使用Teradata模块将Python与Teradata连接

Question

我已经在Windows 7上安装了python 2.7.0和Teradata模块。我无法从python连接和查询TD。

pip install Teradata

现在，我想在我的源代码中导入teradata模块并执行以下操作-

1. 向Teradata触发查询并获取结果集。
2. 检查是否已连接到Teradata。

请帮我编写与我刚接触Python相同的代码，并且没有可用的信息连接到Teradata。

Answer 1

有多种方法可以连接到Teradata并将表导出到Pandas。这是四个+：

使用Teradata模块

# You can install teradata via PIP: pip install teradata
# to get a list of your odbc drivers names, you could do: teradata.tdodbc.drivers
# You don’t need to install teradata odbc driver if using method='rest'.     
# See sending data from df to teradata for connection example

import teradata
import pandas as pd

host,username,password = 'HOST','UID', 'PWD'
#Make a connection
udaExec = teradata.UdaExec (appName="test", version="1.0", logConsole=False)


with udaExec.connect(method="odbc",system=host, username=username,
                            password=password, driver="DRIVERNAME") as connect:

    query = "SELECT * FROM DATABASEX.TABLENAMEX;"

    #Reading query to df
    df = pd.read_sql(query,connect)
    # do something with df,e.g.
    print(df.head()) #to see the first 5 rows

使用TeradataSQL

from @ymzkala：此软件包不需要您安装Teradata驱动程序（此软件包除外）。

# Installing python -m pip install teradatasql

import teradatasql

with teradatasql.connect(host='host', user='username', password='password') as connect:
    df = pd.read_sql(query, connect)

使用pyodbc模块

import pyodbc

 #You can install teradata via PIP: pip install pyodbc
 #to get a list of your odbc drivers names, you could do: pyodbc.drivers()

#Make a connection
link = 'DRIVER={DRIVERNAME};DBCNAME={hostname};UID={uid};PWD={pwd}'.format(
                      DRIVERNAME=DRIVERNAME,hostname=hostname,  
                      uid=username, pwd=password)
with pyodbc.connect(link,autocommit=True) as connect:

    #Reading query to df
    df = pd.read_sql(query,connect)

使用sqlalchemy模块

 #You can install sqlalchemy via PIP: pip install sqlalchemy-teradata
 #Note: It is not pip install sqlalchemy. If you already have sqlalchemy, you still need sqlalchemy-teradata to get teradata dialects

from sqlalchemy import create_engine

#Make a connection

link = 'teradata://{username}:{password}@{hostname}/?driver={DRIVERNAME}'.format(
               username=username,hostname=hostname,DRIVERNAME=DRIVERNAME)

with create_engine(link) as connect:

    #Reading query to df
    df = pd.read_sql(query,connect)

还有第五种方法，使用giraffez模块。我喜欢使用此模块，因为它随MLOAD，FASTLOAD，BULKEXPORT等一起提供。对于初学者来说，唯一的问题是其要求（例如C
/ C ++编译器，Teradata CLIv2和TPT API标头/ lib文件）。

注意：已于2018年7月13日更新，使用上下文管理器确保会话关闭

更新：31-10-2018：使用Teradata将数据从df发送到Teradata

我们可以将数据从df发送到Teradata。避免’odbc‘1
MB限制和odbc驱动程序依赖性，我们可以使用’rest’方法。我们需要主机ip_address，而不是驱动程序参数。 注意：
df中的列顺序应与Teradata表中的列顺序匹配。

import teradata
import pandas as pd

# HOST_IP can be found by executing *>>nslookup viewpoint* or *ping  viewpoint* 
udaExec = teradata.UdaExec (appName="test", version="1.0", logConsole=False) 
with udaExec.connect(method="rest",system="DBName", username="UserName",
                      password="Password", host="HOST_IP_ADDRESS") as connect:

    data = [tuple(x) for x in df.to_records(index=False)]

    connect.executemany("INSERT INTO DATABASE.TABLEWITH5COL values(?,?,?,?,?)",data,batch=True)

使用“ odbc”，您必须将数据分块为少于1MB的块，以避免出现“ [HY001] [Teradata] [ODBC
Teradata驱动程序]内存分配错误”错误：例如

import teradata
import pandas as pd
import numpy as np

udaExec = teradata.UdaExec (appName="test", version="1.0", logConsole=False)

with udaExec.connect(method="odbc",system="DBName", username="UserName",
                      password="Password", driver="DriverName") as connect:

    #We can divide our huge_df to small chuncks. E.g. 100 churchs
    chunks_df = np.array_split(huge_df, 100)

    #Import chuncks to Teradata
    for i,_ in enumerate(chunks_df):

        data = [tuple(x) for x in chuncks_df[i].to_records(index=False)]
        connect.executemany("INSERT INTO DATABASE.TABLEWITH5COL values(?,?,?,?,?)",data,batch=True)