CData Python Connector for JD Edwards

Build 26.0.9771

Automatically Caching Data

Automatically caching data is useful when you do not want to rebuild the cache for each query. When you query data for the first time, the connector automatically initializes and builds a cache in the background. When AutoCache = true, the connector uses the cache for subsequent query executions, resulting in faster response times.

Configuring Automatic Caching

Caching the AccountsPayable.AccountLedger Table

The following example caches the AccountsPayable.AccountLedger table in the file specified by the CacheLocation property of the connection string.

SELECT DocumentNumber, Amount FROM AccountsPayable.AccountLedger WHERE AccountNumber = '10001'

Common Use Case

A common use for automatically caching data is to improve driver performance when making repeated requests to a live data source, such as building a report or creating a visualization. With auto caching enabled, repeated requests to the same data may be executed in a short period of time, but within an allowable tolerance (CacheTolerance) of what is considered "live" data.

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Build 26.0.9771