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  • EMC/Legato/Networker Failed to recover files : Cross Platform Recovery not supported.

    - by marc.riera
    Software used to backup: EMC / Legato Networker legato server : windows legato clients: same hardware (2 years ago fedora something , now ubuntu ) Trying to recover from an old client, which is no longer available. So this is the thing. On 07/20/2008 we backed up a samba server(fedora something) to a tape , setting 1 year as browse policy and retention policy. Now this tape is recyclable. We took down the dns name. We deleted the legato client configuration. That legato client was reinstalled and is doing other stuff on ubuntu 10.04, with a different name but same ip. Now, 2 years and some month later #### Now we need to recover a folder from 2008 backup, on the fedora-samba-server. First thing, legato does not show the client name because the config was deleted. We create it again. We just set the old dns back on track, pointing the same ip, where the old server was, same MAC address ;). We created a new 'old client configuration' pointing to the new server. (different legato ip for client "I suppose" ) The ssid where the needed folder is on 2 tapes, 20 and 22. The index for that backup is on tape 21. We put this tapes on the jukebox (IBMT4000) -- not important for the issue -- All three tapes expired its browsable and recoverable time. So they are on recyclable. We get the clone id from the ssid with following command: mminfo -avot -q "ssid=<ssid>" -r cloneid We set the tapes to notrecyclable nsrmm -S <ssid>/<cloneid> -o notrecyclable We change the retention for the tapes for a future date nsrmm -S <ssid> -e 01/20/2011 We check the dates are correct : mminf -avV -q "ssid=<ssid>" -r ssbrowse(26),ssretent(26),savetime So far its OK. We close the terminal. Restart the server, just for being sure. Finally, we recover the index for that ssid where the folder should be. nsrck -L7 -t "07/20/2008" oldservername.domain.org There, we open the Networker User, select the server, select the old client as source, select the new client as destination. And this is what I get. imgur image of output -- http://i.imgur.com/1nOr8.png Should I understand that I need to install whatsoever operating system that was running on the old "linux server"/"networker client" to be able to restore 26Mb of files? thanks

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  • What's a good box to serve files on my local network, cross platform?

    - by rogpeppe
    I've installed CAT5e cable and gigabit switches in my house with the goal of having an "always-on" file server in the loft, accessible to both my macbook and my partner's Windows box. I'd like to find a solution which: uses minimal power. allows me to access as much disk bandwidth as possible. provides glitch-free file access to both MacOS and Windows. is as cheap as possible, while remaining reliable. Optional, but desirable extras: software or hardware RAID; open source solutions. A SheevaPlug with eSATA seems one possibility, but I'm sure there are any number of other good options.

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  • How can you handle cross-cutting conerns in JAX-WS without Spring or AOP? Handlers?

    - by LES2
    I do have something more specific in mind, however: Each web service method needs to be wrapped with some boiler place code (cross cutting concern, yes, spring AOP would work great here but it either doesn't work or unapproved by gov't architecture group). A simple service call is as follows: @WebMethod... public Foo performFoo(...) { Object result = null; Object something = blah; try { soil(something); result = handlePerformFoo(...); } catch(Exception e) { throw translateException(e); } finally { wash(something); } return result; } protected abstract Foo handlePerformFoo(...); (I hope that's enough context). Basically, I would like a hook (that was in the same thread - like a method invocation interceptor) that could have a before() and after() that could could soil(something) and wash(something) around the method call for every freaking WebMethod. Can't use Spring AOP because my web services are not Spring managed beans :( HELP!!!!! Give advice! Please don't let me copy-paste that boiler plate 1 billion times (as I've been instructed to do). Regards, LES

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  • Cross browser's probelm to highlight option item as bold in form element "select".

    - by Vivek
    Hello All , I am facing one weird cross browsers problem i.e. I want to highlight some of the option items as bold by using CSS class in my form element "select". This all is working fine in firefox only but not in other browsers like safari , chrome and IE .Given below is the code. <html> <head> <title>MAke Heading Bold</title> <style type="text/css"> .mycss {font-weight:bold;} </style> </head> <body> <form name="myform"> <select name="myselect"> <option value="one">one</option> <option value="two" class="mycss">two</option> <option value="three" >Three </option> </select> </form> </body> </html> Please suggest me best possible solution for this . Thanks Vivek

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  • Cross-thread operation not valid: accessed from a thread other than the thread it was created on.

    - by user307524
    Hi, I want to remove checked items from checklistbox (winform control) in class file method which i am calling asynchronously using deletegate. but it showing me this error message:- Cross-thread operation not valid: Control 'checkedListBox1' accessed from a thread other than the thread it was created on. i have tried invoke required but again got the same error. Sample code is below: private void button1_Click(object sender, EventArgs e) { // Create an instance of the test class. Class1 ad = new Class1(); // Create the delegate. AsyncMethodCaller1 caller = new AsyncMethodCaller1(ad.TestMethod1); //callback delegate IAsyncResult result = caller.BeginInvoke(checkedListBox1, new AsyncCallback(CallbackMethod)," "); } In class file code for TestMethod1 is : - private delegate void dlgInvoke(CheckedListBox c, Int32 str); private void Invoke(CheckedListBox c, Int32 str) { if (c.InvokeRequired) { c.Invoke(new dlgInvoke(Invoke), c, str); c.Items.RemoveAt(str); } else { c.Text = ""; } } // The method to be executed asynchronously. public string TestMethod1(CheckedListBox chklist) { for (int i = 0; i < 10; i++) { string chkValue = chklist.CheckedItems[i].ToString(); //do some other database operation based on checked items. Int32 index = chklist.FindString(chkValue); Invoke(chklist, index); } return ""; }

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  • What is preferred strategies for cross browser and multiple styled table in CSS?

    - by jitendra
    What is preferred strategies for cross browser and multiple styled table in CSS? in default css what should i predefined for <table>, td, th , thead, tbody, tfoot I have to work in a project there are so many tables with different color schemes and different type of alignment like in some table , i will need to horizontally align data of cell to right, sometime left, sometime right. same thing for vertical alignment, top, bottom and middle. some table will have thin border on row , some will have thick (same with column border). Some time i want to give different background color to particular row or column or in multiple row or column. So my question is: What code should i keep in css default for all tables and how to handle table with different style using ID and classes in multiple pages. I want to do every presentational thing with css. How to make ID classes for everything using semantic naming ? Which tags related to table can be useful? How to control whole tables styling from one css class?

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  • How can you exclude a large number of records in a cross db query using LINQ2SQL?

    - by tap
    So here is my situation: I have a vendor supplied DB we cannot modify and a custom db that imports data from the vendor app and acts on it. Once records are imported form the vendor app, they cannot appear on the list of records to be imported. Also we only want to display the 250 most recent records that have not been imported. What I originally started with was select the list of ids that have been imported from the custom db, and then query the vendor db, using the list of ids in a .Where(x = !idList.Contains(x.Id)) clause on the remote query. This worked up until we broke 2100 records imported into the custom db, as 2100 is the limit on the number of parameters that can be passed into SQL. After finding out this was the actual problem and not the 'invalid buffer'/'severe error' ADO.Net reported, my solution was to remove the first 2000 ids in the remote query, and then remove the remaining records in the local query. Having to pull back a large number of irrelevant records, just to exclude them, so I can get the correct 250 records seems very inelegant. Is there a better way to do this, short of doing a cross db stored procedure? Thanks in advance.

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  • Penetration testing with Nikto, unknown results found

    - by heldrida
    I've scanned my new webserver and I'm surprised to find that in the results there's programs that I never installed. This is a fresh new install of Ubuntu 12.04 and just installed Php 5.3, mysql, fail2ban, apache2, git, a few other things. Not sure if related, but I've got Wordpress installed but this doesn't have anything to do with myphpnuke does it? I'd like to understand why am I getting this results ? + OSVDB-27071: /phpimageview.php?pic=javascript:alert(8754): PHP Image View 1.0 is vulnerable to Cross Site Scripting (XSS). http://www.cert.org/advisories/CA-2000-02.html. + OSVDB-3931: /myphpnuke/links.php?op=search&query=[script]alert('Vulnerable);[/script]?query=: myphpnuke is vulnerable to Cross Site Scripting (XSS). http://www.cert.org/advisories/CA-2000-02.html. + OSVDB-3931: /myphpnuke/links.php?op=MostPopular&ratenum=[script]alert(document.cookie);[/script]&ratetype=percent: myphpnuke is vulnerable to Cross Site Scripting (XSS). http://www.cert.org/advisories/CA-2000-02.html. + /modules.php?op=modload&name=FAQ&file=index&myfaq=yes&id_cat=1&categories=%3Cimg%20src=javascript:alert(9456);%3E&parent_id=0: Post Nuke 0.7.2.3-Phoenix is vulnerable to Cross Site Scripting (XSS). http://www.cert.org/advisories/CA-2000-02.html. + /modules.php?letter=%22%3E%3Cimg%20src=javascript:alert(document.cookie);%3E&op=modload&name=Members_List&file=index: Post Nuke 0.7.2.3-Phoenix is vulnerable to Cross Site Scripting (XSS). http://www.cert.org/advisories/CA-2000-02.html. + OSVDB-4598: /members.asp?SF=%22;}alert('Vulnerable');function%20x(){v%20=%22: Web Wiz Forums ver. 7.01 and below is vulnerable to Cross Site Scripting (XSS). http://www.cert.org/advisories/CA-2000-02.html. + OSVDB-2946: /forum_members.asp?find=%22;}alert(9823);function%20x(){v%20=%22: Web Wiz Forums ver. 7.01 and below is vulnerable to Cross Site Scripting (XSS). http://www.cert.org/advisories/CA-2000-02.html. Thanks for looking!

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  • Cross-thread operation not valid: Control accessed from a thread other than the thread it was create

    - by SilverHorse
    I have a scenario. (Windows Forms, C#, .NET) There is a main form which hosts some user control. The user control does some heavy data operation, such that if I directly call the Usercontrol_Load method the UI become nonresponsive for the duration for load method execution. To overcome this I load data on different thread (trying to change existing code as little as I can) I used a background worker thread which will be loading the data and when done will notify the application that it has done its work. Now came a real problem. All the UI (main form and its child usercontrols) was created on the primary main thread. In the LOAD method of the usercontrol I'm fetching data based on the values of some control (like textbox) on userControl. The pseudocode would look like this: //CODE 1 UserContrl1_LOadDataMethod() { if(textbox1.text=="MyName") <<======this gives exception { //Load data corresponding to "MyName". //Populate a globale variable List<string> which will be binded to grid at some later stage. } } The Exception it gave was Cross-thread operation not valid: Control accessed from a thread other than the thread it was created on. To know more about this I did some googling and a suggestion came up like using the following code //CODE 2 UserContrl1_LOadDataMethod() { if(InvokeRequired) // Line #1 { this.Invoke(new MethodInvoker(UserContrl1_LOadDataMethod)); return; } if(textbox1.text=="MyName") //<<======Now it wont give exception** { //Load data correspondin to "MyName" //Populate a globale variable List<string> which will be binded to grid at some later stage } } BUT BUT BUT... it seems I'm back to square one. The Application again become nonresponsive. It seems to be due to the execution of line #1 if condition. The loading task is again done by the parent thread and not the third that I spawned. I don't know whether I perceived this right or wrong. I'm new to threading. How do I resolve this and also what is the effect of execution of Line#1 if block? The situation is this: I want to load data into a global variable based on the value of a control. I don't want to change the value of a control from the child thread. I'm not going to do it ever from a child thread. So only accessing the value so that the corresponding data can be fetched from the database.

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  • How can I get penetration depth from Minkowski Portal Refinement / Xenocollide?

    - by Raven Dreamer
    I recently got an implementation of Minkowski Portal Refinement (MPR) successfully detecting collision. Even better, my implementation returns a good estimate (local minimum) direction for the minimum penetration depth. So I took a stab at adjusting the algorithm to return the penetration depth in an arbitrary direction, and was modestly successful - my altered method works splendidly for face-edge collision resolution! What it doesn't currently do, is correctly provide the minimum penetration depth for edge-edge scenarios, such as the case on the right: What I perceive to be happening, is that my current method returns the minimum penetration depth to the nearest vertex - which works fine when the collision is actually occurring on the plane of that vertex, but not when the collision happens along an edge. Is there a way I can alter my method to return the penetration depth to the point of collision, rather than the nearest vertex? Here's the method that's supposed to return the minimum penetration distance along a specific direction: public static Vector3 CalcMinDistance(List<Vector3> shape1, List<Vector3> shape2, Vector3 dir) { //holding variables Vector3 n = Vector3.zero; Vector3 swap = Vector3.zero; // v0 = center of Minkowski sum v0 = Vector3.zero; // Avoid case where centers overlap -- any direction is fine in this case //if (v0 == Vector3.zero) return Vector3.zero; //always pass in a valid direction. // v1 = support in direction of origin n = -dir; //get the differnce of the minkowski sum Vector3 v11 = GetSupport(shape1, -n); Vector3 v12 = GetSupport(shape2, n); v1 = v12 - v11; //if the support point is not in the direction of the origin if (v1.Dot(n) <= 0) { //Debug.Log("Could find no points this direction"); return Vector3.zero; } // v2 - support perpendicular to v1,v0 n = v1.Cross(v0); if (n == Vector3.zero) { //v1 and v0 are parallel, which means //the direction leads directly to an endpoint n = v1 - v0; //shortest distance is just n //Debug.Log("2 point return"); return n; } //get the new support point Vector3 v21 = GetSupport(shape1, -n); Vector3 v22 = GetSupport(shape2, n); v2 = v22 - v21; if (v2.Dot(n) <= 0) { //can't reach the origin in this direction, ergo, no collision //Debug.Log("Could not reach edge?"); return Vector2.zero; } // Determine whether origin is on + or - side of plane (v1,v0,v2) //tests linesegments v0v1 and v0v2 n = (v1 - v0).Cross(v2 - v0); float dist = n.Dot(v0); // If the origin is on the - side of the plane, reverse the direction of the plane if (dist > 0) { //swap the winding order of v1 and v2 swap = v1; v1 = v2; v2 = swap; //swap the winding order of v11 and v12 swap = v12; v12 = v11; v11 = swap; //swap the winding order of v11 and v12 swap = v22; v22 = v21; v21 = swap; //and swap the plane normal n = -n; } /// // Phase One: Identify a portal while (true) { // Obtain the support point in a direction perpendicular to the existing plane // Note: This point is guaranteed to lie off the plane Vector3 v31 = GetSupport(shape1, -n); Vector3 v32 = GetSupport(shape2, n); v3 = v32 - v31; if (v3.Dot(n) <= 0) { //can't enclose the origin within our tetrahedron //Debug.Log("Could not reach edge after portal?"); return Vector3.zero; } // If origin is outside (v1,v0,v3), then eliminate v2 and loop if (v1.Cross(v3).Dot(v0) < 0) { //failed to enclose the origin, adjust points; v2 = v3; v21 = v31; v22 = v32; n = (v1 - v0).Cross(v3 - v0); continue; } // If origin is outside (v3,v0,v2), then eliminate v1 and loop if (v3.Cross(v2).Dot(v0) < 0) { //failed to enclose the origin, adjust points; v1 = v3; v11 = v31; v12 = v32; n = (v3 - v0).Cross(v2 - v0); continue; } bool hit = false; /// // Phase Two: Refine the portal int phase2 = 0; // We are now inside of a wedge... while (phase2 < 20) { phase2++; // Compute normal of the wedge face n = (v2 - v1).Cross(v3 - v1); n.Normalize(); // Compute distance from origin to wedge face float d = n.Dot(v1); // If the origin is inside the wedge, we have a hit if (d > 0 ) { //Debug.Log("Do plane test here"); float T = n.Dot(v2) / n.Dot(dir); Vector3 pointInPlane = (dir * T); return pointInPlane; } // Find the support point in the direction of the wedge face Vector3 v41 = GetSupport(shape1, -n); Vector3 v42 = GetSupport(shape2, n); v4 = v42 - v41; float delta = (v4 - v3).Dot(n); float separation = -(v4.Dot(n)); if (delta <= kCollideEpsilon || separation >= 0) { //Debug.Log("Non-convergance detected"); //Debug.Log("Do plane test here"); return Vector3.zero; } // Compute the tetrahedron dividing face (v4,v0,v1) float d1 = v4.Cross(v1).Dot(v0); // Compute the tetrahedron dividing face (v4,v0,v2) float d2 = v4.Cross(v2).Dot(v0); // Compute the tetrahedron dividing face (v4,v0,v3) float d3 = v4.Cross(v3).Dot(v0); if (d1 < 0) { if (d2 < 0) { // Inside d1 & inside d2 ==> eliminate v1 v1 = v4; v11 = v41; v12 = v42; } else { // Inside d1 & outside d2 ==> eliminate v3 v3 = v4; v31 = v41; v32 = v42; } } else { if (d3 < 0) { // Outside d1 & inside d3 ==> eliminate v2 v2 = v4; v21 = v41; v22 = v42; } else { // Outside d1 & outside d3 ==> eliminate v1 v1 = v4; v11 = v41; v12 = v42; } } } return Vector3.zero; } }

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  • Improving Partitioned Table Join Performance

    - by Paul White
    The query optimizer does not always choose an optimal strategy when joining partitioned tables. This post looks at an example, showing how a manual rewrite of the query can almost double performance, while reducing the memory grant to almost nothing. Test Data The two tables in this example use a common partitioning partition scheme. The partition function uses 41 equal-size partitions: CREATE PARTITION FUNCTION PFT (integer) AS RANGE RIGHT FOR VALUES ( 125000, 250000, 375000, 500000, 625000, 750000, 875000, 1000000, 1125000, 1250000, 1375000, 1500000, 1625000, 1750000, 1875000, 2000000, 2125000, 2250000, 2375000, 2500000, 2625000, 2750000, 2875000, 3000000, 3125000, 3250000, 3375000, 3500000, 3625000, 3750000, 3875000, 4000000, 4125000, 4250000, 4375000, 4500000, 4625000, 4750000, 4875000, 5000000 ); GO CREATE PARTITION SCHEME PST AS PARTITION PFT ALL TO ([PRIMARY]); There two tables are: CREATE TABLE dbo.T1 ( TID integer NOT NULL IDENTITY(0,1), Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T1 PRIMARY KEY CLUSTERED (TID) ON PST (TID) );   CREATE TABLE dbo.T2 ( TID integer NOT NULL, Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T2 PRIMARY KEY CLUSTERED (TID, Column1) ON PST (TID) ); The next script loads 5 million rows into T1 with a pseudo-random value between 1 and 5 for Column1. The table is partitioned on the IDENTITY column TID: INSERT dbo.T1 WITH (TABLOCKX) (Column1) SELECT (ABS(CHECKSUM(NEWID())) % 5) + 1 FROM dbo.Numbers AS N WHERE n BETWEEN 1 AND 5000000; In case you don’t already have an auxiliary table of numbers lying around, here’s a script to create one with 10 million rows: CREATE TABLE dbo.Numbers (n bigint PRIMARY KEY);   WITH L0 AS(SELECT 1 AS c UNION ALL SELECT 1), L1 AS(SELECT 1 AS c FROM L0 AS A CROSS JOIN L0 AS B), L2 AS(SELECT 1 AS c FROM L1 AS A CROSS JOIN L1 AS B), L3 AS(SELECT 1 AS c FROM L2 AS A CROSS JOIN L2 AS B), L4 AS(SELECT 1 AS c FROM L3 AS A CROSS JOIN L3 AS B), L5 AS(SELECT 1 AS c FROM L4 AS A CROSS JOIN L4 AS B), Nums AS(SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS n FROM L5) INSERT dbo.Numbers WITH (TABLOCKX) SELECT TOP (10000000) n FROM Nums ORDER BY n OPTION (MAXDOP 1); Table T1 contains data like this: Next we load data into table T2. The relationship between the two tables is that table 2 contains ‘n’ rows for each row in table 1, where ‘n’ is determined by the value in Column1 of table T1. There is nothing particularly special about the data or distribution, by the way. INSERT dbo.T2 WITH (TABLOCKX) (TID, Column1) SELECT T.TID, N.n FROM dbo.T1 AS T JOIN dbo.Numbers AS N ON N.n >= 1 AND N.n <= T.Column1; Table T2 ends up containing about 15 million rows: The primary key for table T2 is a combination of TID and Column1. The data is partitioned according to the value in column TID alone. Partition Distribution The following query shows the number of rows in each partition of table T1: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T1 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are 40 partitions containing 125,000 rows (40 * 125k = 5m rows). The rightmost partition remains empty. The next query shows the distribution for table 2: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T2 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are roughly 375,000 rows in each partition (the rightmost partition is also empty): Ok, that’s the test data done. Test Query and Execution Plan The task is to count the rows resulting from joining tables 1 and 2 on the TID column: SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; The optimizer chooses a plan using parallel hash join, and partial aggregation: The Plan Explorer plan tree view shows accurate cardinality estimates and an even distribution of rows across threads (click to enlarge the image): With a warm data cache, the STATISTICS IO output shows that no physical I/O was needed, and all 41 partitions were touched: Running the query without actual execution plan or STATISTICS IO information for maximum performance, the query returns in around 2600ms. Execution Plan Analysis The first step toward improving on the execution plan produced by the query optimizer is to understand how it works, at least in outline. The two parallel Clustered Index Scans use multiple threads to read rows from tables T1 and T2. Parallel scan uses a demand-based scheme where threads are given page(s) to scan from the table as needed. This arrangement has certain important advantages, but does result in an unpredictable distribution of rows amongst threads. The point is that multiple threads cooperate to scan the whole table, but it is impossible to predict which rows end up on which threads. For correct results from the parallel hash join, the execution plan has to ensure that rows from T1 and T2 that might join are processed on the same thread. For example, if a row from T1 with join key value ‘1234’ is placed in thread 5’s hash table, the execution plan must guarantee that any rows from T2 that also have join key value ‘1234’ probe thread 5’s hash table for matches. The way this guarantee is enforced in this parallel hash join plan is by repartitioning rows to threads after each parallel scan. The two repartitioning exchanges route rows to threads using a hash function over the hash join keys. The two repartitioning exchanges use the same hash function so rows from T1 and T2 with the same join key must end up on the same hash join thread. Expensive Exchanges This business of repartitioning rows between threads can be very expensive, especially if a large number of rows is involved. The execution plan selected by the optimizer moves 5 million rows through one repartitioning exchange and around 15 million across the other. As a first step toward removing these exchanges, consider the execution plan selected by the optimizer if we join just one partition from each table, disallowing parallelism: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = 1 AND $PARTITION.PFT(T2.TID) = 1 OPTION (MAXDOP 1); The optimizer has chosen a (one-to-many) merge join instead of a hash join. The single-partition query completes in around 100ms. If everything scaled linearly, we would expect that extending this strategy to all 40 populated partitions would result in an execution time around 4000ms. Using parallelism could reduce that further, perhaps to be competitive with the parallel hash join chosen by the optimizer. This raises a question. If the most efficient way to join one partition from each of the tables is to use a merge join, why does the optimizer not choose a merge join for the full query? Forcing a Merge Join Let’s force the optimizer to use a merge join on the test query using a hint: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN); This is the execution plan selected by the optimizer: This plan results in the same number of logical reads reported previously, but instead of 2600ms the query takes 5000ms. The natural explanation for this drop in performance is that the merge join plan is only using a single thread, whereas the parallel hash join plan could use multiple threads. Parallel Merge Join We can get a parallel merge join plan using the same query hint as before, and adding trace flag 8649: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN, QUERYTRACEON 8649); The execution plan is: This looks promising. It uses a similar strategy to distribute work across threads as seen for the parallel hash join. In practice though, performance is disappointing. On a typical run, the parallel merge plan runs for around 8400ms; slower than the single-threaded merge join plan (5000ms) and much worse than the 2600ms for the parallel hash join. We seem to be going backwards! The logical reads for the parallel merge are still exactly the same as before, with no physical IOs. The cardinality estimates and thread distribution are also still very good (click to enlarge): A big clue to the reason for the poor performance is shown in the wait statistics (captured by Plan Explorer Pro): CXPACKET waits require careful interpretation, and are most often benign, but in this case excessive waiting occurs at the repartitioning exchanges. Unlike the parallel hash join, the repartitioning exchanges in this plan are order-preserving ‘merging’ exchanges (because merge join requires ordered inputs): Parallelism works best when threads can just grab any available unit of work and get on with processing it. Preserving order introduces inter-thread dependencies that can easily lead to significant waits occurring. In extreme cases, these dependencies can result in an intra-query deadlock, though the details of that will have to wait for another time to explore in detail. The potential for waits and deadlocks leads the query optimizer to cost parallel merge join relatively highly, especially as the degree of parallelism (DOP) increases. This high costing resulted in the optimizer choosing a serial merge join rather than parallel in this case. The test results certainly confirm its reasoning. Collocated Joins In SQL Server 2008 and later, the optimizer has another available strategy when joining tables that share a common partition scheme. This strategy is a collocated join, also known as as a per-partition join. It can be applied in both serial and parallel execution plans, though it is limited to 2-way joins in the current optimizer. Whether the optimizer chooses a collocated join or not depends on cost estimation. The primary benefits of a collocated join are that it eliminates an exchange and requires less memory, as we will see next. Costing and Plan Selection The query optimizer did consider a collocated join for our original query, but it was rejected on cost grounds. The parallel hash join with repartitioning exchanges appeared to be a cheaper option. There is no query hint to force a collocated join, so we have to mess with the costing framework to produce one for our test query. Pretending that IOs cost 50 times more than usual is enough to convince the optimizer to use collocated join with our test query: -- Pretend IOs are 50x cost temporarily DBCC SETIOWEIGHT(50);   -- Co-located hash join SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (RECOMPILE);   -- Reset IO costing DBCC SETIOWEIGHT(1); Collocated Join Plan The estimated execution plan for the collocated join is: The Constant Scan contains one row for each partition of the shared partitioning scheme, from 1 to 41. The hash repartitioning exchanges seen previously are replaced by a single Distribute Streams exchange using Demand partitioning. Demand partitioning means that the next partition id is given to the next parallel thread that asks for one. My test machine has eight logical processors, and all are available for SQL Server to use. As a result, there are eight threads in the single parallel branch in this plan, each processing one partition from each table at a time. Once a thread finishes processing a partition, it grabs a new partition number from the Distribute Streams exchange…and so on until all partitions have been processed. It is important to understand that the parallel scans in this plan are different from the parallel hash join plan. Although the scans have the same parallelism icon, tables T1 and T2 are not being co-operatively scanned by multiple threads in the same way. Each thread reads a single partition of T1 and performs a hash match join with the same partition from table T2. The properties of the two Clustered Index Scans show a Seek Predicate (unusual for a scan!) limiting the rows to a single partition: The crucial point is that the join between T1 and T2 is on TID, and TID is the partitioning column for both tables. A thread that processes partition ‘n’ is guaranteed to see all rows that can possibly join on TID for that partition. In addition, no other thread will see rows from that partition, so this removes the need for repartitioning exchanges. CPU and Memory Efficiency Improvements The collocated join has removed two expensive repartitioning exchanges and added a single exchange processing 41 rows (one for each partition id). Remember, the parallel hash join plan exchanges had to process 5 million and 15 million rows. The amount of processor time spent on exchanges will be much lower in the collocated join plan. In addition, the collocated join plan has a maximum of 8 threads processing single partitions at any one time. The 41 partitions will all be processed eventually, but a new partition is not started until a thread asks for it. Threads can reuse hash table memory for the new partition. The parallel hash join plan also had 8 hash tables, but with all 5,000,000 build rows loaded at the same time. The collocated plan needs memory for only 8 * 125,000 = 1,000,000 rows at any one time. Collocated Hash Join Performance The collated join plan has disappointing performance in this case. The query runs for around 25,300ms despite the same IO statistics as usual. This is much the worst result so far, so what went wrong? It turns out that cardinality estimation for the single partition scans of table T1 is slightly low. The properties of the Clustered Index Scan of T1 (graphic immediately above) show the estimation was for 121,951 rows. This is a small shortfall compared with the 125,000 rows actually encountered, but it was enough to cause the hash join to spill to physical tempdb: A level 1 spill doesn’t sound too bad, until you realize that the spill to tempdb probably occurs for each of the 41 partitions. As a side note, the cardinality estimation error is a little surprising because the system tables accurately show there are 125,000 rows in every partition of T1. Unfortunately, the optimizer uses regular column and index statistics to derive cardinality estimates here rather than system table information (e.g. sys.partitions). Collocated Merge Join We will never know how well the collocated parallel hash join plan might have worked without the cardinality estimation error (and the resulting 41 spills to tempdb) but we do know: Merge join does not require a memory grant; and Merge join was the optimizer’s preferred join option for a single partition join Putting this all together, what we would really like to see is the same collocated join strategy, but using merge join instead of hash join. Unfortunately, the current query optimizer cannot produce a collocated merge join; it only knows how to do collocated hash join. So where does this leave us? CROSS APPLY sys.partitions We can try to write our own collocated join query. We can use sys.partitions to find the partition numbers, and CROSS APPLY to get a count per partition, with a final step to sum the partial counts. The following query implements this idea: SELECT row_count = SUM(Subtotals.cnt) FROM ( -- Partition numbers SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1 ) AS P CROSS APPLY ( -- Count per collocated join SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals; The estimated plan is: The cardinality estimates aren’t all that good here, especially the estimate for the scan of the system table underlying the sys.partitions view. Nevertheless, the plan shape is heading toward where we would like to be. Each partition number from the system table results in a per-partition scan of T1 and T2, a one-to-many Merge Join, and a Stream Aggregate to compute the partial counts. The final Stream Aggregate just sums the partial counts. Execution time for this query is around 3,500ms, with the same IO statistics as always. This compares favourably with 5,000ms for the serial plan produced by the optimizer with the OPTION (MERGE JOIN) hint. This is another case of the sum of the parts being less than the whole – summing 41 partial counts from 41 single-partition merge joins is faster than a single merge join and count over all partitions. Even so, this single-threaded collocated merge join is not as quick as the original parallel hash join plan, which executed in 2,600ms. On the positive side, our collocated merge join uses only one logical processor and requires no memory grant. The parallel hash join plan used 16 threads and reserved 569 MB of memory:   Using a Temporary Table Our collocated merge join plan should benefit from parallelism. The reason parallelism is not being used is that the query references a system table. We can work around that by writing the partition numbers to a temporary table (or table variable): SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   CREATE TABLE #P ( partition_number integer PRIMARY KEY);   INSERT #P (partition_number) SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1;   SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals;   DROP TABLE #P;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; Using the temporary table adds a few logical reads, but the overall execution time is still around 3500ms, indistinguishable from the same query without the temporary table. The problem is that the query optimizer still doesn’t choose a parallel plan for this query, though the removal of the system table reference means that it could if it chose to: In fact the optimizer did enter the parallel plan phase of query optimization (running search 1 for a second time): Unfortunately, the parallel plan found seemed to be more expensive than the serial plan. This is a crazy result, caused by the optimizer’s cost model not reducing operator CPU costs on the inner side of a nested loops join. Don’t get me started on that, we’ll be here all night. In this plan, everything expensive happens on the inner side of a nested loops join. Without a CPU cost reduction to compensate for the added cost of exchange operators, candidate parallel plans always look more expensive to the optimizer than the equivalent serial plan. Parallel Collocated Merge Join We can produce the desired parallel plan using trace flag 8649 again: SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: One difference between this plan and the collocated hash join plan is that a Repartition Streams exchange operator is used instead of Distribute Streams. The effect is similar, though not quite identical. The Repartition uses round-robin partitioning, meaning the next partition id is pushed to the next thread in sequence. The Distribute Streams exchange seen earlier used Demand partitioning, meaning the next partition id is pulled across the exchange by the next thread that is ready for more work. There are subtle performance implications for each partitioning option, but going into that would again take us too far off the main point of this post. Performance The important thing is the performance of this parallel collocated merge join – just 1350ms on a typical run. The list below shows all the alternatives from this post (all timings include creation, population, and deletion of the temporary table where appropriate) from quickest to slowest: Collocated parallel merge join: 1350ms Parallel hash join: 2600ms Collocated serial merge join: 3500ms Serial merge join: 5000ms Parallel merge join: 8400ms Collated parallel hash join: 25,300ms (hash spill per partition) The parallel collocated merge join requires no memory grant (aside from a paltry 1.2MB used for exchange buffers). This plan uses 16 threads at DOP 8; but 8 of those are (rather pointlessly) allocated to the parallel scan of the temporary table. These are minor concerns, but it turns out there is a way to address them if it bothers you. Parallel Collocated Merge Join with Demand Partitioning This final tweak replaces the temporary table with a hard-coded list of partition ids (dynamic SQL could be used to generate this query from sys.partitions): SELECT row_count = SUM(Subtotals.cnt) FROM ( VALUES (1),(2),(3),(4),(5),(6),(7),(8),(9),(10), (11),(12),(13),(14),(15),(16),(17),(18),(19),(20), (21),(22),(23),(24),(25),(26),(27),(28),(29),(30), (31),(32),(33),(34),(35),(36),(37),(38),(39),(40),(41) ) AS P (partition_number) CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: The parallel collocated hash join plan is reproduced below for comparison: The manual rewrite has another advantage that has not been mentioned so far: the partial counts (per partition) can be computed earlier than the partial counts (per thread) in the optimizer’s collocated join plan. The earlier aggregation is performed by the extra Stream Aggregate under the nested loops join. The performance of the parallel collocated merge join is unchanged at around 1350ms. Final Words It is a shame that the current query optimizer does not consider a collocated merge join (Connect item closed as Won’t Fix). The example used in this post showed an improvement in execution time from 2600ms to 1350ms using a modestly-sized data set and limited parallelism. In addition, the memory requirement for the query was almost completely eliminated  – down from 569MB to 1.2MB. The problem with the parallel hash join selected by the optimizer is that it attempts to process the full data set all at once (albeit using eight threads). It requires a large memory grant to hold all 5 million rows from table T1 across the eight hash tables, and does not take advantage of the divide-and-conquer opportunity offered by the common partitioning. The great thing about the collocated join strategies is that each parallel thread works on a single partition from both tables, reading rows, performing the join, and computing a per-partition subtotal, before moving on to a new partition. From a thread’s point of view… If you have trouble visualizing what is happening from just looking at the parallel collocated merge join execution plan, let’s look at it again, but from the point of view of just one thread operating between the two Parallelism (exchange) operators. Our thread picks up a single partition id from the Distribute Streams exchange, and starts a merge join using ordered rows from partition 1 of table T1 and partition 1 of table T2. By definition, this is all happening on a single thread. As rows join, they are added to a (per-partition) count in the Stream Aggregate immediately above the Merge Join. Eventually, either T1 (partition 1) or T2 (partition 1) runs out of rows and the merge join stops. The per-partition count from the aggregate passes on through the Nested Loops join to another Stream Aggregate, which is maintaining a per-thread subtotal. Our same thread now picks up a new partition id from the exchange (say it gets id 9 this time). The count in the per-partition aggregate is reset to zero, and the processing of partition 9 of both tables proceeds just as it did for partition 1, and on the same thread. Each thread picks up a single partition id and processes all the data for that partition, completely independently from other threads working on other partitions. One thread might eventually process partitions (1, 9, 17, 25, 33, 41) while another is concurrently processing partitions (2, 10, 18, 26, 34) and so on for the other six threads at DOP 8. The point is that all 8 threads can execute independently and concurrently, continuing to process new partitions until the wider job (of which the thread has no knowledge!) is done. This divide-and-conquer technique can be much more efficient than simply splitting the entire workload across eight threads all at once. Related Reading Understanding and Using Parallelism in SQL Server Parallel Execution Plans Suck © 2013 Paul White – All Rights Reserved Twitter: @SQL_Kiwi

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  • Use CompiledQuery.Compile to improve LINQ to SQL performance

    - by Michael Freidgeim
    After reading DLinq (Linq to SQL) Performance and in particular Part 4  I had a few questions. If CompiledQuery.Compile gives so much benefits, why not to do it for all Linq To Sql queries? Is any essential disadvantages of compiling all select queries? What are conditions, when compiling makes whose performance, for how much percentage? World be good to have default on application config level or on DBML level to specify are all select queries to be compiled? And the same questions about Entity Framework CompiledQuery Class. However in comments I’ve found answer  of the author ricom 6 Jul 2007 3:08 AM Compiling the query makes it durable. There is no need for this, nor is there any desire, unless you intend to run that same query many times. SQL provides regular select statements, prepared select statements, and stored procedures for a reason.  Linq now has analogs. Also from 10 Tips to Improve your LINQ to SQL Application Performance   If you are using CompiledQuery make sure that you are using it more than once as it is more costly than normal querying for the first time. The resulting function coming as a CompiledQuery is an object, having the SQL statement and the delegate to apply it.  And your delegate has the ability to replace the variables (or parameters) in the resulting query. However I feel that many developers are not informed enough about benefits of Compile. I think that tools like FxCop and Resharper should check the queries  and suggest if compiling is recommended. Related Articles for LINQ to SQL: MSDN How to: Store and Reuse Queries (LINQ to SQL) 10 Tips to Improve your LINQ to SQL Application Performance Related Articles for Entity Framework: MSDN: CompiledQuery Class Exploring the Performance of the ADO.NET Entity Framework - Part 1 Exploring the Performance of the ADO.NET Entity Framework – Part 2 ADO.NET Entity Framework 4.0: Making it fast through Compiled Query

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  • Compiling JS-Test-Driver Plugin and Installing it on Eclipse 3.5.1 Galileo?

    - by leeand00
    I downloaded the source of the js-test-driver from: http://js-test-driver.googlecode.com/svn/tags/1.2 It compiles just fine, but one of the unit tests fails: [junit] Tests run: 1, Failures: 1, Errors: 0, Time elapsed: 0.012 sec [junit] Test com.google.jstestdriver.eclipse.ui.views.FailureOnlyViewerFilterTest FAILED I am using: - ANT 1.7.1 - javac 1.6.0_12 And I'm trying to install the js-test-driver plugin on Eclipse 3.5.1 Galileo Despite the failed test I installed the plugin into my C:\eclipse\dropins\js-test-driver directory by copying (exporting from svn) the compiled feature and plugins directories there, to see if it would yield any hints to what the problem is. When I started eclipse, added the plugin to the panel using Window-Show View-Other... Other-JsTestDriver The plugin for the panel is added, but it displays the following error instead of the plugin in the panel: Could not create the view: Plugin com.google.jstestdriver.eclipse.ui was unable to load class com.google.jstestdriver.eclipse.ui.views.JsTestDriverView. And then bellow that I get the following stack trace after clicking Details: java.lang.ClassNotFoundException: com.google.jstestdriver.eclipse.ui.views.JsTestDriverView at org.eclipse.osgi.internal.loader.BundleLoader.findClassInternal(BundleLoader.java:494) at org.eclipse.osgi.internal.loader.BundleLoader.findClass(BundleLoader.java:410) at org.eclipse.osgi.internal.loader.BundleLoader.findClass(BundleLoader.java:398) at org.eclipse.osgi.internal.baseadaptor.DefaultClassLoader.loadClass(DefaultClassLoader.java:105) at java.lang.ClassLoader.loadClass(Unknown Source) at org.eclipse.osgi.internal.loader.BundleLoader.loadClass(BundleLoader.java:326) at org.eclipse.osgi.framework.internal.core.BundleHost.loadClass(BundleHost.java:231) at org.eclipse.osgi.framework.internal.core.AbstractBundle.loadClass(AbstractBundle.java:1193) at org.eclipse.core.internal.registry.osgi.RegistryStrategyOSGI.createExecutableExtension(RegistryStrategyOSGI.java:160) at org.eclipse.core.internal.registry.ExtensionRegistry.createExecutableExtension(ExtensionRegistry.java:874) at org.eclipse.core.internal.registry.ConfigurationElement.createExecutableExtension(ConfigurationElement.java:243) at org.eclipse.core.internal.registry.ConfigurationElementHandle.createExecutableExtension(ConfigurationElementHandle.java:51) at org.eclipse.ui.internal.WorkbenchPlugin$1.run(WorkbenchPlugin.java:267) at org.eclipse.swt.custom.BusyIndicator.showWhile(BusyIndicator.java:70) at org.eclipse.ui.internal.WorkbenchPlugin.createExtension(WorkbenchPlugin.java:263) at org.eclipse.ui.internal.registry.ViewDescriptor.createView(ViewDescriptor.java:63) at org.eclipse.ui.internal.ViewReference.createPartHelper(ViewReference.java:324) at org.eclipse.ui.internal.ViewReference.createPart(ViewReference.java:226) at org.eclipse.ui.internal.WorkbenchPartReference.getPart(WorkbenchPartReference.java:595) at org.eclipse.ui.internal.Perspective.showView(Perspective.java:2229) at org.eclipse.ui.internal.WorkbenchPage.busyShowView(WorkbenchPage.java:1067) at org.eclipse.ui.internal.WorkbenchPage$20.run(WorkbenchPage.java:3816) at org.eclipse.swt.custom.BusyIndicator.showWhile(BusyIndicator.java:70) at org.eclipse.ui.internal.WorkbenchPage.showView(WorkbenchPage.java:3813) at org.eclipse.ui.internal.WorkbenchPage.showView(WorkbenchPage.java:3789) at org.eclipse.ui.handlers.ShowViewHandler.openView(ShowViewHandler.java:165) at org.eclipse.ui.handlers.ShowViewHandler.openOther(ShowViewHandler.java:109) at org.eclipse.ui.handlers.ShowViewHandler.execute(ShowViewHandler.java:77) at org.eclipse.ui.internal.handlers.HandlerProxy.execute(HandlerProxy.java:294) at org.eclipse.core.commands.Command.executeWithChecks(Command.java:476) at org.eclipse.core.commands.ParameterizedCommand.executeWithChecks(ParameterizedCommand.java:508) at org.eclipse.ui.internal.handlers.HandlerService.executeCommand(HandlerService.java:169) at org.eclipse.ui.internal.handlers.SlaveHandlerService.executeCommand(SlaveHandlerService.java:241) at org.eclipse.ui.internal.ShowViewMenu$3.run(ShowViewMenu.java:141) at org.eclipse.jface.action.Action.runWithEvent(Action.java:498) at org.eclipse.jface.action.ActionContributionItem.handleWidgetSelection(ActionContributionItem.java:584) at org.eclipse.jface.action.ActionContributionItem.access$2(ActionContributionItem.java:501) at org.eclipse.jface.action.ActionContributionItem$5.handleEvent(ActionContributionItem.java:411) at org.eclipse.swt.widgets.EventTable.sendEvent(EventTable.java:84) at org.eclipse.swt.widgets.Widget.sendEvent(Widget.java:1003) at org.eclipse.swt.widgets.Display.runDeferredEvents(Display.java:3880) at org.eclipse.swt.widgets.Display.readAndDispatch(Display.java:3473) at org.eclipse.ui.internal.Workbench.runEventLoop(Workbench.java:2405) at org.eclipse.ui.internal.Workbench.runUI(Workbench.java:2369) at org.eclipse.ui.internal.Workbench.access$4(Workbench.java:2221) at org.eclipse.ui.internal.Workbench$5.run(Workbench.java:500) at org.eclipse.core.databinding.observable.Realm.runWithDefault(Realm.java:332) at org.eclipse.ui.internal.Workbench.createAndRunWorkbench(Workbench.java:493) at org.eclipse.ui.PlatformUI.createAndRunWorkbench(PlatformUI.java:149) at org.eclipse.ui.internal.ide.application.IDEApplication.start(IDEApplication.java:113) at org.eclipse.equinox.internal.app.EclipseAppHandle.run(EclipseAppHandle.java:194) at org.eclipse.core.runtime.internal.adaptor.EclipseAppLauncher.runApplication(EclipseAppLauncher.java:110) at org.eclipse.core.runtime.internal.adaptor.EclipseAppLauncher.start(EclipseAppLauncher.java:79) at org.eclipse.core.runtime.adaptor.EclipseStarter.run(EclipseStarter.java:368) at org.eclipse.core.runtime.adaptor.EclipseStarter.run(EclipseStarter.java:179) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source) at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source) at java.lang.reflect.Method.invoke(Unknown Source) at org.eclipse.equinox.launcher.Main.invokeFramework(Main.java:559) at org.eclipse.equinox.launcher.Main.basicRun(Main.java:514) at org.eclipse.equinox.launcher.Main.run(Main.java:1311) Additionally, if I go to the settings in Window-Preferences and try to view the JS Test Driver Preferences, I get the following dialog: Problem Occurred Unable to create the selected preference page. com.google.jstestdriver.eclipse.ui.WorkbenchPreferencePage Thank you, Andrew J. Leer

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  • DomainService method not compiling; claims "Return types must be an entity ..."

    - by Duncan Bayne
    I have a WCF RIA Domain Service that contains a method I'd like to invoke when the user clicks a button: [Invoke] public MyEntity PerformAnalysis(int someId) { return new MyEntity(); } However, when I try to compile I'm given the following error: Operation named 'PerformAnalysis' does not conform to the required signature. Return types must be an entity, collection of entities, or one of the predefined serializable types. The thing is, as far as I can tell, MyEntity is an entity: [Serializable] public class MyEntity: EntityObject, IMyEntity { [Key] [DataMember] [Editable(false)] public int DummyKey { get; set; } [DataMember] [Editable(false)] public IEnumerable<SomeOtherEntity> Children { get; set; } } I figure I'm missing something simple here. Could someone please tell me how I can create an invokable method that returns a single MyEntity object?

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  • How do I work around the GCC "error: cast from ‘SourceLocation*’ to ‘int’ loses precision" error when compiling cmockery.c?

    - by Daryl Spitzer
    I need to add unit tests using Cmockery to an existing build environment that uses as hand-crafted Makefile. So I need to figure out how to build cmockery.c (without automake). When I run: g++ -DHAVE_CONFIG_H -DPIC -I ../cmockery-0.1.2 -I /usr/include/malloc -c ../cmockery-0.1.2/cmockery.c -o obj/cmockery.o I get a long list of errors like this: ../cmockery-0.1.2/cmockery.c: In function ‘void initialize_source_location(SourceLocation*)’: ../cmockery-0.1.2/cmockery.c:248: error: cast from ‘SourceLocation*’ to ‘int’ loses precision Here are lines 247:248 of cmockery.c: static void initialize_source_location(SourceLocation * const location) { assert_true(location); assert_true is defined on line 154 of cmockery.h: #define assert_true(c) _assert_true((int)(c), #c, __FILE__, __LINE__) So the problem (as the error states) is GCC doesn't like the cast from ‘SourceLocation*’ to ‘int’. I can build Cmockery using ./configure and make (on Linux, and on Mac OS X if I export CFLAGS=-I/usr/include/malloc first), without any errors. I've tried looking at the command-line that compiles cmockery.c when I run make (after ./configure): gcc -DHAVE_CONFIG_H -I. -I. -I./src -I./src -Isrc/google -I/usr/include/malloc -MT libcmockery_la-cmockery.lo -MD -MP -MF .deps/libcmockery_la-cmockery.Tpo -c src/cmockery.c -fno-common -DPIC -o .libs/libcmockery_la-cmockery.o ...but I don't see any options that might work around this error. In "error: cast from 'void*' to 'int' loses precision", I see I could change (int) in cmockery.h to (intptr_t). And I've confirmed that works. But since I can build Cmockery with ./configure and make, there must be a way to get it to build without modifying the source.

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  • Compiling cpp code in netbeans produce errors, how to solve it ?

    - by Rupertt Wind
    i use the netbeans with MinGW and MYSY make /debugger but when i compile a basic cpp code in it and run it it produces two erorrs this is the code runned and the output![alt text][1] box #include <iostream> void main() { cout << "Hello World!" << endl; cout << "Welcome to C++ Programming" << endl; } output is /usr/bin/make -f nbproject/Makefile-Debug.mk SUBPROJECTS= .build-conf make[1]: Entering directory `/d/Users/Home/Documents/NetBeansProjects/newApp' /usr/bin/make -f nbproject/Makefile-Debug.mk dist/Debug/MinGW-Windows/newapp.exe make[2]: Entering directory `/d/Users/Home/Documents/NetBeansProjects/newApp' mkdir -p dist/Debug/MinGW-Windows g++.exe -o dist/Debug/MinGW-Windows/newapp build/Debug/MinGW-Windows/newmain.o build/Debug/MinGW-Windows/newfile.o build/Debug/MinGW-Windows/main.o build/Debug/MinGW-Windows/newfile.o: In function `main': D:/Users/Home/Documents/NetBeansProjects/newApp/newfile.cpp:5: multiple definition of `main' build/Debug/MinGW-Windows/newmain.o:D:/Users/Home/Documents/NetBeansProjects/newApp/newmain.c:15: first defined here build/Debug/MinGW-Windows/main.o: In function `main': D:/Users/Home/Documents/NetBeansProjects/newApp/main.cpp:13: multiple definition of `main' build/Debug/MinGW-Windows/newmain.o:D:/Users/Home/Documents/NetBeansProjects/newApp/newmain.c:15: first defined here collect2: ld returned 1 exit status make[2]: *** [dist/Debug/MinGW-Windows/newapp.exe] Error 1 make[2]: Leaving directory `/d/Users/Home/Documents/NetBeansProjects/newApp' make[1]: *** [.build-conf] Error 2 make[1]: Leaving directory `/d/Users/Home/Documents/NetBeansProjects/newApp' make: *** [.build-impl] Error 2 BUILD FAILED (exit value 2, total time: 1s) how can i solve this ?

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  • Compiling the icu sqlite extension statically linked to icu.

    - by Georg
    I want to compile the icu sqlite extension statically linked to icu. This is what I've tried, maybe the mistake is obvious to you. cd icu/source ./runConfigureIcu Linux --enable-static --with-packaging-format=archive ... make cd ../../icu-sqlite gcc -o libSqliteIcu.so -shared icu.c -I../icu/source/common -I../icu/source/i18n -L ../icu/source/lib -lsicuuc -lsicui18n -lsicudata ... sqlite3 .load "libSqliteIcu.so" Undefined symbol utf8_countTrailBytes Files icu sqlite extension Download icu.c from sqlite.org ICU 4.2.1 Download ICU4C from icu-project.org My Requirements Runs on Linux & Windows Only one file that I have to distribute: libSqliteIcu.so. Any idea what else I can try? Documentation Sqlite ICU extension's readme ICU's readme

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  • Unit Testing using InternalsVisibleToAttribute requires compiling with /out:filename.ext?

    - by Will Marcouiller
    In my most recent question: Unit Testing Best Practice? / C# InternalsVisibleTo() attribute for VBNET 2.0 while testing?, I was asking about InternalsVisibleToAttribute. I have read the documentation on how to use it, and everything is fine and understood. However, I can't instantiate my class Groupe from my Testing project. I want to be able to instantiate my internal class in my wrapper assembly, from my testing assembly. Any help is appreciated! EDIT #1 Here's the compile-time error I get when I do try to instantiate my type: Erreur 2 'Carra.Exemples.Blocs.ActiveDirectory.Groupe' n'est pas accessible dans ce contexte, car il est 'Private'. C:\Open\Projects\Exemples\Src\Carra.Exemples.Blocs.ActiveDirectory\Carra.Exemples.Blocs.ActiveDirectory.Tests\GroupeTests.vb 9 18 Carra.Exemples.Blocs.ActiveDirectory.Tests (This says that my type is not accessible in this context, because it is private.) But it's Friend (internal)! EDIT #2 Here's a piece of code as suggested for the Groupe class implementing the Public interface IGroupe: #Region "Importations" Imports System.DirectoryServices Imports System.Runtime.CompilerServices #End Region <Assembly: InternalsVisibleTo("Carra.Exemples.Blocs.ActiveDirectory.Tests")> Friend Class Groupe Implements IGroupe #Region "Membres privés" Private _classe As String = "group" Private _domaine As String Private _membres As CustomSet(Of IUtilisateur) Private _groupeNatif As DirectoryEntry #End Region #Region "Constructeurs" Friend Sub New() _membres = New CustomSet(Of IUtilisateur)() _groupeNatif = New DirectoryEntry() End Sub Friend Sub New(ByVal domaine As String) If (String.IsNullOrEmpty(domaine)) Then Throw New ArgumentNullException() _domaine = domaine _membres = New CustomSet(Of IUtilisateur)() _groupeNatif = New DirectoryEntry(domaine) End Sub Friend Sub New(ByVal groupeNatif As DirectoryEntry) _groupeNatif = groupeNatif _domaine = _groupeNatif.Path _membres = New CustomSet(Of IUtilisateur)() End Sub #End Region And the code trying to use it: #Region "Importations" Imports NUnit.Framework Imports Carra.Exemples.Blocs.ActiveDirectory.Tests #End Region <TestFixture()> _ Public Class GroupeTests <Test()> _ Public Sub CreerDefaut() Dim g As Groupe = New Groupe() Assert.IsNotNull(g) Assert.IsInstanceOf(Groupe, g) End Sub End Class EDIT #3 Damn! I have just noticed that I wasn't importing the assembly in my importation region. Nope, didn't solve anything =( Thanks!

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  • Multiple vulnerabilities in Oracle Java Web Console

    - by RitwikGhoshal
    CVE DescriptionCVSSv2 Base ScoreComponentProduct and Resolution CVE-2007-5333 Information Exposure vulnerability 5.0 Apache Tomcat Solaris 10 SPARC: 147673-04 X86: 147674-04 CVE-2007-5342 Permissions, Privileges, and Access Controls vulnerability 6.4 CVE-2007-6286 Request handling vulnerability 4.3 CVE-2008-0002 Information disclosure vulnerability 5.8 CVE-2008-1232 Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting') vulnerability 4.3 CVE-2008-1947 Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting') vulnerability 4.3 CVE-2008-2370 Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal') vulnerability 5.0 CVE-2008-2938 Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal') vulnerability 4.3 CVE-2008-5515 Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal') vulnerability 5.0 CVE-2009-0033 Improper Input Validation vulnerability 5.0 CVE-2009-0580 Information Exposure vulnerability 4.3 CVE-2009-0781 Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting') vulnerability 4.3 CVE-2009-0783 Information Exposure vulnerability 4.6 CVE-2009-2693 Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal') vulnerability 5.8 CVE-2009-2901 Permissions, Privileges, and Access Controls vulnerability 4.3 CVE-2009-2902 Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal') vulnerability 4.3 CVE-2009-3548 Credentials Management vulnerability 7.5 CVE-2010-1157 Information Exposure vulnerability 2.6 CVE-2010-2227 Improper Restriction of Operations within the Bounds of a Memory Buffer vulnerability 6.4 CVE-2010-3718 Directory traversal vulnerability 1.2 CVE-2010-4172 Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting') vulnerability 4.3 CVE-2010-4312 Configuration vulnerability 6.4 CVE-2011-0013 Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting') vulnerability 4.3 CVE-2011-0534 Resource Management Errors vulnerability 5.0 CVE-2011-1184 Permissions, Privileges, and Access Controls vulnerability 5.0 CVE-2011-2204 Information Exposure vulnerability 1.9 CVE-2011-2526 Improper Input Validation vulnerability 4.4 CVE-2011-3190 Permissions, Privileges, and Access Controls vulnerability 7.5 CVE-2011-4858 Resource Management Errors vulnerability 5.0 CVE-2011-5062 Permissions, Privileges, and Access Controls vulnerability 5.0 CVE-2011-5063 Improper Authentication vulnerability 4.3 CVE-2011-5064 Cryptographic Issues vulnerability 4.3 CVE-2012-0022 Numeric Errors vulnerability 5.0 This notification describes vulnerabilities fixed in third-party components that are included in Oracle's product distributions.Information about vulnerabilities affecting Oracle products can be found on Oracle Critical Patch Updates and Security Alerts page.

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  • i want to have some cross browser consistency on my fieldsets, do you know how can i do it?

    - by Omar
    i have this problem with fieldsets... have a look at http://i.imgur.com/IRrXB.png is it possible to achieve what i want with css??? believe me, i tried! as you can see on the img, i just want the look of the legend to be consistent across browsers, i want it to use the width of the fieldset no more (like chrome and ie) no less (like firefox), dont worry about the rounded corners and other issues, thats taken care of. heres the the core i'm using. CSS <style type="text/css"> fieldset {margin: 0 0 10px 0;padding: 0; border:1px solid silver; background-color: #f9f9f9; -moz-border-radius:5px; -webkit-border-radius:5px; border-radius:5px} fieldset p{clear:both;margin:.3em 0;overflow:hidden;} fieldset label{float:left;width:140px;display:block;text-align:right;padding-right:8px;margin-right: 2px;color: #4a4a4a;} fieldset input, fieldset textarea {margin:0;border:1px solid #ddd;padding:3px 5px 3px 5px;} fieldset legend { background: #C6D1E8; position:relative; left: -1px; margin: 0; width: 100%; padding: 0px 5px; font-size: 1.11em; font-weight: bold; text-align:left; border: 1px solid silver; -webkit-border-top-left-radius: 5px; -webkit-border-top-right-radius: 5px; -moz-border-radius-topleft: 5px; -moz-border-radius-topright: 3px; border-top-left-radius: 5px; border-top-right-radius: 5px; } #md {width: 400px;} </style> HTML <div id="md"> <fieldset> <legend>some title</legend> <p> <label>Login</label> <input type="text" /> </p> <p> <label>Password</label> <input type="text" /> </p> <p><label>&nbsp;</label> <input type="submit"> </p> </fieldset> </div>

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  • Can Windows handle inheritance cross the 32-bit/64-bit boundary?

    - by TheBeardyMan
    Is it possible for a child process to inherit a handle from its parent process if one process is 32-bit and the other is 64-bit? HANDLE is a 64 bit type on Win64 and a 32 bit type on Win32, which suggests that even it were supposed to be possible in all cases, there would be some cases where it would fail: a 64-bit parent process, a 32-bit child process, and a handle that can't be represented in 32 bits. Or is naming the object the only way for a 32-bit process and a 64-bit process to get a handle for the same object?

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