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  • What to do if one library is not multi-threaded ?

    - by LB
    Hi, I would like to multi-thread an application, however one library i'm using is not multi-thread capable (i don't know what's the right word ? synchronized ?). What are my options ? As far as i know there's nothing in between threads and processes (Runtime.exec) in java (no abstraction in the jvm to have something like an isolated "java process"). How would you deal with that ?

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  • So…is it a Seek or a Scan?

    - by Paul White
    You’re probably most familiar with the terms ‘Seek’ and ‘Scan’ from the graphical plans produced by SQL Server Management Studio (SSMS).  The image to the left shows the most common ones, with the three types of scan at the top, followed by four types of seek.  You might look to the SSMS tool-tip descriptions to explain the differences between them: Not hugely helpful are they?  Both mention scans and ranges (nothing about seeks) and the Index Seek description implies that it will not scan the index entirely (which isn’t necessarily true). Recall also yesterday’s post where we saw two Clustered Index Seek operations doing very different things.  The first Seek performed 63 single-row seeking operations; and the second performed a ‘Range Scan’ (more on those later in this post).  I hope you agree that those were two very different operations, and perhaps you are wondering why there aren’t different graphical plan icons for Range Scans and Seeks?  I have often wondered about that, and the first person to mention it after yesterday’s post was Erin Stellato (twitter | blog): Before we go on to make sense of all this, let’s look at another example of how SQL Server confusingly mixes the terms ‘Scan’ and ‘Seek’ in different contexts.  The diagram below shows a very simple heap table with two columns, one of which is the non-clustered Primary Key, and the other has a non-unique non-clustered index defined on it.  The right hand side of the diagram shows a simple query, it’s associated query plan, and a couple of extracts from the SSMS tool-tip and Properties windows. Notice the ‘scan direction’ entry in the Properties window snippet.  Is this a seek or a scan?  The different references to Scans and Seeks are even more pronounced in the XML plan output that the graphical plan is based on.  This fragment is what lies behind the single Index Seek icon shown above: You’ll find the same confusing references to Seeks and Scans throughout the product and its documentation. Making Sense of Seeks Let’s forget all about scans for a moment, and think purely about seeks.  Loosely speaking, a seek is the process of navigating an index B-tree to find a particular index record, most often at the leaf level.  A seek starts at the root and navigates down through the levels of the index to find the point of interest: Singleton Lookups The simplest sort of seek predicate performs this traversal to find (at most) a single record.  This is the case when we search for a single value using a unique index and an equality predicate.  It should be readily apparent that this type of search will either find one record, or none at all.  This operation is known as a singleton lookup.  Given the example table from before, the following query is an example of a singleton lookup seek: Sadly, there’s nothing in the graphical plan or XML output to show that this is a singleton lookup – you have to infer it from the fact that this is a single-value equality seek on a unique index.  The other common examples of a singleton lookup are bookmark lookups – both the RID and Key Lookup forms are singleton lookups (an RID lookup finds a single record in a heap from the unique row locator, and a Key Lookup does much the same thing on a clustered table).  If you happen to run your query with STATISTICS IO ON, you will notice that ‘Scan Count’ is always zero for a singleton lookup. Range Scans The other type of seek predicate is a ‘seek plus range scan’, which I will refer to simply as a range scan.  The seek operation makes an initial descent into the index structure to find the first leaf row that qualifies, and then performs a range scan (either backwards or forwards in the index) until it reaches the end of the scan range. The ability of a range scan to proceed in either direction comes about because index pages at the same level are connected by a doubly-linked list – each page has a pointer to the previous page (in logical key order) as well as a pointer to the following page.  The doubly-linked list is represented by the green and red dotted arrows in the index diagram presented earlier.  One subtle (but important) point is that the notion of a ‘forward’ or ‘backward’ scan applies to the logical key order defined when the index was built.  In the present case, the non-clustered primary key index was created as follows: CREATE TABLE dbo.Example ( key_col INTEGER NOT NULL, data INTEGER NOT NULL, CONSTRAINT [PK dbo.Example key_col] PRIMARY KEY NONCLUSTERED (key_col ASC) ) ; Notice that the primary key index specifies an ascending sort order for the single key column.  This means that a forward scan of the index will retrieve keys in ascending order, while a backward scan would retrieve keys in descending key order.  If the index had been created instead on key_col DESC, a forward scan would retrieve keys in descending order, and a backward scan would return keys in ascending order. A range scan seek predicate may have a Start condition, an End condition, or both.  Where one is missing, the scan starts (or ends) at one extreme end of the index, depending on the scan direction.  Some examples might help clarify that: the following diagram shows four queries, each of which performs a single seek against a column holding every integer from 1 to 100 inclusive.  The results from each query are shown in the blue columns, and relevant attributes from the Properties window appear on the right: Query 1 specifies that all key_col values less than 5 should be returned in ascending order.  The query plan achieves this by seeking to the start of the index leaf (there is no explicit starting value) and scanning forward until the End condition (key_col < 5) is no longer satisfied (SQL Server knows it can stop looking as soon as it finds a key_col value that isn’t less than 5 because all later index entries are guaranteed to sort higher). Query 2 asks for key_col values greater than 95, in descending order.  SQL Server returns these results by seeking to the end of the index, and scanning backwards (in descending key order) until it comes across a row that isn’t greater than 95.  Sharp-eyed readers may notice that the end-of-scan condition is shown as a Start range value.  This is a bug in the XML show plan which bubbles up to the Properties window – when a backward scan is performed, the roles of the Start and End values are reversed, but the plan does not reflect that.  Oh well. Query 3 looks for key_col values that are greater than or equal to 10, and less than 15, in ascending order.  This time, SQL Server seeks to the first index record that matches the Start condition (key_col >= 10) and then scans forward through the leaf pages until the End condition (key_col < 15) is no longer met. Query 4 performs much the same sort of operation as Query 3, but requests the output in descending order.  Again, we have to mentally reverse the Start and End conditions because of the bug, but otherwise the process is the same as always: SQL Server finds the highest-sorting record that meets the condition ‘key_col < 25’ and scans backward until ‘key_col >= 20’ is no longer true. One final point to note: seek operations always have the Ordered: True attribute.  This means that the operator always produces rows in a sorted order, either ascending or descending depending on how the index was defined, and whether the scan part of the operation is forward or backward.  You cannot rely on this sort order in your queries of course (you must always specify an ORDER BY clause if order is important) but SQL Server can make use of the sort order internally.  In the four queries above, the query optimizer was able to avoid an explicit Sort operator to honour the ORDER BY clause, for example. Multiple Seek Predicates As we saw yesterday, a single index seek plan operator can contain one or more seek predicates.  These seek predicates can either be all singleton seeks or all range scans – SQL Server does not mix them.  For example, you might expect the following query to contain two seek predicates, a singleton seek to find the single record in the unique index where key_col = 10, and a range scan to find the key_col values between 15 and 20: SELECT key_col FROM dbo.Example WHERE key_col = 10 OR key_col BETWEEN 15 AND 20 ORDER BY key_col ASC ; In fact, SQL Server transforms the singleton seek (key_col = 10) to the equivalent range scan, Start:[key_col >= 10], End:[key_col <= 10].  This allows both range scans to be evaluated by a single seek operator.  To be clear, this query results in two range scans: one from 10 to 10, and one from 15 to 20. Final Thoughts That’s it for today – tomorrow we’ll look at monitoring singleton lookups and range scans, and I’ll show you a seek on a heap table. Yes, a seek.  On a heap.  Not an index! If you would like to run the queries in this post for yourself, there’s a script below.  Thanks for reading! IF OBJECT_ID(N'dbo.Example', N'U') IS NOT NULL BEGIN DROP TABLE dbo.Example; END ; -- Test table is a heap -- Non-clustered primary key on 'key_col' CREATE TABLE dbo.Example ( key_col INTEGER NOT NULL, data INTEGER NOT NULL, CONSTRAINT [PK dbo.Example key_col] PRIMARY KEY NONCLUSTERED (key_col) ) ; -- Non-unique non-clustered index on the 'data' column CREATE NONCLUSTERED INDEX [IX dbo.Example data] ON dbo.Example (data) ; -- Add 100 rows INSERT dbo.Example WITH (TABLOCKX) ( key_col, data ) SELECT key_col = V.number, data = V.number FROM master.dbo.spt_values AS V WHERE V.[type] = N'P' AND V.number BETWEEN 1 AND 100 ; -- ================ -- Singleton lookup -- ================ ; -- Single value equality seek in a unique index -- Scan count = 0 when STATISTIS IO is ON -- Check the XML SHOWPLAN SELECT E.key_col FROM dbo.Example AS E WHERE E.key_col = 32 ; -- =========== -- Range Scans -- =========== ; -- Query 1 SELECT E.key_col FROM dbo.Example AS E WHERE E.key_col <= 5 ORDER BY E.key_col ASC ; -- Query 2 SELECT E.key_col FROM dbo.Example AS E WHERE E.key_col > 95 ORDER BY E.key_col DESC ; -- Query 3 SELECT E.key_col FROM dbo.Example AS E WHERE E.key_col >= 10 AND E.key_col < 15 ORDER BY E.key_col ASC ; -- Query 4 SELECT E.key_col FROM dbo.Example AS E WHERE E.key_col >= 20 AND E.key_col < 25 ORDER BY E.key_col DESC ; -- Final query (singleton + range = 2 range scans) SELECT E.key_col FROM dbo.Example AS E WHERE E.key_col = 10 OR E.key_col BETWEEN 15 AND 20 ORDER BY E.key_col ASC ; -- === TIDY UP === DROP TABLE dbo.Example; © 2011 Paul White email: [email protected] twitter: @SQL_Kiwi

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  • Spooling in SQL execution plans

    - by Rob Farley
    Sewing has never been my thing. I barely even know the terminology, and when discussing this with American friends, I even found out that half the words that Americans use are different to the words that English and Australian people use. That said – let’s talk about spools! In particular, the Spool operators that you find in some SQL execution plans. This post is for T-SQL Tuesday, hosted this month by me! I’ve chosen to write about spools because they seem to get a bad rap (even in my song I used the line “There’s spooling from a CTE, they’ve got recursion needlessly”). I figured it was worth covering some of what spools are about, and hopefully explain why they are remarkably necessary, and generally very useful. If you have a look at the Books Online page about Plan Operators, at http://msdn.microsoft.com/en-us/library/ms191158.aspx, and do a search for the word ‘spool’, you’ll notice it says there are 46 matches. 46! Yeah, that’s what I thought too... Spooling is mentioned in several operators: Eager Spool, Lazy Spool, Index Spool (sometimes called a Nonclustered Index Spool), Row Count Spool, Spool, Table Spool, and Window Spool (oh, and Cache, which is a special kind of spool for a single row, but as it isn’t used in SQL 2012, I won’t describe it any further here). Spool, Table Spool, Index Spool, Window Spool and Row Count Spool are all physical operators, whereas Eager Spool and Lazy Spool are logical operators, describing the way that the other spools work. For example, you might see a Table Spool which is either Eager or Lazy. A Window Spool can actually act as both, as I’ll mention in a moment. In sewing, cotton is put onto a spool to make it more useful. You might buy it in bulk on a cone, but if you’re going to be using a sewing machine, then you quite probably want to have it on a spool or bobbin, which allows it to be used in a more effective way. This is the picture that I want you to think about in relation to your data. I’m sure you use spools every time you use your sewing machine. I know I do. I can’t think of a time when I’ve got out my sewing machine to do some sewing and haven’t used a spool. However, I often run SQL queries that don’t use spools. You see, the data that is consumed by my query is typically in a useful state without a spool. It’s like I can just sew with my cotton despite it not being on a spool! Many of my favourite features in T-SQL do like to use spools though. This looks like a very similar query to before, but includes an OVER clause to return a column telling me the number of rows in my data set. I’ll describe what’s going on in a few paragraphs’ time. So what does a Spool operator actually do? The spool operator consumes a set of data, and stores it in a temporary structure, in the tempdb database. This structure is typically either a Table (ie, a heap), or an Index (ie, a b-tree). If no data is actually needed from it, then it could also be a Row Count spool, which only stores the number of rows that the spool operator consumes. A Window Spool is another option if the data being consumed is tightly linked to windows of data, such as when the ROWS/RANGE clause of the OVER clause is being used. You could maybe think about the type of spool being like whether the cotton is going onto a small bobbin to fit in the base of the sewing machine, or whether it’s a larger spool for the top. A Table or Index Spool is either Eager or Lazy in nature. Eager and Lazy are Logical operators, which talk more about the behaviour, rather than the physical operation. If I’m sewing, I can either be all enthusiastic and get all my cotton onto the spool before I start, or I can do it as I need it. “Lazy” might not the be the best word to describe a person – in the SQL world it describes the idea of either fetching all the rows to build up the whole spool when the operator is called (Eager), or populating the spool only as it’s needed (Lazy). Window Spools are both physical and logical. They’re eager on a per-window basis, but lazy between windows. And when is it needed? The way I see it, spools are needed for two reasons. 1 – When data is going to be needed AGAIN. 2 – When data needs to be kept away from the original source. If you’re someone that writes long stored procedures, you are probably quite aware of the second scenario. I see plenty of stored procedures being written this way – where the query writer populates a temporary table, so that they can make updates to it without risking the original table. SQL does this too. Imagine I’m updating my contact list, and some of my changes move data to later in the book. If I’m not careful, I might update the same row a second time (or even enter an infinite loop, updating it over and over). A spool can make sure that I don’t, by using a copy of the data. This problem is known as the Halloween Effect (not because it’s spooky, but because it was discovered in late October one year). As I’m sure you can imagine, the kind of spool you’d need to protect against the Halloween Effect would be eager, because if you’re only handling one row at a time, then you’re not providing the protection... An eager spool will block the flow of data, waiting until it has fetched all the data before serving it up to the operator that called it. In the query below I’m forcing the Query Optimizer to use an index which would be upset if the Name column values got changed, and we see that before any data is fetched, a spool is created to load the data into. This doesn’t stop the index being maintained, but it does mean that the index is protected from the changes that are being done. There are plenty of times, though, when you need data repeatedly. Consider the query I put above. A simple join, but then counting the number of rows that came through. The way that this has executed (be it ideal or not), is to ask that a Table Spool be populated. That’s the Table Spool operator on the top row. That spool can produce the same set of rows repeatedly. This is the behaviour that we see in the bottom half of the plan. In the bottom half of the plan, we see that the a join is being done between the rows that are being sourced from the spool – one being aggregated and one not – producing the columns that we need for the query. Table v Index When considering whether to use a Table Spool or an Index Spool, the question that the Query Optimizer needs to answer is whether there is sufficient benefit to storing the data in a b-tree. The idea of having data in indexes is great, but of course there is a cost to maintaining them. Here we’re creating a temporary structure for data, and there is a cost associated with populating each row into its correct position according to a b-tree, as opposed to simply adding it to the end of the list of rows in a heap. Using a b-tree could even result in page-splits as the b-tree is populated, so there had better be a reason to use that kind of structure. That all depends on how the data is going to be used in other parts of the plan. If you’ve ever thought that you could use a temporary index for a particular query, well this is it – and the Query Optimizer can do that if it thinks it’s worthwhile. It’s worth noting that just because a Spool is populated using an Index Spool, it can still be fetched using a Table Spool. The details about whether or not a Spool used as a source shows as a Table Spool or an Index Spool is more about whether a Seek predicate is used, rather than on the underlying structure. Recursive CTE I’ve already shown you an example of spooling when the OVER clause is used. You might see them being used whenever you have data that is needed multiple times, and CTEs are quite common here. With the definition of a set of data described in a CTE, if the query writer is leveraging this by referring to the CTE multiple times, and there’s no simplification to be leveraged, a spool could theoretically be used to avoid reapplying the CTE’s logic. Annoyingly, this doesn’t happen. Consider this query, which really looks like it’s using the same data twice. I’m creating a set of data (which is completely deterministic, by the way), and then joining it back to itself. There seems to be no reason why it shouldn’t use a spool for the set described by the CTE, but it doesn’t. On the other hand, if we don’t pull as many columns back, we might see a very different plan. You see, CTEs, like all sub-queries, are simplified out to figure out the best way of executing the whole query. My example is somewhat contrived, and although there are plenty of cases when it’s nice to give the Query Optimizer hints about how to execute queries, it usually doesn’t do a bad job, even without spooling (and you can always use a temporary table). When recursion is used, though, spooling should be expected. Consider what we’re asking for in a recursive CTE. We’re telling the system to construct a set of data using an initial query, and then use set as a source for another query, piping this back into the same set and back around. It’s very much a spool. The analogy of cotton is long gone here, as the idea of having a continual loop of cotton feeding onto a spool and off again doesn’t quite fit, but that’s what we have here. Data is being fed onto the spool, and getting pulled out a second time when the spool is used as a source. (This query is running on AdventureWorks, which has a ManagerID column in HumanResources.Employee, not AdventureWorks2012) The Index Spool operator is sucking rows into it – lazily. It has to be lazy, because at the start, there’s only one row to be had. However, as rows get populated onto the spool, the Table Spool operator on the right can return rows when asked, ending up with more rows (potentially) getting back onto the spool, ready for the next round. (The Assert operator is merely checking to see if we’ve reached the MAXRECURSION point – it vanishes if you use OPTION (MAXRECURSION 0), which you can try yourself if you like). Spools are useful. Don’t lose sight of that. Every time you use temporary tables or table variables in a stored procedure, you’re essentially doing the same – don’t get upset at the Query Optimizer for doing so, even if you think the spool looks like an expensive part of the query. I hope you’re enjoying this T-SQL Tuesday. Why not head over to my post that is hosting it this month to read about some other plan operators? At some point I’ll write a summary post – once I have you should find a comment below pointing at it. @rob_farley

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  • How do I query the gvfs metadata for a specific attribute?

    - by Mathieu Comandon
    A nice feature in evince is that when you close the program and later reopen the same pdf, it automatically jumps to the page you were reading. The problem I have is that I often read ebooks on several computers and I have to find were I was on the last computer I was reading the pdf. I think syncing these bookmarks in UbuntuOne would be a killer feature for people like me who read pdfs on different computers. By investigating a bit, I found where evince was storing this data, it's in the gvfs metadata and it can be accessed for a particular document by typing gvfs-ls -a "metadata::evince::page" myEbook.pdf Rather that querying a particular file, I'd like to query the whole metadata file (located in ~/.local/share/gvfs-metadata/home for the home directory) for any file where this particular attribute is set to some value. The biggest issue is that gvfs metadata and stored in binary files and we all know it's not easy to get something out of a binary file. So, do you know any way to query the gvfs metadata for some attribute?

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  • Should I have link rel=next & prev on URLs which have query variables?

    - by user21100
    For example, I have link rel prev & next set up on these pages of products: site.com?page=2 site.com?page=3 (this is my preferred structure by the way and I'm trying to get all the ugly URLs which are littered with query variables deindexed as they are causing duplicate content). So the above URLs are fine but once a filter to narrow product results is selected, like "price", the URL shows like this: site.com?price[1000-1499]=on site.com?page=2&price[1000-1499]=on As of right now, I am having the link rel prev & next dynamically added to the header of these pages but since I am working on getting these query variable URLs pages deindexed, I am wondering if I should get rid of it on these pages? Any thoughts?

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  • How do I access column data in a previous select statement from a sub-query? [closed]

    - by payling
    PROBLEM How do I access column data in a previous select statement from a sub-query? Below is a simple mock up of what I'm attempting to do. Tables used: Quotes, Users QUOTES TABLE qid, (quote id) owner_uid, creator_uid SQL SYNTAX: SELECT q.qid, q.owner_uid, q.creator_uid, owner.fname, owner.lname FROM quotes q, (SELECT u.fname, u.lname FROM users u WHERE u.uid = q.owner_uid) AS owner WHERE q.qid = '#' SUMMARY I want to be able to use the quote table's owner_uid and specify it for the owner table so I can return all the owner info for that particular quote. The problem is, q.owner_uid is not recognized in the owner sub-query. What am I doing wrong?

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  • The SSIS tuning tip that everyone misses

    - by Rob Farley
    I know that everyone misses this, because I’m yet to find someone who doesn’t have a bit of an epiphany when I describe this. When tuning Data Flows in SQL Server Integration Services, people see the Data Flow as moving from the Source to the Destination, passing through a number of transformations. What people don’t consider is the Source, getting the data out of a database. Remember, the source of data for your Data Flow is not your Source Component. It’s wherever the data is, within your database, probably on a disk somewhere. You need to tune your query to optimise it for SSIS, and this is what most people fail to do. I’m not suggesting that people don’t tune their queries – there’s plenty of information out there about making sure that your queries run as fast as possible. But for SSIS, it’s not about how fast your query runs. Let me say that again, but in bolder text: The speed of an SSIS Source is not about how fast your query runs. If your query is used in a Source component for SSIS, the thing that matters is how fast it starts returning data. In particular, those first 10,000 rows to populate that first buffer, ready to pass down the rest of the transformations on its way to the Destination. Let’s look at a very simple query as an example, using the AdventureWorks database: We’re picking the different Weight values out of the Product table, and it’s doing this by scanning the table and doing a Sort. It’s a Distinct Sort, which means that the duplicates are discarded. It'll be no surprise to see that the data produced is sorted. Obvious, I know, but I'm making a comparison to what I'll do later. Before I explain the problem here, let me jump back into the SSIS world... If you’ve investigated how to tune an SSIS flow, then you’ll know that some SSIS Data Flow Transformations are known to be Blocking, some are Partially Blocking, and some are simply Row transformations. Take the SSIS Sort transformation, for example. I’m using a larger data set for this, because my small list of Weights won’t demonstrate it well enough. Seven buffers of data came out of the source, but none of them could be pushed past the Sort operator, just in case the last buffer contained the data that would be sorted into the first buffer. This is a blocking operation. Back in the land of T-SQL, we consider our Distinct Sort operator. It’s also blocking. It won’t let data through until it’s seen all of it. If you weren’t okay with blocking operations in SSIS, why would you be happy with them in an execution plan? The source of your data is not your OLE DB Source. Remember this. The source of your data is the NCIX/CIX/Heap from which it’s being pulled. Picture it like this... the data flowing from the Clustered Index, through the Distinct Sort operator, into the SELECT operator, where a series of SSIS Buffers are populated, flowing (as they get full) down through the SSIS transformations. Alright, I know that I’m taking some liberties here, because the two queries aren’t the same, but consider the visual. The data is flowing from your disk and through your execution plan before it reaches SSIS, so you could easily find that a blocking operation in your plan is just as painful as a blocking operation in your SSIS Data Flow. Luckily, T-SQL gives us a brilliant query hint to help avoid this. OPTION (FAST 10000) This hint means that it will choose a query which will optimise for the first 10,000 rows – the default SSIS buffer size. And the effect can be quite significant. First let’s consider a simple example, then we’ll look at a larger one. Consider our weights. We don’t have 10,000, so I’m going to use OPTION (FAST 1) instead. You’ll notice that the query is more expensive, using a Flow Distinct operator instead of the Distinct Sort. This operator is consuming 84% of the query, instead of the 59% we saw from the Distinct Sort. But the first row could be returned quicker – a Flow Distinct operator is non-blocking. The data here isn’t sorted, of course. It’s in the same order that it came out of the index, just with duplicates removed. As soon as a Flow Distinct sees a value that it hasn’t come across before, it pushes it out to the operator on its left. It still has to maintain the list of what it’s seen so far, but by handling it one row at a time, it can push rows through quicker. Overall, it’s a lot more work than the Distinct Sort, but if the priority is the first few rows, then perhaps that’s exactly what we want. The Query Optimizer seems to do this by optimising the query as if there were only one row coming through: This 1 row estimation is caused by the Query Optimizer imagining the SELECT operation saying “Give me one row” first, and this message being passed all the way along. The request might not make it all the way back to the source, but in my simple example, it does. I hope this simple example has helped you understand the significance of the blocking operator. Now I’m going to show you an example on a much larger data set. This data was fetching about 780,000 rows, and these are the Estimated Plans. The data needed to be Sorted, to support further SSIS operations that needed that. First, without the hint. ...and now with OPTION (FAST 10000): A very different plan, I’m sure you’ll agree. In case you’re curious, those arrows in the top one are 780,000 rows in size. In the second, they’re estimated to be 10,000, although the Actual figures end up being 780,000. The top one definitely runs faster. It finished several times faster than the second one. With the amount of data being considered, these numbers were in minutes. Look at the second one – it’s doing Nested Loops, across 780,000 rows! That’s not generally recommended at all. That’s “Go and make yourself a coffee” time. In this case, it was about six or seven minutes. The faster one finished in about a minute. But in SSIS-land, things are different. The particular data flow that was consuming this data was significant. It was being pumped into a Script Component to process each row based on previous rows, creating about a dozen different flows. The data flow would take roughly ten minutes to run – ten minutes from when the data first appeared. The query that completes faster – chosen by the Query Optimizer with no hints, based on accurate statistics (rather than pretending the numbers are smaller) – would take a minute to start getting the data into SSIS, at which point the ten-minute flow would start, taking eleven minutes to complete. The query that took longer – chosen by the Query Optimizer pretending it only wanted the first 10,000 rows – would take only ten seconds to fill the first buffer. Despite the fact that it might have taken the database another six or seven minutes to get the data out, SSIS didn’t care. Every time it wanted the next buffer of data, it was already available, and the whole process finished in about ten minutes and ten seconds. When debugging SSIS, you run the package, and sit there waiting to see the Debug information start appearing. You look for the numbers on the data flow, and seeing operators going Yellow and Green. Without the hint, I’d sit there for a minute. With the hint, just ten seconds. You can imagine which one I preferred. By adding this hint, it felt like a magic wand had been waved across the query, to make it run several times faster. It wasn’t the case at all – but it felt like it to SSIS.

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  • MySQL-Cluster or Multi-Master for production? Performance issues?

    - by Phillip Oldham
    We are expanding our network of webservers on EC2 to a number of different regions and currently use master/slave replication. We've found that over the past couple of months our slave has stopped replicating a number of times which required us to clear the db and initialise the replication again. As we're now looking to have servers in 3 different regions we're a little concerned about these MySQL replication errors. We believe they're due to auto_increment values, so we're considering a number of approaches to quell these errors and stabilise replication: Multi-Master replication; 3 masters (one in each region), with the relevant auto_increment offsets, regularly backing up to S3. Or, MySQL-Cluster; 3 nodes (one in each region) with a separate management node which will also aggregate logs and statistics. After investigating it seems they both have down-sides (replication errors for the former, performance issues for the latter). We believe the cluster approach would allow us to manage and add new nodes more easily than the Multi-Master route, and would reduce/eliminate the replication issues we're currently seeing. But performance is a priority. Are the performance issues of MySQL-Cluster as bad as people say?

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  • Chaining IQueryables together

    - by Matt Greer
    I have a RIA Services based app that is using Entity Framework on the server side (possibly not relevant). In my real app, I can do something like this. EntityQuery<Status> query = statusContext.GetStatusesQuery().Where(s => s.Description.Contains("Foo")); Where statusContext is the client side subclass of DomainContext that RIA Services was kind enough to generate for me. The end result is an EntityQuery<Status> object who's Query property is an object that implements IQueryable and represents my where clause. The WebDomainClient is able to take this EntityQuery and not just give me back all of my Statuses but also filtered with my where clause. I am trying to implement this in a mock DomainClient. This MockDomainClient accepts an IQueryably<Entity> which it returns when asked for. But what if the user makes the query and includes the ad hoc additional query? How can I merge the two together? My MockDomainClient is (this is modeled after this blog post) ... public class MockDomainClient : LocalDomainClient { private IQueryable<Entity> _entities; public MockDomainClient(IQueryable<Entity> entities) { _entities = entities; } public override IQueryable<Entity> DoQuery(EntityQuery query) { if (query.Query == null) { return _entities; } // otherwise want the union of _entities and query.Query, query.Query is IQueryable // the below does not work and was a total shot in the dark: //return _entities.Union(query.Query.Cast<Entity>()); } } public abstract class LocalDomainClient : System.ServiceModel.DomainServices.Client.DomainClient { private SynchronizationContext _syncContext; protected LocalDomainClient() { _syncContext = SynchronizationContext.Current; } ... public abstract IQueryable<Entity> DoQuery(EntityQuery query); protected override IAsyncResult BeginQueryCore(EntityQuery query, AsyncCallback callback, object userState) { IQueryable<Entity> localQuery = DoQuery(query); LocalAsyncResult asyncResult = new LocalAsyncResult(callback, userState, localQuery); _syncContext.Post(o => (o as LocalAsyncResult).Complete(), asyncResult); return asyncResult; } ... }

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  • JPA Entity Manager resource handling

    - by chiragshahkapadia
    Every time I call JPA method its creating entity and binding query. My persistence properties are: <property name="hibernate.dialect" value="org.hibernate.dialect.Oracle10gDialect"/> <property name="hibernate.cache.provider_class" value="net.sf.ehcache.hibernate.SingletonEhCacheProvider"/> <property name="hibernate.cache.use_second_level_cache" value="true"/> <property name="hibernate.cache.use_query_cache" value="true"/> And I am creating entity manager the way shown below: emf = Persistence.createEntityManagerFactory("pu"); em = emf.createEntityManager(); em = Persistence.createEntityManagerFactory("pu").createEntityManager(); Is there any nice way to manage entity manager resource instead create new every time or any property can set in persistence. Remember it's JPA. See below binding log every time : 15:35:15,527 INFO [AnnotationBinder] Binding entity from annotated class: * 15:35:15,527 INFO [QueryBinder] Binding Named query: * = * 15:35:15,527 INFO [QueryBinder] Binding Named query: * = * 15:35:15,527 INFO [QueryBinder] Binding Named query: 15:35:15,527 INFO [QueryBinder] Binding Named query: 15:35:15,527 INFO [QueryBinder] Binding Named query: 15:35:15,527 INFO [QueryBinder] Binding Named query: 15:35:15,527 INFO [QueryBinder] Binding Named query: 15:35:15,527 INFO [QueryBinder] Binding Named query: 15:35:15,527 INFO [QueryBinder] Binding Named query: 15:35:15,527 INFO [EntityBinder] Bind entity com.* on table * 15:35:15,542 INFO [HibernateSearchEventListenerRegister] Unable to find org.hibernate.search.event.FullTextIndexEventListener on the classpath. Hibernate Search is not enabled. 15:35:15,542 INFO [NamingHelper] JNDI InitialContext properties:{} 15:35:15,542 INFO [DatasourceConnectionProvider] Using datasource: 15:35:15,542 INFO [SettingsFactory] RDBMS: and Real Application Testing options 15:35:15,542 INFO [SettingsFactory] JDBC driver: Oracle JDBC driver, version: 9.2.0.1.0 15:35:15,542 INFO [Dialect] Using dialect: org.hibernate.dialect.Oracle10gDialect 15:35:15,542 INFO [TransactionFactoryFactory] Transaction strategy: org.hibernate.transaction.JDBCTransactionFactory 15:35:15,542 INFO [TransactionManagerLookupFactory] No TransactionManagerLookup configured (in JTA environment, use of read-write or transactional second-level cache is not recomm ended) 15:35:15,542 INFO [SettingsFactory] Automatic flush during beforeCompletion(): disabled 15:35:15,542 INFO [SettingsFactory] Automatic session close at end of transaction: disabled 15:35:15,542 INFO [SettingsFactory] JDBC batch size: 15 15:35:15,542 INFO [SettingsFactory] JDBC batch updates for versioned data: disabled 15:35:15,542 INFO [SettingsFactory] Scrollable result sets: enabled 15:35:15,542 INFO [SettingsFactory] JDBC3 getGeneratedKeys(): disabled 15:35:15,542 INFO [SettingsFactory] Connection release mode: auto 15:35:15,542 INFO [SettingsFactory] Default batch fetch size: 1 15:35:15,542 INFO [SettingsFactory] Generate SQL with comments: disabled 15:35:15,542 INFO [SettingsFactory] Order SQL updates by primary key: disabled 15:35:15,542 INFO [SettingsFactory] Order SQL inserts for batching: disabled 15:35:15,542 INFO [SettingsFactory] Query translator: org.hibernate.hql.ast.ASTQueryTranslatorFactory 15:35:15,542 INFO [ASTQueryTranslatorFactory] Using ASTQueryTranslatorFactory 15:35:15,542 INFO [SettingsFactory] Query language substitutions: {} 15:35:15,542 INFO [SettingsFactory] JPA-QL strict compliance: enabled 15:35:15,542 INFO [SettingsFactory] Second-level cache: enabled 15:35:15,542 INFO [SettingsFactory] Query cache: enabled 15:35:15,542 INFO [SettingsFactory] Cache region factory : org.hibernate.cache.impl.bridge.RegionFactoryCacheProviderBridge 15:35:15,542 INFO [RegionFactoryCacheProviderBridge] Cache provider: net.sf.ehcache.hibernate.SingletonEhCacheProvider 15:35:15,542 INFO [SettingsFactory] Optimize cache for minimal puts: disabled 15:35:15,542 INFO [SettingsFactory] Structured second-level cache entries: disabled 15:35:15,542 INFO [SettingsFactory] Query cache factory: org.hibernate.cache.StandardQueryCacheFactory 15:35:15,542 INFO [SettingsFactory] Statistics: disabled 15:35:15,542 INFO [SettingsFactory] Deleted entity synthetic identifier rollback: disabled 15:35:15,542 INFO [SettingsFactory] Default entity-mode: pojo 15:35:15,542 INFO [SettingsFactory] Named query checking : enabled 15:35:15,542 INFO [SessionFactoryImpl] building session factory 15:35:15,542 INFO [SessionFactoryObjectFactory] Not binding factory to JNDI, no JNDI name configured 15:35:15,542 INFO [UpdateTimestampsCache] starting update timestamps cache at region: org.hibernate.cache.UpdateTimestampsCache 15:35:15,542 INFO [StandardQueryCache] starting query cache at region: org.hibernate.cache.StandardQueryCache

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  • Activating active PuTTY window in MTPuTTY with AutoHotkey script doesn't work

    - by Piotr Dobrogost
    I'm using Multi-Tabbed PuTTY and I wrote AutoHotKey script to rerun the command which was run as the last one. However the active PuTTY window (inside MTPuTTY) does not get activated thus sending keys has no effect. CTRL+` is a hotkey to Switch between the application and active PuTTY window. How to fix this? WinWait, MTPuTTY (Multi-Tabbed PuTTY), IfWinNotActive, MTPuTTY (Multi-Tabbed PuTTY), , WinActivate, MTPuTTY (Multi-Tabbed PuTTY), WinWaitActive, MTPuTTY (Multi-Tabbed PuTTY), Send, {CTRLDOWN}`{CTRLUP} Send, {UP}{ENTER}

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  • How to do a timestamp comparison with JPA query?

    - by Robert
    We need to make sure only results within the last 30 days are returned for a JPQL query. An example follows: Date now = new Date(); Timestamp thirtyDaysAgo = new Timestamp(now.getTime() - 86400000*30); Query query = em.createQuery( "SELECT msg FROM Message msg "+ "WHERE msg.targetTime < CURRENT_TIMESTAMP AND msg.targetTime > {ts, '"+thirtyDaysAgo+"'}"); List result = query.getResultList(); Here is the error we receive: <openjpa-1.2.3-SNAPSHOT-r422266:907835 nonfatal user error org.apache.openjpa.persistence.ArgumentException: An error occurred while parsing the query filter 'SELECT msg FROM BroadcastMessage msg WHERE msg.targetTime < CURRENT_TIMESTAMP AND msg.targetTime {ts, '2010-04-18 04:15:37.827'}'. Error message: org.apache.openjpa.kernel.jpql.TokenMgrError: Lexical error at line 1, column 217. Encountered: "{" (123), after : "" Help!

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  • Using normalize-string XPath function from SQL XML query ?

    - by Ross Watson
    Hi, is it possible to run an SQL query, with an XPath "where" clause, and to trim trailing spaces before the comparison ? I have an SQL XML column, in which I have XML nodes with attributes which contain trailing spaces. I would like to find a given record, which has a specified attribute value - without the trailing spaces. When I try, I get... "There is no function '{http://www.w3.org/2004/07/xpath-functions}:normalize-space()'" I have tried the following (query 1 works, query 2 doesn't). This is on SQL 2005. declare @test table (data xml) insert into @test values ('<thing xmlns="http://my.org.uk/Things" x="hello " />') -- query 1 ;with xmlnamespaces ('http://my.org.uk/Things' as ns0) select * from @test where data.exist('ns0:thing[@x="hello "]') != 0 -- query 2 ;with xmlnamespaces ('http://my.org.uk/Things' as ns0) select * from @test where data.exist('ns0:thing[normalize-space(@x)="hello "]') != 0 Thanks for any help, Ross

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  • Is it Possible to Query Multiple Databases with WCF Data Services?

    - by Mas
    I have data being inserted into multiple databases with the same schema. The multiple databases exist for performance reasons. I need to create a WCF service that a client can use to query the databases. However from the client's point of view, there is only 1 database. By this I mean when a client performs a query, it should query all databases and return the combined results. I also need to provide the flexibility for the client to define its own queries. Therefore I am looking into WCF Data Services, which provides the very nice functionality for client specified queries. So far, it seems that a DataService can only make a query to a single database. I found no override that would allow me to dispatch queries to multiple databases. Does anyone know if it is possible for a WCF Data Service to query against multiple databases with the same schema?

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  • How do I get a linq to sql group by query into the asp.net mvc view?

    - by Brad Wetli
    Sorry for the newbie question, but I have the following query that groups parking spaces by their garage, but I can't figure out how to iterate the data in the view. I guess I should strongly type the view but am a newbie and having lots of problems figuring this out. Any help would be appreciated. Public Function FindAllSpaces() Implements ISpaceRepository.FindAllSpaces Dim query = _ From s In db.spaces _ Order By s.name Ascending _ Group By s.garageid Into spaces = Group _ Order By garageid Ascending Return query End Function The controller is taking the query object as is and putting it into the viewdata.model and as stated the view is not currently strongly typed as I haven't been able to figure out how to do this. I have run the query successfully in linqpad.

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  • SQL Server Query Slow from PHP, but FAST from SQL Mgt Studio - WHY???

    - by Ray
    I have a fast running query (sub 1 sec) when I execute the query in SQL Server Mgt Studio, but when I run the exact same query in PHP (on the same db instace) using FreeTDS v8, mssql_query(), it takes much longer (70+ seconds). The tables I'm hitting have an index on a date field that I'm using in the Where clause. Could it be that PHP's mssql functions aren't utilizing the index? I have also tried putting the query inside a stored procedure, then executing the SP from PHP - the same results in time difference occurs. I have also tried adding a WITH ( INDEX( .. ) ) clause on the table where that has the date index, but no luck either. Here's the query: SELECT 1 History, h.CUSTNMBR CustNmbr, CONVERT(VARCHAR(10), h.ORDRDATE, 120 ) OrdDate, h.SOPNUMBE OrdNmbr, h.SUBTOTAL OrdTotal, h.CSTPONBR PONmbr, h.SHIPMTHD Shipper, h.VOIDSTTS VoidStatus, h.BACHNUMB BatchNmbr, h.MODIFDT ModifDt FROM SOP30200 h WITH (INDEX (AK2SOP30200)) WHERE h.SOPTYPE = 2 AND h.DOCDATE >= DATEADD(dd, -61, GETDATE()) AND h.VOIDSTTS = 0 AND h.MODIFDT = CONVERT(VARCHAR(10), DATEADD(dd, -1*@daysAgo, GETDATE()) , 120 ) ;

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  • Combining DROP USER and DROP DATABASE with SELECT .. WHERE query?

    - by zsero
    I'd like to make a very simple thing, replicate the functionality of mysql's interactive mysql_secure_installation script. My question is that is there a simple, built-in way in MySQL to combine the output of a SELECT query with the input of a DROP user or DROP database script? For example, if I'd like to drop all users with empty passwords. How could I do that with DROP USER statement? I know an obvious solution would be to run everything for example from a Python script, run a query with mysql -Bse "select..." parse the output with some program construct the drop query run it. Is there an easy way to do it in a simple SQL query? I've seen some example here, but I wouldn't call it simple: http://stackoverflow.com/a/12097567/518169 Would you recommend making a combined query, or just to parse the output using for example Python or bash scripts/sed?

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  • rails 3, active record: any way to tell how many unique values match a "x LIKE ?" query

    - by jpwynn
    I have a query to find all the phone numbers that match a partial expression such as "ends with 234" @matchingphones = Calls.find :all, :conditions => [ "(thephonenumber LIKE ?)", "%234"] The same phone number might be in the database several times, and so might be returned multiple times by this query if it matches. What I need is to know is UNIQUE phone numbers the query returns. For example if the database contains 000-111-1234 * 000-111-3333 000-111-2234 * 000-111-1234 * 000-111-4444 the existing query will return the 3 records marked with * (eg returns one phone number -1234 twice since it's in the database twice) what I need is a query that returns just once instance of each match, in this case 000-111-1234 * 000-111-2234 *

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  • How do I query through a many-to-many relationship using NHibernate Criteria and Lambda Extensions?

    - by Brian Kendig
    In my database I have a Person table and an Event table (parties, meetings, &c.). This many-to-many relationship is represented through an Invitation table. Each Person can have many Invitations. Each Event can also have many Invitations. If I want a list of Events to which a Person is invited, I can use this HQL query: IQuery query = Session.CreateQuery("SELECT i.Event from Invitation i where i.Person = :p"); query.SetParameter("p", person); return query.List<Person>(); How would I write this query with NHibernate criteria and Lambda Extensions?

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  • Plan Caching and Query Memory Part II (Hash Match) – When not to use stored procedure - Most common performance mistake SQL Server developers make.

    - by sqlworkshops
    SQL Server estimates Memory requirement at compile time, when stored procedure or other plan caching mechanisms like sp_executesql or prepared statement are used, the memory requirement is estimated based on first set of execution parameters. This is a common reason for spill over tempdb and hence poor performance. Common memory allocating queries are that perform Sort and do Hash Match operations like Hash Join or Hash Aggregation or Hash Union. This article covers Hash Match operations with examples. It is recommended to read Plan Caching and Query Memory Part I before this article which covers an introduction and Query memory for Sort. In most cases it is cheaper to pay for the compilation cost of dynamic queries than huge cost for spill over tempdb, unless memory requirement for a query does not change significantly based on predicates.   This article covers underestimation / overestimation of memory for Hash Match operation. Plan Caching and Query Memory Part I covers underestimation / overestimation for Sort. It is important to note that underestimation of memory for Sort and Hash Match operations lead to spill over tempdb and hence negatively impact performance. Overestimation of memory affects the memory needs of other concurrently executing queries. In addition, it is important to note, with Hash Match operations, overestimation of memory can actually lead to poor performance.   To read additional articles I wrote click here.   The best way to learn is to practice. To create the below tables and reproduce the behavior, join the mailing list by using this link: www.sqlworkshops.com/ml and I will send you the table creation script. Most of these concepts are also covered in our webcasts: www.sqlworkshops.com/webcasts  Let’s create a Customer’s State table that has 99% of customers in NY and the rest 1% in WA.Customers table used in Part I of this article is also used here.To observe Hash Warning, enable 'Hash Warning' in SQL Profiler under Events 'Errors and Warnings'. --Example provided by www.sqlworkshops.com drop table CustomersState go create table CustomersState (CustomerID int primary key, Address char(200), State char(2)) go insert into CustomersState (CustomerID, Address) select CustomerID, 'Address' from Customers update CustomersState set State = 'NY' where CustomerID % 100 != 1 update CustomersState set State = 'WA' where CustomerID % 100 = 1 go update statistics CustomersState with fullscan go   Let’s create a stored procedure that joins customers with CustomersState table with a predicate on State. --Example provided by www.sqlworkshops.com create proc CustomersByState @State char(2) as begin declare @CustomerID int select @CustomerID = e.CustomerID from Customers e inner join CustomersState es on (e.CustomerID = es.CustomerID) where es.State = @State option (maxdop 1) end go  Let’s execute the stored procedure first with parameter value ‘WA’ – which will select 1% of data. set statistics time on go --Example provided by www.sqlworkshops.com exec CustomersByState 'WA' goThe stored procedure took 294 ms to complete.  The stored procedure was granted 6704 KB based on 8000 rows being estimated.  The estimated number of rows, 8000 is similar to actual number of rows 8000 and hence the memory estimation should be ok.  There was no Hash Warning in SQL Profiler. To observe Hash Warning, enable 'Hash Warning' in SQL Profiler under Events 'Errors and Warnings'.   Now let’s execute the stored procedure with parameter value ‘NY’ – which will select 99% of data. -Example provided by www.sqlworkshops.com exec CustomersByState 'NY' go  The stored procedure took 2922 ms to complete.   The stored procedure was granted 6704 KB based on 8000 rows being estimated.    The estimated number of rows, 8000 is way different from the actual number of rows 792000 because the estimation is based on the first set of parameter value supplied to the stored procedure which is ‘WA’ in our case. This underestimation will lead to spill over tempdb, resulting in poor performance.   There was Hash Warning (Recursion) in SQL Profiler. To observe Hash Warning, enable 'Hash Warning' in SQL Profiler under Events 'Errors and Warnings'.   Let’s recompile the stored procedure and then let’s first execute the stored procedure with parameter value ‘NY’.  In a production instance it is not advisable to use sp_recompile instead one should use DBCC FREEPROCCACHE (plan_handle). This is due to locking issues involved with sp_recompile, refer to our webcasts, www.sqlworkshops.com/webcasts for further details.   exec sp_recompile CustomersByState go --Example provided by www.sqlworkshops.com exec CustomersByState 'NY' go  Now the stored procedure took only 1046 ms instead of 2922 ms.   The stored procedure was granted 146752 KB of memory. The estimated number of rows, 792000 is similar to actual number of rows of 792000. Better performance of this stored procedure execution is due to better estimation of memory and avoiding spill over tempdb.   There was no Hash Warning in SQL Profiler.   Now let’s execute the stored procedure with parameter value ‘WA’. --Example provided by www.sqlworkshops.com exec CustomersByState 'WA' go  The stored procedure took 351 ms to complete, higher than the previous execution time of 294 ms.    This stored procedure was granted more memory (146752 KB) than necessary (6704 KB) based on parameter value ‘NY’ for estimation (792000 rows) instead of parameter value ‘WA’ for estimation (8000 rows). This is because the estimation is based on the first set of parameter value supplied to the stored procedure which is ‘NY’ in this case. This overestimation leads to poor performance of this Hash Match operation, it might also affect the performance of other concurrently executing queries requiring memory and hence overestimation is not recommended.     The estimated number of rows, 792000 is much more than the actual number of rows of 8000.  Intermediate Summary: This issue can be avoided by not caching the plan for memory allocating queries. Other possibility is to use recompile hint or optimize for hint to allocate memory for predefined data range.Let’s recreate the stored procedure with recompile hint. --Example provided by www.sqlworkshops.com drop proc CustomersByState go create proc CustomersByState @State char(2) as begin declare @CustomerID int select @CustomerID = e.CustomerID from Customers e inner join CustomersState es on (e.CustomerID = es.CustomerID) where es.State = @State option (maxdop 1, recompile) end go  Let’s execute the stored procedure initially with parameter value ‘WA’ and then with parameter value ‘NY’. --Example provided by www.sqlworkshops.com exec CustomersByState 'WA' go exec CustomersByState 'NY' go  The stored procedure took 297 ms and 1102 ms in line with previous optimal execution times.   The stored procedure with parameter value ‘WA’ has good estimation like before.   Estimated number of rows of 8000 is similar to actual number of rows of 8000.   The stored procedure with parameter value ‘NY’ also has good estimation and memory grant like before because the stored procedure was recompiled with current set of parameter values.  Estimated number of rows of 792000 is similar to actual number of rows of 792000.    The compilation time and compilation CPU of 1 ms is not expensive in this case compared to the performance benefit.   There was no Hash Warning in SQL Profiler.   Let’s recreate the stored procedure with optimize for hint of ‘NY’. --Example provided by www.sqlworkshops.com drop proc CustomersByState go create proc CustomersByState @State char(2) as begin declare @CustomerID int select @CustomerID = e.CustomerID from Customers e inner join CustomersState es on (e.CustomerID = es.CustomerID) where es.State = @State option (maxdop 1, optimize for (@State = 'NY')) end go  Let’s execute the stored procedure initially with parameter value ‘WA’ and then with parameter value ‘NY’. --Example provided by www.sqlworkshops.com exec CustomersByState 'WA' go exec CustomersByState 'NY' go  The stored procedure took 353 ms with parameter value ‘WA’, this is much slower than the optimal execution time of 294 ms we observed previously. This is because of overestimation of memory. The stored procedure with parameter value ‘NY’ has optimal execution time like before.   The stored procedure with parameter value ‘WA’ has overestimation of rows because of optimize for hint value of ‘NY’.   Unlike before, more memory was estimated to this stored procedure based on optimize for hint value ‘NY’.    The stored procedure with parameter value ‘NY’ has good estimation because of optimize for hint value of ‘NY’. Estimated number of rows of 792000 is similar to actual number of rows of 792000.   Optimal amount memory was estimated to this stored procedure based on optimize for hint value ‘NY’.   There was no Hash Warning in SQL Profiler.   This article covers underestimation / overestimation of memory for Hash Match operation. Plan Caching and Query Memory Part I covers underestimation / overestimation for Sort. It is important to note that underestimation of memory for Sort and Hash Match operations lead to spill over tempdb and hence negatively impact performance. Overestimation of memory affects the memory needs of other concurrently executing queries. In addition, it is important to note, with Hash Match operations, overestimation of memory can actually lead to poor performance.   Summary: Cached plan might lead to underestimation or overestimation of memory because the memory is estimated based on first set of execution parameters. It is recommended not to cache the plan if the amount of memory required to execute the stored procedure has a wide range of possibilities. One can mitigate this by using recompile hint, but that will lead to compilation overhead. However, in most cases it might be ok to pay for compilation rather than spilling sort over tempdb which could be very expensive compared to compilation cost. The other possibility is to use optimize for hint, but in case one sorts more data than hinted by optimize for hint, this will still lead to spill. On the other side there is also the possibility of overestimation leading to unnecessary memory issues for other concurrently executing queries. In case of Hash Match operations, this overestimation of memory might lead to poor performance. When the values used in optimize for hint are archived from the database, the estimation will be wrong leading to worst performance, so one has to exercise caution before using optimize for hint, recompile hint is better in this case.   I explain these concepts with detailed examples in my webcasts (www.sqlworkshops.com/webcasts), I recommend you to watch them. The best way to learn is to practice. To create the above tables and reproduce the behavior, join the mailing list at www.sqlworkshops.com/ml and I will send you the relevant SQL Scripts.  Register for the upcoming 3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop in London, United Kingdom during March 15-17, 2011, click here to register / Microsoft UK TechNet.These are hands-on workshops with a maximum of 12 participants and not lectures. For consulting engagements click here.   Disclaimer and copyright information:This article refers to organizations and products that may be the trademarks or registered trademarks of their various owners. Copyright of this article belongs to R Meyyappan / www.sqlworkshops.com. You may freely use the ideas and concepts discussed in this article with acknowledgement (www.sqlworkshops.com), but you may not claim any of it as your own work. This article is for informational purposes only; you use any of the suggestions given here entirely at your own risk.   R Meyyappan [email protected] LinkedIn: http://at.linkedin.com/in/rmeyyappan

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  • Was Visual Studio 2008 or 2010 written to use multi cores?

    - by Erx_VB.NExT.Coder
    basically i want to know if the visual studio IDE and/or compiler in 2010 was written to make use of a multi core environment (i understand we can target multi core environments in 08 and 10, but that is not my question). i am trying to decide on if i should get a higher clock dual core or a lower clock quad core, as i want to try and figure out which processor will give me the absolute best possible experience with Visual Studio 2010 (ide and background compiler). if they are running the most important section (background compiler and other ide tasks) in one core, then the core will get cut off quicker if running a quad core, esp if background compiler is the heaviest task, i would imagine this would b e difficult to seperate in more then one process, so even if it uses multi cores you might still be better off with going for a higher clock cpu if the majority of the processing is still bound to occur in one core (ie the most significant part of the VS environment). i am a vb programmer, they've made great performance improvements in beta 2, congrats, but i would love to be able to use VS seamlessly... anyone have any ideas? thanks, erx

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  • JPA 2 and Hibernate 3.5.1 MEMBER OF query doesnt work.

    - by Ed_Zero
    I'm trying the following JPQL and it fails misserably: Query query = em.createQuery("SELECT u FROM User u WHERE 'admin' MEMBER OF u.roles"); List users = query.query.getResultList(); I get the following exception: ERROR [main] PARSER.error(454) | <AST>:0:0: unexpected end of subtree java.lang.IllegalArgumentException: org.hibernate.hql.ast.QuerySyntaxException: unexpected end of subtree [SELECT u FROM com.online.data.User u WHERE 'admin' MEMBER OF u.roles] ERROR [main] PARSER.error(454) | <AST>:0:0: expecting "from", found '<ASTNULL>' ... ... Caused by: org.hibernate.hql.ast.QuerySyntaxException: unexpected end of subtree [SELECT u FROM com.online.data.User u WHERE 'admin' MEMBER OF u.roles] I have Spring 3.0.1.RELEASE, Hibernate 3.5.1-Final and maven to glue dependencies. User class: @Entity public class User { @Id @Column(name = "USER_ID") @GeneratedValue(strategy = GenerationType.IDENTITY) private long id; @Column(unique = true, nullable = false) private String username; private boolean enabled; @ElementCollection private Set<String> roles = new HashSet<String>(); ... } Spring configuration: <?xml version="1.0" encoding="UTF-8"?> <beans xmlns="http://www.springframework.org/schema/beans" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:context="http://www.springframework.org/schema/context" xmlns:tx="http://www.springframework.org/schema/tx" xmlns:p="http://www.springframework.org/schema/p" xmlns:aop="http://www.springframework.org/schema/aop" xsi:schemaLocation=" http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans-3.0.xsd http://www.springframework.org/schema/context http://www.springframework.org/schema/tx/spring-context-3.0.xsd http://www.springframework.org/schema/tx http://www.springframework.org/schema/tx/spring-tx-3.0.xsd http://www.springframework.org/schema/aop http://www.springframework.org/schema/aop/spring-aop-3.0.xsd"> <!-- Reading annotation driven configuration --> <tx:annotation-driven transaction-manager="transactionManager" /> <bean class="org.springframework.dao.annotation.PersistenceExceptionTranslationPostProcessor" /> <bean class="org.springframework.orm.jpa.support.PersistenceAnnotationBeanPostProcessor" /> <bean id="dataSource" class="org.apache.commons.dbcp.BasicDataSource" destroy-method="close"> <property name="driverClassName" value="${jdbc.driverClassName}" /> <property name="url" value="${jdbc.url}" /> <property name="username" value="${jdbc.username}" /> <property name="password" value="${jdbc.password}" /> <property name="maxActive" value="100" /> <property name="maxWait" value="1000" /> <property name="poolPreparedStatements" value="true" /> <property name="defaultAutoCommit" value="true" /> </bean> <bean id="transactionManager" class="org.springframework.orm.jpa.JpaTransactionManager"> <property name="entityManagerFactory" ref="entityManagerFactory" /> <property name="dataSource" ref="dataSource" /> </bean> <bean id="entityManagerFactory" class="org.springframework.orm.jpa.LocalContainerEntityManagerFactoryBean"> <property name="dataSource" ref="dataSource" /> <property name="jpaVendorAdapter"> <bean class="org.springframework.orm.jpa.vendor.HibernateJpaVendorAdapter"> <property name="showSql" value="true" /> <property name="databasePlatform" value="${hibernate.dialect}" /> </bean> </property> <property name="loadTimeWeaver"> <bean class="org.springframework.instrument.classloading.InstrumentationLoadTimeWeaver" /> </property> <property name="jpaProperties"> <props> <prop key="hibernate.hbm2ddl.auto">update</prop> <prop key="hibernate.current_session_context_class">thread</prop> <prop key="hibernate.cache.provider_class">org.hibernate.cache.NoCacheProvider</prop> <prop key="hibernate.show_sql">true</prop> <prop key="hibernate.format_sql">false</prop> <prop key="hibernate.show_comments">true</prop> </props> </property> <property name="persistenceUnitName" value="punit" /> </bean> <bean id="JpaTemplate" class="org.springframework.orm.jpa.JpaTemplate"> <property name="entityManagerFactory" ref="entityManagerFactory" /> </bean> </beans> Persistence.xml <?xml version="1.0" encoding="UTF-8"?> <persistence xmlns="http://java.sun.com/xml/ns/persistence" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://java.sun.com/xml/ns/persistence"> <persistence-unit name="punit" transaction-type="RESOURCE_LOCAL" /> </persistence> pom.xml maven dependencies. <dependency> <groupId>org.hibernate</groupId> <artifactId>hibernate</artifactId> <version>${hibernate.version}</version> <type>pom</type> </dependency> <dependency> <groupId>org.hibernate</groupId> <artifactId>hibernate-core</artifactId> <version>${hibernate.version}</version> </dependency> <dependency> <groupId>org.hibernate</groupId> <artifactId>hibernate-annotations</artifactId> <version>${hibernate.version}</version> </dependency> <dependency> <groupId>org.hibernate</groupId> <artifactId>hibernate-entitymanager</artifactId> <version>${hibernate.version}</version> </dependency> <dependency> <groupId>commons-dbcp</groupId> <artifactId>commons-dbcp</artifactId> <version>1.2.2</version> <type>jar</type> </dependency> <dependency> <groupId>org.springframework.security</groupId> <artifactId>spring-security-web</artifactId> <version>${spring.version}</version> </dependency> <dependency> <groupId>org.springframework.security</groupId> <artifactId>spring-security-config</artifactId> <version>${spring.version}</version> </dependency> <dependency> <groupId>org.springframework.security</groupId> <artifactId>spring-security-taglibs</artifactId> <version>${spring.version}</version> </dependency> <dependency> <groupId>org.springframework.security</groupId> <artifactId>spring-security-acl</artifactId> <version>${spring.version}</version> </dependency> <dependency> <groupId>javax.annotation</groupId> <artifactId>jsr250-api</artifactId> <version>1.0</version> </dependency> <properties> <!-- Application settings --> <spring.version>3.0.1.RELEASE</spring.version> <hibernate.version>3.5.1-Final</hibernate.version> Im running a unit test to check the configuration and I am able to run other JPQL queries the only ones that I am unable to run are the IS EMPTY, MEMBER OF conditions. The complete unit test is as follows: TestIntegration @RunWith(SpringJUnit4ClassRunner.class) @ContextConfiguration(locations = { "/spring/dataLayer.xml"}) @Transactional @TransactionConfiguration public class TestUserDaoImplIntegration { @PersistenceContext private EntityManager em; @Test public void shouldTest() throws Exception { try { //WORKS Query query = em.createQuery("SELECT u FROM User u WHERE 'admin' in elements(u.roles)"); List users = query.query.getResultList(); //DOES NOT WORK } catch (Exception e) { e.printStackTrace(); throw e; } try { Query query = em.createQuery("SELECT u FROM User u WHERE 'admin' MEMBER OF u.roles"); List users = query.query.getResultList(); } catch (Exception e) { e.printStackTrace(); throw e; } } }

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  • How to connect a new query script with SSMS add-in?

    - by squillman
    I'm trying to create a SSMS add-in. One of the things I want to do is to create a new query window and programatically connect it to a server instance (in the context of a SQL Login). I can create the new query script window just fine but I can't find how to connect it without first manually connecting to something else (like the Object Explorer). So in other words, if I connect Obect Explorer to a SQL instance manually and then execute the method of my add-in that creates the query window I can connect it using this code: ServiceCache.ScriptFactory.CreateNewBlankScript( Editors.ScriptType.Sql, ServiceCache.ScriptFactory.CurrentlyActiveWndConnectionInfo.UIConnectionInfo, null); But I don't want to rely on CurrentlyActiveWndConnectionInfo.UIConnectionInfo for the connection. I want to set a SQL Login username and password programatically. Does anyone have any ideas? EDIT: I've managed to get the query window connected by setting the last parameter to an instance of System.Data.SqlClient.SqlConnection. However, the connection uses the context of the last login that was connected instead of what I'm trying to set programatically. That is, the user it connects as is the one selected in the Connection Dialog that you get when you click the New Query button and don't have an Object Explorer connected. EDIT2: I'm writing (or hoping to write) an add-in to automatically send a SQL statement and the execution results to our case-tracking system when run against our production servers. One thought I had was to remove write permissions and assign logins through this add-in which will also force the user to enter a case # canceling the statement if it's not there. Another thought I've just had is to inspect the server name in ServiceCache.ScriptFactory.CurrentlyActiveWndConnectionInfo.UIConnectionInfo and compare it to our list of production servers. If it matches and there's no case # then cancel the query.

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  • How do you compare using .NET types in an NHibernate ICriteria query for an ICompositeUserType?

    - by gabe
    I have an answered StackOverflow question about how to combine to legacy CHAR database date and time fields into one .NET DateTime property in my POCO here (thanks much Berryl!). Now i am trying to get a custom ICritera query to work against that very DateTime property to no avail. here's my query: ICriteria criteria = Session.CreateCriteria<InputFileLog>() .Add(Expression.Gt(MembersOf<InputFileLog>.GetName(x => x.FileCreationDateTime), DateTime.Now.AddDays(-14))) .AddOrder(Order.Desc(Projections.Id())) .CreateCriteria(typeof(InputFile).Name) .Add(Expression.Eq(MembersOf<InputFile>.GetName(x => x.Id), inputFileName)); IList<InputFileLog> list = criteria.List<InputFileLog>(); And here's the query it's generating: SELECT this_.input_file_token as input1_9_2_, this_.file_creation_date as file2_9_2_, this_.file_creation_time as file3_9_2_, this_.approval_ind as approval4_9_2_, this_.file_id as file5_9_2_, this_.process_name as process6_9_2_, this_.process_status as process7_9_2_, this_.input_file_name as input8_9_2_, gonogo3_.input_file_token as input1_6_0_, gonogo3_.go_nogo_ind as go2_6_0_, inputfile1_.input_file_name as input1_3_1_, inputfile1_.src_code as src2_3_1_, inputfile1_.process_cat_code as process3_3_1_ FROM input_file_log this_ left outer join go_nogo gonogo3_ on this_.input_file_token=gonogo3_.input_file_token inner join input_file inputfile1_ on this_.input_file_name=inputfile1_.input_file_name WHERE this_.file_creation_date > :p0 and this_.file_creation_time > :p1 and inputfile1_.input_file_name = :p2 ORDER BY this_.input_file_token desc; :p0 = '20100401', :p1 = '15:15:27', :p2 = 'LMCONV_JR' The query is exactly what i would expect, actually, except it doesn't actually give me what i want (all the rows in the last 2 weeks) because in the DB it's doing a greater than comparison using CHARs instead of DATEs. I have no idea how to get the query to convert the CHAR values into a DATE in the query without doing a CreateSQLQuery(), which I would like to avoid. Anyone know how to do this?

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  • Plan Caching and Query Memory Part I – When not to use stored procedure or other plan caching mechanisms like sp_executesql or prepared statement

    - by sqlworkshops
      The most common performance mistake SQL Server developers make: SQL Server estimates memory requirement for queries at compilation time. This mechanism is fine for dynamic queries that need memory, but not for queries that cache the plan. With dynamic queries the plan is not reused for different set of parameters values / predicates and hence different amount of memory can be estimated based on different set of parameter values / predicates. Common memory allocating queries are that perform Sort and do Hash Match operations like Hash Join or Hash Aggregation or Hash Union. This article covers Sort with examples. It is recommended to read Plan Caching and Query Memory Part II after this article which covers Hash Match operations.   When the plan is cached by using stored procedure or other plan caching mechanisms like sp_executesql or prepared statement, SQL Server estimates memory requirement based on first set of execution parameters. Later when the same stored procedure is called with different set of parameter values, the same amount of memory is used to execute the stored procedure. This might lead to underestimation / overestimation of memory on plan reuse, overestimation of memory might not be a noticeable issue for Sort operations, but underestimation of memory will lead to spill over tempdb resulting in poor performance.   This article covers underestimation / overestimation of memory for Sort. Plan Caching and Query Memory Part II covers underestimation / overestimation for Hash Match operation. It is important to note that underestimation of memory for Sort and Hash Match operations lead to spill over tempdb and hence negatively impact performance. Overestimation of memory affects the memory needs of other concurrently executing queries. In addition, it is important to note, with Hash Match operations, overestimation of memory can actually lead to poor performance.   To read additional articles I wrote click here.   In most cases it is cheaper to pay for the compilation cost of dynamic queries than huge cost for spill over tempdb, unless memory requirement for a stored procedure does not change significantly based on predicates.   The best way to learn is to practice. To create the below tables and reproduce the behavior, join the mailing list by using this link: www.sqlworkshops.com/ml and I will send you the table creation script. Most of these concepts are also covered in our webcasts: www.sqlworkshops.com/webcasts   Enough theory, let’s see an example where we sort initially 1 month of data and then use the stored procedure to sort 6 months of data.   Let’s create a stored procedure that sorts customers by name within certain date range.   --Example provided by www.sqlworkshops.com create proc CustomersByCreationDate @CreationDateFrom datetime, @CreationDateTo datetime as begin       declare @CustomerID int, @CustomerName varchar(48), @CreationDate datetime       select @CustomerName = c.CustomerName, @CreationDate = c.CreationDate from Customers c             where c.CreationDate between @CreationDateFrom and @CreationDateTo             order by c.CustomerName       option (maxdop 1)       end go Let’s execute the stored procedure initially with 1 month date range.   set statistics time on go --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-01-31' go The stored procedure took 48 ms to complete.     The stored procedure was granted 6656 KB based on 43199.9 rows being estimated.       The estimated number of rows, 43199.9 is similar to actual number of rows 43200 and hence the memory estimation should be ok.       There was no Sort Warnings in SQL Profiler.      Now let’s execute the stored procedure with 6 month date range. --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-06-30' go The stored procedure took 679 ms to complete.      The stored procedure was granted 6656 KB based on 43199.9 rows being estimated.      The estimated number of rows, 43199.9 is way different from the actual number of rows 259200 because the estimation is based on the first set of parameter value supplied to the stored procedure which is 1 month in our case. This underestimation will lead to sort spill over tempdb, resulting in poor performance.      There was Sort Warnings in SQL Profiler.    To monitor the amount of data written and read from tempdb, one can execute select num_of_bytes_written, num_of_bytes_read from sys.dm_io_virtual_file_stats(2, NULL) before and after the stored procedure execution, for additional information refer to the webcast: www.sqlworkshops.com/webcasts.     Let’s recompile the stored procedure and then let’s first execute the stored procedure with 6 month date range.  In a production instance it is not advisable to use sp_recompile instead one should use DBCC FREEPROCCACHE (plan_handle). This is due to locking issues involved with sp_recompile, refer to our webcasts for further details.   exec sp_recompile CustomersByCreationDate go --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-06-30' go Now the stored procedure took only 294 ms instead of 679 ms.    The stored procedure was granted 26832 KB of memory.      The estimated number of rows, 259200 is similar to actual number of rows of 259200. Better performance of this stored procedure is due to better estimation of memory and avoiding sort spill over tempdb.      There was no Sort Warnings in SQL Profiler.       Now let’s execute the stored procedure with 1 month date range.   --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-01-31' go The stored procedure took 49 ms to complete, similar to our very first stored procedure execution.     This stored procedure was granted more memory (26832 KB) than necessary memory (6656 KB) based on 6 months of data estimation (259200 rows) instead of 1 month of data estimation (43199.9 rows). This is because the estimation is based on the first set of parameter value supplied to the stored procedure which is 6 months in this case. This overestimation did not affect performance, but it might affect performance of other concurrent queries requiring memory and hence overestimation is not recommended. This overestimation might affect performance Hash Match operations, refer to article Plan Caching and Query Memory Part II for further details.    Let’s recompile the stored procedure and then let’s first execute the stored procedure with 2 day date range. exec sp_recompile CustomersByCreationDate go --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-01-02' go The stored procedure took 1 ms.      The stored procedure was granted 1024 KB based on 1440 rows being estimated.      There was no Sort Warnings in SQL Profiler.      Now let’s execute the stored procedure with 6 month date range. --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-06-30' go   The stored procedure took 955 ms to complete, way higher than 679 ms or 294ms we noticed before.      The stored procedure was granted 1024 KB based on 1440 rows being estimated. But we noticed in the past this stored procedure with 6 month date range needed 26832 KB of memory to execute optimally without spill over tempdb. This is clear underestimation of memory and the reason for the very poor performance.      There was Sort Warnings in SQL Profiler. Unlike before this was a Multiple pass sort instead of Single pass sort. This occurs when granted memory is too low.      Intermediate Summary: This issue can be avoided by not caching the plan for memory allocating queries. Other possibility is to use recompile hint or optimize for hint to allocate memory for predefined date range.   Let’s recreate the stored procedure with recompile hint. --Example provided by www.sqlworkshops.com drop proc CustomersByCreationDate go create proc CustomersByCreationDate @CreationDateFrom datetime, @CreationDateTo datetime as begin       declare @CustomerID int, @CustomerName varchar(48), @CreationDate datetime       select @CustomerName = c.CustomerName, @CreationDate = c.CreationDate from Customers c             where c.CreationDate between @CreationDateFrom and @CreationDateTo             order by c.CustomerName       option (maxdop 1, recompile)       end go Let’s execute the stored procedure initially with 1 month date range and then with 6 month date range. --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-01-30' exec CustomersByCreationDate '2001-01-01', '2001-06-30' go The stored procedure took 48ms and 291 ms in line with previous optimal execution times.      The stored procedure with 1 month date range has good estimation like before.      The stored procedure with 6 month date range also has good estimation and memory grant like before because the query was recompiled with current set of parameter values.      The compilation time and compilation CPU of 1 ms is not expensive in this case compared to the performance benefit.     Let’s recreate the stored procedure with optimize for hint of 6 month date range.   --Example provided by www.sqlworkshops.com drop proc CustomersByCreationDate go create proc CustomersByCreationDate @CreationDateFrom datetime, @CreationDateTo datetime as begin       declare @CustomerID int, @CustomerName varchar(48), @CreationDate datetime       select @CustomerName = c.CustomerName, @CreationDate = c.CreationDate from Customers c             where c.CreationDate between @CreationDateFrom and @CreationDateTo             order by c.CustomerName       option (maxdop 1, optimize for (@CreationDateFrom = '2001-01-01', @CreationDateTo ='2001-06-30'))       end go Let’s execute the stored procedure initially with 1 month date range and then with 6 month date range.   --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-01-30' exec CustomersByCreationDate '2001-01-01', '2001-06-30' go The stored procedure took 48ms and 291 ms in line with previous optimal execution times.    The stored procedure with 1 month date range has overestimation of rows and memory. This is because we provided hint to optimize for 6 months of data.      The stored procedure with 6 month date range has good estimation and memory grant because we provided hint to optimize for 6 months of data.       Let’s execute the stored procedure with 12 month date range using the currently cashed plan for 6 month date range. --Example provided by www.sqlworkshops.com exec CustomersByCreationDate '2001-01-01', '2001-12-31' go The stored procedure took 1138 ms to complete.      2592000 rows were estimated based on optimize for hint value for 6 month date range. Actual number of rows is 524160 due to 12 month date range.      The stored procedure was granted enough memory to sort 6 month date range and not 12 month date range, so there will be spill over tempdb.      There was Sort Warnings in SQL Profiler.      As we see above, optimize for hint cannot guarantee enough memory and optimal performance compared to recompile hint.   This article covers underestimation / overestimation of memory for Sort. Plan Caching and Query Memory Part II covers underestimation / overestimation for Hash Match operation. It is important to note that underestimation of memory for Sort and Hash Match operations lead to spill over tempdb and hence negatively impact performance. Overestimation of memory affects the memory needs of other concurrently executing queries. In addition, it is important to note, with Hash Match operations, overestimation of memory can actually lead to poor performance.   Summary: Cached plan might lead to underestimation or overestimation of memory because the memory is estimated based on first set of execution parameters. It is recommended not to cache the plan if the amount of memory required to execute the stored procedure has a wide range of possibilities. One can mitigate this by using recompile hint, but that will lead to compilation overhead. However, in most cases it might be ok to pay for compilation rather than spilling sort over tempdb which could be very expensive compared to compilation cost. The other possibility is to use optimize for hint, but in case one sorts more data than hinted by optimize for hint, this will still lead to spill. On the other side there is also the possibility of overestimation leading to unnecessary memory issues for other concurrently executing queries. In case of Hash Match operations, this overestimation of memory might lead to poor performance. When the values used in optimize for hint are archived from the database, the estimation will be wrong leading to worst performance, so one has to exercise caution before using optimize for hint, recompile hint is better in this case. I explain these concepts with detailed examples in my webcasts (www.sqlworkshops.com/webcasts), I recommend you to watch them. The best way to learn is to practice. To create the above tables and reproduce the behavior, join the mailing list at www.sqlworkshops.com/ml and I will send you the relevant SQL Scripts.     Register for the upcoming 3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop in London, United Kingdom during March 15-17, 2011, click here to register / Microsoft UK TechNet.These are hands-on workshops with a maximum of 12 participants and not lectures. For consulting engagements click here.     Disclaimer and copyright information:This article refers to organizations and products that may be the trademarks or registered trademarks of their various owners. Copyright of this article belongs to R Meyyappan / www.sqlworkshops.com. You may freely use the ideas and concepts discussed in this article with acknowledgement (www.sqlworkshops.com), but you may not claim any of it as your own work. This article is for informational purposes only; you use any of the suggestions given here entirely at your own risk.   R Meyyappan [email protected] LinkedIn: http://at.linkedin.com/in/rmeyyappan

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