![]() ![]() IAM Credentials can be supplied directly to connect(.) using an AWS profile as shown below: import redshift_connector # Connects to Redshift cluster using IAM credentials from default profile defined in ~/.aws/credentials conn = redshift_connector. For exception definitions, please see redshift_connector/error.py Example using IAM Credentials Redshift_connector uses the guideline for exception handling specified in the Python DB-API. paramstyle = 'named' sql = 'insert into foo(bar, jar) VALUES(:p1, :p2)' cursor. execute ( sql, ( 1, "hello world" )) # named redshift_connector. paramstyle = 'numeric' sql = 'insert into foo(bar, jar) VALUES(:1, :2)' cursor. execute ( sql, ( 1, "hello world" )) # numeric redshift_connector. paramstyle = 'qmark' sql = 'insert into foo(bar, jar) VALUES(?, ?)' cursor. Valid values for paramstyle include qmark, numeric, named, format, pyformat. The paramstyle for a cursor can be modified via cursor.paramstyle. autocommit = False Configuring cursor paramstyle # Make sure we're not in a transaction conn. It can be turned on by using the autocommit property of the connection. fetchall () print ( result ) > (, ) Enabling autocommitįollowing the DB-API specification, autocommit is off by default. execute ( "select * from book" ) result : tuple = cursor. executemany ( "insert into book (bookname, author) values ( %s, %s )", ) cursor. execute ( "create Temp table book(bookname varchar,author varchar)" ) cursor. connect ( host = '.', database = 'dev', user = 'awsuser', password = 'my_password' ) cursor : redshift_connector. Please open an issue with our project to request new integrations or get support for a redshift_connector issue seen in an existing integration.īasic Example import redshift_connector # Connects to Redshift cluster using AWS credentials conn = redshift_connector. ![]() Redshift_connector integrates with various open source projects to provide an interface to Amazon Redshift. Please reach out to the team by opening an issue or starting a discussion to help us fill in the gaps in our documentation. We are working to add more documentation and would love your feedback. $ git clone $ cd redshift_connector $ pip install. You may install from source by cloning this repository. Getting Started Install from BinaryĬonda install -c conda-forge redshift_connector This pure Python connector implements Python Database API Specification 2.0. Supported Amazon Redshift features include: Easy integration with pandas and numpy, as well as support for numerous Amazon Redshift specific features help you get the most out of your data This Custom Metric name will be available in the Designer after saving.Redshift_connector is the Amazon Redshift connector for This Data Type is the default selection because it is appropriate for the majority of use cases.įor longer Date Ranges, individual Metric Values will be aggregated for the entire Date Range and the aggregated Value will be displayed for the Custom Metric.Ĭustom Metric Name: Enter a name for your Custom Metric. Event Values: This Data Type should be selected when your Metric Value selection reflects individual Value(s) for the Metric at the specified Date/ Timestamp.This is the main difference between "Total Values" and "Daily Values." In Databox, the latest entry will be displayed for the Custom Metric.įor longer Date Ranges, data will be not aggregated and the most recent Value will be displayed for the selected Date Range. Total Values: This Data Type should be selected when your Metric reflects the current total Value for the Metric.In Databox, the latest daily entry will be displayed for the Custom Metric. Daily Values : This Data Type should be selected when your Metric reflects the most up-to-date Daily Value for the Metric.Data Type: Selecting the appropriate Data Type ensures that your Custom Metrics will be synced correctly in Databox.
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