Concepts Guide

Performance and Caching

This chapter describes the performance features of BEA Liquid Data for WebLogic. It covers these topics:

Overview

Poor performance can outweigh many of the advantages that an otherwise carefully designed data services deployment provides. From a data user's perspective, response times of the data access layer should be close to that of native data access mechanisms.

Liquid Data includes a number of configurable settings and features for ensuring good performance, as well as numerous internal features. With caching, data response times actually improves upon those provided by native access mechanisms.

Query Optimization

The data integration language behind data service functions is the XQuery language. As a declarative language, XQuery affords many opportunities for optimization. In general terms, a declarative language focuses on what needs to be done, not on how things are to be done (as is the case for an imperative language). As such, the Liquid Data engine is free to choose the most effective way to execute a given query, not only for the best performance of the data services layer but also to minimize the burden on the data sources as well.

Among the types of query optimization Liquid Data performs include:

When possible, Liquid Data pushes predicate evaluation, join operations, aggregates, and other data manipulation functions from XQuery to the underlying SQL databases to minimize the number of database calls required and reduce the volume of data returned from those calls.
Liquid Data handles layered data services by a process called view unfolding. Instead of retrieving the full data set encompassed by a multi-level function call, the Liquid Data retrieves only the data relevant to the actual request.
Liquid Data combines multiple "trips" to the same database implied by a function into a single access when sensible.

You can view how the engine has compiled a query using the plan view.

Caching

Caching improves the responsiveness of the client application and minimizes the burden on back-end resources. With caching, Liquid Data stores the results returned from a data service function locally. When a function call is made again with the same parameters, Liquid Data can respond with the cached copy of the data, thereby avoiding repeated calls to the back-end data sources.

Caching with Liquid Data can be managed at a highly granular level. You can enable or disable caching and set the time-to-live on a per function basis. This allows you to apply caching policies as best suited for the type of information. If the information is apt to be long standing without change, the cache can expire that data less frequently. If information changes frequently, you can have the cache expire it frequently as well.

If a client application wants to be sure that it acquires the latest information, it can deliberately bypass the cache, specifying a cache pass through in the data service functions call.

You can manage the cache, for example, by setting time-to-live values and purging the cache through the Liquid Data Administration Console.

Strategies for Security and Caching Policies

You can apply security policies to secure resources by application, data service function, and element. You can also implement data-driven security policies. For more information about security, see Securing Data in the Liquid Data for WebLogic Administration Guide.

The benefits of caching are conferred to calling data services as well as external clients. By caching , you can improve response times for clients and reduce the processing burden on back-end systems. For informaton about caching see Configuring the Results Query Cache in the Liquid Data for WebLogic Administration Guide.