[jira] [Created] (TORQUE-364) RecordMapper very slow on many columns in table

["Max Philipp Wriedt (Jira)" <ji...@apache.org>]

Max Philipp Wriedt created TORQUE-364:
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             Summary: RecordMapper very slow on many columns in table
                 Key: TORQUE-364
                 URL: https://issues.apache.org/jira/browse/TORQUE-364
             Project: Torque
          Issue Type: Improvement
          Components: Runtime, Templates
    Affects Versions: 5.1
            Reporter: Max Philipp Wriedt


When "doSelect()" a large quantity of columns in a table the default RecordMappers generated by Om-Templates (processRow()) cause an  !https://wikimedia.org/api/rest_v1/media/math/render/svg/4441d9689c0e6b2c47994e2f587ac5378faeefba! Problem. (technically O(rows * columns))



Specifically, constantly generating the SQL expression of all possible columns for every row in the result causes excessive use of StringBuilders which slow the mapping process to a crawl.

I currently have two ideas on how best to tackle this problem:
 # Either generate all SQL column expressions once when a (template generated) RecordMapper is first created (using final static String fields) thus reducing the cost for every row to generating all selected column SQL expressions  once(instead of every selected column times every available column)
 # Or (in case the first approach generates unacceptably excessive number of fields for RecordMappers) adjust the RecordMapper API to allow a "prepare(Criteria, int offset)" method to be called once before processing any rows and implement it on generated RecordMappers to scan the Criteria and build two lists: One containing references to the setXXX methods of the mapper in the order they appear in the ResultSet (via the order in the Criteria) and a second list containing the corresponding column offsets. This would allow the processRow method to only iterated over both lists simultaneously and call the referenced methods with the result set and offset immediately. (Alternatively one list using lambdas could be used but I am currently not sure about the stance or impact of these lambdas in the Torque project.)



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