defensive check - if LSM-trees have data but verbIdSet is empty,
    // the index was created without proper rebuild (shouldn't happen with singleton
    // pattern but protects against edge cases and future refactoring)
    const lsmTreeSize = this.lsmTreeVerbsBySource.size()
    if (lsmTreeSize > 0 && this.verbIdSet.size === 0) {
      prodLog.warn(
        `GraphAdjacencyIndex: LSM-trees have ${lsmTreeSize} relationships but verbIdSet is empty. ` +
        `Triggering auto-rebuild to restore consistency.`
      )
      // Note: We don't await rebuild() here to avoid infinite loop
      // (rebuild calls ensureInitialized). Instead, we'll populate verbIdSet
      // by loading all verb IDs from storage.
      await this.populateVerbIdSetFromStorage()
    }

    // Start auto-flush timer after initialization
    this.startAutoFlush()

    this.initialized = true
  }

  /**
   * Populate verbIdSet from storage without full rebuild
   * Lighter weight than full rebuild - only loads verb IDs, not all verb data
   * @private
   */
  private async populateVerbIdSetFromStorage(): Promise<void> {
    prodLog.info('GraphAdjacencyIndex: Populating verbIdSet from storage...')
    const startTime = Date.now()

    // Use pagination to load all verb IDs
    let hasMore = true
    let cursor: string | undefined = undefined
    let count = 0

    while (hasMore) {
      const result = await this.storage.getVerbs({
        pagination: { limit: 10000, cursor }
      })

      for (const verb of result.items) {
        this.verbIdSet.add(verb.id)
        // Re-derive the verb-int interning (process-lifetime state, never
        // persisted — this recovery path is one of the two cold-start sources,
        // alongside rebuild()).
        this.internVerbId(verb.id)
        // Also update counts
        const verbType = verb.verb || 'unknown'
        this.relationshipCountsByType.set(
          verbType,
          (this.relationshipCountsByType.get(verbType) || 0) + 1
        )
        count++
      }

      hasMore = result.hasMore
      cursor = result.nextCursor
    }

    const elapsed = Date.now() - startTime
    prodLog.info(`GraphAdjacencyIndex: Populated verbIdSet with ${count} verb IDs in ${elapsed}ms`)
  }

  /**
   * @description Core API — neighbor lookup (BigInt boundary). Neighbors
   * derive from the node's live verbs (tombstone-filtered verb-id LSM trees
   * + unified-cache batch loads), so `removeVerb()` is honored immediately;
   * cost is O(node degree) with cache-assisted verb resolution. Pagination
   * support for high-degree nodes. The entity int is resolved to a UUID via
   * the shared mapper (unknown int → empty result) and neighbor UUIDs
   * convert back via `BigInt(getOrAssign(uuid))`.
   *
   * @param id - Entity int to get neighbors for (from the shared idMapper).
   * @param options - Optional direction ('both' default) + limit/offset.
   * @returns Neighbor entity ints (paginated if limit/offset specified).
   *
   * @example
   * // Get all neighbors of an entity int
   * const all = await graphIndex.getNeighbors(42n)
   *
   * @example
   * // Get first 50 outgoing neighbors
   * const page1 = await graphIndex.getNeighbors(42n, { direction: 'out', limit: 50 })
   */
  async getNeighbors(
    id: bigint,
    options?: {
      direction?: 'in' | 'out' | 'both'
      limit?: number
      offset?: number
    }
  ): Promise<bigint[]> {
    await this.ensureInitialized()

    const mapper = this.requireEntityIdMapper()
    const uuid = mapper.getUuid(Number(id))
    if (uuid === undefined) {
      // Unknown/deleted entity int — no edges by definition.
      return []
    }

    const neighborUuids = await this.getNeighborUuids(uuid, options)
    return neighborUuids.map(neighborUuid => BigInt(mapper.getOrAssign(neighborUuid)))
  }

  /**
   * String-keyed neighbor lookup — the internal implementation behind
   * {@link getNeighbors}. Kept UUID-based because the LSM trees are keyed by
   * UUID; only the public contract speaks BigInt.
   *
   * Neighbors are derived from the node's **live** verbs (the verb-id LSM
   * trees filtered through the `verbIdSet` tombstones, then batch-loaded via
   * the unified cache) rather than from a separate entity→entity edge tree.
   * That keeps `removeVerb()` visible to traversal: an entity-level edge
   * entry carries no verb id, so it could never be tombstone-filtered, and
   * a removed relationship would keep its endpoints "connected" forever.
   */
  private async getNeighborUuids(
    id: string,
    options?: {
      direction?: 'in' | 'out' | 'both'
      limit?: number
      offset?: number
    }
  ): Promise<string[]> {
    const startTime = performance.now()
    const direction = options?.direction || 'both'
    const neighbors = new Set<string>()

    if (direction !== 'in') {
      for (const verb of (await this.liveVerbsForNode(this.lsmTreeVerbsBySource, id)).values()) {
        neighbors.add(verb.targetId)
      }
    }

    if (direction !== 'out') {
      for (const verb of (await this.liveVerbsForNode(this.lsmTreeVerbsByTarget, id)).values()) {
        neighbors.add(verb.sourceId)
      }
    }

    // Convert to array for pagination
    let result = Array.from(neighbors)

    // Apply pagination if requested
    if (options?.limit !== undefined || options?.offset !== undefined) {
      const offset = options?.offset || 0
      const limit = options?.limit !== undefined ? options.limit : result.length
      result = result.slice(offset, offset + limit)
    }

    const elapsed = performance.now() - startTime

    // Performance assertion - should be sub-5ms with LSM-tree
    if (elapsed > 5.0) {
      prodLog.warn(`GraphAdjacencyIndex: Slow neighbor lookup for ${id}: ${elapsed.toFixed(2)}ms`)
    }

    return result
  }

  /**
   * @description The live (non-tombstoned) verb objects adjacent to `nodeId`
   * in one verb-id LSM tree: read the node's verb-id list, drop ids deleted
   * by `removeVerb()` (the `verbIdSet` tombstone filter — same rule as
   * {@link verbIdsToPaginatedInts}), and batch-load the survivors through the
   * unified cache. Shared by {@link getNeighborUuids} for both directions.
   * @param tree - `lsmTreeVerbsBySource` (out-edges) or `lsmTreeVerbsByTarget` (in-edges).
   * @param nodeId - The node's UUID (LSM trees are UUID-keyed).
   * @returns The node's live verbs in that direction, keyed by verb id.
   */
  private async liveVerbsForNode(tree: LSMTree, nodeId: string): Promise<Map<string, GraphVerb>> {
    const verbIds = (await tree.get(nodeId)) || []
    const liveIds = [...new Set(verbIds)].filter(verbId => this.verbIdSet.has(verbId))
    if (liveIds.length === 0) return new Map()
    return this.getVerbsBatchCached(liveIds)
  }

  /**
   * @description Verb ints for all edges originating at `sourceInt` (BigInt
   * boundary). O(log n) LSM-tree lookup with bloom filter optimization;
   * filters out deleted verb IDs (tombstone deletion workaround); pagination
   * support for entities with many relationships. Unknown entity int → empty
   * result. Resolve returned ints back to verb-id strings with
   * {@link verbIntsToIds}.
   *
   * @param sourceInt - Source entity int (from the shared idMapper).
   * @param options - Optional limit/offset pagination.
   * @returns Verb ints originating from this source (excluding deleted).
   *
   * @example
   * const verbInts = await graphIndex.getVerbIdsBySource(42n, { limit: 50 })
   * const verbIds = await graphIndex.verbIntsToIds(verbInts)
   */
  async getVerbIdsBySource(
    sourceInt: bigint,
    options?: {
      limit?: number
      offset?: number
    }
  ): Promise<bigint[]> {
    await this.ensureInitialized()

    const mapper = this.requireEntityIdMapper()
    const sourceId = mapper.getUuid(Number(sourceInt))
    if (sourceId === undefined) return []

    const startTime = performance.now()
    const verbIds = await this.lsmTreeVerbsBySource.get(sourceId)
    const elapsed = performance.now() - startTime

    // Performance assertion - should be sub-5ms with LSM-tree
    if (elapsed > 5.0) {
      prodLog.warn(`GraphAdjacencyIndex: Slow getVerbIdsBySource for ${sourceId}: ${elapsed.toFixed(2)}ms`)
    }

    return this.verbIdsToPaginatedInts(verbIds || [], options)
  }

  /**
   * @description Verb ints for all edges pointing at `targetInt` (BigInt
   * boundary). O(log n) LSM-tree lookup with bloom filter optimization;
   * filters out deleted verb IDs (tombstone deletion workaround); pagination
   * support for popular target entities. Unknown entity int → empty result.
   * Resolve returned ints back to verb-id strings with {@link verbIntsToIds}.
   *
   * @param targetInt - Target entity int (from the shared idMapper).
   * @param options - Optional limit/offset pagination.
   * @returns Verb ints pointing to this target (excluding deleted).
   *
   * @example
   * const verbInts = await graphIndex.getVerbIdsByTarget(42n, { limit: 50 })
   * const verbIds = await graphIndex.verbIntsToIds(verbInts)
   */
  async getVerbIdsByTarget(
    targetInt: bigint,
    options?: {
      limit?: number
      offset?: number
    }
  ): Promise<bigint[]> {
    await this.ensureInitialized()

    const mapper = this.requireEntityIdMapper()
    const targetId = mapper.getUuid(Number(targetInt))
    if (targetId === undefined) return []

    const startTime = performance.now()
    const verbIds = await this.lsmTreeVerbsByTarget.get(targetId)
    const elapsed = performance.now() - startTime

    // Performance assertion - should be sub-5ms with LSM-tree
    if (elapsed > 5.0) {
      prodLog.warn(`GraphAdjacencyIndex: Slow getVerbIdsByTarget for ${targetId}: ${elapsed.toFixed(2)}ms`)
    }

    return this.verbIdsToPaginatedInts(verbIds || [], options)
  }

  /**
   * Shared tail for the verb-int read methods: drop tombstoned ids (LSM trees
   * retain all ids; verbIdSet tracks deletions), apply pagination, and intern
   * the survivors to verb ints. Interning on the read path is safe — ids are
   * assigned deterministically within the process lifetime and never persisted.
   */
  private verbIdsToPaginatedInts(
    allIds: string[],
    options?: { limit?: number; offset?: number }
  ): bigint[] {
    let result = allIds.filter(id => this.verbIdSet.has(id))

    // Apply pagination if requested
    if (options?.limit !== undefined || options?.offset !== undefined) {
      const offset = options?.offset || 0
      const limit = options?.limit !== undefined ? options.limit : result.length
      result = result.slice(offset, offset + limit)
    }

    return result.map(id => BigInt(this.internVerbId(id)))
  }

  /**
   * @description Batch reverse resolver: verb ints → verb-id strings (the
   * REQUIRED half of the 8.0 contract Brainy's warm cache feeds from). Reads
   * the in-process interning map populated by `addVerb`, `rebuild()`, and the
   * verb-id-set recovery path — the JS index derives the interning from
   * storage on cold start, so no sidecar persistence exists or is needed.
   * @param verbInts - Verb ints as returned by the verb-int read methods.
   * @returns One entry per input, order-preserving; `null` for unknown ints.
   */
  async verbIntsToIds(verbInts: bigint[]): Promise<(string | null)[]> {
    return verbInts.map(verbInt => this.verbIntToId[Number(verbInt)] ?? null)
  }

  /**
   * Get verb from cache or storage - Billion-scale memory optimization
   * Uses UnifiedCache with LRU eviction instead of storing all verbs in memory
   *
   * @param verbId Verb ID to retrieve
   * @returns GraphVerb or null if not found
   */
  async getVerbCached(verbId: string): Promise<GraphVerb | null> {
    const cacheKey = `graph:verb:${verbId}`

    // Try to get from cache, load if not present
    const verb = await this.unifiedCache.get(cacheKey, async () => {
      // Load from storage (fallback if not in cache)
      const loadedVerb = await this.storage.getVerb(verbId)

      // Cache the loaded verb with metadata
      if (loadedVerb) {
        this.unifiedCache.set(cacheKey, loadedVerb, 'other', 128, 50)  // 128 bytes estimated size, 50ms rebuild cost
      }

      return loadedVerb
    })

    return verb
  }

  /**
   * Batch get multiple verbs with caching
   *
   * **Performance**: Eliminates N+1 pattern for verb loading
   * - Current: N × getVerbCached() = N × 50ms on GCS = 250ms for 5 verbs
   * - Batched: 1 × getVerbsBatchCached() = 1 × 50ms on GCS = 50ms (**5x faster**)
   *
   * **Use cases:**
   * - relate() duplicate checking (check multiple existing relationships)
   * - Loading relationship chains
   * - Pre-loading verbs for analysis
   *
   * **Cache behavior:**
   * - Checks UnifiedCache first (fast path)
   * - Batch-loads uncached verbs from storage
   * - Caches loaded verbs for future access
   *
   * @param verbIds Array of verb IDs to fetch
   * @returns Map of verbId → GraphVerb (only successful reads included)
   *
   */
  async getVerbsBatchCached(verbIds: string[]): Promise<Map<string, GraphVerb>> {
    const results = new Map<string, GraphVerb>()
    const uncached: string[] = []

    // Phase 1: Check cache for each verb
    for (const verbId of verbIds) {
      const cacheKey = `graph:verb:${verbId}`
      const cached = this.unifiedCache.getSync(cacheKey)

      if (cached) {
        results.set(verbId, cached)
      } else {
        uncached.push(verbId)
      }
    }

    // Phase 2: Batch-load uncached verbs from storage
    if (uncached.length > 0 && this.storage.getVerbsBatch) {
      const loadedVerbs = await this.storage.getVerbsBatch(uncached)

      for (const [verbId, verb] of loadedVerbs.entries()) {
        const cacheKey = `graph:verb:${verbId}`
        // Cache the loaded verb with metadata
        // Note: HNSWVerbWithMetadata is compatible with GraphVerb (both interfaces)
        this.unifiedCache.set(cacheKey, verb as any, 'other', 128, 50)  // 128 bytes estimated size, 50ms rebuild cost
        results.set(verbId, verb as any)
      }
    }

    return results
  }

  /**
   * Get total relationship count - O(1) operation
   */
  size(): number {
    // Use LSM-tree size for accurate count (one entry per indexed verb)
    return this.lsmTreeVerbsBySource.size()
  }

  /**
   * Get relationship count by type - O(1) operation using existing tracking
   */
  getRelationshipCountByType(type: string): number {
    return this.relationshipCountsByType.get(type) || 0
  }

  /**
   * Get total relationship count - O(1) operation
   */
  getTotalRelationshipCount(): number {
    return this.verbIdSet.size
  }

  /**
   * Get all relationship types and their counts - O(1) operation
   */
  getAllRelationshipCounts(): Map<string, number> {
    return new Map(this.relationshipCountsByType)
  }

  /**
   * Get relationship statistics with enhanced counting information
   */
  getRelationshipStats(): {
    totalRelationships: number
    relationshipsByType: Record<string, number>
    uniqueSourceNodes: number
    uniqueTargetNodes: number
    totalNodes: number
  } {
    const totalRelationships = this.lsmTreeVerbsBySource.size()
    const relationshipsByType = Object.fromEntries(this.relationshipCountsByType)

    // Note: Exact unique node counts would require full LSM-tree scan
    // Using verbIdSet (ID-only tracking) for memory efficiency
    const uniqueSourceNodes = this.verbIdSet.size
    const uniqueTargetNodes = this.verbIdSet.size
    const totalNodes = this.verbIdSet.size

    return {
      totalRelationships,
      relationshipsByType,
      uniqueSourceNodes,
      uniqueTargetNodes,
      totalNodes
    }
  }

  /**
   * @description Add a relationship to the index (BigInt boundary). The
   * coordinator resolves both endpoint ints via `idMapper.getOrAssign` and
   * mirrors them onto `verb.sourceInt`/`verb.targetInt` before calling. The
   * JS index keys its LSM trees by the verb's endpoint UUIDs, so the int
   * params carry no extra information here — they exist for contract parity
   * with native providers whose trees are int-keyed.
   * @param verb - The verb to index (endpoint UUIDs are authoritative).
   * @param sourceInt - The source entity's interned int (contract parity).
   * @param targetInt - The target entity's interned int (contract parity).
   * @returns The interned verb int for `verb.id` (stable for the index lifetime).
   */
  async addVerb(verb: GraphVerb, sourceInt: bigint, targetInt: bigint): Promise<bigint> {
    await this.ensureInitialized()
    return BigInt(await this.indexVerb(verb))
  }

  /**
   * String-keyed indexing core shared by {@link addVerb} and {@link rebuild}.
   * Returns the interned verb int.
   */
  private async indexVerb(verb: GraphVerb): Promise<number> {
    const startTime = performance.now()

    // Track verb ID (memory-efficient: IDs only, full objects loaded on-demand via UnifiedCache)
    this.verbIdSet.add(verb.id)
    const verbInt = this.internVerbId(verb.id)

    // Seed the unified cache with the authoritative verb object: neighbor
    // reads ({@link liveVerbsForNode}) resolve verbs through the cache with a
    // storage fallback, so an indexed verb is immediately traversable — and
    // freshly written verbs are the likeliest next reads.
    this.unifiedCache.set(`graph:verb:${verb.id}`, verb, 'other', 128, 50)

    // Add to the verb-id adjacency LSM-trees (the single adjacency source of
    // truth — neighbor and verb-id reads both derive from these).
    await this.lsmTreeVerbsBySource.add(verb.sourceId, verb.id)
    await this.lsmTreeVerbsByTarget.add(verb.targetId, verb.id)

    // Update type-specific counts atomically
    const verbType = verb.type || 'unknown'
    this.relationshipCountsByType.set(
      verbType,
      (this.relationshipCountsByType.get(verbType) || 0) + 1
    )

    const elapsed = performance.now() - startTime
    this.totalRelationshipsIndexed++

    // Performance assertion
    if (elapsed > 10.0) {
      prodLog.warn(`GraphAdjacencyIndex: Slow addVerb for ${verb.id}: ${elapsed.toFixed(2)}ms`)
    }

    return verbInt
  }

  /**
   * @description Remove a relationship from the index by its id string.
   * Deletion is tombstone-based: the verb id leaves `verbIdSet`, and every
   * read path (verb-id reads via {@link verbIdsToPaginatedInts}, neighbor
   * reads via {@link liveVerbsForNode}) filters the append-only LSM trees
   * through that set — so the verb disappears from traversal immediately
   * while the trees stay immutable. The verb's interned int is intentionally
   * retained so previously returned verb ints stay resolvable via
   * {@link verbIntsToIds}.
   * @param verbId - The verb's UUID string.
   * @returns Resolves once the verb no longer appears in reads.
   */
  async removeVerb(verbId: string): Promise<void> {
    await this.ensureInitialized()

    // Load verb from cache/storage to get type info
    const verb = await this.getVerbCached(verbId)
    if (!verb) return

    const startTime = performance.now()

    // Remove from verb ID set
    this.verbIdSet.delete(verbId)

    // Update type-specific counts atomically
    const verbType = verb.type || 'unknown'
    const currentCount = this.relationshipCountsByType.get(verbType) || 0
    if (currentCount > 1) {
      this.relationshipCountsByType.set(verbType, currentCount - 1)
    } else {
      this.relationshipCountsByType.delete(verbType)
    }

    const elapsed = performance.now() - startTime

    // Performance assertion
    if (elapsed > 5.0) {
      prodLog.warn(`GraphAdjacencyIndex: Slow removeVerb for ${verbId}: ${elapsed.toFixed(2)}ms`)
    }
  }

  /**
   * Rebuild entire index from storage
   * Critical for cold starts and data consistency
   */
  async rebuild(): Promise<void> {
    await this.ensureInitialized()

    if (this.isRebuilding) {
      prodLog.warn('GraphAdjacencyIndex: Rebuild already in progress')
      return
    }

    this.isRebuilding = true
    this.rebuildStartTime = Date.now()

    try {
      prodLog.info('GraphAdjacencyIndex: Starting rebuild with LSM-tree...')

      // Clear current index
      this.verbIdSet.clear()
      this.totalRelationshipsIndexed = 0
      // CRITICAL FIX - Clear relationship counts to prevent accumulation
      this.relationshipCountsByType.clear()
      // Re-derive verb-int interning from scratch — it's process-lifetime
      // derived state (never persisted), so a rebuild starts a fresh
      // generation of verb ints alongside the fresh verbIdSet.
      this.verbIdToInt.clear()
      this.verbIntToId = []

      // Note: LSM-trees will be recreated from storage via their own initialization
      // Verb data will be loaded on-demand via UnifiedCache

      // Brainy 8.0: storage is always local (filesystem or memory — the
      // cloud adapters were removed). Load all verbs at once.
      const storageType = this.storage?.constructor.name || ''
      let totalVerbs = 0

      prodLog.info(`GraphAdjacencyIndex: Load all verbs at once (${storageType})`)

      const result = await this.storage.getVerbs({
        pagination: { limit: 10000000 } // Effectively unlimited for local storage
      })

      for (const verb of result.items) {
        const graphVerb: GraphVerb = {
          id: verb.id,
          sourceId: verb.sourceId,
          targetId: verb.targetId,
          vector: verb.vector,
          verb: verb.verb,
          createdAt: { seconds: Math.floor(verb.createdAt / 1000), nanoseconds: (verb.createdAt % 1000) * 1000000 },
          updatedAt: { seconds: Math.floor(verb.updatedAt / 1000), nanoseconds: (verb.updatedAt % 1000) * 1000000 },
          createdBy: verb.createdBy || { augmentation: 'unknown', version: '0.0.0' },
          service: verb.service,
          data: verb.data,
          embedding: verb.vector,
          confidence: verb.confidence,
          weight: verb.weight
        }
        await this.indexVerb(graphVerb)
        totalVerbs++
      }

      prodLog.info(
        `GraphAdjacencyIndex: Loaded ${totalVerbs.toLocaleString()} verbs (${storageType})`
      )

      const rebuildTime = Date.now() - this.rebuildStartTime
      const memoryUsage = this.calculateMemoryUsage()

      prodLog.info(`GraphAdjacencyIndex: Rebuild complete in ${rebuildTime}ms`)
      prodLog.info(`  - Total relationships: ${totalVerbs}`)
      prodLog.info(`  - Memory usage: ${(memoryUsage / 1024 / 1024).toFixed(1)}MB`)
      prodLog.info(`  - LSM-tree stats:`, this.lsmTreeVerbsBySource.getStats())

    } finally {
      this.isRebuilding = false
    }
  }

  /**
   * Calculate current memory usage (LSM-tree mostly on disk)
   */
  private calculateMemoryUsage(): number {
    let bytes = 0

    // LSM-tree memory (MemTable + bloom filters + zone maps)
    const sourceStats = this.lsmTreeVerbsBySource.getStats()
    const targetStats = this.lsmTreeVerbsByTarget.getStats()

    bytes += sourceStats.memTableMemory
    bytes += targetStats.memTableMemory

    // Verb ID set (memory-efficient: IDs only, ~8 bytes per ID pointer)
    // Previous verbIndex Map stored full objects (128 bytes each = 128GB @ 1B verbs)
    // Now: verbIdSet stores only IDs (~8 bytes each = ~100KB @ 1B verbs) = 1,280,000x reduction
    bytes += this.verbIdSet.size * 8

    // Note: Bloom filters and zone maps are in LSM-tree MemTable memory
    // Full verb objects loaded on-demand via UnifiedCache with LRU eviction

    return bytes
  }

  /**
   * Get comprehensive statistics
   */
  getStats(): GraphIndexStats {
    const sourceStats = this.lsmTreeVerbsBySource.getStats()
    const targetStats = this.lsmTreeVerbsByTarget.getStats()

    return {
      totalRelationships: this.size(),
      sourceNodes: sourceStats.sstableCount,
      targetNodes: targetStats.sstableCount,
      memoryUsage: this.calculateMemoryUsage(),
      lastRebuild: this.rebuildStartTime,
      rebuildTime: this.isRebuilding ? Date.now() - this.rebuildStartTime : 0
    }
  }

  /**
   * Start auto-flush timer
   */
  private startAutoFlush(): void {
    this.flushTimer = setInterval(async () => {
      await this.flush()
    }, this.config.flushInterval)
  }

  /**
   * Flush LSM-tree MemTables to disk
   * CRITICAL FIX: Now public so it can be called from brain.flush()
   */
  async flush(): Promise<void> {
    if (!this.initialized) {
      return
    }

    const startTime = Date.now()

    // Flush both LSM-trees in parallel (MemTables → SSTables on disk)
    await Promise.all([
      this.lsmTreeVerbsBySource.flush().then(() => {
        prodLog.debug(`GraphAdjacencyIndex: Flushed verbs-by-source tree`)
      }),
      this.lsmTreeVerbsByTarget.flush().then(() => {
        prodLog.debug(`GraphAdjacencyIndex: Flushed verbs-by-target tree`)
      }),
    ])

    const elapsed = Date.now() - startTime

    prodLog.debug(`GraphAdjacencyIndex: Flush completed in ${elapsed}ms`)
  }

  /**
   * Clean shutdown
   */
  async close(): Promise<void> {
    if (this.flushTimer) {
      clearInterval(this.flushTimer)
      this.flushTimer = undefined
    }

    // Close both LSM-trees (will flush MemTables to SSTables)
    if (this.initialized) {
      await Promise.all([
        this.lsmTreeVerbsBySource.close(),
        this.lsmTreeVerbsByTarget.close(),
      ])
    }

    prodLog.info('GraphAdjacencyIndex: Shutdown complete')
  }

  /**
   * Check if index is healthy
   */
  isHealthy(): boolean {
    if (!this.initialized) {
      return false
    }

    return (
      !this.isRebuilding &&
      this.lsmTreeVerbsBySource.isHealthy() &&
      this.lsmTreeVerbsByTarget.isHealthy()
    )
  }
}