As mentioned earlier, you can execute a dynamic SQL directly or inside your stored procedure based on your requirement. This works fine for me but the first part where creating an Lambda function that will connect to Redshift takes some time. But by using this library, it means that you will be running your code on the driver node while all your workers are idle. Redshift plpgsql conditional statements are a useful and important part of the plpgsql language. Please review the document "Creating Stored Procedures in Amazon Redshift" for more information on getting started with stored procedures. Because stored procedure names can be overloaded, you must include the argument list for the procedure. You would need to run some code externally that triggers the stored procedure. Therefore, the function is complete after initiating the process in Amazon Redshift without caring about the query completion. Hi. We need to identify the employee and manager hierarchy. SVL_STORED_PROC_CALL is visible to all users. I am able to run it like this without errors: import psycopg2 con=psycopg2.connect("dbname=my_db host=my_host port=5439 user=my_user password=my_pw") cur = con.cursor() cur.execute('CALL sp_dim_date();') The procedure contains a number of RAISE, both NOTICE and INFO. From the docs . Although the stored procedure may take around 6 minutes to complete, the Lambda function finishes in seconds. For example, an AWS Lambda function can be configured to run on a schedule. The procedure is stored in the database and is available for any user with sufficient privileges to run. Redshift stored procedures are used to encapsulate business logic such as transformation, data validation, etc. But your options are: 1- Use a pyodbc library to connect and execute your procedure. For example, instead of a full table you could also use a subquery in parentheses. This is primarily because the execution from Lambda on Amazon Redshift was asynchronous. Typically, stored procedure returns a unique value, it can also return result set in the form of cursor or temporary tables. For example, you can also set session specific variables dynamically inside the stored procedure and execute the queries. Running a stored procedure through a JDBC connection from azure databricks is not supported as of now. It could then use psycopg2 to connect to the Amazon Redshift cluster and execute an SQL command to run the stored procedure. Grants the EXECUTE privilege on a specific stored procedure. The JDBC table that should be read. You can use Redshift control structures to perform some critical decisions based on data and manipulate SQL data in a flexible and powerful way. EXECUTE ON PROCEDURE procedure_name. You can define an Amazon Redshift stored procedure using the PostgreSQL procedural language PL/pgSQL to perform a set of SQL queries and logical operations. Redshift Recursive Query Alternative using Stored Procedure. But, if you don’t know the depth of the recursive query hierarchy, you have to use Redshift stored procedure to identify the depth dynamically. You can also use the stored procedure to return the result set in your applications. Stored Procedures are now supported in Amazon Redshift from version 1.0.7287 (late April 2019). I created an AWS Lambda function to connect Amazon Redshift database using .NET and PostgreSQL packages And then using the Event Rules within CloudWatch dashboard I created a schedule to execute AWS Lambda periodically. I am running a Redshift stored procedure from Python using psycopg2. For more information, see Naming stored procedures. You can query the system view SVL_STORED_PROC_CALL to get information about stored procedure calls, including start time, end time, and whether a call is aborted. Redshift Dynamic SQL Queries. Note that anything that is valid in a FROM clause of a SQL query can be used. 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