Abstract
is the processing of groups of elements. From an application standpoint, batch processing means reading data from a persistent store, doing something with the data, and then possibly storing the processed data in the persistent store as a single conceptual operation. Batch processing, which allows for the automation and sharing of computer resources, is usually run when computer resources are less busy.
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Notes
- 1.
To be sure, this is not the only way to accomplish the application of discounts based on specific circumstances; a rules engine would work as well, and would probably be more appropriate than altering your actual data.
- 2.
This number is pulled out of a hat. It’s a good number and an excellent starting point, but if you really want to know what the best number of operations in a single transaction would be, you have to roll up your sleeves and test. Unfortunately, “best number of operations” is difficult to define in a general sense (outside of “does it work?”), so there’s no good way to demonstrate such a test.
- 3.
Hey, there’s that Hat of Generally Useful Numbers again!
- 4.
You can commit a transaction and start a new one to do a “batch operation” as well, but that would invalidate our (somewhat artificial) requirement to maintain a single transaction. In general, we’ve been closing the Session when committing transactions, mostly to explicitly clear the Session's first-level cache.
- 5.
What we’d prefer is that books with Hibernate in their titles get a $10 premium. But “discount” it is!
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© 2015 Joseph Ottinger
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Ottinger, J., Guruzu, S., Mak, G. (2015). Batch Processing and Native SQL. In: Hibernate Recipes. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-0127-5_11
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DOI: https://doi.org/10.1007/978-1-4842-0127-5_11
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Publisher Name: Apress, Berkeley, CA
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Online ISBN: 978-1-4842-0127-5
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