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Things worth to mention and remember (IV) - Data Loading

In this part of the series I'll cover some basics about data loading:

1. If you want to load a large amount of data quickly into a table or partition that gets truncated before the load and indexes also need to be maintained, then it is probably faster to set the the indexes to unusable before the load and rebuild them afterwards instead of letting the insert maintain the indexes. Note that even with a direct-path insert the index maintenance of usable indexes will generate undo and redo, whereas a separate index rebuild doesn't generate undo and can be also be run as a nologging operation if desired. However, as always, test for your particular situation/configuration as the index maintenance as part of direct-path inserts are quite efficient and therefore might not be that much slower than separate index rebuild steps.

The Core Performance Fundamentals Of Oracle Data Warehousing – Data Loading

[back to Introduction] Getting flat file data into your Oracle data warehouse is likely a daily (or more possibly frequent) task, but it certainly does not have to be a difficult one.  Bulk loading data rates are governed by the following operations and hardware resources: How fast can the data be read How fast can data be written out How much CPU power is available I’m always a bit amazed (and depressed) when I hear people complain that their data loading rates are slow and they proceed to tell me things like: The source files reside on a shared NFS filer (or similar) and it has just a single GbE (1 Gigabit Ethernet) network path to the Oracle database host(s). The source files reside on this internal disk volume which consists of a two disk mirror (or a volume with very few spindles). Maybe it’s not entirely obvious so let me spell it out (as I did in this tweet): One can not load data into a database faster than it can be delivered from the source. Database systems must obey the laws of physics! Or putting it another way: Don’t fall victim to slow data loading because of a slow performing data source. [...]