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  • Customized listfield with image displaying from a url

    - by arunabha
    I am displaying a customized list field with text on the right side and image on the left side.The image comes from a URL dynamically.Initially i am placing a blank image on the left of the list field,then call URLBitmapField class's setURL method,which actually does the processing and places the processed image on top of the blank image.The image gets displayed on the list field,but to see that processed image i need to press any key or click on the list field items.I want the processed image to be displayed automatically in the list field after the processing.Can anyone tell me where i am getting wrong? import java.util.Vector; import net.rim.device.api.system.Bitmap; import net.rim.device.api.system.Display; import net.rim.device.api.ui.ContextMenu; import net.rim.device.api.ui.DrawStyle; import net.rim.device.api.ui.Field; import net.rim.device.api.ui.Font; import net.rim.device.api.ui.Graphics; import net.rim.device.api.ui.Manager; import net.rim.device.api.ui.MenuItem; import net.rim.device.api.ui.UiApplication; import net.rim.device.api.ui.component.BitmapField; import net.rim.device.api.ui.component.Dialog; import net.rim.device.api.ui.component.LabelField; import net.rim.device.api.ui.component.ListField; import net.rim.device.api.ui.component.ListFieldCallback; import net.rim.device.api.ui.component.NullField; import net.rim.device.api.ui.container.FullScreen; import net.rim.device.api.ui.container.MainScreen; import net.rim.device.api.ui.container.VerticalFieldManager; import net.rim.device.api.util.Arrays; import net.rim.device.api.ui.component.ListField; public class TaskListField extends UiApplication { // statics // ------------------------------------------------------------------ public static void main(String[] args) { TaskListField theApp = new TaskListField(); theApp.enterEventDispatcher(); } public TaskListField() { pushScreen(new TaskList()); } } class TaskList extends MainScreen implements ListFieldCallback { private Vector rows; private Bitmap p1; private Bitmap p2; private Bitmap p3; String Task; ListField listnew = new ListField(); private VerticalFieldManager metadataVFM; TableRowManager row; public TaskList() { super(); URLBitmapField artistImgField; listnew.setRowHeight(80); listnew.setCallback(this); rows = new Vector(); for (int x = 0; x <3; x++) { row = new TableRowManager(); artistImgField = new URLBitmapField(Bitmap .getBitmapResource("res/images/bg.jpg")); row.add(artistImgField); String photoURL = "someimagefrmurl.jpg"; Log.info(photoURL); // strip white spaces in the url, which is causing the // images to not display properly for (int i = 0; i < photoURL.length(); i++) { if (photoURL.charAt(i) == ' ') { photoURL = photoURL.substring(0, i) + "%20" + photoURL.substring(i + 1, photoURL.length()); } } Log.info("Processed URL: " + photoURL); artistImgField.setURL(photoURL); LabelField task = new LabelField("Display"); row.add(task); LabelField task1 = new LabelField( "Now Playing" + String.valueOf(x)); Font myFont = Font.getDefault().derive(Font.PLAIN, 12); task1.setFont(myFont); row.add(task1); rows.addElement(row); } listnew.setSize(rows.size()); this.add(listnew); //listnew.invalidate(); } // ListFieldCallback Implementation public void drawListRow(ListField listField, Graphics g, int index, int y, int width) { TableRowManager rowManager = (TableRowManager) rows.elementAt(index); rowManager.drawRow(g, 0, y, width, listnew.getRowHeight()); } protected void drawFocus(Graphics graphics, boolean on) { } private class TableRowManager extends Manager { public TableRowManager() { super(0); } // Causes the fields within this row manager to be layed out then // painted. public void drawRow(Graphics g, int x, int y, int width, int height) { // Arrange the cell fields within this row manager. layout(width, height); // Place this row manager within its enclosing list. setPosition(x, y); // Apply a translating/clipping transformation to the graphics // context so that this row paints in the right area. g.pushRegion(getExtent()); // Paint this manager's controlled fields. subpaint(g); g.setColor(0x00CACACA); g.drawLine(0, 0, getPreferredWidth(), 0); // Restore the graphics context. g.popContext(); } // Arrages this manager's controlled fields from left to right within // the enclosing table's columns. protected void sublayout(int width, int height) { // set the size and position of each field. int fontHeight = Font.getDefault().getHeight(); int preferredWidth = getPreferredWidth(); // start with the Bitmap Field of the priority icon Field field = getField(0); layoutChild(field, 146,80); setPositionChild(field, 0, 0); // set the task name label field field = getField(1); layoutChild(field, preferredWidth - 16, fontHeight + 1); setPositionChild(field, 149, 3); // set the list name label field field = getField(2); layoutChild(field, 150, fontHeight + 1); setPositionChild(field, 149, fontHeight + 6); setExtent(360, 480); } // The preferred width of a row is defined by the list renderer. public int getPreferredWidth() { return listnew.getWidth(); } // The preferred height of a row is the "row height" as defined in the // enclosing list. public int getPreferredHeight() { return listnew.getRowHeight(); } } public Object get(ListField listField, int index) { // TODO Auto-generated method stub return null; } public int getPreferredWidth(ListField listField) { return 0; } public int indexOfList(ListField listField, String prefix, int start) { // TODO Auto-generated method stub return 0; } }

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  • Set postion in customized list field in blackberry

    - by arunabha
    I want three list field items to be displayed, from bottom to top. I am able to display three list field items, but they display from top to bottom. I have tried setting the position, but it isn't working. import java.util.Vector; import net.rim.device.api.system.Bitmap; import net.rim.device.api.system.Display; import net.rim.device.api.ui.ContextMenu; import net.rim.device.api.ui.DrawStyle; import net.rim.device.api.ui.Field; import net.rim.device.api.ui.Font; import net.rim.device.api.ui.Graphics; import net.rim.device.api.ui.Manager; import net.rim.device.api.ui.MenuItem; import net.rim.device.api.ui.UiApplication; import net.rim.device.api.ui.component.BitmapField; import net.rim.device.api.ui.component.Dialog; import net.rim.device.api.ui.component.LabelField; import net.rim.device.api.ui.component.ListField; import net.rim.device.api.ui.component.ListFieldCallback; import net.rim.device.api.ui.component.NullField; import net.rim.device.api.ui.container.FullScreen; import net.rim.device.api.ui.container.MainScreen; import net.rim.device.api.util.Arrays; import net.rim.device.api.ui.component.ListField; /** * @author Jason Emerick */ public class TaskListField extends UiApplication { //statics ------------------------------------------------------------------ public static void main(String[] args) { TaskListField theApp = new TaskListField(); theApp.enterEventDispatcher(); } public TaskListField() { pushScreen(new TaskList()); } } /*class List extends FullScreen { TaskList tl; List(){ super(); TaskList tl=new TaskList(); } }*/ class TaskList extends MainScreen implements ListFieldCallback { private Vector rows; private Bitmap p1; private Bitmap p2; private Bitmap p3; String Task; ListField listnew=new ListField(); public TaskList() { super(); listnew.setRowHeight(50); //setEmptyString("Hooray, no tasks here!", DrawStyle.HCENTER); listnew.setCallback(this); p1 = Bitmap.getBitmapResource("1.png"); p2 = Bitmap.getBitmapResource("2.png"); p3 = Bitmap.getBitmapResource("3.png"); rows = new Vector(); for (int x = 0; x < 3; x++) { TableRowManager row = new TableRowManager(); if (x== 0) { Task="On Air Now"; } if (x== 1) { Task="Music Channel"; } if (x==2) { Task="News Channel"; } // SET THE PRIORITY BITMAP FIELD // if high priority, display p1 bitmap if (x % 2 == 0) { row.add(new BitmapField(p1)); } // if priority is 2, set p2 bitmap else if (x % 3 == 0) { row.add(new BitmapField(p2)); } // if priority is 3, set p3 bitmap else { row.add(new BitmapField(p3)); } // SET THE TASK NAME LABELFIELD // if overdue, bold/underline LabelField task = new LabelField(Task, DrawStyle.ELLIPSIS); // if due today, bold if (x % 2 == 0) { task.setFont(Font.getDefault().derive( Font.BOLD)); } else { task.setFont(Font.getDefault().derive(Font.BOLD)); } row.add(task); LabelField task1 = new LabelField("Now Playing" + String.valueOf(x), DrawStyle.ELLIPSIS); // if due today, bold /* if (x % 2 == 0) { task.setFont(Font.getDefault().derive( Font.BOLD)); } else { task.setFont(Font.getDefault().derive(Font.BOLD)); }*/ Font myFont = Font.getDefault().derive(Font.PLAIN, 12); task1.setFont(myFont); row.add(task1); // SET THE DUE DATE/TIME row.add(new LabelField("", DrawStyle.ELLIPSIS | LabelField.USE_ALL_WIDTH | DrawStyle.RIGHT) { protected void paint(Graphics graphics) { graphics.setColor(0x00878787); super.paint(graphics); } }); rows.addElement(row); } listnew.setSize(rows.size()); this.add(listnew); } // ListFieldCallback Implementation public void drawListRow(ListField listField, Graphics g, int index, int y, int width) { //TaskList list =(TaskListField) listnew; TableRowManager rowManager = (TableRowManager)rows .elementAt(index); rowManager.drawRow(g, 0, y, width, listnew.getRowHeight()); } private class TableRowManager extends Manager { public TableRowManager() { super(0); } // Causes the fields within this row manager to be layed out then // painted. public void drawRow(Graphics g, int x, int y, int width, int height) { // Arrange the cell fields within this row manager. layout(0, 1); // Place this row manager within its enclosing list. setPosition(x,y); // Apply a translating/clipping transformation to the graphics // context so that this row paints in the right area. g.pushRegion(getExtent()); // Paint this manager's controlled fields. subpaint(g); g.setColor(0x00CACACA); g.drawLine(0, 0, getPreferredWidth(), 0); // Restore the graphics context. g.popContext(); } // Arrages this manager's controlled fields from left to right within // the enclosing table's columns. protected void sublayout(int width, int height) { // set the size and position of each field. int fontHeight = Font.getDefault().getHeight(); int preferredWidth = getPreferredWidth(); // start with the Bitmap Field of the priority icon /* Field field = getField(0); layoutChild(field, 0, 0); setPositionChild(field, 150, 300);*/ // set the task name label field /* field = getField(1); layoutChild(field, preferredWidth - 16, fontHeight + 1); setPositionChild(field, 34, 3); // set the list name label field field = getField(2); layoutChild(field, 150, fontHeight + 1); setPositionChild(field, 34, fontHeight + 6);*/ // set the due time name label field /* field = getField(3); layoutChild(field, 150, fontHeight + 1); setPositionChild(field,4,340);*/ /* layoutChild(listnew, preferredWidth, fontHeight); setPositionChild(listnew, 3, 396);*/ setExtent(360, 480); } // The preferred width of a row is defined by the list renderer. public int getPreferredWidth() { return getWidth(); } // The preferred height of a row is the "row height" as defined in the // enclosing list. public int getPreferredHeight() { return listnew.getRowHeight(); } } public Object get(ListField listField, int index) { // TODO Auto-generated method stub return null; } public int getPreferredWidth(ListField listField) { // TODO Auto-generated method stub return 0; } public int indexOfList(ListField listField, String prefix, int start) { // TODO Auto-generated method stub return 0; } }

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  • Create a class that inherets DrawableGameComponent in XNA as a CLASS with custom functions

    - by user3675013
    using Microsoft.Xna.Framework.Graphics; using Microsoft.Xna.Framework.Media; using Microsoft.Xna.Framework; using Microsoft.Xna.Framework.Content; namespace TileEngine { class Renderer : DrawableGameComponent { public Renderer(Game game) : base(game) { } SpriteBatch spriteBatch ; protected override void LoadContent() { base.LoadContent(); } public override void Draw(GameTime gameTime) { base.Draw(gameTime); } public override void Update(GameTime gameTime) { base.Update(gameTime); } public override void Initialize() { base.Initialize(); } public RenderTarget2D new_texture(int width, int height) { Texture2D TEX = new Texture2D(GraphicsDevice, width, height); //create the texture to render to RenderTarget2D Mine = new RenderTarget2D(GraphicsDevice, width, height); GraphicsDevice.SetRenderTarget(Mine); //set the render device to the reference provided //maybe base.draw can be used with spritebatch. Idk. We'll see if the order of operation //works out. Wish I could call base.draw here. return Mine; //I'm hoping that this returns the same instance and not a copy. } public void draw_texture(int width, int height, RenderTarget2D Mine) { GraphicsDevice.SetRenderTarget(null); //Set the renderer to render to the backbuffer again Rectangle drawrect = new Rectangle(0, 0, width, height); //Set the rendering size to what we want spriteBatch.Begin(); //This uses spritebatch to draw the texture directly to the screen spriteBatch.Draw(Mine, drawrect, Color.White); //This uses the color white spriteBatch.End(); //ends the spritebatch //Call base.draw after this since it doesn't seem to recognize inside the function //maybe base.draw can be used with spritebatch. Idk. We'll see if the order of operation //works out. Wish I could call base.draw here. } } } I solved a previous issue where I wasn't allowed to access GraphicsDevice outside the main Default 'main' class Ie "Game" or "Game1" etc. Now I have a new issue. FYi no one told me that it would be possible to use GraphicsDevice References to cause it to not be null by using the drawable class. (hopefully after this last bug is solved it doesn't still return null) Anyways at present the problem is that I can't seem to get it to initialize as an instance in my main program. Ie Renderer tileClipping; and I'm unable to use it such as it is to be noted i haven't even gotten to testing these two steps below but before it compiled but when those functions of this class were called it complained that it can't render to a null device. Which meant that the device wasn't being initialized. I had no idea why. It took me hours to google this. I finally figured out the words I needed.. which were "do my rendering in XNA in a seperate class" now I haven't used the addcomponent function because I don't want it to only run these functions automatically and I want to be able to call the custom ones. In a nutshell what I want is: *access to rendering targets and graphics device OUTSIDE default class *passing of Rendertarget2D (which contain textures and textures should automatically be passed with a rendering target? ) *the device should be passed to this function as well OR the device should be passed to this function as a byproduct of passing the rendertarget (which is automatically associated with the render device it was given originally) *I'm assuming I'm dealing with abstracted pointers here so when I pass a class object or instance, I should be recieving the SAME object , I referenced, and not a copy that has only the lifespan of the function running. *the purpose for all these options: I want to initialize new 2d textures on the fly to customize tileclipping and even the X , y Offsets of where a WHOLE texture will be rendered, and the X and Y offsets of where tiles will be rendered ON that surface. This is why. And I'll be doing region based lighting effects per tile or even per 8X8 pixel spaces.. we'll see I'll also be doing sprite rotations on the whole texture then copying it again to a circular masked texture, and then doing a second copy for only solid tiles for masked rotated collisions on sprites. I'll be checking the masked pixels for my collision, and using raycasting possibly to check for collisions on those areas. The sprite will stay in the center, when this rotation happens. Here is a detailed diagram: http://i.stack.imgur.com/INf9K.gif I'll be using texture2D for steps 4-6 I suppose for steps 1 as well. Now ontop of that, the clipping size (IE the sqaure rendered) will be able to be shrunk or increased, on a per frame basis Therefore I can't use the same static size for my main texture2d and I can't use just the backbuffer Or we get the annoying flicker. Also I will have multiple instances of the renderer class so that I can freely pass textures around as if they are playing cards (in a sense) layering them ontop of eachother, cropping them how i want and such. and then using spritebatch to simply draw them at the locations I want. Hopefully this makes sense, and yes I will be planning on using alpha blending but only after all tiles have been drawn.. The masked collision is important and Yes I am avoiding using math on the tile rendering and instead resorting to image manipulation in video memory which is WHY I need this to work the way I'm intending it to work and not in the default way that XNA seems to handle graphics. Thanks to anyone willing to help. I hate the code form offered, because then I have to rely on static presence of an update function. What if I want to kill that update function or that object, but have it in memory, but just have it temporarily inactive? I'm making the assumption here the update function of one of these gamecomponents is automatic ? Anyways this is as detailed as I can make this post hopefully someone can help me solve the issue. Instead of tell me "derrr don't do it this wayyy" which is what a few people told me (but they don't understand the actual goal I have in mind) I'm trying to create basically a library where I can copy images freely no matter the size, i just have to specify the size in the function then as long as a reference to that object exists it should be kept alive? right? :/ anyways.. Anything else? I Don't know. I understand object oriented coding but I don't understand this XNA It's beggining to feel impossible to do anything custom in it without putting ALL my rendering code into the draw function of the main class tileClipping.new_texture(GraphicsDevice, width, height) tileClipping.Draw_texture(...)

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  • 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 { 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  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.

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