767 lines
55 KiB
Plaintext
767 lines
55 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "8b22a2d1",
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"metadata": {},
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"source": [
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"# Introduction\n",
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"\n",
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"Bonjour et bienvenue dans le notebook du projet sur les bases de données tablebase.\n",
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"\n",
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"Dans ce notebook. Nous allons montrer comme fonctionne le stockage d'une base de données tablebases en prenant comme exemple la table base \"syzygy\" avec comme taille 3 à 5 pièces pour des raisons de taille.\n",
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"Nous allons expliquer comment les finales ( ou position d'échecs) sont stockés dans le format key-value puis nous verrons ensuite les performances de la base à l'aide de Redis.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "78fdf40a",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-06T09:21:56.560113Z",
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"start_time": "2025-12-06T09:21:56.104871Z"
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}
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},
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"outputs": [],
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"source": [
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"#import du projet\n",
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"import redis\n",
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"import time\n",
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"import chess\n",
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"import chess.syzygy\n",
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"import random\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import os\n",
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"import hashlib\n",
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"from IPython.display import display\n",
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"from scipy import stats\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d584aba1",
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"metadata": {},
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"source": [
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"## Peuplement de Redis\n",
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"\n",
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"Dans un premier temps, on ajoute les positions de la tablebase à Redis afin de pouvoir les utilisers derrières pour faire les tests."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "e6058974",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-06T09:22:49.905572Z",
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"start_time": "2025-12-06T09:22:06.592819Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Configs ok: {'KRvK', 'KQvKP', 'KRvKB', 'KNvK', 'KRBvKP', 'KBvK', 'KBNvK', 'KPvK', 'KPvKP', 'KRPvKP', 'KQvK'}\n"
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]
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},
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{
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"data": {
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"image/svg+xml": [
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"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"390\" height=\"390\"><desc><pre>. . . . k . . .\n",
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". . . . . . . .\n",
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". . . n . . . .\n",
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". . . . . . . .\n",
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". . . . . . . p\n",
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". P . . . . . .\n",
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". . . . B . . .\n",
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". . . . K . . .</pre></desc><defs><g id=\"white-pawn\" class=\"white pawn\"><path d=\"M22.5 9c-2.21 0-4 1.79-4 4 0 .89.29 1.71.78 2.38C17.33 16.5 16 18.59 16 21c0 2.03.94 3.84 2.41 5.03-3 1.06-7.41 5.55-7.41 13.47h23c0-7.92-4.41-12.41-7.41-13.47 1.47-1.19 2.41-3 2.41-5.03 0-2.41-1.33-4.5-3.28-5.62.49-.67.78-1.49.78-2.38 0-2.21-1.79-4-4-4z\" fill=\"#fff\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" /></g><g id=\"white-bishop\" class=\"white bishop\" fill=\"none\" fill-rule=\"evenodd\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><g fill=\"#fff\" stroke-linecap=\"butt\"><path d=\"M9 36c3.39-.97 10.11.43 13.5-2 3.39 2.43 10.11 1.03 13.5 2 0 0 1.65.54 3 2-.68.97-1.65.99-3 .5-3.39-.97-10.11.46-13.5-1-3.39 1.46-10.11.03-13.5 1-1.354.49-2.323.47-3-.5 1.354-1.94 3-2 3-2zM15 32c2.5 2.5 12.5 2.5 15 0 .5-1.5 0-2 0-2 0-2.5-2.5-4-2.5-4 5.5-1.5 6-11.5-5-15.5-11 4-10.5 14-5 15.5 0 0-2.5 1.5-2.5 4 0 0-.5.5 0 2zM25 8a2.5 2.5 0 1 1-5 0 2.5 2.5 0 1 1 5 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xlink:href=\"#white-pawn\" transform=\"translate(60, 240)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(330, 195)\" /><use href=\"#black-knight\" xlink:href=\"#black-knight\" transform=\"translate(150, 105)\" /><use href=\"#black-king\" xlink:href=\"#black-king\" transform=\"translate(195, 15)\" /></svg>"
|
|
],
|
|
"text/plain": [
|
|
"Board('4k3/8/3n4/8/7p/1P6/4B3/4K3 b - - 0 1')"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Récupération des positions de la tablebase KBNvK\n",
|
|
"Récupération des positions de la tablebase KPvK\n",
|
|
"Récupération des positions de la tablebase KPvKP\n",
|
|
"Récupération des positions de la tablebase KQvK\n",
|
|
"Récupération des positions de la tablebase KQvKP\n",
|
|
"Récupération des positions de la tablebase KRBvKP\n",
|
|
"Récupération des positions de la tablebase KRPvKP\n",
|
|
"Récupération des positions de la tablebase KRvK\n",
|
|
"Récupération des positions de la tablebase KRvKB\n",
|
|
"Récupération des positions de la tablebase KBvK\n",
|
|
"Récupération des positions de la tablebase KNvK\n",
|
|
"Total positions récupérées: 55000\n",
|
|
"Ajoute de toutes les positions des tablebases dans Redis terminé!\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"def get_250_random_position(tablebases, config):\n",
|
|
" positions = []\n",
|
|
" tries = 0\n",
|
|
"\n",
|
|
" while len(positions) < 10000 and tries < 5000:\n",
|
|
" board = generate_board_from_config(config)\n",
|
|
" positions.append(board)\n",
|
|
" tries += 1\n",
|
|
"\n",
|
|
" return positions\n",
|
|
"\n",
|
|
"def generate_board_from_config(config):\n",
|
|
" \"\"\"\n",
|
|
" Génère un board contenant exactement le matériel d'une config Syzygy. Exemples de config :\n",
|
|
" - 'KQvK', 'KPvKP'\n",
|
|
" Cette fonction est important car il faut générer les positions qui dont les informations sont dans la tablebases.\n",
|
|
" \"\"\"\n",
|
|
"\n",
|
|
" piece_map = {\"K\": chess.KING,\"Q\": chess.QUEEN,\"R\": chess.ROOK,\"B\": chess.BISHOP,\"N\": chess.KNIGHT,\"P\": chess.PAWN}\n",
|
|
"\n",
|
|
" white_str, black_str = config.split(\"v\") #Sépare la config entre les 2 couleurs\n",
|
|
"\n",
|
|
" board = chess.Board(None) # donne un plateau vide\n",
|
|
"\n",
|
|
" # boucles pour placer les pièces sur le plateau\n",
|
|
" for p in white_str:\n",
|
|
" piece = piece_map[p]\n",
|
|
" sq = random.choice([s for s in chess.SQUARES if board.piece_at(s) is None]) #choisi une pièce et vérifie que il n'y a pas déjà une pièce dessus\n",
|
|
" board.set_piece_at(sq, chess.Piece(piece, chess.WHITE))\n",
|
|
"\n",
|
|
" for p in black_str:\n",
|
|
" piece = piece_map[p]\n",
|
|
" sq = random.choice([s for s in chess.SQUARES if board.piece_at(s) is None])#choisi une pièce et vérifie que il n'y a pas déjà une pièce dessus\n",
|
|
" board.set_piece_at(sq, chess.Piece(piece, chess.BLACK))\n",
|
|
"\n",
|
|
" # désactive les règles des échecs.\n",
|
|
" board.castling_rights = 0\n",
|
|
" board.ep_square = None\n",
|
|
"\n",
|
|
" # Donne une couleur aléatoire pour savoir quelle couleur doit jouer le prochain coup.\n",
|
|
" board.turn = random.choice([chess.WHITE, chess.BLACK])\n",
|
|
"\n",
|
|
" if not board.is_valid():\n",
|
|
" return generate_board_from_config(config)\n",
|
|
"\n",
|
|
" return board\n",
|
|
"\n",
|
|
"def add_tablebase_to_redis():\n",
|
|
" #Connect to Redis server\n",
|
|
" redis_server = redis.Redis(host='localhost', port=6379, db=0)\n",
|
|
" \n",
|
|
" tablebases_path = \"tablebases/\" #Chemin vers les tables bases Syzygy\n",
|
|
"\n",
|
|
" tablebases = chess.syzygy.open_tablebase(tablebases_path)\n",
|
|
"\n",
|
|
" available_tables = set()\n",
|
|
" for f in os.listdir(tablebases_path):\n",
|
|
" if f.endswith(\".rtbw\") or f.endswith(\".rtbz\"):\n",
|
|
" available_tables.add(f.split(\".\")[0])\n",
|
|
"\n",
|
|
" print(\"Configs ok:\", available_tables)\n",
|
|
"\n",
|
|
" position = chess.Board(\"4k3/8/3n4/8/7p/1P6/4B3/4K3 b - - 0 1\") #Position d'exemple\n",
|
|
" display(position) \n",
|
|
" \n",
|
|
" configs = [\"KBNvK\",\"KPvK\",\"KPvKP\",\"KQvK\",\"KQvKP\",\"KRBvKP\",\"KRPvKP\",\"KRvK\",\"KRvKB\",\"KBvK\",\"KNvK\"]\n",
|
|
" config_valide = [c for c in configs if c in available_tables]\n",
|
|
" #Configurations de toute les tablebases que l'on utilisera dans le projet.\n",
|
|
" all_positions = []\n",
|
|
" for config in config_valide:\n",
|
|
" print(f\"Récupération des positions de la tablebase {config}\")\n",
|
|
" position = get_250_random_position(tablebases,config)\n",
|
|
" all_positions.extend(position) #Ajout des positions générées à la liste globale\n",
|
|
" \n",
|
|
" print(f\"Total positions récupérées: {len(all_positions)}\")\n",
|
|
" print(\"Ajoute de toutes les positions des tablebases dans Redis terminé!\")\n",
|
|
"\n",
|
|
"if __name__ == \"__main__\":\n",
|
|
" add_tablebase_to_redis() "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "1b443c8c",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Accès à Redis\n",
|
|
"\n",
|
|
"Nous avons rajouter 250 positions apparentant à chacune des tablesbases présent dans le dossier. Il y a des configurations ayant 3,4 ainsi que 5 pièces pour voir si cela a une incidence sur le temps d'accès à Redis.\n",
|
|
"\n",
|
|
"Pour la suite, nous allons donc pouvoir passer sur Redis pour vérifier cette hypothèse et faire d'autres tests sur notre base.\n",
|
|
"\n",
|
|
"## Benchmark redis"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "8d49b928-7522-4727-b6d8-224e2bd3a1b7",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-12-06T09:27:00.752325Z",
|
|
"start_time": "2025-12-06T09:27:00.748915Z"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def benchmark_redis(redis_serveur, pos):\n",
|
|
" latency = []\n",
|
|
" start_time_all = time.time()\n",
|
|
"\n",
|
|
" for board in pos:\n",
|
|
" fen = board.fen()\n",
|
|
" start_individual = time.time()\n",
|
|
" redis_serveur.hset(fen, mapping={\"board\": board.fen()})\n",
|
|
" latency.append(time.time() - start_individual)\n",
|
|
"\n",
|
|
" total_time_all=time.time()-start_time_all\n",
|
|
" latency= np.array(latency)\n",
|
|
"\n",
|
|
" return{\n",
|
|
" \"count\": len(pos),\n",
|
|
" \"average_latency\": np.mean(latency),\n",
|
|
" \"p50\": np.percentile(latency,50),\n",
|
|
" \"p90\": np.percentile(latency,90),\n",
|
|
" \"p95\": np.percentile(latency,95),\n",
|
|
" \"p99\": np.percentile(latency,99),\n",
|
|
" \"std\": np.std(latency),\n",
|
|
" \"min\": np.min(latency),\n",
|
|
" \"max\": np.max(latency),\n",
|
|
" \"total_time\": total_time_all,\n",
|
|
" \"latency\": latency,\n",
|
|
" \"rps\": len(latency)/total_time_all,\n",
|
|
" }\n",
|
|
"\n",
|
|
"def benchmark_syzygy(tablebases, pos):\n",
|
|
" latency = []\n",
|
|
" start_time_all = time.time()\n",
|
|
"\n",
|
|
" for board in pos:\n",
|
|
" try:\n",
|
|
" start = time.time()\n",
|
|
" tablebases.probe_wdl(board)\n",
|
|
" tablebases.probe_dtz(board)\n",
|
|
" latency.append(time.time() - start)\n",
|
|
" except chess.syzygy.MissingTableError:\n",
|
|
" continue\n",
|
|
"\n",
|
|
" total_time_all=time.time()-start_time_all\n",
|
|
"\n",
|
|
" return{\n",
|
|
" \"count\": len(pos),\n",
|
|
" \"average_latency\": np.mean(latency),\n",
|
|
" \"p50\": np.percentile(latency,50),\n",
|
|
" \"p90\": np.percentile(latency,90),\n",
|
|
" \"p95\": np.percentile(latency,95),\n",
|
|
" \"p99\": np.percentile(latency,99),\n",
|
|
" \"std\": np.std(latency),\n",
|
|
" \"min\": np.min(latency),\n",
|
|
" \"max\": np.max(latency),\n",
|
|
" \"total_time\": total_time_all,\n",
|
|
" \"latency\": latency,\n",
|
|
" \"rps\": len(latency)/total_time_all\n",
|
|
" }\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "0d955b0d-dc80-4518-954b-7c2df295adca",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-12-06T10:18:18.501308Z",
|
|
"start_time": "2025-12-06T10:18:18.393765Z"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"BENCHMARK SYZYGY\n",
|
|
"BENCHMARK REDIS\n",
|
|
"Redis latency array length: 55000\n",
|
|
"Syzygy latency array length: 50215\n",
|
|
" test statistic p-value \\\n",
|
|
"0 paired t-test 93.454797 0.0 \n",
|
|
"1 KS-test 0.690232 0.0 \n",
|
|
"\n",
|
|
" interpretation \n",
|
|
"0 H0: mean latency equal (Redis vs Syzygy) \n",
|
|
"1 H0: same latency distribution (Redis vs Syzygy) \n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"def statistical_tests(bench_redis, bench_syzygy):\n",
|
|
" redis_lat = np.array(bench_redis[\"latency\"], dtype=np.float64)\n",
|
|
" syz_lat = np.array(bench_syzygy[\"latency\"], dtype=np.float64)\n",
|
|
" \n",
|
|
" min_len = min(len(redis_lat), len(syz_lat))\n",
|
|
" redis_lat = redis_lat[:min_len]\n",
|
|
" syz_lat = syz_lat[:min_len]\n",
|
|
"\n",
|
|
" # PAIRED T-Test (Comparaison median)\n",
|
|
" t_stat, t_p = stats.ttest_rel(syz_lat, redis_lat)\n",
|
|
"\n",
|
|
" # KS Test (Comparaison distribution)\n",
|
|
" ks_stat, ks_p = stats.ks_2samp(syz_lat, redis_lat)\n",
|
|
"\n",
|
|
" df_stats = pd.DataFrame({\n",
|
|
" \"test\": [\"paired t-test\", \"KS-test\"],\n",
|
|
" \"statistic\": [t_stat, ks_stat],\n",
|
|
" \"p-value\": [t_p, ks_p],\n",
|
|
" \"interpretation\": [\n",
|
|
" \"H0: mean latency equal (Redis vs Syzygy)\",\n",
|
|
" \"H0: same latency distribution (Redis vs Syzygy)\"\n",
|
|
" ]\n",
|
|
" })\n",
|
|
"\n",
|
|
" print(df_stats)\n",
|
|
" return df_stats\n",
|
|
"\n",
|
|
"\n",
|
|
"def run_experiment_test_latency():\n",
|
|
" redis_server = redis.Redis(host=\"localhost\", port=6379, db=0)\n",
|
|
" tablebases = chess.syzygy.open_tablebase(\"tablebases/\")\n",
|
|
"\n",
|
|
" configs = [\"KBNvK\",\"KPvK\",\"KPvKP\",\"KQvK\",\"KQvKP\",\"KRBvKP\",\"KRPvKP\",\"KRvK\",\"KRvKB\",\"KBvK\",\"KNvK\"]\n",
|
|
"\n",
|
|
" all_positions = []\n",
|
|
" for config in configs:\n",
|
|
" all_positions += get_250_random_position(tablebases, config)\n",
|
|
"\n",
|
|
" # Benchmark Syzygy\n",
|
|
" bench_syzygy = benchmark_syzygy(tablebases, all_positions)\n",
|
|
" print(\"BENCHMARK SYZYGY\")\n",
|
|
" #print(bench_syzygy)\n",
|
|
"\n",
|
|
" # Benchmark Redis\n",
|
|
" bench_redis = benchmark_redis(redis_server, all_positions)\n",
|
|
" print(\"BENCHMARK REDIS\")\n",
|
|
" #print(bench_redis)\n",
|
|
"\n",
|
|
" # Length info\n",
|
|
" print(f\"Redis latency array length: {len(bench_redis['latency'])}\")\n",
|
|
" print(f\"Syzygy latency array length: {len(bench_syzygy['latency'])}\")\n",
|
|
"\n",
|
|
" return {\n",
|
|
" \"bench_syzygy\": bench_syzygy,\n",
|
|
" \"bench_redis\": bench_redis\n",
|
|
" }\n",
|
|
"\n",
|
|
"if __name__ == \"__main__\":\n",
|
|
" results = run_experiment_test_latency()\n",
|
|
" statistical_tests(results[\"bench_redis\"], results[\"bench_syzygy\"])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "37b01080",
|
|
"metadata": {},
|
|
"source": [
|
|
"Cette partie teste les accès Redis, afin de savoir si le nombre de pièces de la configuration importe sur le temps d'accès : "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "a68060e1-0052-4446-a0d3-40bc1756f210",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"=== Benchmark Redis GET : 3pieces ===\n",
|
|
"\n",
|
|
"=== Benchmark Redis GET : 4pieces ===\n",
|
|
"\n",
|
|
"=== Benchmark Redis GET : 5pieces ===\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'3pieces': {'mean_ms': np.float64(0.4743400499864947),\n",
|
|
" 'median_ms': np.float64(0.4443719999471796),\n",
|
|
" 'min_ms': np.float64(0.30335500014189165),\n",
|
|
" 'max_ms': np.float64(3.0856469993523206),\n",
|
|
" 'std_ms': np.float64(0.2824736354026385)},\n",
|
|
" '4pieces': {'mean_ms': np.float64(0.45132315999580896),\n",
|
|
" 'median_ms': np.float64(0.4520549996414047),\n",
|
|
" 'min_ms': np.float64(0.2539389997764374),\n",
|
|
" 'max_ms': np.float64(0.838474999909522),\n",
|
|
" 'std_ms': np.float64(0.13465422472708688)},\n",
|
|
" '5pieces': {'mean_ms': np.float64(0.5181703400103288),\n",
|
|
" 'median_ms': np.float64(0.4918450003970065),\n",
|
|
" 'min_ms': np.float64(0.3080989999943995),\n",
|
|
" 'max_ms': np.float64(1.0084479999932228),\n",
|
|
" 'std_ms': np.float64(0.11421552341082152)}}"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"\"\"\"\n",
|
|
"redis_client : instance redis.Redis déjà créée\n",
|
|
"test_positions : dict {\"3men\": [...], \"4men\": [...], \"5men\": [...]}\n",
|
|
"\"\"\"\n",
|
|
"def benchmark_redis_get_comparison(redis_client, test_positions: dict, iterations=100):\n",
|
|
"\n",
|
|
" results = {}\n",
|
|
"\n",
|
|
" for label, fens in test_positions.items():\n",
|
|
" print(f\"\\n=== Benchmark Redis GET : {label} ===\")\n",
|
|
"\n",
|
|
" times = np.zeros(iterations)\n",
|
|
"\n",
|
|
" for i in range(iterations):\n",
|
|
" fen = random.choice(fens)\n",
|
|
" key = f\"tb:{fen}\"\n",
|
|
"\n",
|
|
" start = time.perf_counter()\n",
|
|
" _ = redis_client.get(key)\n",
|
|
" times[i] = time.perf_counter() - start\n",
|
|
"\n",
|
|
" results[label] = {\n",
|
|
" \"mean_ms\": np.mean(times) * 1000,\n",
|
|
" \"median_ms\": np.median(times) * 1000,\n",
|
|
" \"min_ms\": np.min(times) * 1000,\n",
|
|
" \"max_ms\": np.max(times) * 1000,\n",
|
|
" \"std_ms\": np.std(times) * 1000,\n",
|
|
" }\n",
|
|
" return results\n",
|
|
"\n",
|
|
"\n",
|
|
"#IMPORTANT : Es-ce qu'on garde ca comme ca ? \n",
|
|
"TEST_POSITIONS = {\n",
|
|
" \"3pieces\": [\n",
|
|
" \"K7/8/8/8/8/8/8/k7 w - - 0 1\",\n",
|
|
" \"KB6/8/8/8/8/8/8/k7 w - - 0 1\",\n",
|
|
" \"KN6/8/8/8/8/8/8/k7 w - - 0 1\",\n",
|
|
" ],\n",
|
|
" \"4pieces\": [\n",
|
|
" \"KQ6/8/8/8/8/8/8/k7 w - - 0 1\",\n",
|
|
" \"KP6/8/8/8/8/8/8/k7 w - - 0 1\",\n",
|
|
" \"KQ6/kr6/8/8/8/8/8/8 w - - 0 1\",\n",
|
|
" ],\n",
|
|
" \"5pieces\": [\n",
|
|
" \"KQ6/8/8/Kp6/8/8/8/k7 w - - 0 1\",\n",
|
|
" \"KR6/1p6/8/8/8/8/8/k7 w - - 0 1\",\n",
|
|
" \"KRP5/1p6/8/8/8/8/8/k7 w - - 0 1\",\n",
|
|
" ],\n",
|
|
"}\n",
|
|
"\n",
|
|
"redis_server = redis.Redis(host=\"localhost\", port=6379, db=0)\n",
|
|
"benchmark_redis_get_comparison(redis_server, TEST_POSITIONS, 100)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "a68060e1-0052-4446-a0d3-40bc1756f210",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"MEMORY USAGE REDIS\n",
|
|
"Memory Redis: 9.43M\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'\\n#Test Mémoire Syzygy\\nimport psutil\\n\\ntablebases = chess.syzygy.open_tablebase(\"tablebases/\")\\n\\n#Récuperer les données de proccesus de Syzygy\\nprocess = psutil.Process(os.getpid())\\nmem_bytes = process.memory_info().rss\\nmem_mb = mem_bytes / (1024**2)\\nprint(\"MEMORY USAGE SYZYGY\")\\nprint(f\"Memory Syzygy: {mem_mb}\")\\n'"
|
|
]
|
|
},
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"#Test Mémoire Redis ( Stockage du Redis )\n",
|
|
"redis_server = redis.Redis(host=\"localhost\", port=6379, db=0)\n",
|
|
"info= redis_server.info(\"memory\")\n",
|
|
"mem_human = info[\"used_memory_human\"]\n",
|
|
"print(\"MEMORY USAGE REDIS\")\n",
|
|
"print(f\"Memory Redis: {mem_human}\")\n",
|
|
"\n",
|
|
"\"\"\"\n",
|
|
"#Test Mémoire Syzygy\n",
|
|
"import psutil\n",
|
|
"\n",
|
|
"tablebases = chess.syzygy.open_tablebase(\"tablebases/\")\n",
|
|
"\n",
|
|
"#Récuperer les données de proccesus de Syzygy\n",
|
|
"process = psutil.Process(os.getpid())\n",
|
|
"mem_bytes = process.memory_info().rss\n",
|
|
"mem_mb = mem_bytes / (1024**2)\n",
|
|
"print(\"MEMORY USAGE SYZYGY\")\n",
|
|
"print(f\"Memory Syzygy: {mem_mb}\")\n",
|
|
"\"\"\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "d6d03e08",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def benchmark_hash(redis_serveur, pos):\n",
|
|
" latency = []\n",
|
|
" start_time_all = time.perf_counter()\n",
|
|
" \n",
|
|
" for board in pos:\n",
|
|
" fen = board.fen()\n",
|
|
" key = hashlib.md5(fen.encode()).hexdigest()[:16]\n",
|
|
" \n",
|
|
" start_individual = time.perf_counter()\n",
|
|
" results = redis_serveur.hgetall(key)\n",
|
|
" latency.append(time.perf_counter() - start_individual)\n",
|
|
" \n",
|
|
" total_time_all = time.perf_counter() - start_time_all\n",
|
|
" latency = np.array(latency)\n",
|
|
" \n",
|
|
" return {\n",
|
|
" \"count\": len(pos),\n",
|
|
" \"average_latency\": latency.mean(),\n",
|
|
" \"p50\": np.percentile(latency, 50),\n",
|
|
" \"p90\": np.percentile(latency, 90),\n",
|
|
" \"p95\": np.percentile(latency, 95),\n",
|
|
" \"p99\": np.percentile(latency, 99),\n",
|
|
" \"std\": latency.std(),\n",
|
|
" \"min\": latency.min(),\n",
|
|
" \"max\": latency.max(),\n",
|
|
" \"total_time\": total_time_all,\n",
|
|
" \"latency\": latency,\n",
|
|
" \"rps\": len(latency) / total_time_all,\n",
|
|
" }"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "46157e5c",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Configs ok: {'KRvK', 'KQvKP', 'KRvKB', 'KNvK', 'KRBvKP', 'KBvK', 'KBNvK', 'KPvK', 'KPvKP', 'KRPvKP', 'KQvK'}\n",
|
|
"Récupération des positions de la tablebase KBNvK\n",
|
|
"Récupération des positions de la tablebase KPvK\n",
|
|
"Récupération des positions de la tablebase KPvKP\n",
|
|
"Récupération des positions de la tablebase KQvK\n",
|
|
"Récupération des positions de la tablebase KQvKP\n",
|
|
"Récupération des positions de la tablebase KRBvKP\n",
|
|
"Récupération des positions de la tablebase KRPvKP\n",
|
|
"Récupération des positions de la tablebase KRvK\n",
|
|
"Récupération des positions de la tablebase KRvKB\n",
|
|
"Total positions récupérées: 2250\n",
|
|
"Ajoute de toutes les positions hashés des tablebases dans Redis terminé!\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"def get_250_random_position(tablebases, config):\n",
|
|
" positions = []\n",
|
|
" tries = 0\n",
|
|
"\n",
|
|
" while len(positions) < 250 and tries < 500:\n",
|
|
" board = generate_board_from_config(config)\n",
|
|
" try:\n",
|
|
" tablebases.probe_wdl(board) # thử probe WDL\n",
|
|
" positions.append(board)\n",
|
|
" except chess.syzygy.MissingTableError:\n",
|
|
" pass # bỏ qua nếu table không tồn tại\n",
|
|
" tries += 1\n",
|
|
" return positions\n",
|
|
"\n",
|
|
"def add_tablebase_to_redis():\n",
|
|
" h = hashlib.new('sha512_256')\n",
|
|
" #Connect to Redis server\n",
|
|
" redis_server = redis.Redis(host='localhost', port=6379, db=1) #db1 pour H4\n",
|
|
" \n",
|
|
" tablebases_path = \"tablebases/\" #Chemin vers les tables bases Syzygy\n",
|
|
"\n",
|
|
" tablebases = chess.syzygy.open_tablebase(tablebases_path)\n",
|
|
"\n",
|
|
" available_tables = set()\n",
|
|
" for f in os.listdir(tablebases_path):\n",
|
|
" if f.endswith(\".rtbw\") or f.endswith(\".rtbz\"):\n",
|
|
" available_tables.add(f.split(\".\")[0])\n",
|
|
"\n",
|
|
" print(\"Configs ok:\", available_tables)\n",
|
|
" \n",
|
|
" configs = [\"KBNvK\",\"KPvK\",\"KPvKP\",\"KQvK\",\"KQvKP\",\"KRBvKP\",\"KRPvKP\",\"KRvK\",\"KRvKB\"]\n",
|
|
" config_valide = [c for c in configs if c in available_tables]\n",
|
|
" #Configurations de toute les tablebases que l'on utilisera dans le projet.\n",
|
|
" all_positions = []\n",
|
|
" for config in config_valide:\n",
|
|
" print(f\"Récupération des positions de la tablebase {config}\")\n",
|
|
" position = get_250_random_position(tablebases,config)\n",
|
|
" all_positions.extend(position) #Ajout des positions générées à la liste globale\n",
|
|
" \n",
|
|
" print(f\"Total positions récupérées: {len(all_positions)}\")\n",
|
|
" for position in all_positions:\n",
|
|
" try:\n",
|
|
" fen = position.fen()\n",
|
|
" key = hashlib.md5(fen.encode()).hexdigest()[:16] #Limite à 16 caractères\n",
|
|
" wdl = tablebases.probe_wdl(position)\n",
|
|
" dtz = tablebases.probe_dtz(position)\n",
|
|
" redis_server.hset(key, mapping={\"wdl\": wdl, \"dtz\": dtz}) \n",
|
|
" except (KeyError,chess.syzygy.MissingTableError): #vérifie que les positions générés appartiennent bien au table du projet seulement\n",
|
|
" continue \n",
|
|
" \n",
|
|
" print(\"Ajoute de toutes les positions hashés des tablebases dans Redis terminé!\")\n",
|
|
"\n",
|
|
"if __name__ == \"__main__\":\n",
|
|
" add_tablebase_to_redis()\n",
|
|
" \n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "099ce824",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"BENCHMARK REDIS (FEN)\n",
|
|
"{'count': 2250, 'average_latency': np.float64(0.0007091928058200412), 'p50': np.float64(0.0006681680679321289), 'p90': np.float64(0.0009292125701904298), 'p95': np.float64(0.00102473497390747), 'p99': np.float64(0.0013124537467956525), 'std': np.float64(0.00022609616592649846), 'min': np.float64(0.0004031658172607422), 'max': np.float64(0.004224300384521484), 'total_time': 2.1053922176361084, 'latency': array([0.00342536, 0.00068593, 0.00071573, ..., 0.00083399, 0.00082922,\n",
|
|
" 0.00101638], shape=(2250,)), 'rps': 1068.6844860318968}\n",
|
|
"BENCHMARK REDIS (HASH)\n",
|
|
"{'count': 2250, 'average_latency': np.float64(0.0004489188648941409), 'p50': np.float64(0.0004307850003897329), 'p90': np.float64(0.0005560255000091274), 'p95': np.float64(0.0006055185504919784), 'p99': np.float64(0.000822205319836939), 'std': np.float64(0.00018291225647658574), 'min': np.float64(0.00024306299928866792), 'max': np.float64(0.003079133999563055), 'total_time': 1.5033779259993025, 'latency': array([0.00307913, 0.00059742, 0.00061489, ..., 0.00038755, 0.00035628,\n",
|
|
" 0.00050254], shape=(2250,)), 'rps': 1496.6296638314775}\n",
|
|
"Redis FEN latency array length: 2250\n",
|
|
"Redis Hash latency array length: 2250\n",
|
|
"\n",
|
|
"============================================================\n",
|
|
"MEMORY USAGE BY KEY (ESTIMATION)\n",
|
|
"============================================================\n",
|
|
"Taille moyenne clé FEN: 136.46 bytes\n",
|
|
"Taille moyenne clé Hash: 97.84 bytes\n",
|
|
"\n",
|
|
"Estimation pour 66009 clés:\n",
|
|
" FEN: 8.59 MB\n",
|
|
" Hash: 6.16 MB\n",
|
|
" Gain: 28.3%\n",
|
|
"============================================================\n",
|
|
" test statistic p-value \\\n",
|
|
"0 paired t-test -44.041712 4.787276e-306 \n",
|
|
"1 KS-test 0.691556 0.000000e+00 \n",
|
|
"\n",
|
|
" interpretation \n",
|
|
"0 H0: mean latency equal (Redis vs Syzygy) \n",
|
|
"1 H0: same latency distribution (Redis vs Syzygy) \n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"def run_experiment():\n",
|
|
" redis_server = redis.Redis(host=\"localhost\", port=6379, db=0)\n",
|
|
" redis_hash = redis.Redis(host=\"localhost\", port=6379, db=1)\n",
|
|
" tablebases = chess.syzygy.open_tablebase(\"tablebases/\")\n",
|
|
" configs = [\"KBNvK\",\"KPvK\",\"KPvKP\",\"KQvK\",\"KQvKP\",\"KRBvKP\",\"KRPvKP\",\"KRvK\",\"KRvKB\"]\n",
|
|
" all_positions = []\n",
|
|
" for config in configs:\n",
|
|
" all_positions += get_250_random_position(tablebases, config)\n",
|
|
" valid_positions = []\n",
|
|
" for board in all_positions:\n",
|
|
" try:\n",
|
|
" tablebases.probe_wdl(board)\n",
|
|
" valid_positions.append(board)\n",
|
|
" except chess.syzygy.MissingTableError:\n",
|
|
" continue\n",
|
|
" print(\"BENCHMARK REDIS (FEN)\")\n",
|
|
" bench_redis = benchmark_redis(redis_server, valid_positions)\n",
|
|
" print(bench_redis)\n",
|
|
" print(\"BENCHMARK REDIS (HASH)\")\n",
|
|
" bench_hash = benchmark_hash(redis_hash, valid_positions)\n",
|
|
" print(bench_hash)\n",
|
|
" print(f\"Redis FEN latency array length: {len(bench_redis['latency'])}\")\n",
|
|
" print(f\"Redis Hash latency array length: {len(bench_hash['latency'])}\")\n",
|
|
" print(\"\\n\" + \"=\" * 60)\n",
|
|
" print(\"MEMORY USAGE BY KEY (ESTIMATION)\")\n",
|
|
" print(\"=\" * 60)\n",
|
|
"\n",
|
|
" # Échantillonner 50 clés de chaque base\n",
|
|
" sample_fen = [redis_server.randomkey() for _ in range(50)]\n",
|
|
" mem_per_key_fen = [redis_server.memory_usage(k) for k in sample_fen if k]\n",
|
|
" avg_fen = np.mean(mem_per_key_fen)\n",
|
|
"\n",
|
|
" sample_hash = [redis_hash.randomkey() for _ in range(50)]\n",
|
|
" mem_per_key_hash = [redis_hash.memory_usage(k) for k in sample_hash if k]\n",
|
|
" avg_hash = np.mean(mem_per_key_hash)\n",
|
|
"\n",
|
|
" nb_keys = redis_server.dbsize()\n",
|
|
" estimated_fen = avg_fen * nb_keys\n",
|
|
" estimated_hash = avg_hash * nb_keys\n",
|
|
"\n",
|
|
" print(f\"Taille moyenne clé FEN: {avg_fen:.2f} bytes\")\n",
|
|
" print(f\"Taille moyenne clé Hash: {avg_hash:.2f} bytes\")\n",
|
|
" print(f\"\\nEstimation pour {nb_keys} clés:\")\n",
|
|
" print(f\" FEN: {estimated_fen / (1024*1024):.2f} MB\")\n",
|
|
" print(f\" Hash: {estimated_hash / (1024*1024):.2f} MB\")\n",
|
|
" print(f\" Gain: {(1 - avg_hash/avg_fen) * 100:.1f}%\")\n",
|
|
" print(\"=\" * 60)\n",
|
|
" \n",
|
|
" return {\n",
|
|
" \"bench_redis\": bench_redis,\n",
|
|
" \"bench_hash\": bench_hash\n",
|
|
" }\n",
|
|
"\n",
|
|
"if __name__ == \"__main__\":\n",
|
|
" results = run_experiment()\n",
|
|
" statistical_tests(results[\"bench_redis\"], results[\"bench_hash\"])\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.12"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|