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  • Intger encoding and decoding problem

    - by aASDASD
    I have a long list of integers, and i need to reduce this down to a single integer. The integer list can be anywhere from 0 to 300 ints long (about). I need to be able to encode/decode. Is there a better option than a lookup table?

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  • C++ WCHAR: Cannot allocate an array of constant size 0

    - by Rosarch
    I'm trying to create a WCHAR: LONG bufferSize = foo.bar() + 1; WCHAR wszBaz[bufferSize]; The compiler issues an error: error C2057: expected constant expression error C2466: cannot allocate an array of constant size 0 error C2133: 'wszBaz' unknown size What am I doing wrong? UPDATE: I added const but it still gives the same error: const LONG bufferSize = foo.bar() + 1; WCHAR wszBaz[bufferSize];

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  • In Vim, how to swap 2 non adjacent patterns?

    - by ThG
    I have lines of text, all with the same structure, and would like to make a permutation of 2 elements on all lines: 1257654 some text (which may be long) #Foo 1543098 some other text #Barbar 1238769 whatever #Baz 2456874 something else #Quux I want to obtain : #Foo some text (which may be long) 1257654 #Barbar some other text 1543098 #Baz whatever 1238769 #Quux something else 2456874 This is where I am stuck : :%s/\(\d\{7\}\)\(#.\{-}\)/\2\1/ Where did I go wrong ?

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  • c language: printf help

    - by dydx
    Hi again, here is my coding which gives me the error 'warning: unknown conversion type character 0x20 in format' int subtotal; long long a,b,c,d,e,f,g,h,i,j,k,l,m; subtotal = (1*(a+c+e+g+i+k))+(3*(b+d+f+h+j+l)); printf(" = %d % 10 = %d; (10 - %d) % 10 = %lld\n", subtotal,subtotal%10,subtotal%10,m); any idea why this is wrong?

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  • Multilingual dates in gridview

    - by Moox
    I'm replacing a website that was previously HTML only. I put a GridView on the French and the English page and I'm getting 2 fields: A date and a currency. For the date, I need to transform it into a long date format. The English part isn't very hard, but when I get to the French page, I can't transform it into a long date format with French words (i.e.:Mardi instead of Tuesday). Is there any way to format the string with French date words instead?

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  • Dynamic allocation in C

    - by Kerby82
    I'm writing a program and I have the following problem: char *tmp; sprintf (tmp,"%ld",(long)time_stamp_for_file_name); Could someone explain how much memory allocate for the string tmp. How many chars are a long variable? Thank you, I would appreciate also a link to an exahustive resource on this kind of information. Thank you

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  • Moving to 64-bit on OS X?

    - by ML
    Hi All, What is the best practice for moving to 64-bit on OS X? Using the 10.6 SDK and 64-bit intel as my SDK and target. I have int32 types to change Does OS X have an 'int64' or would one use a 'long long'? Where might I find a resource to available data types? What other issues are there?

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  • CUDA memory transfer issue

    - by Vaibhav Sundriyal
    I am trying to execute a code which first transfers data from CPU to GPU memory and vice-versa. In spite of increasing the volume of data, the data transfer time remains the same as if no data transfer is actually taking place. I am posting the code. #include <stdio.h> /* Core input/output operations */ #include <stdlib.h> /* Conversions, random numbers, memory allocation, etc. */ #include <math.h> /* Common mathematical functions */ #include <time.h> /* Converting between various date/time formats */ #include <cuda.h> /* CUDA related stuff */ #include <sys/time.h> __global__ void device_volume(float *x_d,float *y_d) { int index = blockIdx.x * blockDim.x + threadIdx.x; } int main(void) { float *x_h,*y_h,*x_d,*y_d,*z_h,*z_d; long long size=9999999; long long nbytes=size*sizeof(float); timeval t1,t2; double et; x_h=(float*)malloc(nbytes); y_h=(float*)malloc(nbytes); z_h=(float*)malloc(nbytes); cudaMalloc((void **)&x_d,size*sizeof(float)); cudaMalloc((void **)&y_d,size*sizeof(float)); cudaMalloc((void **)&z_d,size*sizeof(float)); gettimeofday(&t1,NULL); cudaMemcpy(x_d, x_h, nbytes, cudaMemcpyHostToDevice); cudaMemcpy(y_d, y_h, nbytes, cudaMemcpyHostToDevice); cudaMemcpy(z_d, z_h, nbytes, cudaMemcpyHostToDevice); gettimeofday(&t2,NULL); et = (t2.tv_sec - t1.tv_sec) * 1000.0; // sec to ms et += (t2.tv_usec - t1.tv_usec) / 1000.0; // us to ms printf("\n %ld\t\t%f\t\t",nbytes,et); et=0.0; //printf("%f %d\n",seconds,CLOCKS_PER_SEC); // launch a kernel with a single thread to greet from the device //device_volume<<<1,1>>>(x_d,y_d); gettimeofday(&t1,NULL); cudaMemcpy(x_h, x_d, nbytes, cudaMemcpyDeviceToHost); cudaMemcpy(y_h, y_d, nbytes, cudaMemcpyDeviceToHost); cudaMemcpy(z_h, z_d, nbytes, cudaMemcpyDeviceToHost); gettimeofday(&t2,NULL); et = (t2.tv_sec - t1.tv_sec) * 1000.0; // sec to ms et += (t2.tv_usec - t1.tv_usec) / 1000.0; // us to ms printf("%f\n",et); cudaFree(x_d); cudaFree(y_d); cudaFree(z_d); return 0; } Can anybody help me with this issue? Thanks

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  • access javascrip variable in java

    - by chetan
    function test(custId){ List<Account> list=accountDelegate.findAccountEntityByGroupId((long)%> custId<%)%>; <script> } Here my question is how to send custId to java method as parameter from java script.where java method takes long argument.

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  • Number of the different elements in an array.

    - by AB
    Is it possible to compute the number of the different elements in an array in linear time and constant space? Let us say it's an array of long integers, and you can not allocate an array of length sizeof(long). P.S. Not homework, just curious. I've got a book that sort of implies that it is possible.

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  • cut text after (x) amount of characters

    - by Blackbird
    This is in wordpress (not sure that makes a difference) This bit of php outputs the post title <?php echo $data['nameofpost']; ?> It's simple text which can be anywhere up to 100 chars long. What i'd like is if the chars outputted are over 20 long to display '...' or simply nothing at all. Thanks

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  • Capturing and Transforming ASP.NET Output with Response.Filter

    - by Rick Strahl
    During one of my Handlers and Modules session at DevConnections this week one of the attendees asked a question that I didn’t have an immediate answer for. Basically he wanted to capture response output completely and then apply some filtering to the output – effectively injecting some additional content into the page AFTER the page had completely rendered. Specifically the output should be captured from anywhere – not just a page and have this code injected into the page. Some time ago I posted some code that allows you to capture ASP.NET Page output by overriding the Render() method, capturing the HtmlTextWriter() and reading its content, modifying the rendered data as text then writing it back out. I’ve actually used this approach on a few occasions and it works fine for ASP.NET pages. But this obviously won’t work outside of the Page class environment and it’s not really generic – you have to create a custom page class in order to handle the output capture. [updated 11/16/2009 – updated ResponseFilterStream implementation and a few additional notes based on comments] Enter Response.Filter However, ASP.NET includes a Response.Filter which can be used – well to filter output. Basically Response.Filter is a stream through which the OutputStream is piped back to the Web Server (indirectly). As content is written into the Response object, the filter stream receives the appropriate Stream commands like Write, Flush and Close as well as read operations although for a Response.Filter that’s uncommon to be hit. The Response.Filter can be programmatically replaced at runtime which allows you to effectively intercept all output generation that runs through ASP.NET. A common Example: Dynamic GZip Encoding A rather common use of Response.Filter hooking up code based, dynamic  GZip compression for requests which is dead simple by applying a GZipStream (or DeflateStream) to Response.Filter. The following generic routines can be used very easily to detect GZip capability of the client and compress response output with a single line of code and a couple of library helper routines: WebUtils.GZipEncodePage(); which is handled with a few lines of reusable code and a couple of static helper methods: /// <summary> ///Sets up the current page or handler to use GZip through a Response.Filter ///IMPORTANT:  ///You have to call this method before any output is generated! /// </summary> public static void GZipEncodePage() {     HttpResponse Response = HttpContext.Current.Response;     if(IsGZipSupported())     {         stringAcceptEncoding = HttpContext.Current.Request.Headers["Accept-Encoding"];         if(AcceptEncoding.Contains("deflate"))         {             Response.Filter = newSystem.IO.Compression.DeflateStream(Response.Filter,                                        System.IO.Compression.CompressionMode.Compress);             Response.AppendHeader("Content-Encoding", "deflate");         }         else        {             Response.Filter = newSystem.IO.Compression.GZipStream(Response.Filter,                                       System.IO.Compression.CompressionMode.Compress);             Response.AppendHeader("Content-Encoding", "gzip");                            }     }     // Allow proxy servers to cache encoded and unencoded versions separately    Response.AppendHeader("Vary", "Content-Encoding"); } /// <summary> /// Determines if GZip is supported /// </summary> /// <returns></returns> public static bool IsGZipSupported() { string AcceptEncoding = HttpContext.Current.Request.Headers["Accept-Encoding"]; if (!string.IsNullOrEmpty(AcceptEncoding) && (AcceptEncoding.Contains("gzip") || AcceptEncoding.Contains("deflate"))) return true; return false; } GZipStream and DeflateStream are streams that are assigned to Response.Filter and by doing so apply the appropriate compression on the active Response. Response.Filter content is chunked So to implement a Response.Filter effectively requires only that you implement a custom stream and handle the Write() method to capture Response output as it’s written. At first blush this seems very simple – you capture the output in Write, transform it and write out the transformed content in one pass. And that indeed works for small amounts of content. But you see, the problem is that output is written in small buffer chunks (a little less than 16k it appears) rather than just a single Write() statement into the stream, which makes perfect sense for ASP.NET to stream data back to IIS in smaller chunks to minimize memory usage en route. Unfortunately this also makes it a more difficult to implement any filtering routines since you don’t directly get access to all of the response content which is problematic especially if those filtering routines require you to look at the ENTIRE response in order to transform or capture the output as is needed for the solution the gentleman in my session asked for. So in order to address this a slightly different approach is required that basically captures all the Write() buffers passed into a cached stream and then making the stream available only when it’s complete and ready to be flushed. As I was thinking about the implementation I also started thinking about the few instances when I’ve used Response.Filter implementations. Each time I had to create a new Stream subclass and create my custom functionality but in the end each implementation did the same thing – capturing output and transforming it. I thought there should be an easier way to do this by creating a re-usable Stream class that can handle stream transformations that are common to Response.Filter implementations. Creating a semi-generic Response Filter Stream Class What I ended up with is a ResponseFilterStream class that provides a handful of Events that allow you to capture and/or transform Response content. The class implements a subclass of Stream and then overrides Write() and Flush() to handle capturing and transformation operations. By exposing events it’s easy to hook up capture or transformation operations via single focused methods. ResponseFilterStream exposes the following events: CaptureStream, CaptureString Captures the output only and provides either a MemoryStream or String with the final page output. Capture is hooked to the Flush() operation of the stream. TransformStream, TransformString Allows you to transform the complete response output with events that receive a MemoryStream or String respectively and can you modify the output then return it back as a return value. The transformed output is then written back out in a single chunk to the response output stream. These events capture all output internally first then write the entire buffer into the response. TransformWrite, TransformWriteString Allows you to transform the Response data as it is written in its original chunk size in the Stream’s Write() method. Unlike TransformStream/TransformString which operate on the complete output, these events only see the current chunk of data written. This is more efficient as there’s no caching involved, but can cause problems due to searched content splitting over multiple chunks. Using this implementation, creating a custom Response.Filter transformation becomes as simple as the following code. To hook up the Response.Filter using the MemoryStream version event: ResponseFilterStream filter = new ResponseFilterStream(Response.Filter); filter.TransformStream += filter_TransformStream; Response.Filter = filter; and the event handler to do the transformation: MemoryStream filter_TransformStream(MemoryStream ms) { Encoding encoding = HttpContext.Current.Response.ContentEncoding; string output = encoding.GetString(ms.ToArray()); output = FixPaths(output); ms = new MemoryStream(output.Length); byte[] buffer = encoding.GetBytes(output); ms.Write(buffer,0,buffer.Length); return ms; } private string FixPaths(string output) { string path = HttpContext.Current.Request.ApplicationPath; // override root path wonkiness if (path == "/") path = ""; output = output.Replace("\"~/", "\"" + path + "/").Replace("'~/", "'" + path + "/"); return output; } The idea of the event handler is that you can do whatever you want to the stream and return back a stream – either the same one that’s been modified or a brand new one – which is then sent back to as the final response. The above code can be simplified even more by using the string version events which handle the stream to string conversions for you: ResponseFilterStream filter = new ResponseFilterStream(Response.Filter); filter.TransformString += filter_TransformString; Response.Filter = filter; and the event handler to do the transformation calling the same FixPaths method shown above: string filter_TransformString(string output) { return FixPaths(output); } The events for capturing output and capturing and transforming chunks work in a very similar way. By using events to handle the transformations ResponseFilterStream becomes a reusable component and we don’t have to create a new stream class or subclass an existing Stream based classed. By the way, the example used here is kind of a cool trick which transforms “~/” expressions inside of the final generated HTML output – even in plain HTML controls not HTML controls – and transforms them into the appropriate application relative path in the same way that ResolveUrl would do. So you can write plain old HTML like this: <a href=”~/default.aspx”>Home</a>  and have it turned into: <a href=”/myVirtual/default.aspx”>Home</a>  without having to use an ASP.NET control like Hyperlink or Image or having to constantly use: <img src=”<%= ResolveUrl(“~/images/home.gif”) %>” /> in MVC applications (which frankly is one of the most annoying things about MVC especially given the path hell that extension-less and endpoint-less URLs impose). I can’t take credit for this idea. While discussing the Response.Filter issues on Twitter a hint from Dylan Beattie who pointed me at one of his examples which does something similar. I thought the idea was cool enough to use an example for future demos of Response.Filter functionality in ASP.NET next I time I do the Modules and Handlers talk (which was great fun BTW). How practical this is is debatable however since there’s definitely some overhead to using a Response.Filter in general and especially on one that caches the output and the re-writes it later. Make sure to test for performance anytime you use Response.Filter hookup and make sure it' doesn’t end up killing perf on you. You’ve been warned :-}. How does ResponseFilterStream work? The big win of this implementation IMHO is that it’s a reusable  component – so for implementation there’s no new class, no subclassing – you simply attach to an event to implement an event handler method with a straight forward signature to retrieve the stream or string you’re interested in. The implementation is based on a subclass of Stream as is required in order to handle the Response.Filter requirements. What’s different than other implementations I’ve seen in various places is that it supports capturing output as a whole to allow retrieving the full response output for capture or modification. The exception are the TransformWrite and TransformWrite events which operate only active chunk of data written by the Response. For captured output, the Write() method captures output into an internal MemoryStream that is cached until writing is complete. So Write() is called when ASP.NET writes to the Response stream, but the filter doesn’t pass on the Write immediately to the filter’s internal stream. The data is cached and only when the Flush() method is called to finalize the Stream’s output do we actually send the cached stream off for transformation (if the events are hooked up) and THEN finally write out the returned content in one big chunk. Here’s the implementation of ResponseFilterStream: /// <summary> /// A semi-generic Stream implementation for Response.Filter with /// an event interface for handling Content transformations via /// Stream or String. /// <remarks> /// Use with care for large output as this implementation copies /// the output into a memory stream and so increases memory usage. /// </remarks> /// </summary> public class ResponseFilterStream : Stream { /// <summary> /// The original stream /// </summary> Stream _stream; /// <summary> /// Current position in the original stream /// </summary> long _position; /// <summary> /// Stream that original content is read into /// and then passed to TransformStream function /// </summary> MemoryStream _cacheStream = new MemoryStream(5000); /// <summary> /// Internal pointer that that keeps track of the size /// of the cacheStream /// </summary> int _cachePointer = 0; /// <summary> /// /// </summary> /// <param name="responseStream"></param> public ResponseFilterStream(Stream responseStream) { _stream = responseStream; } /// <summary> /// Determines whether the stream is captured /// </summary> private bool IsCaptured { get { if (CaptureStream != null || CaptureString != null || TransformStream != null || TransformString != null) return true; return false; } } /// <summary> /// Determines whether the Write method is outputting data immediately /// or delaying output until Flush() is fired. /// </summary> private bool IsOutputDelayed { get { if (TransformStream != null || TransformString != null) return true; return false; } } /// <summary> /// Event that captures Response output and makes it available /// as a MemoryStream instance. Output is captured but won't /// affect Response output. /// </summary> public event Action<MemoryStream> CaptureStream; /// <summary> /// Event that captures Response output and makes it available /// as a string. Output is captured but won't affect Response output. /// </summary> public event Action<string> CaptureString; /// <summary> /// Event that allows you transform the stream as each chunk of /// the output is written in the Write() operation of the stream. /// This means that that it's possible/likely that the input /// buffer will not contain the full response output but only /// one of potentially many chunks. /// /// This event is called as part of the filter stream's Write() /// operation. /// </summary> public event Func<byte[], byte[]> TransformWrite; /// <summary> /// Event that allows you to transform the response stream as /// each chunk of bytep[] output is written during the stream's write /// operation. This means it's possibly/likely that the string /// passed to the handler only contains a portion of the full /// output. Typical buffer chunks are around 16k a piece. /// /// This event is called as part of the stream's Write operation. /// </summary> public event Func<string, string> TransformWriteString; /// <summary> /// This event allows capturing and transformation of the entire /// output stream by caching all write operations and delaying final /// response output until Flush() is called on the stream. /// </summary> public event Func<MemoryStream, MemoryStream> TransformStream; /// <summary> /// Event that can be hooked up to handle Response.Filter /// Transformation. Passed a string that you can modify and /// return back as a return value. The modified content /// will become the final output. /// </summary> public event Func<string, string> TransformString; protected virtual void OnCaptureStream(MemoryStream ms) { if (CaptureStream != null) CaptureStream(ms); } private void OnCaptureStringInternal(MemoryStream ms) { if (CaptureString != null) { string content = HttpContext.Current.Response.ContentEncoding.GetString(ms.ToArray()); OnCaptureString(content); } } protected virtual void OnCaptureString(string output) { if (CaptureString != null) CaptureString(output); } protected virtual byte[] OnTransformWrite(byte[] buffer) { if (TransformWrite != null) return TransformWrite(buffer); return buffer; } private byte[] OnTransformWriteStringInternal(byte[] buffer) { Encoding encoding = HttpContext.Current.Response.ContentEncoding; string output = OnTransformWriteString(encoding.GetString(buffer)); return encoding.GetBytes(output); } private string OnTransformWriteString(string value) { if (TransformWriteString != null) return TransformWriteString(value); return value; } protected virtual MemoryStream OnTransformCompleteStream(MemoryStream ms) { if (TransformStream != null) return TransformStream(ms); return ms; } /// <summary> /// Allows transforming of strings /// /// Note this handler is internal and not meant to be overridden /// as the TransformString Event has to be hooked up in order /// for this handler to even fire to avoid the overhead of string /// conversion on every pass through. /// </summary> /// <param name="responseText"></param> /// <returns></returns> private string OnTransformCompleteString(string responseText) { if (TransformString != null) TransformString(responseText); return responseText; } /// <summary> /// Wrapper method form OnTransformString that handles /// stream to string and vice versa conversions /// </summary> /// <param name="ms"></param> /// <returns></returns> internal MemoryStream OnTransformCompleteStringInternal(MemoryStream ms) { if (TransformString == null) return ms; //string content = ms.GetAsString(); string content = HttpContext.Current.Response.ContentEncoding.GetString(ms.ToArray()); content = TransformString(content); byte[] buffer = HttpContext.Current.Response.ContentEncoding.GetBytes(content); ms = new MemoryStream(); ms.Write(buffer, 0, buffer.Length); //ms.WriteString(content); return ms; } /// <summary> /// /// </summary> public override bool CanRead { get { return true; } } public override bool CanSeek { get { return true; } } /// <summary> /// /// </summary> public override bool CanWrite { get { return true; } } /// <summary> /// /// </summary> public override long Length { get { return 0; } } /// <summary> /// /// </summary> public override long Position { get { return _position; } set { _position = value; } } /// <summary> /// /// </summary> /// <param name="offset"></param> /// <param name="direction"></param> /// <returns></returns> public override long Seek(long offset, System.IO.SeekOrigin direction) { return _stream.Seek(offset, direction); } /// <summary> /// /// </summary> /// <param name="length"></param> public override void SetLength(long length) { _stream.SetLength(length); } /// <summary> /// /// </summary> public override void Close() { _stream.Close(); } /// <summary> /// Override flush by writing out the cached stream data /// </summary> public override void Flush() { if (IsCaptured && _cacheStream.Length > 0) { // Check for transform implementations _cacheStream = OnTransformCompleteStream(_cacheStream); _cacheStream = OnTransformCompleteStringInternal(_cacheStream); OnCaptureStream(_cacheStream); OnCaptureStringInternal(_cacheStream); // write the stream back out if output was delayed if (IsOutputDelayed) _stream.Write(_cacheStream.ToArray(), 0, (int)_cacheStream.Length); // Clear the cache once we've written it out _cacheStream.SetLength(0); } // default flush behavior _stream.Flush(); } /// <summary> /// /// </summary> /// <param name="buffer"></param> /// <param name="offset"></param> /// <param name="count"></param> /// <returns></returns> public override int Read(byte[] buffer, int offset, int count) { return _stream.Read(buffer, offset, count); } /// <summary> /// Overriden to capture output written by ASP.NET and captured /// into a cached stream that is written out later when Flush() /// is called. /// </summary> /// <param name="buffer"></param> /// <param name="offset"></param> /// <param name="count"></param> public override void Write(byte[] buffer, int offset, int count) { if ( IsCaptured ) { // copy to holding buffer only - we'll write out later _cacheStream.Write(buffer, 0, count); _cachePointer += count; } // just transform this buffer if (TransformWrite != null) buffer = OnTransformWrite(buffer); if (TransformWriteString != null) buffer = OnTransformWriteStringInternal(buffer); if (!IsOutputDelayed) _stream.Write(buffer, offset, buffer.Length); } } The key features are the events and corresponding OnXXX methods that handle the event hookups, and the Write() and Flush() methods of the stream implementation. All the rest of the members tend to be plain jane passthrough stream implementation code without much consequence. I do love the way Action<t> and Func<T> make it so easy to create the event signatures for the various events – sweet. A few Things to consider Performance Response.Filter is not great for performance in general as it adds another layer of indirection to the ASP.NET output pipeline, and this implementation in particular adds a memory hit as it basically duplicates the response output into the cached memory stream which is necessary since you may have to look at the entire response. If you have large pages in particular this can cause potentially serious memory pressure in your server application. So be careful of wholesale adoption of this (or other) Response.Filters. Make sure to do some performance testing to ensure it’s not killing your app’s performance. Response.Filter works everywhere A few questions came up in comments and discussion as to capturing ALL output hitting the site and – yes you can definitely do that by assigning a Response.Filter inside of a module. If you do this however you’ll want to be very careful and decide which content you actually want to capture especially in IIS 7 which passes ALL content – including static images/CSS etc. through the ASP.NET pipeline. So it is important to filter only on what you’re looking for – like the page extension or maybe more effectively the Response.ContentType. Response.Filter Chaining Originally I thought that filter chaining doesn’t work at all due to a bug in the stream implementation code. But it’s quite possible to assign multiple filters to the Response.Filter property. So the following actually works to both compress the output and apply the transformed content: WebUtils.GZipEncodePage(); ResponseFilterStream filter = new ResponseFilterStream(Response.Filter); filter.TransformString += filter_TransformString; Response.Filter = filter; However the following does not work resulting in invalid content encoding errors: ResponseFilterStream filter = new ResponseFilterStream(Response.Filter); filter.TransformString += filter_TransformString; Response.Filter = filter; WebUtils.GZipEncodePage(); In other words multiple Response filters can work together but it depends entirely on the implementation whether they can be chained or in which order they can be chained. In this case running the GZip/Deflate stream filters apparently relies on the original content length of the output and chokes when the content is modified. But if attaching the compression first it works fine as unintuitive as that may seem. Resources Download example code Capture Output from ASP.NET Pages © Rick Strahl, West Wind Technologies, 2005-2010Posted in ASP.NET  

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  • value types in the vm

    - by john.rose
    value types in the vm p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} p.p2 {margin: 0.0px 0.0px 14.0px 0.0px; font: 14.0px Times} p.p3 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times} p.p4 {margin: 0.0px 0.0px 15.0px 0.0px; font: 14.0px Times} p.p5 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier} p.p6 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier; min-height: 17.0px} p.p7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p8 {margin: 0.0px 0.0px 0.0px 36.0px; text-indent: -36.0px; font: 14.0px Times; min-height: 18.0px} p.p9 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p10 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; color: #000000} li.li1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} li.li7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} span.s1 {font: 14.0px Courier} span.s2 {color: #000000} span.s3 {font: 14.0px Courier; color: #000000} ol.ol1 {list-style-type: decimal} Or, enduring values for a changing world. Introduction A value type is a data type which, generally speaking, is designed for being passed by value in and out of methods, and stored by value in data structures. The only value types which the Java language directly supports are the eight primitive types. Java indirectly and approximately supports value types, if they are implemented in terms of classes. For example, both Integer and String may be viewed as value types, especially if their usage is restricted to avoid operations appropriate to Object. In this note, we propose a definition of value types in terms of a design pattern for Java classes, accompanied by a set of usage restrictions. We also sketch the relation of such value types to tuple types (which are a JVM-level notion), and point out JVM optimizations that can apply to value types. This note is a thought experiment to extend the JVM’s performance model in support of value types. The demonstration has two phases.  Initially the extension can simply use design patterns, within the current bytecode architecture, and in today’s Java language. But if the performance model is to be realized in practice, it will probably require new JVM bytecode features, changes to the Java language, or both.  We will look at a few possibilities for these new features. An Axiom of Value In the context of the JVM, a value type is a data type equipped with construction, assignment, and equality operations, and a set of typed components, such that, whenever two variables of the value type produce equal corresponding values for their components, the values of the two variables cannot be distinguished by any JVM operation. Here are some corollaries: A value type is immutable, since otherwise a copy could be constructed and the original could be modified in one of its components, allowing the copies to be distinguished. Changing the component of a value type requires construction of a new value. The equals and hashCode operations are strictly component-wise. If a value type is represented by a JVM reference, that reference cannot be successfully synchronized on, and cannot be usefully compared for reference equality. A value type can be viewed in terms of what it doesn’t do. We can say that a value type omits all value-unsafe operations, which could violate the constraints on value types.  These operations, which are ordinarily allowed for Java object types, are pointer equality comparison (the acmp instruction), synchronization (the monitor instructions), all the wait and notify methods of class Object, and non-trivial finalize methods. The clone method is also value-unsafe, although for value types it could be treated as the identity function. Finally, and most importantly, any side effect on an object (however visible) also counts as an value-unsafe operation. A value type may have methods, but such methods must not change the components of the value. It is reasonable and useful to define methods like toString, equals, and hashCode on value types, and also methods which are specifically valuable to users of the value type. Representations of Value Value types have two natural representations in the JVM, unboxed and boxed. An unboxed value consists of the components, as simple variables. For example, the complex number x=(1+2i), in rectangular coordinate form, may be represented in unboxed form by the following pair of variables: /*Complex x = Complex.valueOf(1.0, 2.0):*/ double x_re = 1.0, x_im = 2.0; These variables might be locals, parameters, or fields. Their association as components of a single value is not defined to the JVM. Here is a sample computation which computes the norm of the difference between two complex numbers: double distance(/*Complex x:*/ double x_re, double x_im,         /*Complex y:*/ double y_re, double y_im) {     /*Complex z = x.minus(y):*/     double z_re = x_re - y_re, z_im = x_im - y_im;     /*return z.abs():*/     return Math.sqrt(z_re*z_re + z_im*z_im); } A boxed representation groups component values under a single object reference. The reference is to a ‘wrapper class’ that carries the component values in its fields. (A primitive type can naturally be equated with a trivial value type with just one component of that type. In that view, the wrapper class Integer can serve as a boxed representation of value type int.) The unboxed representation of complex numbers is practical for many uses, but it fails to cover several major use cases: return values, array elements, and generic APIs. The two components of a complex number cannot be directly returned from a Java function, since Java does not support multiple return values. The same story applies to array elements: Java has no ’array of structs’ feature. (Double-length arrays are a possible workaround for complex numbers, but not for value types with heterogeneous components.) By generic APIs I mean both those which use generic types, like Arrays.asList and those which have special case support for primitive types, like String.valueOf and PrintStream.println. Those APIs do not support unboxed values, and offer some problems to boxed values. Any ’real’ JVM type should have a story for returns, arrays, and API interoperability. The basic problem here is that value types fall between primitive types and object types. Value types are clearly more complex than primitive types, and object types are slightly too complicated. Objects are a little bit dangerous to use as value carriers, since object references can be compared for pointer equality, and can be synchronized on. Also, as many Java programmers have observed, there is often a performance cost to using wrapper objects, even on modern JVMs. Even so, wrapper classes are a good starting point for talking about value types. If there were a set of structural rules and restrictions which would prevent value-unsafe operations on value types, wrapper classes would provide a good notation for defining value types. This note attempts to define such rules and restrictions. Let’s Start Coding Now it is time to look at some real code. Here is a definition, written in Java, of a complex number value type. @ValueSafe public final class Complex implements java.io.Serializable {     // immutable component structure:     public final double re, im;     private Complex(double re, double im) {         this.re = re; this.im = im;     }     // interoperability methods:     public String toString() { return "Complex("+re+","+im+")"; }     public List<Double> asList() { return Arrays.asList(re, im); }     public boolean equals(Complex c) {         return re == c.re && im == c.im;     }     public boolean equals(@ValueSafe Object x) {         return x instanceof Complex && equals((Complex) x);     }     public int hashCode() {         return 31*Double.valueOf(re).hashCode()                 + Double.valueOf(im).hashCode();     }     // factory methods:     public static Complex valueOf(double re, double im) {         return new Complex(re, im);     }     public Complex changeRe(double re2) { return valueOf(re2, im); }     public Complex changeIm(double im2) { return valueOf(re, im2); }     public static Complex cast(@ValueSafe Object x) {         return x == null ? ZERO : (Complex) x;     }     // utility methods and constants:     public Complex plus(Complex c)  { return new Complex(re+c.re, im+c.im); }     public Complex minus(Complex c) { return new Complex(re-c.re, im-c.im); }     public double abs() { return Math.sqrt(re*re + im*im); }     public static final Complex PI = valueOf(Math.PI, 0.0);     public static final Complex ZERO = valueOf(0.0, 0.0); } This is not a minimal definition, because it includes some utility methods and other optional parts.  The essential elements are as follows: The class is marked as a value type with an annotation. The class is final, because it does not make sense to create subclasses of value types. The fields of the class are all non-private and final.  (I.e., the type is immutable and structurally transparent.) From the supertype Object, all public non-final methods are overridden. The constructor is private. Beyond these bare essentials, we can observe the following features in this example, which are likely to be typical of all value types: One or more factory methods are responsible for value creation, including a component-wise valueOf method. There are utility methods for complex arithmetic and instance creation, such as plus and changeIm. There are static utility constants, such as PI. The type is serializable, using the default mechanisms. There are methods for converting to and from dynamically typed references, such as asList and cast. The Rules In order to use value types properly, the programmer must avoid value-unsafe operations.  A helpful Java compiler should issue errors (or at least warnings) for code which provably applies value-unsafe operations, and should issue warnings for code which might be correct but does not provably avoid value-unsafe operations.  No such compilers exist today, but to simplify our account here, we will pretend that they do exist. A value-safe type is any class, interface, or type parameter marked with the @ValueSafe annotation, or any subtype of a value-safe type.  If a value-safe class is marked final, it is in fact a value type.  All other value-safe classes must be abstract.  The non-static fields of a value class must be non-public and final, and all its constructors must be private. Under the above rules, a standard interface could be helpful to define value types like Complex.  Here is an example: @ValueSafe public interface ValueType extends java.io.Serializable {     // All methods listed here must get redefined.     // Definitions must be value-safe, which means     // they may depend on component values only.     List<? extends Object> asList();     int hashCode();     boolean equals(@ValueSafe Object c);     String toString(); } //@ValueSafe inherited from supertype: public final class Complex implements ValueType { … The main advantage of such a conventional interface is that (unlike an annotation) it is reified in the runtime type system.  It could appear as an element type or parameter bound, for facilities which are designed to work on value types only.  More broadly, it might assist the JVM to perform dynamic enforcement of the rules for value types. Besides types, the annotation @ValueSafe can mark fields, parameters, local variables, and methods.  (This is redundant when the type is also value-safe, but may be useful when the type is Object or another supertype of a value type.)  Working forward from these annotations, an expression E is defined as value-safe if it satisfies one or more of the following: The type of E is a value-safe type. E names a field, parameter, or local variable whose declaration is marked @ValueSafe. E is a call to a method whose declaration is marked @ValueSafe. E is an assignment to a value-safe variable, field reference, or array reference. E is a cast to a value-safe type from a value-safe expression. E is a conditional expression E0 ? E1 : E2, and both E1 and E2 are value-safe. Assignments to value-safe expressions and initializations of value-safe names must take their values from value-safe expressions. A value-safe expression may not be the subject of a value-unsafe operation.  In particular, it cannot be synchronized on, nor can it be compared with the “==” operator, not even with a null or with another value-safe type. In a program where all of these rules are followed, no value-type value will be subject to a value-unsafe operation.  Thus, the prime axiom of value types will be satisfied, that no two value type will be distinguishable as long as their component values are equal. More Code To illustrate these rules, here are some usage examples for Complex: Complex pi = Complex.valueOf(Math.PI, 0); Complex zero = pi.changeRe(0);  //zero = pi; zero.re = 0; ValueType vtype = pi; @SuppressWarnings("value-unsafe")   Object obj = pi; @ValueSafe Object obj2 = pi; obj2 = new Object();  // ok List<Complex> clist = new ArrayList<Complex>(); clist.add(pi);  // (ok assuming List.add param is @ValueSafe) List<ValueType> vlist = new ArrayList<ValueType>(); vlist.add(pi);  // (ok) List<Object> olist = new ArrayList<Object>(); olist.add(pi);  // warning: "value-unsafe" boolean z = pi.equals(zero); boolean z1 = (pi == zero);  // error: reference comparison on value type boolean z2 = (pi == null);  // error: reference comparison on value type boolean z3 = (pi == obj2);  // error: reference comparison on value type synchronized (pi) { }  // error: synch of value, unpredictable result synchronized (obj2) { }  // unpredictable result Complex qq = pi; qq = null;  // possible NPE; warning: “null-unsafe" qq = (Complex) obj;  // warning: “null-unsafe" qq = Complex.cast(obj);  // OK @SuppressWarnings("null-unsafe")   Complex empty = null;  // possible NPE qq = empty;  // possible NPE (null pollution) The Payoffs It follows from this that either the JVM or the java compiler can replace boxed value-type values with unboxed ones, without affecting normal computations.  Fields and variables of value types can be split into their unboxed components.  Non-static methods on value types can be transformed into static methods which take the components as value parameters. Some common questions arise around this point in any discussion of value types. Why burden the programmer with all these extra rules?  Why not detect programs automagically and perform unboxing transparently?  The answer is that it is easy to break the rules accidently unless they are agreed to by the programmer and enforced.  Automatic unboxing optimizations are tantalizing but (so far) unreachable ideal.  In the current state of the art, it is possible exhibit benchmarks in which automatic unboxing provides the desired effects, but it is not possible to provide a JVM with a performance model that assures the programmer when unboxing will occur.  This is why I’m writing this note, to enlist help from, and provide assurances to, the programmer.  Basically, I’m shooting for a good set of user-supplied “pragmas” to frame the desired optimization. Again, the important thing is that the unboxing must be done reliably, or else programmers will have no reason to work with the extra complexity of the value-safety rules.  There must be a reasonably stable performance model, wherein using a value type has approximately the same performance characteristics as writing the unboxed components as separate Java variables. There are some rough corners to the present scheme.  Since Java fields and array elements are initialized to null, value-type computations which incorporate uninitialized variables can produce null pointer exceptions.  One workaround for this is to require such variables to be null-tested, and the result replaced with a suitable all-zero value of the value type.  That is what the “cast” method does above. Generically typed APIs like List<T> will continue to manipulate boxed values always, at least until we figure out how to do reification of generic type instances.  Use of such APIs will elicit warnings until their type parameters (and/or relevant members) are annotated or typed as value-safe.  Retrofitting List<T> is likely to expose flaws in the present scheme, which we will need to engineer around.  Here are a couple of first approaches: public interface java.util.List<@ValueSafe T> extends Collection<T> { … public interface java.util.List<T extends Object|ValueType> extends Collection<T> { … (The second approach would require disjunctive types, in which value-safety is “contagious” from the constituent types.) With more transformations, the return value types of methods can also be unboxed.  This may require significant bytecode-level transformations, and would work best in the presence of a bytecode representation for multiple value groups, which I have proposed elsewhere under the title “Tuples in the VM”. But for starters, the JVM can apply this transformation under the covers, to internally compiled methods.  This would give a way to express multiple return values and structured return values, which is a significant pain-point for Java programmers, especially those who work with low-level structure types favored by modern vector and graphics processors.  The lack of multiple return values has a strong distorting effect on many Java APIs. Even if the JVM fails to unbox a value, there is still potential benefit to the value type.  Clustered computing systems something have copy operations (serialization or something similar) which apply implicitly to command operands.  When copying JVM objects, it is extremely helpful to know when an object’s identity is important or not.  If an object reference is a copied operand, the system may have to create a proxy handle which points back to the original object, so that side effects are visible.  Proxies must be managed carefully, and this can be expensive.  On the other hand, value types are exactly those types which a JVM can “copy and forget” with no downside. Array types are crucial to bulk data interfaces.  (As data sizes and rates increase, bulk data becomes more important than scalar data, so arrays are definitely accompanying us into the future of computing.)  Value types are very helpful for adding structure to bulk data, so a successful value type mechanism will make it easier for us to express richer forms of bulk data. Unboxing arrays (i.e., arrays containing unboxed values) will provide better cache and memory density, and more direct data movement within clustered or heterogeneous computing systems.  They require the deepest transformations, relative to today’s JVM.  There is an impedance mismatch between value-type arrays and Java’s covariant array typing, so compromises will need to be struck with existing Java semantics.  It is probably worth the effort, since arrays of unboxed value types are inherently more memory-efficient than standard Java arrays, which rely on dependent pointer chains. It may be sufficient to extend the “value-safe” concept to array declarations, and allow low-level transformations to change value-safe array declarations from the standard boxed form into an unboxed tuple-based form.  Such value-safe arrays would not be convertible to Object[] arrays.  Certain connection points, such as Arrays.copyOf and System.arraycopy might need additional input/output combinations, to allow smooth conversion between arrays with boxed and unboxed elements. Alternatively, the correct solution may have to wait until we have enough reification of generic types, and enough operator overloading, to enable an overhaul of Java arrays. Implicit Method Definitions The example of class Complex above may be unattractively complex.  I believe most or all of the elements of the example class are required by the logic of value types. If this is true, a programmer who writes a value type will have to write lots of error-prone boilerplate code.  On the other hand, I think nearly all of the code (except for the domain-specific parts like plus and minus) can be implicitly generated. Java has a rule for implicitly defining a class’s constructor, if no it defines no constructors explicitly.  Likewise, there are rules for providing default access modifiers for interface members.  Because of the highly regular structure of value types, it might be reasonable to perform similar implicit transformations on value types.  Here’s an example of a “highly implicit” definition of a complex number type: public class Complex implements ValueType {  // implicitly final     public double re, im;  // implicitly public final     //implicit methods are defined elementwise from te fields:     //  toString, asList, equals(2), hashCode, valueOf, cast     //optionally, explicit methods (plus, abs, etc.) would go here } In other words, with the right defaults, a simple value type definition can be a one-liner.  The observant reader will have noticed the similarities (and suitable differences) between the explicit methods above and the corresponding methods for List<T>. Another way to abbreviate such a class would be to make an annotation the primary trigger of the functionality, and to add the interface(s) implicitly: public @ValueType class Complex { … // implicitly final, implements ValueType (But to me it seems better to communicate the “magic” via an interface, even if it is rooted in an annotation.) Implicitly Defined Value Types So far we have been working with nominal value types, which is to say that the sequence of typed components is associated with a name and additional methods that convey the intention of the programmer.  A simple ordered pair of floating point numbers can be variously interpreted as (to name a few possibilities) a rectangular or polar complex number or Cartesian point.  The name and the methods convey the intended meaning. But what if we need a truly simple ordered pair of floating point numbers, without any further conceptual baggage?  Perhaps we are writing a method (like “divideAndRemainder”) which naturally returns a pair of numbers instead of a single number.  Wrapping the pair of numbers in a nominal type (like “QuotientAndRemainder”) makes as little sense as wrapping a single return value in a nominal type (like “Quotient”).  What we need here are structural value types commonly known as tuples. For the present discussion, let us assign a conventional, JVM-friendly name to tuples, roughly as follows: public class java.lang.tuple.$DD extends java.lang.tuple.Tuple {      double $1, $2; } Here the component names are fixed and all the required methods are defined implicitly.  The supertype is an abstract class which has suitable shared declarations.  The name itself mentions a JVM-style method parameter descriptor, which may be “cracked” to determine the number and types of the component fields. The odd thing about such a tuple type (and structural types in general) is it must be instantiated lazily, in response to linkage requests from one or more classes that need it.  The JVM and/or its class loaders must be prepared to spin a tuple type on demand, given a simple name reference, $xyz, where the xyz is cracked into a series of component types.  (Specifics of naming and name mangling need some tasteful engineering.) Tuples also seem to demand, even more than nominal types, some support from the language.  (This is probably because notations for non-nominal types work best as combinations of punctuation and type names, rather than named constructors like Function3 or Tuple2.)  At a minimum, languages with tuples usually (I think) have some sort of simple bracket notation for creating tuples, and a corresponding pattern-matching syntax (or “destructuring bind”) for taking tuples apart, at least when they are parameter lists.  Designing such a syntax is no simple thing, because it ought to play well with nominal value types, and also with pre-existing Java features, such as method parameter lists, implicit conversions, generic types, and reflection.  That is a task for another day. Other Use Cases Besides complex numbers and simple tuples there are many use cases for value types.  Many tuple-like types have natural value-type representations. These include rational numbers, point locations and pixel colors, and various kinds of dates and addresses. Other types have a variable-length ‘tail’ of internal values. The most common example of this is String, which is (mathematically) a sequence of UTF-16 character values. Similarly, bit vectors, multiple-precision numbers, and polynomials are composed of sequences of values. Such types include, in their representation, a reference to a variable-sized data structure (often an array) which (somehow) represents the sequence of values. The value type may also include ’header’ information. Variable-sized values often have a length distribution which favors short lengths. In that case, the design of the value type can make the first few values in the sequence be direct ’header’ fields of the value type. In the common case where the header is enough to represent the whole value, the tail can be a shared null value, or even just a null reference. Note that the tail need not be an immutable object, as long as the header type encapsulates it well enough. This is the case with String, where the tail is a mutable (but never mutated) character array. Field types and their order must be a globally visible part of the API.  The structure of the value type must be transparent enough to have a globally consistent unboxed representation, so that all callers and callees agree about the type and order of components  that appear as parameters, return types, and array elements.  This is a trade-off between efficiency and encapsulation, which is forced on us when we remove an indirection enjoyed by boxed representations.  A JVM-only transformation would not care about such visibility, but a bytecode transformation would need to take care that (say) the components of complex numbers would not get swapped after a redefinition of Complex and a partial recompile.  Perhaps constant pool references to value types need to declare the field order as assumed by each API user. This brings up the delicate status of private fields in a value type.  It must always be possible to load, store, and copy value types as coordinated groups, and the JVM performs those movements by moving individual scalar values between locals and stack.  If a component field is not public, what is to prevent hostile code from plucking it out of the tuple using a rogue aload or astore instruction?  Nothing but the verifier, so we may need to give it more smarts, so that it treats value types as inseparable groups of stack slots or locals (something like long or double). My initial thought was to make the fields always public, which would make the security problem moot.  But public is not always the right answer; consider the case of String, where the underlying mutable character array must be encapsulated to prevent security holes.  I believe we can win back both sides of the tradeoff, by training the verifier never to split up the components in an unboxed value.  Just as the verifier encapsulates the two halves of a 64-bit primitive, it can encapsulate the the header and body of an unboxed String, so that no code other than that of class String itself can take apart the values. Similar to String, we could build an efficient multi-precision decimal type along these lines: public final class DecimalValue extends ValueType {     protected final long header;     protected private final BigInteger digits;     public DecimalValue valueOf(int value, int scale) {         assert(scale >= 0);         return new DecimalValue(((long)value << 32) + scale, null);     }     public DecimalValue valueOf(long value, int scale) {         if (value == (int) value)             return valueOf((int)value, scale);         return new DecimalValue(-scale, new BigInteger(value));     } } Values of this type would be passed between methods as two machine words. Small values (those with a significand which fits into 32 bits) would be represented without any heap data at all, unless the DecimalValue itself were boxed. (Note the tension between encapsulation and unboxing in this case.  It would be better if the header and digits fields were private, but depending on where the unboxing information must “leak”, it is probably safer to make a public revelation of the internal structure.) Note that, although an array of Complex can be faked with a double-length array of double, there is no easy way to fake an array of unboxed DecimalValues.  (Either an array of boxed values or a transposed pair of homogeneous arrays would be reasonable fallbacks, in a current JVM.)  Getting the full benefit of unboxing and arrays will require some new JVM magic. Although the JVM emphasizes portability, system dependent code will benefit from using machine-level types larger than 64 bits.  For example, the back end of a linear algebra package might benefit from value types like Float4 which map to stock vector types.  This is probably only worthwhile if the unboxing arrays can be packed with such values. More Daydreams A more finely-divided design for dynamic enforcement of value safety could feature separate marker interfaces for each invariant.  An empty marker interface Unsynchronizable could cause suitable exceptions for monitor instructions on objects in marked classes.  More radically, a Interchangeable marker interface could cause JVM primitives that are sensitive to object identity to raise exceptions; the strangest result would be that the acmp instruction would have to be specified as raising an exception. @ValueSafe public interface ValueType extends java.io.Serializable,         Unsynchronizable, Interchangeable { … public class Complex implements ValueType {     // inherits Serializable, Unsynchronizable, Interchangeable, @ValueSafe     … It seems possible that Integer and the other wrapper types could be retro-fitted as value-safe types.  This is a major change, since wrapper objects would be unsynchronizable and their references interchangeable.  It is likely that code which violates value-safety for wrapper types exists but is uncommon.  It is less plausible to retro-fit String, since the prominent operation String.intern is often used with value-unsafe code. We should also reconsider the distinction between boxed and unboxed values in code.  The design presented above obscures that distinction.  As another thought experiment, we could imagine making a first class distinction in the type system between boxed and unboxed representations.  Since only primitive types are named with a lower-case initial letter, we could define that the capitalized version of a value type name always refers to the boxed representation, while the initial lower-case variant always refers to boxed.  For example: complex pi = complex.valueOf(Math.PI, 0); Complex boxPi = pi;  // convert to boxed myList.add(boxPi); complex z = myList.get(0);  // unbox Such a convention could perhaps absorb the current difference between int and Integer, double and Double. It might also allow the programmer to express a helpful distinction among array types. As said above, array types are crucial to bulk data interfaces, but are limited in the JVM.  Extending arrays beyond the present limitations is worth thinking about; for example, the Maxine JVM implementation has a hybrid object/array type.  Something like this which can also accommodate value type components seems worthwhile.  On the other hand, does it make sense for value types to contain short arrays?  And why should random-access arrays be the end of our design process, when bulk data is often sequentially accessed, and it might make sense to have heterogeneous streams of data as the natural “jumbo” data structure.  These considerations must wait for another day and another note. More Work It seems to me that a good sequence for introducing such value types would be as follows: Add the value-safety restrictions to an experimental version of javac. Code some sample applications with value types, including Complex and DecimalValue. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. A staggered roll-out like this would decouple language changes from bytecode changes, which is always a convenient thing. A similar investigation should be applied (concurrently) to array types.  In this case, it seems to me that the starting point is in the JVM: Add an experimental unboxing array data structure to a production JVM, perhaps along the lines of Maxine hybrids.  No bytecode or language support is required at first; everything can be done with encapsulated unsafe operations and/or method handles. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. That’s enough musing me for now.  Back to work!

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    - by Torandi
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  • Java login through LDAP

    - by Salda
    I am starting to write an application for our office and the first step is authentication through LDAP where all users already exist. Everything I need is a code in Java to check if the pair <nick,password> is right. Google found me many links, but I think that I will find here the most sofisticate, short and up to date solution (I don't want to read all articles like 2 whole days to do something so simple). I have written many dkBs of code in C++, but in Java I am total noob and I haven't coded anything with LDAP yet so try to be simple if speaking in Java and LDAP terms if possible.

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  • How to monitor Windows Server events with Centreon Nagios

    - by Miss M
    I want to monitor events on a Windows Exchange Server (Windows 2008 R2) and have installed NSclient ++ so I can use Centreon Nagios to monitor it. I did a bit of research and came across this question that I found somewhat helpful: How to monitor Windows host with Nagios? Nick Kavadias gave a good answer but it did not provide an explanation on how to configure the Nagios config file in such a way that it would monitor a specific service on the server. So, how do I set up a service in Nagios in such a way that it will detect when a windows event occurs on a server?

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  • Windows 2003 Server - File Permissions

    - by nickstan
    I have a Windows 2003 web server with a tree of folders that contains around 100GB of small images. I need to update the permissions on this folder to add a new user with access. I tried to do this by right clicking on the folder and adding the new user but the process never completed. I left it running for around an hour but it started to heavily impact the peformance of the server. Is there any other way to change these folder permissions without affecting server performance? Many Thanks Nick

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