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zeCmyMR!XFqDDS?OHM*B4Sj=#HA-}NIJDr;N#h1L|THklY)q;n+klW9W(hDz~p*2D6 zI^8WZ@QspmZAe6Bi~Ri|?CEbWkb^DN(NUypFW3D3RAAsDp>kQB;c;nqVlE~;X}&J} z#OJpyiK2(!^s^2k-)I6eM+jb;BeIUi=iO-Dfkl3uh00}tZYb^F>#vri$K(FhplXU+ zIWJhA))7AUNeVf38#z!gh%>zV>_no@xML#$^t|uA9cdIMAbBjgWLV<5e5ZN$qi&%i z->ThY^H)Cx9pfJ|dqK@rFHqC2aZ2sVDMJ1avs9;1E6C;H`Vwv0J}lW7<%#yer(GM2 zw-;1Vf;jGOa={RkceIHwiq%XhySHWFaBwu{PQ9;wy=BnJ6Q{sO_92#QKZ0C8&cKlH$TH{5Ur=<#|6dzz zR1vv*`WfnS&<3D1N!=*uAcV{%Em+ZORw^aEFOA1nAc4ue|QVO1w^jb^ z{8!%nY(v{r9*?z;*61uazup{}h00A{o16x^=g;h8K85pPH37pNSw8kIw%lWHF1&xc z=wRaCU)lHNZ=8&ksnwfvE#lW-|1XtE;;jE(J0?%tCh^%_=RS`@-miIIHazS8weO2= z?fhFy?%!T5dBc@C%tRsXT*jOG$<<$ee%$j+@@&!H_x6|82j5)|E4&ywUIEi!-pz!K zb+M`9d|!UsUw^ZGldx&oj1@C0emW%kue#BHU;9_Zzt+Dywn=WjQn%*o{Rm$Mhomh# zR!hBjzoC9{{QlJa_cQBO6rL)c<^9W6`>W5IniZLsf!gACyP8)Y?GpV}YPcGF*(E5h z4Elg6v=})0(3g5<{>}Ou<^Om5Xa4)j_m)ha@v`9mvpZ+MdgQyPqEId9j?KFNdtLTh z^|x-QVqgfll5|F<_I=EQz2#Z+K6#b=dVl|Fg|^&{)!*k_THict!}oL{-Qy-Z?L)Sk@d){Jr`A$3B@;kn_dNMz#@!YYvt@k24zsN@X^o-_zC)+3RWE@h{ zJ!W)Q>iy*Zi9e%l#nVk29Ok@b{*wJ}--X}LwnwNkFdQ?ljsI_3yFczl_4~Sz{dK{@ zHS@RE_rJZierHpx$Faj+od5j)iM#f{=lyH@SN8p!J;31kzbt-wcG3RheNvxyPq_t4 ftMsCK43q!aI|O}xblL5yJ;+i|S3j3^P6 #pagebreak(weak: true) -== #ref-glossary(term: "PostgreSQL")[PostgreSQL], un système open source +== PostgreSQL, un système open source Ce document est axé sur le travail réalisé sur une base de données. L'application utilisant une base de données #ref-glossary(term: "PostgreSQL")[PostgreSQL], je présente dans cette section ce qu'est #ref-glossary(term: "PostgreSQL")[PostgreSQL], les avantages de cette technologie, les inconvénients et enfin une conclusion à cette section orientée. -=== Présentation de #ref-glossary(term: "PostgreSQL")[PostgreSQL] +=== Présentation de PostgreSQL #ref-glossary(term: "PostgreSQL")[PostgreSQL] est un système de gestion de base de données relationnelle (#ref-glossary(term: "SGBDR")[SGBDR]). Le projet est initié en 1986 par Michael Stonebraker et Andrew Yu à l'Université de Californie à Berkeley. @@ -92,9 +93,9 @@ Comme dit précédemment, #ref-glossary(term: "PostgreSQL")[PostgreSQL] est un # - Le partitionnement - La gestion du cache - Des notions de concurrence et d'isolation - - De la réplication et du #ref-glossary(term: "Sharding")[Sharding]. + - De la réplication et du #ref-glossary(term: "Sharding")[sharding]. -=== Les avantages de #ref-glossary(term: "PostgreSQL")[PostgreSQL] +=== Les avantages de PostgreSQL #ref-glossary(term: "PostgreSQL")[PostgreSQL] est un #ref-glossary(term: "SGBDR")[SGBDR] très populaire pour plusieurs raisons : - Il est open source, ce qui signifie qu'il est gratuit et que son code source est disponible pour tous. Cela permet à la communauté de développeurs de contribuer à son amélioration et de créer des extensions pour ajouter des fonctionnalités supplémentaires. @@ -107,7 +108,7 @@ Il est également important de noter que #ref-glossary(term: "PostgreSQL")[Postg Cette gestion de données complexe permet une ouverture sur d'autres systèmes, particulièrement QGIS, un système d'informations géographiques, et ainsi d'étendre les fonctionnalités proposées par ce système. En type de fichiers volumineux, on peut par exemple citer les fichiers MAJICS, RPG, référentiels vecteurs, etc. -=== Les inconvénients de #ref-glossary(term: "PostgreSQL")[PostgreSQL] +=== Les inconvénients de PostgreSQL #ref-glossary(term: "PostgreSQL")[PostgreSQL] présente aussi quelques inconvénients qu'il faut prendre en compte: - Il peut être plus complexe à installer et à configurer que d'autres #ref-glossary(term: "SGBDR")[SGBDR], tels que MySQL ou SQLite. @@ -129,9 +130,9 @@ Par exemple, dans le cas où Google déploierait des changements de code majeurs La majorité des technologies utilisées par InfSuite suivent ce système de versionnement. Malheureusement, réaliser de nouveaux développements pour l'application tout en surveillant les mises à jour des composants externes, analyser leur impact, les intégrer à l'application dès que ces dernières sont disponibles, n'est clairement pas possible. La stratégie de mises à jour utilisée est donc d'effectuer occasionnellement ces mises à jour, soit lors de moments clés (de nouvelles fonctionnalités importantes, des mises à jour de sécurité, des gains de performances, etc.), soit spontanément pour maintenir l'environnement à jour. -Ce cas de figure est présent à toutes les couches de l'architecture logicielle d'InfSuite, front-end, back-end, bases de données. Depuis un certain temps, l'application InfSuite utilise la version 14 du #ref-glossary(term: "SGBDR")[SGBDR] #ref-glossary(term: "PostgreSQL")[PostgreSQL]. Sortie en septembre 2021, cette version est maintenant dépréciée en faveur de la dernière version stable de l'application 16. Cependant, la version 14 a été adoptée très tôt par InfSuite et est restée depuis la version utilisée dans l'environnement InfSuite. +Ce cas de figure est présent à toutes les couches de l'architecture logicielle d'InfSuite, front-end, back-end, bases de données. Depuis un certain temps, l'application InfSuite utilise la version 12 du #ref-glossary(term: "SGBDR")[SGBDR] #ref-glossary(term: "PostgreSQL")[PostgreSQL]. Sortie en septembre 2019, cette version est maintenant dépréciée en faveur de la dernière version stable de l'application 16. Cependant, la version 12 a été adoptée très tôt par InfSuite et est restée depuis la version utilisée dans l'environnement InfSuite. -Pour suivre la stratégie de mise à jour de l'application, le sujet d'une mise à jour de la base de données d'InfSuite, de la version 14 vers la version stable la plus récente, a été mis sur la table. Le chef de projet en charge d'InfSuite a alors demandé une expertise pour la réalisation de cette migration pour s'assurer de la viabilité, mais également d'envisager, si possible, de réaliser cette migration en utilisant les nouveautés disponibles pour cette migration. +Pour suivre la stratégie de mise à jour de l'application, le sujet d'une mise à jour de la base de données d'InfSuite, de la version 12 vers la version stable la plus récente, a été mis sur la table. Le chef de projet en charge d'InfSuite a alors demandé une expertise pour la réalisation de cette migration pour s'assurer de la viabilité, mais également d'envisager, si possible, de réaliser cette migration en utilisant les nouveautés disponibles pour cette migration. Je vais donc chercher à savoir quelles sont les étapes préliminaires pour s'assurer de la viabilité d'une telle migration, comment réaliser cette migration de manière optimale dans le contexte d'InfSuite et comment s'assurer de la qualité de la réalisation de cette dernière. #pagebreak(weak: true) diff --git a/chapters/réalisation.typ b/chapters/réalisation.typ index 91093c0..68f49ed 100644 --- a/chapters/réalisation.typ +++ b/chapters/réalisation.typ @@ -75,7 +75,7 @@ Si je souhaite par exemple mettre à jour un package dans le projet "Geometry", === Compatibilité et décision -La première étape est de valider la compatibilité entre la version 14 et 16 de #ref-glossary(term: "PostgreSQL")[PostgreSQL]. Une première manière de valider cela est d'aller sur les notes de mise à jour sur le site officiel et de chercher les changements qui peuvent affecter la compatibilité entre les deux bases. +La première étape est de valider la compatibilité entre la version 12 et 16 de #ref-glossary(term: "PostgreSQL")[PostgreSQL]. Une première manière de valider cela est d'aller sur les notes de mise à jour sur le site officiel et de chercher les changements qui peuvent affecter la compatibilité entre les deux bases. Après avoir analysé en détail les changements, il y a trois grandes catégories. Il y a l'impact sur l'administration système, celui sur les requêtes SQL et celui sur l'incompatibilité des données. La partie administration système n'est pas gérée par les développeurs d'InfSuite, elle est gérée par d'autres employés de l'entreprise, je peux donc me concentrer sur la partie requêtes et données. @@ -130,47 +130,47 @@ La dernière étape permettant de valider cette étape de migration est de s'ass Pour effectuer un test de performance, le système utilise une fonctionnalité gourmande en ressource, les groupes. Dans l'environnement InfSuite, il est possible d'afficher sur la carte les objets d'infrastructure. Par défaut tous les objets d'un DataOwner sont affichés. Pour permettre d'axer son travail sur des objets d'infrastructures particuliers, alors il est possible d'appliquer un filtre à toutes ces entités. Le mode "Groupe" de l'application permet de créer alors deux types différents de filtres. -Le premier type de filtres est un filtre statique. Peu gourmand en ressource et très simple d'utilisation, on peut y ajouter des entités manuellement et cela va permettre de n'afficher que les objets sélectionnés dans l'application. Fonctionnellement parlant ce n'est qu'une relation de base de données. +Le premier type de filtre est un filtre statique. Peu gourmand en ressource et très simple d'utilisation, on peut y ajouter des entités manuellement et cela va permettre de n'afficher que les objets sélectionnés dans l'application. Fonctionnellement parlant ce n'est qu'une relation de base de données. Le second type de filtre, plus complexe, est le filtre dynamique. Les filtres dynamiques permettent de gérer automatiquement les entités qui seront incluses ou exclues de l'affichage. Pour ce type de filtre, il est possible de créer des règles de filtrage avancées pour permettre à l'utilisateur plus de flexibilité.\ -Un peu à la manière d'une requête SQL, il est possible de construire une requête qui va être exécutée pour permettre de retrouver les entités à partir de critères. Il est par exemple possible de demander de filtrer les ouvrages par position géographique en ne prenant en compte que ceux au-dessus d'une certaine latitude et de rajouter à ce filtre uniquement les ouvrages qui contiennent un certain numéro dans leur nom.\ -Il est possible de créer des règles avec chaque propriété d'un objet d'infrastructure, d'appliquer un ordre pour les conditions, de faire des agrégats, des jointures... Les utilisateurs ont la liberté de créer leur propre requête, une requête pouvant devenir rapidement complexe, elle peut donc prendre plus de temps à filtrer les objets d'infrastructures pour les retourner à l'utilisateur.\ +Un peu à la manière d'une requête SQL, l'utilisateur peut construire une requête qui va être exécutée pour permettre de retrouver les entités à partir de critères. La requête offre la possibilité de filtrer les ouvrages par position géographique en ne prenant en compte que ceux au-dessus d'une certaine latitude et de rajouter à ce filtre uniquement les ouvrages qui contiennent un certain numéro dans leur nom.\ +Les règles peuvent être construites en utilisant chaque propriété d'un objet d'infrastructure, d'appliquer un ordre pour les conditions, de faire des agrégats, des jointures... Les utilisateurs ont la liberté de créer leur propre requête, une requête pouvant devenir rapidement complexe, elle peut donc prendre plus de temps à filtrer les objets d'infrastructures pour les retourner à l'utilisateur.\ Étant utilisé par les services #ref-glossary(term: "GIS")[GIS] de l'application, s'assurer de la performance de ce système est primordial. Ce que l'outil permet de tester est ce système de groupe/filtres. Il va chercher les différents filtres existants en base de données, peu importe le client qui a pu les créer, et les exécuter.\ Cette méthode permet de connaître les performances réelles de la base dans un cas pratique spécifique à l'application et non pas d'avoir des performances théoriques fournies par les développeurs du #ref-glossary(term: "SGBDR")[SGBDR] qui peuvent être tournées en leur faveur. Nous cherchons donc à savoir si le chiffre de dix pourcents en gain de performances est réel ou non. -L'objectif est donc de comparer les performances de la base de production encore sous #ref-glossary(term: "PostgreSQL")[PostgreSQL] 14, avec les performances d'une base de développement installée pour l'occasion, elle, sous #ref-glossary(term: "PostgreSQL")[PostgreSQL] 16. Après avoir exécuté l'outil de benchmark sur les deux bases, je récupère les données brutes en sortie de programme pour les analyser. Ci-dessous un exemple de données de sorties fournies par le programme après exécution. +L'objectif est donc de comparer les performances de la base de production encore sous #ref-glossary(term: "PostgreSQL")[PostgreSQL] 12, avec les performances d'une base de développement installée pour l'occasion, elle, sous #ref-glossary(term: "PostgreSQL")[PostgreSQL] 16. Après avoir exécuté l'outil de benchmark sur les deux bases, je récupère les données brutes en sortie de programme pour les analyser. Ci-dessous un exemple de données de sorties fournies par le programme après exécution. -```csv -eruid,description -batman,uses technology -superman,flies through the air -spiderman,uses a web -ghostrider, rides a motorcycle -#GROUP_OBJECT_PROFILE#accessgroupGroupProfile -cn,description -daredevil,this group represents daredevils -superhero,this group represents superheroes -#GROUP_OBJECT_PROFILE#aixaccessgroupGroupProfile -aixgroupadminlist,ibm-aixprojectnamelist -eadmins,eadmingroup -eguests,eguestgroup -``` +#text( + ```csv +Group_Id;Mandant;Group_Name;Count_Ios;Load_Duration_In_Ms;Load_Per_Io_In_Ms +0000-00000-0000;CANTON_AG;1 Erhaltungsplanung Gesamt;995;946;0,950753769 +0000-00000-0000;CANTON_AG;2 Arbeitsplanung BT;184;159;0,864130435 +0000-00000-0000;CANTON_AG;3 Arbeitsplanung EM-KB;4739;139;0,029331083 +0000-00000-0000;CANTON_AG;4 Inspektionsplanung EM-KB;4739;113;0,023844693 +0000-00000-0000;CANTON_AG;5 Zustandsklassen;4739;115;0,024266723 +0000-00000-0000;CANTON_AG;6 Kontroll-Auswertungen;4739;116;0,024477738 +0000-00000-0000;CANTON_AG;ASTRA Kreuzungsobjekte;79;219;2,772151899 +0000-00000-0000;CANTON_AG;Bauwerke ohne Code und in Betrieb;0;166;166 +0000-00000-0000;CANTON_AG;Bauwerke ohne Eigentümer und in Betrieb;0;171;171 +0000-00000-0000;CANTON_AG;Bauwerke ohne Gemeinde und in Betrieb;0;148;148 +0000-00000-0000;CANTON_AG;Bauwerke ohne Status;35;225;6,428571429 +```) Les données sont au format CSV, je peux ainsi les importer dans un logiciel tableur tel qu'Excel pour les manipuler et les comparer. L'application de production étant accessibles aux clients de manière continue, les données stockées peuvent changer très rapidement et présenter un delta avec les données sauvegardées à posteriori lors la mise en place de la base de développement.\ Cette différence doit être prise en compte dans la comparaison des données puisque je dois alors utiliser des fonctions plus poussées d'Excel pour retrouver les données similaires entre les deux tables et en comparer les résultats.\ J'obtiens dans les données les informations d'identification des groupes testés, le client à qui ils appartiennent, le nombre d'ouvrages que les groupes contiennent après filtrage, le temps total d'exécution du groupe et le temps moyen calculée pour le chargement d'un objet d'infrastructure. L'unité de temps est donnée en millisecondes. -Pour comparer les performances de la base, je commence par récupérer les données globales des deux bases, à savoir le nombre total d'objets d'infrastructures qui ont étés traités et le temps global d'exécution de traitement. Pour connaître le temps total qu'a mis la base pour traiter tous les groupes, j'utilise la requête ``` =SUM(pg14[Load_Duration_In_Ms])``` qui fait une simple somme de tous les résultats de la colonne ``` Load_Duration_In_Ms```. Je fais la même chose pour les résultats des deux bases et pour la colonne ``` Count_Ios``` que je réutilise avec un simple calcul de différence dans la requête ``` =Total_Ios_Count_14 - Total_Ios_Count_16```. Comme le présente le tableau des résultats @result_sheet_benchmark_1, les données dans la base de production ont déjà changées, il faut donc que je tienne compte dans mes résultats. +Pour comparer les performances de la base, je commence par récupérer les données globales des deux bases, à savoir le nombre total d'objets d'infrastructures qui ont étés traités et le temps global d'exécution de traitement. Pour connaître le temps total qu'a mis la base pour traiter tous les groupes, j'utilise la requête ``` =SUM(pg12[Load_Duration_In_Ms])``` qui fait une simple somme de tous les résultats de la colonne ``` Load_Duration_In_Ms```. Je fais la même chose pour les résultats des deux bases et pour la colonne ``` Count_Ios``` que je réutilise avec un simple calcul de différence dans la requête ``` =Total_Ios_Count_12 - Total_Ios_Count_16```. Comme le présente le tableau des résultats @result_sheet_benchmark_1, les données dans la base de production ont déjà changées, il faut donc que je tienne compte dans mes résultats. #figure( caption: "Comparatif brut des performances entre les deux bases", table( columns: (auto, auto, auto, auto), align: horizon, - table.header([], [*Postgres_14*], [*Postgres_16*], [*Gap_From_14_To_16*]), + table.header([], [*Postgres_12*], [*Postgres_16*], [*Gap_From_12_To_16*]), [*Total_Io_Count*], [323527], [318634], [-4893], [*Total_Duration*], [3577961], [3450208], [-127753], [*Identical_Ios_Duration*], [3499476], [3438261], [-61215], @@ -179,18 +179,18 @@ Pour comparer les performances de la base, je commence par récupérer les donn ) Structure du tableau de résultats: -- La colonne *``` Gap_From_14_To_16```* contient les résultats des comparaisons des données obtenues entre Postgres 14 et Postgres 16. +- La colonne *``` Gap_From_12_To_16```* contient les résultats des comparaisons des données obtenues entre Postgres 12 et Postgres 16. - La ligne *``` Total_Io_Count```* indique le nombre total d'objets d'infrastructures qui ont été filtrés par les différents groupes traités. - La ligne *``` Total_Duration```* indique le temps total de traitement (en millisecondes) de tous les groupes. - La ligne *``` Identical_Ios_Duration```* indique le temps de traitement total (en millisecondes) uniquement pour les #ref-glossary(term: "OIs")[OIs] identiques entre les deux bases. - La ligne *``` Average_Ios_Load_Duration```* indique le temps de traitement moyen écoulé (en millisecondes) par objet d'infrastructure. -Dans les réultats obtenus, je remarque rapidement que le temps de traitement entre les deux bases n'est pas drastiquement différent. Pour le temps de traitement global des objets d'infrastructure identiques entre les deux bases, on ne gagne que 0.23 milliseconde après la migration. Pour filtrer mes résultats, j'utilise la méthode ``` =SUM(SUMIF(pg14[Group_Id]; VLOOKUP(pg14[Group_Id]; pg16[Group_Id]; 1;FALSE); pg14[Load_Duration_In_Ms]))``` qui va exclusivement récupérer les lignes pour lesquelles les groupes analysés sont présents dans les deux bases, puis faire la somme des résultats de performances. +Dans les réultats obtenus, je remarque rapidement que le temps de traitement entre les deux bases n'est pas drastiquement différent. Pour le temps de traitement global des objets d'infrastructure identiques entre les deux bases, on ne gagne que 0.23 milliseconde après la migration. Pour filtrer mes résultats, j'utilise la méthode ``` =SUM(SUMIF(pg12[Group_Id]; VLOOKUP(pg12[Group_Id]; pg16[Group_Id]; 1;FALSE); pg12[Load_Duration_In_Ms]))``` qui va exclusivement récupérer les lignes pour lesquelles les groupes analysés sont présents dans les deux bases, puis faire la somme des résultats de performances. Pour terminer mon analyse, je transforme les résultats en pourcentages dans le tableau @perf_gain_sheet_1. Avec ça je peux comparer les dix pourcents de performances annoncées avec les résultats réels obtenus. #figure( - caption: "Résultat des gains de performances entre PGSQL 14 et 16", + caption: "Résultat des gains de performances entre PGSQL 12 et 16", table( columns: (auto, auto), align: horizon, @@ -253,14 +253,14 @@ C'est en me rappelant cela que j'ai réalisé que les tests ont été réalisés La mise en production étant un environnement sensible et n'ayant pas les compétences d'administrateur système nécessaires, je n'ai pas pu participer à la mise en place de la mise en production des changements. J'ai cependant pu vérifier que tout fonctionnait comme attendu en inspectant l'environnement de production. -Une fois le serveur de production mis à jour avec les nouveaux éléments, j'effectue un nouveau test de performance pour valider que le biais de différence de performance entre les environnements est bien la source du problème. Je conserve les données obtenues précédemment pour la base #ref-glossary(term: "PostgreSQL")[PostgreSQL] 14 et je récupère le résultat de l'outil de benchmark une fois exécuté sur la nouvelle base et sur le même environnement pour une comparaison correcte. +Une fois le serveur de production mis à jour avec les nouveaux éléments, j'effectue un nouveau test de performance pour valider que le biais de différence de performance entre les environnements est bien la source du problème. Je conserve les données obtenues précédemment pour la base #ref-glossary(term: "PostgreSQL")[PostgreSQL] 12 et je récupère le résultat de l'outil de benchmark une fois exécuté sur la nouvelle base et sur le même environnement pour une comparaison correcte. #figure( caption: "Nouveau comparatif brut des performances entre les deux bases", table( columns: (auto, auto, auto, auto), align: horizon, - table.header([], [*Postgres_14*], [*Postgres_16*], [*Gap_From_14_To_16*]), + table.header([], [*Postgres_12*], [*Postgres_16*], [*Gap_From_12_To_16*]), [*Total_Io_Count*], [323527], [346761], [23234], [*Total_Duration*], [3577961], [4025135], [-447174], [*Identical_Ios_Duration*], [3577257], [3980590], [403333], @@ -271,7 +271,7 @@ Une fois le serveur de production mis à jour avec les nouveaux éléments, j'ef Dans le tableau @result_sheet_benchmark_2 les résultats bruts ne donnent pas forcément de gros indices sur les différences par rapport à la première analyse comportant potentiellement un biais. Je constate une inversion sur la quantité de données traitées, cette fois-ci la nouvelle base de données à eu plus d'#ref-glossary(term: "OIs")[OIs] à traiter et par conséquent les temps de traitements ont aussi augmenté. Une comparaison avancée me permet d'obtenir un comparatif plus explicite entre les deux bases. #figure( - caption: "Résultat des gains réels de performances entre PGSQL 14 et 16", + caption: "Résultat des gains réels de performances entre PGSQL 12 et 16", table( columns: (auto, auto), align: horizon, diff --git a/main.pdf b/main.pdf index 401723f3c657f20ddc80d89aaa5972444785decc..b97bde80648a1d8a282f5d9bf718435a55168c87 100644 GIT binary patch delta 303216 zcmZU)Wl-Kf6z9u>ySuwXahKxm?(Pl`E=46sgCi6M>+gqTn=}izOjjE(H3p*<>B2Dmr4H1!>KMll>mmxX{B8L^z)s|FH zO;HhnFe-E5AG>{!64}01+7h7W`$O(kPNzTnl z&cn{j%Ff5j-G~E40GY;sjP_q7<^K`n2lES)lbnrQ9Z^IC(aPD<#oQhdktd_S1_l?3 zi-*1SdIki71aUO#!kdB8dQW*9cM)VE(+IgxGsxQ55TMw2I5KXexT%4Q00bXwt(NQV z=DXaQ#qxNbTb5WTJT1?jz6@R%U(?*tVT;;266UX|x;T^fJbgqOOAMf*XsPkMEs z{OSWRJwVnzsy!Nbs+7%8_e>e+2frvbp-q`9lBQx$Oh&J<+>DfGatQJa~N{CUUngIz{$l9SrAMVW%vO(52nh&H0Ra(aM z292_czZE8GWExw4ke%`aZK+she>J6Tii=8{6ax~yBjm$VhZoZx}@Rp8Z^I+2)Ug|uDtHha{C3fQJ~?>)K?0LaP4WNvVS;PZyi_GGU0-U5OM 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