teradata python,使用Teradata模块将Python与Teradata连接

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

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

Firing queries to teradata and get result set.

Check if connection is made to 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.

解决方案

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

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)

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)

#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)

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).

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

Update: 31-10-2018: Using teradata to send data from df to teradata

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)

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)