使用 Teradata 模块连接 Python 与 Teradata

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 模块并执行类似 -

的操作

触发对 teradata 的查询并获取结果集。
检查是否已连接到 teradata。

请帮助我编写代码，因为我是 Python 的新手，而且我没有可用的信息来连接到 teradata。

Answer 1

从互联网下载 Teradata Python 模块和 python pyodbc.pyd。使用 cmd install setup.py.

安装

这是连接到 teradata 和提取数据的示例脚本：

import teradata
import pyodbc
import sys



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

session = udaExec.connect(method="odbc", dsn="prod32",
        username="PRODRUN", password="PRODRUN");

i = 0
REJECTED = 'R';

f = file("output.txt","w");sys.stdout=f

cursor =  session.cursor();

ff_remaining = 0;

cnt = cursor.execute("SELECT  SEQ_NO,FRQFBKDC,PNR_RELOC FROM ttemp.ffremaining ORDER BY 1,2,3 ").rowcount;
rows = cursor.execute("SELECT  SEQ_NO,FRQFBKDC,PNR_RELOC FROM ttemp.ffremaining ORDER BY 1,2,3 ").fetchall();


for i in range(cnt):
    ff_remaining = cursor.execute("select count(*) as coun from  ttemp.ffretroq_paxoff where seq_no=? and status <> ?",(rows[i].seq_no,REJECTED)).fetchall();
    print ff_remaining[0].coun, rows[i].seq_no, REJECTED;

Answer 2

有多种方法可以连接到 Teradata 并将 table 导出到 Pandas。这里有四个+:

Using teradata module

# 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

Using TeradataSQL

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)

Using pyodbc module

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 Module

 #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 module。我喜欢使用这个模块，因为它带有 MLOAD、FASTLOAD、BULKEXPORT 等。初学者唯一的问题是它的要求（例如 C/C++ 编译器、Teradata CLIv2 和 TPT API headers/lib 文件).

注意：更新于 13-07-2018，使用上下文管理器确保会话关闭

更新：2018 年 31 月 10 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 驱动依赖，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 table 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'，您必须将数据分块到小于 1MB 的块以避免“[HY001][Teradata][ODBC Teradata Driver] 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)

Answer 3

添加到 answer, you can use the teradatasql package (found on pypi)。此软件包不需要您安装 Teradata 驱动程序（此软件包除外）。像这样使用它：

import teradatasql
import pandas as pd

with teradatasql.connect(host='host', user='username', password='password') as connect:
    data = pd.read_sql('select top 5 * from table_name;', connect)

使用 Teradata 模块连接 Python 与 Teradata

Connecting Python with Teradata using Teradata module

使用sqlalchemy Module