Efficient Data Handling Techniques in Python for Oracle Database
summary
Live Jupyter demos use cx_Oracle with Oracle Instant Client to show concrete gains: execute_many replaces thousand-loop inserts (example: 1,000 executes ≈2.0s vs one execute_many ≈0.1s), array fetching tuning (prefetch_rows and array_size) cuts network round trips and speeds queries, and batching loads 10,000 CSV rows in ~0.5s. Session pools with fixed size improve stability, use session callbacks to set state once, and SODA provides JSON document storage backed by SQL.
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chapters (19)
- 0:00Start
- 0:45Christopher Jones
- 4:26Installation
- 4:50Oracle Instant Client
- 7:44Easy Connect Syntax
- 9:29Connection Type
- 10:41Session Pool
- 11:29Session Pool Technology
- 15:35Autonomous Database Cloud
- 16:42Autonomous Transaction Processing
- 21:13Tuning Parameters
- 25:18Bind Variables
- 27:11Insert Data in a Loop
- 30:12Auto Commit Mode
- 32:39Set Input Sizes
- 34:08Queries
- 34:43Soda Is a Document Style Api
- 35:31Auto Commit
- 39:53Tuning Features
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