Merge pull request #467 from softeerbootcamp-2nd/refactor/cache-enhance

Refactor/cache enhance

backend-recommend/recommend.py

@@ -4,6 +4,7 @@
 from mlxtend.frequent_patterns import association_rules
 import pymysql
 import time
+import redisConnection
 import os
 from dotenv import load_dotenv
 from flask import jsonify
@@ -13,7 +14,7 @@
 def mysql_create_session():
     conn = pymysql.connect(host=os.getenv('host'), user=os.getenv('user'), password=os.getenv('password'), db=os.getenv('db'))
     cur = conn.cursor()
-    return conn, cur
+    return conn, cur    
 
 def recByApriori(body):
     start = time.time()
@@ -32,26 +33,32 @@ def recByApriori(body):
     input = set(input)
     dataset = []
 
-    conn, cur = mysql_create_session()
-    try:
-        cur.execute('SELECT hm.history_id, sh.sold_count, sh.sold_options_id FROM SalesHistory sh INNER JOIN HistoryModelMapper hm ON sh.history_id = hm.history_id WHERE sh.car_id = %s AND hm.model_id IN (%s, %s, %s) GROUP BY hm.history_id HAVING COUNT(DISTINCT hm.model_id) = 3;', (carId, powerTrainId, bodyTypeId, operationId))
-        dbRow = cur.fetchall()
-    finally:
-        conn.close()
-
-    for j in range(len(dbRow)):
-        oneRow = dbRow[j][2]
-        if(oneRow == ''):
-            continue
-        options = oneRow.split(",")
-        for i in range(int(dbRow[j][1])):
-            dataset.append(options)
+    df = redisConnection.redis_getData()
+    if df is None:
+        conn, cur = mysql_create_session()
+        try:
+            cur.execute('SELECT hm.history_id, sh.sold_count, sh.sold_options_id FROM SalesHistory sh INNER JOIN HistoryModelMapper hm ON sh.history_id = hm.history_id WHERE sh.car_id = %s AND hm.model_id IN (%s, %s, %s) GROUP BY hm.history_id HAVING COUNT(DISTINCT hm.model_id) = 3;', (carId, powerTrainId, bodyTypeId, operationId))
+            dbRow = cur.fetchall()
+        finally:
+            conn.close()
+
+        for j in range(len(dbRow)):
+            oneRow = dbRow[j][2]
+            if(oneRow == ''):
+                continue
+            options = oneRow.split(",")
+            for i in range(int(dbRow[j][1])):
+                dataset.append(options)
+
+        start = time.time()
+        te = TransactionEncoder()
+        te_ary = te.fit(dataset).transform(dataset)
+        df = pd.DataFrame(te_ary, columns=te.columns_)
+        redisConnection.redis_setData(df)
 
-    start = time.time()
-    te = TransactionEncoder()
-    te_ary = te.fit(dataset).transform(dataset)
-    df = pd.DataFrame(te_ary, columns=te.columns_)
     df = df.iloc[:100000]
+
+
     frequent_itemsets = apriori(df, min_support=0.01, use_colnames=True)
 
     result_itemsets = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.1) 

backend-recommend/redisConnection.py

@@ -0,0 +1,22 @@
+import io
+import redis
+import pandas as pd
+import os
+
+redis_pool = redis.ConnectionPool(host='3.37.253.113', port=6379, password=1234, db=0)
+
+def redis_getData():
+    with redis.StrictRedis(connection_pool=redis_pool) as conn:
+        bytes_object = conn.get("rec_dataframe")
+        if bytes_object is None:
+            return None
+        buffer = io.BytesIO(bytes_object)
+        return pd.read_pickle(buffer) 
+
+def redis_setData(df):
+    with redis.StrictRedis(connection_pool=redis_pool) as conn:
+        buffer = io.BytesIO()
+        df.to_pickle(buffer)
+        buffer.seek(0)
+        bytes_object = buffer.read()
+        conn.set('rec_dataframe', bytes_object, 60*60*24*7)