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This query unpivots (or splits) the employee yearly values from each source row to a separate row per order year, generating the following output:

To get a connection to a database, you ask the runtime for a reference to the right factory, and then get a connection from the factory, as shown in Listing 10-4 . You use the name of the database type (System.Data.SqlClient or System.Data.SqlServerCe.3.5, for example) . After getting the right kind of factory, you ask it to create a connection for you .

17-1

One of the things that management seems to love the most about a database full of bugs is the reports they can generate to represent the variety of information available in the system. No magic formula or query tells whether your project is ready to ship or if it is in trouble, but there are countless methods of examining the data. Teams at Microsoft examine bug data in hundreds of different permutations. Some examples of potential bug metrics to examine along with potential uses are listed in Table 9-5. Table 9-5: Metrics and Purpose Open table as spreadsheet Metric Metric use

These results show little difference, except that slightly more user CPU time is used when using the times method rather than using for. You can use this same technique to test different ways of calculating the same answers in your code, and optimize your code to use the fastest methods. Benchmark also includes a way to make completing multiple tests more convenient. You can rewrite the preceding benchmarking scenario like this: require 'benchmark' iterations = 1000000 Benchmark.bm do |bm| bm.report("for:") do for i in 1..iterations do x = i end end bm.report("times:") do iterations.times do |i| x = i end end end The primary difference with using the bm method is that it allows you to collect a group of benchmark tests together and display the results in a prettier way. Example output for the preceding code is as follows: user system for: 0.850000 0.000000 times: 0.970000 0.010000 total 0.850000 ( 0.980000 ( real 0.967980) 1.301703)

11-46

Display Name Network Location Awareness Network Store Interface Service Offline Files Parental Controls Peer Name Resolution Protocol Peer Networking Grouping Peer Networking Identity Manager Performance Logs And Alerts Plug And Play PnP-X IP Bus Enumerator PNRP Machine Name Publication Service Portable Device Enumerator Service Print Spooler Problem Reports and Solutions Control Panel Program Compatibility Assistant Service Protected Storage Quality Windows Audio Video Experience ReadyBoost Remote Access Auto Connection Manager Remote Access Connection Manager Remote Procedure Call (RPC) Remote Procedure Call (RPC) Locator Remote Registry Routing And Remote Access Secondary Logon Security Accounts Manager Security Center Server Shell Hardware Detection SL UI Notification Service Smart Card 25

void M3() { Employee e; Int32 year; e = new Manager(); e = Employee.Lookup("Joe"); year = e.GetYearsEmployed(); e.GeProgressReport(); }

for embedded systems, real-time systems, and other systems with strict speed or space restrictions can still benefit from this information. The need to measure the impact of each and every attempt at code tuning has been a constant since Donald Knuth published his study of Fortran programs in 1971. According to the measurements in this chapter, the effect of any specific optimization is actually less predictable than it was 10 years ago. The effect of each code tuning is affected by the programming language, compiler, compiler version, code libraries, library versions, and compiler settings, among other things. Code tuning invariably involves tradeoffs among complexity, readability, simplicity, and maintainability on the one hand and a desire to improve performance on the other. It introduces a high degree of maintenance overhead because of all the reprofiling that s required. I have found that insisting on measurable improvement is a good way to resist the temptation to optimize prematurely and to enforce a bias toward clear, straightforward code. If an optimization is important enough to haul out the profiler and measure the optimization s effect, then it s probably important enough to allow as long as it works. But if an optimization isn t important enough to haul out the profiling machinery, then it isn t important enough to degrade readability, maintainability, and other code characteristics. The impact of unmeasured code tuning on performance is speculative at best, whereas the impact on readability is as certain as it is detrimental.