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PyCharm でCData ODBC Driver for Spark を使ってみた

Spark にODBC データソースとしてPyCharm から連携。

CData ODBC Drivers は、ODBC をサポートするあらゆる環境から利用可能です。 本記事では、PyCharm からのCData ODBC Driver for Spark の利用を説明します。ODBC Deriver をデータソースとして設定する方法、データソースをクエリするPyCharm のベーシックな方法を含みます。

To begin, this tutorial will assume that you have already installed the CData ODBC Driver for Spark as well as PyCharm.

Pyodbc をプロジェクトに追加

Follow the steps below to add the pyodbc module to your project.

  1. Click File -> Settings to open the project settings window.
  2. Click Project Interpreter from the Project: YourProjectName menu.
  3. To add pyodbc, click the + button and enter pyodbc.
  4. Click Install Package to install pyodbc.

Spark への接続

You can now connect with an ODBC connection string or a DSN. See the Getting Started section in the CData driver documentation for a guide to creating a DSN on your OS.

Set the Server, Database, User, and Password connection properties to connect to SparkSQL.

Below is the syntax for a DSN:

[CData SparkSQL Source] Driver = CData ODBC Driver for Spark Description = My Description Server = 127.0.0.1

Spark へのクエリの実行

Instantiate a Cursor and use the execute method of the Cursor class to execute any SQL statement.

import pyodbc cnxn = pyodbc.connect('DRIVER={CData ODBC Driver for SparkSQL};Server = 127.0.0.1;') cursor = cnxn.cursor() cursor.execute("SELECT City, Balance FROM Customers WHERE Country = 'US'") rows = cursor.fetchall() for row in rows: print(row.City, row.Balance)

After connecting to Spark in PyCharm using the CData ODBC Driver, you will be able to build Python apps with access to Spark data as if it were a standard database. If you have any questions, comments, or feedback regarding this tutorial, please contact us at support@cdata.com.

 
 
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