Attempted to use DBConnection Pool to reduce memory consumption, but it does not work well with the multi-processing architecture of Python.

src/DDBBase.py

@@ -5,7 +5,7 @@ import dolphindb as ddb
 class DDBBase(object):
 
     ddb_config = {
-        'host' : '192.168.1.167',
+        'host' : '192.168.1.7',
         'username' : 'admin',
         'password' : '123456',
     }

src/loader/DDBHFTLoader.py

@@ -94,8 +94,10 @@ class DDBHFTLoader(DDBLoader):
 
     num_workers = 8
     default_table_capacity = 10000
+    # TODO: 这里需要饮用自身文件的绝对路径，然后再寻找其父目录
     ddb_dump_journal_fname = '../assets/ddb_dump_journal.csv'
 
+    ddb_sess_pool = ddb.DBConnectionPool(DDBLoader.ddb_config['host'], 8848, num_workers)
 
     def init_ddb_database(self, df_calendar):
         """
@@ -432,8 +434,8 @@ class DDBHFTLoader(DDBLoader):
         self.dump_journal_writer.flush()
 
 
-    @staticmethod
-    def make_stock_daily_df(blob, type_name, stock_id):
+    @classmethod
+    def make_stock_daily_df(cls, blob, type_name, stock_id):
         """
         用于做多进程录入ddb的函数
         """
@@ -463,11 +465,17 @@ class DDBHFTLoader(DDBLoader):
 
         #print(f"Did create ddb table for dataframe of shape {df.shape}")
         # self.make_table_skeleton(type_name, df.shape[0])
+
+        del(blob)
+        del(dataArray)
+        del(data_dict_list)
+        del(array_type_list)
+
         return df
 
 
-    @staticmethod
-    def dump_stock_daily_to_ddb(row, type_name, stock_id):
+    @classmethod
+    def dump_stock_daily_to_ddb(cls, row, type_name, stock_id):
         """
         用于做多进程录入ddb的函数
         """
@@ -479,15 +487,24 @@ class DDBHFTLoader(DDBLoader):
 
         ddb_sess.upload({df_table_name : df})
         # 因为在做Tick数据的时候，偶然发生'CHUNK[xxx] does not exist.'，所以在这里使用`append!`函数代换一下试试
-        #ddb_sess.run("append!(loadTable('{dbPath}', `{partitioned_table_name}), {df_table_name})".format(
-        ddb_sess.run("tableInsert(loadTable('{dbPath}', `{partitioned_table_name}), {df_table_name})".format(
+        ddb_sess.run("append!(loadTable('{dbPath}', `{partitioned_table_name}), {df_table_name})".format(
+        #ddb_sess.run("tableInsert(loadTable('{dbPath}', `{partitioned_table_name}), {df_table_name})".format(
             dbPath = DDBHFTLoader.ddb_path,
             partitioned_table_name = type_name + DDBHFTLoader.ddb_partition_table_suffix,
             df_table_name = df_table_name
         ))
 
+        #cls.ddb_sess_pool.runTaskAsyn("""
+        #    tableInsert{{ loadTable("{dbPath}", "{partitioned_table_name}") }}
+        #""".format(
+        #    dbPath = DDBHFTLoader.ddb_path,
+        #    partitioned_table_name = type_name + DDBHFTLoader.ddb_partition_table_suffix,
+        #), df).result()
+
         # 由于不是复用`DDBHFTLoader`对象内部的Session，因此如果不手动关闭就会造成内存逐渐泄漏
-        ddb_sess.close()
         del(df)
+        ddb_sess.undefAll()
+        ddb_sess.close()
+        del(ddb_sess)
 
 

src/run.py

@@ -46,8 +46,8 @@ def main():
 	# TODO: 
 	# 可以使用`Fire`库，对函数调用再做一次封装，就可以避免每次运行不同参数时候需要修改内部多处的代码。
 
-    # create_hft_data()
-    create_pit_data()
+    create_hft_data()
+    #create_pit_data()
 
 
 if __name__ == '__main__':