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Showing posts with the label PL/SQL error handling

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8 Ways to Optimize AWS Glue Jobs in a Nutshell

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  Improving the performance of AWS Glue jobs involves several strategies that target different aspects of the ETL (Extract, Transform, Load) process. Here are some key practices. 1. Optimize Job Scripts Partitioning : Ensure your data is properly partitioned. Partitioning divides your data into manageable chunks, allowing parallel processing and reducing the amount of data scanned. Filtering : Apply pushdown predicates to filter data early in the ETL process, reducing the amount of data processed downstream. Compression : Use compressed file formats (e.g., Parquet, ORC) for your data sources and sinks. These formats not only reduce storage costs but also improve I/O performance. Optimize Transformations : Minimize the number of transformations and actions in your script. Combine transformations where possible and use DataFrame APIs which are optimized for performance. 2. Use Appropriate Data Formats Parquet and ORC : These columnar formats are efficient for storage and querying, signif
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PL/SQL Sample code and error handling mechanism

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SAMPLE PL/SQL CREATE TABLE dummy ( dummy_value VARCHAR2(1)); DECLARE -- Define local variable. my_string VARCHAR2(1) := ' '; my_number NUMBER; BEGIN -- Select a white space into a local variable. SELECT ' ' INTO my_string FROM dummy; -- Attempt to assign a single white space to a number. my_number := TO_NUMBER(my_string); EXCEPTION WHEN no_data_found THEN dbms_output.put_line('SELECT-INTO'||CHR(10)||SQLERRM); END; / Output and Error: The program returns the following output, which illustrates formatting user- defined exceptions.  The CHR(10) inserts a line return and provides a clean break between the program's SQLCODE and SQLERRM messages: RAISE my_error SQLCODE [1]  SQLERRM [User-Defined Exception]
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