es查询出结果分组聚合，写入文件

utils/base_dataclean_pg.py

@@ -1,5 +1,6 @@
 #encoding=utf-8
 import json
+import codecs,os
 import time,datetime
 import traceback
 from datetime import datetime, timedelta
@@ -192,7 +193,7 @@ def get_menu_group_data(index,startTime,endTime):
                 "count": bucket['doc_count'],
                 "jobnum": bucket['key']['trojan_type'] ,
                 "ip":bucket['key']['sip'],
-                "menu_name": bucket['key']['worm_family'],
+                "menu": bucket['key']['worm_family'],
             }
             datas.append(data)
             after_key = bucket["key"]
@@ -204,17 +205,145 @@ def get_menu_group_data(index,startTime,endTime):
 
 def clean_data(read_index,start,end):
     data_ip = get_ip_group_data(read_index,start,end)
-    print "data_ip:"+str(len(data_ip))
+    # print "data_ip:"+str(len(data_ip))
     data_account = get_account_group_data(read_index,start,end)
-    print "data_ip:"+str(len(data_account))
+    # print "data_account:"+str(len(data_account))
     data_interface = get_interface_group_data(read_index,start,end)
-    print "data_ip:"+str(len(data_interface))
+    # print "data_interface:"+str(len(data_interface))
     data_menu = get_menu_group_data(read_index,start,end)
-    print "data_ip:"+str(len(data_menu))
+    # print "data_menu:"+str(len(data_menu))
     res_data = data_ip+data_account+data_interface+data_menu
     
     #todo 读取上一次5分钟的文件，与这5分钟的文件合并
     #合并完成后 写文件
+    group_and_write_to_file(data_ip, data_account, data_interface, data_menu, start)
+    
+    
+def group_and_write_to_file(data_ip, data_account, data_interface, data_menu, start):
+    ipGroupStr = "ip,jobnum"
+    ipGroup = group_and_sum(data_ip, ipGroupStr)
+    accountGroupStr = "account,jobnum"
+    accountGroup = group_and_sum(data_account, accountGroupStr)
+    interfaceGroupStr = "interface,ip,account,jobnum"
+    interfaceGroup = group_and_sum(data_interface, interfaceGroupStr)
+    menuGroupStr = "menu,ip,account,jobnum"
+    menuGroup = group_and_sum(data_menu, menuGroupStr)
+    
+    
+    data = {}
+    data["ip"] = ipGroup
+    data["account"] = accountGroup
+    data["interface"] = interfaceGroup
+    data["menu"] = menuGroup
+    
+    # 获取当前工作目录
+    current_dir = os.getcwd()
+    time_struct = time.gmtime(long(start) / 1000.0)  # UTC时间
+    date_time = time.strftime('%Y-%m-%d', time_struct)
+
+    file_path = os.path.join(current_dir, 'files/' + date_time + '.json')
+    
+    all_data = [data]
+    
+    if os.path.exists(file_path):
+        # 打开文件并读取内容
+        with codecs.open(file_path, 'r', encoding='utf-8') as file:
+            content = file.read()
+            old_json_data = json.loads(content)
+            all_data = [data, old_json_data]
+        
+    merged_data = merge_data(all_data)
+    
+    # 使用codecs模块以UTF-8编码打开文件
+    f = codecs.open(file_path, 'w', encoding='utf-8')
+
+    json_data = json.dumps(merged_data)
+    
+    # 写入Unicode字符串
+    f.write(json_data)
+
+    # 关闭文件
+    f.close()
+    
+    
+    
+def group_and_sum(data, by_fields="ip,jobnum"):
+    # 将by_fields转换为列表
+    by_fields_list = by_fields.split(',')
+
+    aggregated_data = {}
+
+    # 遍历数据，进行聚合
+    for record in data:
+        # 确保所有字段都存在于记录中
+        if all(field in record for field in by_fields_list):
+            # 构建聚合键
+            key = tuple(record[field] for field in by_fields_list)
+            
+            # 如果键不存在，则初始化计数器
+            if key not in aggregated_data:
+                aggregated_data[key] = 0
+            
+            # 累加count值
+            aggregated_data[key] += record['count']
+
+    # 将结果转换为期望的输出格式
+    output = [
+        dict({field: value for (field, value) in zip(by_fields_list, key)}, count=count)
+        for key, count in aggregated_data.items()
+    ]
+    
+    return output
+    
+def merge_data(datasets):
+    #  初始化一个空的字典来保存合并后的数据
+    merged_data = {
+        "ip": [],
+        "account": [],
+        "interface": [],
+        "menu": []
+    }
+
+    # 遍历所有数据集
+    for dataset in datasets:
+        # 遍历数据集中的每个类别
+        for category, items in dataset.items():
+            # 将当前数据集的项目添加到合并数据的相应类别中
+            merged_data[category].extend(items)
+
+    # 定义一个字典来存储聚合后的数据
+    aggregated_data = {
+        "ip": [],
+        "account": [],
+        "interface": [],
+        "menu": []
+    }
+
+    # 遍历所有类别
+    for category in aggregated_data:
+        # 创建一个字典来存储每个类别的聚合数据
+        category_data = {}
+
+        # 如果当前类别存在于merged_data中
+        if category in merged_data:
+            for item in merged_data[category]:
+                # 确定非计数字段
+                keys_to_use = [k for k in item if k != 'count']
+                # 使用元组作为键，包含所有非计数字段
+                key_tuple = tuple(item[k] for k in keys_to_use)
+
+                if key_tuple not in category_data:
+                    category_data[key_tuple] = item['count']
+                else:
+                    category_data[key_tuple] += item['count']
+
+        # 将聚合后的数据转换回原始格式
+        aggregated_data[category] = [
+            dict(zip(keys_to_use, key_tuple) + [('count', count)])
+            for key_tuple, count in category_data.items()
+        ]
+
+    return aggregated_data
     
 #入口
 def entry(start,end):