Created statistic cache

src/main.py

@@ -1,4 +1,4 @@
-from typing import Union
+from typing import Union, Dict
 from pydantic import BaseModel
 from sqlmodel import Session
 from threading import Thread
@@ -25,6 +25,7 @@ def refresh_data():
     data_connection.delete_database()
     data_connection.create_db_and_tables()
     with Session(data_connection.get_db_engine()) as session:
+        statistic_creator.clear_cache()
         data_connection.get_user_collection(session)
         data_connection.get_user_owned_collection(session)
         data_connection.get_user_wishlist_collection(session)
@@ -219,8 +220,8 @@ def get_winrate(player_name: str | None = None, session: Session = Depends(get_s
 
     return statistic_to_return
 
-@app.get('/statistics/winrate_over_time', response_model=statistic_classes.TimeLineStatistic)
+@app.get('/statistics/winrate_over_time', response_model=Union[statistic_classes.TimeLineStatistic, Dict[str,statistic_classes.TimeLineStatistic]])
-def get_winrate_over_time(player_name: str, day_step: int = 1, session: Session=Depends(get_session)):
+def get_winrate_over_time(player_name: str | None = None, day_step: int = 1, session: Session=Depends(get_session)):
     statistic_to_return = statistic_creator.get_winrate_over_time(session, player_name, day_step)
 
     return statistic_to_return

src/modules/statistic_creator.py

@@ -1,14 +1,44 @@
 from __future__ import annotations
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Union, Dict
 if TYPE_CHECKING:
     from src.main import BoardgameFilterParams, PlayFilterParams
 
+import hashlib
+
 from src.classes import statistic_classes
 from src.modules import data_connection
 from datetime import date, timedelta, datetime
 from sqlmodel import Session
 
+cached_statistics = {}
+
+def clear_cache():
+    global cached_statistics
+    cached_statistics = {}
+
+def get_from_cache(argument_dict: dict):
+
+    #Session is random for each request
+    if 'session' in argument_dict:
+        del argument_dict['session']
+
+    md5hash = hashlib.md5(str(argument_dict).encode('utf-8')).hexdigest()
+    
+    if md5hash in cached_statistics:
+        return md5hash, cached_statistics[md5hash]
+    else:
+        return md5hash, None    
+
 def get_total_owned_games(session: Session, filtering_query: BoardgameFilterParams = None) -> statistic_classes.NumberStatistic:
+
+    statistic_name = 'Amount of games in owned collection'
+
+    md5hash, cached_statistic = get_from_cache({**locals()})
+
+    if cached_statistic != None:
+        return cached_statistic
+    
+
     owned_collection = data_connection.get_user_owned_collection(session)
 
     if filtering_query != None:
@@ -17,16 +47,27 @@ def get_total_owned_games(session: Session, filtering_query: BoardgameFilterPara
     total_owned_games = len(owned_collection)
 
     statistic_dict = {
-        "name":"Amount of games in owned collection",
+        "name":statistic_name,
         "result":total_owned_games
     }
 
     statistic_to_return = statistic_classes.NumberStatistic.model_validate(statistic_dict)
 
+    cached_statistics[md5hash] = statistic_to_return
+
     return statistic_to_return
 
 
 def get_total_owned_collection_cost(session: Session, filtering_query: BoardgameFilterParams = None) -> statistic_classes.NumberStatistic:
+
+    statistic_name = 'Total cost of the owned collection'
+
+    md5hash, cached_statistic = get_from_cache({**locals()})
+
+    if cached_statistic != None:
+        return cached_statistic
+    
+
     owned_collection = data_connection.get_user_owned_collection(session)
 
     if filtering_query != None:
@@ -36,16 +77,27 @@ def get_total_owned_collection_cost(session: Session, filtering_query: Boardgame
     total_cost = sum([boardgame.owned_info.price_paid for boardgame in owned_collection])
 
     statistic_dict = {
-        "name":"Total cost of the owned collection",
+        "name":statistic_name,
         "result":total_cost
     }
 
     statistic_to_return = statistic_classes.NumberStatistic.model_validate(statistic_dict)
 
+    cached_statistics[md5hash] = statistic_to_return
+
     return statistic_to_return
 
 
 def get_amount_of_games_over_time(session: Session, filtering_query: BoardgameFilterParams = None, day_step: int = 1) -> statistic_classes.TimeLineStatistic:
+
+    statistic_name = 'Amount of games in owned collection over time'
+
+    md5hash, cached_statistic = get_from_cache({**locals()})
+
+    if cached_statistic != None:
+        return cached_statistic
+    
+
     def daterange(start_date: date, end_date: date, day_step):
         days = int((end_date - start_date).days)
         for n in range(0, days, day_step):
@@ -65,16 +117,27 @@ def get_amount_of_games_over_time(session: Session, filtering_query: BoardgameFi
         timeline_dict[current_date] = len(games_in_collection_at_date)
     
     statistic_dict = {
-        "name":"Amount of games in owned collection over time",
+        "name":statistic_name,
         "result":timeline_dict
     }
 
     statistic_to_return = statistic_classes.TimeLineStatistic.model_validate(statistic_dict)
 
+    cached_statistics[md5hash] = statistic_to_return
+
     return statistic_to_return
 
 
 def get_amount_of_games_played_per_year(session: Session, filtering_query: PlayFilterParams = None) -> statistic_classes.TimeLineStatistic:
+
+    statistic_name = 'Amount of games played per year'
+
+    md5hash, cached_statistic = get_from_cache({**locals()})
+
+    if cached_statistic != None:
+        return cached_statistic
+    
+
     all_plays = data_connection.get_plays(session)
 
     all_plays.sort(key= lambda x: x.play_date)
@@ -99,15 +162,26 @@ def get_amount_of_games_played_per_year(session: Session, filtering_query: PlayF
 
 
     statistic_dict = {
-        "name":"Amount of games played per year",
+        "name":statistic_name,
         "result":years_plays_dict
     }
 
     statistic_to_return = statistic_classes.TimeLineStatistic.model_validate(statistic_dict)
 
+    cached_statistics[md5hash] = statistic_to_return
+
     return statistic_to_return
 
 def get_most_expensive_games(session: Session, filtering_query: BoardgameFilterParams = None, top_amount: int = 10) -> statistic_classes.GamesStatistic:
+
+    statistic_name = 'Most expensive games'
+
+    md5hash, cached_statistic = get_from_cache({**locals()})
+
+    if cached_statistic != None:
+        return cached_statistic
+    
+
     most_expensive_games = data_connection.get_user_owned_collection(session)
 
     most_expensive_games = filtering_query.do_filtering(most_expensive_games)
@@ -117,16 +191,27 @@ def get_most_expensive_games(session: Session, filtering_query: BoardgameFilterP
     most_expensive_games = most_expensive_games[0:top_amount]
 
     statistic_dict = {
-        "name":"Most expensive games",
+        "name":statistic_name,
         "result":most_expensive_games
     }
 
     statistic_to_return = statistic_classes.GamesStatistic.model_validate(statistic_dict)
 
+    cached_statistics[md5hash] = statistic_to_return
+
     return statistic_to_return
 
 
 def get_shelf_of_shame(session: Session, filtering_query: BoardgameFilterParams = None) -> statistic_classes.GamesStatistic:
+
+    statistic_name = "Shelf of Shame"
+
+    md5hash, cached_statistic = get_from_cache({**locals()})
+
+    if cached_statistic != None:
+        return cached_statistic
+    
+
     boardgames_in_collection = data_connection.get_user_collection(session)
 
     owned_boardgames = data_connection.get_user_owned_collection(session)
@@ -145,24 +230,33 @@ def get_shelf_of_shame(session: Session, filtering_query: BoardgameFilterParams
     owned_boardgames_no_plays.sort(key=lambda x: x.name)
 
     statistic_dict = {
-        "name":"Shelf of Shame",
+        "name":statistic_name,
         "result":owned_boardgames_no_plays
     }
 
     statistic_to_return = statistic_classes.GamesStatistic.model_validate(statistic_dict)
 
+    cached_statistics[md5hash] = statistic_to_return
+
     return statistic_to_return
 
 
 def get_winrate(session: Session, player_name: str | None = None):
 
+    statistic_name = 'Player winrate'
+
+    md5hash, cached_statistic = get_from_cache({**locals()})
+
+    if cached_statistic != None:
+        return cached_statistic
+
     if player_name == None:
         players_to_calculate = data_connection.get_all_players(session)
     else:
         players_to_calculate = [data_connection.get_player(player_name.title(), session)]
     
     statistic_dict = {
-        'name': 'Player winrate',
+        'name': statistic_name,
         'result': {}
     }
 
@@ -177,43 +271,73 @@ def get_winrate(session: Session, player_name: str | None = None):
 
     statistic_to_return = statistic_classes.PlayerStatistic.model_validate(statistic_dict)
 
+    cached_statistics[md5hash] = statistic_to_return
+
     return statistic_to_return
 
         
-def get_winrate_over_time(session: Session, player_name: str, day_step = 1):
+def get_winrate_over_time(session: Session, player_name: str | None = None, day_step = 1) -> Dict[str,statistic_classes.TimeLineStatistic]:
 
+    statistic_name = 'Player winrate over time'
+
+    md5hash, cached_statistic = get_from_cache({**locals()})
+
+    if cached_statistic != None:
+        return cached_statistic
+    
     def daterange(start_date: date, end_date: date, day_step):
         days = int((end_date - start_date).days)
         for n in range(0, days, day_step):
             yield start_date + timedelta(n)
 
+    if player_name == None:
+        wanted_players = data_connection.get_all_players(session)
+    else:
+        wanted_players = [data_connection.get_player(player_name, session)]
 
-    wanted_player = data_connection.get_player(player_name, session)
+    dict_to_return = {}
-    all_playplayers = [playplayer for playplayer in wanted_player.playplayers]
-    all_playplayers.sort(key=lambda x: x.play.play_date)
 
-    start_date = all_playplayers[0].play.play_date
+    start_date = datetime.today().date()
 
-    timeline_dict = {}
+    for wanted_player in wanted_players:
 
-    for current_date in daterange(start_date, date.today(), day_step):
+        all_playplayers = [playplayer for playplayer in wanted_player.playplayers]
-        playplayers_at_date = list(filter(lambda playplayer: playplayer.play.play_date <= current_date, all_playplayers))
+        all_playplayers.sort(key=lambda x: x.play.play_date)
-        total_games_played = len(playplayers_at_date)
+
-        total_games_won = 0
+        if start_date > all_playplayers[0].play.play_date:
-        for playplayer in playplayers_at_date:
+            start_date = all_playplayers[0].play.play_date
-            if playplayer.has_won:
+
-                total_games_won += 1
+    for wanted_player in wanted_players:
-        
+        all_playplayers = [playplayer for playplayer in wanted_player.playplayers]
-        timeline_dict[current_date] = total_games_won / total_games_played
+        all_playplayers.sort(key=lambda x: x.play.play_date)
+
+        timeline_dict = {}
+
+        for current_date in daterange(start_date, date.today(), day_step):
+            playplayers_at_date = list(filter(lambda playplayer: playplayer.play.play_date <= current_date, all_playplayers))
+            total_games_played = len(playplayers_at_date)
+            total_games_won = 0
+            for playplayer in playplayers_at_date:
+                if playplayer.has_won:
+                    total_games_won += 1
+            
+            if total_games_played != 0:
+                timeline_dict[current_date] = total_games_won / total_games_played
+            else:
+                timeline_dict[current_date] = 0
 
 
-    statistic_dict = {
+        statistic_dict = {
-        'name': 'Player winrate over time',
+            'name': statistic_name,
-        'result': timeline_dict
+            'result': timeline_dict
-    }
+        }
 
-    statistic_to_return = statistic_classes.TimeLineStatistic.model_validate(statistic_dict)
+        statistic_to_return = statistic_classes.TimeLineStatistic.model_validate(statistic_dict)
 
-    return statistic_to_return
+        dict_to_return[wanted_player.name] = statistic_to_return
+
+    cached_statistics[md5hash] = dict_to_return
+
+    return dict_to_return