使用Teradata模块将Python与Teradata连接 [英] Connecting Python with Teradata using Teradata module

问题描述

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.

I have installed python 2.7.0 and Teradata module on Windows 7. I am not able to connect and query TD from python.

pip install Teradata

现在，我想在源代码中导入teradata模块并执行--p这样的操作

Now I want to import teradata module in my source code and perform operations like -

1. 对Teradata进行查询并获取结果集.
2. 检查是否已与Teradata建立连接.

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

Please help me writing code for the same as I am new to Python and there is no information available with me to connect to teradata.

推荐答案

有多种方法可以连接到Teradata并将表导出到Pandas.这是四个以上:

There are a number of ways to connect to Teradata and export table to Pandas. Here are four+:

# 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).

from @ymzkala : This package doesn't require you to install Teradata drivers (other than this package).

# 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，唯一的问题是它的要求(例如C/C ++编译器，Teradata CLIv2和TPT API标头/lib文件).

There is a fifth way, using giraffez module. I enjoy using this module as it come with MLOAD, FASTLOAD, BULKEXPORT etc. The only issue for beginners is its requirements (e.g C/C++ compiler ,Teradata CLIv2 and TPT API headers/lib files).

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

Note: Updated 13-07-2018, using of context manager to ensure closing of sessions

更新时间:2018年10月31日: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.

We can send data from df to Teradata. Avoiding 'odbc' 1 MB limit and odbc driver dependency, we can use 'rest' method. We need host ip_address, instead of driver argument. NB: The order of columns in df should match the order of columns in Teradata table.

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'，您必须将数据分块到小于1MB的块，以免出现"[HY001] [Teradata] [ODBC Teradata驱动程序]内存分配错误"错误:例如

Using 'odbc', you have to chunk your data to less than 1MB chunks to avoid "[HY001][Teradata][ODBC Teradata Driver] Memory allocation error" error: E.g.

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)

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