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  • QT List of Callbacks

    - by Talguy
    I am trying to make a real-time data collection application that has timed task. Each task can have a different or the same update period. I would like to store the task with the common update period in a list where I can iterate through it and call the function that I registered in the list. How would I go about adding callbacks to a data structure like a list or vector? Can I store slots in them?

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  • Can I Have Polymorphic Containers With Value Semantics in C++11?

    - by John Dibling
    This is a sequel to a related post which asked the eternal question: Can I have polymorphic containers with value semantics in C++? The question was asked slightly incorrectly. It should have been more like: Can I have STL containers of a base type stored by-value in which the elements exhibit polymorphic behavior? If you are asking the question in terms of C++, the answer is "no." At some point, you will slice objects stored by-value. Now I ask the question again, but strictly in terms of C++11. With the changes to the language and the standard libraries, is it now possible to store polymorphic objects by value in an STL container? I'm well aware of the possibility of storing a smart pointer to the base class in the container -- this is not what I'm looking for, as I'm trying to construct objects on the stack without using new. Consider if you will (from the linked post) as basic C++ example: #include <iostream> using namespace std; class Parent { public: Parent() : parent_mem(1) {} virtual void write() { cout << "Parent: " << parent_mem << endl; } int parent_mem; }; class Child : public Parent { public: Child() : child_mem(2) { parent_mem = 2; } void write() { cout << "Child: " << parent_mem << ", " << child_mem << endl; } int child_mem; }; int main(int, char**) { // I can have a polymorphic container with pointer semantics vector<Parent*> pointerVec; pointerVec.push_back(new Parent()); pointerVec.push_back(new Child()); pointerVec[0]->write(); pointerVec[1]->write(); // Output: // // Parent: 1 // Child: 2, 2 // But I can't do it with value semantics vector<Parent> valueVec; valueVec.push_back(Parent()); valueVec.push_back(Child()); // gets turned into a Parent object :( valueVec[0].write(); valueVec[1].write(); // Output: // // Parent: 1 // Parent: 2 }

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  • Weird seg fault problem

    - by bluedaemon
    Greetings, I'm having a weird seg fault problem. My application dumps a core file at runtime. After digging into it I found it died in this block: #include <lib1/c.h> ... x::c obj; obj.func1(); I defined class c in a library lib1: namespace x { struct c { c(); ~c(); void fun1(); vector<char *> _data; }; } x::c::c() { } x::c::~c() { for ( int i = 0; i < _data.size(); ++i ) delete _data[i]; } I could not figure it out for some time till I ran nm on the lib1.so file: there are more function definitions than I defined: x::c::c() x::c::c() x::c::~c() x::c::~c() x::c::func1() x::c::func2() After searching in code base I found someone else defined a class with same name in same namespace, but in another library lib2 as follows: namespace x { struct c { c(); ~c(); void func2(); vector<string> strs_; }; } x::c::c() { } x::c::~c() { } My application links to lib2, which has dependency on lib1. This interesting behavior brings several questions: Why would it even work? I would expect a "multiple definitions" error while linking against lib2 (which depends upon lib1) but never had such. The application seems to be doing what's defined in func1 except it dumps a core at runtime. After attaching debugger, I found my application calls the ctor of class c in lib2, then calls func1 (defined in lib1). When going out of scope it calls dtor of class c in lib2, where the seg fault occurs. Can anybody teach me how this could even occur? How can I prevent such problems from happening again? Is there any C++ syntax I can use? Forgot to mention I'm using g++ 4.1 on RHEL4, thank you very much!

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  • [C++] Converting Integer to a Byte Representation

    - by bobber205
    How can I convert a integer to it's byte representation. I want to take an integer and return a vector that has contains 1's and 0's of the integers byte representation. I'm having a heck of a time trying to do this myself so I'd thought I would ask to see if there was a built in library function that could help. Thanks!

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  • R: optimal way of computing the "product" of two vectors

    - by Musa
    Hi, Let's assume that I have a vector r <- rnorm(4) and a matrix W of dimension 20000*200 for example: W <- matrix(rnorm(20000*200),20000,200) I want to compute a new matrix M of dimension 5000*200 such that m11 <- r%*%W[1:4,1], m21 <- r%*%W[5:8,1], m12 <- r%*%W[1:4,2] etc. (i.e. grouping rows 4-by-4 and computing the product). What's the optimal (speed,memory) way of doing this? Thanks in advance.

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  • in R, how can i save the value of "print"

    - by alex
    in R , when i use "print", i can see all the values, but how can i save this as a vector for example, in for loop, for (i in 1:10), i want the value of A , when i= 1,2,3,4..... but if i use the x=A, it only have the final value of A which is the value when i = 10. so , how can i save the vaule in print(A)

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  • R: How to replace elements of a data.frame?

    - by John
    I'm trying to replace elements of a data.frame containing "#N/A" with "NULL", and I'm running into problems: foo <- data.frame("day"= c(1, 3, 5, 7), "od" = c(0.1, "#N/A", 0.4, 0.8)) indices_of_NAs <- which(foo == "#N/A") replace(foo, indices_of_NAs, "NULL") Error in [<-.data.frame(*tmp*, list, value = "NULL") : new columns would leave holes after existing columns I think that the problem is that my index is treating the data.frame as a vector, but that the replace function is treating it differently somehow, but I'm not sure what the issue is?

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  • C++ return object

    - by Pauff
    I have a class that has a vector of objects. What do I need to do to return one of this objects and change it outside the class, keeping the changings? Is it possible to do with regular pointers? Is there a standard procedure? (And yes, my background is in Java.)

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  • Creating a thematic map

    - by jsharma
    This post describes how to create a simple thematic map, just a state population layer, with no underlying map tile layer. The map shows states color-coded by total population. The map is interactive with info-windows and can be panned and zoomed. The sample code demonstrates the following: Displaying an interactive vector layer with no background map tile layer (i.e. purpose and use of the Universe object) Using a dynamic (i.e. defined via the javascript client API) color bucket style Dynamically changing a layer's rendering style Specifying which attribute value to use in determining the bucket, and hence style, for a feature (FoI) The result is shown in the screenshot below. The states layer was defined, and stored in the user_sdo_themes view of the mvdemo schema, using MapBuilder. The underlying table is defined as SQL> desc states_32775  Name                                      Null?    Type ----------------------------------------- -------- ----------------------------  STATE                                              VARCHAR2(26)  STATE_ABRV                                         VARCHAR2(2) FIPSST                                             VARCHAR2(2) TOTPOP                                             NUMBER PCTSMPLD                                           NUMBER LANDSQMI                                           NUMBER POPPSQMI                                           NUMBER ... MEDHHINC NUMBER AVGHHINC NUMBER GEOM32775 MDSYS.SDO_GEOMETRY We'll use the TOTPOP column value in the advanced (color bucket) style for rendering the states layers. The predefined theme (US_STATES_BI) is defined as follows. SQL> select styling_rules from user_sdo_themes where name='US_STATES_BI'; STYLING_RULES -------------------------------------------------------------------------------- <?xml version="1.0" standalone="yes"?> <styling_rules highlight_style="C.CB_QUAL_8_CLASS_DARK2_1"> <hidden_info> <field column="STATE" name="Name"/> <field column="POPPSQMI" name="POPPSQMI"/> <field column="TOTPOP" name="TOTPOP"/> </hidden_info> <rule column="TOTPOP"> <features style="states_totpop"> </features> <label column="STATE_ABRV" style="T.BLUE_SERIF_10"> 1 </label> </rule> </styling_rules> SQL> The theme definition specifies that the state, poppsqmi, totpop, state_abrv, and geom columns will be queried from the states_32775 table. The state_abrv value will be used to label the state while the totpop value will be used to determine the color-fill from those defined in the states_totpop advanced style. The states_totpop style, which we will not use in our demo, is defined as shown below. SQL> select definition from user_sdo_styles where name='STATES_TOTPOP'; DEFINITION -------------------------------------------------------------------------------- <?xml version="1.0" ?> <AdvancedStyle> <BucketStyle> <Buckets default_style="C.S02_COUNTRY_AREA"> <RangedBucket seq="0" label="10K - 5M" low="10000" high="5000000" style="C.SEQ6_01" /> <RangedBucket seq="1" label="5M - 12M" low="5000001" high="1.2E7" style="C.SEQ6_02" /> <RangedBucket seq="2" label="12M - 20M" low="1.2000001E7" high="2.0E7" style="C.SEQ6_04" /> <RangedBucket seq="3" label="&gt; 20M" low="2.0000001E7" high="5.0E7" style="C.SEQ6_05" /> </Buckets> </BucketStyle> </AdvancedStyle> SQL> The demo defines additional advanced styles via the OM.style object and methods and uses those instead when rendering the states layer.   Now let's look at relevant snippets of code that defines the map extent and zoom levels (i.e. the OM.universe),  loads the states predefined vector layer (OM.layer), and sets up the advanced (color bucket) style. Defining the map extent and zoom levels. function initMap() {   //alert("Initialize map view");     // define the map extent and number of zoom levels.   // The Universe object is similar to the map tile layer configuration   // It defines the map extent, number of zoom levels, and spatial reference system   // well-known ones (like web mercator/google/bing or maps.oracle/elocation are predefined   // The Universe must be defined when there is no underlying map tile layer.   // When there is a map tile layer then that defines the map extent, srid, and zoom levels.      var uni= new OM.universe.Universe(     {         srid : 32775,         bounds : new OM.geometry.Rectangle(                         -3280000, 170000, 2300000, 3200000, 32775),         numberOfZoomLevels: 8     }); The srid specifies the spatial reference system which is Equal-Area Projection (United States). SQL> select cs_name from cs_srs where srid=32775 ; CS_NAME --------------------------------------------------- Equal-Area Projection (United States) The bounds defines the map extent. It is a Rectangle defined using the lower-left and upper-right coordinates and srid. Loading and displaying the states layer This is done in the states() function. The full code is at the end of this post, however here's the snippet which defines the states VectorLayer.     // States is a predefined layer in user_sdo_themes     var  layer2 = new OM.layer.VectorLayer("vLayer2",     {         def:         {             type:OM.layer.VectorLayer.TYPE_PREDEFINED,             dataSource:"mvdemo",             theme:"us_states_bi",             url: baseURL,             loadOnDemand: false         },         boundingTheme:true      }); The first parameter is a layer name, the second is an object literal for a layer config. The config object has two attributes: the first is the layer definition, the second specifies whether the layer is a bounding one (i.e. used to determine the current map zoom and center such that the whole layer is displayed within the map window) or not. The layer config has the following attributes: type - specifies whether is a predefined one, a defined via a SQL query (JDBC), or in a json-format file (DATAPACK) theme - is the predefined theme's name url - is the location of the mapviewer server loadOnDemand - specifies whether to load all the features or just those that lie within the current map window and load additional ones as needed on a pan or zoom The code snippet below dynamically defines an advanced style and then uses it, instead of the 'states_totpop' style, when rendering the states layer. // override predefined rendering style with programmatic one    var theRenderingStyle =      createBucketColorStyle('YlBr5', colorSeries, 'States5', true);   // specify which attribute is used in determining the bucket (i.e. color) to use for the state   // It can be an array because the style could be a chart type (pie/bar)   // which requires multiple attribute columns     // Use the STATE.TOTPOP column (aka attribute) value here    layer2.setRenderingStyle(theRenderingStyle, ["TOTPOP"]); The style itself is defined in the createBucketColorStyle() function. Dynamically defining an advanced style The advanced style used here is a bucket color style, i.e. a color style is associated with each bucket. So first we define the colors and then the buckets.     numClasses = colorSeries[colorName].classes;    // create Color Styles    for (var i=0; i < numClasses; i++)    {         theStyles[i] = new OM.style.Color(                      {fill: colorSeries[colorName].fill[i],                        stroke:colorSeries[colorName].stroke[i],                       strokeOpacity: useGradient? 0.25 : 1                      });    }; numClasses is the number of buckets. The colorSeries array contains the color fill and stroke definitions and is: var colorSeries = { //multi-hue color scheme #10 YlBl. "YlBl3": {   classes:3,                  fill: [0xEDF8B1, 0x7FCDBB, 0x2C7FB8],                  stroke:[0xB5DF9F, 0x72B8A8, 0x2872A6]   }, "YlBl5": {   classes:5,                  fill:[0xFFFFCC, 0xA1DAB4, 0x41B6C4, 0x2C7FB8, 0x253494],                  stroke:[0xE6E6B8, 0x91BCA2, 0x3AA4B0, 0x2872A6, 0x212F85]   }, //multi-hue color scheme #11 YlBr.  "YlBr3": {classes:3,                  fill:[0xFFF7BC, 0xFEC44F, 0xD95F0E],                  stroke:[0xE6DEA9, 0xE5B047, 0xC5360D]   }, "YlBr5": {classes:5,                  fill:[0xFFFFD4, 0xFED98E, 0xFE9929, 0xD95F0E, 0x993404],                  stroke:[0xE6E6BF, 0xE5C380, 0xE58A25, 0xC35663, 0x8A2F04]     }, etc. Next we create the bucket style.    bucketStyleDef = {       numClasses : colorSeries[colorName].classes, //      classification: 'custom',  //since we are supplying all the buckets //      buckets: theBuckets,       classification: 'logarithmic',  // use a logarithmic scale       styles: theStyles,       gradient:  useGradient? 'linear' : 'off' //      gradient:  useGradient? 'radial' : 'off'     };    theBucketStyle = new OM.style.BucketStyle(bucketStyleDef);    return theBucketStyle; A BucketStyle constructor takes a style definition as input. The style definition specifies the number of buckets (numClasses), a classification scheme (which can be equal-ranged, logarithmic scale, or custom), the styles for each bucket, whether to use a gradient effect, and optionally the buckets (required when using a custom classification scheme). The full source for the demo <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd"> <html> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <title>Oracle Maps V2 Thematic Map Demo</title> <script src="http://localhost:8080/mapviewer/jslib/v2/oraclemapsv2.js" type="text/javascript"> </script> <script type="text/javascript"> //var $j = jQuery.noConflict(); var baseURL="http://localhost:8080/mapviewer"; // location of mapviewer OM.gv.proxyEnabled =false; // no mvproxy needed OM.gv.setResourcePath(baseURL+"/jslib/v2/images/"); // location of resources for UI elements like nav panel buttons var map = null; // the client mapviewer object var statesLayer = null, stateCountyLayer = null; // The vector layers for states and counties in a state var layerName="States"; // initial map center and zoom var mapCenterLon = -20000; var mapCenterLat = 1750000; var mapZoom = 2; var mpoint = new OM.geometry.Point(mapCenterLon,mapCenterLat,32775); var currentPalette = null, currentStyle=null; // set an onchange listener for the color palette select list // initialize the map // load and display the states layer $(document).ready( function() { $("#demo-htmlselect").change(function() { var theColorScheme = $(this).val(); useSelectedColorScheme(theColorScheme); }); initMap(); states(); } ); /** * color series from ColorBrewer site (http://colorbrewer2.org/). */ var colorSeries = { //multi-hue color scheme #10 YlBl. "YlBl3": { classes:3, fill: [0xEDF8B1, 0x7FCDBB, 0x2C7FB8], stroke:[0xB5DF9F, 0x72B8A8, 0x2872A6] }, "YlBl5": { classes:5, fill:[0xFFFFCC, 0xA1DAB4, 0x41B6C4, 0x2C7FB8, 0x253494], stroke:[0xE6E6B8, 0x91BCA2, 0x3AA4B0, 0x2872A6, 0x212F85] }, //multi-hue color scheme #11 YlBr. "YlBr3": {classes:3, fill:[0xFFF7BC, 0xFEC44F, 0xD95F0E], stroke:[0xE6DEA9, 0xE5B047, 0xC5360D] }, "YlBr5": {classes:5, fill:[0xFFFFD4, 0xFED98E, 0xFE9929, 0xD95F0E, 0x993404], stroke:[0xE6E6BF, 0xE5C380, 0xE58A25, 0xC35663, 0x8A2F04] }, // single-hue color schemes (blues, greens, greys, oranges, reds, purples) "Purples5": {classes:5, fill:[0xf2f0f7, 0xcbc9e2, 0x9e9ac8, 0x756bb1, 0x54278f], stroke:[0xd3d3d3, 0xd3d3d3, 0xd3d3d3, 0xd3d3d3, 0xd3d3d3] }, "Blues5": {classes:5, fill:[0xEFF3FF, 0xbdd7e7, 0x68aed6, 0x3182bd, 0x18519C], stroke:[0xd3d3d3, 0xd3d3d3, 0xd3d3d3, 0xd3d3d3, 0xd3d3d3] }, "Greens5": {classes:5, fill:[0xedf8e9, 0xbae4b3, 0x74c476, 0x31a354, 0x116d2c], stroke:[0xd3d3d3, 0xd3d3d3, 0xd3d3d3, 0xd3d3d3, 0xd3d3d3] }, "Greys5": {classes:5, fill:[0xf7f7f7, 0xcccccc, 0x969696, 0x636363, 0x454545], stroke:[0xd3d3d3, 0xd3d3d3, 0xd3d3d3, 0xd3d3d3, 0xd3d3d3] }, "Oranges5": {classes:5, fill:[0xfeedde, 0xfdb385, 0xfd8d3c, 0xe6550d, 0xa63603], stroke:[0xd3d3d3, 0xd3d3d3, 0xd3d3d3, 0xd3d3d3, 0xd3d3d3] }, "Reds5": {classes:5, fill:[0xfee5d9, 0xfcae91, 0xfb6a4a, 0xde2d26, 0xa50f15], stroke:[0xd3d3d3, 0xd3d3d3, 0xd3d3d3, 0xd3d3d3, 0xd3d3d3] } }; function createBucketColorStyle( colorName, colorSeries, rangeName, useGradient) { var theBucketStyle; var bucketStyleDef; var theStyles = []; var theColors = []; var aBucket, aStyle, aColor, aRange; var numClasses ; numClasses = colorSeries[colorName].classes; // create Color Styles for (var i=0; i < numClasses; i++) { theStyles[i] = new OM.style.Color( {fill: colorSeries[colorName].fill[i], stroke:colorSeries[colorName].stroke[i], strokeOpacity: useGradient? 0.25 : 1 }); }; bucketStyleDef = { numClasses : colorSeries[colorName].classes, // classification: 'custom', //since we are supplying all the buckets // buckets: theBuckets, classification: 'logarithmic', // use a logarithmic scale styles: theStyles, gradient: useGradient? 'linear' : 'off' // gradient: useGradient? 'radial' : 'off' }; theBucketStyle = new OM.style.BucketStyle(bucketStyleDef); return theBucketStyle; } function initMap() { //alert("Initialize map view"); // define the map extent and number of zoom levels. // The Universe object is similar to the map tile layer configuration // It defines the map extent, number of zoom levels, and spatial reference system // well-known ones (like web mercator/google/bing or maps.oracle/elocation are predefined // The Universe must be defined when there is no underlying map tile layer. // When there is a map tile layer then that defines the map extent, srid, and zoom levels. var uni= new OM.universe.Universe( { srid : 32775, bounds : new OM.geometry.Rectangle( -3280000, 170000, 2300000, 3200000, 32775), numberOfZoomLevels: 8 }); map = new OM.Map( document.getElementById('map'), { mapviewerURL: baseURL, universe:uni }) ; var navigationPanelBar = new OM.control.NavigationPanelBar(); map.addMapDecoration(navigationPanelBar); } // end initMap function states() { //alert("Load and display states"); layerName = "States"; if(statesLayer) { // states were already visible but the style may have changed // so set the style to the currently selected one var theData = $('#demo-htmlselect').val(); setStyle(theData); } else { // States is a predefined layer in user_sdo_themes var layer2 = new OM.layer.VectorLayer("vLayer2", { def: { type:OM.layer.VectorLayer.TYPE_PREDEFINED, dataSource:"mvdemo", theme:"us_states_bi", url: baseURL, loadOnDemand: false }, boundingTheme:true }); // add drop shadow effect and hover style var shadowFilter = new OM.visualfilter.DropShadow({opacity:0.5, color:"#000000", offset:6, radius:10}); var hoverStyle = new OM.style.Color( {stroke:"#838383", strokeThickness:2}); layer2.setHoverStyle(hoverStyle); layer2.setHoverVisualFilter(shadowFilter); layer2.enableFeatureHover(true); layer2.enableFeatureSelection(false); layer2.setLabelsVisible(true); // override predefined rendering style with programmatic one var theRenderingStyle = createBucketColorStyle('YlBr5', colorSeries, 'States5', true); // specify which attribute is used in determining the bucket (i.e. color) to use for the state // It can be an array because the style could be a chart type (pie/bar) // which requires multiple attribute columns // Use the STATE.TOTPOP column (aka attribute) value here layer2.setRenderingStyle(theRenderingStyle, ["TOTPOP"]); currentPalette = "YlBr5"; var stLayerIdx = map.addLayer(layer2); //alert('State Layer Idx = ' + stLayerIdx); map.setMapCenter(mpoint); map.setMapZoomLevel(mapZoom) ; // display the map map.init() ; statesLayer=layer2; // add rt-click event listener to show counties for the state layer2.addListener(OM.event.MouseEvent.MOUSE_RIGHT_CLICK,stateRtClick); } // end if } // end states function setStyle(styleName) { // alert("Selected Style = " + styleName); // there may be a counties layer also displayed. // that wll have different bucket ranges so create // one style for states and one for counties var newRenderingStyle = null; if (layerName === "States") { if(/3/.test(styleName)) { newRenderingStyle = createBucketColorStyle(styleName, colorSeries, 'States3', false); currentStyle = createBucketColorStyle(styleName, colorSeries, 'Counties3', false); } else { newRenderingStyle = createBucketColorStyle(styleName, colorSeries, 'States5', false); currentStyle = createBucketColorStyle(styleName, colorSeries, 'Counties5', false); } statesLayer.setRenderingStyle(newRenderingStyle, ["TOTPOP"]); if (stateCountyLayer) stateCountyLayer.setRenderingStyle(currentStyle, ["TOTPOP"]); } } // end setStyle function stateRtClick(evt){ var foi = evt.feature; //alert('Rt-Click on State: ' + foi.attributes['_label_'] + // ' with pop ' + foi.attributes['TOTPOP']); // display another layer with counties info // layer may change on each rt-click so create and add each time. var countyByState = null ; // the _label_ attribute of a feature in this case is the state abbreviation // we will use that to query and get the counties for a state var sqlText = "select totpop,geom32775 from counties_32775_moved where state_abrv="+ "'"+foi.getAttributeValue('_label_')+"'"; // alert(sqlText); if (currentStyle === null) currentStyle = createBucketColorStyle('YlBr5', colorSeries, 'Counties5', false); /* try a simple style instead new OM.style.ColorStyle( { stroke: "#B8F4FF", fill: "#18E5F4", fillOpacity:0 } ); */ // remove existing layer if any if(stateCountyLayer) map.removeLayer(stateCountyLayer); countyByState = new OM.layer.VectorLayer("stCountyLayer", {def:{type:OM.layer.VectorLayer.TYPE_JDBC, dataSource:"mvdemo", sql:sqlText, url:baseURL}}); // url:baseURL}, // renderingStyle:currentStyle}); countyByState.setVisible(true); // specify which attribute is used in determining the bucket (i.e. color) to use for the state countyByState.setRenderingStyle(currentStyle, ["TOTPOP"]); var ctLayerIdx = map.addLayer(countyByState); // alert('County Layer Idx = ' + ctLayerIdx); //map.addLayer(countyByState); stateCountyLayer = countyByState; } // end stateRtClick function useSelectedColorScheme(theColorScheme) { if(map) { // code to update renderStyle goes here //alert('will try to change render style'); setStyle(theColorScheme); } else { // do nothing } } </script> </head> <body bgcolor="#b4c5cc" style="height:100%;font-family:Arial,Helvetica,Verdana"> <h3 align="center">State population thematic map </h3> <div id="demo" style="position:absolute; left:68%; top:44px; width:28%; height:100%"> <HR/> <p/> Choose Color Scheme: <select id="demo-htmlselect"> <option value="YlBl3"> YellowBlue3</option> <option value="YlBr3"> YellowBrown3</option> <option value="YlBl5"> YellowBlue5</option> <option value="YlBr5" selected="selected"> YellowBrown5</option> <option value="Blues5"> Blues</option> <option value="Greens5"> Greens</option> <option value="Greys5"> Greys</option> <option value="Oranges5"> Oranges</option> <option value="Purples5"> Purples</option> <option value="Reds5"> Reds</option> </select> <p/> </div> <div id="map" style="position:absolute; left:10px; top:50px; width:65%; height:75%; background-color:#778f99"></div> <div style="position:absolute;top:85%; left:10px;width:98%" class="noprint"> <HR/> <p> Note: This demo uses HTML5 Canvas and requires IE9+, Firefox 10+, or Chrome. No map will show up in IE8 or earlier. </p> </div> </body> </html>

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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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  • How to calculate checksum?

    - by Patel Rikin
    I m developing instrument driver and i want to know how to calculate checksum of frame. Explanation: Expressed by characters [0-9] and [A-F]. Characters beginning from the character after [STX] and until [ETB] or [ETX] (including [ETB] or [ETX]) are added in binary. The 2-digit numbers, which represent the least significant 8 bits in hexadecimal code, are converted to ASCII characters [0-9] and [A-F]. The most significant digit is stored in CHK1 and the least significant digit in CHK2. This is sample frame : <STX>2Q|1|2^1||||20011001153000<CR><ETX><CHK1><CHK2><CR><LF> and i want to know what is value of chk1 and chk2 and i am new in this so i m totally blank about how to calculate checksum i am not getting above 3rd and 4th point. can any one provide sample code for c#. Please help me.

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  • Fixing up Visual Studio&rsquo;s gitignore , using IFix

    - by terje
    Originally posted on: http://geekswithblogs.net/terje/archive/2014/06/13/fixing-up-visual-studiorsquos-gitignore--using-ifix.aspxDownload tool Is there anything wrong with the built-in Visual Studio gitignore ???? Yes, there is !  First, some background: When you set up a git repo, it should be small and not contain anything not really needed.  One thing you should not have in your git repo is binary files. These binary files may come from two sources, one is the output files, in the bin and obj folders.  If you have a  gitignore file present, which you should always have (!!), these folders are excluded by the standard included file (the one included when you choose Team Explorer/Settings/GitIgnore – Add.) The other source are the packages folder coming from your NuGet setup.  You do use NuGet, right ?  Of course you do !  But, that gitignore file doesn’t have any exclude clause for those folders.  You have to add that manually.  (It will very probably be included in some upcoming update or release).  This is one thing that is missing from the built-in gitignore. To add those few lines is a no-brainer, you just include this: # NuGet Packages packages/* *.nupkg # Enable "build/" folder in the NuGet Packages folder since # NuGet packages use it for MSBuild targets. # This line needs to be after the ignore of the build folder # (and the packages folder if the line above has been uncommented) !packages/build/ Now, if you are like me, and you probably are, you add git repo’s faster than you can code, and you end up with a bunch of repo’s, and then start to wonder: Did I fix up those gitignore files, or did I forget it? The next thing you learn, for example by reading this blog post, is that the “standard” latest Visual Studio gitignore file exist at https://github.com/github/gitignore, and you locate it under the file name VisualStudio.gitignore.  Here you will find all the new stuff, for example, the exclusion of the roslyn ide folders was commited on May 24th.  So, you think, all is well, Visual Studio will use this file …..     I am very sorry, it won’t. Visual Studio comes with a gitignore file that is baked into the release, and that is by this time “very old”.  The one at github is the latest.  The included gitignore miss the exclusion of the nuget packages folder, it also miss a lot of new stuff, like the Roslyn stuff. So, how do you fix this ?  … note .. while we wait for the next version… You can manually update it for every single repo you create, which works, but it does get boring after a few times, doesn’t it ? IFix Enter IFix ,  install it from here. IFix is a command line utility (and the installer adds it to the system path, you might need to reboot), and one of the commands is gitignore If you run it from a directory, it will check and optionally fix all gitignores in all git repo’s in that folder or below.  So, start up by running it from your C:/<user>/source/repos folder. To run it in check mode – which will not change anything, just do a check: IFix  gitignore --check What it will do is to check if the gitignore file is present, and if it is, check if the packages folder has been excluded.  If you want to see those that are ok, add the --verbose command too.  The result may look like this: Fixing missing packages Let us fix a single repo by adding the missing packages structure,  using IFix --fix We first check, then fix, then check again to verify that the gitignore is correct, and that the “packages/” part has been added. If we open up the .gitignore, we see that the block shown below has been added to the end of the .gitignore file.   Comparing and fixing with latest standard Visual Studio gitignore (from github) Now, this tells you if you miss the nuget packages folder, but what about the latest gitignore from github ? You can check for this too, just add the option –merge (why this is named so will be clear later down) So, IFix gitignore --check –merge The result may come out like this  (sorry no colors, not got that far yet here): As you can see, one repo has the latest gitignore (test1), the others are missing either 57 or 150 lines.  IFix has three ways to fix this: --add --merge --replace The options work as follows: Add:  Used to add standard gitignore in the cases where a .gitignore file is missing, and only that, that means it won’t touch other existing gitignores. Merge: Used to merge in the missing lines from the standard into the gitignore file.  If gitignore file is missing, the whole standard will be added. Replace: Used to force a complete replacement of the existing gitignore with the standard one. The Add and Replace options can be used without Fix, which means they will actually do the action. If you combine with --check it will otherwise not touch any files, just do a verification.  So a Merge Check will  tell you if there is any difference between the local gitignore and the standard gitignore, a Compare in effect. When you do a Fix Merge it will combine the local gitignore with the standard, and add what is missing to the end of the local gitignore. It may mean some things may be doubled up if they are spelled a bit differently.  You might also see some extra comments added, but they do no harm. Init new repo with standard gitignore One cool thing is that with a new repo, or a repo that is missing its gitignore, you can grab the latest standard just by using either the Add or the Replace command, both will in effect do the same in this case. So, IFix gitignore --add will add it in, as in the complete example below, where we set up a new git repo and add in the latest standard gitignore: Notes The project is open sourced at github, and you can also report issues there.

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  • Toorcon 15 (2013)

    - by danx
    The Toorcon gang (senior staff): h1kari (founder), nfiltr8, and Geo Introduction to Toorcon 15 (2013) A Tale of One Software Bypass of MS Windows 8 Secure Boot Breaching SSL, One Byte at a Time Running at 99%: Surviving an Application DoS Security Response in the Age of Mass Customized Attacks x86 Rewriting: Defeating RoP and other Shinanighans Clowntown Express: interesting bugs and running a bug bounty program Active Fingerprinting of Encrypted VPNs Making Attacks Go Backwards Mask Your Checksums—The Gorry Details Adventures with weird machines thirty years after "Reflections on Trusting Trust" Introduction to Toorcon 15 (2013) Toorcon 15 is the 15th annual security conference held in San Diego. I've attended about a third of them and blogged about previous conferences I attended here starting in 2003. As always, I've only summarized the talks I attended and interested me enough to write about them. Be aware that I may have misrepresented the speaker's remarks and that they are not my remarks or opinion, or those of my employer, so don't quote me or them. Those seeking further details may contact the speakers directly or use The Google. For some talks, I have a URL for further information. A Tale of One Software Bypass of MS Windows 8 Secure Boot Andrew Furtak and Oleksandr Bazhaniuk Yuri Bulygin, Oleksandr ("Alex") Bazhaniuk, and (not present) Andrew Furtak Yuri and Alex talked about UEFI and Bootkits and bypassing MS Windows 8 Secure Boot, with vendor recommendations. They previously gave this talk at the BlackHat 2013 conference. MS Windows 8 Secure Boot Overview UEFI (Unified Extensible Firmware Interface) is interface between hardware and OS. UEFI is processor and architecture independent. Malware can replace bootloader (bootx64.efi, bootmgfw.efi). Once replaced can modify kernel. Trivial to replace bootloader. Today many legacy bootkits—UEFI replaces them most of them. MS Windows 8 Secure Boot verifies everything you load, either through signatures or hashes. UEFI firmware relies on secure update (with signed update). You would think Secure Boot would rely on ROM (such as used for phones0, but you can't do that for PCs—PCs use writable memory with signatures DXE core verifies the UEFI boat loader(s) OS Loader (winload.efi, winresume.efi) verifies the OS kernel A chain of trust is established with a root key (Platform Key, PK), which is a cert belonging to the platform vendor. Key Exchange Keys (KEKs) verify an "authorized" database (db), and "forbidden" database (dbx). X.509 certs with SHA-1/SHA-256 hashes. Keys are stored in non-volatile (NV) flash-based NVRAM. Boot Services (BS) allow adding/deleting keys (can't be accessed once OS starts—which uses Run-Time (RT)). Root cert uses RSA-2048 public keys and PKCS#7 format signatures. SecureBoot — enable disable image signature checks SetupMode — update keys, self-signed keys, and secure boot variables CustomMode — allows updating keys Secure Boot policy settings are: always execute, never execute, allow execute on security violation, defer execute on security violation, deny execute on security violation, query user on security violation Attacking MS Windows 8 Secure Boot Secure Boot does NOT protect from physical access. Can disable from console. Each BIOS vendor implements Secure Boot differently. There are several platform and BIOS vendors. It becomes a "zoo" of implementations—which can be taken advantage of. Secure Boot is secure only when all vendors implement it correctly. Allow only UEFI firmware signed updates protect UEFI firmware from direct modification in flash memory protect FW update components program SPI controller securely protect secure boot policy settings in nvram protect runtime api disable compatibility support module which allows unsigned legacy Can corrupt the Platform Key (PK) EFI root certificate variable in SPI flash. If PK is not found, FW enters setup mode wich secure boot turned off. Can also exploit TPM in a similar manner. One is not supposed to be able to directly modify the PK in SPI flash from the OS though. But they found a bug that they can exploit from User Mode (undisclosed) and demoed the exploit. It loaded and ran their own bootkit. The exploit requires a reboot. Multiple vendors are vulnerable. They will disclose this exploit to vendors in the future. Recommendations: allow only signed updates protect UEFI fw in ROM protect EFI variable store in ROM Breaching SSL, One Byte at a Time Yoel Gluck and Angelo Prado Angelo Prado and Yoel Gluck, Salesforce.com CRIME is software that performs a "compression oracle attack." This is possible because the SSL protocol doesn't hide length, and because SSL compresses the header. CRIME requests with every possible character and measures the ciphertext length. Look for the plaintext which compresses the most and looks for the cookie one byte-at-a-time. SSL Compression uses LZ77 to reduce redundancy. Huffman coding replaces common byte sequences with shorter codes. US CERT thinks the SSL compression problem is fixed, but it isn't. They convinced CERT that it wasn't fixed and they issued a CVE. BREACH, breachattrack.com BREACH exploits the SSL response body (Accept-Encoding response, Content-Encoding). It takes advantage of the fact that the response is not compressed. BREACH uses gzip and needs fairly "stable" pages that are static for ~30 seconds. It needs attacker-supplied content (say from a web form or added to a URL parameter). BREACH listens to a session's requests and responses, then inserts extra requests and responses. Eventually, BREACH guesses a session's secret key. Can use compression to guess contents one byte at-a-time. For example, "Supersecret SupersecreX" (a wrong guess) compresses 10 bytes, and "Supersecret Supersecret" (a correct guess) compresses 11 bytes, so it can find each character by guessing every character. To start the guess, BREACH needs at least three known initial characters in the response sequence. Compression length then "leaks" information. Some roadblocks include no winners (all guesses wrong) or too many winners (multiple possibilities that compress the same). The solutions include: lookahead (guess 2 or 3 characters at-a-time instead of 1 character). Expensive rollback to last known conflict check compression ratio can brute-force first 3 "bootstrap" characters, if needed (expensive) block ciphers hide exact plain text length. Solution is to align response in advance to block size Mitigations length: use variable padding secrets: dynamic CSRF tokens per request secret: change over time separate secret to input-less servlets Future work eiter understand DEFLATE/GZIP HTTPS extensions Running at 99%: Surviving an Application DoS Ryan Huber Ryan Huber, Risk I/O Ryan first discussed various ways to do a denial of service (DoS) attack against web services. One usual method is to find a slow web page and do several wgets. Or download large files. Apache is not well suited at handling a large number of connections, but one can put something in front of it Can use Apache alternatives, such as nginx How to identify malicious hosts short, sudden web requests user-agent is obvious (curl, python) same url requested repeatedly no web page referer (not normal) hidden links. hide a link and see if a bot gets it restricted access if not your geo IP (unless the website is global) missing common headers in request regular timing first seen IP at beginning of attack count requests per hosts (usually a very large number) Use of captcha can mitigate attacks, but you'll lose a lot of genuine users. Bouncer, goo.gl/c2vyEc and www.github.com/rawdigits/Bouncer Bouncer is software written by Ryan in netflow. Bouncer has a small, unobtrusive footprint and detects DoS attempts. It closes blacklisted sockets immediately (not nice about it, no proper close connection). Aggregator collects requests and controls your web proxies. Need NTP on the front end web servers for clean data for use by bouncer. Bouncer is also useful for a popularity storm ("Slashdotting") and scraper storms. Future features: gzip collection data, documentation, consumer library, multitask, logging destroyed connections. Takeaways: DoS mitigation is easier with a complete picture Bouncer designed to make it easier to detect and defend DoS—not a complete cure Security Response in the Age of Mass Customized Attacks Peleus Uhley and Karthik Raman Peleus Uhley and Karthik Raman, Adobe ASSET, blogs.adobe.com/asset/ Peleus and Karthik talked about response to mass-customized exploits. Attackers behave much like a business. "Mass customization" refers to concept discussed in the book Future Perfect by Stan Davis of Harvard Business School. Mass customization is differentiating a product for an individual customer, but at a mass production price. For example, the same individual with a debit card receives basically the same customized ATM experience around the world. Or designing your own PC from commodity parts. Exploit kits are another example of mass customization. The kits support multiple browsers and plugins, allows new modules. Exploit kits are cheap and customizable. Organized gangs use exploit kits. A group at Berkeley looked at 77,000 malicious websites (Grier et al., "Manufacturing Compromise: The Emergence of Exploit-as-a-Service", 2012). They found 10,000 distinct binaries among them, but derived from only a dozen or so exploit kits. Characteristics of Mass Malware: potent, resilient, relatively low cost Technical characteristics: multiple OS, multipe payloads, multiple scenarios, multiple languages, obfuscation Response time for 0-day exploits has gone down from ~40 days 5 years ago to about ~10 days now. So the drive with malware is towards mass customized exploits, to avoid detection There's plenty of evicence that exploit development has Project Manager bureaucracy. They infer from the malware edicts to: support all versions of reader support all versions of windows support all versions of flash support all browsers write large complex, difficult to main code (8750 lines of JavaScript for example Exploits have "loose coupling" of multipe versions of software (adobe), OS, and browser. This allows specific attacks against specific versions of multiple pieces of software. Also allows exploits of more obscure software/OS/browsers and obscure versions. Gave examples of exploits that exploited 2, 3, 6, or 14 separate bugs. However, these complete exploits are more likely to be buggy or fragile in themselves and easier to defeat. Future research includes normalizing malware and Javascript. Conclusion: The coming trend is that mass-malware with mass zero-day attacks will result in mass customization of attacks. x86 Rewriting: Defeating RoP and other Shinanighans Richard Wartell Richard Wartell The attack vector we are addressing here is: First some malware causes a buffer overflow. The malware has no program access, but input access and buffer overflow code onto stack Later the stack became non-executable. The workaround malware used was to write a bogus return address to the stack jumping to malware Later came ASLR (Address Space Layout Randomization) to randomize memory layout and make addresses non-deterministic. The workaround malware used was to jump t existing code segments in the program that can be used in bad ways "RoP" is Return-oriented Programming attacks. RoP attacks use your own code and write return address on stack to (existing) expoitable code found in program ("gadgets"). Pinkie Pie was paid $60K last year for a RoP attack. One solution is using anti-RoP compilers that compile source code with NO return instructions. ASLR does not randomize address space, just "gadgets". IPR/ILR ("Instruction Location Randomization") randomizes each instruction with a virtual machine. Richard's goal was to randomize a binary with no source code access. He created "STIR" (Self-Transofrming Instruction Relocation). STIR disassembles binary and operates on "basic blocks" of code. The STIR disassembler is conservative in what to disassemble. Each basic block is moved to a random location in memory. Next, STIR writes new code sections with copies of "basic blocks" of code in randomized locations. The old code is copied and rewritten with jumps to new code. the original code sections in the file is marked non-executible. STIR has better entropy than ASLR in location of code. Makes brute force attacks much harder. STIR runs on MS Windows (PEM) and Linux (ELF). It eliminated 99.96% or more "gadgets" (i.e., moved the address). Overhead usually 5-10% on MS Windows, about 1.5-4% on Linux (but some code actually runs faster!). The unique thing about STIR is it requires no source access and the modified binary fully works! Current work is to rewrite code to enforce security policies. For example, don't create a *.{exe,msi,bat} file. Or don't connect to the network after reading from the disk. Clowntown Express: interesting bugs and running a bug bounty program Collin Greene Collin Greene, Facebook Collin talked about Facebook's bug bounty program. Background at FB: FB has good security frameworks, such as security teams, external audits, and cc'ing on diffs. But there's lots of "deep, dark, forgotten" parts of legacy FB code. Collin gave several examples of bountied bugs. Some bounty submissions were on software purchased from a third-party (but bounty claimers don't know and don't care). We use security questions, as does everyone else, but they are basically insecure (often easily discoverable). Collin didn't expect many bugs from the bounty program, but they ended getting 20+ good bugs in first 24 hours and good submissions continue to come in. Bug bounties bring people in with different perspectives, and are paid only for success. Bug bounty is a better use of a fixed amount of time and money versus just code review or static code analysis. The Bounty program started July 2011 and paid out $1.5 million to date. 14% of the submissions have been high priority problems that needed to be fixed immediately. The best bugs come from a small % of submitters (as with everything else)—the top paid submitters are paid 6 figures a year. Spammers like to backstab competitors. The youngest sumitter was 13. Some submitters have been hired. Bug bounties also allows to see bugs that were missed by tools or reviews, allowing improvement in the process. Bug bounties might not work for traditional software companies where the product has release cycle or is not on Internet. Active Fingerprinting of Encrypted VPNs Anna Shubina Anna Shubina, Dartmouth Institute for Security, Technology, and Society (I missed the start of her talk because another track went overtime. But I have the DVD of the talk, so I'll expand later) IPsec leaves fingerprints. Using netcat, one can easily visually distinguish various crypto chaining modes just from packet timing on a chart (example, DES-CBC versus AES-CBC) One can tell a lot about VPNs just from ping roundtrips (such as what router is used) Delayed packets are not informative about a network, especially if far away from the network More needed to explore about how TCP works in real life with respect to timing Making Attacks Go Backwards Fuzzynop FuzzyNop, Mandiant This talk is not about threat attribution (finding who), product solutions, politics, or sales pitches. But who are making these malware threats? It's not a single person or group—they have diverse skill levels. There's a lot of fat-fingered fumblers out there. Always look for low-hanging fruit first: "hiding" malware in the temp, recycle, or root directories creation of unnamed scheduled tasks obvious names of files and syscalls ("ClearEventLog") uncleared event logs. Clearing event log in itself, and time of clearing, is a red flag and good first clue to look for on a suspect system Reverse engineering is hard. Disassembler use takes practice and skill. A popular tool is IDA Pro, but it takes multiple interactive iterations to get a clean disassembly. Key loggers are used a lot in targeted attacks. They are typically custom code or built in a backdoor. A big tip-off is that non-printable characters need to be printed out (such as "[Ctrl]" "[RightShift]") or time stamp printf strings. Look for these in files. Presence is not proof they are used. Absence is not proof they are not used. Java exploits. Can parse jar file with idxparser.py and decomile Java file. Java typially used to target tech companies. Backdoors are the main persistence mechanism (provided externally) for malware. Also malware typically needs command and control. Application of Artificial Intelligence in Ad-Hoc Static Code Analysis John Ashaman John Ashaman, Security Innovation Initially John tried to analyze open source files with open source static analysis tools, but these showed thousands of false positives. Also tried using grep, but tis fails to find anything even mildly complex. So next John decided to write his own tool. His approach was to first generate a call graph then analyze the graph. However, the problem is that making a call graph is really hard. For example, one problem is "evil" coding techniques, such as passing function pointer. First the tool generated an Abstract Syntax Tree (AST) with the nodes created from method declarations and edges created from method use. Then the tool generated a control flow graph with the goal to find a path through the AST (a maze) from source to sink. The algorithm is to look at adjacent nodes to see if any are "scary" (a vulnerability), using heuristics for search order. The tool, called "Scat" (Static Code Analysis Tool), currently looks for C# vulnerabilities and some simple PHP. Later, he plans to add more PHP, then JSP and Java. For more information see his posts in Security Innovation blog and NRefactory on GitHub. Mask Your Checksums—The Gorry Details Eric (XlogicX) Davisson Eric (XlogicX) Davisson Sometimes in emailing or posting TCP/IP packets to analyze problems, you may want to mask the IP address. But to do this correctly, you need to mask the checksum too, or you'll leak information about the IP. Problem reports found in stackoverflow.com, sans.org, and pastebin.org are usually not masked, but a few companies do care. If only the IP is masked, the IP may be guessed from checksum (that is, it leaks data). Other parts of packet may leak more data about the IP. TCP and IP checksums both refer to the same data, so can get more bits of information out of using both checksums than just using one checksum. Also, one can usually determine the OS from the TTL field and ports in a packet header. If we get hundreds of possible results (16x each masked nibble that is unknown), one can do other things to narrow the results, such as look at packet contents for domain or geo information. With hundreds of results, can import as CSV format into a spreadsheet. Can corelate with geo data and see where each possibility is located. Eric then demoed a real email report with a masked IP packet attached. Was able to find the exact IP address, given the geo and university of the sender. Point is if you're going to mask a packet, do it right. Eric wouldn't usually bother, but do it correctly if at all, to not create a false impression of security. Adventures with weird machines thirty years after "Reflections on Trusting Trust" Sergey Bratus Sergey Bratus, Dartmouth College (and Julian Bangert and Rebecca Shapiro, not present) "Reflections on Trusting Trust" refers to Ken Thompson's classic 1984 paper. "You can't trust code that you did not totally create yourself." There's invisible links in the chain-of-trust, such as "well-installed microcode bugs" or in the compiler, and other planted bugs. Thompson showed how a compiler can introduce and propagate bugs in unmodified source. But suppose if there's no bugs and you trust the author, can you trust the code? Hell No! There's too many factors—it's Babylonian in nature. Why not? Well, Input is not well-defined/recognized (code's assumptions about "checked" input will be violated (bug/vunerabiliy). For example, HTML is recursive, but Regex checking is not recursive. Input well-formed but so complex there's no telling what it does For example, ELF file parsing is complex and has multiple ways of parsing. Input is seen differently by different pieces of program or toolchain Any Input is a program input executes on input handlers (drives state changes & transitions) only a well-defined execution model can be trusted (regex/DFA, PDA, CFG) Input handler either is a "recognizer" for the inputs as a well-defined language (see langsec.org) or it's a "virtual machine" for inputs to drive into pwn-age ELF ABI (UNIX/Linux executible file format) case study. Problems can arise from these steps (without planting bugs): compiler linker loader ld.so/rtld relocator DWARF (debugger info) exceptions The problem is you can't really automatically analyze code (it's the "halting problem" and undecidable). Only solution is to freeze code and sign it. But you can't freeze everything! Can't freeze ASLR or loading—must have tables and metadata. Any sufficiently complex input data is the same as VM byte code Example, ELF relocation entries + dynamic symbols == a Turing Complete Machine (TM). @bxsays created a Turing machine in Linux from relocation data (not code) in an ELF file. For more information, see Rebecca "bx" Shapiro's presentation from last year's Toorcon, "Programming Weird Machines with ELF Metadata" @bxsays did same thing with Mach-O bytecode Or a DWARF exception handling data .eh_frame + glibc == Turning Machine X86 MMU (IDT, GDT, TSS): used address translation to create a Turning Machine. Page handler reads and writes (on page fault) memory. Uses a page table, which can be used as Turning Machine byte code. Example on Github using this TM that will fly a glider across the screen Next Sergey talked about "Parser Differentials". That having one input format, but two parsers, will create confusion and opportunity for exploitation. For example, CSRs are parsed during creation by cert requestor and again by another parser at the CA. Another example is ELF—several parsers in OS tool chain, which are all different. Can have two different Program Headers (PHDRs) because ld.so parses multiple PHDRs. The second PHDR can completely transform the executable. This is described in paper in the first issue of International Journal of PoC. Conclusions trusting computers not only about bugs! Bugs are part of a problem, but no by far all of it complex data formats means bugs no "chain of trust" in Babylon! (that is, with parser differentials) we need to squeeze complexity out of data until data stops being "code equivalent" Further information See and langsec.org. USENIX WOOT 2013 (Workshop on Offensive Technologies) for "weird machines" papers and videos.

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  • nm-applet gone?

    - by welp
    nm-applet seems to have disappeared from my system. I am running 12.10. Here's what I get when I check my package manager logs: ? ~ grep network-manager /var/log/dpkg.log 2012-10-06 10:37:08 upgrade network-manager-gnome:amd64 0.9.6.2-0ubuntu5 0.9.6.2-0ubuntu6 2012-10-06 10:37:08 status half-configured network-manager-gnome:amd64 0.9.6.2-0ubuntu5 2012-10-06 10:37:08 status unpacked network-manager-gnome:amd64 0.9.6.2-0ubuntu5 2012-10-06 10:37:08 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu5 2012-10-06 10:37:08 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu5 2012-10-06 10:37:08 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu5 2012-10-06 10:37:08 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu5 2012-10-06 10:37:08 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu5 2012-10-06 10:37:08 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu5 2012-10-06 10:37:08 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu5 2012-10-06 10:37:09 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu5 2012-10-06 10:37:09 status unpacked network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-06 10:37:09 status unpacked network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-06 10:39:50 configure network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-06 10:39:50 status unpacked network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-06 10:39:50 status unpacked network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-06 10:39:50 status half-configured network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-06 10:39:50 status installed network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-28 22:27:23 status installed network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-28 22:27:23 remove network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-28 22:27:23 status half-configured network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-28 22:27:23 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-28 22:27:23 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-28 22:27:23 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-28 22:27:23 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-28 22:27:23 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-28 22:27:23 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-28 22:27:23 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-28 22:27:23 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-28 22:27:23 status config-files network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-28 22:27:23 status config-files network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-31 19:58:03 install network-manager-gnome:amd64 0.9.6.2-0ubuntu6 0.9.6.2-0ubuntu6 2012-10-31 19:58:03 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-31 19:58:03 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-31 19:58:03 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-31 19:58:03 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-31 19:58:03 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-31 19:58:03 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-31 19:58:03 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-31 19:58:03 status half-installed network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-31 19:58:03 status unpacked network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-31 19:58:03 status unpacked network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-31 19:58:06 configure network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-31 19:58:06 status unpacked network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-31 19:58:07 status unpacked network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-31 19:58:07 status half-configured network-manager-gnome:amd64 0.9.6.2-0ubuntu6 2012-10-31 19:58:07 status installed network-manager-gnome:amd64 0.9.6.2-0ubuntu6 ? ~ Unfortunately, I cannot find network-manager-applet package at all: ? ~ apt-cache search network-manager-applet ? ~ Here are the contents of /etc/apt/sources.list: ? ~ cat /etc/apt/sources.list # deb cdrom:[Ubuntu 12.04 LTS _Precise Pangolin_ - Release amd64 (20120425)]/ dists/precise/main/binary-i386/ # deb cdrom:[Ubuntu 12.04 LTS _Precise Pangolin_ - Release amd64 (20120425)]/ dists/precise/restricted/binary-i386/ # deb cdrom:[Ubuntu 12.04 LTS _Precise Pangolin_ - Release amd64 (20120425)]/ precise main restricted # See http://help.ubuntu.com/community/UpgradeNotes for how to upgrade to # newer versions of the distribution. deb http://gb.archive.ubuntu.com/ubuntu/ quantal main restricted deb-src http://gb.archive.ubuntu.com/ubuntu/ quantal main restricted ## Major bug fix updates produced after the final release of the ## distribution. deb http://gb.archive.ubuntu.com/ubuntu/ quantal-updates main restricted deb-src http://gb.archive.ubuntu.com/ubuntu/ quantal-updates main restricted ## N.B. software from this repository is ENTIRELY UNSUPPORTED by the Ubuntu ## team. Also, please note that software in universe WILL NOT receive any ## review or updates from the Ubuntu security team. deb http://gb.archive.ubuntu.com/ubuntu/ quantal universe deb-src http://gb.archive.ubuntu.com/ubuntu/ quantal universe deb http://gb.archive.ubuntu.com/ubuntu/ quantal-updates universe deb-src http://gb.archive.ubuntu.com/ubuntu/ quantal-updates universe ## N.B. software from this repository is ENTIRELY UNSUPPORTED by the Ubuntu ## team, and may not be under a free licence. Please satisfy yourself as to ## your rights to use the software. Also, please note that software in ## multiverse WILL NOT receive any review or updates from the Ubuntu ## security team. deb http://gb.archive.ubuntu.com/ubuntu/ quantal multiverse deb-src http://gb.archive.ubuntu.com/ubuntu/ quantal multiverse deb http://gb.archive.ubuntu.com/ubuntu/ quantal-updates multiverse deb-src http://gb.archive.ubuntu.com/ubuntu/ quantal-updates multiverse ## N.B. software from this repository may not have been tested as ## extensively as that contained in the main release, although it includes ## newer versions of some applications which may provide useful features. ## Also, please note that software in backports WILL NOT receive any review ## or updates from the Ubuntu security team. deb http://gb.archive.ubuntu.com/ubuntu/ quantal-backports main restricted universe multiverse deb-src http://gb.archive.ubuntu.com/ubuntu/ quantal-backports main restricted universe multiverse deb http://security.ubuntu.com/ubuntu quantal-security main restricted deb-src http://security.ubuntu.com/ubuntu quantal-security main restricted deb http://security.ubuntu.com/ubuntu quantal-security universe deb-src http://security.ubuntu.com/ubuntu quantal-security universe deb http://security.ubuntu.com/ubuntu quantal-security multiverse deb-src http://security.ubuntu.com/ubuntu quantal-security multiverse ## Uncomment the following two lines to add software from Canonical's ## 'partner' repository. ## This software is not part of Ubuntu, but is offered by Canonical and the ## respective vendors as a service to Ubuntu users. # deb http://archive.canonical.com/ubuntu precise partner # deb-src http://archive.canonical.com/ubuntu precise partner ## This software is not part of Ubuntu, but is offered by third-party ## developers who want to ship their latest software. deb http://extras.ubuntu.com/ubuntu quantal main deb-src http://extras.ubuntu.com/ubuntu quantal main ? ~ Right now, I can't think of anything else. Happy to provide more info upon request.

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  • BSON Serialization

    BSON is a binary-encoded serialization of JSON-like documents, which essentially means its an efficient way of transfering information. Part of my work on the MongoDB NoRM drivers, discussed in more details by Rob Conery, is to write an efficient and maintainable BSON serializer and deserializer. The goal of the serializer is that you give it a .NET object and you get a byte array out of it which represents valid BSON. The deserializer does the opposite - give it a byte array and out pops your object....Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • QotD: Alex Buckley announcing Java™ SE 8 Early Access Builds with Type Annotation Support

    - by $utils.escapeXML($entry.author)
    I am pleased to announce that binary builds of the JSR 308 Reference Implementation are available at http://jdk8.java.net/type-annotations/.Please see the Type Annotations project page for a link to the JSR 308 Specification. There is also a changelog, which is important to review as there have been significant spec changes in 2012.The builds were generated from the type-annotations/type-annotations forest on 9/9. This forest is regularly updated from jdk8/jdk8 and jdk8/tl.Alex Buckley in a post on the type-annotations-dev mailing list.If you want to play with repeating annotations, check out http://jdk8.java.net/type-annotations/ ... thanks to superior code wrangling by Joel Franck (repeating annotations) and Werner Dietl (type annotations), support for repeating annotations on declarations is included in the build.Alex Buckley in a post on the enhanced-metadata-spec-discuss mailing list.

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  • Installing gflags 12.04

    - by Ritwik G
    I want to install libgflags-dev. According to this launchpad link, it is present in the ubuntu repositories : https://launchpad.net/ubuntu/+source/gflags and http://packages.ubuntu.com/raring/libdevel/libgflags-dev But when i try to sudo apt-get install it, I get E: Unable to locate package libgflags-dev Why is this happening ? I have all multiverse and universe enabled from software center. How to install it from apt-get ? P.S. I know i can simply download the binary and make install it, but I want to know how to do this from apt-get.

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  • NetBackup-pal is muködik az Oracle Database 11gR2 mentés Exadata V2 környezetben

    - by Fekete Zoltán
    A Veritas NetBackup szoftverrel is menthetok az Oracle 11gR2 adatbázisok az Oracle Enterprise Linux-on is (RMAN-t használva), 64-bites környezetben. A dokumentumokban a Red Hat-re vonatkozó infót kell keresnünk, mivel http://seer.entsupport.symantec.com/docs/337048.htm szerint "Oracle Enterprise Linux (OEL)" Supported based on NetBackup Red Hat Enterprise Linux 4.x/5.x Client, Server, and Oracle Agent support. BMR is not supported. NetBackup compatibility listák: http://seer.entsupport.symantec.com/docs/303344.htm - A NetBackup 7 kompatibilis az Oracle Exadata V2-vel: http://seer.entsupport.symantec.com/docs/340295.htm - A NetBackup 6.x verziókra telepíteni kell a következo patch-et: NB_6.5.5_ET1940073_1_347227.zip is a NetBackup 6.5.5 EEB (Emergency Engineering Binary) for Oracle Clients. http://seer.entsupport.symantec.com/docs/347227.htm és http://support.veritas.com/docs/279048.

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  • How do you uninstall wine 1.5?

    - by jeff
    I installed Wine through the terminal using these commands: sudo add-apt-repository ppa:ubuntu-wine/ppa sudo apt-get update sudo apt-get install wine1.5 I know that I've removed at least part of wine. I removed the .wine folder in my home folder and I managed to remove the repository via this command: sudo apt-add-repository --remove ppa:ubuntu-wine/ppa Now when I try to install wine 1.4 via Ubuntu software center, it tells me I must remove these items to install wine: Microsoft windows compatibility layer (binary emulator and library) wine1.5 Microsoft windows compatibility layer (64-bit support) wine1.5-amd64 Microsoft windows compatibility layer (32-bit support) wine1.5-i386:i386 I've already tried this command: sudo apt-get --purge remove wine but it said that wine wasn't installed. Some assistance would be appreciated.

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  • JIT compiler for C, C++, and the likes

    - by Ebrahim
    Is there any just-in-time compiler out there for compiled languages, such as C and C++? (The first names that come to mind are Clang and LLVM! But I don't think they currently support it.) Explanation: I think the software could benefit from runtime profiling feedback and aggressively optimized recompilation of hotspots at runtime, even for compiled-to-machine languages like C and C++. Profile-guided optimization does a similar job, but with the difference a JIT would be more flexible in different environments. In PGO you run your binary prior to releasing it. After you released it, it would use no environment/input feedbacks collected at runtime. So if the input pattern is changed, it is probe to performance penalty. But JIT works well even in that conditions. However I think it is controversial wether the JIT compiling performance benefit outweights its own overhead. Edit: Grammar

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