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  • MySQL query being performed when PHP if condition not met?

    - by Ryan
    The script I'm using is if($profile['username'] == $user['username']) { $db->query("UPDATE users SET newcomments = 0 WHERE username = '$user[username]'"); echo "This is a test"; } (Note that $db-query is exactly the same as mysql_query) For some very odd reason, the MySQL query is being performed even if the defined condition is false The "This is a test" works properly and only appears when the condition is met, but the MySQL query is performed anyway Whats the problem with it?

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  • XMLReader : how to catch syntax errors in the xml file ?

    - by mishal153
    Hi, I have an XML file with syntax errors. eg. <Viewport thisisbad Left="0" Top="0" Width="1280" Height="720" > When i create an XML reader it does not throw any errors. I there a way to do syntax checking automatically, like XMLDocument does ? I have tried setting various XmlReaderSettings flags but found nothing useful.

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  • Is there a good, free raw HTML editor with auto-complete, auto-formatting and syntax highlighting?

    - by joshcomley
    I'm used to Visual Studio, so in an ideal world I would like something that meets the following criteria: Lightweight CSS auto-complete HTML auto-complete CSS auto-formatting HTML auto-formatting Syntax highlighting Notepad2 has syntax highlighting, but no auto-complete and no auto-formatting. Any thoughts? Please don't answer with "Visual Studio"! I'm after something very lightweight (if it exists).

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  • how to solve syntax error in procedure in mysql?

    - by Shooter
    Hi all, i am executing a procedure in mysql, procedure is- delimiter $$ create procedure getFacility (in id int, out MyFacility VARCHAR(200)) begin select Facility into MyFacility from facilities where FacilityID = id ; end $$ delimiter ; and it is giving error below- Error Code : 1064 You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near 'end $$ delimiter' at line 1 (0 ms taken) how to solve this error? Thanks, Yugal

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  • Can i get RSpec to generate specs with expect syntax?

    - by papirtiger
    When generating specs with : rails g controller Home index A spec is generated with the older object.should syntax require 'spec_helper' describe HomeController do describe "GET 'index'" do it "returns http success" do get 'index' response.should be_success end end end Is it possible to configure the generator to use the expect syntax instead? Desired output: require 'spec_helper' describe HomeController do describe "GET 'index'" do it "returns http success" do get 'index' expect(response).to be_success end end end in config/application.rb: config.generators do |g| g.test_framework :rspec, fixture: true g.fixture_replacement :factory_girl, dir: 'spec/factories' g.view_specs false g.stylesheets = false g.javascripts = false end

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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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  • What is the bash syntax to create a new directory in the directory above?

    - by mozerella
    I aim to make a script for mogrify. The mogrify command will resize images in a directory and put the resized images into a directory on the same directory level, with the same name as the work directory, but with a suffix (_a). The new directory will be moved to another collection later on. Something like this, #!/bin/bash mkdir ../n_a for file in *{.JPG|.jpg}; do mogrify -path ../n_a -resize 1200x1200 -quality 96;done I'm guessing ../ denotes the parent dir when working in a child directory, but I need help here. Edit: "n" needs to be replaced with the syntax for the working directory name. Sorry there was a typo as well third script line, should have read n not x Edit2: This script does exactly what I need and it's silent. #!/bin/bash DEST="../${PWD##*/}_a" mkdir -p $DEST mogrify -path $DEST -resize 1200x1200 -quality 96 *.jpg *.JPG thanks to vgoff for the correct PWD syntax and cesareriva http://www.cesareriva.com/archives/722 for showing me the DEST function. Something else: ${PWD##*/}_a is not caring for spaces in the directory name and the script fails. An empty dir is created in the same dir as the images. Found it out now, it needs quotations on the $DEST too, presumably to help mkdir create the dir with a space in the name, and mogrify to write the files to the right place, like this #!/bin/bash DEST="../${PWD##*/}_a" mkdir -p "$DEST" mogrify -path "$DEST" -resize 1200x1200 -quality 96 *.jpg *.JPG

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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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  • Much Ado About Nothing: Stub Objects

    - by user9154181
    The Solaris 11 link-editor (ld) contains support for a new type of object that we call a stub object. A stub object is a shared object, built entirely from mapfiles, that supplies the same linking interface as the real object, while containing no code or data. Stub objects cannot be executed — the runtime linker will kill any process that attempts to load one. However, you can link to a stub object as a dependency, allowing the stub to act as a proxy for the real version of the object. You may well wonder if there is a point to producing an object that contains nothing but linking interface. As it turns out, stub objects are very useful for building large bodies of code such as Solaris. In the last year, we've had considerable success in applying them to one of our oldest and thorniest build problems. In this discussion, I will describe how we came to invent these objects, and how we apply them to building Solaris. This posting explains where the idea for stub objects came from, and details our long and twisty journey from hallway idea to standard link-editor feature. I expect that these details are mainly of interest to those who work on Solaris and its makefiles, those who have done so in the past, and those who work with other similar bodies of code. A subsequent posting will omit the history and background details, and instead discuss how to build and use stub objects. If you are mainly interested in what stub objects are, and don't care about the underlying software war stories, I encourage you to skip ahead. The Long Road To Stubs This all started for me with an email discussion in May of 2008, regarding a change request that was filed in 2002, entitled: 4631488 lib/Makefile is too patient: .WAITs should be reduced This CR encapsulates a number of cronic issues with Solaris builds: We build Solaris with a parallel make (dmake) that tries to build as much of the code base in parallel as possible. There is a lot of code to build, and we've long made use of parallelized builds to get the job done quicker. This is even more important in today's world of massively multicore hardware. Solaris contains a large number of executables and shared objects. Executables depend on shared objects, and shared objects can depend on each other. Before you can build an object, you need to ensure that the objects it needs have been built. This implies a need for serialization, which is in direct opposition to the desire to build everying in parallel. To accurately build objects in the right order requires an accurate set of make rules defining the things that depend on each other. This sounds simple, but the reality is quite complex. In practice, having programmers explicitly specify these dependencies is a losing strategy: It's really hard to get right. It's really easy to get it wrong and never know it because things build anyway. Even if you get it right, it won't stay that way, because dependencies between objects can change over time, and make cannot help you detect such drifing. You won't know that you got it wrong until the builds break. That can be a long time after the change that triggered the breakage happened, making it hard to connect the cause and the effect. Usually this happens just before a release, when the pressure is on, its hard to think calmly, and there is no time for deep fixes. As a poor compromise, the libraries in core Solaris were built using a set of grossly incomplete hand written rules, supplemented with a number of dmake .WAIT directives used to group the libraries into sets of non-interacting groups that can be built in parallel because we think they don't depend on each other. From time to time, someone will suggest that we could analyze the built objects themselves to determine their dependencies and then generate make rules based on those relationships. This is possible, but but there are complications that limit the usefulness of that approach: To analyze an object, you have to build it first. This is a classic chicken and egg scenario. You could analyze the results of a previous build, but then you're not necessarily going to get accurate rules for the current code. It should be possible to build the code without having a built workspace available. The analysis will take time, and remember that we're constantly trying to make builds faster, not slower. By definition, such an approach will always be approximate, and therefore only incremantally more accurate than the hand written rules described above. The hand written rules are fast and cheap, while this idea is slow and complex, so we stayed with the hand written approach. Solaris was built that way, essentially forever, because these are genuinely difficult problems that had no easy answer. The makefiles were full of build races in which the right outcomes happened reliably for years until a new machine or a change in build server workload upset the accidental balance of things. After figuring out what had happened, you'd mutter "How did that ever work?", add another incomplete and soon to be inaccurate make dependency rule to the system, and move on. This was not a satisfying solution, as we tend to be perfectionists in the Solaris group, but we didn't have a better answer. It worked well enough, approximately. And so it went for years. We needed a different approach — a new idea to cut the Gordian Knot. In that discussion from May 2008, my fellow linker-alien Rod Evans had the initial spark that lead us to a game changing series of realizations: The link-editor is used to link objects together, but it only uses the ELF metadata in the object, consisting of symbol tables, ELF versioning sections, and similar data. Notably, it does not look at, or understand, the machine code that makes an object useful at runtime. If you had an object that only contained the ELF metadata for a dependency, but not the code or data, the link-editor would find it equally useful for linking, and would never know the difference. Call it a stub object. In the core Solaris OS, we require all objects to be built with a link-editor mapfile that describes all of its publically available functions and data. Could we build a stub object using the mapfile for the real object? It ought to be very fast to build stub objects, as there are no input objects to process. Unlike the real object, stub objects would not actually require any dependencies, and so, all of the stubs for the entire system could be built in parallel. When building the real objects, one could link against the stub objects instead of the real dependencies. This means that all the real objects can be built built in parallel too, without any serialization. We could replace a system that requires perfect makefile rules with a system that requires no ordering rules whatsoever. The results would be considerably more robust. We immediately realized that this idea had potential, but also that there were many details to sort out, lots of work to do, and that perhaps it wouldn't really pan out. As is often the case, it would be necessary to do the work and see how it turned out. Following that conversation, I set about trying to build a stub object. We determined that a faithful stub has to do the following: Present the same set of global symbols, with the same ELF versioning, as the real object. Functions are simple — it suffices to have a symbol of the right type, possibly, but not necessarily, referencing a null function in its text segment. Copy relocations make data more complicated to stub. The possibility of a copy relocation means that when you create a stub, the data symbols must have the actual size of the real data. Any error in this will go uncaught at link time, and will cause tragic failures at runtime that are very hard to diagnose. For reasons too obscure to go into here, involving tentative symbols, it is also important that the data reside in bss, or not, matching its placement in the real object. If the real object has more than one symbol pointing at the same data item, we call these aliased symbols. All data symbols in the stub object must exhibit the same aliasing as the real object. We imagined the stub library feature working as follows: A command line option to ld tells it to produce a stub rather than a real object. In this mode, only mapfiles are examined, and any object or shared libraries on the command line are are ignored. The extra information needed (function or data, size, and bss details) would be added to the mapfile. When building the real object instead of the stub, the extra information for building stubs would be validated against the resulting object to ensure that they match. In exploring these ideas, I immediately run headfirst into the reality of the original mapfile syntax, a subject that I would later write about as The Problem(s) With Solaris SVR4 Link-Editor Mapfiles. The idea of extending that poor language was a non-starter. Until a better mapfile syntax became available, which seemed unlikely in 2008, the solution could not involve extentions to the mapfile syntax. Instead, we cooked up the idea (hack) of augmenting mapfiles with stylized comments that would carry the necessary information. A typical definition might look like: # DATA(i386) __iob 0x3c0 # DATA(amd64,sparcv9) __iob 0xa00 # DATA(sparc) __iob 0x140 iob; A further problem then became clear: If we can't extend the mapfile syntax, then there's no good way to extend ld with an option to produce stub objects, and to validate them against the real objects. The idea of having ld read comments in a mapfile and parse them for content is an unacceptable hack. The entire point of comments is that they are strictly for the human reader, and explicitly ignored by the tool. Taking all of these speed bumps into account, I made a new plan: A perl script reads the mapfiles, generates some small C glue code to produce empty functions and data definitions, compiles and links the stub object from the generated glue code, and then deletes the generated glue code. Another perl script used after both objects have been built, to compare the real and stub objects, using data from elfdump, and validate that they present the same linking interface. By June 2008, I had written the above, and generated a stub object for libc. It was a useful prototype process to go through, and it allowed me to explore the ideas at a deep level. Ultimately though, the result was unsatisfactory as a basis for real product. There were so many issues: The use of stylized comments were fine for a prototype, but not close to professional enough for shipping product. The idea of having to document and support it was a large concern. The ideal solution for stub objects really does involve having the link-editor accept the same arguments used to build the real object, augmented with a single extra command line option. Any other solution, such as our prototype script, will require makefiles to be modified in deeper ways to support building stubs, and so, will raise barriers to converting existing code. A validation script that rederives what the linker knew when it built an object will always be at a disadvantage relative to the actual linker that did the work. A stub object should be identifyable as such. In the prototype, there was no tag or other metadata that would let you know that they weren't real objects. Being able to identify a stub object in this way means that the file command can tell you what it is, and that the runtime linker can refuse to try and run a program that loads one. At that point, we needed to apply this prototype to building Solaris. As you might imagine, the task of modifying all the makefiles in the core Solaris code base in order to do this is a massive task, and not something you'd enter into lightly. The quality of the prototype just wasn't good enough to justify that sort of time commitment, so I tabled the project, putting it on my list of long term things to think about, and moved on to other work. It would sit there for a couple of years. Semi-coincidentally, one of the projects I tacked after that was to create a new mapfile syntax for the Solaris link-editor. We had wanted to do something about the old mapfile syntax for many years. Others before me had done some paper designs, and a great deal of thought had already gone into the features it should, and should not have, but for various reasons things had never moved beyond the idea stage. When I joined Sun in late 2005, I got involved in reviewing those things and thinking about the problem. Now in 2008, fresh from relearning for the Nth time why the old mapfile syntax was a huge impediment to linker progress, it seemed like the right time to tackle the mapfile issue. Paving the way for proper stub object support was not the driving force behind that effort, but I certainly had them in mind as I moved forward. The new mapfile syntax, which we call version 2, integrated into Nevada build snv_135 in in February 2010: 6916788 ld version 2 mapfile syntax PSARC/2009/688 Human readable and extensible ld mapfile syntax In order to prove that the new mapfile syntax was adequate for general purpose use, I had also done an overhaul of the ON consolidation to convert all mapfiles to use the new syntax, and put checks in place that would ensure that no use of the old syntax would creep back in. That work went back into snv_144 in June 2010: 6916796 OSnet mapfiles should use version 2 link-editor syntax That was a big putback, modifying 517 files, adding 18 new files, and removing 110 old ones. I would have done this putback anyway, as the work was already done, and the benefits of human readable syntax are obvious. However, among the justifications listed in CR 6916796 was this We anticipate adding additional features to the new mapfile language that will be applicable to ON, and which will require all sharable object mapfiles to use the new syntax. I never explained what those additional features were, and no one asked. It was premature to say so, but this was a reference to stub objects. By that point, I had already put together a working prototype link-editor with the necessary support for stub objects. I was pleased to find that building stubs was indeed very fast. On my desktop system (Ultra 24), an amd64 stub for libc can can be built in a fraction of a second: % ptime ld -64 -z stub -o stubs/libc.so.1 -G -hlibc.so.1 \ -ztext -zdefs -Bdirect ... real 0.019708910 user 0.010101680 sys 0.008528431 In order to go from prototype to integrated link-editor feature, I knew that I would need to prove that stub objects were valuable. And to do that, I knew that I'd have to switch the Solaris ON consolidation to use stub objects and evaluate the outcome. And in order to do that experiment, ON would first need to be converted to version 2 mapfiles. Sub-mission accomplished. Normally when you design a new feature, you can devise reasonably small tests to show it works, and then deploy it incrementally, letting it prove its value as it goes. The entire point of stub objects however was to demonstrate that they could be successfully applied to an extremely large and complex code base, and specifically to solve the Solaris build issues detailed above. There was no way to finesse the matter — in order to move ahead, I would have to successfully use stub objects to build the entire ON consolidation and demonstrate their value. In software, the need to boil the ocean can often be a warning sign that things are trending in the wrong direction. Conversely, sometimes progress demands that you build something large and new all at once. A big win, or a big loss — sometimes all you can do is try it and see what happens. And so, I spent some time staring at ON makefiles trying to get a handle on how things work, and how they'd have to change. It's a big and messy world, full of complex interactions, unspecified dependencies, special cases, and knowledge of arcane makefile features... ...and so, I backed away, put it down for a few months and did other work... ...until the fall, when I felt like it was time to stop thinking and pondering (some would say stalling) and get on with it. Without stubs, the following gives a simplified high level view of how Solaris is built: An initially empty directory known as the proto, and referenced via the ROOT makefile macro is established to receive the files that make up the Solaris distribution. A top level setup rule creates the proto area, and performs operations needed to initialize the workspace so that the main build operations can be launched, such as copying needed header files into the proto area. Parallel builds are launched to build the kernel (usr/src/uts), libraries (usr/src/lib), and commands. The install makefile target builds each item and delivers a copy to the proto area. All libraries and executables link against the objects previously installed in the proto, implying the need to synchronize the order in which things are built. Subsequent passes run lint, and do packaging. Given this structure, the additions to use stub objects are: A new second proto area is established, known as the stub proto and referenced via the STUBROOT makefile macro. The stub proto has the same structure as the real proto, but is used to hold stub objects. All files in the real proto are delivered as part of the Solaris product. In contrast, the stub proto is used to build the product, and then thrown away. A new target is added to library Makefiles called stub. This rule builds the stub objects. The ld command is designed so that you can build a stub object using the same ld command line you'd use to build the real object, with the addition of a single -z stub option. This means that the makefile rules for building the stub objects are very similar to those used to build the real objects, and many existing makefile definitions can be shared between them. A new target is added to the Makefiles called stubinstall which delivers the stub objects built by the stub rule into the stub proto. These rules reuse much of existing plumbing used by the existing install rule. The setup rule runs stubinstall over the entire lib subtree as part of its initialization. All libraries and executables link against the objects in the stub proto rather than the main proto, and can therefore be built in parallel without any synchronization. There was no small way to try this that would yield meaningful results. I would have to take a leap of faith and edit approximately 1850 makefiles and 300 mapfiles first, trusting that it would all work out. Once the editing was done, I'd type make and see what happened. This took about 6 weeks to do, and there were many dark days when I'd question the entire project, or struggle to understand some of the many twisted and complex situations I'd uncover in the makefiles. I even found a couple of new issues that required changes to the new stub object related code I'd added to ld. With a substantial amount of encouragement and help from some key people in the Solaris group, I eventually got the editing done and stub objects for the entire workspace built. I found that my desktop system could build all the stub objects in the workspace in roughly a minute. This was great news, as it meant that use of the feature is effectively free — no one was likely to notice or care about the cost of building them. After another week of typing make, fixing whatever failed, and doing it again, I succeeded in getting a complete build! The next step was to remove all of the make rules and .WAIT statements dedicated to controlling the order in which libraries under usr/src/lib are built. This came together pretty quickly, and after a few more speed bumps, I had a workspace that built cleanly and looked like something you might actually be able to integrate someday. This was a significant milestone, but there was still much left to do. I turned to doing full nightly builds. Every type of build (open, closed, OpenSolaris, export, domestic) had to be tried. Each type failed in a new and unique way, requiring some thinking and rework. As things came together, I became aware of things that could have been done better, simpler, or cleaner, and those things also required some rethinking, the seeking of wisdom from others, and some rework. After another couple of weeks, it was in close to final form. My focus turned towards the end game and integration. This was a huge workspace, and needed to go back soon, before changes in the gate would made merging increasingly difficult. At this point, I knew that the stub objects had greatly simplified the makefile logic and uncovered a number of race conditions, some of which had been there for years. I assumed that the builds were faster too, so I did some builds intended to quantify the speedup in build time that resulted from this approach. It had never occurred to me that there might not be one. And so, I was very surprised to find that the wall clock build times for a stock ON workspace were essentially identical to the times for my stub library enabled version! This is why it is important to always measure, and not just to assume. One can tell from first principles, based on all those removed dependency rules in the library makefile, that the stub object version of ON gives dmake considerably more opportunities to overlap library construction. Some hypothesis were proposed, and shot down: Could we have disabled dmakes parallel feature? No, a quick check showed things being build in parallel. It was suggested that we might be I/O bound, and so, the threads would be mostly idle. That's a plausible explanation, but system stats didn't really support it. Plus, the timing between the stub and non-stub cases were just too suspiciously identical. Are our machines already handling as much parallelism as they are capable of, and unable to exploit these additional opportunities? Once again, we didn't see the evidence to back this up. Eventually, a more plausible and obvious reason emerged: We build the libraries and commands (usr/src/lib, usr/src/cmd) in parallel with the kernel (usr/src/uts). The kernel is the long leg in that race, and so, wall clock measurements of build time are essentially showing how long it takes to build uts. Although it would have been nice to post a huge speedup immediately, we can take solace in knowing that stub objects simplify the makefiles and reduce the possibility of race conditions. The next step in reducing build time should be to find ways to reduce or overlap the uts part of the builds. When that leg of the build becomes shorter, then the increased parallelism in the libs and commands will pay additional dividends. Until then, we'll just have to settle for simpler and more robust. And so, I integrated the link-editor support for creating stub objects into snv_153 (November 2010) with 6993877 ld should produce stub objects PSARC/2010/397 ELF Stub Objects followed by the work to convert the ON consolidation in snv_161 (February 2011) with 7009826 OSnet should use stub objects 4631488 lib/Makefile is too patient: .WAITs should be reduced This was a huge putback, with 2108 modified files, 8 new files, and 2 removed files. Due to the size, I was allowed a window after snv_160 closed in which to do the putback. It went pretty smoothly for something this big, a few more preexisting race conditions would be discovered and addressed over the next few weeks, and things have been quiet since then. Conclusions and Looking Forward Solaris has been built with stub objects since February. The fact that developers no longer specify the order in which libraries are built has been a big success, and we've eliminated an entire class of build error. That's not to say that there are no build races left in the ON makefiles, but we've taken a substantial bite out of the problem while generally simplifying and improving things. The introduction of a stub proto area has also opened some interesting new possibilities for other build improvements. As this article has become quite long, and as those uses do not involve stub objects, I will defer that discussion to a future article.

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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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  • 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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  • 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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  • 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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  • 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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  • Why can't I pass a form field of type file to a CFFUNCTION using structure syntax?

    - by Eric Belair
    I'm trying to pass a form field of type "file" to a CFFUNCTION. The argument type is "any". Here is the syntax I am trying to use (pseudocode): <cfloop from="1" to="5" index="i"> <cfset fieldname = "attachment" & i /> <cfinvoke component="myComponent" method="attachFile"> <cfinvokeargument name="attachment" value="#FORM[fieldname]#" /> </cfinvoke> </cfloop> The loop is being done because there are five form fields named "attachment1", "attachment2", et al. This throws an exception in the function: coldfusion.tagext.io.FileTag$FormFileNotFoundException: The form field C:\ColdFusion8\...\neotmp25080.tmp did not contain a file. However, this syntax DOES work: <cfloop from="1" to="5" index="i"> <cfinvoke component="myComponent" method="attachFile"> <cfinvokeargument name="attachment" value="FORM.attachment#i#" /> </cfinvoke> </cfloop> I don't like writing code like that in the second example. It just seems like bad practice to me. So, can anyone tell me how to use structure syntax to properly pass a file type form field to a CFFUNCTION??

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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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