[{"data":1,"prerenderedAt":1911},["ShallowReactive",2],{"docs-page-fr-ca-\u002Ffr-ca\u002Fdocs\u002Fservices\u002Frds":3,"docs-navigation-fr-ca":1814,"docs-surround-fr-ca-\u002Ffr-ca\u002Fdocs\u002Fservices\u002Frds":1906},{"id":4,"title":5,"body":6,"claimDependencies":1796,"description":1800,"extension":1801,"locale":1802,"meta":1803,"missingMedia":1804,"navigation":501,"owner":1805,"path":1806,"reviewStatus":1807,"seo":1808,"staleLinks":1809,"stem":1810,"technicalRisk":1811,"translationKey":1812,"__hash__":1813},"docs_fr_ca\u002Ffr-ca\u002Fdocs\u002F5.services\u002F5.rds.md","Optimisation RDS",{"type":7,"value":8,"toc":1761},"minimark",[9,13,18,21,50,54,59,66,71,82,87,107,112,133,139,143,146,151,157,161,178,183,194,198,202,205,211,264,270,284,289,300,306,314,318,321,326,340,345,359,364,378,383,394,398,401,406,411,417,421,425,431,434,438,444,447,451,457,461,464,469,782,787,1068,1072,1076,1079,1139,1143,1148,1162,1167,1181,1185,1190,1201,1206,1220,1224,1228,1233,1244,1249,1260,1264,1268,1279,1283,1294,1298,1302,1313,1317,1331,1335,1339,1350,1354,1368,1372,1376,1381,1384,1398,1401,1406,1409,1423,1428,1431,1448,1452,1457,1460,1471,1476,1496,1501,1503,1512,1516,1537,1541,1545,1548,1559,1563,1566,1580,1584,1602,1606,1609,1681,1690,1694,1697,1729,1732,1737,1757],[10,11,12],"p",{},"Jetscale fournit une optimisation des coûts alimentée par l'IA pour Amazon RDS (Relational Database Service), incluant les instances autonomes et les clusters Aurora. Nos agents spécialisés analysent vos charges de travail de base de données pour identifier les opportunités de dimensionnement approprié et les améliorations de configuration.",[14,15,17],"h2",{"id":16},"vue-densemble","Vue d'ensemble",[10,19,20],{},"Jetscale optimise les ressources RDS en analysant :",[22,23,24,32,38,44],"ul",{},[25,26,27,31],"li",{},[28,29,30],"strong",{},"Utilisation des instances"," : modèles de CPU, mémoire, réseau et connexions",[25,33,34,37],{},[28,35,36],{},"Analyse des coûts"," : dépenses actuelles vs. configuration optimale",[25,39,40,43],{},[28,41,42],{},"Métriques de performance"," : performance des requêtes, latence de réplication, taux de succès du cache tampon",[25,45,46,49],{},[28,47,48],{},"Haute disponibilité"," : configurations Multi-AZ pour les instances autonomes",[14,51,53],{"id":52},"types-rds-pris-en-charge","Types RDS pris en charge",[55,56,58],"h3",{"id":57},"clusters-aurora","Clusters Aurora",[10,60,61,62,65],{},"Les clusters Aurora sont notre ",[28,63,64],{},"cible d'optimisation principale",". Nous analysons le cluster dans son ensemble plutôt que les instances individuelles.",[10,67,68],{},[28,69,70],{},"Architecture du cluster :",[72,73,78],"pre",{"className":74,"code":76,"language":77},[75],"language-text","RdsDbCluster (Cible d'optimisation)\n├── RdsDbInstance (Writer) - Facturable\n├── RdsDbInstance (Reader) - Facturable\n├── RdsDbInstance (Reader) - Facturable\n└── AuroraDbClusterStorage - Facturable (I\u002FO + Stockage)\n","text",[79,80,76],"code",{"__ignoreMap":81},"",[10,83,84],{},[28,85,86],{},"Ce que nous optimisons :",[22,88,89,95,101],{},[25,90,91,94],{},[28,92,93],{},"Classe d'instance"," : dimensionner correctement tous les nœuds du cluster pour correspondre à la charge de travail réelle (tous les nœuds utilisent la même classe d'instance)",[25,96,97,100],{},[28,98,99],{},"Migration Graviton"," : basculer vers des instances ARM (r6g, r7g, m6g, m7g) pour 10-40% d'économies",[25,102,103,106],{},[28,104,105],{},"Configuration du stockage"," : Aurora Standard vs. I\u002FO-Optimized selon les modèles d'I\u002FO",[10,108,109],{},[28,110,111],{},"Configurations de stockage :",[22,113,114,124],{},[25,115,116,119,120,123],{},[28,117,118],{},"Aurora Standard"," (",[79,121,122],{},"aurora",") : coût de calcul inférieur, frais d'I\u002FO de 0,20 $\u002Fmillion",[25,125,126,119,129,132],{},[28,127,128],{},"Aurora I\u002FO-Optimized",[79,130,131],{},"aurora-iopt1",") : coût de calcul supérieur (+20%), frais d'I\u002FO nuls",[10,134,135,138],{},[28,136,137],{},"Important :"," Toutes les instances d'un cluster Aurora doivent utiliser la même classe d'instance. Jetscale dimensionne pour le nœud avec la plus forte utilisation de ressources afin d'assurer des performances adéquates pour tous les membres du cluster.",[55,140,142],{"id":141},"instances-rds-autonomes","Instances RDS autonomes",[10,144,145],{},"Les instances RDS traditionnelles (MySQL, PostgreSQL, MariaDB, SQL Server, Oracle) sont optimisées individuellement.",[10,147,148],{},[28,149,150],{},"Architecture d'instance :",[72,152,155],{"className":153,"code":154,"language":77},[75],"RdsDbInstance (Cible d'optimisation, Facturable)\n└── RdsDbInstanceStorage - Facturable\n",[79,156,154],{"__ignoreMap":81},[10,158,159],{},[28,160,86],{},[22,162,163,168,172],{},[25,164,165,167],{},[28,166,93],{}," : dimensionner correctement vers la famille appropriée (t3, m5, r5, r6g, etc.)",[25,169,170,100],{},[28,171,99],{},[25,173,174,177],{},[28,175,176],{},"Configuration Multi-AZ"," : désactiver Multi-AZ pour ~50% d'économies lorsque la haute disponibilité n'est pas critique (réduit la disponibilité)",[10,179,180],{},[28,181,182],{},"Modèle de facturation :",[22,184,185,188,191],{},[25,186,187],{},"Facturation à la seconde avec un minimum de 10 minutes",[25,189,190],{},"Reserved Instances : jusqu'à 66% d'économies (engagement 1 an ou 3 ans)",[25,192,193],{},"Database Savings Plans : flexibilité supplémentaire entre les familles d'instances",[14,195,197],{"id":196},"comment-jetscale-optimise-rds","Comment Jetscale optimise RDS",[55,199,201],{"id":200},"_1-collecte-de-données","1. Collecte de données",[10,203,204],{},"Jetscale analyse plusieurs sources de données :",[10,206,207,210],{},[28,208,209],{},"Métriques CloudWatch"," (fenêtre glissante de 14 jours) :",[22,212,213,219,225,231,241,250,258],{},[25,214,215,218],{},[79,216,217],{},"CPUUtilization"," - Utilisation CPU de l'instance",[25,220,221,224],{},[79,222,223],{},"FreeableMemory"," - Mémoire disponible",[25,226,227,230],{},[79,228,229],{},"DatabaseConnections"," - Connexions actives",[25,232,233,236,237,240],{},[79,234,235],{},"ReadLatency"," \u002F ",[79,238,239],{},"WriteLatency"," - Performance du stockage",[25,242,243,236,246,249],{},[79,244,245],{},"ReadThroughput",[79,247,248],{},"WriteThroughput"," - I\u002FO disque",[25,251,252,236,255],{},[79,253,254],{},"NetworkReceiveThroughput",[79,256,257],{},"NetworkTransmitThroughput",[25,259,260,263],{},[79,261,262],{},"AuroraBinlogReplicaLag"," (Aurora uniquement)",[10,265,266,269],{},[28,267,268],{},"Données API RDS"," :",[22,271,272,275,278,281],{},[25,273,274],{},"Classe d'instance et version du moteur",[25,276,277],{},"Type de stockage (Aurora Standard vs I\u002FO-Optimized)",[25,279,280],{},"Statut Multi-AZ (instances autonomes uniquement)",[25,282,283],{},"Membres du cluster et rôles (Aurora uniquement)",[10,285,286,269],{},[28,287,288],{},"Données Cost Explorer",[22,290,291,294,297],{},[25,292,293],{},"Dépenses mensuelles actuelles par instance\u002Fcluster",[25,295,296],{},"Tendances de coûts historiques",[25,298,299],{},"Utilisation des Reserved Instances",[10,301,302,305],{},[28,303,304],{},"AWS Compute Optimizer"," (si activé) :",[22,307,308,311],{},[25,309,310],{},"Recommandations de dimensionnement générées par AWS",[25,312,313],{},"Évaluations des risques de performance",[55,315,317],{"id":316},"_2-analyse","2. Analyse",[10,319,320],{},"Nos agents IA effectuent une analyse approfondie :",[10,322,323],{},[28,324,325],{},"Modèles d'utilisation :",[22,327,328,331,334,337],{},[25,329,330],{},"Utilisation maximale vs. moyenne sur toutes les métriques",[25,332,333],{},"Modèles horaires (identifier les périodes d'inactivité)",[25,335,336],{},"Modèles hebdomadaires (charge week-end vs. semaine)",[25,338,339],{},"Tendances de croissance au fil du temps",[10,341,342],{},[28,343,344],{},"Évaluation de la performance :",[22,346,347,350,353,356],{},[25,348,349],{},"Taux de succès du cache tampon (indique l'adéquation de la mémoire)",[25,351,352],{},"Débit de stockage vs. IOPS provisionnées",[25,354,355],{},"Débit réseau vs. limites de l'instance",[25,357,358],{},"Modèles de latence de réplication",[10,360,361],{},[28,362,363],{},"Modélisation des coûts :",[22,365,366,369,372,375],{},[25,367,368],{},"Répartition des coûts actuels (calcul, stockage, I\u002FO, sauvegarde)",[25,370,371],{},"Coût projeté pour les configurations alternatives",[25,373,374],{},"Opportunités de Reserved Instance \u002F Savings Plan",[25,376,377],{},"Analyse de la prime Multi-AZ",[10,379,380],{},[28,381,382],{},"Évaluation des risques :",[22,384,385,388,391],{},[25,386,387],{},"Calcul de la marge (tampon au-dessus de l'utilisation maximale)",[25,389,390],{},"Probabilité de dégradation des performances",[25,392,393],{},"Évaluation de l'impact sur la disponibilité",[55,395,397],{"id":396},"_3-recommandations","3. Recommandations",[10,399,400],{},"Jetscale génère des recommandations spécifiques et actionnables :",[402,403,405],"h4",{"id":404},"dimensionnement-approprié-des-instances","Dimensionnement approprié des instances",[10,407,408],{},[28,409,410],{},"Exemple de recommandation :",[72,412,415],{"className":413,"code":414,"language":77},[75],"Ressource : production-postgres-db\nActuel : db.r5.2xlarge (8 vCPUs, 64 GB RAM)\nRecommandé : db.r5.xlarge (4 vCPUs, 32 GB RAM)\n\nImpact sur les coûts :\n- Actuel : 730 $\u002Fmois\n- Projeté : 365 $\u002Fmois\n- Économies : 365 $\u002Fmois (50%), 4 380 $\u002Fan\n\nAnalyse de performance :\n- CPU moyen : 15-25%\n- CPU maximal : 35%\n- Mémoire moyenne : 30-40%\n- La recommandation fournit une marge de 2x au-dessus du pic\n\nRisque : Faible - Marge amplement maintenue\n",[79,416,414],{"__ignoreMap":81},[402,418,420],{"id":419},"optimisation-multi-az","Optimisation Multi-AZ",[10,422,423],{},[28,424,410],{},[72,426,429],{"className":427,"code":428,"language":77},[75],"Ressource : staging-postgres-db\nActuel : db.m5.large avec Multi-AZ activé\nRecommandé : db.m5.large avec Multi-AZ désactivé\n\nImpact sur les coûts :\n- Actuel : 280 $\u002Fmois (prime Multi-AZ incluse)\n- Projeté : 140 $\u002Fmois (Single-AZ)\n- Économies : 140 $\u002Fmois (50%), 1 680 $\u002Fan\n\nAnalyse de disponibilité :\n- Environnement : Staging\u002FNon-Production\n- SLA actuel : 99,95% (Multi-AZ)\n- SLA projeté : 99,9% (Single-AZ)\n- RPO\u002FRTO : Acceptable pour la charge de travail de staging\n\nRisque : Faible - Environnement de non-production\nNote : Multi-AZ fournit un basculement automatique en ~2 minutes\n",[79,430,428],{"__ignoreMap":81},[402,432,128],{"id":433},"aurora-io-optimized",[10,435,436],{},[28,437,410],{},[72,439,442],{"className":440,"code":441,"language":77},[75],"Ressource : customer-aurora-cluster\nActuel : Configuration Standard\nRecommandé : Configuration I\u002FO-Optimized\n\nImpact sur les coûts :\n- Actuel : 584 $\u002Fmois (instances) + 375 $\u002Fmois (I\u002FO) = 959 $\u002Fmois\n- Projeté : 701 $\u002Fmois (instances) + 0 $ (I\u002FO) = 701 $\u002Fmois\n- Économies : 258 $\u002Fmois (27%), 3 096 $\u002Fan\n\nAnalyse I\u002FO :\n- Requêtes I\u002FO mensuelles : 1,9 milliard\n- Coût I\u002FO à 0,20 $\u002Fmillion : 375 $\u002Fmois\n- Prime I\u002FO-Optimized : augmentation de 20% du coût d'instance (117 $\u002Fmois)\n- Économies nettes : 258 $\u002Fmois\n\nRecommandation : Basculer vers I\u002FO-Optimized\nSeuil : Bénéfique lorsque les coûts I\u002FO > 15% des coûts d'instance\n",[79,443,441],{"__ignoreMap":81},[402,445,99],{"id":446},"migration-graviton",[10,448,449],{},[28,450,410],{},[72,452,455],{"className":453,"code":454,"language":77},[75],"Ressource : api-database-cluster\nActuel : 3x db.r5.xlarge (basé Intel)\nRecommandé : 3x db.r6g.xlarge (basé Graviton2)\n\nImpact sur les coûts :\n- Actuel : 1 095 $\u002Fmois (3 instances @ 365 $\u002Fmois chacune)\n- Projeté : 876 $\u002Fmois (3 instances @ 292 $\u002Fmois chacune)\n- Économies : 219 $\u002Fmois (20%), 2 628 $\u002Fan\n\nAnalyse de performance :\n- Graviton2 offre des performances équivalentes ou supérieures\n- Coût inférieur de 20% par instance\n- Moteur : aurora-postgresql 14.7 (compatible Graviton)\n- Même configuration vCPU et mémoire\n\nRisque : Très faible - Performance Graviton éprouvée\nNote : Nécessite une vérification de compatibilité de version du moteur\n",[79,456,454],{"__ignoreMap":81},[55,458,460],{"id":459},"_4-génération-terraform","4. Génération Terraform",[10,462,463],{},"Pour chaque recommandation, Jetscale génère du code Terraform prêt pour la production :",[10,465,466],{},[28,467,468],{},"Exemple : Dimensionnement approprié d'instance",[72,470,474],{"className":471,"code":472,"language":473,"meta":81,"style":81},"language-hcl shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","# Optimisation d'instance RDS\n# Généré par Jetscale le 2024-01-15\n# ID de recommandation : rec_rds_001\n\nresource \"aws_db_instance\" \"production_postgres\" {\n  identifier = \"production-postgres-db\"\n\n  # Précédent : db.r5.2xlarge (730 $\u002Fmois)\n  # Optimisé : db.r5.xlarge (365 $\u002Fmois)\n  # Réduction des coûts : 50% (365 $\u002Fmois, 4 380 $\u002Fan)\n  #\n  # Justification :\n  # - Utilisation CPU moyenne : 15-25%\n  # - Utilisation CPU maximale : 35%\n  # - Utilisation mémoire moyenne : 30-40%\n  # - La nouvelle instance fournit une marge de 2x au-dessus de l'utilisation maximale\n  # - La surveillance des performances ne montre aucune pression mémoire\n  instance_class = \"db.r5.xlarge\"\n\n  engine         = \"postgres\"\n  engine_version = \"14.7\"\n\n  allocated_storage     = 500\n  storage_type          = \"gp3\"\n  iops                  = 3000\n  storage_encrypted     = true\n\n  multi_az = true\n\n  # Configuration existante préservée\n  db_name  = var.db_name\n  username = var.db_username\n  password = var.db_password\n\n  vpc_security_group_ids = var.security_group_ids\n  db_subnet_group_name   = var.db_subnet_group_name\n\n  backup_retention_period = 7\n  backup_window          = \"03:00-04:00\"\n  maintenance_window     = \"mon:04:00-mon:05:00\"\n\n  skip_final_snapshot = false\n  final_snapshot_identifier = \"${var.identifier}-final-snapshot\"\n\n  tags = merge(\n    var.tags,\n    {\n      \"jetscale:optimized\" = \"true\"\n      \"jetscale:recommendation\" = \"rec_rds_001\"\n    }\n  )\n}\n","hcl",[79,475,476,484,490,496,503,509,515,520,526,532,538,544,550,556,562,568,574,580,586,591,597,603,608,614,620,626,632,637,643,648,654,660,666,672,677,683,689,694,700,706,712,717,723,729,734,740,746,752,758,764,770,776],{"__ignoreMap":81},[477,478,481],"span",{"class":479,"line":480},"line",1,[477,482,483],{},"# Optimisation d'instance RDS\n",[477,485,487],{"class":479,"line":486},2,[477,488,489],{},"# Généré par Jetscale le 2024-01-15\n",[477,491,493],{"class":479,"line":492},3,[477,494,495],{},"# ID de recommandation : rec_rds_001\n",[477,497,499],{"class":479,"line":498},4,[477,500,502],{"emptyLinePlaceholder":501},true,"\n",[477,504,506],{"class":479,"line":505},5,[477,507,508],{},"resource \"aws_db_instance\" \"production_postgres\" {\n",[477,510,512],{"class":479,"line":511},6,[477,513,514],{},"  identifier = \"production-postgres-db\"\n",[477,516,518],{"class":479,"line":517},7,[477,519,502],{"emptyLinePlaceholder":501},[477,521,523],{"class":479,"line":522},8,[477,524,525],{},"  # Précédent : db.r5.2xlarge (730 $\u002Fmois)\n",[477,527,529],{"class":479,"line":528},9,[477,530,531],{},"  # Optimisé : db.r5.xlarge (365 $\u002Fmois)\n",[477,533,535],{"class":479,"line":534},10,[477,536,537],{},"  # Réduction des coûts : 50% (365 $\u002Fmois, 4 380 $\u002Fan)\n",[477,539,541],{"class":479,"line":540},11,[477,542,543],{},"  #\n",[477,545,547],{"class":479,"line":546},12,[477,548,549],{},"  # Justification :\n",[477,551,553],{"class":479,"line":552},13,[477,554,555],{},"  # - Utilisation CPU moyenne : 15-25%\n",[477,557,559],{"class":479,"line":558},14,[477,560,561],{},"  # - Utilisation CPU maximale : 35%\n",[477,563,565],{"class":479,"line":564},15,[477,566,567],{},"  # - Utilisation mémoire moyenne : 30-40%\n",[477,569,571],{"class":479,"line":570},16,[477,572,573],{},"  # - La nouvelle instance fournit une marge de 2x au-dessus de l'utilisation maximale\n",[477,575,577],{"class":479,"line":576},17,[477,578,579],{},"  # - La surveillance des performances ne montre aucune pression mémoire\n",[477,581,583],{"class":479,"line":582},18,[477,584,585],{},"  instance_class = \"db.r5.xlarge\"\n",[477,587,589],{"class":479,"line":588},19,[477,590,502],{"emptyLinePlaceholder":501},[477,592,594],{"class":479,"line":593},20,[477,595,596],{},"  engine         = \"postgres\"\n",[477,598,600],{"class":479,"line":599},21,[477,601,602],{},"  engine_version = \"14.7\"\n",[477,604,606],{"class":479,"line":605},22,[477,607,502],{"emptyLinePlaceholder":501},[477,609,611],{"class":479,"line":610},23,[477,612,613],{},"  allocated_storage     = 500\n",[477,615,617],{"class":479,"line":616},24,[477,618,619],{},"  storage_type          = \"gp3\"\n",[477,621,623],{"class":479,"line":622},25,[477,624,625],{},"  iops                  = 3000\n",[477,627,629],{"class":479,"line":628},26,[477,630,631],{},"  storage_encrypted     = true\n",[477,633,635],{"class":479,"line":634},27,[477,636,502],{"emptyLinePlaceholder":501},[477,638,640],{"class":479,"line":639},28,[477,641,642],{},"  multi_az = true\n",[477,644,646],{"class":479,"line":645},29,[477,647,502],{"emptyLinePlaceholder":501},[477,649,651],{"class":479,"line":650},30,[477,652,653],{},"  # Configuration existante préservée\n",[477,655,657],{"class":479,"line":656},31,[477,658,659],{},"  db_name  = var.db_name\n",[477,661,663],{"class":479,"line":662},32,[477,664,665],{},"  username = var.db_username\n",[477,667,669],{"class":479,"line":668},33,[477,670,671],{},"  password = var.db_password\n",[477,673,675],{"class":479,"line":674},34,[477,676,502],{"emptyLinePlaceholder":501},[477,678,680],{"class":479,"line":679},35,[477,681,682],{},"  vpc_security_group_ids = var.security_group_ids\n",[477,684,686],{"class":479,"line":685},36,[477,687,688],{},"  db_subnet_group_name   = var.db_subnet_group_name\n",[477,690,692],{"class":479,"line":691},37,[477,693,502],{"emptyLinePlaceholder":501},[477,695,697],{"class":479,"line":696},38,[477,698,699],{},"  backup_retention_period = 7\n",[477,701,703],{"class":479,"line":702},39,[477,704,705],{},"  backup_window          = \"03:00-04:00\"\n",[477,707,709],{"class":479,"line":708},40,[477,710,711],{},"  maintenance_window     = \"mon:04:00-mon:05:00\"\n",[477,713,715],{"class":479,"line":714},41,[477,716,502],{"emptyLinePlaceholder":501},[477,718,720],{"class":479,"line":719},42,[477,721,722],{},"  skip_final_snapshot = false\n",[477,724,726],{"class":479,"line":725},43,[477,727,728],{},"  final_snapshot_identifier = \"${var.identifier}-final-snapshot\"\n",[477,730,732],{"class":479,"line":731},44,[477,733,502],{"emptyLinePlaceholder":501},[477,735,737],{"class":479,"line":736},45,[477,738,739],{},"  tags = merge(\n",[477,741,743],{"class":479,"line":742},46,[477,744,745],{},"    var.tags,\n",[477,747,749],{"class":479,"line":748},47,[477,750,751],{},"    {\n",[477,753,755],{"class":479,"line":754},48,[477,756,757],{},"      \"jetscale:optimized\" = \"true\"\n",[477,759,761],{"class":479,"line":760},49,[477,762,763],{},"      \"jetscale:recommendation\" = \"rec_rds_001\"\n",[477,765,767],{"class":479,"line":766},50,[477,768,769],{},"    }\n",[477,771,773],{"class":479,"line":772},51,[477,774,775],{},"  )\n",[477,777,779],{"class":479,"line":778},52,[477,780,781],{},"}\n",[10,783,784],{},[28,785,786],{},"Exemple : Aurora I\u002FO-Optimized",[72,788,790],{"className":471,"code":789,"language":473,"meta":81,"style":81},"# Optimisation I\u002FO du cluster Aurora\n# Généré par Jetscale le 2024-01-15\n\nresource \"aws_rds_cluster\" \"customer_aurora\" {\n  cluster_identifier = \"customer-aurora-cluster\"\n\n  engine         = \"aurora-postgresql\"\n  engine_version = \"15.4\"\n  engine_mode    = \"provisioned\"\n\n  # Basculer vers la configuration I\u002FO-Optimized\n  # Précédent : Standard (Coûts I\u002FO élevés : 375 $\u002Fmois)\n  # Optimisé : I\u002FO-Optimized (prime d'instance de 20%, coûts I\u002FO de 0 $)\n  # Économies nettes : 258 $\u002Fmois (27%), 3 096 $\u002Fan\n  #\n  # Analyse :\n  # - I\u002FO mensuelles : 1,9 milliard de requêtes\n  # - Coût I\u002FO (Standard) : 375 $\u002Fmois\n  # - Prime d'instance (I\u002FO-Optimized) : 117 $\u002Fmois\n  # - Bénéfice net : 258 $\u002Fmois\n  storage_type = \"aurora-iopt1\"\n\n  master_username = var.master_username\n  master_password = var.master_password\n\n  database_name = var.database_name\n\n  vpc_security_group_ids = var.vpc_security_group_ids\n  db_subnet_group_name   = var.db_subnet_group_name\n\n  backup_retention_period = 7\n  preferred_backup_window = \"03:00-04:00\"\n  preferred_maintenance_window = \"mon:04:00-mon:05:00\"\n\n  enabled_cloudwatch_logs_exports = [\"postgresql\"]\n\n  tags = merge(\n    var.tags,\n    {\n      \"jetscale:optimized\" = \"true\"\n      \"jetscale:storage-mode\" = \"io-optimized\"\n    }\n  )\n}\n\nresource \"aws_rds_cluster_instance\" \"customer_aurora_instances\" {\n  count = 3\n\n  identifier         = \"customer-aurora-${count.index + 1}\"\n  cluster_identifier = aws_rds_cluster.customer_aurora.id\n\n  instance_class = \"db.r6g.xlarge\"\n  engine         = aws_rds_cluster.customer_aurora.engine\n  engine_version = aws_rds_cluster.customer_aurora.engine_version\n\n  publicly_accessible = false\n\n  tags = var.tags\n}\n",[79,791,792,797,801,805,810,815,819,824,829,834,838,843,848,853,858,862,867,872,877,882,887,892,896,901,906,910,915,919,924,928,932,936,941,946,950,955,959,963,967,971,975,980,984,988,992,996,1001,1006,1010,1015,1020,1024,1029,1035,1041,1046,1052,1057,1063],{"__ignoreMap":81},[477,793,794],{"class":479,"line":480},[477,795,796],{},"# Optimisation I\u002FO du cluster Aurora\n",[477,798,799],{"class":479,"line":486},[477,800,489],{},[477,802,803],{"class":479,"line":492},[477,804,502],{"emptyLinePlaceholder":501},[477,806,807],{"class":479,"line":498},[477,808,809],{},"resource \"aws_rds_cluster\" \"customer_aurora\" {\n",[477,811,812],{"class":479,"line":505},[477,813,814],{},"  cluster_identifier = \"customer-aurora-cluster\"\n",[477,816,817],{"class":479,"line":511},[477,818,502],{"emptyLinePlaceholder":501},[477,820,821],{"class":479,"line":517},[477,822,823],{},"  engine         = \"aurora-postgresql\"\n",[477,825,826],{"class":479,"line":522},[477,827,828],{},"  engine_version = \"15.4\"\n",[477,830,831],{"class":479,"line":528},[477,832,833],{},"  engine_mode    = \"provisioned\"\n",[477,835,836],{"class":479,"line":534},[477,837,502],{"emptyLinePlaceholder":501},[477,839,840],{"class":479,"line":540},[477,841,842],{},"  # Basculer vers la configuration I\u002FO-Optimized\n",[477,844,845],{"class":479,"line":546},[477,846,847],{},"  # Précédent : Standard (Coûts I\u002FO élevés : 375 $\u002Fmois)\n",[477,849,850],{"class":479,"line":552},[477,851,852],{},"  # Optimisé : I\u002FO-Optimized (prime d'instance de 20%, coûts I\u002FO de 0 $)\n",[477,854,855],{"class":479,"line":558},[477,856,857],{},"  # Économies nettes : 258 $\u002Fmois (27%), 3 096 $\u002Fan\n",[477,859,860],{"class":479,"line":564},[477,861,543],{},[477,863,864],{"class":479,"line":570},[477,865,866],{},"  # Analyse :\n",[477,868,869],{"class":479,"line":576},[477,870,871],{},"  # - I\u002FO mensuelles : 1,9 milliard de requêtes\n",[477,873,874],{"class":479,"line":582},[477,875,876],{},"  # - Coût I\u002FO (Standard) : 375 $\u002Fmois\n",[477,878,879],{"class":479,"line":588},[477,880,881],{},"  # - Prime d'instance (I\u002FO-Optimized) : 117 $\u002Fmois\n",[477,883,884],{"class":479,"line":593},[477,885,886],{},"  # - Bénéfice net : 258 $\u002Fmois\n",[477,888,889],{"class":479,"line":599},[477,890,891],{},"  storage_type = \"aurora-iopt1\"\n",[477,893,894],{"class":479,"line":605},[477,895,502],{"emptyLinePlaceholder":501},[477,897,898],{"class":479,"line":610},[477,899,900],{},"  master_username = var.master_username\n",[477,902,903],{"class":479,"line":616},[477,904,905],{},"  master_password = var.master_password\n",[477,907,908],{"class":479,"line":622},[477,909,502],{"emptyLinePlaceholder":501},[477,911,912],{"class":479,"line":628},[477,913,914],{},"  database_name = var.database_name\n",[477,916,917],{"class":479,"line":634},[477,918,502],{"emptyLinePlaceholder":501},[477,920,921],{"class":479,"line":639},[477,922,923],{},"  vpc_security_group_ids = var.vpc_security_group_ids\n",[477,925,926],{"class":479,"line":645},[477,927,688],{},[477,929,930],{"class":479,"line":650},[477,931,502],{"emptyLinePlaceholder":501},[477,933,934],{"class":479,"line":656},[477,935,699],{},[477,937,938],{"class":479,"line":662},[477,939,940],{},"  preferred_backup_window = \"03:00-04:00\"\n",[477,942,943],{"class":479,"line":668},[477,944,945],{},"  preferred_maintenance_window = \"mon:04:00-mon:05:00\"\n",[477,947,948],{"class":479,"line":674},[477,949,502],{"emptyLinePlaceholder":501},[477,951,952],{"class":479,"line":679},[477,953,954],{},"  enabled_cloudwatch_logs_exports = [\"postgresql\"]\n",[477,956,957],{"class":479,"line":685},[477,958,502],{"emptyLinePlaceholder":501},[477,960,961],{"class":479,"line":691},[477,962,739],{},[477,964,965],{"class":479,"line":696},[477,966,745],{},[477,968,969],{"class":479,"line":702},[477,970,751],{},[477,972,973],{"class":479,"line":708},[477,974,757],{},[477,976,977],{"class":479,"line":714},[477,978,979],{},"      \"jetscale:storage-mode\" = \"io-optimized\"\n",[477,981,982],{"class":479,"line":719},[477,983,769],{},[477,985,986],{"class":479,"line":725},[477,987,775],{},[477,989,990],{"class":479,"line":731},[477,991,781],{},[477,993,994],{"class":479,"line":736},[477,995,502],{"emptyLinePlaceholder":501},[477,997,998],{"class":479,"line":742},[477,999,1000],{},"resource \"aws_rds_cluster_instance\" \"customer_aurora_instances\" {\n",[477,1002,1003],{"class":479,"line":748},[477,1004,1005],{},"  count = 3\n",[477,1007,1008],{"class":479,"line":754},[477,1009,502],{"emptyLinePlaceholder":501},[477,1011,1012],{"class":479,"line":760},[477,1013,1014],{},"  identifier         = \"customer-aurora-${count.index + 1}\"\n",[477,1016,1017],{"class":479,"line":766},[477,1018,1019],{},"  cluster_identifier = aws_rds_cluster.customer_aurora.id\n",[477,1021,1022],{"class":479,"line":772},[477,1023,502],{"emptyLinePlaceholder":501},[477,1025,1026],{"class":479,"line":778},[477,1027,1028],{},"  instance_class = \"db.r6g.xlarge\"\n",[477,1030,1032],{"class":479,"line":1031},53,[477,1033,1034],{},"  engine         = aws_rds_cluster.customer_aurora.engine\n",[477,1036,1038],{"class":479,"line":1037},54,[477,1039,1040],{},"  engine_version = aws_rds_cluster.customer_aurora.engine_version\n",[477,1042,1044],{"class":479,"line":1043},55,[477,1045,502],{"emptyLinePlaceholder":501},[477,1047,1049],{"class":479,"line":1048},56,[477,1050,1051],{},"  publicly_accessible = false\n",[477,1053,1055],{"class":479,"line":1054},57,[477,1056,502],{"emptyLinePlaceholder":501},[477,1058,1060],{"class":479,"line":1059},58,[477,1061,1062],{},"  tags = var.tags\n",[477,1064,1066],{"class":479,"line":1065},59,[477,1067,781],{},[14,1069,1071],{"id":1070},"bonnes-pratiques","Bonnes pratiques",[55,1073,1075],{"id":1074},"surveillance-après-les-modifications","Surveillance après les modifications",[10,1077,1078],{},"Après avoir appliqué les recommandations Jetscale :",[1080,1081,1082,1102,1122],"ol",{},[25,1083,1084,1087,1088],{},[28,1085,1086],{},"Premières 24 heures"," : Surveiller de près les métriques clés",[22,1089,1090,1093,1096,1099],{},[25,1091,1092],{},"Utilisation CPU et mémoire",[25,1094,1095],{},"Connexions à la base de données",[25,1097,1098],{},"Latence des requêtes (p50, p95, p99)",[25,1100,1101],{},"IOPS et débit du stockage",[25,1103,1104,1107,1108],{},[28,1105,1106],{},"Semaine 1"," : Valider la performance",[22,1109,1110,1113,1116,1119],{},[25,1111,1112],{},"Comparer les temps d'exécution des requêtes",[25,1114,1115],{},"Vérifier les indicateurs de pression mémoire",[25,1117,1118],{},"Surveiller la latence de réplication (le cas échéant)",[25,1120,1121],{},"Examiner les taux d'erreur de l'application",[25,1123,1124,1127,1128],{},[28,1125,1126],{},"Semaines 2-4"," : Confirmer les économies de coûts",[22,1129,1130,1133,1136],{},[25,1131,1132],{},"Vérifier que la facturation AWS reflète les économies attendues",[25,1134,1135],{},"S'assurer qu'il n'y a pas de frais inattendus",[25,1137,1138],{},"Vérifier les recommandations de Reserved Instance",[55,1140,1142],{"id":1141},"stratégie-de-test","Stratégie de test",[10,1144,1145],{},[28,1146,1147],{},"Test en pré-production :",[1080,1149,1150,1153,1156,1159],{},[25,1151,1152],{},"Appliquer les modifications d'abord à l'environnement dev\u002Fstaging",[25,1154,1155],{},"Exécuter des tests de charge simulant le trafic de pointe",[25,1157,1158],{},"Surveiller pendant 48-72 heures sous charge réaliste",[25,1160,1161],{},"Valider les procédures de sauvegarde\u002Frestauration",[10,1163,1164],{},[28,1165,1166],{},"Déploiement en production :",[1080,1168,1169,1172,1175,1178],{},[25,1170,1171],{},"Planifier les modifications pendant les fenêtres de maintenance",[25,1173,1174],{},"Utiliser des déploiements blue\u002Fgreen pour Aurora (temps d'arrêt nul)",[25,1176,1177],{},"Avoir un plan de retour arrière prêt",[25,1179,1180],{},"Surveiller activement pendant et après la modification",[55,1182,1184],{"id":1183},"reserved-instances-savings-plans","Reserved Instances & Savings Plans",[10,1186,1187],{},[28,1188,1189],{},"Quand acheter :",[22,1191,1192,1195,1198],{},[25,1193,1194],{},"Bases de données stables et de longue durée (> 1 an)",[25,1196,1197],{},"Après le dimensionnement approprié (ne pas réserver d'instances surdimensionnées)",[25,1199,1200],{},"Lorsque les économies d'engagement > coût d'opportunité",[10,1202,1203],{},[28,1204,1205],{},"Recommandations Jetscale :",[22,1207,1208,1211,1214,1217],{},[25,1209,1210],{},"Nous analysons l'utilisation des RI et suggérons des achats optimaux",[25,1212,1213],{},"Envisager 1 an plutôt que 3 ans pour la flexibilité",[25,1215,1216],{},"Les RI flexibles en taille permettent des changements de famille d'instances",[25,1218,1219],{},"Combiner avec les Compute Savings Plans pour une flexibilité maximale",[14,1221,1223],{"id":1222},"modèles-doptimisation-courants","Modèles d'optimisation courants",[55,1225,1227],{"id":1226},"modèle-1-base-de-données-oltp-sur-provisionnée","Modèle 1 : Base de données OLTP sur-provisionnée",[10,1229,1230],{},[28,1231,1232],{},"Symptômes :",[22,1234,1235,1238,1241],{},[25,1236,1237],{},"Utilisation CPU \u003C 20% en moyenne",[25,1239,1240],{},"Utilisation mémoire \u003C 40%",[25,1242,1243],{},"Pics de connexion peu fréquents",[10,1245,1246],{},[28,1247,1248],{},"Recommandation Jetscale :",[22,1250,1251,1254,1257],{},[25,1252,1253],{},"Réduire de 1-2 tailles d'instance",[25,1255,1256],{},"Économies typiques : 50-66%",[25,1258,1259],{},"Risque : Faible (marge de 2-3x maintenue)",[55,1261,1263],{"id":1262},"modèle-2-coûts-io-élevés-sur-aurora","Modèle 2 : Coûts I\u002FO élevés sur Aurora",[10,1265,1266],{},[28,1267,1232],{},[22,1269,1270,1273,1276],{},[25,1271,1272],{},"Coûts I\u002FO > 15% de la facture RDS totale",[25,1274,1275],{},"Requêtes fréquentes en lecture intensive",[25,1277,1278],{},"Analyses de grandes tables",[10,1280,1281],{},[28,1282,1248],{},[22,1284,1285,1288,1291],{},[25,1286,1287],{},"Basculer vers la configuration I\u002FO-Optimized",[25,1289,1290],{},"Économies typiques : 20-40% sur le coût total du cluster",[25,1292,1293],{},"Avantage supplémentaire : coûts prévisibles",[55,1295,1297],{"id":1296},"modèle-3-multi-az-non-production","Modèle 3 : Multi-AZ non-production",[10,1299,1300],{},[28,1301,1232],{},[22,1303,1304,1307,1310],{},[25,1305,1306],{},"Environnements dev\u002Fstaging\u002Ftest avec Multi-AZ activé",[25,1308,1309],{},"Haute disponibilité non requise pour la non-production",[25,1311,1312],{},"Tolérance acceptable aux temps d'arrêt",[10,1314,1315],{},[28,1316,1248],{},[22,1318,1319,1322,1325,1328],{},[25,1320,1321],{},"Désactiver Multi-AZ pour les charges de travail non-production",[25,1323,1324],{},"Économies typiques : 50% sur les coûts d'instance",[25,1326,1327],{},"Compromis : basculement manuel requis vs. automatique",[25,1329,1330],{},"Idéal pour : développement, staging, test, analytique",[55,1332,1334],{"id":1333},"modèle-4-opportunités-graviton-manquées","Modèle 4 : Opportunités Graviton manquées",[10,1336,1337],{},[28,1338,1232],{},[22,1340,1341,1344,1347],{},[25,1342,1343],{},"Utilisation d'instances Intel (r5, m5, r6i)",[25,1345,1346],{},"Versions de moteur compatibles disponibles",[25,1348,1349],{},"Pas de dépendances spécifiques ARM",[10,1351,1352],{},[28,1353,1248],{},[22,1355,1356,1359,1362,1365],{},[25,1357,1358],{},"Migrer vers des instances Graviton (r6g, r7g, m6g, m7g)",[25,1360,1361],{},"Économies typiques : 10-40% avec des performances identiques ou meilleures",[25,1363,1364],{},"Exigences : vérifier la compatibilité de version du moteur",[25,1366,1367],{},"Idéal pour : la plupart des moteurs RDS modernes (PostgreSQL 12+, MySQL 8+, MariaDB 10.4+)",[14,1369,1371],{"id":1370},"dépannage","Dépannage",[55,1373,1375],{"id":1374},"préoccupations-concernant-les-recommandations","Préoccupations concernant les recommandations",[10,1377,1378],{},[28,1379,1380],{},"Q : La réduction de mon instance impactera-t-elle les performances ?",[10,1382,1383],{},"R : Jetscale maintient une marge de 2-3x au-dessus de l'utilisation maximale. Nous analysons :",[22,1385,1386,1389,1392,1395],{},[25,1387,1388],{},"Utilisation CPU\u002Fmémoire P99 sur 14 jours",[25,1390,1391],{},"Taux de succès du cache tampon",[25,1393,1394],{},"Points de saturation réseau et stockage",[25,1396,1397],{},"Tendances de croissance",[10,1399,1400],{},"Les recommandations ne sont émises que si le risque de performance est Faible ou Très Faible.",[10,1402,1403],{},[28,1404,1405],{},"Q : Qu'en est-il des pics de trafic soudains ?",[10,1407,1408],{},"R : Notre analyse inclut :",[22,1410,1411,1414,1417,1420],{},[25,1412,1413],{},"Métriques P99 (99e percentile), pas seulement les moyennes",[25,1415,1416],{},"Tampon pour les pics inattendus (2-3x au-dessus du pic)",[25,1418,1419],{},"Modèles de pics historiques",[25,1421,1422],{},"Validation des contrôles de santé au niveau application",[10,1424,1425],{},[28,1426,1427],{},"Q : Puis-je tester avant de m'engager ?",[10,1429,1430],{},"R : Oui ! Appliquez d'abord les modifications à dev\u002Fstaging :",[1080,1432,1433,1436,1439,1442,1445],{},[25,1434,1435],{},"Jetscale génère Terraform pour tous les environnements",[25,1437,1438],{},"Testez en non-production pendant 48-72 heures",[25,1440,1441],{},"Surveillez les métriques de performance",[25,1443,1444],{},"Retour arrière si nécessaire (simple revert Terraform)",[25,1446,1447],{},"Appliquez à la production une fois validé",[55,1449,1451],{"id":1450},"problèmes-de-performance-après-optimisation","Problèmes de performance après optimisation",[10,1453,1454],{},[28,1455,1456],{},"Symptôme : Latence accrue des requêtes",[10,1458,1459],{},"Causes possibles :",[22,1461,1462,1465,1468],{},[25,1463,1464],{},"Taille insuffisante du pool de connexions pour une instance plus petite",[25,1466,1467],{},"Pression mémoire causant une augmentation des I\u002FO disque",[25,1469,1470],{},"Les paramètres du groupe de paramètres nécessitent un ajustement",[10,1472,1473],{},[28,1474,1475],{},"Résolution :",[1080,1477,1478,1483,1486,1489],{},[25,1479,1480,1481],{},"Vérifier la métrique ",[79,1482,223],{},[25,1484,1485],{},"Examiner les journaux de requêtes lentes",[25,1487,1488],{},"Augmenter la taille de l'instance d'un niveau si nécessaire",[25,1490,1491,1492,1495],{},"Ajuster ",[79,1493,1494],{},"shared_buffers"," ou le paramètre équivalent",[10,1497,1498],{},[28,1499,1500],{},"Symptôme : Coûts I\u002FO élevés sur Aurora",[10,1502,1459],{},[22,1504,1505,1508,1510],{},[25,1506,1507],{},"Utilisation d'Aurora Standard avec une charge de travail I\u002FO élevée",[25,1509,1275],{},[25,1511,1278],{},[10,1513,1514],{},[28,1515,1475],{},[1080,1517,1518,1521,1524,1534],{},[25,1519,1520],{},"Vérifier le pourcentage de coût I\u002FO de la facture RDS totale",[25,1522,1523],{},"Si les coûts I\u002FO > 15% des coûts d'instance, envisager I\u002FO-Optimized",[25,1525,1526,1527,1530,1531],{},"Surveiller les métriques ",[79,1528,1529],{},"VolumeReadIOPs"," et ",[79,1532,1533],{},"VolumeWriteIOPs",[25,1535,1536],{},"I\u002FO-Optimized ajoute 20% au coût d'instance mais élimine tous les frais I\u002FO",[14,1538,1540],{"id":1539},"considérations-de-sécurité","Considérations de sécurité",[55,1542,1544],{"id":1543},"chiffrement","Chiffrement",[10,1546,1547],{},"Les recommandations Jetscale préservent les paramètres de chiffrement existants :",[22,1549,1550,1553,1556],{},[25,1551,1552],{},"État du chiffrement du stockage maintenu",[25,1554,1555],{},"Clés KMS inchangées",[25,1557,1558],{},"Exigences de connexion TLS\u002FSSL préservées",[55,1560,1562],{"id":1561},"conformité","Conformité",[10,1564,1565],{},"Les modifications d'instance maintiennent la conformité :",[22,1567,1568,1571,1574,1577],{},[25,1569,1570],{},"Configuration Multi-AZ modifiée uniquement lorsque explicitement recommandé",[25,1572,1573],{},"Moteur et version du moteur jamais modifiés",[25,1575,1576],{},"Associations VPC et groupes de sécurité préservés",[25,1578,1579],{},"Groupes de paramètres existants maintenus",[55,1581,1583],{"id":1582},"identifiants","Identifiants",[22,1585,1586,1593,1596,1599],{},[25,1587,1588,1589,1592],{},"Terraform utilise ",[79,1590,1591],{},"var.db_password"," (externalisé)",[25,1594,1595],{},"Mots de passe maîtres non exposés dans le code généré",[25,1597,1598],{},"Les secrets doivent utiliser AWS Secrets Manager",[25,1600,1601],{},"Politiques de rotation inchangées",[14,1603,1605],{"id":1604},"intégration-api","Intégration API",[10,1607,1608],{},"Jetscale fournit un accès API pour l'optimisation programmatique :",[72,1610,1614],{"className":1611,"code":1612,"language":1613,"meta":81,"style":81},"language-bash shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","# Lister les recommandations RDS\nGET \u002Fapi\u002Fv1\u002Frecommendations?resource_type=rds\n\n# Obtenir les détails d'une recommandation spécifique\nGET \u002Fapi\u002Fv1\u002Frecommendations\u002F{recommendation_id}\n\n# Approuver une recommandation (génère Terraform)\nPOST \u002Fapi\u002Fv1\u002Frecommendations\u002F{recommendation_id}\u002Fapprove\n\n# Récupérer le Terraform généré\nGET \u002Fapi\u002Fv1\u002Frecommendations\u002F{recommendation_id}\u002Fterraform\n","bash",[79,1615,1616,1622,1632,1636,1641,1648,1652,1657,1665,1669,1674],{"__ignoreMap":81},[477,1617,1618],{"class":479,"line":480},[477,1619,1621],{"class":1620},"sHwdD","# Lister les recommandations RDS\n",[477,1623,1624,1628],{"class":479,"line":486},[477,1625,1627],{"class":1626},"sBMFI","GET",[477,1629,1631],{"class":1630},"sfazB"," \u002Fapi\u002Fv1\u002Frecommendations?resource_type=rds\n",[477,1633,1634],{"class":479,"line":492},[477,1635,502],{"emptyLinePlaceholder":501},[477,1637,1638],{"class":479,"line":498},[477,1639,1640],{"class":1620},"# Obtenir les détails d'une recommandation spécifique\n",[477,1642,1643,1645],{"class":479,"line":505},[477,1644,1627],{"class":1626},[477,1646,1647],{"class":1630}," \u002Fapi\u002Fv1\u002Frecommendations\u002F{recommendation_id}\n",[477,1649,1650],{"class":479,"line":511},[477,1651,502],{"emptyLinePlaceholder":501},[477,1653,1654],{"class":479,"line":517},[477,1655,1656],{"class":1620},"# Approuver une recommandation (génère Terraform)\n",[477,1658,1659,1662],{"class":479,"line":522},[477,1660,1661],{"class":1626},"POST",[477,1663,1664],{"class":1630}," \u002Fapi\u002Fv1\u002Frecommendations\u002F{recommendation_id}\u002Fapprove\n",[477,1666,1667],{"class":479,"line":528},[477,1668,502],{"emptyLinePlaceholder":501},[477,1670,1671],{"class":479,"line":534},[477,1672,1673],{"class":1620},"# Récupérer le Terraform généré\n",[477,1675,1676,1678],{"class":479,"line":540},[477,1677,1627],{"class":1626},[477,1679,1680],{"class":1630}," \u002Fapi\u002Fv1\u002Frecommendations\u002F{recommendation_id}\u002Fterraform\n",[10,1682,1683,1684,1689],{},"Consultez notre ",[1685,1686,1688],"a",{"href":1687},"\u002Ffr-ca\u002Fdocs\u002Fapi-reference","Documentation API"," pour une référence complète.",[14,1691,1693],{"id":1692},"support","Support",[10,1695,1696],{},"Besoin d'aide avec l'optimisation RDS ?",[22,1698,1699,1709,1718],{},[25,1700,1701,1704,1705],{},[28,1702,1703],{},"Email"," : ",[1685,1706,1708],{"href":1707},"mailto:support@jetscale.ai","support@jetscale.ai",[25,1710,1711,1704,1714],{},[28,1712,1713],{},"Documentation",[1685,1715,1717],{"href":1716},"\u002Ffr-ca\u002Fdocs\u002Ffaq","FAQ",[25,1719,1720,1704,1723],{},[28,1721,1722],{},"GitHub Issues",[1685,1724,1728],{"href":1725,"rel":1726},"https:\u002F\u002Fgithub.com\u002FJetscale-ai\u002Fjetscale-docs\u002Fissues",[1727],"nofollow","Signaler un problème",[1730,1731],"hr",{},[10,1733,1734],{},[28,1735,1736],{},"Documentation connexe :",[22,1738,1739,1745,1751],{},[25,1740,1741],{},[1685,1742,1744],{"href":1743},"\u002Ffr-ca\u002Fdocs\u002Fservices\u002Felasticache","Optimisation ElastiCache",[25,1746,1747],{},[1685,1748,1750],{"href":1749},"\u002Ffr-ca\u002Fdocs\u002Fservices\u002Fec2","Optimisation EC2",[25,1752,1753],{},[1685,1754,1756],{"href":1755},"\u002Ffr-ca\u002Fdocs\u002Fservices\u002Febs","Optimisation EBS",[1758,1759,1760],"style",{},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html pre.shiki code .sHwdD, html code.shiki .sHwdD{--shiki-light:#90A4AE;--shiki-light-font-style:italic;--shiki-default:#546E7A;--shiki-default-font-style:italic;--shiki-dark:#676E95;--shiki-dark-font-style:italic}html pre.shiki code .sBMFI, html code.shiki .sBMFI{--shiki-light:#E2931D;--shiki-default:#FFCB6B;--shiki-dark:#FFCB6B}html pre.shiki code .sfazB, html code.shiki .sfazB{--shiki-light:#91B859;--shiki-default:#C3E88D;--shiki-dark:#C3E88D}",{"title":81,"searchDepth":486,"depth":486,"links":1762},[1763,1764,1768,1774,1779,1785,1789,1794,1795],{"id":16,"depth":486,"text":17},{"id":52,"depth":486,"text":53,"children":1765},[1766,1767],{"id":57,"depth":492,"text":58},{"id":141,"depth":492,"text":142},{"id":196,"depth":486,"text":197,"children":1769},[1770,1771,1772,1773],{"id":200,"depth":492,"text":201},{"id":316,"depth":492,"text":317},{"id":396,"depth":492,"text":397},{"id":459,"depth":492,"text":460},{"id":1070,"depth":486,"text":1071,"children":1775},[1776,1777,1778],{"id":1074,"depth":492,"text":1075},{"id":1141,"depth":492,"text":1142},{"id":1183,"depth":492,"text":1184},{"id":1222,"depth":486,"text":1223,"children":1780},[1781,1782,1783,1784],{"id":1226,"depth":492,"text":1227},{"id":1262,"depth":492,"text":1263},{"id":1296,"depth":492,"text":1297},{"id":1333,"depth":492,"text":1334},{"id":1370,"depth":486,"text":1371,"children":1786},[1787,1788],{"id":1374,"depth":492,"text":1375},{"id":1450,"depth":492,"text":1451},{"id":1539,"depth":486,"text":1540,"children":1790},[1791,1792,1793],{"id":1543,"depth":492,"text":1544},{"id":1561,"depth":492,"text":1562},{"id":1582,"depth":492,"text":1583},{"id":1604,"depth":486,"text":1605},{"id":1692,"depth":486,"text":1693},[1797,1798,1799],"optimization-methodology","provider-service-support","savings-methodology","Jetscale fournit une optimisation des coûts alimentée par l'IA pour Amazon RDS , incluant les instances autonomes et les clusters Aurora. Nos agents spécialisés analysent vos...","md","fr-ca",{},[],"Cloud Optimization","\u002Ffr-ca\u002Fdocs\u002Fservices\u002Frds","legacy-import",{"title":5,"description":1800},[],"fr-ca\u002Fdocs\u002F5.services\u002F5.rds","high","services\u002Frds","NGKTGAD0r8DdDQALdTy5TtG3UR-TYqXJy3-2r3Xb8OE",[1815],{"title":1816,"path":1817,"stem":1802,"children":1818,"page":1905},"Fr Ca","\u002Ffr-ca",[1819],{"title":1820,"path":1821,"stem":1822,"children":1823},"Documentation Jetscale","\u002Ffr-ca\u002Fdocs","fr-ca\u002Fdocs\u002Findex",[1824,1825,1829,1833,1837,1841,1863,1867,1896,1900,1903],{"title":1820,"path":1821,"stem":1822},{"title":1826,"path":1827,"stem":1828},"Démarrage avec Jetscale","\u002Ffr-ca\u002Fdocs\u002Fgetting-started","fr-ca\u002Fdocs\u002F1.getting-started",{"title":1830,"path":1831,"stem":1832},"Guide de Configuration AWS","\u002Ffr-ca\u002Fdocs\u002Faws-setup","fr-ca\u002Fdocs\u002F2.aws-setup",{"title":1834,"path":1835,"stem":1836},"Guide de Configuration Azure","\u002Ffr-ca\u002Fdocs\u002Fazure-setup","fr-ca\u002Fdocs\u002F3.azure-setup",{"title":1838,"path":1839,"stem":1840},"Comment fonctionne Jetscale","\u002Ffr-ca\u002Fdocs\u002Fhow-it-works","fr-ca\u002Fdocs\u002F4.how-it-works",{"title":1842,"path":1843,"stem":1844,"children":1845},"Intégrations","\u002Ffr-ca\u002Fdocs\u002Fintegrations","fr-ca\u002Fdocs\u002F4.integrations\u002Findex",[1846,1847,1851,1855,1859],{"title":1842,"path":1843,"stem":1844},{"title":1848,"path":1849,"stem":1850},"Intégration GitHub","\u002Ffr-ca\u002Fdocs\u002Fintegrations\u002Fgithub","fr-ca\u002Fdocs\u002F4.integrations\u002F1.github",{"title":1852,"path":1853,"stem":1854},"Intégration Jira","\u002Ffr-ca\u002Fdocs\u002Fintegrations\u002Fjira","fr-ca\u002Fdocs\u002F4.integrations\u002F2.jira",{"title":1856,"path":1857,"stem":1858},"Intégration Slack","\u002Ffr-ca\u002Fdocs\u002Fintegrations\u002Fslack","fr-ca\u002Fdocs\u002F4.integrations\u002F3.slack",{"title":1860,"path":1861,"stem":1862},"Intégration Bitbucket","\u002Ffr-ca\u002Fdocs\u002Fintegrations\u002Fbitbucket","fr-ca\u002Fdocs\u002F4.integrations\u002F4.bitbucket",{"title":1864,"path":1865,"stem":1866},"Analyse Propulsée par l'IA","\u002Ffr-ca\u002Fdocs\u002Fai-analysis","fr-ca\u002Fdocs\u002F5.ai-analysis",{"title":1868,"path":1869,"stem":1870,"children":1871},"Services pris en charge","\u002Ffr-ca\u002Fdocs\u002Fservices","fr-ca\u002Fdocs\u002F5.services\u002Findex",[1872,1873,1875,1877,1881,1883,1884,1888,1892],{"title":1868,"path":1869,"stem":1870},{"title":1756,"path":1755,"stem":1874},"fr-ca\u002Fdocs\u002F5.services\u002F1.ebs",{"title":1750,"path":1749,"stem":1876},"fr-ca\u002Fdocs\u002F5.services\u002F2.ec2",{"title":1878,"path":1879,"stem":1880},"Optimisation EKS","\u002Ffr-ca\u002Fdocs\u002Fservices\u002Feks","fr-ca\u002Fdocs\u002F5.services\u002F3.eks",{"title":1744,"path":1743,"stem":1882},"fr-ca\u002Fdocs\u002F5.services\u002F4.elasticache",{"title":5,"path":1806,"stem":1810},{"title":1885,"path":1886,"stem":1887},"Optimisation S3","\u002Ffr-ca\u002Fdocs\u002Fservices\u002Fs3","fr-ca\u002Fdocs\u002F5.services\u002F6.s3",{"title":1889,"path":1890,"stem":1891},"Optimisation Azure SQL","\u002Ffr-ca\u002Fdocs\u002Fservices\u002Fazure-sql","fr-ca\u002Fdocs\u002F5.services\u002F7.azure-sql",{"title":1893,"path":1894,"stem":1895},"Optimisation des machines virtuelles Azure","\u002Ffr-ca\u002Fdocs\u002Fservices\u002Fazure-vm","fr-ca\u002Fdocs\u002F5.services\u002F8.azure-vm",{"title":1897,"path":1898,"stem":1899},"Flux de Recommandations","\u002Ffr-ca\u002Fdocs\u002Frecommendation-workflow","fr-ca\u002Fdocs\u002F6.recommendation-workflow",{"title":1901,"path":1687,"stem":1902},"Référence API","fr-ca\u002Fdocs\u002F7.api-reference",{"title":1717,"path":1716,"stem":1904},"fr-ca\u002Fdocs\u002F8.faq",false,[1907,1909],{"title":1744,"path":1743,"stem":1882,"description":1908,"children":-1},"Jetscale propose une optimisation des couts basee sur l'IA pour Amazon ElastiCache, incluant les clusters Redis et Memcached. Nos agents specialises analysent vos charges de...",{"title":1885,"path":1886,"stem":1887,"description":1910,"children":-1},"Jetscale fournit une optimisation des coûts pilotée par l'IA pour les buckets Amazon S3 . Nos agents spécialisés analysent vos charges de travail de stockage pour identifier...",1788219271618]