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  • Making body(box2d) a spite(andengine) in android

    - by Kadir
    I can't make body(box2d) a spite(andengine) and at the same time i wanna apply MoveModifier to sprite which is body.if i can make just body,it works namely the srites can collide.if i can apply just MoveModifier to sprites,the sprites can move where i want.but i wanna make body(they can collide) and apply MoveModifier(they can move where i want) at the same time.How can i do? this my code just run MoveModifier not as body at the same time. circles[i] = new Sprite(startX, startY, textRegCircle[i]); body[i] = PhysicsFactory.createCircleBody(physicsWorld, circles[i], BodyType.DynamicBody, FIXTURE_DEF); physicsWorld.registerPhysicsConnector(new PhysicsConnector(circles[i], body[i], true, true)); circles[i].registerEntityModifier( (IEntityModifier) new SequenceEntityModifier ( new MoveModifier(10.0f, circles[i].getX(), circles[i].getX(), circles[i].getY(),CAMERA_HEIGHT+64.0f))); scene.getLastChild().attachChild(circles[i]); scene.registerUpdateHandler(physicsWorld);

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  • Applying prerecorded animations to models with the same skeleton

    - by Jeremias Pflaumbaum
    well my question sounds a bit like, how do I apply mo-cap animations to my model, but thats not really it I guess. Animations and model share the same skeleton, but the models vary in size and proportion, but I still want to be able to apply any animation to any model. I think this should be possible since the models got the same skeleton bone structure and the bones are always in the same area only their position varies from model to model. In particular Im trying to apply this to 2D characters that got 2arm, 2legs, a head and a body, but if you got anything related to that topic even if its 3D related or keywords, articles, books whatever Im gratefull for everything cause Im a bit stuck at the moment. cheers Jery

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  • Understanding Unity3d physics: where is the force applied?

    - by Heisenbug
    I'm trying to understand which is the right way to apply forces to a RigidBody. I noticed that there are AddForce and AddRelativeForce methods, one applied in world space coordinate system meanwhile the other in the local space. The thing that I do not understand is the following: usually in physics library (es. Bullet) we can specify the force vector and also the force application point. How can I do this in Unity? Is it possible to apply a force vector in a specific point relative to the given RigidBody coordinate system? Where does AddForce apply the force?

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  • cgaffinetransformrotate jagged edges

    - by Raj
    I am trying to apply cgaffinetransformrotate transform to uiimageview. However the image edges seems to be jaded. Is there any workaround this problem. If I apply rotate angle to 90% than I don't see these jaded edges. I need to apply smaller angles, this is where I see the problem Thanks,

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  • explanation about prototype.js function binding code

    - by resopollution
    From: http://ejohn.org/apps/learn/#2 Function.prototype.bind = function(){ var fn = this, args = Array.prototype.slice.call(arguments), object = args.shift(); return function(){ return fn.apply(object, args.concat(Array.prototype.slice.call(arguments))); }; }; Can anyone tell me why the second return is necessary (before fn.apply)? Also, can anyone explain why args.concat is necessary? Why wouldn't it be re-written as: fn.apply(object, args) instead of return fn.apply(object, args.concat(Array.prototype.slice.call(arguments)));

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  • How to Generate XML from Database

    - by Nisarg Mehta
    Hi , I am fetching data from two tables CARRIER_IFTA ,IFTA_NAME. My Select Query is like below.. SELECT t1.IFTA_LICENSE_NUMBER,t1.IFTA_BASE_STATE,t2.NAME_TYPE,t2.NAME from CARRIER_IFTA t1 inner join IFTA_NAME t2 on t1.IFTA_LICENSE_NUMBER=t2.IFTA_LICENSE_NUMBER My Data is coming in this way... IFTA_LICENSE_NUMBER IFTA_BASE_STATE NAME_TYPE NAME -------------------------------------------------------- 630908333 US LG XYZ 630908333 US MG PQR 730908344 US LG ABC Now using XSLT I want to generate XML like this <T0019> <IFTA_ACCOUNT> <IFTA_LICENSE_NUMBER>630908333</IFTA_LICENSE_NUMBER> <IFTA_BASE_STATE>US</IFTA_BASE_STATE> <IFTA_NAME> <NAME_TYPE>LG<NAME_TYPE> <NAME>XYZ</NAME> </IFTA_NAME> <IFTA_NAME> <NAME_TYPE>MG<NAME_TYPE> <NAME>PQR</NAME> <IFTA_NAME> </IFTA_ACCOUNT> <IFTA_ACCOUNT> <IFTA_LICENSE_NUMBER>730908344</IFTA_LICENSE_NUMBER> <IFTA_BASE_STATE>US</IFTA_BASE_STATE> <IFTA_NAME> <NAME_TYPE>LG<NAME_TYPE> <NAME>ABC</NAME> </IFTA_NAME> </IFTA_ACCOUNT> </T0019> i have used below xslt but it is not giveng me desire result ... <xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="2.0"> <xsl:template match="/ROWSET"> <xsl:element name="T0019"> <xsl:apply-templates select="IFTAACCOUNT"/> </xsl:element> </xsl:template> <xsl:template match="IFTAACCOUNT"> <xsl:element name="IFTAACCOUNT"> <xsl:apply-templates select="IFTA_CARRIER_ID_NUMBER"/> </xsl:element> </xsl:template> <xsl:template match="IFTA_LICENSE_NUMBER"> <xsl:element name="IFTA_LICENSE_NUMBER"> <xsl:apply-templates /> </xsl:element> </xsl:template> <xsl:template match="IFTA_BASE_STATE"> <xsl:element name="IFTA_BASE_STATE"> <xsl:apply-templates /> </xsl:element> </xsl:template> <xsl:template match="IRP_NAME"> <IRP_NAME> <xsl:apply-templates select="NAME"/> <xsl:apply-templates select="NAME_TYPE"/> </IRP_NAME> </xsl:template> <xsl:template match="NAME"> <xsl:element name="NAME"> <xsl:value-of select="." /> </xsl:element> </xsl:template> <xsl:template match="NAME_TYPE"> <xsl:element name="NAME_TYPE"> <xsl:apply-templates /> </xsl:element> </xsl:template> </xsl:stylesheet> but it is not giving desire result ... Please help me ... Thanks in Advance...

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  • simple scala question about httpparser

    - by kula
    hi all. i'm a scala newbee. i have one question. in my code ,i try to import httpparse library like this scalac -classpath /home/kula/code/201005/kookle/lib/htmlparser.jar crawler.scala and i run this code. scala main and it tell me that java.lang.NoClassDefFoundError: org/htmlparser/Parser at FetchActor$$anonfun$act$1$$anonfun$apply$1.apply(crawler.scala:21) at FetchActor$$anonfun$act$1$$anonfun$apply$1.apply(crawler.scala:13) at scala.actors.Reaction.run(Reaction.scala:78) at scala.actors.FJTask$Wrap.run(Unknown Source) at scala.actors.FJTaskRunner.scanWhileIdling(Unknown Source) at scala.actors.FJTaskRunner.run(Unknown Source) i check the file./home/kula/code/201005/kookle/lib/htmlparser.jar and it is no problem.anyone can tell me how cause this bug?

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  • How to approach parallel processing of messages?

    - by Dan
    I am redesigning the messaging system for my app to use intel threading building blocks and am stumped trying to decide between two possible approaches. Basically, I have a sequence of message objects and for each message type, a sequence of handlers. For each message object, I apply each handler registered for that message objects type. The sequential version would be something like this (pseudocode): for each message in message_sequence <- SEQUENTIAL for each handler in (handler_table for message.type) apply handler to message <- SEQUENTIAL The first approach which I am considering processes the message objects in turn (sequentially) and applies the handlers concurrently. Pros: predictable ordering of messages (ie, we are guaranteed a FIFO processing order) (potentially) lower latency of processing each message Cons: more processing resources available than handlers for a single message type (bad parallelization) bad use of processor cache since message objects need to be copied for each handler to use large overhead for small handlers The pseudocode of this approach would be as follows: for each message in message_sequence <- SEQUENTIAL parallel_for each handler in (handler_table for message.type) apply handler to message <- PARALLEL The second approach is to process the messages in parallel and apply the handlers to each message sequentially. Pros: better use of processor cache (keeps the message object local to all handlers which will use it) small handlers don't impose as much overhead (as long as there are other handlers also to be run) more messages are expected than there are handlers, so the potential for parallelism is greater Cons: Unpredictable ordering - if message A is sent before message B, they may both be processed at the same time, or B may finish processing before all of A's handlers are finished (order is non-deterministic) The pseudocode is as follows: parallel_for each message in message_sequence <- PARALLEL for each handler in (handler_table for message.type) apply handler to message <- SEQUENTIAL The second approach has more advantages than the first, but non-deterministic ordering is a big disadvantage.. Which approach would you choose and why? Are there any other approaches I should consider (besides the obvious third approach: parallel messages and parallel handlers, which has the disadvantages of both and no real redeeming factors as far as I can tell)? Thanks!

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  • Applying a pixel shader effect to a portion of an image

    - by Nick
    I have a ScrollViewer that contains a very large video (16 megapixel @ 10fps) and I want to apply a pixel shader effect to it. Given the size of the images I can't apply the effect directly to the image. So I apply the effect to the ScrollContentPresenter in the control style. Which is great, everything runs nice and fast. However, I'm also rendering annotations inside of the ScrollContentPresenter which I do NOT want effects applied to (but they need to move and scale along with the image). Is there to apply the effect just to the clipped and displayed portion of the image or do I need to build a rather more complex control?

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  • scala 2.8 breakout

    - by oxbow_lakes
    In Scala 2.8, there is an object in scala.collection.package.scala: def breakOut[From, T, To](implicit b : CanBuildFrom[Nothing, T, To]) = new CanBuildFrom[From, T, To] { def apply(from: From) = b.apply() ; def apply() = b.apply() } I have been told that this results in: > import scala.collection.breakOut > val map : Map[Int,String] = List("London", "Paris").map(x => (x.length, x))(breakOut) map: Map[Int,String] = Map(6 -> London, 5 -> Paris) What is going on here? Why is breakOut being called as an argument to my List?

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  • Generate XML from Database using XSLT

    - by Nisarg Mehta
    Hi , I am fetching data from two tables CARRIER_IFTA ,IFTA_NAME. My Select Query is like below.. SELECT t1.IFTA_LICENSE_NUMBER,t1.IFTA_BASE_STATE,t2.NAME_TYPE,t2.NAME from CARRIER_IFTA t1 inner join IFTA_NAME t2 on t1.IFTA_LICENSE_NUMBER=t2.IFTA_LICENSE_NUMBER My Data is coming in this way... IFTA_LICENSE_NUMBER IFTA_BASE_STATE NAME_TYPE NAME -------------------------------------------------------- 630908333 US LG XYZ 630908333 US MG PQR 730908344 US LG ABC Now using XSLT I want to generate XML like this <T0019> <IFTA_ACCOUNT> <IFTA_LICENSE_NUMBER>630908333</IFTA_LICENSE_NUMBER> <IFTA_BASE_STATE>US</IFTA_BASE_STATE> <IFTA_NAME> <NAME_TYPE>LG<NAME_TYPE> <NAME>XYZ</NAME> </IFTA_NAME> <IFTA_NAME> <NAME_TYPE>MG<NAME_TYPE> <NAME>PQR</NAME> <IFTA_NAME> </IFTA_ACCOUNT> <IFTA_ACCOUNT> <IFTA_LICENSE_NUMBER>730908344</IFTA_LICENSE_NUMBER> <IFTA_BASE_STATE>US</IFTA_BASE_STATE> <IFTA_NAME> <NAME_TYPE>LG<NAME_TYPE> <NAME>ABC</NAME> </IFTA_NAME> </IFTA_ACCOUNT> </T0019> i have used below xslt but it is not giveng me desire result ... <xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="2.0"> <xsl:template match="/ROWSET"> <xsl:element name="T0019"> <xsl:apply-templates select="IFTAACCOUNT"/> </xsl:element> </xsl:template> <xsl:template match="IFTAACCOUNT"> <xsl:element name="IFTAACCOUNT"> <xsl:apply-templates select="IFTA_CARRIER_ID_NUMBER"/> </xsl:element> </xsl:template> <xsl:template match="IFTA_LICENSE_NUMBER"> <xsl:element name="IFTA_LICENSE_NUMBER"> <xsl:apply-templates /> </xsl:element> </xsl:template> <xsl:template match="IFTA_BASE_STATE"> <xsl:element name="IFTA_BASE_STATE"> <xsl:apply-templates /> </xsl:element> </xsl:template> <xsl:template match="IRP_NAME"> <IRP_NAME> <xsl:apply-templates select="NAME"/> <xsl:apply-templates select="NAME_TYPE"/> </IRP_NAME> </xsl:template> <xsl:template match="NAME"> <xsl:element name="NAME"> <xsl:value-of select="." /> </xsl:element> </xsl:template> <xsl:template match="NAME_TYPE"> <xsl:element name="NAME_TYPE"> <xsl:apply-templates /> </xsl:element> </xsl:template> </xsl:stylesheet> but it is not giving desire result ... Please help me ... Thanks in Advance...

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  • Do you leave Windows Automatic Updates enabled on your production IIS server?

    - by Nobody
    If you were running a 24/7 website on Windows Server 2003 (IIS6). Would you leave the Windows automatic update feature enabled or would you turn it off? When enabled, you always get the latest security patches and bug fixes automatically as soon as they're available, which is the most secure choice. However, the machine will sometimes get automatically rebooted to apply the updates leading to a couple of minutes of downtime in the middle of the night. Also, I've seen rare occasions where the machine does not restart correctly resulting in further downtime. If auto updates are off, when do you apply the patches? I guess you have to use a load balancer with multiple web servers and rotate them out of the production site, apply patches manually, and put them back in. This can be logistically inconvenient when the load balancer is managed by a hosting company. You will also have machines in production that don't always have the latest security patches and you have to routinely spend time deciding which patches to apply and when.

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  • Specific template for the first element.

    - by Kalinin
    I have a template: <xsl:template match="paragraph"> ... </xsl:template> I call it: <xsl:apply-templates select="paragraph"/> For the first element I need to do: <xsl:template match="paragraph[1]"> ... <xsl:apply-templates select="."/><!-- I understand that this does not work --> ... </xsl:template> How to call <xsl:apply-templates select="paragraph"/> (for the first element paragraph) from the template <xsl:template match="paragraph[1]">? So far that I have something like a loop. I solve this problem so (but I do not like it): <xsl:for-each select="paragraph"> <xsl:choose> <xsl:when test="position() = 1"> ... <xsl:apply-templates select="."/> ... </xsl:when> <xsl:otherwise> <xsl:apply-templates select="."/> </xsl:otherwise> </xsl:choose> </xsl:for-each>

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  • Scala : reference is ambiguous (imported twice)

    - by tk
    I want to use a method as a parameter of another method of the same class. I have a class and objects which are companions: class mM(var elem:Matrix){ //apply a function on a dimension rows (1) or cols (2) def app(func:Iterable[Double]=>Double)(dim : Int) : Matrix = { ... } //utility function def logsumexp(): Double = {...} } object mM{ def apply(elem:Matrix):mM={new mM(elem)} def logsumexp(elem:Iterable[Double]): Double ={ this.apply(elem.asInstanceOf[Matrix]).logsumexp() } } Normally I use logsumexp like this mM(matrix).logsumexp but if want to apply it to the rows I can't use mM(matrix).app(mM.logsumexp)(1), I get the error: error: reference to mM is ambiguous; it is imported twice in the same scope by import mM and import mM What is the most elegant solution ? Should I change logsumexp() to another class ? Thanks,=)

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  • Writing lambda functions in Scala

    - by user2433237
    I'm aware that you can write anonymous functions in Scala but I'm having trouble trying to convert a piece of code from Scheme. Could anyone help me convert this to Scala? (define apply-env (lambda (env search-sym) (cases environment env (empty-env () (eopl:error 'apply-env "No binding for ~s" search-sym)) (extend-env (var val saved-env) (if (eqv? search-sym var) val (apply-env saved-env search-sym))) (extend-env-rec (p-name b-var p-body saved-env) (if (eqv? search-sym p-name) (proc-val (procedure b-var p-body env)) (apply-env saved-env search-sym)))))) Thanks in advance

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  • SQL Spatial: Getting “nearest” calculations working properly

    - by Rob Farley
    If you’ve ever done spatial work with SQL Server, I hope you’ve come across the ‘nearest’ problem. You have five thousand stores around the world, and you want to identify the one that’s closest to a particular place. Maybe you want the store closest to the LobsterPot office in Adelaide, at -34.925806, 138.605073. Or our new US office, at 42.524929, -87.858244. Or maybe both! You know how to do this. You don’t want to use an aggregate MIN or MAX, because you want the whole row, telling you which store it is. You want to use TOP, and if you want to find the closest store for multiple locations, you use APPLY. Let’s do this (but I’m going to use addresses in AdventureWorks2012, as I don’t have a list of stores). Oh, and before I do, let’s make sure we have a spatial index in place. I’m going to use the default options. CREATE SPATIAL INDEX spin_Address ON Person.Address(SpatialLocation); And my actual query: WITH MyLocations AS (SELECT * FROM (VALUES ('LobsterPot Adelaide', geography::Point(-34.925806, 138.605073, 4326)),                        ('LobsterPot USA', geography::Point(42.524929, -87.858244, 4326))                ) t (Name, Geo)) SELECT l.Name, a.AddressLine1, a.City, s.Name AS [State], c.Name AS Country FROM MyLocations AS l CROSS APPLY (     SELECT TOP (1) *     FROM Person.Address AS ad     ORDER BY l.Geo.STDistance(ad.SpatialLocation)     ) AS a JOIN Person.StateProvince AS s     ON s.StateProvinceID = a.StateProvinceID JOIN Person.CountryRegion AS c     ON c.CountryRegionCode = s.CountryRegionCode ; Great! This is definitely working. I know both those City locations, even if the AddressLine1s don’t quite ring a bell. I’m sure I’ll be able to find them next time I’m in the area. But of course what I’m concerned about from a querying perspective is what’s happened behind the scenes – the execution plan. This isn’t pretty. It’s not using my index. It’s sucking every row out of the Address table TWICE (which sucks), and then it’s sorting them by the distance to find the smallest one. It’s not pretty, and it takes a while. Mind you, I do like the fact that it saw an indexed view it could use for the State and Country details – that’s pretty neat. But yeah – users of my nifty website aren’t going to like how long that query takes. The frustrating thing is that I know that I can use the index to find locations that are within a particular distance of my locations quite easily, and Microsoft recommends this for solving the ‘nearest’ problem, as described at http://msdn.microsoft.com/en-au/library/ff929109.aspx. Now, in the first example on this page, it says that the query there will use the spatial index. But when I run it on my machine, it does nothing of the sort. I’m not particularly impressed. But what we see here is that parallelism has kicked in. In my scenario, it’s split the data up into 4 threads, but it’s still slow, and not using my index. It’s disappointing. But I can persuade it with hints! If I tell it to FORCESEEK, or use my index, or even turn off the parallelism with MAXDOP 1, then I get the index being used, and it’s a thing of beauty! Part of the plan is here: It’s massive, and it’s ugly, and it uses a TVF… but it’s quick. The way it works is to hook into the GeodeticTessellation function, which is essentially finds where the point is, and works out through the spatial index cells that surround it. This then provides a framework to be able to see into the spatial index for the items we want. You can read more about it at http://msdn.microsoft.com/en-us/library/bb895265.aspx#tessellation – including a bunch of pretty diagrams. One of those times when we have a much more complex-looking plan, but just because of the good that’s going on. This tessellation stuff was introduced in SQL Server 2012. But my query isn’t using it. When I try to use the FORCESEEK hint on the Person.Address table, I get the friendly error: Msg 8622, Level 16, State 1, Line 1 Query processor could not produce a query plan because of the hints defined in this query. Resubmit the query without specifying any hints and without using SET FORCEPLAN. And I’m almost tempted to just give up and move back to the old method of checking increasingly large circles around my location. After all, I can even leverage multiple OUTER APPLY clauses just like I did in my recent Lookup post. WITH MyLocations AS (SELECT * FROM (VALUES ('LobsterPot Adelaide', geography::Point(-34.925806, 138.605073, 4326)),                        ('LobsterPot USA', geography::Point(42.524929, -87.858244, 4326))                ) t (Name, Geo)) SELECT     l.Name,     COALESCE(a1.AddressLine1,a2.AddressLine1,a3.AddressLine1),     COALESCE(a1.City,a2.City,a3.City),     s.Name AS [State],     c.Name AS Country FROM MyLocations AS l OUTER APPLY (     SELECT TOP (1) *     FROM Person.Address AS ad     WHERE l.Geo.STDistance(ad.SpatialLocation) < 1000     ORDER BY l.Geo.STDistance(ad.SpatialLocation)     ) AS a1 OUTER APPLY (     SELECT TOP (1) *     FROM Person.Address AS ad     WHERE l.Geo.STDistance(ad.SpatialLocation) < 5000     AND a1.AddressID IS NULL     ORDER BY l.Geo.STDistance(ad.SpatialLocation)     ) AS a2 OUTER APPLY (     SELECT TOP (1) *     FROM Person.Address AS ad     WHERE l.Geo.STDistance(ad.SpatialLocation) < 20000     AND a2.AddressID IS NULL     ORDER BY l.Geo.STDistance(ad.SpatialLocation)     ) AS a3 JOIN Person.StateProvince AS s     ON s.StateProvinceID = COALESCE(a1.StateProvinceID,a2.StateProvinceID,a3.StateProvinceID) JOIN Person.CountryRegion AS c     ON c.CountryRegionCode = s.CountryRegionCode ; But this isn’t friendly-looking at all, and I’d use the method recommended by Isaac Kunen, who uses a table of numbers for the expanding circles. It feels old-school though, when I’m dealing with SQL 2012 (and later) versions. So why isn’t my query doing what it’s supposed to? Remember the query... WITH MyLocations AS (SELECT * FROM (VALUES ('LobsterPot Adelaide', geography::Point(-34.925806, 138.605073, 4326)),                        ('LobsterPot USA', geography::Point(42.524929, -87.858244, 4326))                ) t (Name, Geo)) SELECT l.Name, a.AddressLine1, a.City, s.Name AS [State], c.Name AS Country FROM MyLocations AS l CROSS APPLY (     SELECT TOP (1) *     FROM Person.Address AS ad     ORDER BY l.Geo.STDistance(ad.SpatialLocation)     ) AS a JOIN Person.StateProvince AS s     ON s.StateProvinceID = a.StateProvinceID JOIN Person.CountryRegion AS c     ON c.CountryRegionCode = s.CountryRegionCode ; Well, I just wasn’t reading http://msdn.microsoft.com/en-us/library/ff929109.aspx properly. The following requirements must be met for a Nearest Neighbor query to use a spatial index: A spatial index must be present on one of the spatial columns and the STDistance() method must use that column in the WHERE and ORDER BY clauses. The TOP clause cannot contain a PERCENT statement. The WHERE clause must contain a STDistance() method. If there are multiple predicates in the WHERE clause then the predicate containing STDistance() method must be connected by an AND conjunction to the other predicates. The STDistance() method cannot be in an optional part of the WHERE clause. The first expression in the ORDER BY clause must use the STDistance() method. Sort order for the first STDistance() expression in the ORDER BY clause must be ASC. All the rows for which STDistance returns NULL must be filtered out. Let’s start from the top. 1. Needs a spatial index on one of the columns that’s in the STDistance call. Yup, got the index. 2. No ‘PERCENT’. Yeah, I don’t have that. 3. The WHERE clause needs to use STDistance(). Ok, but I’m not filtering, so that should be fine. 4. Yeah, I don’t have multiple predicates. 5. The first expression in the ORDER BY is my distance, that’s fine. 6. Sort order is ASC, because otherwise we’d be starting with the ones that are furthest away, and that’s tricky. 7. All the rows for which STDistance returns NULL must be filtered out. But I don’t have any NULL values, so that shouldn’t affect me either. ...but something’s wrong. I do actually need to satisfy #3. And I do need to make sure #7 is being handled properly, because there are some situations (eg, differing SRIDs) where STDistance can return NULL. It says so at http://msdn.microsoft.com/en-us/library/bb933808.aspx – “STDistance() always returns null if the spatial reference IDs (SRIDs) of the geography instances do not match.” So if I simply make sure that I’m filtering out the rows that return NULL… …then it’s blindingly fast, I get the right results, and I’ve got the complex-but-brilliant plan that I wanted. It just wasn’t overly intuitive, despite being documented. @rob_farley

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  • How can I keep the the logic to translate a ViewModel's values to a Where clause to apply to a linq query out of My Controller?

    - by Mr. Manager
    This same problem keeps cropping up. I have a viewModel that doesn't have any persistent backing. It is just a ViewModel to generate a search input form. I want to build a large where clause from the values the user entered. If the Action Accepts as a parameter SearchViewModel How do I do this without passing my viewModel to my service layer? Service shouldn't know about ViewModels right? Oh and if I serialize it, then it would be a big string and the key/values would be strongly typed. SearchViewModel this is just a snippet. [Display(Name="Address")] public string AddressKeywords { get; set; } /// <summary> /// Gets or sets the census. /// </summary> public string Census { get; set; } /// <summary> /// Gets or sets the lot block sub. /// </summary> public string LotBlockSub { get; set; } /// <summary> /// Gets or sets the owner keywords. /// </summary> [Display(Name="Owner")] public string OwnerKeywords { get; set; } In my controller action I was thinking of something like this. but I would think all this logic doesn't belong in my Controller. ActionResult GetSearchResults(SearchViewModel model){ var query = service.GetAllParcels(); if(model.Census != null){ query = query.Where(x=>x.Census == model.Census); } if (model.OwnerKeywords != null){ query = query.Where(x=>x.Owners == model.OwnerKeywords); } return View(query.ToList()); }

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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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  • SQL SERVER – DMV – sys.dm_os_waiting_tasks and sys.dm_exec_requests – Wait Type – Day 4 of 28

    - by pinaldave
    Previously, we covered the DMV sys.dm_os_wait_stats, and also saw how it can be useful to identify the major resource bottleneck. However, at the same time, we discussed that this is only useful when we are looking at an instance-level picture. Quite often we want to know about the processes going in our server at the given instant. Here is the query for the same. This DMV is written taking the following into consideration: we want to analyze the queries that are currently running or which have recently ran and their plan is still in the cache. SELECT dm_ws.wait_duration_ms, dm_ws.wait_type, dm_es.status, dm_t.TEXT, dm_qp.query_plan, dm_ws.session_ID, dm_es.cpu_time, dm_es.memory_usage, dm_es.logical_reads, dm_es.total_elapsed_time, dm_es.program_name, DB_NAME(dm_r.database_id) DatabaseName, -- Optional columns dm_ws.blocking_session_id, dm_r.wait_resource, dm_es.login_name, dm_r.command, dm_r.last_wait_type FROM sys.dm_os_waiting_tasks dm_ws INNER JOIN sys.dm_exec_requests dm_r ON dm_ws.session_id = dm_r.session_id INNER JOIN sys.dm_exec_sessions dm_es ON dm_es.session_id = dm_r.session_id CROSS APPLY sys.dm_exec_sql_text (dm_r.sql_handle) dm_t CROSS APPLY sys.dm_exec_query_plan (dm_r.plan_handle) dm_qp WHERE dm_es.is_user_process = 1 GO You can change CROSS APPLY to OUTER APPLY if you want to see all the details which are omitted because of the plan cache. Let us analyze the result of the above query and see how it can be helpful to identify the query and the kind of wait type it creates. Click to Enlarage The above query will return various columns. There are various columns that provide very important details. e.g. wait_duration_ms – it indicates current wait for the query that executes at that point of time. wait_type – it indicates the current wait type for the query text – indicates the query text query_plan – when clicked on the same, it will display the query plans There are many other important information like CPU_time, memory_usage, and logical_reads, which can be read from the query as well. In future posts on this series, we will see how once identified wait type we can attempt to reduce the same. Read all the post in the Wait Types and Queue series. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: DMV, Pinal Dave, PostADay, SQL, SQL Authority, SQL DMV, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • Add Transitions to Slideshows in PowerPoint 2010

    - by DigitalGeekery
    Sitting through PowerPoint presentation can sometimes get a little boring. You can make your slideshows more interesting by adding transitions between the slides in your presentations. Transitions certainly aren’t new to PowerPoint, but Office 2010 adds a number of exciting new transitions and options. Add Transitions Select the slide to which you want to apply a transition. On the Transitions tab, select the More button to reveal the all transition options in the gallery.   Select the transition you’d like to apply to your slide. The transitions are divided into three types…Subtle, Exciting, and Dynamic Content. You can hover your mouse over each item in the gallery to preview the transition with Live Preview. You can adjust many of the transitions using Effect Options. The options will vary depending on which transition you’ve selected.   You can add additional customizations in the Timing Group. You can add sound by selecting one of the options in the Sound dropdown list…   You can change the duration of the transition… Or choose to advance the slide On Mouse Click (default) or automatically after a certain period of time.   If you’d like to apply one transition to every slide in your presentation, select the Apply To All button. You can preview your transition by clicking the Preview button on the Transitions tab. A few clicks is all it takes to add a little energy and excitement to an otherwise dry presentation.   Are you looking for more ways to spice up your PowerPoint 2010 slideshows? You could try adding animation to text and images, or adding video from the web. Similar Articles Productive Geek Tips Insert Tables Into PowerPoint 2007Bring Office 2003 Menus Back to 2010 with UBitMenuEmbed True Type Fonts in Word and PowerPoint 2007 DocumentsHow to Add Video from the Web in PowerPoint 2010Add Artistic Effects to Your Pictures in Office 2010 TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips HippoRemote Pro 2.2 Xobni Plus for Outlook All My Movies 5.9 CloudBerry Online Backup 1.5 for Windows Home Server Windows Media Player Plus! – Cool WMP Enhancer Get Your Team’s World Cup Schedule In Google Calendar Backup Drivers With Driver Magician TubeSort: YouTube Playlist Organizer XPS file format & XPS Viewer Explained Microsoft Office Web Apps Guide

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  • C# Extension Methods - To Extend or Not To Extend...

    - by James Michael Hare
    I've been thinking a lot about extension methods lately, and I must admit I both love them and hate them. They are a lot like sugar, they taste so nice and sweet, but they'll rot your teeth if you eat them too much.   I can't deny that they aren't useful and very handy. One of the major components of the Shared Component library where I work is a set of useful extension methods. But, I also can't deny that they tend to be overused and abused to willy-nilly extend every living type.   So what constitutes a good extension method? Obviously, you can write an extension method for nearly anything whether it is a good idea or not. Many times, in fact, an idea seems like a good extension method but in retrospect really doesn't fit.   So what's the litmus test? To me, an extension method should be like in the movies when a person runs into their twin, separated at birth. You just know you're related. Obviously, that's hard to quantify, so let's try to put a few rules-of-thumb around them.   A good extension method should:     Apply to any possible instance of the type it extends.     Simplify logic and improve readability/maintainability.     Apply to the most specific type or interface applicable.     Be isolated in a namespace so that it does not pollute IntelliSense.     So let's look at a few examples in relation to these rules.   The first rule, to me, is the most important of all. Once again, it bears repeating, a good extension method should apply to all possible instances of the type it extends. It should feel like the long lost relative that should have been included in the original class but somehow was missing from the family tree.    Take this nifty little int extension, I saw this once in a blog and at first I really thought it was pretty cool, but then I started noticing a code smell I couldn't quite put my finger on. So let's look:       public static class IntExtensinos     {         public static int Seconds(int num)         {             return num * 1000;         }           public static int Minutes(int num)         {             return num * 60000;         }     }     This is so you could do things like:       ...     Thread.Sleep(5.Seconds());     ...     proxy.Timeout = 1.Minutes();     ...     Awww, you say, that's cute! Well, that's the problem, it's kitschy and it doesn't always apply (and incidentally you could achieve the same thing with TimeStamp.FromSeconds(5)). It's syntactical candy that looks cool, but tends to rot and pollute the code. It would allow things like:       total += numberOfTodaysOrders.Seconds();     which makes no sense and should never be allowed. The problem is you're applying an extension method to a logical domain, not a type domain. That is, the extension method Seconds() doesn't really apply to ALL ints, it applies to ints that are representative of time that you want to convert to milliseconds.    Do you see what I mean? The two problems, in a nutshell, are that a) Seconds() called off a non-time value makes no sense and b) calling Seconds() off something to pass to something that does not take milliseconds will be off by a factor of 1000 or worse.   Thus, in my mind, you should only ever have an extension method that applies to the whole domain of that type.   For example, this is one of my personal favorites:       public static bool IsBetween<T>(this T value, T low, T high)         where T : IComparable<T>     {         return value.CompareTo(low) >= 0 && value.CompareTo(high) <= 0;     }   This allows you to check if any IComparable<T> is within an upper and lower bound. Think of how many times you type something like:       if (response.Employee.Address.YearsAt >= 2         && response.Employee.Address.YearsAt <= 10)     {     ...     }     Now, you can instead type:       if(response.Employee.Address.YearsAt.IsBetween(2, 10))     {     ...     }     Note that this applies to all IComparable<T> -- that's ints, chars, strings, DateTime, etc -- and does not depend on any logical domain. In addition, it satisfies the second point and actually makes the code more readable and maintainable.   Let's look at the third point. In it we said that an extension method should fit the most specific interface or type possible. Now, I'm not saying if you have something that applies to enumerables, you create an extension for List, Array, Dictionary, etc (though you may have reasons for doing so), but that you should beware of making things TOO general.   For example, let's say we had an extension method like this:       public static T ConvertTo<T>(this object value)     {         return (T)Convert.ChangeType(value, typeof(T));     }         This lets you do more fluent conversions like:       double d = "5.0".ConvertTo<double>();     However, if you dig into Reflector (LOVE that tool) you will see that if the type you are calling on does not implement IConvertible, what you convert to MUST be the exact type or it will throw an InvalidCastException. Now this may or may not be what you want in this situation, and I leave that up to you. Things like this would fail:       object value = new Employee();     ...     // class cast exception because typeof(IEmployee) != typeof(Employee)     IEmployee emp = value.ConvertTo<IEmployee>();       Yes, that's a downfall of working with Convertible in general, but if you wanted your fluent interface to be more type-safe so that ConvertTo were only callable on IConvertibles (and let casting be a manual task), you could easily make it:         public static T ConvertTo<T>(this IConvertible value)     {         return (T)Convert.ChangeType(value, typeof(T));     }         This is what I mean by choosing the best type to extend. Consider that if we used the previous (object) version, every time we typed a dot ('.') on an instance we'd pull up ConvertTo() whether it was applicable or not. By filtering our extension method down to only valid types (those that implement IConvertible) we greatly reduce our IntelliSense pollution and apply a good level of compile-time correctness.   Now my fourth rule is just my general rule-of-thumb. Obviously, you can make extension methods as in-your-face as you want. I included all mine in my work libraries in its own sub-namespace, something akin to:       namespace Shared.Core.Extensions { ... }     This is in a library called Shared.Core, so just referencing the Core library doesn't pollute your IntelliSense, you have to actually do a using on Shared.Core.Extensions to bring the methods in. This is very similar to the way Microsoft puts its extension methods in System.Linq. This way, if you want 'em, you use the appropriate namespace. If you don't want 'em, they won't pollute your namespace.   To really make this work, however, that namespace should only include extension methods and subordinate types those extensions themselves may use. If you plant other useful classes in those namespaces, once a user includes it, they get all the extensions too.   Also, just as a personal preference, extension methods that aren't simply syntactical shortcuts, I like to put in a static utility class and then have extension methods for syntactical candy. For instance, I think it imaginable that any object could be converted to XML:       namespace Shared.Core     {         // A collection of XML Utility classes         public static class XmlUtility         {             ...             // Serialize an object into an xml string             public static string ToXml(object input)             {                 var xs = new XmlSerializer(input.GetType());                   // use new UTF8Encoding here, not Encoding.UTF8. The later includes                 // the BOM which screws up subsequent reads, the former does not.                 using (var memoryStream = new MemoryStream())                 using (var xmlTextWriter = new XmlTextWriter(memoryStream, new UTF8Encoding()))                 {                     xs.Serialize(xmlTextWriter, input);                     return Encoding.UTF8.GetString(memoryStream.ToArray());                 }             }             ...         }     }   I also wanted to be able to call this from an object like:       value.ToXml();     But here's the problem, if i made this an extension method from the start with that one little keyword "this", it would pop into IntelliSense for all objects which could be very polluting. Instead, I put the logic into a utility class so that users have the choice of whether or not they want to use it as just a class and not pollute IntelliSense, then in my extensions namespace, I add the syntactical candy:       namespace Shared.Core.Extensions     {         public static class XmlExtensions         {             public static string ToXml(this object value)             {                 return XmlUtility.ToXml(value);             }         }     }   So now it's the best of both worlds. On one hand, they can use the utility class if they don't want to pollute IntelliSense, and on the other hand they can include the Extensions namespace and use as an extension if they want. The neat thing is it also adheres to the Single Responsibility Principle. The XmlUtility is responsible for converting objects to XML, and the XmlExtensions is responsible for extending object's interface for ToXml().

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  • Evolution sending Email Settings

    - by Jack
    Can receive Comcast mail with evolution but cannot send. The SMTP settings record more digits when clicking the Server Configuration apply key. Sending ([email protected]:tenz222) port587 After clicking apply, receive a "Error while sending message" host lookup failed etc When rechecking,the sever settings are, ([email protected]%253atenz222) Attempted this process many times with same result. Now need to be proactive and ask for help. Will phone for help anywhere, anytime in USA. Thanks

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  • List of events triggered on pages matching regex

    - by Cubius
    Is there a way to get the grouped list of events (such as in Top events) which were triggered on pages matching a regular expression? I may add the Page secondary dimension in Top events and apply the regex filter but this way I won't get a grouped list. I may apply the filter to Events - Pages report but this way the events will be grouped only inside pages whilst I need global grouping. Any suggestions?

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  • Landscape-like tool to distribute security upgrades to OS?

    - by Ichikata
    i'm looking for an alternative to Landscape, Spacewalk (for RHEL), or CTL to perform a specific job. I need to control and apply OS upgrades on ubuntu systems, for 100+ servers, and so far i wasn't that lucky. I've tried Approx tool (similar to apt-proxy), but it just caches the content, and what i really need to do is set update milestones, apply the upgrades to QA servers, validate, then Stage environment, and so on to Production. I hope I was clear enough, any answer will be much appreciated.

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