/**
 * @module tests/benchmarks/model-b-write-perf.spike
 * @description SPIKE (throwaway, spike/model-b-write-perf branch) — measures the
 * cost of Model B (per-write immutable history). We do NOT build Model B to measure
 * it: `transact([1 op])` already does exactly what Model B would make every single-op
 * write do (read+save before-image → write delta → write manifest → sync). So this
 * benchmarks today's single-op path (`add()`/`update()`) against `transact([1])`
 * (Model B per-write, un-optimized upper bound) and `transact([K])` (group-commit
 * amortization), on memory (isolates CPU/retention overhead, sync is a no-op) and
 * filesystem-on-NVMe (adds real fsync), with embedding neutralized (precomputed
 * vector + a constant embeddings provider) so the throughput delta is pure
 * commit-path overhead. Also measures on-disk history growth (bytes/write retained).
 *
 * Run: node_modules/.bin/tsx tests/benchmarks/model-b-write-perf.spike.ts
 * Optional smaller run: SPIKE_SCALE=small node_modules/.bin/tsx tests/benchmarks/model-b-write-perf.spike.ts
 */

import { rmSync, mkdirSync, existsSync } from 'node:fs'
import { execSync } from 'node:child_process'
import { Brainy } from '../../src/index.js'
import { NounType } from '../../src/types/graphTypes.js'
import type { BrainyConfig } from '../../src/types/brainy.types.js'

// Silence brainy's verbose per-brain init logging — it floods stdout (≈1 MB at
// small scale across dozens of fresh brains) and the I/O measurably slows the run.
// Capture the real console.log first, then no-op the noisy channels; `say()` is the
// benchmark's own output channel and is unaffected.
const _log = console.log.bind(console)
const say = (...a: any[]) => _log(...a)
for (const m of ['log', 'info', 'debug', 'warn'] as const) {
  ;(console as any)[m] = () => {}
}

// ---- config ---------------------------------------------------------------

const SMALL = process.env.SPIKE_SCALE === 'small'
const DIM = 384
// NVMe ext4 (NOT /tmp — that is tmpfs/RAM and would understate fsync).
const FS_ROOT = '/media/dpsifr/storage/home/Projects/brainy/.spike-data'

// Scales: memory is cheap (CPU only); filesystem pays fsync per transact so it is smaller.
const N_MEM = SMALL ? 2_000 : 20_000
const N_FS = SMALL ? 500 : 4_000
const K = 100 // group-commit batch size for the transact([K]) amortization run
const WARMUP = SMALL ? 100 : 500
const REPEATS = 3 // median of N timed repeats

// ---- helpers --------------------------------------------------------------

/** Deterministic, cheap, VARIED unit vector per index (avoids embedding AND a
 *  degenerate all-identical-vector HNSW graph). No model, ~µs. */
function vec(i: number): number[] {
  const v = new Array<number>(DIM)
  let mag = 0
  for (let d = 0; d < DIM; d++) {
    const x = Math.sin((i + 1) * 0.001 + d * 0.1) * 0.5 + Math.cos(d * 0.05) * 0.3
    v[d] = x
    mag += x * x
  }
  mag = Math.sqrt(mag) || 1
  for (let d = 0; d < DIM; d++) v[d] /= mag
  return v
}

const CONST_VEC = vec(0)

/** Build a brain with embedding fully neutralized:
 *  - a constant 'embeddings' provider → skips the WASM eager-init (the provider
 *    "owns" embeddings) and makes any embed call O(1);
 *  - every write also passes a precomputed `vector`, so embed is never even called. */
async function mkBrain(backend: 'memory' | 'filesystem', dir?: string): Promise<Brainy> {
  const storage: BrainyConfig['storage'] =
    backend === 'memory' ? { type: 'memory' } : { type: 'filesystem', path: dir! }
  const brain = new Brainy({
    requireSubtype: false,
    storage,
    // CRITICAL: skip the WASM embedder eager-init (90-140s compile per brain) —
    // we never embed (precomputed vectors), so the model must not load at all.
    eagerEmbeddings: false,
    index: { m: 16, efConstruction: 200, efSearch: 50 },
    cache: { maxSize: 1000, ttl: 3600 }
  } as BrainyConfig)
  brain.use({
    name: 'const-embedder',
    activate: async (ctx: any) => {
      ctx.registerProvider('embeddings', async () => CONST_VEC)
      return true
    }
  } as any)
  await brain.init()
  return brain
}

function freshDir(name: string): string {
  const dir = `${FS_ROOT}/${name}`
  if (existsSync(dir)) rmSync(dir, { recursive: true, force: true })
  mkdirSync(dir, { recursive: true })
  return dir
}

function dirBytes(dir: string): number {
  try {
    const out = execSync(`du -sb ${dir}`, { encoding: 'utf8' })
    return parseInt(out.split(/\s+/)[0], 10)
  } catch {
    return -1
  }
}

interface Timing {
  perSec: number
  msPerOp: number
}

/** Median-of-REPEATS timing of one full N-write workload built by `make`.
 *  `make` returns an async fn that performs ALL N writes on a FRESH brain. */
async function timeWorkload(
  label: string,
  build: () => Promise<{ run: () => Promise<void>; n: number; cleanup: () => Promise<void> }>
): Promise<Timing> {
  const rates: number[] = []
  for (let r = 0; r < REPEATS; r++) {
    const { run, n, cleanup } = await build()
    const t0 = process.hrtime.bigint()
    await run()
    const t1 = process.hrtime.bigint()
    await cleanup()
    const sec = Number(t1 - t0) / 1e9
    rates.push(n / sec)
  }
  rates.sort((a, b) => a - b)
  const perSec = rates[Math.floor(rates.length / 2)]
  return { perSec, msPerOp: 1000 / perSec }
}

// ---- write workloads ------------------------------------------------------
// Every workload produces N entities and is identical except for the write path,
// so index-growth + payload costs cancel in the A-vs-B delta.

function payload(i: number) {
  return {
    type: NounType.Document,
    subtype: 'note',
    data: `doc ${i}`,
    vector: vec(i),
    metadata: { i, batch: 'spike', tag: i % 7 }
  }
}

/** CREATE via single-op add() (today's path). */
function createSingleOp(backend: 'memory' | 'filesystem', n: number, dirName: string) {
  return async () => {
    const dir = backend === 'filesystem' ? freshDir(dirName) : undefined
    const brain = await mkBrain(backend, dir)
    return {
      n,
      run: async () => {
        for (let i = 0; i < n; i++) await brain.add(payload(i))
      },
      cleanup: async () => {
        await brain.close()
        if (dir) rmSync(dir, { recursive: true, force: true })
      }
    }
  }
}

/** CREATE via transact([batch]) — batch=1 is Model B per-write upper bound. */
function createTransact(backend: 'memory' | 'filesystem', n: number, batch: number, dirName: string) {
  return async () => {
    const dir = backend === 'filesystem' ? freshDir(dirName) : undefined
    const brain = await mkBrain(backend, dir)
    return {
      n,
      run: async () => {
        for (let i = 0; i < n; i += batch) {
          const ops = []
          for (let j = i; j < Math.min(i + batch, n); j++) {
            ops.push({ op: 'add' as const, ...payload(j) })
          }
          await brain.transact(ops as any)
        }
      },
      cleanup: async () => {
        await brain.close()
        if (dir) rmSync(dir, { recursive: true, force: true })
      }
    }
  }
}

/** UPDATE churn (the case where Model B's before-image = a FULL old record).
 *  Seeds n entities via fast single-op add(), then times n updates via the path. */
function updateSingleOp(backend: 'memory' | 'filesystem', n: number, dirName: string) {
  return async () => {
    const dir = backend === 'filesystem' ? freshDir(dirName) : undefined
    const brain = await mkBrain(backend, dir)
    const ids: string[] = []
    for (let i = 0; i < n; i++) ids.push(await brain.add(payload(i)))
    return {
      n,
      run: async () => {
        for (let i = 0; i < n; i++) await brain.update({ id: ids[i], metadata: { i, rev: 1 } })
      },
      cleanup: async () => {
        await brain.close()
        if (dir) rmSync(dir, { recursive: true, force: true })
      }
    }
  }
}

function updateTransact(backend: 'memory' | 'filesystem', n: number, batch: number, dirName: string) {
  return async () => {
    const dir = backend === 'filesystem' ? freshDir(dirName) : undefined
    const brain = await mkBrain(backend, dir)
    const ids: string[] = []
    for (let i = 0; i < n; i++) ids.push(await brain.add(payload(i)))
    return {
      n,
      run: async () => {
        for (let i = 0; i < n; i += batch) {
          const ops = []
          for (let j = i; j < Math.min(i + batch, n); j++) {
            ops.push({ op: 'update' as const, id: ids[j], metadata: { i: j, rev: 1 } })
          }
          await brain.transact(ops as any)
        }
      },
      cleanup: async () => {
        await brain.close()
        if (dir) rmSync(dir, { recursive: true, force: true })
      }
    }
  }
}

// ---- history growth (filesystem only) -------------------------------------

/** Disk bytes for N adds via single-op vs via transact([1]); delta = retained history. */
async function historyGrowthAdd(n: number) {
  const dSingle = freshDir('hist-add-single')
  let b = await mkBrain('filesystem', dSingle)
  for (let i = 0; i < n; i++) await b.add(payload(i))
  await b.close()
  const singleBytes = dirBytes(dSingle)

  const dTx = freshDir('hist-add-tx')
  b = await mkBrain('filesystem', dTx)
  for (let i = 0; i < n; i++) await b.transact([{ op: 'add', ...payload(i) }] as any)
  await b.close()
  const txBytes = dirBytes(dTx)

  rmSync(dSingle, { recursive: true, force: true })
  rmSync(dTx, { recursive: true, force: true })
  return { n, singleBytes, txBytes, retainedTotal: txBytes - singleBytes, retainedPerWrite: (txBytes - singleBytes) / n }
}

/** Update-churn: n entities, each updated M times via transact([1]); disk delta vs the
 *  same entities updated via single-op update() (no history). Captures full-record before-images. */
async function historyGrowthUpdateChurn(n: number, churn: number) {
  const seed = (b: Brainy) => (async () => {
    const ids: string[] = []
    for (let i = 0; i < n; i++) ids.push(await b.add(payload(i)))
    return ids
  })()

  const dSingle = freshDir('hist-upd-single')
  let b = await mkBrain('filesystem', dSingle)
  let ids = await seed(b)
  for (let c = 0; c < churn; c++) for (let i = 0; i < n; i++) await b.update({ id: ids[i], metadata: { i, rev: c } })
  await b.close()
  const singleBytes = dirBytes(dSingle)

  const dTx = freshDir('hist-upd-tx')
  b = await mkBrain('filesystem', dTx)
  ids = await seed(b)
  for (let c = 0; c < churn; c++) for (let i = 0; i < n; i++) await b.transact([{ op: 'update', id: ids[i], metadata: { i, rev: c } }] as any)
  await b.close()
  const txBytes = dirBytes(dTx)

  rmSync(dSingle, { recursive: true, force: true })
  rmSync(dTx, { recursive: true, force: true })
  const writes = n * churn
  return { writes, singleBytes, txBytes, retainedTotal: txBytes - singleBytes, retainedPerWrite: (txBytes - singleBytes) / writes }
}

// ---- driver ---------------------------------------------------------------

async function main() {
  say(`[spike] start (scale=${SMALL ? 'small' : 'full'}); verifying no-WASM embedding...`)
  // Sanity: a brain must construct + init fast (no WASM compile).
  {
    const t = process.hrtime.bigint()
    const b = await mkBrain('memory')
    await b.add(payload(0))
    await b.close()
    say(`[spike] brain init + 1 write OK in ${(Number(process.hrtime.bigint() - t) / 1e6).toFixed(0)}ms (must be <2000ms or WASM is loading)`)
  }
  if (existsSync(FS_ROOT)) rmSync(FS_ROOT, { recursive: true, force: true })
  mkdirSync(FS_ROOT, { recursive: true })

  const results: any = { meta: { N_MEM, N_FS, K, WARMUP, REPEATS, DIM, dim: DIM, small: SMALL }, throughput: {}, history: {} }

  // Warmup (JIT/alloc) on a memory brain.
  {
    const b = await mkBrain('memory')
    for (let i = 0; i < WARMUP; i++) await b.add(payload(i))
    for (let i = 0; i < WARMUP; i += K) await b.transact([{ op: 'add', ...payload(i) }] as any)
    await b.close()
  }

  const fmt = (t: Timing) => `${Math.round(t.perSec).toLocaleString()}/s (${t.msPerOp.toFixed(3)} ms)`

  for (const backend of ['memory', 'filesystem'] as const) {
    const n = backend === 'memory' ? N_MEM : N_FS
    say(`\n========== ${backend.toUpperCase()} backend (N=${n.toLocaleString()}) ==========`)

    // CREATE
    const addSingle = await timeWorkload('add-single', createSingleOp(backend, n, 'add-single'))
    const addTx1 = await timeWorkload('add-tx1', createTransact(backend, n, 1, 'add-tx1'))
    const addTxK = await timeWorkload('add-txK', createTransact(backend, n, K, 'add-txK'))
    say(`CREATE  add()          ${fmt(addSingle)}`)
    say(`CREATE  transact([1])  ${fmt(addTx1)}   <- Model B per-write (slowdown ${(addSingle.perSec / addTx1.perSec).toFixed(2)}x)`)
    say(`CREATE  transact([${K}]) ${fmt(addTxK)}   <- group-commit (slowdown vs add() ${(addSingle.perSec / addTxK.perSec).toFixed(2)}x)`)

    // UPDATE
    const updSingle = await timeWorkload('upd-single', updateSingleOp(backend, n, 'upd-single'))
    const updTx1 = await timeWorkload('upd-tx1', updateTransact(backend, n, 1, 'upd-tx1'))
    const updTxK = await timeWorkload('upd-txK', updateTransact(backend, n, K, 'upd-txK'))
    say(`UPDATE  update()       ${fmt(updSingle)}`)
    say(`UPDATE  transact([1])  ${fmt(updTx1)}   <- Model B per-write (slowdown ${(updSingle.perSec / updTx1.perSec).toFixed(2)}x)`)
    say(`UPDATE  transact([${K}]) ${fmt(updTxK)}   <- group-commit (slowdown vs update() ${(updSingle.perSec / updTxK.perSec).toFixed(2)}x)`)

    results.throughput[backend] = {
      n,
      create: { add: addSingle, tx1: addTx1, txK: addTxK, slowdownTx1: addSingle.perSec / addTx1.perSec, slowdownTxK: addSingle.perSec / addTxK.perSec },
      update: { single: updSingle, tx1: updTx1, txK: updTxK, slowdownTx1: updSingle.perSec / updTx1.perSec, slowdownTxK: updSingle.perSec / updTxK.perSec }
    }
  }

  // HISTORY GROWTH (filesystem, NVMe)
  say(`\n========== HISTORY GROWTH (filesystem NVMe) ==========`)
  const hN = SMALL ? 500 : 3_000
  const addGrowth = await historyGrowthAdd(hN)
  say(`ADD-only (${hN.toLocaleString()} adds): retained history = ${(addGrowth.retainedTotal / 1024).toFixed(0)} KiB total, ${addGrowth.retainedPerWrite.toFixed(0)} bytes/write`)
  const churn = SMALL ? 3 : 5
  const updGrowth = await historyGrowthUpdateChurn(SMALL ? 200 : 1_000, churn)
  say(`UPDATE-churn (${updGrowth.writes.toLocaleString()} updates): retained history = ${(updGrowth.retainedTotal / 1024 / 1024).toFixed(1)} MiB total, ${updGrowth.retainedPerWrite.toFixed(0)} bytes/write`)
  results.history = { addGrowth, updGrowth }

  // persist raw JSON for the verification workflow
  const outPath = `${FS_ROOT}/../tests/benchmarks/_spike-results.json`
  const fs = await import('node:fs')
  fs.writeFileSync(`/media/dpsifr/storage/home/Projects/brainy/tests/benchmarks/_spike-results.json`, JSON.stringify(results, null, 2))
  say(`\nRaw results → tests/benchmarks/_spike-results.json`)

  // cleanup data root
  if (existsSync(FS_ROOT)) rmSync(FS_ROOT, { recursive: true, force: true })
}

main()
  .then(() => {
    say('[spike] done.')
    process.exit(0) // force exit — brain caches/timers can keep the loop alive
  })
  .catch((e) => {
    console.error(e)
    process.exit(1)
  })