Search Results

Search found 65160 results on 2607 pages for 'compile time constant'.

facebook twitter digg google delicious technorati stumbleupon myspace wordpress linkedin gmail igoogle windows live tumblr viadeo yahoo buzz yahoo mail yahoo bookmarks favorites email print

Page 186/2607 | < Previous Page | 182 183 184 185 186 187 188 189 190 191 192 193  | Next Page >

  • Why do I have to enter my password every time I activate / deactivate AirPort (WiFi) on my MacBook P

    - by Another Registered User
    I use Snow Leopard, and I'm used to activate / deactivate WiFi like 20 times per day. The reason is that WiFi stops working properly after a few minutes of use. So every time I try to surf, I must stop/reactivate it first. But now, suddenly I have to enter my user password every time I want to do it. It's so annoying! The dialogue details say: Right: com.apple.airport.power Program: SystemUIServer What can I do that the Mac won't ask me for the password every time? It's hard enough that I have to stop/reactivate WiFi all the time (hardware bug). I have a admin account with full rights.

    Read the article

  • i7 4770k or i7 4930k - Which for faster compile times? [on hold]

    - by Chumm
    I've looked up comparisons and found that single core performance seems to be better on i7 4770k, but has less cores that the i7 4930k. Would VS take advantage of extra cores when compiling, or would the difference be negible. I'm looking to buy the PC primarily for programming, so which would be better for visual studio? I already have the rest of my build ready, I just need to decide on this! :) thanks

    Read the article

  • Common Network Administrator Tools

    - by No Time
    I would like to make a custom clump of Network Admin packages, to be able to carry on a thumb drive, to administer Debian based machines. Examples of what I would include so far: nmap traceroute vnstat zenmap * I know every situation may be different, but I would like to build a toolbox I could bring everywhere, and am looking for advice on other tools which would work. (If there is a similar question, I am fine being directed there)

    Read the article

  • How to do safely test Biztalk app by manipulating the Windows OS system time w/o breaking the Active Directory?

    - by melaos
    i have a biztalk - window service tied middleware application which talks to other system. recently we had a request to test for scenarios which relates to the date. as we have a lot of places in the application which uses the .net Datetime.Now value, we don't really want to go into the code level and change all these values. so we're looking at the simplest way to test which is to just change the OS time. but what we notice is that sometimes when we change the system date time, we will get account lock out due to Active Directory. So my question is what's a good and safe way that i can test for future dates, etc by changing the windows OS system date time but without causing any issues with the Active Directory. And where can i find out more about AD and how it issues token and what's the correlation with the system date time changes. Thanks! ~m

    Read the article

  • Are elements returned by Linq-to-Entities query streamed from the DB one at the time or are they retrieved all at once?

    - by carewithl
    Are elements returned by Linq-to-Entities query streamed from the database one at the time ( as they are requested ) or are they retrieved all at once: SampleContext context = new SampleContext(); // SampleContext derives from ObjectContext var search = context.Contacts; foreach (var contact in search) { Console.WriteLine(contact.ContactID); // is each Contact retrieved from the DB // only when foreach requests it? } thank you in advance

    Read the article

  • Can I autogenerate/compile code on-the-fly, at runtime, based upon values (like key/value pairs) parsed out of a configuration file?

    - by Kumba
    This might be a doozy for some. I'm not sure if it's even 100% implementable, but I wanted to throw the idea out there to see if I'm really off of my rocker yet. I have a set of classes that mimics enums (see my other questions for specific details/examples). For 90% of my project, I can compile everything in at design time. But the remaining 10% is going to need to be editable w/o re-compiling the project in VS 2010. This remaining 10% will be based on a templated version of my Enums class, but will generate code at runtime, based upon data values sourced in from external configuration files. To keep this question small, see this SO question for an idea of what my Enums class looks like. The templated fields, per that question, will be the MaxEnums Int32, Names String() array, and Values array, plus each shared implementation of the Enums sub-class (which themselves, represent the Enums that I use elsewhere in my code). I'd ideally like to parse values from a simple text file (INI-style) of key/value pairs: [Section1] Enum1=enum_one Enum2=enum_two Enum3=enum_three So that the following code would be generated (and compiled) at runtime (comments/supporting code stripped to reduce question size): Friend Shared ReadOnly MaxEnums As Int32 = 3 Private Shared ReadOnly _Names As String() = New String() _ {"enum_one", "enum_two", "enum_three"} Friend Shared ReadOnly Enum1 As New Enums(_Names(0), 1) Friend Shared ReadOnly Enum2 As New Enums(_Names(1), 2) Friend Shared ReadOnly Enum3 As New Enums(_Names(2), 4) Friend Shared ReadOnly Values As Enums() = New Enums() _ {Enum1, Enum2, Enum3} I'm certain this would need to be generated in MSIL code, and I know from reading that the two components to look at are CodeDom and Reflection.Emit, but I was wondering if anyone had working examples (or pointers to working examples) versus really long articles. I'm a hands-on learner, so I have to have example code to play with. Thanks!

    Read the article

  • Python, dictionaries, and chi-square contingency table

    - by rohanbk
    I have a file which contains several lines in the following format (word, time that the word occurred in, and frequency of documents containing the given word within the given instance in time): #inputfile <word, time, frequency> apple, 1, 3 banana, 1, 2 apple, 2, 1 banana, 2, 4 orange, 3, 1 I have Python class below that I used to create 2-D dictionaries to store the above file using as the key, and frequency as the value: class Ddict(dict): ''' 2D dictionary class ''' def __init__(self, default=None): self.default = default def __getitem__(self, key): if not self.has_key(key): self[key] = self.default() return dict.__getitem__(self, key) wordtime=Ddict(dict) # Store each inputfile entry with a <word,time> key timeword=Ddict(dict) # Store each inputfile entry with a <time,word> key # Loop over every line of the inputfile for line in open('inputfile'): word,time,count=line.split(',') # If <word,time> already a key, increment count try: wordtime[word][time]+=count # Otherwise, create the key except KeyError: wordtime[word][time]=count # If <time,word> already a key, increment count try: timeword[time][word]+=count # Otherwise, create the key except KeyError: timeword[time][word]=count The question that I have pertains to calculating certain things while iterating over the entries in this 2D dictionary. For each word 'w' at each time 't', calculate: The number of documents with word 'w' within time 't'. (a) The number of documents without word 'w' within time 't'. (b) The number of documents with word 'w' outside time 't'. (c) The number of documents without word 'w' outside time 't'. (d) Each of the items above represents one of the cells of a chi-square contingency table for each word and time. Can all of these be calculated within a single loop or do they need to be done one at a time? Ideally, I would like the output to be what's below, where a,b,c,d are all the items calculated above: print "%s, %s, %s, %s" %(a,b,c,d)

    Read the article

  • Can a conforming C implementation #define NULL to be something wacky

    - by janks
    I'm asking because of the discussion that's been provoked in this thread: http://stackoverflow.com/questions/2597142/when-was-the-null-macro-not-0/2597232 Trying to have a serious back-and-forth discussion using comments under other people's replies is not easy or fun. So I'd like to hear what our C experts think without being restricted to 500 characters at a time. The C standard has precious few words to say about NULL and null pointer constants. There's only two relevant sections that I can find. First: 3.2.2.3 Pointers An integral constant expression with the value 0, or such an expression cast to type void * , is called a null pointer constant. If a null pointer constant is assigned to or compared for equality to a pointer, the constant is converted to a pointer of that type. Such a pointer, called a null pointer, is guaranteed to compare unequal to a pointer to any object or function. and second: 4.1.5 Common definitions <stddef.h> The macros are NULL which expands to an implementation-defined null pointer constant; The question is, can NULL expand to an implementation-defined null pointer constant that is different from the ones enumerated in 3.2.2.3? In particular, could it be defined as: #define NULL __builtin_magic_null_pointer Or even: #define NULL ((void*)-1) My reading of 3.2.2.3 is that it specifies that an integral constant expression of 0, and an integral constant expression of 0 cast to type void* must be among the forms of null pointer constant that the implementation recognizes, but that it isn't meant to be an exhaustive list. I believe that the implementation is free to recognize other source constructs as null pointer constants, so long as no other rules are broken. So for example, it is provable that #define NULL (-1) is not a legal definition, because in if (NULL) do_stuff(); do_stuff() must not be called, whereas with if (-1) do_stuff(); do_stuff() must be called; since they are equivalent, this cannot be a legal definition of NULL. But the standard says that integer-to-pointer conversions (and vice-versa) are implementation-defined, therefore it could define the conversion of -1 to a pointer as a conversion that produces a null pointer. In which case if ((void*)-1) would evaluate to false, and all would be well. So what do other people think? I'd ask for everybody to especially keep in mind the "as-if" rule described in 2.1.2.3 Program execution. It's huge and somewhat roundabout, so I won't paste it here, but it essentially says that an implementation merely has to produce the same observable side-effects as are required of the abstract machine described by the standard. It says that any optimizations, transformations, or whatever else the compiler wants to do to your program are perfectly legal so long as the observable side-effects of the program aren't changed by them. So if you are looking to prove that a particular definition of NULL cannot be legal, you'll need to come up with a program that can prove it. Either one like mine that blatantly breaks other clauses in the standard, or one that can legally detect whatever magic the compiler has to do to make the strange NULL definition work. Steve Jessop found an example of way for a program to detect that NULL isn't defined to be one of the two forms of null pointer constants in 3.2.2.3, which is to stringize the constant: #define stringize_helper(x) #x #define stringize(x) stringize_helper(x) Using this macro, one could puts(stringize(NULL)); and "detect" that NULL does not expand to one of the forms in 3.2.2.3. Is that enough to render other definitions illegal? I just don't know. Thanks!

    Read the article

  • How to use reflection to call a method and pass parameters whose types are unknown at compile time?

    - by MandoMando
    I'd like to call methods of a class dynamically with parameter values that are "parsed" from a string input. For example: I'd like to call the following program with these commands: c:myprog.exe MethodA System.Int32 777 c:myprog.exe MethodA System.float 23.17 c:myprog.exe MethodB System.Int32& 777 c:myprog.exe MethodC System.Int32 777 System.String ThisCanBeDone static void Main(string[] args) { ClassA aa = new ClassA(); System.Type[] types = new Type[args.Length / 2]; object[] ParamArray = new object[types.Length]; for (int i=0; i < types.Length; i++) { types[i] = System.Type.GetType(args[i*2 + 1]); // LINE_X: this will obviously cause runtime error invalid type/casting ParamArray[i] = args[i*2 + 2]; MethodInfo callInfo = aa.GetType().GetMethod(args[0],types); callInfo.Invoke(aa, ParamArray); } // In a non-changeable classlib: public class ClassA { public void MethodA(int i) { Console.Write(i.ToString()); } public void MethodA(float f) { Console.Write(f.ToString()); } public void MethodB(ref int i) { Console.Write(i.ToString()); i++; } public void MethodC(int i, string s) { Console.Write(s + i.ToString()); } public void MethodA(object o) { Console.Write("Argg! Type Trapped!"); } } "LINE_X" in the above code is the sticky part. For one, I have no idea how to assign value to a int or a ref int parameter even after I create it using Activator.CreatInstance or something else. The typeConverter does come to mind, but then that requires an explicit compile type casting as well. Am I looking at CLR with JavaScript glasses or there is way to do this?

    Read the article

  • How do I compile and build the taf2-curb Ruby gem on Windows XP with MinGW?

    - by Laran Evans
    How do I compile and build the taf2-curb Ruby gem on Windows XP with MinGW? I tried this, but I'm kinda fishing, unsuccessfully. C:\Documents and Settings\Megem install taf2-curb -- --with-curl-include=C:/curl-7.19.5-devel-mingw32/include --with-curl-dir=C:/curl-7.19.5 --with-curl-lib=C:/curl-7.19.5-devel-mingw32/lib --prefix=C:/MinGW --with-curllib Bulk updating Gem source index for: http://gems.rubyforge.org Updating metadata for 73 gems from http://gems.rubyonrails.org ......................................................................... complete Bulk updating Gem source index for: http://gems.github.com Building native extensions. This could take a while... ERROR: Error installing taf2-curb: ERROR: Failed to build gem native extension. C:/Ruby/bin/ruby.exe extconf.rb install taf2-curb -- --with-curl-include=C:/curl-7.19.5-devel-mingw32/include --with-cur l-dir=C:/curl-7.19.5 --with-curl-lib=C:/curl-7.19.5-devel-mingw32/lib --prefix=C:/MinGW --with-curllib checking for curl-config... no checking for main() in true.lib... no *** extconf.rb failed *** Could not create Makefile due to some reason, probably lack of necessary libraries and/or headers. Check the mkmf.log file for more details. You may need configuration options. Provided configuration options: --with-opt-dir --without-opt-dir --with-opt-include --without-opt-include=${opt-dir}/include --with-opt-lib --without-opt-lib=${opt-dir}/lib --with-make-prog --srcdir=. --curdir --ruby=C:/Ruby/bin/ruby --with-curl-dir --with-curl-include=${curl-dir}/include --with-curl-lib=${curl-dir}/lib --with-curllib extconf.rb:9: Can't find libcurl or curl/curl.h (RuntimeError) Try passing --with-curl-dir or --with-curl-lib and --with-curl-include options to extconf. Gem files will remain installed in C:/Ruby/lib/ruby/gems/1.8/gems/taf2-curb-0.4.8.0 for inspection. Results logged to C:/Ruby/lib/ruby/gems/1.8/gems/taf2-curb-0.4.8.0/ext/gem_make.out C:\Documents and Settings\Me I've installed curl-7.19.5 and curl-7.19.5-devel-mingw from this url: http://curl.haxx.se/download.html Help! And thanks!

    Read the article

  • I want tell the VC++ Compiler to compile all code. Can it be done?

    - by KGB
    I am using VS2005 VC++ for unmanaged C++. I have VSTS and am trying to use the code coverage tool to accomplish two things with regards to unit tests: See how much of my referenced code under test is getting executed See how many methods of my code under test (if any) are not unit tested at all Setting up the VSTS code coverage tool (see the link text) and accomplishing task #1 was straightforward. However #2 has been a surprising challenge for me. Here is my test code. class CodeCoverageTarget { public: std::string ThisMethodRuns() { return "Running"; } std::string ThisMethodDoesNotRun() { return "Not Running"; } }; #include <iostream> #include "CodeCoverageTarget.h" using namespace std; int main() { CodeCoverageTarget cct; cout<<cct.ThisMethodRuns()<<endl; } When both methods are defined within the class as above the compiler automatically eliminates the ThisMethodDoesNotRun() from the obj file. If I move it's definition outside the class then it is included in the obj file and the code coverage tool shows it has not been exercised at all. Under most circumstances I want the compiler to do this elimination for me but for the code coverage tool it defeats a significant portion of the value (e.g. finding untested methods). I have tried a number of things to tell the compiler to stop being smart for me and compile everything but I am stumped. It would be nice if the code coverage tool compensated for this (I suppose by scanning the source and matching it up with the linker output) but I didn't find anything to suggest it has a special mode to be turned on. Am I totally missing something simple here or is this not possible with the VC++ compiler + VSTS code coverage tool? Thanks in advance, KGB

    Read the article

  • Replacing symbol from object file at compile time. For example swapping out main

    - by Anthony Sottile
    Here's the use case: I have a .cpp file which has functions implemented in it. For sake of example say it has the following: [main.cpp] #include <iostream> int foo(int); int foo(int a) { return a * a; } int main() { for (int i = 0; i < 5; i += 1) { std::cout << foo(i) << std::endl; } return 0; } I want to perform some amount of automated testing on the function foo in this file but would need to replace out the main() function to do my testing. Preferably I'd like to have a separate file like this that I could link in over top of that one: [mymain.cpp] #include <iostream> #include <cassert> extern int foo(int); int main() { assert(foo(1) == 1); assert(foo(2) == 4); assert(foo(0) == 0); assert(foo(-2) == 4); return 0; } I'd like (if at all possible) to avoid changing the original .cpp file in order to do this -- though this would be my approach if this is not possible: do a replace for "(\s)main\s*\(" == "\1__oldmain\(" compile as usual. The environment I am targeting is a linux environment with g++.

    Read the article

  • The Unspoken - The Why of GC Ergonomics

    - by jonthecollector
    Do you use GC ergonomics, -XX:+UseAdaptiveSizePolicy, with the UseParallelGC collector? The jist of GC ergonomics for that collector is that it tries to grow or shrink the heap to meet a specified goal. The goals that you can choose are maximum pause time and/or throughput. Don't get too excited there. I'm speaking about UseParallelGC (the throughput collector) so there are definite limits to what pause goals can be achieved. When you say out loud "I don't care about pause times, give me the best throughput I can get" and then say to yourself "Well, maybe 10 seconds really is too long", then think about a pause time goal. By default there is no pause time goal and the throughput goal is high (98% of the time doing application work and 2% of the time doing GC work). You can get more details on this in my very first blog. GC ergonomics The UseG1GC has its own version of GC ergonomics, but I'll be talking only about the UseParallelGC version. If you use this option and wanted to know what it (GC ergonomics) was thinking, try -XX:AdaptiveSizePolicyOutputInterval=1 This will print out information every i-th GC (above i is 1) about what the GC ergonomics to trying to do. For example, UseAdaptiveSizePolicy actions to meet *** throughput goal *** GC overhead (%) Young generation: 16.10 (attempted to grow) Tenured generation: 4.67 (attempted to grow) Tenuring threshold: (attempted to decrease to balance GC costs) = 1 GC ergonomics tries to meet (in order) Pause time goal Throughput goal Minimum footprint The first line says that it's trying to meet the throughput goal. UseAdaptiveSizePolicy actions to meet *** throughput goal *** This run has the default pause time goal (i.e., no pause time goal) so it is trying to reach a 98% throughput. The lines Young generation: 16.10 (attempted to grow) Tenured generation: 4.67 (attempted to grow) say that we're currently spending about 16% of the time doing young GC's and about 5% of the time doing full GC's. These percentages are a decaying, weighted average (earlier contributions to the average are given less weight). The source code is available as part of the OpenJDK so you can take a look at it if you want the exact definition. GC ergonomics is trying to increase the throughput by growing the heap (so says the "attempted to grow"). The last line Tenuring threshold: (attempted to decrease to balance GC costs) = 1 says that the ergonomics is trying to balance the GC times between young GC's and full GC's by decreasing the tenuring threshold. During a young collection the younger objects are copied to the survivor spaces while the older objects are copied to the tenured generation. Younger and older are defined by the tenuring threshold. If the tenuring threshold hold is 4, an object that has survived fewer than 4 young collections (and has remained in the young generation by being copied to the part of the young generation called a survivor space) it is younger and copied again to a survivor space. If it has survived 4 or more young collections, it is older and gets copied to the tenured generation. A lower tenuring threshold moves objects more eagerly to the tenured generation and, conversely a higher tenuring threshold keeps copying objects between survivor spaces longer. The tenuring threshold varies dynamically with the UseParallelGC collector. That is different than our other collectors which have a static tenuring threshold. GC ergonomics tries to balance the amount of work done by the young GC's and the full GC's by varying the tenuring threshold. Want more work done in the young GC's? Keep objects longer in the survivor spaces by increasing the tenuring threshold. This is an example of the output when GC ergonomics is trying to achieve a pause time goal UseAdaptiveSizePolicy actions to meet *** pause time goal *** GC overhead (%) Young generation: 20.74 (no change) Tenured generation: 31.70 (attempted to shrink) The pause goal was set at 50 millisecs and the last GC was 0.415: [Full GC (Ergonomics) [PSYoungGen: 2048K-0K(26624K)] [ParOldGen: 26095K-9711K(28992K)] 28143K-9711K(55616K), [Metaspace: 1719K-1719K(2473K/6528K)], 0.0758940 secs] [Times: user=0.28 sys=0.00, real=0.08 secs] The full collection took about 76 millisecs so GC ergonomics wants to shrink the tenured generation to reduce that pause time. The previous young GC was 0.346: [GC (Allocation Failure) [PSYoungGen: 26624K-2048K(26624K)] 40547K-22223K(56768K), 0.0136501 secs] [Times: user=0.06 sys=0.00, real=0.02 secs] so the pause time there was about 14 millisecs so no changes are needed. If trying to meet a pause time goal, the generations are typically shrunk. With a pause time goal in play, watch the GC overhead numbers and you will usually see the cost of setting a pause time goal (i.e., throughput goes down). If the pause goal is too low, you won't achieve your pause time goal and you will spend all your time doing GC. GC ergonomics is meant to be simple because it is meant to be used by anyone. It was not meant to be mysterious and so this output was added. If you don't like what GC ergonomics is doing, you can turn it off with -XX:-UseAdaptiveSizePolicy, but be pre-warned that you have to manage the size of the generations explicitly. If UseAdaptiveSizePolicy is turned off, the heap does not grow. The size of the heap (and the generations) at the start of execution is always the size of the heap. I don't like that and tried to fix it once (with some help from an OpenJDK contributor) but it unfortunately never made it out the door. I still have hope though. Just a side note. With the default throughput goal of 98% the heap often grows to it's maximum value and stays there. Definitely reduce the throughput goal if footprint is important. Start with -XX:GCTimeRatio=4 for a more modest throughput goal (%20 of the time spent in GC). A higher value means a smaller amount of time in GC (as the throughput goal).

    Read the article

  • IDEA modular problem (jsp)

    - by Jeriho
    I have project in with 2 separate modules(frontend and backend, first depends on second). When I'm trying to access backend code from frontend code, things going fine. Things turn for the worse when I do the same from jsp. This is stacktrase for simple accessign bean <jsp:useBean id="mybean" class="backend.main.MyBean" scope="request"></jsp:useBean> org.apache.jasper.JasperException: /results.jsp(9,0) The value for the useBean class attribute backend.main.MyBean is invalid. org.apache.jasper.compiler.DefaultErrorHandler.jspError(DefaultErrorHandler.java:40) org.apache.jasper.compiler.ErrorDispatcher.dispatch(ErrorDispatcher.java:407) org.apache.jasper.compiler.ErrorDispatcher.jspError(ErrorDispatcher.java:148) org.apache.jasper.compiler.Generator$GenerateVisitor.visit(Generator.java:1220) org.apache.jasper.compiler.Node$UseBean.accept(Node.java:1178) org.apache.jasper.compiler.Node$Nodes.visit(Node.java:2361) org.apache.jasper.compiler.Node$Visitor.visitBody(Node.java:2411) org.apache.jasper.compiler.Node$Visitor.visit(Node.java:2417) org.apache.jasper.compiler.Node$Root.accept(Node.java:495) org.apache.jasper.compiler.Node$Nodes.visit(Node.java:2361) org.apache.jasper.compiler.Generator.generate(Generator.java:3416) org.apache.jasper.compiler.Compiler.generateJava(Compiler.java:231) org.apache.jasper.compiler.Compiler.compile(Compiler.java:347) org.apache.jasper.compiler.Compiler.compile(Compiler.java:327) org.apache.jasper.compiler.Compiler.compile(Compiler.java:314) org.apache.jasper.JspCompilationContext.compile(JspCompilationContext.java:589) org.apache.jasper.servlet.JspServletWrapper.service(JspServletWrapper.java:317) org.apache.jasper.servlet.JspServlet.serviceJspFile(JspServlet.java:313) org.apache.jasper.servlet.JspServlet.service(JspServlet.java:260) javax.servlet.http.HttpServlet.service(HttpServlet.java:717) And this error will appear if I try to access regular class: An error occurred at line: 12 in the jsp file: /results.jsp backend.main.RegularClass cannot be resolved to a type Stacktrace: org.apache.jasper.compiler.DefaultErrorHandler.javacError(DefaultErrorHandler.java:92) org.apache.jasper.compiler.ErrorDispatcher.javacError(ErrorDispatcher.java:330) org.apache.jasper.compiler.JDTCompiler.generateClass(JDTCompiler.java:439) org.apache.jasper.compiler.Compiler.compile(Compiler.java:349) org.apache.jasper.compiler.Compiler.compile(Compiler.java:327) org.apache.jasper.compiler.Compiler.compile(Compiler.java:314) org.apache.jasper.JspCompilationContext.compile(JspCompilationContext.java:589) org.apache.jasper.servlet.JspServletWrapper.service(JspServletWrapper.java:317) org.apache.jasper.servlet.JspServlet.serviceJspFile(JspServlet.java:313) org.apache.jasper.servlet.JspServlet.service(JspServlet.java:260) javax.servlet.http.HttpServlet.service(HttpServlet.java:717) Sorry for so many stacktraces.

    Read the article

  • Java: refactoring static constants

    - by akf
    We are in the process of refactoring some code. There is a feature that we have developed in one project that we would like to now use in other projects. We are extracting the foundation of this feature and making it a full-fledged project which can then be imported by its current project and others. This effort has been relatively straight-forward but we have one headache. When the framework in question was originally developed, we chose to keep a variety of constant values defined as static fields in a single class. Over time this list of static members grew. The class is used in very many places in our code. In our current refactoring, we will be elevating some of the members of this class to our new framework, but leaving others in place. Our headache is in extracting the foundation members of this class to be used in our new project, and more specifically, how we should address those extracted members in our existing code. We know that we can have our existing Constants class subclass this new project's Constants class and it would inherit all of the parent's static members. This would allow us to effect the change without touching the code that uses these members to change the class name on the static reference. However, the tight coupling inherent in this choice doesn't feel right. before: public class ConstantsA { public static final String CONSTANT1 = "constant.1"; public static final String CONSTANT2 = "constant.2"; public static final String CONSTANT3 = "constant.3"; } after: public class ConstantsA extends ConstantsB { public static final String CONSTANT1 = "constant.1"; } public class ConstantsB { public static final String CONSTANT2 = "constant.2"; public static final String CONSTANT3 = "constant.3"; } In our existing code branch, all of the above would be accessible in this manner: ConstantsA.CONSTANT2 I would like to solicit arguments about whether this is 'acceptable' and/or what the best practices are.

    Read the article

  • Question about <foreach> task and the failonerror attribute?

    - by Mike M
    Hi guys, I have made a build file for the automated compilation of Oracle Forms files. An excerpt of the code is as follows: <target name="build" description="compiles the source code"> ... <foreach item="File" property="filename" failonerror="false" > <in> <items basedir="${source.directory}\${project.type}\Forms"> <include name="*.fmb" /> </items> </in> <do> <exec program="${forms.path}" workingdir="${source.directory}\${project.type}\Forms" commandline="module=${filename} userid=${username}/${password}@${database} batch=yes module_type=form compile_all=yes window_state=minimize" /> </do> </foreach> ... </target> The build file navigates to the directory containing the forms that the user desires fo compile and attempts to compile each form. The failonerror attribute is set to false so that the build file does not exit if a compilation error occurs. Unfortunately, however, though this prevents the build file from exiting when a compilation error occurs, it also appears to make the build file exit the task. This is a problem because, unless the form that does not compile successfully is the last to be tested (based on the filename of the form in alphanumerical decsending order), there will be one or more forms that the build file does not attempt to compile. So, for example, if the folder containing the forms that are desired to be compiled contains 10 forms and the first form does not compile successfully, the build file will not attempt to compile the remaining 9 forms (ie exit the task). Is there a way to make the build file attempt to compile remaining forms after encountering after failing to compile a form? Thanks in advance!

    Read the article

  • Using R to Analyze G1GC Log Files

    - by user12620111
    Using R to Analyze G1GC Log Files body, td { font-family: sans-serif; background-color: white; font-size: 12px; margin: 8px; } tt, code, pre { font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace; } h1 { font-size:2.2em; } h2 { font-size:1.8em; } h3 { font-size:1.4em; } h4 { font-size:1.0em; } h5 { font-size:0.9em; } h6 { font-size:0.8em; } a:visited { color: rgb(50%, 0%, 50%); } pre { margin-top: 0; max-width: 95%; border: 1px solid #ccc; white-space: pre-wrap; } pre code { display: block; padding: 0.5em; } code.r, code.cpp { background-color: #F8F8F8; } table, td, th { border: none; } blockquote { color:#666666; margin:0; padding-left: 1em; border-left: 0.5em #EEE solid; } hr { height: 0px; border-bottom: none; border-top-width: thin; border-top-style: dotted; border-top-color: #999999; } @media print { * { background: transparent !important; color: black !important; filter:none !important; -ms-filter: none !important; } body { font-size:12pt; max-width:100%; } a, a:visited { text-decoration: underline; } hr { visibility: hidden; page-break-before: always; } pre, blockquote { padding-right: 1em; page-break-inside: avoid; } tr, img { page-break-inside: avoid; } img { max-width: 100% !important; } @page :left { margin: 15mm 20mm 15mm 10mm; } @page :right { margin: 15mm 10mm 15mm 20mm; } p, h2, h3 { orphans: 3; widows: 3; } h2, h3 { page-break-after: avoid; } } pre .operator, pre .paren { color: rgb(104, 118, 135) } pre .literal { color: rgb(88, 72, 246) } pre .number { color: rgb(0, 0, 205); } pre .comment { color: rgb(76, 136, 107); } pre .keyword { color: rgb(0, 0, 255); } pre .identifier { color: rgb(0, 0, 0); } pre .string { color: rgb(3, 106, 7); } var hljs=new function(){function m(p){return p.replace(/&/gm,"&").replace(/"}while(y.length||w.length){var v=u().splice(0,1)[0];z+=m(x.substr(q,v.offset-q));q=v.offset;if(v.event=="start"){z+=t(v.node);s.push(v.node)}else{if(v.event=="stop"){var p,r=s.length;do{r--;p=s[r];z+=("")}while(p!=v.node);s.splice(r,1);while(r'+M[0]+""}else{r+=M[0]}O=P.lR.lastIndex;M=P.lR.exec(L)}return r+L.substr(O,L.length-O)}function J(L,M){if(M.sL&&e[M.sL]){var r=d(M.sL,L);x+=r.keyword_count;return r.value}else{return F(L,M)}}function I(M,r){var L=M.cN?'':"";if(M.rB){y+=L;M.buffer=""}else{if(M.eB){y+=m(r)+L;M.buffer=""}else{y+=L;M.buffer=r}}D.push(M);A+=M.r}function G(N,M,Q){var R=D[D.length-1];if(Q){y+=J(R.buffer+N,R);return false}var P=q(M,R);if(P){y+=J(R.buffer+N,R);I(P,M);return P.rB}var L=v(D.length-1,M);if(L){var O=R.cN?"":"";if(R.rE){y+=J(R.buffer+N,R)+O}else{if(R.eE){y+=J(R.buffer+N,R)+O+m(M)}else{y+=J(R.buffer+N+M,R)+O}}while(L1){O=D[D.length-2].cN?"":"";y+=O;L--;D.length--}var r=D[D.length-1];D.length--;D[D.length-1].buffer="";if(r.starts){I(r.starts,"")}return R.rE}if(w(M,R)){throw"Illegal"}}var E=e[B];var D=[E.dM];var A=0;var x=0;var y="";try{var s,u=0;E.dM.buffer="";do{s=p(C,u);var t=G(s[0],s[1],s[2]);u+=s[0].length;if(!t){u+=s[1].length}}while(!s[2]);if(D.length1){throw"Illegal"}return{r:A,keyword_count:x,value:y}}catch(H){if(H=="Illegal"){return{r:0,keyword_count:0,value:m(C)}}else{throw H}}}function g(t){var p={keyword_count:0,r:0,value:m(t)};var r=p;for(var q in e){if(!e.hasOwnProperty(q)){continue}var s=d(q,t);s.language=q;if(s.keyword_count+s.rr.keyword_count+r.r){r=s}if(s.keyword_count+s.rp.keyword_count+p.r){r=p;p=s}}if(r.language){p.second_best=r}return p}function i(r,q,p){if(q){r=r.replace(/^((]+|\t)+)/gm,function(t,w,v,u){return w.replace(/\t/g,q)})}if(p){r=r.replace(/\n/g,"")}return r}function n(t,w,r){var x=h(t,r);var v=a(t);var y,s;if(v){y=d(v,x)}else{return}var q=c(t);if(q.length){s=document.createElement("pre");s.innerHTML=y.value;y.value=k(q,c(s),x)}y.value=i(y.value,w,r);var u=t.className;if(!u.match("(\\s|^)(language-)?"+v+"(\\s|$)")){u=u?(u+" "+v):v}if(/MSIE [678]/.test(navigator.userAgent)&&t.tagName=="CODE"&&t.parentNode.tagName=="PRE"){s=t.parentNode;var p=document.createElement("div");p.innerHTML=""+y.value+"";t=p.firstChild.firstChild;p.firstChild.cN=s.cN;s.parentNode.replaceChild(p.firstChild,s)}else{t.innerHTML=y.value}t.className=u;t.result={language:v,kw:y.keyword_count,re:y.r};if(y.second_best){t.second_best={language:y.second_best.language,kw:y.second_best.keyword_count,re:y.second_best.r}}}function o(){if(o.called){return}o.called=true;var r=document.getElementsByTagName("pre");for(var p=0;p|=||=||=|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~";this.ER="(?![\\s\\S])";this.BE={b:"\\\\.",r:0};this.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[this.BE],r:0};this.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[this.BE],r:0};this.CLCM={cN:"comment",b:"//",e:"$"};this.CBLCLM={cN:"comment",b:"/\\*",e:"\\*/"};this.HCM={cN:"comment",b:"#",e:"$"};this.NM={cN:"number",b:this.NR,r:0};this.CNM={cN:"number",b:this.CNR,r:0};this.BNM={cN:"number",b:this.BNR,r:0};this.inherit=function(r,s){var p={};for(var q in r){p[q]=r[q]}if(s){for(var q in s){p[q]=s[q]}}return p}}();hljs.LANGUAGES.cpp=function(){var a={keyword:{"false":1,"int":1,"float":1,"while":1,"private":1,"char":1,"catch":1,"export":1,virtual:1,operator:2,sizeof:2,dynamic_cast:2,typedef:2,const_cast:2,"const":1,struct:1,"for":1,static_cast:2,union:1,namespace:1,unsigned:1,"long":1,"throw":1,"volatile":2,"static":1,"protected":1,bool:1,template:1,mutable:1,"if":1,"public":1,friend:2,"do":1,"return":1,"goto":1,auto:1,"void":2,"enum":1,"else":1,"break":1,"new":1,extern:1,using:1,"true":1,"class":1,asm:1,"case":1,typeid:1,"short":1,reinterpret_cast:2,"default":1,"double":1,register:1,explicit:1,signed:1,typename:1,"try":1,"this":1,"switch":1,"continue":1,wchar_t:1,inline:1,"delete":1,alignof:1,char16_t:1,char32_t:1,constexpr:1,decltype:1,noexcept:1,nullptr:1,static_assert:1,thread_local:1,restrict:1,_Bool:1,complex:1},built_in:{std:1,string:1,cin:1,cout:1,cerr:1,clog:1,stringstream:1,istringstream:1,ostringstream:1,auto_ptr:1,deque:1,list:1,queue:1,stack:1,vector:1,map:1,set:1,bitset:1,multiset:1,multimap:1,unordered_set:1,unordered_map:1,unordered_multiset:1,unordered_multimap:1,array:1,shared_ptr:1}};return{dM:{k:a,i:"",k:a,r:10,c:["self"]}]}}}();hljs.LANGUAGES.r={dM:{c:[hljs.HCM,{cN:"number",b:"\\b0[xX][0-9a-fA-F]+[Li]?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+(?:[eE][+\\-]?\\d*)?L\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+\\.(?!\\d)(?:i\\b)?",e:hljs.IMMEDIATE_RE,r:1},{cN:"number",b:"\\b\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"keyword",b:"(?:tryCatch|library|setGeneric|setGroupGeneric)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\.",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\d+(?![\\w.])",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\b(?:function)",e:hljs.IMMEDIATE_RE,r:2},{cN:"keyword",b:"(?:if|in|break|next|repeat|else|for|return|switch|while|try|stop|warning|require|attach|detach|source|setMethod|setClass)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"literal",b:"(?:NA|NA_integer_|NA_real_|NA_character_|NA_complex_)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"literal",b:"(?:NULL|TRUE|FALSE|T|F|Inf|NaN)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"identifier",b:"[a-zA-Z.][a-zA-Z0-9._]*\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"|=||   Using R to Analyze G1GC Log Files   Using R to Analyze G1GC Log Files Introduction Working in Oracle Platform Integration gives an engineer opportunities to work on a wide array of technologies. My team’s goal is to make Oracle applications run best on the Solaris/SPARC platform. When looking for bottlenecks in a modern applications, one needs to be aware of not only how the CPUs and operating system are executing, but also network, storage, and in some cases, the Java Virtual Machine. I was recently presented with about 1.5 GB of Java Garbage First Garbage Collector log file data. If you’re not familiar with the subject, you might want to review Garbage First Garbage Collector Tuning by Monica Beckwith. The customer had been running Java HotSpot 1.6.0_31 to host a web application server. I was told that the Solaris/SPARC server was running a Java process launched using a commmand line that included the following flags: -d64 -Xms9g -Xmx9g -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -XX:InitiatingHeapOccupancyPercent=80 -XX:PermSize=256m -XX:MaxPermSize=256m -XX:+PrintGC -XX:+PrintGCTimeStamps -XX:+PrintHeapAtGC -XX:+PrintGCDateStamps -XX:+PrintFlagsFinal -XX:+DisableExplicitGC -XX:+UnlockExperimentalVMOptions -XX:ParallelGCThreads=8 Several sources on the internet indicate that if I were to print out the 1.5 GB of log files, it would require enough paper to fill the bed of a pick up truck. Of course, it would be fruitless to try to scan the log files by hand. Tools will be required to summarize the contents of the log files. Others have encountered large Java garbage collection log files. There are existing tools to analyze the log files: IBM’s GC toolkit The chewiebug GCViewer gchisto HPjmeter Instead of using one of the other tools listed, I decide to parse the log files with standard Unix tools, and analyze the data with R. Data Cleansing The log files arrived in two different formats. I guess that the difference is that one set of log files was generated using a more verbose option, maybe -XX:+PrintHeapAtGC, and the other set of log files was generated without that option. Format 1 In some of the log files, the log files with the less verbose format, a single trace, i.e. the report of a singe garbage collection event, looks like this: {Heap before GC invocations=12280 (full 61): garbage-first heap total 9437184K, used 7499918K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 1 young (4096K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. 2014-05-14T07:24:00.988-0700: 60586.353: [GC pause (young) 7324M->7320M(9216M), 0.1567265 secs] Heap after GC invocations=12281 (full 61): garbage-first heap total 9437184K, used 7496533K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 0 young (0K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. } A simple grep can be used to extract a summary: $ grep "\[ GC pause (young" g1gc.log 2014-05-13T13:24:35.091-0700: 3.109: [GC pause (young) 20M->5029K(9216M), 0.0146328 secs] 2014-05-13T13:24:35.440-0700: 3.459: [GC pause (young) 9125K->6077K(9216M), 0.0086723 secs] 2014-05-13T13:24:37.581-0700: 5.599: [GC pause (young) 25M->8470K(9216M), 0.0203820 secs] 2014-05-13T13:24:42.686-0700: 10.704: [GC pause (young) 44M->15M(9216M), 0.0288848 secs] 2014-05-13T13:24:48.941-0700: 16.958: [GC pause (young) 51M->20M(9216M), 0.0491244 secs] 2014-05-13T13:24:56.049-0700: 24.066: [GC pause (young) 92M->26M(9216M), 0.0525368 secs] 2014-05-13T13:25:34.368-0700: 62.383: [GC pause (young) 602M->68M(9216M), 0.1721173 secs] But that format wasn't easily read into R, so I needed to be a bit more tricky. I used the following Unix command to create a summary file that was easy for R to read. $ echo "SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime" $ grep "\[GC pause (young" g1gc.log | grep -v mark | sed -e 's/[A-SU-z\(\),]/ /g' -e 's/->/ /' -e 's/: / /g' | more SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime 2014-05-13T13:24:35.091-0700 3.109 20 5029 9216 0.0146328 2014-05-13T13:24:35.440-0700 3.459 9125 6077 9216 0.0086723 2014-05-13T13:24:37.581-0700 5.599 25 8470 9216 0.0203820 2014-05-13T13:24:42.686-0700 10.704 44 15 9216 0.0288848 2014-05-13T13:24:48.941-0700 16.958 51 20 9216 0.0491244 2014-05-13T13:24:56.049-0700 24.066 92 26 9216 0.0525368 2014-05-13T13:25:34.368-0700 62.383 602 68 9216 0.1721173 Format 2 In some of the log files, the log files with the more verbose format, a single trace, i.e. the report of a singe garbage collection event, was more complicated than Format 1. Here is a text file with an example of a single G1GC trace in the second format. As you can see, it is quite complicated. It is nice that there is so much information available, but the level of detail can be overwhelming. I wrote this awk script (download) to summarize each trace on a single line. #!/usr/bin/env awk -f BEGIN { printf("SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize\n") } ###################### # Save count data from lines that are at the start of each G1GC trace. # Each trace starts out like this: # {Heap before GC invocations=14 (full 0): # garbage-first heap total 9437184K, used 325496K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) ###################### /{Heap.*full/{ gsub ( "\\)" , "" ); nf=split($0,a,"="); split(a[2],b," "); getline; if ( match($0, "first") ) { G1GC=1; IncrementalCount=b[1]; FullCount=substr( b[3], 1, length(b[3])-1 ); } else { G1GC=0; } } ###################### # Pull out time stamps that are in lines with this format: # 2014-05-12T14:02:06.025-0700: 94.312: [GC pause (young), 0.08870154 secs] ###################### /GC pause/ { DateTime=$1; SecondsSinceLaunch=substr($2, 1, length($2)-1); } ###################### # Heap sizes are in lines that look like this: # [ 4842M->4838M(9216M)] ###################### /\[ .*]$/ { gsub ( "\\[" , "" ); gsub ( "\ \]" , "" ); gsub ( "->" , " " ); gsub ( "\\( " , " " ); gsub ( "\ \)" , " " ); split($0,a," "); if ( split(a[1],b,"M") > 1 ) {BeforeSize=b[1]*1024;} if ( split(a[1],b,"K") > 1 ) {BeforeSize=b[1];} if ( split(a[2],b,"M") > 1 ) {AfterSize=b[1]*1024;} if ( split(a[2],b,"K") > 1 ) {AfterSize=b[1];} if ( split(a[3],b,"M") > 1 ) {TotalSize=b[1]*1024;} if ( split(a[3],b,"K") > 1 ) {TotalSize=b[1];} } ###################### # Emit an output line when you find input that looks like this: # [Times: user=1.41 sys=0.08, real=0.24 secs] ###################### /\[Times/ { if (G1GC==1) { gsub ( "," , "" ); split($2,a,"="); UserTime=a[2]; split($3,a,"="); SysTime=a[2]; split($4,a,"="); RealTime=a[2]; print DateTime,SecondsSinceLaunch,IncrementalCount,FullCount,UserTime,SysTime,RealTime,BeforeSize,AfterSize,TotalSize; G1GC=0; } } The resulting summary is about 25X smaller that the original file, but still difficult for a human to digest. SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ... 2014-05-12T18:36:34.669-0700: 3985.744 561 0 0.57 0.06 0.16 1724416 1720320 9437184 2014-05-12T18:36:34.839-0700: 3985.914 562 0 0.51 0.06 0.19 1724416 1720320 9437184 2014-05-12T18:36:35.069-0700: 3986.144 563 0 0.60 0.04 0.27 1724416 1721344 9437184 2014-05-12T18:36:35.354-0700: 3986.429 564 0 0.33 0.04 0.09 1725440 1722368 9437184 2014-05-12T18:36:35.545-0700: 3986.620 565 0 0.58 0.04 0.17 1726464 1722368 9437184 2014-05-12T18:36:35.726-0700: 3986.801 566 0 0.43 0.05 0.12 1726464 1722368 9437184 2014-05-12T18:36:35.856-0700: 3986.930 567 0 0.30 0.04 0.07 1726464 1723392 9437184 2014-05-12T18:36:35.947-0700: 3987.023 568 0 0.61 0.04 0.26 1727488 1723392 9437184 2014-05-12T18:36:36.228-0700: 3987.302 569 0 0.46 0.04 0.16 1731584 1724416 9437184 Reading the Data into R Once the GC log data had been cleansed, either by processing the first format with the shell script, or by processing the second format with the awk script, it was easy to read the data into R. g1gc.df = read.csv("summary.txt", row.names = NULL, stringsAsFactors=FALSE,sep="") str(g1gc.df) ## 'data.frame': 8307 obs. of 10 variables: ## $ row.names : chr "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ... ## $ SecondsSinceLaunch: num 1.16 1.47 1.97 3.83 6.1 ... ## $ IncrementalCount : int 0 1 2 3 4 5 6 7 8 9 ... ## $ FullCount : int 0 0 0 0 0 0 0 0 0 0 ... ## $ UserTime : num 0.11 0.05 0.04 0.21 0.08 0.26 0.31 0.33 0.34 0.56 ... ## $ SysTime : num 0.04 0.01 0.01 0.05 0.01 0.06 0.07 0.06 0.07 0.09 ... ## $ RealTime : num 0.02 0.02 0.01 0.04 0.02 0.04 0.05 0.04 0.04 0.06 ... ## $ BeforeSize : int 8192 5496 5768 22528 24576 43008 34816 53248 55296 93184 ... ## $ AfterSize : int 1400 1672 2557 4907 7072 14336 16384 18432 19456 21504 ... ## $ TotalSize : int 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 ... head(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount ## 1 2014-05-12T14:00:32.868-0700: 1.161 0 ## 2 2014-05-12T14:00:33.179-0700: 1.472 1 ## 3 2014-05-12T14:00:33.677-0700: 1.969 2 ## 4 2014-05-12T14:00:35.538-0700: 3.830 3 ## 5 2014-05-12T14:00:37.811-0700: 6.103 4 ## 6 2014-05-12T14:00:41.428-0700: 9.720 5 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 1 0 0.11 0.04 0.02 8192 1400 9437184 ## 2 0 0.05 0.01 0.02 5496 1672 9437184 ## 3 0 0.04 0.01 0.01 5768 2557 9437184 ## 4 0 0.21 0.05 0.04 22528 4907 9437184 ## 5 0 0.08 0.01 0.02 24576 7072 9437184 ## 6 0 0.26 0.06 0.04 43008 14336 9437184 Basic Statistics Once the data has been read into R, simple statistics are very easy to generate. All of the numbers from high school statistics are available via simple commands. For example, generate a summary of every column: summary(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount FullCount ## Length:8307 Min. : 1 Min. : 0 Min. : 0.0 ## Class :character 1st Qu.: 9977 1st Qu.:2048 1st Qu.: 0.0 ## Mode :character Median :12855 Median :4136 Median : 12.0 ## Mean :12527 Mean :4156 Mean : 31.6 ## 3rd Qu.:15758 3rd Qu.:6262 3rd Qu.: 61.0 ## Max. :55484 Max. :8391 Max. :113.0 ## UserTime SysTime RealTime BeforeSize ## Min. :0.040 Min. :0.0000 Min. : 0.0 Min. : 5476 ## 1st Qu.:0.470 1st Qu.:0.0300 1st Qu.: 0.1 1st Qu.:5137920 ## Median :0.620 Median :0.0300 Median : 0.1 Median :6574080 ## Mean :0.751 Mean :0.0355 Mean : 0.3 Mean :5841855 ## 3rd Qu.:0.920 3rd Qu.:0.0400 3rd Qu.: 0.2 3rd Qu.:7084032 ## Max. :3.370 Max. :1.5600 Max. :488.1 Max. :8696832 ## AfterSize TotalSize ## Min. : 1380 Min. :9437184 ## 1st Qu.:5002752 1st Qu.:9437184 ## Median :6559744 Median :9437184 ## Mean :5785454 Mean :9437184 ## 3rd Qu.:7054336 3rd Qu.:9437184 ## Max. :8482816 Max. :9437184 Q: What is the total amount of User CPU time spent in garbage collection? sum(g1gc.df$UserTime) ## [1] 6236 As you can see, less than two hours of CPU time was spent in garbage collection. Is that too much? To find the percentage of time spent in garbage collection, divide the number above by total_elapsed_time*CPU_count. In this case, there are a lot of CPU’s and it turns out the the overall amount of CPU time spent in garbage collection isn’t a problem when viewed in isolation. When calculating rates, i.e. events per unit time, you need to ask yourself if the rate is homogenous across the time period in the log file. Does the log file include spikes of high activity that should be separately analyzed? Averaging in data from nights and weekends with data from business hours may alias problems. If you have a reason to suspect that the garbage collection rates include peaks and valleys that need independent analysis, see the “Time Series” section, below. Q: How much garbage is collected on each pass? The amount of heap space that is recovered per GC pass is surprisingly low: At least one collection didn’t recover any data. (“Min.=0”) 25% of the passes recovered 3MB or less. (“1st Qu.=3072”) Half of the GC passes recovered 4MB or less. (“Median=4096”) The average amount recovered was 56MB. (“Mean=56390”) 75% of the passes recovered 36MB or less. (“3rd Qu.=36860”) At least one pass recovered 2GB. (“Max.=2121000”) g1gc.df$Delta = g1gc.df$BeforeSize - g1gc.df$AfterSize summary(g1gc.df$Delta) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0 3070 4100 56400 36900 2120000 Q: What is the maximum User CPU time for a single collection? The worst garbage collection (“Max.”) is many standard deviations away from the mean. The data appears to be right skewed. summary(g1gc.df$UserTime) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.040 0.470 0.620 0.751 0.920 3.370 sd(g1gc.df$UserTime) ## [1] 0.3966 Basic Graphics Once the data is in R, it is trivial to plot the data with formats including dot plots, line charts, bar charts (simple, stacked, grouped), pie charts, boxplots, scatter plots histograms, and kernel density plots. Histogram of User CPU Time per Collection I don't think that this graph requires any explanation. hist(g1gc.df$UserTime, main="User CPU Time per Collection", xlab="Seconds", ylab="Frequency") Box plot to identify outliers When the initial data is viewed with a box plot, you can see the one crazy outlier in the real time per GC. Save this data point for future analysis and drop the outlier so that it’s not throwing off our statistics. Now the box plot shows many outliers, which will be examined later, using times series analysis. Notice that the scale of the x-axis changes drastically once the crazy outlier is removed. par(mfrow=c(2,1)) boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(dominated by a crazy outlier)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") crazy.outlier.df=g1gc.df[g1gc.df$RealTime > 400,] g1gc.df=g1gc.df[g1gc.df$RealTime < 400,] boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(crazy outlier excluded)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") box(which = "outer", lty = "solid") Here is the crazy outlier for future analysis: crazy.outlier.df ## row.names SecondsSinceLaunch IncrementalCount ## 8233 2014-05-12T23:15:43.903-0700: 20741 8316 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 8233 112 0.55 0.42 488.1 8381440 8235008 9437184 ## Delta ## 8233 146432 R Time Series Data To analyze the garbage collection as a time series, I’ll use Z’s Ordered Observations (zoo). “zoo is the creator for an S3 class of indexed totally ordered observations which includes irregular time series.” require(zoo) ## Loading required package: zoo ## ## Attaching package: 'zoo' ## ## The following objects are masked from 'package:base': ## ## as.Date, as.Date.numeric head(g1gc.df[,1]) ## [1] "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" ## [3] "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ## [5] "2014-05-12T14:00:37.811-0700:" "2014-05-12T14:00:41.428-0700:" options("digits.secs"=3) times=as.POSIXct( g1gc.df[,1], format="%Y-%m-%dT%H:%M:%OS%z:") g1gc.z = zoo(g1gc.df[,-c(1)], order.by=times) head(g1gc.z) ## SecondsSinceLaunch IncrementalCount FullCount ## 2014-05-12 17:00:32.868 1.161 0 0 ## 2014-05-12 17:00:33.178 1.472 1 0 ## 2014-05-12 17:00:33.677 1.969 2 0 ## 2014-05-12 17:00:35.538 3.830 3 0 ## 2014-05-12 17:00:37.811 6.103 4 0 ## 2014-05-12 17:00:41.427 9.720 5 0 ## UserTime SysTime RealTime BeforeSize AfterSize ## 2014-05-12 17:00:32.868 0.11 0.04 0.02 8192 1400 ## 2014-05-12 17:00:33.178 0.05 0.01 0.02 5496 1672 ## 2014-05-12 17:00:33.677 0.04 0.01 0.01 5768 2557 ## 2014-05-12 17:00:35.538 0.21 0.05 0.04 22528 4907 ## 2014-05-12 17:00:37.811 0.08 0.01 0.02 24576 7072 ## 2014-05-12 17:00:41.427 0.26 0.06 0.04 43008 14336 ## TotalSize Delta ## 2014-05-12 17:00:32.868 9437184 6792 ## 2014-05-12 17:00:33.178 9437184 3824 ## 2014-05-12 17:00:33.677 9437184 3211 ## 2014-05-12 17:00:35.538 9437184 17621 ## 2014-05-12 17:00:37.811 9437184 17504 ## 2014-05-12 17:00:41.427 9437184 28672 Example of Two Benchmark Runs in One Log File The data in the following graph is from a different log file, not the one of primary interest to this article. I’m including this image because it is an example of idle periods followed by busy periods. It would be uninteresting to average the rate of garbage collection over the entire log file period. More interesting would be the rate of garbage collect in the two busy periods. Are they the same or different? Your production data may be similar, for example, bursts when employees return from lunch and idle times on weekend evenings, etc. Once the data is in an R Time Series, you can analyze isolated time windows. Clipping the Time Series data Flashing back to our test case… Viewing the data as a time series is interesting. You can see that the work intensive time period is between 9:00 PM and 3:00 AM. Lets clip the data to the interesting period:     par(mfrow=c(2,1)) plot(g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Complete Log File", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") clipped.g1gc.z=window(g1gc.z, start=as.POSIXct("2014-05-12 21:00:00"), end=as.POSIXct("2014-05-13 03:00:00")) plot(clipped.g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Limited to Benchmark Execution", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") box(which = "outer", lty = "solid") Cumulative Incremental and Full GC count Here is the cumulative incremental and full GC count. When the line is very steep, it indicates that the GCs are repeating very quickly. Notice that the scale on the Y axis is different for full vs. incremental. plot(clipped.g1gc.z[,c(2:3)], main="Cumulative Incremental and Full GC count", xlab="Time of Day", col="#1b9e77") GC Analysis of Benchmark Execution using Time Series data In the following series of 3 graphs: The “After Size” show the amount of heap space in use after each garbage collection. Many Java objects are still referenced, i.e. alive, during each garbage collection. This may indicate that the application has a memory leak, or may indicate that the application has a very large memory footprint. Typically, an application's memory footprint plateau's in the early stage of execution. One would expect this graph to have a flat top. The steep decline in the heap space may indicate that the application crashed after 2:00. The second graph shows that the outliers in real execution time, discussed above, occur near 2:00. when the Java heap seems to be quite full. The third graph shows that Full GCs are infrequent during the first few hours of execution. The rate of Full GC's, (the slope of the cummulative Full GC line), changes near midnight.   plot(clipped.g1gc.z[,c("AfterSize","RealTime","FullCount")], xlab="Time of Day", col=c("#1b9e77","red","#1b9e77")) GC Analysis of heap recovered Each GC trace includes the amount of heap space in use before and after the individual GC event. During garbage coolection, unreferenced objects are identified, the space holding the unreferenced objects is freed, and thus, the difference in before and after usage indicates how much space has been freed. The following box plot and bar chart both demonstrate the same point - the amount of heap space freed per garbage colloection is surprisingly low. par(mfrow=c(2,1)) boxplot(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", horizontal = TRUE, col="red") hist(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", breaks=100, col="red") box(which = "outer", lty = "solid") This graph is the most interesting. The dark blue area shows how much heap is occupied by referenced Java objects. This represents memory that holds live data. The red fringe at the top shows how much data was recovered after each garbage collection. barplot(clipped.g1gc.z[,c("AfterSize","Delta")], col=c("#7570b3","#e7298a"), xlab="Time of Day", border=NA) legend("topleft", c("Live Objects","Heap Recovered on GC"), fill=c("#7570b3","#e7298a")) box(which = "outer", lty = "solid") When I discuss the data in the log files with the customer, I will ask for an explaination for the large amount of referenced data resident in the Java heap. There are two are posibilities: There is a memory leak and the amount of space required to hold referenced objects will continue to grow, limited only by the maximum heap size. After the maximum heap size is reached, the JVM will throw an “Out of Memory” exception every time that the application tries to allocate a new object. If this is the case, the aplication needs to be debugged to identify why old objects are referenced when they are no longer needed. The application has a legitimate requirement to keep a large amount of data in memory. The customer may want to further increase the maximum heap size. Another possible solution would be to partition the application across multiple cluster nodes, where each node has responsibility for managing a unique subset of the data. Conclusion In conclusion, R is a very powerful tool for the analysis of Java garbage collection log files. The primary difficulty is data cleansing so that information can be read into an R data frame. Once the data has been read into R, a rich set of tools may be used for thorough evaluation.

    Read the article

  • PL/SQL pre-compile and Code Quality checks in an automatted build environment?

    - by Lars Corneliussen
    We build software using Hudson and Maven. We have C#, java and last, but not least PL/SQL sources (sprocs, packages, DDL, crud) For C# and Java we do unit tests and code analysis, but we don't really know the health of our PL/SQL sources before we actually publish them to the target database. Requirements There are a couple of things we wan't to test in the following priority: Are the sources valid, hence "compilable"? For packages, with respect to a certain database, would they compile? Code Quality: Do we have code flaws like duplicates, too complex methods or other violations to a defined set of rules? Also, the tool must run head-less (commandline, ant, ...) we wan't to do analysis on a partial code base (changed sources only) Tools We did a little research and found the following tools that could potencially help: Cast Application Intelligence Platform (AIP): Seems to be a server that grasps information about "anything". Couldn't find a console version that would export in readable format. Toad for Oracle: The Professional version is said to include something called Xpert validates a set of rules against a code base. Sonar + PL/SQL-Plugin: Uses Toad for Oracle to display code-health the sonar-way. This is for browsing the current state of the code base. Semantic Designs DMSToolkit: Quite general analysis of source code base. Commandline available? Semantic Designs Clones Detector: Detects clones. But also via command line? Fortify Source Code Analyzer: Seems to be focussed on security issues. But maybe it is extensible? more... So far, Toad for Oracle together with Sonar seems to be an elegant solution. But may be we are missing something here? Any ideas? Other products? Experiences? Related Questions on SO: http://stackoverflow.com/questions/531430/any-static-code-analysis-tools-for-stored-procedures http://stackoverflow.com/questions/839707/any-code-quality-tool-for-pl-sql http://stackoverflow.com/questions/956104/is-there-a-static-analysis-tool-for-python-ruby-sql-cobol-perl-and-pl-sql

    Read the article

  • DataTables warning (table id = 'example-advanced'): Cannot reinitialise DataTable while using treetable and datatable at the same time

    - by Nyaro
    DataTables warning (table id = 'example-advanced'): Cannot reinitialise DataTable while using treetable and datatable at the same time. Here is my code: <script src="jquery-1.7.2.min.js"></script> <script src='jquery.dataTables.min.js'></script> <script src="jquery.treetable.js"></script> <script> $("#example-advanced").treetable({ expandable: true }); </script> <script> $('#example-advanced').dataTable( { "bSort": false } ); </script> Actually I wanted to get rid of the sorting part of the datatable coz it was giving error in treetable display so i want the sorting part from the datatable out and keep other functions like search and pagination. Please help me out. Thanks in advance.

    Read the article

facebook twitter digg google delicious technorati stumbleupon myspace wordpress linkedin gmail igoogle windows live tumblr viadeo yahoo buzz yahoo mail yahoo bookmarks favorites email print

< Previous Page | 182 183 184 185 186 187 188 189 190 191 192 193  | Next Page >