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Table 7 1: Mapping of SQL types to Java types SQL Type BOOLEAN SMALLINT INTEGER BIGINT REAL DOUBLE DECIMAL NUMERIC CHAR VARCHAR DATE TIMESTAMP Java Type Boolean Short int long float double java.math.BigDecimal java.math.BigDecimal char java.lang.String java.sql.Date java.sql.Timestamp 126

Components of the Servlet Address SPECIFIES The address of the host, which is also known as the loop back address The port number through which the J2EE server accepts the client requests The name of the Web context for this application name The name of the alias that was specified in the deploy tool

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The Stifle AntiPattern makes the database a bottleneck by making inefficient use of the network pipeline between J2EE database clients and database servers. Ultimately, this leads to poor performance and unhappy users. Following are the symptoms and consequences of Stifle:

Figure 4.17 Infrared spectrum of an unknown hydrocarbon (C8 H16 ) (cf. SAQ 4.12). From Stuart, B., Modern Infrared Spectroscopy, ACOL Series, Wiley, Chichester, UK, 1996. University of Greenwich, and reproduced by permission of the University of Greenwich.

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seems very little left for consciousness to do. So, rather than asking how subjective experience can arise from activity in neurons, I ask the question, What is consciousness for Or more particularly, Why does my brain make me experience myself as a free agent My assumption is that we get some advantage from experiencing ourselves as free agents. So the question is: What is this advantage My answer is, for the moment, pure speculation.

23: Managing Transactions with JTA/JTS To get a better idea of the types of benchmarking they do, take a brief look at each of the benchmarking categories the TPMC provides: TPC This benchmark is probably one of the more comprehensive and complex of the four, because of the way it is executed The TPC benchmark is an online transaction processing (OLTP) benchmark that measures the number of new order transactions per minute This benchmark can be further refined to indicate how much a particular transaction costs in terms of resources, and how fast it may be carried out This benchmark simulates a small order entry environment that supports activities such as entering orders, delivering orders, recording payments, checking the status of orders, and monitoring inventory levels A mixture of five different transaction types of varying complexity are either executed immediately, as they would be online, or queued for deferred execution.

Other reactive procedures were also implemented in the 2004 panel to identify possible keying errors in earnings amounts; even though they seem unlikely to have had any noticeable impact on amount transitions, we note this fact in the interest of completeness. Amount reports that deviated (up or down) from the previous wave s report by more than some threshold percentage were flagged for the interviewer s immediate but off stage attention, to be corrected if necessary or to be accepted as initially entered. Our pretest experience leads us to suspect that these flags were elicited only rarely, but there are no hard data to test this assertion.

Since the goal of these benchmarks is to simulate real world transaction processing performance as accurately as possible, the benchmark environment is further enhanced by the following attributes: Simultaneous execution of multiple transaction types of varying complexity Online and deferred transaction execution Multiple users Moderate system and application execution time Non uniform distribution of data access through primary and secondary keys Databases consisting of many tables with different sizes, attributes and relationships Contention against data resources during read and write TPC H This benchmark differs from the first in that is designed to measure transaction performance in a decision support environment Service industries that provide high volume call support, such as call centers or business offices of public utilities, are two examples of such environments This benchmark is designed to measure the number of queries per hour that a system can perform.

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Typically this benchmark is run in an environment that simulates large volumes of data, executing queries of varying complexity that provide, in some cases, mission critical information TPC R This benchmark, like TPC H, is also a decision support based benchmark However, it differs in that it enables optimizations based on advanced knowledge of the queries being performed This benchmark also measures the number of transactions per hour TPC W This benchmark is designed to measure the number of Web transactions per second A Web transaction can be anything from requesting a Web page (which could be static or dynamically generated) to updating a database The benchmark is designed to simulate an online retail store, and each transaction is subject to a response time constraint To maximize the accuracy of the results, this benchmark uses real world attributes similar to those used by the TPC benchmark..

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