""" This file is intended to perform read/writes against an SQL database such as PostgreSQL, Redshift, Mysql, MsSQL ... LICENSE (MIT) Copyright 2016-2020, The Phi Technology LLC Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. @TODO: - Migrate SQLite to SQL hierarchy - Include Write in Chunks from pandas """ import psycopg2 as pg import mysql.connector as my import sys import sqlalchemy if sys.version_info[0] > 2 : from transport.common import Reader, Writer #, factory else: from common import Reader,Writer import json from google.oauth2 import service_account from google.cloud import bigquery as bq from multiprocessing import Lock, RLock import pandas as pd import numpy as np import nzpy as nz #--- netezza drivers import sqlite3 import copy import os class SQLRW : lock = RLock() MAX_CHUNK = 2000000 DRIVERS = {"postgresql":pg,"redshift":pg,"mysql":my,"mariadb":my,"netezza":nz} REFERENCE = { "netezza":{"port":5480,"handler":nz,"dtype":"VARCHAR(512)"}, "postgresql":{"port":5432,"handler":pg,"dtype":"VARCHAR"}, "redshift":{"port":5432,"handler":pg,"dtype":"VARCHAR"}, "mysql":{"port":3360,"handler":my,"dtype":"VARCHAR(256)"}, "mariadb":{"port":3360,"handler":my,"dtype":"VARCHAR(256)"}, } def __init__(self,**_args): _info = {} _info['dbname'] = _args['db'] if 'db' in _args else _args['database'] self.table = _args['table'] if 'table' in _args else None self.fields = _args['fields'] if 'fields' in _args else [] self.schema = _args['schema'] if 'schema' in _args else '' self._chunks = 1 if 'chunks' not in _args else int(_args['chunks']) self._provider = _args['provider'] if 'provider' in _args else None # _info['host'] = 'localhost' if 'host' not in _args else _args['host'] # _info['port'] = SQLWriter.REFERENCE[_provider]['port'] if 'port' not in _args else _args['port'] _info['host'] = _args['host'] if 'host' in _args else '' _info['port'] = _args['port'] if 'port' in _args else '' # if 'host' in _args : # _info['host'] = 'localhost' if 'host' not in _args else _args['host'] # # _info['port'] = SQLWriter.PROVIDERS[_args['provider']] if 'port' not in _args else _args['port'] # _info['port'] = SQLWriter.REFERENCE[_provider]['port'] if 'port' not in _args else _args['port'] self.lock = False if 'lock' not in _args else _args['lock'] if 'username' in _args or 'user' in _args: key = 'username' if 'username' in _args else 'user' _info['user'] = _args[key] _info['password'] = _args['password'] if 'password' in _args else '' if 'auth_file' in _args : _auth = json.loads( open(_args['auth_file']).read() ) key = 'username' if 'username' in _auth else 'user' _info['user'] = _auth[key] _info['password'] = _auth['password'] if 'password' in _auth else '' _info['host'] = _auth['host'] if 'host' in _auth else _info['host'] _info['port'] = _auth['port'] if 'port' in _auth else _info['port'] if 'database' in _auth: _info['dbname'] = _auth['database'] self.table = _auth['table'] if 'table' in _auth else self.table # # We need to load the drivers here to see what we are dealing with ... # _handler = SQLWriter.REFERENCE[_provider]['handler'] _handler = _args['driver'] #-- handler to the driver self._dtype = _args['default']['type'] if 'default' in _args and 'type' in _args['default'] else 'VARCHAR(256)' # self._provider = _args['provider'] # self._dtype = SQLWriter.REFERENCE[_provider]['dtype'] if 'dtype' not in _args else _args['dtype'] # self._provider = _provider if _handler == nz : _info['database'] = _info['dbname'] _info['securityLevel'] = 0 del _info['dbname'] if _handler == my : _info['database'] = _info['dbname'] del _info['dbname'] if _handler == sqlite3 : _info = {'path':_info['dbname'],'isolation_level':'IMMEDIATE'} if _handler != sqlite3 : self.conn = _handler.connect(**_info) else: self.conn = _handler.connect(_info['path'],isolation_level='IMMEDIATE') self._engine = _args['sqlalchemy'] if 'sqlalchemy' in _args else None def meta(self,**_args): schema = [] try: if self._engine : table = _args['table'] if 'table' in _args else self.table if sqlalchemy.__version__.startswith('1.') : _m = sqlalchemy.MetaData(bind=self._engine) _m.reflect() else: _m = sqlalchemy.MetaData() _m.reflect(bind=self._engine) schema = [{"name":_attr.name,"type":str(_attr.type)} for _attr in _m.tables[table].columns] # # Some house keeping work _m = {'BIGINT':'INTEGER','TEXT':'STRING','DOUBLE_PRECISION':'FLOAT','NUMERIC':'FLOAT','DECIMAL':'FLOAT','REAL':'FLOAT'} for _item in schema : if _item['type'] in _m : _item['type'] = _m[_item['type']] except Exception as e: print (e) pass return schema def _tablename(self,name) : return self.schema +'.'+name if self.schema not in [None, ''] and '.' not in name else name def has(self,**_args): return self.meta(**_args) # found = False # try: # table = self._tablename(_args['table'])if 'table' in _args else self._tablename(self.table) # sql = "SELECT * FROM :table LIMIT 1".replace(":table",table) # if self._engine : # _conn = self._engine.connect() # else: # _conn = self.conn # found = pd.read_sql(sql,_conn).shape[0] # found = True # except Exception as e: # print (e) # pass # finally: # if not self._engine : # _conn.close() # return found def isready(self): _sql = "SELECT * FROM :table LIMIT 1".replace(":table",self.table) try: _conn = self.conn if not hasattr(self,'_engine') else self._engine return pd.read_sql(_sql,_conn).columns.tolist() except Exception as e: pass return False def apply(self,_sql): """ This function applies a command and/or a query against the current relational data-store :param _sql insert/select statement @TODO: Store procedure calls """ # _out = None try: if _sql.lower().startswith('select') : _conn = self._engine if self._engine else self.conn return pd.read_sql(_sql,_conn) else: # Executing a command i.e no expected return values ... cursor = self.conn.cursor() cursor.execute(_sql) self.conn.commit() except Exception as e : print (e) finally: if not self._engine : self.conn.commit() # cursor.close() def close(self): try: self.conn.close() except Exception as error : print (error) pass class SQLReader(SQLRW,Reader) : def __init__(self,**_args): super().__init__(**_args) def read(self,**_args): if 'sql' in _args : _sql = (_args['sql']) else: if 'table' in _args : table = _args['table'] else: table = self.table # table = self.table if self.table is not None else _args['table'] _sql = "SELECT :fields FROM "+self._tablename(table) if 'filter' in _args : _sql = _sql +" WHERE "+_args['filter'] if 'fields' in _args : _fields = _args['fields'] else: _fields = '*' if not self.fields else ",".join(self.fields) _sql = _sql.replace(":fields",_fields) # # At this point we have a query we can execute gracefully if 'limit' in _args : _sql = _sql + " LIMIT "+str(_args['limit']) # # @TODO: # It is here that we should inspect to see if there are any pre/post conditions # return self.apply(_sql) def close(self) : try: self.conn.close() except Exception as error : print (error) pass class SQLWriter(SQLRW,Writer): def __init__(self,**_args) : super().__init__(**_args) # # In the advent that data typing is difficult to determine we can inspect and perform a default case # This slows down the process but improves reliability of the data # NOTE: Proper data type should be set on the target system if their source is unclear. self._cast = False if 'cast' not in _args else _args['cast'] def init(self,fields=None): # if not fields : # try: # table = self._tablename(self.table) # self.fields = pd.read_sql_query("SELECT * FROM :table LIMIT 1".replace(":table",table),self.conn).columns.tolist() # except Exception as e: # pass # finally: # pass # else: self.fields = fields; def make(self,**_args): table = self._tablename(self.table) if 'table' not in _args else self._tablename(_args['table']) if 'fields' in _args : fields = _args['fields'] # table = self._tablename(self.table) sql = " ".join(["CREATE TABLE",table," (", ",".join([ name +' '+ self._dtype for name in fields]),")"]) else: schema = _args['schema'] if 'schema' in _args else [] _map = _args['map'] if 'map' in _args else {} sql = [] # ["CREATE TABLE ",_args['table'],"("] for _item in schema : _type = _item['type'] if _type in _map : _type = _map[_type] sql = sql + [" " .join([_item['name'], ' ',_type])] sql = ",".join(sql) # table = self._tablename(_args['table']) sql = ["CREATE TABLE ",table,"( ",sql," )"] sql = " ".join(sql) cursor = self.conn.cursor() try: cursor.execute(sql) except Exception as e : print (e) # print (sql) pass finally: # cursor.close() self.conn.commit() pass def write(self,info,**_args): """ :param info writes a list of data to a given set of fields """ # inspect = False if 'inspect' not in _args else _args['inspect'] # cast = False if 'cast' not in _args else _args['cast'] # if not self.fields : # if type(info) == list : # _fields = info[0].keys() # elif type(info) == dict : # _fields = info.keys() # elif type(info) == pd.DataFrame : # _fields = info.columns.tolist() # # _fields = info.keys() if type(info) == dict else info[0].keys() # # _fields = list (_fields) # self.init(_fields) try: table = _args['table'] if 'table' in _args else self.table # # In SQL, schema can stand for namespace or the structure of a table # In case we have a list, we are likely dealing with table structure # if 'schema' in _args : if type(_args['schema']) == str : self.schema = _args['schema'] if 'schema' in _args else self.schema elif type(_args['schema']) == list and len(_args['schema']) > 0 and not self.has(table=table): # # There is a messed up case when an empty array is passed (no table should be created) # self.make(table=table,schema=_args['schema']) pass # self.schema = _args['schema'] if 'schema' in _args else self.schema table = self._tablename(table) _sql = "INSERT INTO :table (:fields) VALUES (:values)".replace(":table",table) #.replace(":table",self.table).replace(":fields",_fields) if type(info) == list : _info = pd.DataFrame(info) elif type(info) == dict : _info = pd.DataFrame([info]) else: _info = pd.DataFrame(info) if _info.shape[0] == 0 : return if self.lock : SQLRW.lock.acquire() # # we will adjust the chunks here in case we are not always sure of the if self._chunks == 1 and _info.shape[0] > SQLRW.MAX_CHUNK : self._chunks = 10 _indexes = np.array_split(np.arange(_info.shape[0]),self._chunks) for i in _indexes : # # In case we have an invalid chunk ... if _info.iloc[i].shape[0] == 0 : continue # # We are enabling writing by chunks/batches because some persistent layers have quotas or limitations on volume of data if self._engine is not None: # pd.to_sql(_info,self._engine) if self.schema in ['',None] : rows = _info.iloc[i].to_sql(table,self._engine,if_exists='append',index=False) else: # # Writing with schema information ... rows = _info.iloc[i].to_sql(self.table,self._engine,schema=self.schema,if_exists='append',index=False) else: _fields = ",".join(self.fields) _sql = _sql.replace(":fields",_fields) values = ", ".join("?"*len(self.fields)) if self._provider == 'netezza' else ",".join(["%s" for name in self.fields]) _sql = _sql.replace(":values",values) cursor = self.conn.cursor() cursor.executemany(_sql,_info.iloc[i].values.tolist()) cursor.close() # cursor.commit() # self.conn.commit() except Exception as e: print(e) pass finally: if self._engine is None : self.conn.commit() if self.lock : SQLRW.lock.release() # cursor.close() pass def close(self): try: self.conn.close() finally: pass class BigQuery: def __init__(self,**_args): path = _args['service_key'] if 'service_key' in _args else _args['private_key'] self.credentials = service_account.Credentials.from_service_account_file(path) self.dataset = _args['dataset'] if 'dataset' in _args else None self.path = path self.dtypes = _args['dtypes'] if 'dtypes' in _args else None self.table = _args['table'] if 'table' in _args else None self.client = bq.Client.from_service_account_json(self.path) def meta(self,**_args): """ This function returns meta data for a given table or query with dataset/table properly formatted :param table name of the name WITHOUT including dataset :param sql sql query to be pulled, """ table = _args['table'] if 'table' in _args else self.table try: if table : _dataset = self.dataset if 'dataset' not in _args else _args['dataset'] sql = f"""SELECT column_name as name, data_type as type FROM {_dataset}.INFORMATION_SCHEMA.COLUMNS WHERE table_name = '{table}' """ _info = {'credentials':self.credentials,'dialect':'standard'} return pd.read_gbq(sql,**_info).to_dict(orient='records') # return self.read(sql=sql).to_dict(orient='records') # ref = self.client.dataset(self.dataset).table(table) # _schema = self.client.get_table(ref).schema # return [{"name":_item.name,"type":_item.field_type,"description":( "" if not hasattr(_item,"description") else _item.description )} for _item in _schema] else : return [] except Exception as e: return [] def has(self,**_args): found = False try: _has = self.meta(**_args) found = _has is not None and len(_has) > 0 except Exception as e: pass return found class BQReader(BigQuery,Reader) : def __init__(self,**_args): super().__init__(**_args) def apply(self,sql): return self.read(sql=sql) def read(self,**_args): SQL = None table = self.table if 'table' not in _args else _args['table'] if 'sql' in _args : SQL = _args['sql'] elif table: table = "".join(["`",table,"`"]) if '.' in table else "".join(["`:dataset.",table,"`"]) SQL = "SELECT * FROM :table ".replace(":table",table) if not SQL : return None if SQL and 'limit' in _args: SQL += " LIMIT "+str(_args['limit']) if (':dataset' in SQL or ':DATASET' in SQL) and self.dataset: SQL = SQL.replace(':dataset',self.dataset).replace(':DATASET',self.dataset) _info = {'credentials':self.credentials,'dialect':'standard'} return pd.read_gbq(SQL,**_info) if SQL else None # return self.client.query(SQL).to_dataframe() if SQL else None class BQWriter(BigQuery,Writer): lock = Lock() def __init__(self,**_args): super().__init__(**_args) self.parallel = False if 'lock' not in _args else _args['lock'] self.table = _args['table'] if 'table' in _args else None self.mode = {'if_exists':'append','chunksize':900000,'destination_table':self.table,'credentials':self.credentials} self._chunks = 1 if 'chunks' not in _args else int(_args['chunks']) def write(self,_info,**_args) : try: if self.parallel or 'lock' in _args : BQWriter.lock.acquire() _args['table'] = self.table if 'table' not in _args else _args['table'] self._write(_info,**_args) finally: if self.parallel: BQWriter.lock.release() def _write(self,_info,**_args) : _df = None if type(_info) in [list,pd.DataFrame] : if type(_info) == list : _df = pd.DataFrame(_info) elif type(_info) == pd.DataFrame : _df = _info if '.' not in _args['table'] : self.mode['destination_table'] = '.'.join([self.dataset,_args['table']]) else: self.mode['destination_table'] = _args['table'].strip() if 'schema' in _args : self.mode['table_schema'] = _args['schema'] # # Let us insure that the types are somewhat compatible ... # _map = {'INTEGER':np.int64,'DATETIME':'datetime64[ns]','TIMESTAMP':'datetime64[ns]','FLOAT':np.float64,'DOUBLE':np.float64,'STRING':str} # _mode = copy.deepcopy(self.mode) _mode = self.mode # _df.to_gbq(**self.mode) #if_exists='append',destination_table=partial,credentials=credentials,chunksize=90000) # # Let us adjust the chunking here self._chunkks = 10 if _df.shape[0] > SQLRW.MAX_CHUNK and self._chunks == 1 else self._chunks _indexes = np.array_split(np.arange(_df.shape[0]),self._chunks) for i in _indexes : _df.iloc[i].to_gbq(**self.mode) pass # # Aliasing the big query classes allowing it to be backward compatible # BigQueryReader = BQReader BigQueryWriter = BQWriter