修改测试文件

tensnsorflow/multi.py

@@ -24,25 +24,48 @@ def feature_engineer():
         .set("spark.tispark.pd.addresses", "172.16.40.158:2379").set("spark.io.compression.codec", "lzf")
 
     spark = SparkSession.builder.config(conf=sparkConf).enableHiveSupport().getOrCreate()
-    ti = pti.TiContext(spark)
-    ti.tidbMapDatabase("jerry_test")
-    spark.sparkContext.setLogLevel("WARN")
-    sql = "select e.y,e.z,e.stat_date,e.ucity_id,feat.level2_ids,e.ccity_name,u.device_type,u.manufacturer," \
-          "u.channel,c.top,e.device_id,cut.time,dl.app_list,e.diary_service_id,feat.level3_ids," \
-          "k.treatment_method,k.price_min,k.price_max,k.treatment_time,k.maintain_time,k.recover_time " \
-          "from esmm_train_data e left join user_feature u on e.device_id = u.device_id " \
-          "left join cid_type_top c on e.device_id = c.device_id " \
-          "left join cid_time_cut cut on e.cid_id = cut.cid " \
-          "left join device_app_list dl on e.device_id = dl.device_id " \
-          "left join diary_feat feat on e.cid_id = feat.diary_id " \
-          "left join train_Knowledge_network_data k on feat.level2 = k.level2_id " \
-          "where e.stat_date >= '{}'".format(start)
+    # ti = pti.TiContext(spark)
+    # ti.tidbMapDatabase("jerry_test")
+    # spark.sparkContext.setLogLevel("WARN")
+    # sql = "select e.y,e.z,e.stat_date,e.ucity_id,feat.level2_ids,e.ccity_name,u.device_type,u.manufacturer," \
+    #       "u.channel,c.top,e.device_id,cut.time,dl.app_list,e.diary_service_id,feat.level3_ids," \
+    #       "k.treatment_method,k.price_min,k.price_max,k.treatment_time,k.maintain_time,k.recover_time " \
+    #       "from esmm_train_data e left join user_feature u on e.device_id = u.device_id " \
+    #       "left join cid_type_top c on e.device_id = c.device_id " \
+    #       "left join cid_time_cut cut on e.cid_id = cut.cid " \
+    #       "left join device_app_list dl on e.device_id = dl.device_id " \
+    #       "left join diary_feat feat on e.cid_id = feat.diary_id " \
+    #       "left join train_Knowledge_network_data k on feat.level2 = k.level2_id " \
+    #       "where e.stat_date >= '{}'".format(start)
+    #
+    # df = spark.sql(sql)
+    # # print(df.count())
+    # df.show(6)
+
+    url = "jdbc:mysql://172.16.30.143:3306/zhengxing"
+    jdbcDF = spark.read.format("jdbc").option("driver", "com.mysql.jdbc.Driver").option("url", url) \
+        .option("dbtable", "api_service").option("user", 'work').option("password", 'BJQaT9VzDcuPBqkd').load()
+    jdbcDF.createOrReplaceTempView("api_service")
+    jdbc = spark.read.format("jdbc").option("driver", "com.mysql.jdbc.Driver").option("url", url) \
+        .option("dbtable", "api_doctor").option("user", 'work').option("password", 'BJQaT9VzDcuPBqkd').load()
+    jdbc.createOrReplaceTempView("api_doctor")
 
+    sql = "select s.id,d.hospital_id from api_service s left join api_doctor d on s.doctor_id = d.id"
+          # "where s.id in {}".format(service_id)
     df = spark.sql(sql)
     df.show(6)
     print(df.count())
 
 
+    # db = pymysql.connect(host='172.16.30.143', port=3306, user='work',
+    #                      passwd='BJQaT9VzDcuPBqkd', db='zhengxing')
+
+
+    # df = df.drop_duplicates(["ucity_id", "level2_ids", "ccity_name", "device_type", "manufacturer",
+    #                           "channel", "top", "time", "stat_date", "app_list", "hospital_id", "level3_ids"])
+
+
+
 
     # df = df.rename(columns={0: "y", 1: "z", 2: "stat_date", 3: "ucity_id", 4: "clevel2_id", 5: "ccity_name",
     #                         6: "device_type", 7: "manufacturer", 8: "channel", 9: "top", 10: "device_id",