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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 { font-size:12pt; max-width:100%; } a, a:visited { text-decoration: underline; } hr { visibility: hidden; page-break-before: always; } pre, blockquote { padding-right: 1em; page-break-inside: avoid; } tr, img { page-break-inside: avoid; } img { max-width: 100% !important; } @page :left { margin: 15mm 20mm 15mm 10mm; } @page :right { margin: 15mm 10mm 15mm 20mm; } p, h2, h3 { orphans: 3; widows: 3; } h2, h3 { page-break-after: avoid; } } pre .operator, pre .paren { color: rgb(104, 118, 135) } pre .literal { color: rgb(88, 72, 246) } pre .number { color: rgb(0, 0, 205); } pre .comment { color: rgb(76, 136, 107); } pre .keyword { color: rgb(0, 0, 255); } pre .identifier { color: rgb(0, 0, 0); } pre .string { color: rgb(3, 106, 7); } var hljs=new function(){function m(p){return p.replace(/&/gm,"&").replace(/"}while(y.length||w.length){var v=u().splice(0,1)[0];z+=m(x.substr(q,v.offset-q));q=v.offset;if(v.event=="start"){z+=t(v.node);s.push(v.node)}else{if(v.event=="stop"){var 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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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  • Project Server 2010 Beta - can't connect Project client to projects hosted on server

    - by Chris W
    We have a Project 2010 Beta installed on SharePoint 2010 Beta as test set-up whilst we wait for the release versions. Whilst the installation seemed to complete without any issues we're unable to open any projects within the Project 2010 client app. Project pops up an error 'Could not retrieve server initialization data'. My local event logs list some errors from MSSOAP that simply state an unanticipated error occurred during the processing of the request. The server doesn't log any errors. The Sharepoint set up is a farm containing 3 SharePoint servers. I log in to server 'PORTAL' but the Project Server stuff is configured to run on one of the other SharePoint boxes. I presume others have managed to get this working - has anyone got any ideas as to what could be wrong. Everything is patched correctly as far as I can tell.

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  • OpenVPN - Ubuntu 10.04 - Client Can't Connect to Server - Linux Route Add Command Failed

    - by nicorellius
    I suppose this could be asked on Server Fault as well, but it is specific to the client so I thought I'd start here. I have keys for access to an OpenVPN server already in place. I have used these keys to connect already, but using a Windows XP machine. On a Ubuntu machine, I installed OpenVPN and then configured client.conf file so that I could run: sudo openvpn --config client.conf And it seems correct but I still can't connect and get these errors and lines of output: Mon May 31 14:34:57 2010 ERROR: Linux route add command failed: external program exited with error status: 7 Mon May 31 14:34:57 2010 /sbin/route add -net 10.8.0.1 netmask 255.255.255.255 gw 10.8.0.17 SIOCADDRT: File exists Mon May 31 14:34:57 2010 ERROR: Linux route add command failed: external program exited with error status: 7 Mon May 31 14:34:57 2010 Initialization Sequence Completed I searched the net for forums and ideas and tried some file moving and renaming but still ended up in the same place.

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  • Wine MFC80.DLL Issue

    - by Kush
    I am getting the following error when trying to run winetricks vcrun2005. "vcrun2005 install completed, but installed file /home/compuz/.wine/dosdevices/c:/windows/winsxs/x86_Microsoft.VC80.MFC_1fc8b3b9a1e18e3b_8.0.50727.6195_x-ww_150c9e8b/mfc80.dll not found" I then tried the following command: winetricks dlls list | grep mfc80 This gave the following output: vcrun2005 Visual C++ 2005 libraries (mfc80,msvcp80,msvcr80) (Microsoft, 2011) [downloadable,cached] Here mfc80 is colored red. This is the complete log: $ winetricks vcrun2005 Executing w_do_call vcrun2005 Executing load_vcrun2005 Executing mkdir -p /home/compuz/.cache/winetricks/vcrun2005 Using native,builtin override for following DLLs: msvcr80 Executing winetricks_early_wine regedit C:\windows\Temp\_vcrun2005\override-dll.reg Executing wine vcredist_x86.EXE fixme:advapi:DecryptFileA "C:\\users\\compuz\\Temp\\IXP000.TMP\\" 00000000 wine: Call from 0x7bc49070 to unimplemented function SHLWAPI.dll.SHRegGetValueW, aborting err:module:attach_process_dlls "urlmon.dll" failed to initialize, aborting err:module:LdrInitializeThunk Main exe initialization for L"C:\\windows\\system32\\msiexec.exe" failed, status 80000100 ------------------------------------------------------ vcrun2005 install completed, but installed file /home/compuz/.wine/dosdevices/c:/windows/winsxs/x86_Microsoft.VC80.MFC_1fc8b3b9a1e18e3b_8.0.50727.6195_x-ww_150c9e8b/mfc80.dll not found How can I fix this issue?

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  • Crash when attempting to install 32bit delphi service on 2008 r2

    - by Oded
    I have an old 32bit delphi application (with no source code), that is used as a windows service. It runs fine on windows 2003 32bit. I do not know if it has been created as a service originally, or converted to one later on. It is supposed to get installed to the server using a /install flag on the command line. When attempting to install it on a Windows 2008 R2 virtual machine, I am getting an APPCRASH event in the event log. The service is supposed to read a blob from a remote SQL Server instance and write it out to the local HD. It also reads some initialization data from the registry. Is there any way I can install this application as a service on windows 2008 r2 64bit? If not, are there any workarounds I can try? What are your suggestions?

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  • PHP-FPM bind issues

    - by Leo
    I have a setup of a mac server running Nginx 1.2.3, PHP-FPM 5.3.10 on OSX Lion. It works fine with Nginx on port 8080 ( using port fordwaring on from 80 ) and PHP-FPM listening on 9000. Both are running as /System/LaunchDaemons. My issue is that the system log is full of these errors ( every ten seconds ) which despite the fact the system works I don't like. Sep 10 19:59:05 mac-server php-fpm[974]: [10-Sep-2012 19:59:05] ERROR: unable to bind listening socket for address '127.0.0.1:9000': Address already in use (48) Sep 10 19:59:05 mac-server php-fpm[974]: [10-Sep-2012 19:59:05] ERROR: FPM initialization failed Sep 10 19:59:05 mac-server com.apple.launchd[1] (php-fpm): Throttling respawn: Will start in 10 seconds I have pretty much an out of the box php-fpm config. Could someone please point me to what configuration variables may be causing this problem. There are not any errorneous LaunchAgent/LaunchDaemons floating around either?

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  • Vmware Workstation in Ubuntu 9.10 Karmic Koala

    - by kzh
    I am unable to get vmware workstation to work in Ubuntu 9.10. The same installer worked fine in 9.04. Installation is successful, but initialization is not. Does anybody know a fix? $ lsb_release -a No LSB modules are available. Distributor ID: Ubuntu Description: Ubuntu 9.10 Release: 9.10 Codename: karmic VMware-Workstation-for-Linux-64bit-6.5.1-126130.x86_64 I don't think my key will work for version 7, sadly. I have a free 365 day student evaluation license.

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  • OS X not booting after upgrading LibXML2

    - by Federico
    I tried upgrading the version of LibXML2 in my machine to run some propietary software. Compiled as 32-bit (-arch i386) and restarted to see if it worked. Now when I try to boot I can only get into the spinning screen and it just sits there. Restarting in verbose mode (Cmd-V) does not show any error in the process, it just freezes after "AppleIntelCPUPowerManagement: initialization complete". I can't from any CD either (if I press c on startup it just spins the disk around and then stops in the same spinner screen). I've also reset the PRAM/NVRAM to discard any problems with that and it´s still not fixed. Any pointers of what might´ve gone wrong? (Latest gen MBP, running 10.6.3)

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  • LSI 9211-8i won't boot in KVM

    - by Paul McMillan
    I'm running ubuntu 11.04 with an LSI 9211-8i in IT mode with the latest firmware (10). I'm using KVM with VT-d enabled to pass the entire PCIe device through to the guest OS. I have the device disabled in the system BIOS. When I boot my virtual machine, the adapter quits during bios initialization with the following error: Unable to load LSI Corporation MPT BIOS MPT BIOS Fault 0Ch encountered at adapter PCI(00h,04h,00h) Press any key to continue... I know the virtualization is correctly enabled. I've blacklisted the kernel modules in the host OS. Has anyone else encountered this error? I'm about out of ideas here. Edit: I contacted LSI tech support about this, and they suggested that I try RedHat and Xen. Apparently they don't test or support anything else.

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  • Parallel prologue and epilogue in Grid Engine

    - by ajdecon
    We have a cluster being used to run MPI jobs for a customer. Previously this cluster used Torque as the scheduler, but we are transitioning to Grid Engine 6.2u5 (for some other features). Unfortunately, we are having trouble duplicating some of our maintenance scripts in the Grid Engine environment. In Torque, we have a prologue.parallel script which is used to carry out an automated health-check on the node. If this script returns a fail condition, Torque will helpfully offline the node and re-queue the job to use a different group of nodes. In Grid Engine, however, the queue "prolog" only runs on the head node of the job. We can manually run our prologue script from the startmpi.sh initialization script, for the mpi parallel environment; but I can't figure out how to detect a fail condition and carry out the same "mark offline and requeue" procedure. Any suggestions?

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  • Unknown protocol when trying to connect to remote host wit stunnel

    - by RaYell
    I'm trying to set up a stunnel for WebDav on Windows. I want to connect 80 port on my local interface to 443 on another machine in my network. I can ping the machine remote machine. However when I use the tunnel, I'm getting this error all the time SSL state (accept): before/accept initialization SSL_accept: 140760FC: error:140760FC:SSL routines:SSL23_GET_CLIENT_HELLO:unknown protocol There is nothing in the logs on the other machine and here's my stunnel connection config [https] accept = 127.0.0.2:80 connect = 10.0.0.60:443 verify = 0 I've set it up to accept all certificates so this shouldn't be a problem with a self-signed certificate remote host uses. Does anyone knows what might be the problem that this connection cannot be eastablished?

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  • Dell R610 memory configuration for all 12 slots

    - by Neal
    I purchased 12 sticks of RAM on eBay to go into a Dell R610 server. The RAM is ECC REG PC3-12800 DDR3-1600 yet when I occupy all 12 slots with this ram I get the following error on boot: MEMORY Initialization Warning: Memory Size May be Reduced MEMBIST failure – The following DIMM has been disabled by Bios: DIMM B2 MEMBIST failure – The following DIMM has been disabled by Bios: DIMM B5 I am using all of the latest versions, BIOS, etc. I am using 2 x x5660 processors. What is causing this issue and is it correctable? If this RAM is incorrect what is correct to maximize the RAM on this server? Thank you.

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  • Can't Remove Logical Drive/Array from HP P400

    - by Myles
    This is my first post here. Thank you in advance for any assistance with this matter. I'm trying to remove a logical drive (logical drive 2) and an array (array "B") from my Smart Array P400. The host is a DL580 G5 running 64-bit Red Hat Enterprise Linux Server release 5.7 (Tikanga). I am unable to remove the array using either hpacucli or cpqacuxe. I believe it is because of "OS Status: LOCKED". The file system that lives on this array has been unmounted. I do not want to reboot the host. Is there some way to "release" this logical drive so I can remove the array? Note that I do not need to preserve the data on logical drive 2. I intend to physically remove the drives from the machine and replace them with larger drives. I'm using the cciss kernel module that ships with Red Hat 5.7. Here is some information pertaining to the host and the P400 configuration: [root@gort ~]# cat /etc/redhat-release Red Hat Enterprise Linux Server release 5.7 (Tikanga) [root@gort ~]# uname -a Linux gort 2.6.18-274.el5 #1 SMP Fri Jul 8 17:36:59 EDT 2011 x86_64 x86_64 x86_64 GNU/Linux [root@gort ~]# rpm -qa | egrep '^(hp|cpq)' cpqacuxe-9.30-15.0 hp-health-9.25-1551.7.rhel5 hpsmh-7.1.2-3 hpdiags-9.3.0-466 hponcfg-3.1.0-0 hp-snmp-agents-9.25-2384.8.rhel5 hpacucli-9.30-15.0 [root@gort ~]# hpacucli HP Array Configuration Utility CLI 9.30.15.0 Detecting Controllers...Done. Type "help" for a list of supported commands. Type "exit" to close the console. => ctrl all show config detail Smart Array P400 in Slot 0 (Embedded) Bus Interface: PCI Slot: 0 Cache Serial Number: PA82C0J9SVW34U RAID 6 (ADG) Status: Enabled Controller Status: OK Hardware Revision: D Firmware Version: 7.22 Rebuild Priority: Medium Expand Priority: Medium Surface Scan Delay: 15 secs Surface Scan Mode: Idle Wait for Cache Room: Disabled Surface Analysis Inconsistency Notification: Disabled Post Prompt Timeout: 0 secs Cache Board Present: True Cache Status: OK Cache Ratio: 25% Read / 75% Write Drive Write Cache: Disabled Total Cache Size: 256 MB Total Cache Memory Available: 208 MB No-Battery Write Cache: Disabled Cache Backup Power Source: Batteries Battery/Capacitor Count: 1 Battery/Capacitor Status: OK SATA NCQ Supported: True Logical Drive: 1 Size: 136.7 GB Fault Tolerance: RAID 1 Heads: 255 Sectors Per Track: 32 Cylinders: 35132 Strip Size: 128 KB Full Stripe Size: 128 KB Status: OK Caching: Enabled Unique Identifier: 600508B100184A395356573334550002 Disk Name: /dev/cciss/c0d0 Mount Points: /boot 101 MB, /tmp 7.8 GB, /usr 3.9 GB, /usr/local 2.0 GB, /var 3.9 GB, / 2.0 GB, /local 113.2 GB OS Status: LOCKED Logical Drive Label: A0027AA78DEE Mirror Group 0: physicaldrive 1I:1:2 (port 1I:box 1:bay 2, SAS, 146 GB, OK) Mirror Group 1: physicaldrive 1I:1:1 (port 1I:box 1:bay 1, SAS, 146 GB, OK) Drive Type: Data Array: A Interface Type: SAS Unused Space: 0 MB Status: OK Array Type: Data physicaldrive 1I:1:1 Port: 1I Box: 1 Bay: 1 Status: OK Drive Type: Data Drive Interface Type: SAS Size: 146 GB Rotational Speed: 10000 Firmware Revision: HPDE Serial Number: 3NM57RF40000983878FX Model: HP DG146BB976 Current Temperature (C): 29 Maximum Temperature (C): 35 PHY Count: 2 PHY Transfer Rate: Unknown, Unknown physicaldrive 1I:1:2 Port: 1I Box: 1 Bay: 2 Status: OK Drive Type: Data Drive Interface Type: SAS Size: 146 GB Rotational Speed: 10000 Firmware Revision: HPDE Serial Number: 3NM55VQC000098388524 Model: HP DG146BB976 Current Temperature (C): 29 Maximum Temperature (C): 36 PHY Count: 2 PHY Transfer Rate: Unknown, Unknown Logical Drive: 2 Size: 546.8 GB Fault Tolerance: RAID 5 Heads: 255 Sectors Per Track: 32 Cylinders: 65535 Strip Size: 64 KB Full Stripe Size: 256 KB Status: OK Caching: Enabled Parity Initialization Status: Initialization Completed Unique Identifier: 600508B100184A395356573334550003 Disk Name: /dev/cciss/c0d1 Mount Points: None OS Status: LOCKED Logical Drive Label: A5C9C6F81504 Drive Type: Data Array: B Interface Type: SAS Unused Space: 0 MB Status: OK Array Type: Data physicaldrive 1I:1:3 Port: 1I Box: 1 Bay: 3 Status: OK Drive Type: Data Drive Interface Type: SAS Size: 146 GB Rotational Speed: 10000 Firmware Revision: HPDE Serial Number: 3NM2H5PE00009802NK19 Model: HP DG146ABAB4 Current Temperature (C): 30 Maximum Temperature (C): 37 PHY Count: 1 PHY Transfer Rate: Unknown physicaldrive 1I:1:4 Port: 1I Box: 1 Bay: 4 Status: OK Drive Type: Data Drive Interface Type: SAS Size: 146 GB Rotational Speed: 10000 Firmware Revision: HPDE Serial Number: 3NM28YY400009750MKPJ Model: HP DG146ABAB4 Current Temperature (C): 31 Maximum Temperature (C): 36 PHY Count: 1 PHY Transfer Rate: 3.0Gbps physicaldrive 2I:1:5 Port: 2I Box: 1 Bay: 5 Status: OK Drive Type: Data Drive Interface Type: SAS Size: 146 GB Rotational Speed: 10000 Firmware Revision: HPDE Serial Number: 3NM2FGYV00009802N3GN Model: HP DG146ABAB4 Current Temperature (C): 30 Maximum Temperature (C): 38 PHY Count: 1 PHY Transfer Rate: Unknown physicaldrive 2I:1:6 Port: 2I Box: 1 Bay: 6 Status: OK Drive Type: Data Drive Interface Type: SAS Size: 146 GB Rotational Speed: 10000 Firmware Revision: HPDE Serial Number: 3NM8AFAK00009920MMV1 Model: HP DG146BB976 Current Temperature (C): 31 Maximum Temperature (C): 41 PHY Count: 2 PHY Transfer Rate: Unknown, Unknown physicaldrive 2I:1:7 Port: 2I Box: 1 Bay: 7 Status: OK Drive Type: Data Drive Interface Type: SAS Size: 146 GB Rotational Speed: 10000 Firmware Revision: HPDE Serial Number: 3NM2FJQD00009801MSHQ Model: HP DG146ABAB4 Current Temperature (C): 29 Maximum Temperature (C): 39 PHY Count: 1 PHY Transfer Rate: Unknown

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  • Installed mountain lion, getting a python virtual env error?

    - by user27449
    I recently installed mountain lion (10.8) and when I open up my terminal I get this message: Traceback (most recent call last): File "<string>", line 1, in <module> ImportError: No module named virtualenvwrapper.hook_loader virtualenvwrapper.sh: There was a problem running the initialization hooks. If Python could not import the module virtualenvwrapper.hook_loader, check that virtualenv has been installed for VIRTUALENVWRAPPER_PYTHON=/usr/bin/python and that PATH is set properly. Before I try and fix this, I was hoping someone could guide me as I haven't touched python in a while and I don't want to mess up this installation.

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  • JVM memory initializazion error after windows update

    - by gianni
    We have three Windows Server 2003 with 2 GB RAM. Server1 tomcat 5.5.25 jvm version SUN 1.6.0_11-b03 Server2 tomcat 5.5.25 jvm version SUN 1.6.0_14-b08 Server3 tomcat 6.0.18 jvm version SUN 1.6.0_14-b08 For the three servers JVM parameters are: -XX:MaxPermSize=256m -Dcatalina.base=C:\Programmi\Apache Group\apache-tomcat-5.5.25 -Dcatalina.home=C:\Programmi\Apache Group\apache-tomcat-5.5.25 -Djava.endorsed.dirs=C:\Programmi\Apache Group\apache-tomcat-5.5.25\common\endorsed -Djava.io.tmpdir=C:\Programmi\Apache Group\apache-tomcat-5.5.25\temp vfprintf -Xms512m -Xmx1024m For some months everithing worked fine. Last friday we installed some windows updates. After the reboot tomcat doesnt start with error: Error occurred during initialization of VM Could not reserve enough space for object heap We reduced the parameter -Xmx1024m to -Xmx768m and now tomcat starts. But we need greater max heap size What happened to our servers ? Thanks in advance.

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  • INNODB mysql. Plugin disabled

    - by alexcunn
    When I startup mysql on my unbuntu server I will get a message. 121122 17:39:37 [Note] Plugin 'FEDERATED' is disabled. 121122 17:39:37 InnoDB: The InnoDB memory heap is disabled 121122 17:39:37 InnoDB: Mutexes and rw_locks use GCC atomic builtins 121122 17:39:37 InnoDB: Compressed tables use zlib 1.2.3.4 121122 17:39:37 InnoDB: Initializing buffer pool, size = 128.0M InnoDB: mmap(137363456 bytes) failed; errno 12 121122 17:39:37 InnoDB: Completed initialization of buffer pool 121122 17:39:37 InnoDB: Fatal error: cannot allocate memory for the buffer pool 121122 17:39:37 [ERROR] Plugin 'InnoDB' init function returned error. 121122 17:39:37 [ERROR] Plugin 'InnoDB' registration as a STORAGE ENGINE failed. 121122 17:39:37 [ERROR] Unknown/unsupported storage engine: InnoDB 121122 17:39:37 [ERROR] Aborting 121122 17:39:37 [Note] mysqld: Shutdown complete a few times I have got a message saying that the plugin is disabled. I use webmin to configure it. Could that be a problem?

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  • HDD is not recognized/initialized via USB, only via SATA - is a reformat through USB a bad idea?

    - by Wuschelbeutel Kartoffelhuhn
    I have a 4TB Hitachi HDD that I purchased in Europe (I use it as a backup disk); I use Windows 7. When I connect it to a SATA port, it is recognized in Windows Explorer and gives no problems, even after transferring 3TB at a time or after being on for days. When I connect it via a SATA-to-USB2.0 adapter, it is also recognized, but when I transfer a large amount of data, it will intermittently stop being recognized by Windows Explorer and cancel the transfer. When I connect it via an external enclosure (which is technically a SATA-to-USB3.0 adapter), it does not display at all in Windows Explorer, but Disk Management will show the drive, albeit uninitialized (prompts for format). I only got the external enclosure because I want to backup my files more conveniently (instead of having to open the computer case each time). Do you advise against reformat/initialization via the external enclosure? Can it screw up things in an irrevocable way (Master Boot Record etc.)?

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  • Unable to enable InnoDB in mySQL on Ubuntu 10.04

    - by spowers
    I am trying to enable InnDB on my linux server. I have installed Ubuntu 10.04 JeOS on an ESX server. I then installed mySQL and tomcat using aptitude. However when I use SHOW ENGINES; in mySQL it does not appear that InnoDB was installed. I then tried following the directions in the documentation. http://dev.mysql.com/doc/refman/5.1/en/innodb.html However I get the following when trying to enable a plugin: ERROR 1123 (HY000): Can't initialize function 'InnoDB'; Plugin initialization function failed. I would appreciate some advice as to how to approach this problem.

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  • Unable to enable InnoDB in mySQL on Ubuntu 10.04

    - by spowers
    I am trying to enable InnDB on my linux server. I have installed Ubuntu 10.04 JeOS on an ESX server. I then installed mySQL and tomcat using aptitude. However when I use SHOW ENGINES; in mySQL it does not appear that InnoDB was installed. I then tried following the directions in the documentation. http://dev.mysql.com/doc/refman/5.1/en/innodb.html However I get the following when trying to enable a plugin: ERROR 1123 (HY000): Can't initialize function 'InnoDB'; Plugin initialization function failed. I would appreciate some advice as to how to approach this problem.

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  • OpenVPN - Ubunut 10.04 - Client Can't Connect to Server - Linux Route Add Command Failed

    - by nicorellius
    I suppose this could be asked on Server Fault as well, but it is specific to the client so I thought I'd start here. I have keys for a OpenVPN server already in place. I have used these keys to connect already, but using a Windows XP machine. I started by building the client.conf file so that I could run: sudo openvpn --config client.conf And it seems correct but I still can't connect and get these errors and lines of output: Mon May 31 14:34:57 2010 ERROR: Linux route add command failed: external program exited with error status: 7 Mon May 31 14:34:57 2010 /sbin/route add -net 10.8.0.1 netmask 255.255.255.255 gw 10.8.0.17 SIOCADDRT: File exists Mon May 31 14:34:57 2010 ERROR: Linux route add command failed: external program exited with error status: 7 Mon May 31 14:34:57 2010 Initialization Sequence Completed I searched the net for forums and ideas and tried some file moving and renaming but still ended up in the same place.

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  • Unknown protocol when trying to connect to remote host with stunnel

    - by RaYell
    I'm trying to set up a stunnel for WebDav on Windows. I want to connect 80 port on my local interface to 443 on another machine in my network. I can ping the machine remote machine. However when I use the tunnel, I'm getting this error all the time SSL state (accept): before/accept initialization SSL_accept: 140760FC: error:140760FC:SSL routines:SSL23_GET_CLIENT_HELLO:unknown protocol There is nothing in the logs on the other machine and here's my stunnel connection config [https] accept = 127.0.0.2:80 connect = 10.0.0.60:443 verify = 0 I've set it up to accept all certificates so this shouldn't be a problem with a self-signed certificate remote host uses. Does anyone knows what might be the problem that this connection cannot be eastablished?

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  • Tomcat 6.0 service does not start: java/lang/NoClassDefFoundError

    - by James Lawruk
    I installed Tomcat on my Windows XP PC. But I can't get it to start. I start the service and I see the progress bar, but it fails. I installed the Java Runtime Environment Version 6 and created JRE_HOME Environment variable. (C:\Program Files\Java\jre6) Any ideas? Thanks. Below is the jakarta_serviceYYYMMDD.log entries: [2010-04-08 14:22:42] [info] Commons Daemon procrun (1.0.2.0) started [2010-04-08 14:22:42] [info] Running Service... [2010-04-08 14:22:42] [info] Starting service... [2010-04-08 14:22:42] [info] Error occurred during initialization of VM [2010-04-08 14:22:42] [info] java/lang/NoClassDefFoundError [2010-04-08 14:22:42] [info] : java/lang/Object

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  • JVM memory initializazion error after windows update

    - by gianni
    We have three Windows Server 2003 with 2 GB RAM. Server1 tomcat 5.5.25 jvm version SUN 1.6.0_11-b03 Server2 tomcat 5.5.25 jvm version SUN 1.6.0_14-b08 Server3 tomcat 6.0.18 jvm version SUN 1.6.0_14-b08 For the three servers JVM parameters are: -XX:MaxPermSize=256m -Dcatalina.base=C:\Programmi\Apache Group\apache-tomcat-5.5.25 -Dcatalina.home=C:\Programmi\Apache Group\apache-tomcat-5.5.25 -Djava.endorsed.dirs=C:\Programmi\Apache Group\apache-tomcat-5.5.25\common\endorsed -Djava.io.tmpdir=C:\Programmi\Apache Group\apache-tomcat-5.5.25\temp vfprintf -Xms512m -Xmx1024m For some months everithing worked fine. Last friday we installed some windows updates. After the reboot tomcat doesnt start with error: Error occurred during initialization of VM Could not reserve enough space for object heap We reduced the parameter -Xmx1024m to -Xmx768m and now tomcat starts. But we need greater max heap size What happened to our servers ? Thanks in advance.

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  • Windows 7 setup can not find hard disk

    - by Akshay Kulkarni
    Previously I had an old Seagate barracuda 160GB HDD which got crashed some days before. Yesterday I bought new Hard Disk Seagate Barracuda 500GB (ST500DM002). I just replaced the 500GB hard disk with 160GB one leaving data and power cable intact and untouched. And ideally this new hard disk should start functioning. I tried to install Windows 7 with DVD the setup says you don't have any hard disks installed on your machine. I rechecked connection tried with Win XP setup but continued receiving same error. Do I need to do some initialization stuff with hard disk before installing setup? If so how to do it. If not then is there any problem with my newly bought hard disk? Thanks In Advance.

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