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Keep in mind that querying a database is an I/O-bound process, especially if the database is remote If you have to execute several independent queries it's sometimes not very efficient to do them one after another Instead, it's wiser to make use of idle CPU cycles that are available and have these queries execute in parallel The easiest way to do this in Java is by using multiple threads Let's return to our hypothetical example of a remote database with 50 ms of network roundtrip time Those 50 ms are lost n the client machine unless concurrent computation is going to be performed Similarly, the database server machine is idle from the time it replies until the time the next query arrives But by using multiple threads, you can request both in parallel and decrease the overall execution time for the two statements by up to half[*].

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In this chapter, we looked at the basic and the not-so-basic strategies for querying a database using JDBC As described, JDBC allows you to use some simple classes and ethods to perform all of the basic types of queries you'll need to do However, by understanding some of the more advanced features of JDBC, such as connection pooling and the ability to precompile SQL statements, you'll be able to write high-performance queries One of the challenges of JDBC, and SQL in particular, is simplicity Complex data models can often demand multiline, cryptic queries that are incomprehensible to all but the person who wrote the code Instead, try to identify those queries and make use of views and stored procedures as necessary, so that the SQL written is minimal and optimized On a large development project, with many engineers trying to query a common database, there's the problem of consistency among SQL queries that have the same purpose Midway through the project, people realize that everyone has his or her own way of navigating to a common piece of data So the SQL being executed is not only complex but inconsistent across the group Changes to the data model, which are inevitable, threaten to unleash chaos The solution is to identify query needs and make it easy and intuitive for engineers to get at the data they want.

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Scalability is almost as easy to measure as performance is We know that scalability refers to an application's ability to accommodate rising resource demand gracefully, without a noticeable loss in QoS To measure scalability, it would seem that we need to calculate how well increasing demand is handled But how exactly do we do this Let's consider a simple example Suppose that we deploy an online banking application One type of request that clients can make is to view recent bank transactions Suppose that when a single client connects to the system, it takes a speedy 10 ms of server-side time to process this request Note that network latency and other client or network issues affecting the delivery of the response will increase the end-to-end response time; for example, maybe end-to-end response time will be 1,000 ms for a single client But, to keep our example simple, let's consider ust server-side time Next, suppose that 50 users simultaneously want to view their recent transactions, and that it takes an average of 500 ms of server-side time to process each of these 50 concurrent requests Obviously, our server-side response time has slowed because of the concurrency of demands That is to be expected Our next question might be: How well does our application scale To answer this, we need some scalability metrics, such as the following:.

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Scalability is almost as easy to measure as performance is We know that scalability refers to an application's ability to accommodate rising resource demand gracefully, without a noticeable loss in QoS To measure scalability, it would seem that we need to calculate how well increasing demand is handled But how exactly do we do this Let's consider a simple example Suppose that we deploy an online banking application One type of request that clients can make is to view recent bank transactions Suppose that when a single client connects to the system, it takes a speedy 10 ms of server-side time to process this request Note that network latency and other client or network issues affecting the delivery of the response will increase the end-to-end response time; for example, maybe end-to-end response time will be 1,000 ms for a single client But, to keep our example simple, let's consider just server-side time Next, suppose that 50 users simultaneously want to view their recent transactions, and that it takes an average of 500 ms of server ide time to process each of these 50 concurrent requests Obviously, our server-side response time has slowed because of the concurrency of demands That is to be expected Our next question might be: How well does our application scale To answer this, we need some scalability metrics, such as the following:.

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Bar Code Encoder In VB.NET Using Barcode printer for . Decode DataMatrix In VS .NET Using Barcode reader for VS .State stored within SPM is not durable With simple data types not a lot of overhead is involved Storage of complex data types is not supported The SPM is not designed for a big, scalable solution The state stored within SPM is lost either as soon as the Enterprise Services application is restarted (if the property release mode is set to Process), or if the last reference to it is released (with the property release mode set to Standard) SPM state is bound to a single Enterprise Services application; it cannot be accessed from a different application that might be invoked from within the same activity However, it is possible to share SPM state with different objects in the same application Similar to stateful components, the size is limited within the memory boundaries of a process.Related:

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It may be a valid claim, or it may be nothing more than a false benefit that is derived from overworking system administrators rather than from any great economy that a product delivers.) The case for virtualizing storage is equally simple. torage virtualization improves one or more of these qualities, increasing the value that an enterprise can derive from its data storage assets. For example:. Printing Interleaved 2 Of 5 In .NET Using Barcode encoder for Visual .Related:

By using a simple dilution technique to normalize the thanol content of samples to 20% prior to analysis, ethyl carbamate could be rapidly (40 min) and reliably measured to low concentrations (42 g L 1) with a method performance that was comparable to GC/MS De Melo Abreu et al (75) validated the HPLC-FLD method of Herbert et al (53) by collaborative trial Despite a limited number of participants (six), the results obtained by laboratories using the HPLC-FLD method (two) were comparable to those using GC/MS Although HPLC-FLD may be suitable for the analysis of ethyl carbamate in alcoholic beverages, there might be limitations for other matrices Park et al (63) used propyl carbamate as internal standard and liquid liquid extraction (ethyl acetate) to isolate ethyl carbamate from soy sauce prior to derivatization with 9-xanthydrol.

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Both ethyl carbamate and propyl carbamate were well separated and free from interfering peaks in the chromatograms, but the LOD in soy sauce was about ve times higher (20 g L 1) than that obtained by Herbert et al for alcoholic beverages In the same publication, Park et al (63) also described a method for the analysis of soy sauce using HPLC/MS/MS Samples were prepared by SPE using dichloromethane for elution, and propyl carbamate as internal standard After removal of the solvent, residues were dissolved in water, ltered, and injected directly into an HPLC/MS/MS system that was tted with an electrospray interface (ESI) The SRM precursor to product ion transitions was m/z 90 62 for ethyl carbamate and m/z 104 62 for propyl carbamate The LOD for ethyl carbamate in soy sauce using this technique was 0.

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05 g L 1 for a sample-to- nal-extract ratio of 5 g mL 1 It is likely that with further development, for example, from the use In Visual Basic NET Using Barcode encoder for VS Related: .

Even though JDBC transactions allow us to enforce an all-or-nothing style of execution when we update data, each executeUpdate() can result in a call to the database As we saw in previous chapters, this can lead to very inefficient execution, particularly if the database is remote and substantial network latency exists Still, there are many times when batch updates are relevant Consider a B2B application that integrates two applications through a messaging-based design One application periodically shovels new product orders to the second application so that they can be added to the company-wide database If the first application sends 1,000 orders, this normally requires 1,000 executeUpdate() calls In JDBC 20, however, there's a simple solution for this problem: the JDBC batch updates feature The concept is simple: div>. In Visual Basic .NET Using Barcode encoder for .NET . In Visual Studio .NET Using Barcode creator for ASP .Related:

Narrow Your Search to a Specific Domain. Deploy code 39 in .net generate, create barcode 39 none with .net projects. Another useful way to narrow your search is .Related:

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vb.net qr code reader

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vb.net qr code scanner

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