--? 1 - L’objectif de ce TP étant d’étudier l’optimisation et les index, 
--? mettez à jour les statistiques utilisées par PostgreSQL pour établir les plans d’exécution afin de partir sur de bonnes bases.

-- TODO : ANALYZE table_name;
ANALYSE; -- Pour tout d'un coup.


--? 2 - Lors du TP précédent, vous avez écrit une requête permettant de répondre à la question
--? suivante :
--? Quel est le joueur qui a fait le plus rapidement 6 fautes personnelles ? Donner la date de cette performance et le nom des deux équipes qui se sont affrontées lors de ce match.
--? Afficher le plan d’exécution détaillé avec le temps d’exécution de cette requête.

EXPLAIN ANALYZE
    SELECT p.name , t1.abbreviation , t2.abbreviation , g.dateGame
    FROM Player p, Team t1, Team t2, GameDetail gd, Game g
    WHERE p.id = gd.idPlayer AND g.id = gd.idGame AND t1.id = gd.idTeam
    AND ((g.idHomeTeam = t1.id AND g.idVisitorTeam = t2.id)
    OR (g.idVisitorTeam = t1.id AND g.idHomeTeam = t2.id))
    AND gd.personnalFoul > 6;


--                                                                                             QUERY PLAN                                                                                             
-- ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
--  Nested Loop  (cost=1001.37..16948.56 rows=1 width=49) (actual time=85.074..86.860 rows=1 loops=1)
--    Join Filter: ((((g.idhometeam)::text = (t1.id)::text) AND ((g.idvisitorteam)::text = (t2.id)::text)) OR (((g.idvisitorteam)::text = (t1.id)::text) AND ((g.idhometeam)::text = (t2.id)::text)))
--    Rows Removed by Join Filter: 1
--    ->  Nested Loop  (cost=1000.97..16940.34 rows=1 width=93) (actual time=85.034..86.818 rows=1 loops=1)
--          Join Filter: ((gd.idteam)::text = (t1.id)::text)
--          ->  Nested Loop  (cost=1000.57..16933.54 rows=1 width=50) (actual time=84.985..86.769 rows=1 loops=1)
--                ->  Nested Loop  (cost=1000.28..16925.24 rows=1 width=33) (actual time=84.973..86.756 rows=1 loops=1)
--                      ->  Gather  (cost=1000.00..16916.93 rows=1 width=26) (actual time=83.900..85.640 rows=1 loops=1)
--                            Workers Planned: 2
--                            Workers Launched: 2
--                            ->  Parallel Seq Scan on gamedetail gd  (cost=0.00..15916.83 rows=1 width=26) (actual time=56.639..67.325 rows=0 loops=3)
--                                  Filter: (personnalfoul > '6'::numeric)
--                                  Rows Removed by Filter: 215221
--                      ->  Index Scan using player_pkey on player p  (cost=0.28..8.30 rows=1 width=19) (actual time=1.005..1.005 rows=1 loops=1)
--                            Index Cond: ((id)::text = (gd.idplayer)::text)
--                ->  Index Scan using game_pkey on game g  (cost=0.29..8.30 rows=1 width=35) (actual time=0.008..0.008 rows=1 loops=1)
--                      Index Cond: (id = gd.idgame)
--          ->  Bitmap Heap Scan on team t1  (cost=0.40..4.42 rows=190 width=54) (actual time=0.012..0.012 rows=1 loops=1)
--                Recheck Cond: (((g.idhometeam)::text = (id)::text) OR ((g.idvisitorteam)::text = (id)::text))
--                Heap Blocks: exact=1
--                ->  BitmapOr  (cost=0.40..0.40 rows=2 width=0) (actual time=0.007..0.008 rows=0 loops=1)
--                      ->  Bitmap Index Scan on team_pkey  (cost=0.00..0.15 rows=1 width=0) (actual time=0.005..0.005 rows=1 loops=1)
--                            Index Cond: ((id)::text = (g.idhometeam)::text)
--                      ->  Bitmap Index Scan on team_pkey  (cost=0.00..0.15 rows=1 width=0) (actual time=0.001..0.001 rows=1 loops=1)
--                            Index Cond: ((id)::text = (g.idvisitorteam)::text)
--    ->  Bitmap Heap Scan on team t2  (cost=0.40..4.42 rows=190 width=54) (actual time=0.003..0.004 rows=2 loops=1)
--          Recheck Cond: (((g.idvisitorteam)::text = (id)::text) OR ((g.idhometeam)::text = (id)::text))
--          Heap Blocks: exact=1
--          ->  BitmapOr  (cost=0.40..0.40 rows=2 width=0) (actual time=0.003..0.003 rows=0 loops=1)
--                ->  Bitmap Index Scan on team_pkey  (cost=0.00..0.15 rows=1 width=0) (actual time=0.001..0.001 rows=1 loops=1)
--                      Index Cond: ((id)::text = (g.idvisitorteam)::text)
--                ->  Bitmap Index Scan on team_pkey  (cost=0.00..0.15 rows=1 width=0) (actual time=0.001..0.001 rows=1 loops=1)
--                      Index Cond: ((id)::text = (g.idhometeam)::text)
--  Planning Time: 1.956 ms
--  Execution Time: 87.197 ms
-- (35 rows)

--? 3 - En indexant une colonne, il est possible de grandement améliorer le temps d’exécution de la requête. Laquelle ? Tester et comparer le temps d’exécution.
CREATE INDEX idx ON GameDetail(personnalFoul);

EXPLAIN ANALYZE
    SELECT p.name , t1.abbreviation , t2.abbreviation , g.dateGame
    FROM Player p, Team t1, Team t2, GameDetail gd, Game g
    WHERE p.id = gd.idPlayer AND g.id = gd.idGame AND t1.id = gd.idTeam
    AND ((g.idHomeTeam = t1.id AND g.idVisitorTeam = t2.id)
    OR (g.idVisitorTeam = t1.id AND g.idHomeTeam = t2.id))
    AND gd.personnalFoul > 6;


--                                                                                             QUERY PLAN                                                                                             
-- ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
--  Nested Loop  (cost=1.79..40.07 rows=1 width=49) (actual time=0.201..0.214 rows=1 loops=1)
--    Join Filter: ((((g.idhometeam)::text = (t1.id)::text) AND ((g.idvisitorteam)::text = (t2.id)::text)) OR (((g.idvisitorteam)::text = (t1.id)::text) AND ((g.idhometeam)::text = (t2.id)::text)))
--    Rows Removed by Join Filter: 1
--    ->  Nested Loop  (cost=1.39..31.85 rows=1 width=93) (actual time=0.174..0.183 rows=1 loops=1)
--          Join Filter: ((gd.idteam)::text = (t1.id)::text)
--          ->  Nested Loop  (cost=0.99..25.05 rows=1 width=50) (actual time=0.128..0.134 rows=1 loops=1)
--                ->  Nested Loop  (cost=0.71..16.75 rows=1 width=33) (actual time=0.093..0.097 rows=1 loops=1)
--                      ->  Index Scan using idx on gamedetail gd  (cost=0.42..8.44 rows=1 width=26) (actual time=0.044..0.046 rows=1 loops=1)
--                            Index Cond: (personnalfoul > '6'::numeric)
--                      ->  Index Scan using player_pkey on player p  (cost=0.28..8.30 rows=1 width=19) (actual time=0.031..0.032 rows=1 loops=1)
--                            Index Cond: ((id)::text = (gd.idplayer)::text)
--                ->  Index Scan using game_pkey on game g  (cost=0.29..8.30 rows=1 width=35) (actual time=0.029..0.030 rows=1 loops=1)
--                      Index Cond: (id = gd.idgame)
--          ->  Bitmap Heap Scan on team t1  (cost=0.40..4.42 rows=190 width=54) (actual time=0.032..0.035 rows=1 loops=1)
--                Recheck Cond: (((g.idhometeam)::text = (id)::text) OR ((g.idvisitorteam)::text = (id)::text))
--                Heap Blocks: exact=1
--                ->  BitmapOr  (cost=0.40..0.40 rows=2 width=0) (actual time=0.020..0.022 rows=0 loops=1)
--                      ->  Bitmap Index Scan on team_pkey  (cost=0.00..0.15 rows=1 width=0) (actual time=0.012..0.012 rows=1 loops=1)
--                            Index Cond: ((id)::text = (g.idhometeam)::text)
--                      ->  Bitmap Index Scan on team_pkey  (cost=0.00..0.15 rows=1 width=0) (actual time=0.003..0.003 rows=1 loops=1)
--                            Index Cond: ((id)::text = (g.idvisitorteam)::text)
--    ->  Bitmap Heap Scan on team t2  (cost=0.40..4.42 rows=190 width=54) (actual time=0.012..0.014 rows=2 loops=1)
--          Recheck Cond: (((g.idvisitorteam)::text = (id)::text) OR ((g.idhometeam)::text = (id)::text))
--          Heap Blocks: exact=1
--          ->  BitmapOr  (cost=0.40..0.40 rows=2 width=0) (actual time=0.009..0.010 rows=0 loops=1)
--                ->  Bitmap Index Scan on team_pkey  (cost=0.00..0.15 rows=1 width=0) (actual time=0.004..0.004 rows=1 loops=1)
--                      Index Cond: ((id)::text = (g.idvisitorteam)::text)
--                ->  Bitmap Index Scan on team_pkey  (cost=0.00..0.15 rows=1 width=0) (actual time=0.003..0.003 rows=1 loops=1)
--                      Index Cond: ((id)::text = (g.idhometeam)::text)
--  Planning Time: 2.722 ms
--  Execution Time: 0.558 ms
-- (31 rows)

--? 4 - Trouver le nom de l’équipe ayant gagné un match avec le plus grand écart de points, avec le score final.

SELECT t1.nickname, abs(g.ptsAway - g.ptsHome), ptsAway, ptsHome
FROM Team t1, Team t2, Game g
WHERE t1.id = g.idHomeTeam AND t2.id = g.idVisitorTeam AND g.homeTeamWins = TRUE
GROUP BY t1.nickname, abs(g.ptsAway - g.ptsHome), ptsAway, ptsHome
HAVING abs(g.ptsAway - g.ptsHome) >= ALL(SELECT abs(g.ptsAway - g.ptsHome)
                                         FROM Team t3, Team t4, Game g
                                         WHERE t3.id = g.idHomeTeam AND t4.id = g.idVisitorTeam AND g.homeTeamWins = true);

--  nickname  | abs | ptsaway | ptshome 
-- -----------+-----+---------+---------
--  Grizzlies |  73 |      79 |     152
-- (1 row)

--? 5 - Analyser le plan d’exécution.

--! devrait marcher normalement (sans HAVING)==> OUI
--? Mais change pas grand chose ????!!!!

EXPLAIN ANALYZE
    SELECT t1.nickname, abs(g.ptsAway - g.ptsHome), ptsAway, ptsHome
    FROM Team t1, Team t2, Game g
    WHERE t1.id = g.idHomeTeam AND t2.id = g.idVisitorTeam AND g.homeTeamWins = TRUE
    AND abs(g.ptsAway - g.ptsHome) >= ALL(SELECT abs(g.ptsAway - g.ptsHome)
                                            FROM Team t1, Team t2, Game g
                                            WHERE t1.id = g.idHomeTeam AND t2.id = g.idVisitorTeam AND g.homeTeamWins = true);

EXPLAIN ANALYZE
    -- SELECT t1.nickname, abs(g.ptsAway - g.ptsHome), ptsAway, ptsHome
    -- FROM Team t1, Team t2, Game g
    -- WHERE t1.id = g.idHomeTeam AND t2.id = g.idVisitorTeam AND g.homeTeamWins = TRUE
    -- GROUP BY t1.nickname, abs(g.ptsAway - g.ptsHome), ptsAway, ptsHome
    -- HAVING abs(g.ptsAway - g.ptsHome) >= ALL(SELECT abs(g.ptsAway - g.ptsHome)
    --                                         FROM Team t1, Team t2, Game g
    --                                         WHERE t1.id = g.idHomeTeam AND t2.id = g.idVisitorTeam AND g.homeTeamWins = true);

--                                                                QUERY PLAN                                                               
-- ----------------------------------------------------------------------------------------------------------------------------------------
--  HashAggregate  (cost=1065.09..7344429.42 rows=7569 width=120) (actual time=73.608..80.050 rows=1 loops=1)
--    Group Key: t1.nickname, abs((g.ptsaway - g.ptshome)), g.ptsaway, g.ptshome
--    Filter: (SubPlan 1)
--    Batches: 1  Memory Usage: 1433kB
--    Rows Removed by Filter: 11863
--    ->  Hash Join  (cost=28.55..885.16 rows=15138 width=120) (actual time=0.139..26.082 rows=15138 loops=1)
--          Hash Cond: ((g.idvisitorteam)::text = (t2.id)::text)
--          ->  Hash Join  (cost=14.28..754.57 rows=15138 width=99) (actual time=0.060..15.497 rows=15138 loops=1)
--                Hash Cond: ((g.idhometeam)::text = (t1.id)::text)
--                ->  Seq Scan on game g  (cost=0.00..699.67 rows=15138 width=32) (actual time=0.014..8.369 rows=15138 loops=1)
--                      Filter: hometeamwins
--                      Rows Removed by Filter: 10629
--                ->  Hash  (cost=11.90..11.90 rows=190 width=116) (actual time=0.027..0.027 rows=30 loops=1)
--                      Buckets: 1024  Batches: 1  Memory Usage: 10kB
--                      ->  Seq Scan on team t1  (cost=0.00..11.90 rows=190 width=116) (actual time=0.004..0.012 rows=30 loops=1)
--          ->  Hash  (cost=11.90..11.90 rows=190 width=38) (actual time=0.054..0.054 rows=30 loops=1)
--                Buckets: 1024  Batches: 1  Memory Usage: 10kB
--                ->  Seq Scan on team t2  (cost=0.00..11.90 rows=190 width=38) (actual time=0.024..0.032 rows=30 loops=1)
--    SubPlan 1
--      ->  Materialize  (cost=28.55..960.85 rows=15138 width=32) (actual time=0.000..0.002 rows=9 loops=11864)
--            ->  Hash Join  (cost=28.55..885.16 rows=15138 width=32) (actual time=0.096..14.368 rows=15138 loops=1)
--                  Hash Cond: ((g_1.idvisitorteam)::text = (t2_1.id)::text)
--                  ->  Hash Join  (cost=14.28..754.57 rows=15138 width=21) (actual time=0.032..8.845 rows=15138 loops=1)
--                        Hash Cond: ((g_1.idhometeam)::text = (t1_1.id)::text)
--                        ->  Seq Scan on game g_1  (cost=0.00..699.67 rows=15138 width=32) (actual time=0.010..5.103 rows=15138 loops=1)
--                              Filter: hometeamwins
--                              Rows Removed by Filter: 10629
--                        ->  Hash  (cost=11.90..11.90 rows=190 width=38) (actual time=0.016..0.016 rows=30 loops=1)
--                              Buckets: 1024  Batches: 1  Memory Usage: 10kB
--                              ->  Seq Scan on team t1_1  (cost=0.00..11.90 rows=190 width=38) (actual time=0.002..0.009 rows=30 loops=1)
--                  ->  Hash  (cost=11.90..11.90 rows=190 width=38) (actual time=0.039..0.039 rows=30 loops=1)
--                        Buckets: 1024  Batches: 1  Memory Usage: 10kB
--                        ->  Seq Scan on team t2_1  (cost=0.00..11.90 rows=190 width=38) (actual time=0.028..0.032 rows=30 loops=1)
--  Planning Time: 0.916 ms
--  Execution Time: 80.363 ms
-- (35 rows)

--! Optimiser le JOIN

--? 6 - Comment améliorer les performances de cette requête ? Tester et comparer. ==> But : faire des index

CREATE INDEX idx2 ON Game(idHomeTeam, idVisitorTeam);

--? 7 - Trouver les joueurs ayant fait plus de 5 interceptions (steal) tout en ayant commencé au centre.

SELECT p.id, p.name
FROM Player p, GameDetail gd
WHERE p.id = gd.idPlayer AND gd.steals >= 5 AND gd.startposition = 'C';

--    id    |        name         
-- ---------+---------------------
--  1630596 | Evan Mobley
--  1628389 | Bam Adebayo
--  202326  | DeMarcus Cousins
--  203991  | Clint Capela
--  1628389 | Bam Adebayo
--  203083  | Andre Drummond
--  202696  | Nikola Vucevic
--  203110  | Draymond Green
--  203083  | Andre Drummond
--  203954  | Joel Embiid
--  202326  | DeMarcus Cousins
--  1628976 | Wendell Carter Jr.
--  202696  | Nikola Vucevic
--  203999  | Nikola Jokic
--  1626167 | Myles Turner
--  203496  | Robert Covington

--? 8 - Analyser le plan d’exécution

EXPLAIN ANALYZE 
    SELECT p.id, p.name
    FROM Player p, GameDetail gd
    WHERE p.id = gd.idPlayer AND gd.steals >= 5 AND gd.startposition = 'C';


--                                                              QUERY PLAN                                                              
-- -------------------------------------------------------------------------------------------------------------------------------------
--  Gather  (cost=1000.28..17684.64 rows=201 width=19) (actual time=1.410..80.509 rows=137 loops=1)
--    Workers Planned: 2
--    Workers Launched: 2
--    ->  Nested Loop  (cost=0.28..16664.54 rows=84 width=19) (actual time=2.226..64.468 rows=46 loops=3)
--          ->  Parallel Seq Scan on gamedetail gd  (cost=0.00..16589.40 rows=84 width=6) (actual time=2.122..64.080 rows=46 loops=3)
--                Filter: ((steals >= '5'::numeric) AND (startposition = 'C'::bpchar))
--                Rows Removed by Filter: 215176
--          ->  Index Scan using player_pkey on player p  (cost=0.28..0.89 rows=1 width=19) (actual time=0.007..0.007 rows=1 loops=137)
--                Index Cond: ((id)::text = (gd.idplayer)::text)
--  Planning Time: 0.988 ms
--  Execution Time: 81.023 ms
-- (11 rows)

--? 9 - Comment améliorer les performances de cette requête ? Tester et comparer.

CREATE INDEX idx3 ON GameDetail(startposition, steals);

EXPLAIN ANALYZE 
    SELECT p.id, p.name
    FROM Player p, GameDetail gd
    WHERE p.id = gd.idPlayer AND gd.steals >= 5 AND gd.startposition = 'C';


--                                                         QUERY PLAN                                                         
-- ---------------------------------------------------------------------------------------------------------------------------
--  Hash Join  (cost=82.42..810.23 rows=201 width=19) (actual time=5.164..6.532 rows=137 loops=1)
--    Hash Cond: ((gd.idplayer)::text = (p.id)::text)
--    ->  Bitmap Heap Scan on gamedetail gd  (cost=6.49..733.77 rows=201 width=6) (actual time=0.648..1.910 rows=137 loops=1)
--          Recheck Cond: ((startposition = 'C'::bpchar) AND (steals >= '5'::numeric))
--          Heap Blocks: exact=135
--          ->  Bitmap Index Scan on idx3  (cost=0.00..6.43 rows=201 width=0) (actual time=0.567..0.568 rows=137 loops=1)
--                Index Cond: ((startposition = 'C'::bpchar) AND (steals >= '5'::numeric))
--    ->  Hash  (cost=43.75..43.75 rows=2575 width=19) (actual time=4.471..4.471 rows=2575 loops=1)
--          Buckets: 4096  Batches: 1  Memory Usage: 165kB
--          ->  Seq Scan on player p  (cost=0.00..43.75 rows=2575 width=19) (actual time=0.027..2.996 rows=2575 loops=1)
--  Planning Time: 1.538 ms
--  Execution Time: 6.690 ms
-- (12 rows)


--? 10 - Supprimer tout les index précédents. 

--? Pour s'aider : commande \di qui affiche tous les index de votre base. 
--? (Remarquez qu’il existe plusieurs index dont le nom se termine par _pkey. 
--? Ce sont des index créés automatiquement sur les clés primaires des tables. 
--? Inutile de les supprimer.)

\di
DROP INDEX idx;
DROP INDEX idx2;
DROP INDEX idx3;

\di

--? 11 - Les deux requêtes ci-dessous calculent la même chose. Que font-elles?
SELECT t.nickname, t.city, count(*)
FROM Team t, Game g
WHERE (t.id = g.idHomeTeam AND g.homeTeamWins) OR (t.id = g.idVisitorTeam
    AND NOT g.homeTeamWins) 
GROUP BY t.nickname , t.city ORDER BY 3 DESC;

-- * Celle ci affiche le surnom, la ville de l'équipe ainsi que le nombre de match victorieux au total (exterieur ou non)
-- * De la table game et team puis koin la team soit en equipe maison ou externe au terrain (si maison, alors victorieuse (impossible en exterieur))
-- * Enfin, on ordonne le tout par nombre de match gagné


--    nickname    |     city      | count 
-- ---------------+---------------+-------
--  Spurs         | San Antonio   |  1166
--  Heat          | Miami         |  1037
--  Celtics       | Boston        |  1011
--  Mavericks     | Dallas        |  1002
--  Warriors      | Golden State  |   982
--  Rockets       | Houston       |   982
--  Nuggets       | Denver        |   968
--  Lakers        | Los Angeles   |   929
--  Jazz          | Utah          |   925
--  Thunder       | Oklahoma City |   911
--  Pacers        | Indiana       |   905
--  Cavaliers     | Cleveland     |   899
--  Clippers      | Los Angeles   |   892
--  Raptors       | Toronto       |   884
--  Suns          | Phoenix       |   881
--  Bulls         | Chicago       |   866
--  Trail Blazers | Portland      |   850
--  Grizzlies     | Memphis       |   850
--  Pistons       | Detroit       |   835
--  Bucks         | Milwaukee     |   816
--  Hawks         | Atlanta       |   804
--  Magic         | Orlando       |   761
--  76ers         | Philadelphia  |   760
--  Pelicans      | New Orleans   |   759
--  Nets          | Brooklyn      |   750
--  Wizards       | Washington    |   736
--  Kings         | Sacramento    |   674
--  Knicks        | New York      |   657
--  Timberwolves  | Minnesota     |   654
--  Hornets       | Charlotte     |   621
-- (30 rows)


SELECT t.nickname, t.city, hw.nb+vw.nb 
FROM Team t, (SELECT t.id, count(*) nb
              FROM Team t, Game g
              WHERE t.id = g.idHomeTeam AND g.homeTeamWins
              GROUP BY t.id) hw,

              (SELECT t.id, count(*) nb
              FROM Team t, Game g
              WHERE t.id = g.idVisitorTeam AND NOT g.homeTeamWins
              GROUP BY t.id) vw 

WHERE t.id = hw.id AND t.id = vw.id
ORDER BY 3 DESC;

-- * Pour celle ci, on affiche le surnom, la ville de l'équipe ainsi que son nombre de win maison + son nombre de win exterieur
-- * Depuis la table team et deux autres tables qui contiennent le nombre de win hom et le nombre de win exterieur
-- * On join la team à ses deux win 
-- * Puis on le trie par nombre de win total.

--    nickname    |     city      | ?column? 
-- ---------------+---------------+----------
--  Spurs         | San Antonio   |     1166
--  Heat          | Miami         |     1037
--  Celtics       | Boston        |     1011
--  Mavericks     | Dallas        |     1002
--  Rockets       | Houston       |      982
--  Warriors      | Golden State  |      982
--  Nuggets       | Denver        |      968
--  Lakers        | Los Angeles   |      929
--  Jazz          | Utah          |      925
--  Thunder       | Oklahoma City |      911
--  Pacers        | Indiana       |      905
--  Cavaliers     | Cleveland     |      899
--  Clippers      | Los Angeles   |      892
--  Raptors       | Toronto       |      884
--  Suns          | Phoenix       |      881
--  Bulls         | Chicago       |      866
--  Trail Blazers | Portland      |      850
--  Grizzlies     | Memphis       |      850
--  Pistons       | Detroit       |      835
--  Bucks         | Milwaukee     |      816
--  Hawks         | Atlanta       |      804
--  Magic         | Orlando       |      761
--  76ers         | Philadelphia  |      760
--  Pelicans      | New Orleans   |      759
--  Nets          | Brooklyn      |      750
--  Wizards       | Washington    |      736
--  Kings         | Sacramento    |      674
--  Knicks        | New York      |      657
--  Timberwolves  | Minnesota     |      654
--  Hornets       | Charlotte     |      621
-- (30 rows)

--? 12 - Sans les exécuter, laquelle de ces deux requêtes est la plus rapide selon vous ? Justifier.
-- * Selon moi, Je pense que la deuxième est meilleur car d'une part elle n'as pas de GROUP BY, ce qui devrait etre moins gourmand.
-- * D'autre part, elle calcule séparement le home du away ce qui, je pense, en indexant devrait etre plus rapide.

--? 13 - Vérifier votre réponse en affichant le temps d’exécution de chaque requête.

EXPLAIN ANALYZE 
    SELECT t.nickname, t.city, count(*)
    FROM Team t, Game g
    WHERE (t.id = g.idHomeTeam AND g.homeTeamWins) OR (t.id = g.idVisitorTeam
        AND NOT g.homeTeamWins)
    GROUP BY t.nickname , t.city ORDER BY 3 DESC;

--                                                                            QUERY PLAN                                                                           
-- ----------------------------------------------------------------------------------------------------------------------------------------------------------------
--  Sort  (cost=65828.12..65828.60 rows=190 width=164) (actual time=159.839..159.842 rows=30 loops=1)
--    Sort Key: (count(*)) DESC
--    Sort Method: quicksort  Memory: 27kB
--    ->  HashAggregate  (cost=65819.03..65820.93 rows=190 width=164) (actual time=159.815..159.820 rows=30 loops=1)
--          Group Key: t.nickname, t.city
--          Batches: 1  Memory Usage: 40kB
--          ->  Nested Loop  (cost=0.00..65626.02 rows=25735 width=156) (actual time=0.072..152.901 rows=25767 loops=1)
--                Join Filter: ((((t.id)::text = (g.idhometeam)::text) AND g.hometeamwins) OR (((t.id)::text = (g.idvisitorteam)::text) AND (NOT g.hometeamwins)))
--                Rows Removed by Join Filter: 747243
--                ->  Seq Scan on game g  (cost=0.00..699.67 rows=19523 width=23) (actual time=0.026..5.395 rows=25767 loops=1)
--                      Filter: (hometeamwins OR (NOT hometeamwins))
--                ->  Materialize  (cost=0.00..12.85 rows=190 width=194) (actual time=0.000..0.001 rows=30 loops=25767)
--                      ->  Seq Scan on team t  (cost=0.00..11.90 rows=190 width=194) (actual time=0.008..0.016 rows=30 loops=1)
--  Planning Time: 0.486 ms
--  Execution Time: 159.978 ms
-- (15 rows)

-- * Pour cette premiere question, cela bloque bien à l'endroit que je pensais
-- * En indexant, on devrait surement avoir de meilleures résultats.


EXPLAIN ANALYZE 
    SELECT t.nickname, t.city, hw.nb+vw.nb 
    FROM Team t, (SELECT t.id, count(*) nb
                FROM Team t, Game g
                WHERE t.id = g.idHomeTeam AND g.homeTeamWins
                GROUP BY t.id) hw,

                (SELECT t.id, count(*) nb
                FROM Team t, Game g
                WHERE t.id = g.idVisitorTeam AND NOT g.homeTeamWins
                GROUP BY t.id) vw 

    WHERE t.id = hw.id AND t.id = vw.id
    ORDER BY 3 DESC;

--                                                                       QUERY PLAN                                                                       
-- -------------------------------------------------------------------------------------------------------------------------------------------------------
--  Sort  (cost=1658.81..1659.29 rows=190 width=164) (actual time=34.733..34.742 rows=30 loops=1)
--    Sort Key: ((hw.nb + vw.nb)) DESC
--    Sort Method: quicksort  Memory: 27kB
--    ->  Hash Join  (cost=1638.23..1651.62 rows=190 width=164) (actual time=34.660..34.698 rows=30 loops=1)
--          Hash Cond: ((t.id)::text = (vw.id)::text)
--          ->  Hash Join  (cost=836.44..848.85 rows=190 width=240) (actual time=19.458..19.477 rows=30 loops=1)
--                Hash Cond: ((t.id)::text = (hw.id)::text)
--                ->  Seq Scan on team t  (cost=0.00..11.90 rows=190 width=194) (actual time=0.030..0.034 rows=30 loops=1)
--                ->  Hash  (cost=834.06..834.06 rows=190 width=46) (actual time=19.374..19.376 rows=30 loops=1)
--                      Buckets: 1024  Batches: 1  Memory Usage: 10kB
--                      ->  Subquery Scan on hw  (cost=830.26..834.06 rows=190 width=46) (actual time=19.302..19.317 rows=30 loops=1)
--                            ->  HashAggregate  (cost=830.26..832.16 rows=190 width=46) (actual time=19.300..19.308 rows=30 loops=1)
--                                  Group Key: t_1.id
--                                  Batches: 1  Memory Usage: 40kB
--                                  ->  Hash Join  (cost=14.28..754.57 rows=15138 width=38) (actual time=0.048..13.843 rows=15138 loops=1)
--                                        Hash Cond: ((g.idhometeam)::text = (t_1.id)::text)
--                                        ->  Seq Scan on game g  (cost=0.00..699.67 rows=15138 width=11) (actual time=0.009..7.778 rows=15138 loops=1)
--                                              Filter: hometeamwins
--                                              Rows Removed by Filter: 10629
--                                        ->  Hash  (cost=11.90..11.90 rows=190 width=38) (actual time=0.024..0.025 rows=30 loops=1)
--                                              Buckets: 1024  Batches: 1  Memory Usage: 10kB
--                                              ->  Seq Scan on team t_1  (cost=0.00..11.90 rows=190 width=38) (actual time=0.004..0.010 rows=30 loops=1)
--          ->  Hash  (cost=799.41..799.41 rows=190 width=46) (actual time=15.177..15.180 rows=30 loops=1)
--                Buckets: 1024  Batches: 1  Memory Usage: 10kB
--                ->  Subquery Scan on vw  (cost=795.61..799.41 rows=190 width=46) (actual time=15.132..15.148 rows=30 loops=1)
--                      ->  HashAggregate  (cost=795.61..797.51 rows=190 width=46) (actual time=15.128..15.139 rows=30 loops=1)
--                            Group Key: t_2.id
--                            Batches: 1  Memory Usage: 40kB
--                            ->  Hash Join  (cost=14.28..742.47 rows=10629 width=38) (actual time=0.110..11.306 rows=10629 loops=1)
--                                  Hash Cond: ((g_1.idvisitorteam)::text = (t_2.id)::text)
--                                  ->  Seq Scan on game g_1  (cost=0.00..699.67 rows=10629 width=11) (actual time=0.037..7.024 rows=10629 loops=1)
--                                        Filter: (NOT hometeamwins)
--                                        Rows Removed by Filter: 15138
--                                  ->  Hash  (cost=11.90..11.90 rows=190 width=38) (actual time=0.044..0.045 rows=30 loops=1)
--                                        Buckets: 1024  Batches: 1  Memory Usage: 10kB
--                                        ->  Seq Scan on team t_2  (cost=0.00..11.90 rows=190 width=38) (actual time=0.012..0.020 rows=30 loops=1)
--  Planning Time: 0.826 ms
--  Execution Time: 35.012 ms
-- (38 rows)

-- * La différence est choquante, je ne m'attendais pas à ca mais je n'étais pas du tout sur la bonne raison.
-- * On dirait que c'est grace à la jointure des id que l'on gagne bcp de temps car comme on l'a vu précédemment, 
-- * les PK sont déjà indéxé de base ce qui améliore l'éxécution de bcp. Mais ce point serait bien à revoir pour moi.


--? 14 - Lister les joueurs ayant marqué plus de 40 points dans un match, avec le nombre de matchs
--? pour lesquels le joueur a réussi cette performance.

SELECT p.id, p.name, count(*)
FROM Player p, GameDetail gd
WHERE p.id = gd.idPlayer AND gd.points >= 40
GROUP BY p.id, p.name
ORDER BY 3 DESC;

-- * Meme structure que la première donc devrait etre moins opti

--    id    |          name           | count 
-- ---------+-------------------------+-------
--  201935  | James Harden            |   112
--  977     | Kobe Bryant             |   106
--  2544    | LeBron James            |    97
--  201142  | Kevin Durant            |    73
--  201939  | Stephen Curry           |    64
--  201566  | Russell Westbrook       |    57
--  203081  | Damian Lillard          |    44
--  2546    | Carmelo Anthony         |    41
--  2548    | Dwyane Wade             |    41
--  203076  | Anthony Davis           |    38
--  947     | Allen Iverson           |    32
--  203507  | Giannis Antetokounmpo   |    32
--  203078  | Bradley Beal            |    30
--  2240    | Gilbert Arenas          |    29
--  202681  | Kyrie Irving            |    27
--  203954  | Joel Embiid             |    24
--  1503    | Tracy McGrady           |    24
--  1629027 | Trae Young              |    23
--  2405    | Amar'e Stoudemire       |    22
--  1717    | Dirk Nowitzki           |    22
--  1713    | Vince Carter            |    22
--  1626164 | Devin Booker            |    20
--  1629029 | Luka Doncic             |    20
--  202331  | Paul George             |    19
--  202689  | Kemba Walker            |    19
--  201942  | DeMar DeRozan           |    16
--  203897  | Zach LaVine             |    15
--  202691  | Klay Thompson           |    14
--  1628369 | Jayson Tatum            |    14
--  1628378 | Donovan Mitchell        |    13
--  202326  | DeMarcus Cousins        |    13
--  203999  | Nikola Jokic            |    12
--  2072    | Michael Redd            |    12
--  101108  | Chris Paul              |    11
--  200746  | LaMarcus Aldridge       |    11
--  201933  | Blake Griffin           |    11
--  201567  | Kevin Love              |    11
--  2547    | Chris Bosh              |    10
--  203468  | CJ McCollum             |    10
--  101145  | Monta Ellis             |    10
--  202695  | Kawhi Leonard           |    10
--  1626157 | Karl-Anthony Towns      |    10
--  202710  | Jimmy Butler            |    10
--  2037    | Jamal Crawford          |     9

--? 15 - Quel index pourrait permettre d’améliorer le temps d’exécution de la requête ? Justifier votre réponse.

EXPLAIN ANALYZE 
    SELECT p.id, p.name, count(*)
    FROM Player p, GameDetail gd
    WHERE p.id = gd.idPlayer AND gd.points >= 40
    GROUP BY p.id, p.name
    ORDER BY 3 DESC;

--                                                                           QUERY PLAN                                                                          
-- --------------------------------------------------------------------------------------------------------------------------------------------------------------
--  Sort  (cost=17293.10..17297.02 rows=1570 width=27) (actual time=166.819..168.162 rows=196 loops=1)
--    Sort Key: (count(*)) DESC
--    Sort Method: quicksort  Memory: 40kB
--    ->  Finalize GroupAggregate  (cost=17025.10..17209.76 rows=1570 width=27) (actual time=166.127..168.110 rows=196 loops=1)
--          Group Key: p.id
--          ->  Gather Merge  (cost=17025.10..17187.52 rows=1308 width=27) (actual time=166.117..168.026 rows=376 loops=1)
--                Workers Planned: 2
--                Workers Launched: 2
--                ->  Partial GroupAggregate  (cost=16025.07..16036.52 rows=654 width=27) (actual time=152.688..152.792 rows=125 loops=3)
--                      Group Key: p.id
--                      ->  Sort  (cost=16025.07..16026.71 rows=654 width=19) (actual time=152.674..152.698 rows=546 loops=3)
--                            Sort Key: p.id
--                            Sort Method: quicksort  Memory: 71kB
--                            Worker 0:  Sort Method: quicksort  Memory: 61kB
--                            Worker 1:  Sort Method: quicksort  Memory: 64kB
--                            ->  Hash Join  (cost=75.94..15994.49 rows=654 width=19) (actual time=2.969..152.075 rows=546 loops=3)
--                                  Hash Cond: ((gd.idplayer)::text = (p.id)::text)
--                                  ->  Parallel Seq Scan on gamedetail gd  (cost=0.00..15916.83 rows=654 width=6) (actual time=1.040..149.731 rows=546 loops=3)
--                                        Filter: (points >= '40'::numeric)
--                                        Rows Removed by Filter: 214675
--                                  ->  Hash  (cost=43.75..43.75 rows=2575 width=19) (actual time=1.770..1.771 rows=2575 loops=3)
--                                        Buckets: 4096  Batches: 1  Memory Usage: 165kB
--                                        ->  Seq Scan on player p  (cost=0.00..43.75 rows=2575 width=19) (actual time=0.051..0.730 rows=2575 loops=3)
--  Planning Time: 1.028 ms
--  Execution Time: 168.314 ms
-- (25 rows)

-- ! DONC :

CREATE INDEX idx ON GameDetail(points);

--                                                                  QUERY PLAN                                                                 
-- --------------------------------------------------------------------------------------------------------------------------------------------
--  Sort  (cost=4622.02..4625.93 rows=1566 width=27) (actual time=17.991..18.038 rows=196 loops=1)
--    Sort Key: (count(*)) DESC
--    Sort Method: quicksort  Memory: 40kB
--    ->  HashAggregate  (cost=4523.26..4538.92 rows=1566 width=27) (actual time=17.787..17.860 rows=196 loops=1)
--          Group Key: p.id
--          Batches: 1  Memory Usage: 97kB
--          ->  Hash Join  (cost=108.50..4515.43 rows=1566 width=19) (actual time=2.438..16.954 rows=1639 loops=1)
--                Hash Cond: ((gd.idplayer)::text = (p.id)::text)
--                ->  Bitmap Heap Scan on gamedetail gd  (cost=32.56..4435.37 rows=1566 width=6) (actual time=0.956..14.274 rows=1639 loops=1)
--                      Recheck Cond: (points >= '40'::numeric)
--                      Heap Blocks: exact=1504
--                      ->  Bitmap Index Scan on idx  (cost=0.00..32.17 rows=1566 width=0) (actual time=0.633..0.634 rows=1639 loops=1)
--                            Index Cond: (points >= '40'::numeric)
--                ->  Hash  (cost=43.75..43.75 rows=2575 width=19) (actual time=1.456..1.458 rows=2575 loops=1)
--                      Buckets: 4096  Batches: 1  Memory Usage: 165kB
--                      ->  Seq Scan on player p  (cost=0.00..43.75 rows=2575 width=19) (actual time=0.022..0.606 rows=2575 loops=1)
--  Planning Time: 2.630 ms
--  Execution Time: 18.270 ms
-- (18 rows)

--? 16 - Lister les joueurs ayant réalisé un total d’assists et de vols supérieur à 22 pendant un match.
SELECT p.id, p.name, assists, blockedShots
FROM Player p, GameDetail gd
WHERE p.id = gd.idPlayer AND assists > 22 AND blockedShots > 22;

--  id | name | assists | blockedshots 
-- ----+------+---------+--------------
-- (0 rows)


-- ! Ou ??? :

SELECT p.id, p.name, assists, blockedShots
FROM Player p, GameDetail gd
WHERE p.id = gd.idPlayer AND (assists + blockedShots) > 22;

--    id   |       name        | assists | blockedshots 
-- --------+-------------------+---------+--------------
--  201566 | Russell Westbrook |      24 |            1
--  201196 | Ramon Sessions    |      24 |            1
--  959    | Steve Nash        |      23 |            1
--  200765 | Rajon Rondo       |      23 |            1
--  200765 | Rajon Rondo       |      24 |            0
--  201566 | Russell Westbrook |      24 |            0
--  200765 | Rajon Rondo       |      25 |            0
--  201566 | Russell Westbrook |      22 |            1
-- (8 rows)

--? 17 - Quel index pourrait permettre d’améliorer le temps d’exécution de la requête ? Justifier votre
--? réponse.

EXPLAIN ANALYZE 
    SELECT p.id, p.name, gd.assists, gd.blockedShots
    FROM Player p, GameDetail gd
    WHERE p.id = gd.idPlayer AND (gd.assists + gd.blockedShots) > 22;

--                                                         QUERY PLAN                                                        
-- --------------------------------------------------------------------------------------------------------------------------
--  Hash Join  (cost=75.94..22880.79 rows=215221 width=26) (actual time=19.979..161.473 rows=8 loops=1)
--    Hash Cond: ((gd.idplayer)::text = (p.id)::text)
--    ->  Seq Scan on gamedetail gd  (cost=0.00..22238.96 rows=215221 width=13) (actual time=18.738..160.218 rows=8 loops=1)
--          Filter: ((assists + blockedshots) > '22'::numeric)
--          Rows Removed by Filter: 645656
--    ->  Hash  (cost=43.75..43.75 rows=2575 width=19) (actual time=1.201..1.202 rows=2575 loops=1)
--          Buckets: 4096  Batches: 1  Memory Usage: 165kB
--          ->  Seq Scan on player p  (cost=0.00..43.75 rows=2575 width=19) (actual time=0.023..0.512 rows=2575 loops=1)
--  Planning Time: 1.002 ms
--  Execution Time: 161.535 ms
-- (10 rows)

--! CREATE INDEX idx2 ON GameDetail(assists, blockedshots); PAS OK
-- * JSP la