1. `DDBDailyFactor.py` => `DDBFactor.py`

2. 在`DDBLoader.py`中增加了`DDBPITLoader`类，专门用于向DolphinDB里面添加PIT数据
3. 其他一些优化

DDBFactor.py

@@ -10,6 +10,9 @@ from DDBLoader import DDBLoader
 
 
 def load_ddb_table(hft_tbl_name):
+    """
+    这是一个用来简化载入分区表过程的语法糖，但似乎需要预先调用这个函数的场景并不多，简化效果不是很明显。
+    """
     def decorator(func):
         @functools.wraps(func)
         def wrapper(self, *args, **kwargs):

DDBLoader.py

@@ -2,6 +2,8 @@ import importlib
 import gzip
 import pickle
 import functools
+import abc
+import warnings
 
 from pprint import pprint
 from pathlib import Path
@@ -11,6 +13,8 @@ from multiprocessing import Pool
 
 import numpy as np
 import pandas as pd
+from pandas.core.common import SettingWithCopyWarning
+warnings.simplefilter(action="ignore", category=SettingWithCopyWarning)
 
 import dolphindb as ddb
 import dolphindb.settings as keys
@@ -20,58 +24,64 @@ import sqlalchemy as sa
 import ProtoBuffEntitys
 
 
-def make_stock_daily_df(blob, type_name, stock_id):
+
+class DDBLoader(object):
     """
-    用于做多进程录入ddb的函数
+    - 放了几个公用的配置字段，包括：
+        1. SQL-Server的链接参数
+        2. DolphinDB的链接参数
+
+    - 放了几个@abstractmethod在里面，不过如果不需要使用多态特性，那应该用处不大：
+        1. create_ddb_database
+        2. create_ddb_partition_table
     """
-    blob = gzip.decompress(blob)
-    dataArray = eval(f"ProtoBuffEntitys.{type_name}Message_pb2.{type_name}Array()")
-    dataArray.ParseFromString(blob)
 
-    data_dict_list = [
+    mssql_config = {
-        {field.name : val for field, val in entry.ListFields()}
+        'host' : '192.168.1.7',
-        for entry in dataArray.dataArray
+        'username' : 'sa',
-    ]
+        'password' : 'passw0rd!'
+    }
 
-    array_type_list = [
+    ddb_config = {
-        field.name 
+        'host' : '192.168.1.167',
-        for field, val in dataArray.dataArray[0].ListFields()
+        'username' : 'admin',
-        if isinstance(field.default_value, list)
+        'password' : '123456'
-    ]
+    }
-    #pprint(array_type_list)
 
-    df = pd.DataFrame(data_dict_list)
-    #df['code'] = make_symbol(df['code'])
-    df['code'] = stock_id
-    df['m_nDate'] = make_date(df['m_nDate'])
-    df['m_nTime'] = df['m_nDate'] + make_time(df['m_nTime'])
-    for field_name in array_type_list:
-        df[field_name] = make_nparray(df[field_name])
 
-    #print(f"Did create ddb table for dataframe of shape {df.shape}")
+    def __init__(self):
-    # self.make_table_skeleton(type_name, df.shape[0])
+        self.mssql_engine = sa.create_engine(
-    return df
+            "mssql+pyodbc://{username}:{password}@{host}/master?driver=ODBC+Driver+18+for+SQL+Server".format(**self.mssql_config),
+            connect_args = {
+                "TrustServerCertificate": "yes"
+            }, echo=False
+        )
 
+        self.ddb_sess = ddb.session(self.ddb_config['host'], 8848)
+        self.ddb_sess.login(self.ddb_config['username'], self.ddb_config['password'])
 
-def dump_stock_daily_to_ddb(row, type_name, stock_id):
+
+    @abc.abstractmethod
+    def create_ddb_database(self, *args, **kwargs):
         """
-    用于做多进程录入ddb的函数
+        创建database函数，需要被子类具体实现。
         """
-    df_table_name = type_name
+        return
-    df = make_stock_daily_df(row[2], type_name, stock_id)
 
-    ddb_sess = ddb.session(DDBLoader.ddb_config['host'], 8848)
+    @abc.abstractmethod
-    ddb_sess.login(DDBLoader.ddb_config['username'], DDBLoader.ddb_config['password'])
+    def create_ddb_partition_table(self, *args, **kwargs):
+        """
+        创建分区表函数，需要被子类具体实现。
+        """
+        return 
 
-    ddb_sess.upload({df_table_name : df})
-    ddb_sess.run("tableInsert(loadTable('{dbPath}', `{partitioned_table_name}), {df_table_name})".format(
-        dbPath = DDBLoader.ddb_path,
-        partitioned_table_name = type_name + DDBLoader.ddb_partition_table_suffix,
-        df_table_name = df_table_name
-    ))
 
+    @staticmethod
+    def tscode_to_windcode(series):
+        return series.apply(lambda x : x[2:] + '.' + x[:2])
 
 
+    @staticmethod
     def make_symbol(series):
         return series.astype('int32').astype('str')\
             .apply(str.zfill, args=(6,))\
@@ -80,15 +90,20 @@ def make_symbol(series):
                 else code + '.SZ')
 
 
+    @staticmethod
     def make_date(series):
+        # 特别是对于分红表，如果某些关键日期还未公布，则会填充0，导致日期解析失败
+        series.loc[series == 0] = np.nan
         return pd.to_datetime(
             series.astype(str), format='%Y%m%d')
 
 
+    @staticmethod
     def make_nparray(series):
         return series.apply(lambda x : np.array(x))
 
 
+    @staticmethod
     def make_time(series):
         s_hr = series // 10000000 * 3600000
         s_min = series % 10000000 // 100000 * 60000
@@ -97,7 +112,377 @@ def make_time(series):
         return pd.to_timedelta(s_hr + s_min + s_sec + s_ms, unit='ms')
 
 
-class DDBLoader(object):
+
+class DDBPITLoader(DDBLoader):
+
+    ddb_path = "dfs://pit_stock_ts"
+    ddb_dbname = "ddb_pit_stock_ts"
+
+    num_code_partition = 50
+
+    table_name_mapping = {
+        #'CBS_AFTER_ADJ' : 'bs_common_adj',
+        #'CBS_BEFORE_ADJ' : 'bs_common_ori',
+        #'CCFS_AFTER_ADJ' : 'cfs_common_adj',
+        #'CCFS_BEFORE_ADJ' : 'cfs_common_ori',
+        #'CIS_AFTER_ADJ' : 'is_common_adj',
+        #'CIS_BEFORE_ADJ' : 'is_common_ori',
+        'DIV_WIND' : 'divident',
+        #'EP_WIND' : 'earnings_preannouncement',
+        #'PEE_WIND' : 'preliminary_earnings_estimate'
+    }
+
+    meta_col_config = {
+        'WIND_CODE' : ('code', 'SYMBOL'),
+        # mssql表中不需要记录的meta字段，在这里直接设置为None
+        'IntCode' : None,
+        'ACTUAL_ANN_DT' : None,
+        'ReportPeriod' : ('report_period', 'DATE'),
+        'AppearInPeriod' : ('appear_in_period', 'DATE'),
+        'AppearAtDate' : ('appear_at_date', 'DATE')
+    }
+
+    date_col_set = {
+        'report_period',
+        'appear_in_period',
+        'appear_at_date',
+        'ReportPeriod',
+        'AppearInPeriod',
+        'AppearAtDate',
+        'EQY_RECORD_DT',
+        'EX_DT',
+        'DVD_PAYOUT_DT',
+        'S_DIV_PRELANDATE',
+        'S_DIV_SMTGDATE',
+        'DVD_ANN_DT',
+        'S_DIV_PREANNDT'
+    }
+
+    ddb_type_mapping = {
+        'float' : 'DOUBLE',
+        'int' : 'INT',
+        'text' : 'STRING',
+        'varchar' : 'STRING',
+        'str' : 'STRING'
+    }
+
+    # 基本面数据库现在存放在91服务器之上
+    mssql_config = {
+        'host' : '192.168.1.91',
+        'username' : 'sa',
+        'password' : 'xn.123',
+        'dbname' : 'tr_statement'
+    }
+
+
+    def __init__(self):
+        super().__init__()
+        # 重新设定mssql_engine对象，此时我们需要使用基本面数据库
+        self.mssql_engine = sa.create_engine(
+            "mssql+pyodbc://{username}:{password}@{host}/{dbname}?driver=ODBC+Driver+18+for+SQL+Server".format(**self.mssql_config),
+            connect_args = {
+                "TrustServerCertificate": "yes"
+            }, echo=False
+        )
+
+
+    def create_ddb_database(self):
+        self.ddb_sess.run("""
+            {dbName} = database(
+                directory = '{dbPath}',
+                partitionType = HASH,
+                partitionScheme = [SYMBOL, {num_code_partition}],
+                engine = 'TSDB'
+            )
+        """.format(
+            dbName = self.ddb_dbname,
+            dbPath = self.ddb_path,
+            num_code_partition = self.num_code_partition
+        ))
+
+
+    def _make_col_config(self, mssql_table_name):
+        """
+        Return:
+            mssql_col_name_list, ddb_col_name_list, ddb_col_type_list 
+        """
+        with self.mssql_engine.connect() as conn:
+            col_sp_list = list(conn.execute(f"exec sp_columns {mssql_table_name}").fetchall())
+
+        mssql_col_name_list, ddb_col_name_list, ddb_col_type_list = \
+                [], [], []
+
+        for col_sp in col_sp_list:
+            _col_name = col_sp[3]
+            _col_type = col_sp[5]
+
+            # 对于meta字段，需要根据meta配置表来进行处理
+            if _col_name in self.meta_col_config:
+                # 跳过mssql表中 不需要记录的meta字段
+                if self.meta_col_config[_col_name] is None:
+                    continue
+                # 字段名和字段类型都要进行映射
+                mssql_col_name_list.append(_col_name)
+                ddb_col_name_list.append(self.meta_col_config[_col_name][0])
+                ddb_col_type_list.append(self.meta_col_config[_col_name][1])
+            # 对于非meta字段，仅需要检查是否是float类型，对于float类型设置类型为DOUBLE
+            else:
+                # 需要之后被转换成DATE的字段，一般在原表中为为INT类型
+                if _col_name in self.date_col_set:
+                    mssql_col_name_list.append(_col_name)
+                    ddb_col_name_list.append(_col_name)
+                    ddb_col_type_list.append('DATE')
+                # 按照对照表进行类型转换
+                elif _col_type in self.ddb_type_mapping:
+                    mssql_col_name_list.append(_col_name)
+                    ddb_col_name_list.append(_col_name)
+                    ddb_col_type_list.append(self.ddb_type_mapping[_col_type])
+                # 对照表中没有的字段类型，就不加入到字段列表中了
+                else:
+                    print(f"!**Unrecognized type '{_col_type}' for column {_col_name}, will skip.")
+
+        return mssql_col_name_list, ddb_col_name_list, ddb_col_type_list
+
+
+    def create_ddb_partition_table(self, mssql_table_name):
+        """创建分区表"""
+        memory_table_name = mssql_table_name
+        partition_table_name = self.table_name_mapping[mssql_table_name]
+
+        mssql_col_name_list, ddb_col_name_list, ddb_col_type_list = \
+                self._make_col_config(mssql_table_name)
+
+        # 根据是否
+        if 'appear_in_period' in ddb_col_name_list:
+            compress_methods = """{
+                'report_period' : 'delta', 
+                'appear_in_period' : 'delta', 
+                'appear_at_date' : 'delta'
+            }"""
+        else:
+            compress_methods = """{
+                'report_period' : 'delta',
+                'appear_at_date' : 'delta'
+            }"""
+
+        # 因为已经根据`appear_in_period`分列了调整前和调整后，因此不需要对它再进行排序了
+        sort_columns = "`code`report_period`appear_at_date"
+
+        # 1. 先创建内存表，内存表中设定好列名和列类型
+        # 2. 然后根据内存表创建分区表，设定分区列等信息
+        self.ddb_sess.run("""
+            {memory_table_name} = table(
+                {capacity}:0,
+                {column_name_list},
+                [{column_type_list}]
+            );
+
+            if (existsTable("{ddb_path}", "{partition_table_name}")) {{
+                dropTable({ddb_dbname}, "{partition_table_name}");
+            }}
+
+            {partition_table_name} = createPartitionedTable(
+                dbHandle = {ddb_dbname},
+                table = {memory_table_name},
+                tableName = "{partition_table_name}",
+                partitionColumns = 'code',
+                compressMethods = {compress_methods},
+                sortColumns = {sort_columns}
+            );
+        """.format(
+            ddb_dbname = self.ddb_dbname,
+            ddb_path = self.ddb_path,
+            memory_table_name = memory_table_name,
+            partition_table_name = partition_table_name,
+            capacity = 10,
+            column_name_list = '`' + '`'.join(ddb_col_name_list),
+            column_type_list = ','.join(ddb_col_type_list),
+            compress_methods = compress_methods.replace('\n', '').replace(' ', ''),
+            sort_columns = sort_columns
+        ))
+        print('-' * 80)
+        print(f"Did create parition table <{partition_table_name}>:")
+        pprint(self.ddb_sess.run(f"schema({partition_table_name});"))
+        return partition_table_name, mssql_col_name_list
+
+
+    def create_ddb_partition_tables(self):
+        for mssql_table_name in self.table_name_mapping:
+            self.create_ddb_partition_table(mssql_table_name)
+
+    
+    def _dump_pit_to_ddb(self, mssql_table_name):
+        print('Will work on table', mssql_table_name)
+        # 返回的`mssql_col_name_list`可以用来对SQL-Server获取的dataframe进行列过滤
+        partition_table_name, mssql_col_name_list = \
+                self.create_ddb_partition_table(mssql_table_name)
+        
+        with self.mssql_engine.connect() as conn:
+            stat = f"select distinct [WIND_CODE] from {mssql_table_name}"
+            stock_id_list = list(conn.execute(stat).fetchall())
+
+            with tqdm(stock_id_list) as pbar:
+                for (stock_id,) in pbar:
+                    pbar.set_description(f"Will work on {stock_id}") 
+                    #pbar.set_description(f"Will fetch all data of {stock_id} from SQL Server")
+                    stat = """
+                        select * from {mssql_table_name}
+                        where WIND_CODE='{stock_id}' and AppearAtDate>0
+                    """.format(
+                        mssql_table_name = mssql_table_name,
+                        stock_id = stock_id
+                    )
+                    row_list = list(conn.execute(stat).fetchall())
+                    num_rows = len(row_list)
+
+                    # 因为对AppearAtDate做了过滤，所以有可能得到一个空的数据集
+                    if num_rows == 0:
+                        print(f"Will skip {stock_id} due to empty result.")
+                        continue
+
+                    #pbar.set_description(f"Will work on dumping job on {stock_id} of len {num_rows}")
+                    # 这里需要对select语句获取的所有列进行一次过滤，以保证和partition table中的列一致
+                    df = pd.DataFrame(row_list)[mssql_col_name_list]
+                    # 需要把部分字段的int字段类型转换成DATE字段类型
+                    for df_col in df.columns:
+                        if df_col in self.date_col_set:
+                            df[df_col] = DDBLoader.make_date(df[df_col])
+                    # 因为在做数据库View的时候已经做过一轮转换了，所以这里就不需要再次转换了
+                    #df['WIND_CODE'] = DDBLoader.tscode_to_windcode(df['WIND_CODE'])
+
+                    self.ddb_sess.upload({mssql_table_name : df})
+                    self.ddb_sess.run(f"{partition_table_name}.tableInsert({mssql_table_name})")
+
+
+    def dump_pit_to_ddb(self):
+        for mssql_table_name in self.table_name_mapping:
+            self._dump_pit_to_ddb(mssql_table_name)
+
+
+class DDBDailyLoader(DDBLoader):
+
+    ddb_path = "dfs://daily_stock_ts"
+    ddb_dbname = "db_daily_stock_ts"
+
+    daily_kline_cols = [
+        'code', 'm_nDate', 
+        'open', 'high', 'low', 'close', 'vol',
+        'amount', 'cjbs', 'yclose', 
+        'PctChg', 'IsZt', 'IsDt', 'IsST', 'IsGoDelist',
+        'FloatShares', 'MarketValues',
+        'factor'
+    ]
+
+    daily_kline_col_types = [
+        'SYMBOL', 'DATE',
+        'DOUBLE', 'DOUBLE', 'DOUBLE', 'DOUBLE', 'DOUBLE', 
+        'DOUBLE', 'INT', 'DOUBLE',
+        'DOUBLE', 'INT', 'INT', 'INT', 'INT',
+        'DOUBLE', 'DOUBLE',
+        'DOUBLE'
+    ]
+
+
+    def create_ddb_database(self):
+        # TODO: daily数据库已经在DDBDailyFactor中被创建了
+        #       后续可以迁移过来，不过现在就暂时先不管了
+        pass
+
+
+    def load_ddb_database(self):
+        self.ddb_sess.run("""
+            {dbName} = database(directory='{dbPath}')
+        """.format(
+            dbName = self.ddb_dbname,
+            dbPath = self.ddb_path
+        ))
+        print('Did load database from', self.ddb_path)
+
+
+    def create_ddb_partition_table(self, memory_table_name, partition_table_name):
+        # TODO: 现在只做一个日频行情数据表，今后可能考虑把基本面数据也迁移过来
+
+        # 由于日频行情数据的表结构相对简单，所以直接把表结构写在这里代码里即可
+        # 搬迁数据的时候需要考虑按照逐个股票来搬迁，以免造成对内存的巨大压力
+        self.ddb_sess.run("""
+            // 确保删除原表
+            if (existsTable("{ddb_daily_path}", "{partition_table_name}")) {{
+                dropTable({ddb_daily_dbname}, "{partition_table_name}");
+            }}
+
+            // 然后根据内存表的结构，创建持久化的分区表
+            {partition_table_name} = {ddb_daily_dbname}.createPartitionedTable(
+                table = {memory_table_name},
+                tableName = "{partition_table_name}",
+                partitionColumns = `code,
+                sortColumns = `code`m_nDate,
+                compressMethods = {{m_nDate:"delta"}}
+            );
+        """.format(
+            ddb_daily_path = self.ddb_path,
+            ddb_daily_dbname = self.ddb_dbname,
+            memory_table_name = memory_table_name,
+            partition_table_name = partition_table_name,
+        ))
+    
+
+    def create_ddb_memory_table(self, memory_table_name, capacity):
+        self.ddb_sess.run("""
+            // 先创建一个空的内存表用来表征结构，如果无需插入数据，capacity可以设为10
+            {memory_table_name} = table({capacity}:0, {col_names}, [{col_types}]);
+        """.format(
+            memory_table_name = memory_table_name,
+            capacity = capacity,
+            col_names =  '`' + '`'.join(self.daily_kline_cols),
+            col_types = ', '.join(self.daily_kline_col_types)
+        ))
+
+
+    def dump_daily_kline_to_ddb(self):
+        # 先创建一个分区表，然后再逐个股票的数据插入
+        # 1. 需要额外控制在插入第一个股票数据的时候创建分区表比较麻烦
+        # 2. python程序中的dataframe直接上传到dolphindb内存表，不需要考虑内存表字段类型，分区表中设置好即可
+
+        memory_table_name = 'daily_kline_mem'
+        partition_table_name = 'daily_kline'
+
+        self.create_ddb_memory_table(memory_table_name, 10)
+        print('Did create ddb memory table.')
+        pprint(self.ddb_sess.run(f"schema({memory_table_name})"))
+        self.create_ddb_partition_table(memory_table_name, partition_table_name)
+        print('Did create ddb partition table.')
+        pprint(self.ddb_sess.run(f"schema({partition_table_name})"))
+
+        with self.mssql_engine.connect() as conn:
+            stat = "select distinct [StockID] from [StockDaily].dbo.[DailyKLine]"
+            stock_id_list = list(conn.execute(stat).fetchall())
+
+            with tqdm(stock_id_list) as pbar:
+                for (stock_id,) in pbar:
+                    pbar.set_description(f"Will work on {stock_id}") 
+                    #pbar.set_description(f"Will fetch all data of {stock_id} from SQL Server")
+                    stat = """
+                        select * from [StockDaily].dbo.[DailyKLine]
+                        where StockID='{stock_id}'
+                    """.format(
+                        stock_id = stock_id
+                    )
+                    row_list = list(conn.execute(stat).fetchall())
+                    num_rows = len(row_list)
+
+                    #pbar.set_description(f"Will work on dumping job on {stock_id} of len {num_rows}")
+                    df = pd.DataFrame(row_list)
+                    df['date'] = DDBLoader.make_date(df['date'])
+                    df['StockID'] = DDBLoader.tscode_to_windcode(df['StockID'])
+                    self.ddb_sess.upload({memory_table_name : df})
+                    #print('Did upload dataframe to ddb.')
+                    #pprint(self.ddb_sess.run(f"schema({memory_table_name})"))
+                    #break
+                    self.ddb_sess.run(f"{partition_table_name}.tableInsert({memory_table_name})")
+
+
+
+class DDBHFTLoader(DDBLoader):
     """
     0. 从sql-server中读取calendar数据，并创建成员变量df_calendar，df_calendar可以保存在本地pickle作为缓存
         |- `def make_calendar_df(self) -> df_calendar`
@@ -162,18 +547,6 @@ class DDBLoader(object):
         13 : 'INT',
     }
 
-    mssql_config = {
-        'host' : '192.168.1.7',
-        'username' : 'sa',
-        'password' : 'passw0rd!'
-    }
-
-    ddb_config = {
-        'host' : '192.168.1.7',
-        'username' : 'admin',
-        'password' : '123456'
-    }
-
     # this value may be used by factor makers, which may loop through code partitions
     num_code_partition = 50
 
@@ -182,18 +555,6 @@ class DDBLoader(object):
     ddb_dump_journal_fname = 'ddb_dump_journal.csv'
 
 
-    def __init__(self):
-        self.mssql_engine = sa.create_engine(
-            "mssql+pyodbc://{username}:{password}@{host}/master?driver=ODBC+Driver+18+for+SQL+Server".format(**self.mssql_config),
-            connect_args = {
-                "TrustServerCertificate": "yes"
-            }, echo=False
-        )
-
-        self.ddb_sess = ddb.session(self.ddb_config['host'], 8848)
-        self.ddb_sess.login(self.ddb_config['username'], self.ddb_config['password'])
-
-
     def init_ddb_database(self, df_calendar):
         """
         1. 创建ddb_database
@@ -490,6 +851,7 @@ class DDBLoader(object):
                 print("Will create new Pool object, but this is not encourage for large batch work.")
                 pool = Pool(self.num_worker)
 
+            # 在单个股票内部，对不同日期进行并行处理，对内存使用较为友好，不需要同时载入多个股票海量的全历史数据
             with tqdm(total=num_rows, leave=False) as sub_pbar:
                 for _ in pool.imap_unordered(
                     functools.partial(
@@ -505,18 +867,85 @@ class DDBLoader(object):
         self.dump_journal_writer.flush()
 
 
+    @staticmethod
+    def make_stock_daily_df(blob, type_name, stock_id):
+        """
+        用于做多进程录入ddb的函数
+        """
+        blob = gzip.decompress(blob)
+        dataArray = eval(f"ProtoBuffEntitys.{type_name}Message_pb2.{type_name}Array()")
+        dataArray.ParseFromString(blob)
+
+        data_dict_list = [
+            {field.name : val for field, val in entry.ListFields()}
+            for entry in dataArray.dataArray
+        ]
+
+        array_type_list = [
+            field.name 
+            for field, val in dataArray.dataArray[0].ListFields()
+            if isinstance(field.default_value, list)
+        ]
+        #pprint(array_type_list)
+
+        df = pd.DataFrame(data_dict_list)
+        #df['code'] = make_symbol(df['code'])
+        df['code'] = stock_id
+        df['m_nDate'] = make_date(df['m_nDate'])
+        df['m_nTime'] = df['m_nDate'] + make_time(df['m_nTime'])
+        for field_name in array_type_list:
+            df[field_name] = make_nparray(df[field_name])
+
+        #print(f"Did create ddb table for dataframe of shape {df.shape}")
+        # self.make_table_skeleton(type_name, df.shape[0])
+        return df
+
+
+    @staticmethod
+    def dump_stock_daily_to_ddb(row, type_name, stock_id):
+        """
+        用于做多进程录入ddb的函数
+        """
+        df_table_name = type_name
+        df = make_stock_daily_df(row[2], type_name, stock_id)
+
+        ddb_sess = ddb.session(DDBLoader.ddb_config['host'], 8848)
+        ddb_sess.login(DDBLoader.ddb_config['username'], DDBLoader.ddb_config['password'])
+
+        ddb_sess.upload({df_table_name : df})
+        ddb_sess.run("tableInsert(loadTable('{dbPath}', `{partitioned_table_name}), {df_table_name})".format(
+            dbPath = DDBLoader.ddb_path,
+            partitioned_table_name = type_name + DDBLoader.ddb_partition_table_suffix,
+            df_table_name = df_table_name
+        ))
+
+
+
 def main():
-    loader = DDBLoader()
-    df_calendar = loader.make_calendar_df()
 
-    loader.init_ddb_database(df_calendar)
+    # PIT基本面数据
-    print('Did finish init_ddb_database')
+    loader = DDBPITLoader()
+    loader.create_ddb_database()
+    #loader.create_ddb_partition_tables()
+    loader.dump_pit_to_ddb()
+
+    # 日频行情数据
+    #loader = DDBDailyLoader()
+    #loader.load_ddb_database()
+    #loader.dump_daily_kline_to_ddb()
+
+
+    # 高频数据
+    #df_calendar = loader.make_calendar_df()
+
+    #loader.init_ddb_database(df_calendar)
+    #print('Did finish init_ddb_database')
 
     #loader.load_ddb_database()
     #print('Did load ddb database')
 
-    loader.init_ddb_table_data(df_calendar)
+    #loader.init_ddb_table_data(df_calendar)
-    print('Did finish init_table_data')
+    #print('Did finish init_table_data')
 
 
 if __name__ == '__main__':

ddb.ipynb

@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 66,
    "id": "5d0f471e-682e-43cc-abdb-7e52f3bbd707",
    "metadata": {},
    "outputs": [],
@@ -23,30 +23,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 67,
    "id": "ed17fd0b-9b36-47e4-9ab6-11459a3621fb",
    "metadata": {},
    "outputs": [],
    "source": [
     "# backup(backup_path, sql_obj, force, parallel)\n",
     "code = \"\"\"\n",
-    "    backup('/data/dolphindb/backup/', <select * from loadTable(\"dfs://hft_stock_ts\", \"KLinePartitioned\")>, false, true)\n",
+    "    backup('/data/dolphindb/backup/', <select * from loadTable(\"dfs://hft_stock_ts\", \"OrderPartitioned\")>, true, true)\n",
     "\"\"\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 68,
    "id": "8b7dae3d-aef1-4c50-92b2-460d4fea0a96",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0"
+       "114350"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -55,6 +55,27 @@
     "sess.run(code)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "bf3bc38e-74cb-4549-bdca-6a3d2a601488",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2287.0"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "114350 / 50"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,