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  • Django get() query not working

    - by pimcoooooooo
    this_category = Category.objects.get(name=cat_name) gives error: get() takes exactly 2 non-keyword arguments (1 given) I am using the appengine helper, so maybe that is causing problems. Category is my model. Category.objects.all() works fine. Filter is also similarily not working. Thanks,

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  • .NET DB Query Without Allocations?

    - by Michael Covelli
    I have been given the task of re-writing some libraries written in C# so that there are no allocations once startup is completed. I just got to one project that does some DB queries over an OdbcConnection every 30 seconds. I've always just used .ExecuteReader() which creates an OdbcDataReader. Is there any pattern (like the SocketAsyncEventArgs socket pattern) that lets you re-use your own OdbcDataReader? Or some other clever way to avoid allocations? I haven't bothered to learn LINQ since all the dbs at work are Oracle based and the last I checked, there was no official Linq To Oracle provider. But if there's a way to do this in Linq, I could use one of the third-party ones.

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  • php: parse error on mysql query

    - by dwstein
    I'm getting the following error: Parse error: syntax error, unexpected T_VARIABLE in /home/a4999406/public_html/willingLog.html on line 48 on the following code (line 48 is first row of this code): $rows = mysql_num_rows($result); for ($j=0; $j<$rows: ++$j) { echo 'ID: ' . mysql_result($result, $j, 'id') . '<br />'; echo 'First: ' . mysql_result($result, $j, 'first') . '<br />'; echo 'Last: ' . mysql_result($result, $j, 'last') . '<br />'; echo 'Email: ' . mysql_result($result, $j, 'email') . '<br />'; } Anyone know what i'm doing wrong?

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  • Using query to change table mapping

    - by crapbag
    I have a table mytable( id, key, value). I realize that key is generating a lot of data redundancy since my key is a string. (my keys are really long, but repetititve) How do I build a separate table out that has (key, keyID) and then alternate my table to be mytable( id, keyID, value) and keyTable(keyID, key) ?

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  • mysql query for change in values in a logging table

    - by kiasectomondo
    I have a table like this: Index , PersonID , ItemCount , UnixTimeStamp 1 , 1 , 1 , 1296000000 2 , 1 , 2 , 1296000100 3 , 2 , 4 , 1296003230 4 , 2 , 6 , 1296093949 5 , 1 , 0 , 1296093295 Time and index always go up. Its basically a logging table to log the itemcount each time it changes. I get the most recent ItemCount for each Person like this: SELECT * FROM table a INNER JOIN ( SELECT MAX(index) as i FROM table GROUP BY PersonID) b ON a.index = b.i; What I want to do is get get the most recent record for each PersonID that is at least 24 hours older than the most recent record for each Person ID. Then I want to take the difference in ItemCount between these two to get a change in itemcount for each person over the last 24 hours: personID ChangeInItemCountOverAtLeast24Hours 1 3 2 -11 3 6 Im sort of stuck with what to do next. How can I join another itemcount based on latest adjusted timestamp of individual rows?

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  • TIME REDUCE(OPTIMISE QUERY)

    - by user2527657
    select a.userid,(select firstName from user where userid=NOTUSED.userid) as z, (select max(login_time) from userLoginTime AS b where userid = a.user_id GROUP BY b.user_id ORDER BY b.user_id) as y From(SELECT DISTINCT a.user_id FROM user AS a LEFT OUTER JOIN (SELECT (userid) FROM userlogintime where serialid=15400012)AS b ON user.user_id = b.user_id where a.Serialid=15400012 AND b.userid IS NULL) NOTUSED, Relation r, user a where r.childuserid = NOTUSED.userid and guarduserid = a.userid

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  • basic json > struct question

    - by danwoods
    I'm working with twitter's api, trying to get the json data from http://search.twitter.com/trends/current.json which looks like: {"as_of":1268069036,"trends":{"2010-03-08 17:23:56":[{"name":"Happy Women's Day","query":"\"Happy Women's Day\" OR \"Women's Day\""},{"name":"#MusicMonday","query":"#MusicMonday"},{"name":"#MM","query":"#MM"},{"name":"Oscars","query":"Oscars OR #oscars"},{"name":"#nooffense","query":"#nooffense"},{"name":"Hurt Locker","query":"\"Hurt Locker\""},{"name":"Justin Bieber","query":"\"Justin Bieber\""},{"name":"Cmon","query":"Cmon"},{"name":"My World 2","query":"\"My World 2\""},{"name":"Sandra Bullock","query":"\"Sandra Bullock\""}]}} My structs look like: type trend struct { name string query string } type trends struct { id string arr_of_trends []trend } type Trending struct { as_of string trends_obj trends } and then I parse the JSON into a variable of type Trending. I'm very new to JSON so my main concern is making sure I've have the data structure correctly setup to hold the returned json data. I'm writing this in 'Go' for a project for school. (This is not part of a particular assignment, just something I'm demo-ing for a presentation on the language)

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  • Can I use TCP as DNS query protocol on Mac OS?

    - by Brian
    Hi, I'm using Mac OS, Snow Leopard 10.6.2, and I'm suffering from UDP packet loss during DNS query. So my web browser is too slow to surf internet nicely. But it worked very well when I tried a DNS query on TCP using dig command. However, I can't find some control switch to change to use TCP during DNS query. Is there a way to change it in Mac OS? Thank you.

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  • Can I use TCP as DNS query protocol on Mac OS?

    - by Brian
    Hi, I'm using Mac OS, Snow Leopard 10.6.2, and I'm suffering from UDP packet loss during DNS query. So I tried DNS query as TCP using dig command, it worked very well. However, I can't find some control switch to change to use TCP during DNS query. Is there a way to change it in Mac OS? Thank you.

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  • How to get just value from database query in Excel?

    - by Corin
    I'm creating a spreadsheet as a collection point of information from a number of MS Access databases. I will run a query on each database to get a count of records in a particular table. Each database has the same structure but different content as they are used in different situations. So the query returns a single value, rec_count. I've figured out how to create that query, save it and then use it as the data source. So far so good. The problem is that Excel treats the query results as a table. So instead of getting just the single value the query returns, I also get the field name. Thus the result takes up two cells instead of one. When linking in the data source, I only see Table, PivotTable Report and PivotChart as options for viewing the data. I don't want any of those. I just want the single value without any formatting, column headers, etc. Is there a way to do this is Excel 2007?

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  • How to find and fix performance problems in ORM powered applications

    - by FransBouma
    Once in a while we get requests about how to fix performance problems with our framework. As it comes down to following the same steps and looking into the same things every single time, I decided to write a blogpost about it instead, so more people can learn from this and solve performance problems in their O/R mapper powered applications. In some parts it's focused on LLBLGen Pro but it's also usable for other O/R mapping frameworks, as the vast majority of performance problems in O/R mapper powered applications are not specific for a certain O/R mapper framework. Too often, the developer looks at the wrong part of the application, trying to fix what isn't a problem in that part, and getting frustrated that 'things are so slow with <insert your favorite framework X here>'. I'm in the O/R mapper business for a long time now (almost 10 years, full time) and as it's a small world, we O/R mapper developers know almost all tricks to pull off by now: we all know what to do to make task ABC faster and what compromises (because there are almost always compromises) to deal with if we decide to make ABC faster that way. Some O/R mapper frameworks are faster in X, others in Y, but you can be sure the difference is mainly a result of a compromise some developers are willing to deal with and others aren't. That's why the O/R mapper frameworks on the market today are different in many ways, even though they all fetch and save entities from and to a database. I'm not suggesting there's no room for improvement in today's O/R mapper frameworks, there always is, but it's not a matter of 'the slowness of the application is caused by the O/R mapper' anymore. Perhaps query generation can be optimized a bit here, row materialization can be optimized a bit there, but it's mainly coming down to milliseconds. Still worth it if you're a framework developer, but it's not much compared to the time spend inside databases and in user code: if a complete fetch takes 40ms or 50ms (from call to entity object collection), it won't make a difference for your application as that 10ms difference won't be noticed. That's why it's very important to find the real locations of the problems so developers can fix them properly and don't get frustrated because their quest to get a fast, performing application failed. Performance tuning basics and rules Finding and fixing performance problems in any application is a strict procedure with four prescribed steps: isolate, analyze, interpret and fix, in that order. It's key that you don't skip a step nor make assumptions: these steps help you find the reason of a problem which seems to be there, and how to fix it or leave it as-is. Skipping a step, or when you assume things will be bad/slow without doing analysis will lead to the path of premature optimization and won't actually solve your problems, only create new ones. The most important rule of finding and fixing performance problems in software is that you have to understand what 'performance problem' actually means. Most developers will say "when a piece of software / code is slow, you have a performance problem". But is that actually the case? If I write a Linq query which will aggregate, group and sort 5 million rows from several tables to produce a resultset of 10 rows, it might take more than a couple of milliseconds before that resultset is ready to be consumed by other logic. If I solely look at the Linq query, the code consuming the resultset of the 10 rows and then look at the time it takes to complete the whole procedure, it will appear to me to be slow: all that time taken to produce and consume 10 rows? But if you look closer, if you analyze and interpret the situation, you'll see it does a tremendous amount of work, and in that light it might even be extremely fast. With every performance problem you encounter, always do realize that what you're trying to solve is perhaps not a technical problem at all, but a perception problem. The second most important rule you have to understand is based on the old saying "Penny wise, Pound Foolish": the part which takes e.g. 5% of the total time T for a given task isn't worth optimizing if you have another part which takes a much larger part of the total time T for that same given task. Optimizing parts which are relatively insignificant for the total time taken is not going to bring you better results overall, even if you totally optimize that part away. This is the core reason why analysis of the complete set of application parts which participate in a given task is key to being successful in solving performance problems: No analysis -> no problem -> no solution. One warning up front: hunting for performance will always include making compromises. Fast software can be made maintainable, but if you want to squeeze as much performance out of your software, you will inevitably be faced with the dilemma of compromising one or more from the group {readability, maintainability, features} for the extra performance you think you'll gain. It's then up to you to decide whether it's worth it. In almost all cases it's not. The reason for this is simple: the vast majority of performance problems can be solved by implementing the proper algorithms, the ones with proven Big O-characteristics so you know the performance you'll get plus you know the algorithm will work. The time taken by the algorithm implementing code is inevitable: you already implemented the best algorithm. You might find some optimizations on the technical level but in general these are minor. Let's look at the four steps to see how they guide us through the quest to find and fix performance problems. Isolate The first thing you need to do is to isolate the areas in your application which are assumed to be slow. For example, if your application is a web application and a given page is taking several seconds or even minutes to load, it's a good candidate to check out. It's important to start with the isolate step because it allows you to focus on a single code path per area with a clear begin and end and ignore the rest. The rest of the steps are taken per identified problematic area. Keep in mind that isolation focuses on tasks in an application, not code snippets. A task is something that's started in your application by either another task or the user, or another program, and has a beginning and an end. You can see a task as a piece of functionality offered by your application.  Analyze Once you've determined the problem areas, you have to perform analysis on the code paths of each area, to see where the performance problems occur and which areas are not the problem. This is a multi-layered effort: an application which uses an O/R mapper typically consists of multiple parts: there's likely some kind of interface (web, webservice, windows etc.), a part which controls the interface and business logic, the O/R mapper part and the RDBMS, all connected with either a network or inter-process connections provided by the OS or other means. Each of these parts, including the connectivity plumbing, eat up a part of the total time it takes to complete a task, e.g. load a webpage with all orders of a given customer X. To understand which parts participate in the task / area we're investigating and how much they contribute to the total time taken to complete the task, analysis of each participating task is essential. Start with the code you wrote which starts the task, analyze the code and track the path it follows through your application. What does the code do along the way, verify whether it's correct or not. Analyze whether you have implemented the right algorithms in your code for this particular area. Remember we're looking at one area at a time, which means we're ignoring all other code paths, just the code path of the current problematic area, from begin to end and back. Don't dig in and start optimizing at the code level just yet. We're just analyzing. If your analysis reveals big architectural stupidity, it's perhaps a good idea to rethink the architecture at this point. For the rest, we're analyzing which means we collect data about what could be wrong, for each participating part of the complete application. Reviewing the code you wrote is a good tool to get deeper understanding of what is going on for a given task but ultimately it lacks precision and overview what really happens: humans aren't good code interpreters, computers are. We therefore need to utilize tools to get deeper understanding about which parts contribute how much time to the total task, triggered by which other parts and for example how many times are they called. There are two different kind of tools which are necessary: .NET profilers and O/R mapper / RDBMS profilers. .NET profiling .NET profilers (e.g. dotTrace by JetBrains or Ants by Red Gate software) show exactly which pieces of code are called, how many times they're called, and the time it took to run that piece of code, at the method level and sometimes even at the line level. The .NET profilers are essential tools for understanding whether the time taken to complete a given task / area in your application is consumed by .NET code, where exactly in your code, the path to that code, how many times that code was called by other code and thus reveals where hotspots are located: the areas where a solution can be found. Importantly, they also reveal which areas can be left alone: remember our penny wise pound foolish saying: if a profiler reveals that a group of methods are fast, or don't contribute much to the total time taken for a given task, ignore them. Even if the code in them is perhaps complex and looks like a candidate for optimization: you can work all day on that, it won't matter.  As we're focusing on a single area of the application, it's best to start profiling right before you actually activate the task/area. Most .NET profilers support this by starting the application without starting the profiling procedure just yet. You navigate to the particular part which is slow, start profiling in the profiler, in your application you perform the actions which are considered slow, and afterwards you get a snapshot in the profiler. The snapshot contains the data collected by the profiler during the slow action, so most data is produced by code in the area to investigate. This is important, because it allows you to stay focused on a single area. O/R mapper and RDBMS profiling .NET profilers give you a good insight in the .NET side of things, but not in the RDBMS side of the application. As this article is about O/R mapper powered applications, we're also looking at databases, and the software making it possible to consume the database in your application: the O/R mapper. To understand which parts of the O/R mapper and database participate how much to the total time taken for task T, we need different tools. There are two kind of tools focusing on O/R mappers and database performance profiling: O/R mapper profilers and RDBMS profilers. For O/R mapper profilers, you can look at LLBLGen Prof by hibernating rhinos or the Linq to Sql/LLBLGen Pro profiler by Huagati. Hibernating rhinos also have profilers for other O/R mappers like NHibernate (NHProf) and Entity Framework (EFProf) and work the same as LLBLGen Prof. For RDBMS profilers, you have to look whether the RDBMS vendor has a profiler. For example for SQL Server, the profiler is shipped with SQL Server, for Oracle it's build into the RDBMS, however there are also 3rd party tools. Which tool you're using isn't really important, what's important is that you get insight in which queries are executed during the task / area we're currently focused on and how long they took. Here, the O/R mapper profilers have an advantage as they collect the time it took to execute the query from the application's perspective so they also collect the time it took to transport data across the network. This is important because a query which returns a massive resultset or a resultset with large blob/clob/ntext/image fields takes more time to get transported across the network than a small resultset and a database profiler doesn't take this into account most of the time. Another tool to use in this case, which is more low level and not all O/R mappers support it (though LLBLGen Pro and NHibernate as well do) is tracing: most O/R mappers offer some form of tracing or logging system which you can use to collect the SQL generated and executed and often also other activity behind the scenes. While tracing can produce a tremendous amount of data in some cases, it also gives insight in what's going on. Interpret After we've completed the analysis step it's time to look at the data we've collected. We've done code reviews to see whether we've done anything stupid and which parts actually take place and if the proper algorithms have been implemented. We've done .NET profiling to see which parts are choke points and how much time they contribute to the total time taken to complete the task we're investigating. We've performed O/R mapper profiling and RDBMS profiling to see which queries were executed during the task, how many queries were generated and executed and how long they took to complete, including network transportation. All this data reveals two things: which parts are big contributors to the total time taken and which parts are irrelevant. Both aspects are very important. The parts which are irrelevant (i.e. don't contribute significantly to the total time taken) can be ignored from now on, we won't look at them. The parts which contribute a lot to the total time taken are important to look at. We now have to first look at the .NET profiler results, to see whether the time taken is consumed in our own code, in .NET framework code, in the O/R mapper itself or somewhere else. For example if most of the time is consumed by DbCommand.ExecuteReader, the time it took to complete the task is depending on the time the data is fetched from the database. If there was just 1 query executed, according to tracing or O/R mapper profilers / RDBMS profilers, check whether that query is optimal, uses indexes or has to deal with a lot of data. Interpret means that you follow the path from begin to end through the data collected and determine where, along the path, the most time is contributed. It also means that you have to check whether this was expected or is totally unexpected. My previous example of the 10 row resultset of a query which groups millions of rows will likely reveal that a long time is spend inside the database and almost no time is spend in the .NET code, meaning the RDBMS part contributes the most to the total time taken, the rest is compared to that time, irrelevant. Considering the vastness of the source data set, it's expected this will take some time. However, does it need tweaking? Perhaps all possible tweaks are already in place. In the interpret step you then have to decide that further action in this area is necessary or not, based on what the analysis results show: if the analysis results were unexpected and in the area where the most time is contributed to the total time taken is room for improvement, action should be taken. If not, you can only accept the situation and move on. In all cases, document your decision together with the analysis you've done. If you decide that the perceived performance problem is actually expected due to the nature of the task performed, it's essential that in the future when someone else looks at the application and starts asking questions you can answer them properly and new analysis is only necessary if situations changed. Fix After interpreting the analysis results you've concluded that some areas need adjustment. This is the fix step: you're actively correcting the performance problem with proper action targeted at the real cause. In many cases related to O/R mapper powered applications it means you'll use different features of the O/R mapper to achieve the same goal, or apply optimizations at the RDBMS level. It could also mean you apply caching inside your application (compromise memory consumption over performance) to avoid unnecessary re-querying data and re-consuming the results. After applying a change, it's key you re-do the analysis and interpretation steps: compare the results and expectations with what you had before, to see whether your actions had any effect or whether it moved the problem to a different part of the application. Don't fall into the trap to do partly analysis: do the full analysis again: .NET profiling and O/R mapper / RDBMS profiling. It might very well be that the changes you've made make one part faster but another part significantly slower, in such a way that the overall problem hasn't changed at all. Performance tuning is dealing with compromises and making choices: to use one feature over the other, to accept a higher memory footprint, to go away from the strict-OO path and execute queries directly onto the RDBMS, these are choices and compromises which will cross your path if you want to fix performance problems with respect to O/R mappers or data-access and databases in general. In most cases it's not a big issue: alternatives are often good choices too and the compromises aren't that hard to deal with. What is important is that you document why you made a choice, a compromise: which analysis data, which interpretation led you to the choice made. This is key for good maintainability in the years to come. Most common performance problems with O/R mappers Below is an incomplete list of common performance problems related to data-access / O/R mappers / RDBMS code. It will help you with fixing the hotspots you found in the interpretation step. SELECT N+1: (Lazy-loading specific). Lazy loading triggered performance bottlenecks. Consider a list of Orders bound to a grid. You have a Field mapped onto a related field in Order, Customer.CompanyName. Showing this column in the grid will make the grid fetch (indirectly) for each row the Customer row. This means you'll get for the single list not 1 query (for the orders) but 1+(the number of orders shown) queries. To solve this: use eager loading using a prefetch path to fetch the customers with the orders. SELECT N+1 is easy to spot with an O/R mapper profiler or RDBMS profiler: if you see a lot of identical queries executed at once, you have this problem. Prefetch paths using many path nodes or sorting, or limiting. Eager loading problem. Prefetch paths can help with performance, but as 1 query is fetched per node, it can be the number of data fetched in a child node is bigger than you think. Also consider that data in every node is merged on the client within the parent. This is fast, but it also can take some time if you fetch massive amounts of entities. If you keep fetches small, you can use tuning parameters like the ParameterizedPrefetchPathThreshold setting to get more optimal queries. Deep inheritance hierarchies of type Target Per Entity/Type. If you use inheritance of type Target per Entity / Type (each type in the inheritance hierarchy is mapped onto its own table/view), fetches will join subtype- and supertype tables in many cases, which can lead to a lot of performance problems if the hierarchy has many types. With this problem, keep inheritance to a minimum if possible, or switch to a hierarchy of type Target Per Hierarchy, which means all entities in the inheritance hierarchy are mapped onto the same table/view. Of course this has its own set of drawbacks, but it's a compromise you might want to take. Fetching massive amounts of data by fetching large lists of entities. LLBLGen Pro supports paging (and limiting the # of rows returned), which is often key to process through large sets of data. Use paging on the RDBMS if possible (so a query is executed which returns only the rows in the page requested). When using paging in a web application, be sure that you switch server-side paging on on the datasourcecontrol used. In this case, paging on the grid alone is not enough: this can lead to fetching a lot of data which is then loaded into the grid and paged there. Keep note that analyzing queries for paging could lead to the false assumption that paging doesn't occur, e.g. when the query contains a field of type ntext/image/clob/blob and DISTINCT can't be applied while it should have (e.g. due to a join): the datareader will do DISTINCT filtering on the client. this is a little slower but it does perform paging functionality on the data-reader so it won't fetch all rows even if the query suggests it does. Fetch massive amounts of data because blob/clob/ntext/image fields aren't excluded. LLBLGen Pro supports field exclusion for queries. You can exclude fields (also in prefetch paths) per query to avoid fetching all fields of an entity, e.g. when you don't need them for the logic consuming the resultset. Excluding fields can greatly reduce the amount of time spend on data-transport across the network. Use this optimization if you see that there's a big difference between query execution time on the RDBMS and the time reported by the .NET profiler for the ExecuteReader method call. Doing client-side aggregates/scalar calculations by consuming a lot of data. If possible, try to formulate a scalar query or group by query using the projection system or GetScalar functionality of LLBLGen Pro to do data consumption on the RDBMS server. It's far more efficient to process data on the RDBMS server than to first load it all in memory, then traverse the data in-memory to calculate a value. Using .ToList() constructs inside linq queries. It might be you use .ToList() somewhere in a Linq query which makes the query be run partially in-memory. Example: var q = from c in metaData.Customers.ToList() where c.Country=="Norway" select c; This will actually fetch all customers in-memory and do an in-memory filtering, as the linq query is defined on an IEnumerable<T>, and not on the IQueryable<T>. Linq is nice, but it can often be a bit unclear where some parts of a Linq query might run. Fetching all entities to delete into memory first. To delete a set of entities it's rather inefficient to first fetch them all into memory and then delete them one by one. It's more efficient to execute a DELETE FROM ... WHERE query on the database directly to delete the entities in one go. LLBLGen Pro supports this feature, and so do some other O/R mappers. It's not always possible to do this operation in the context of an O/R mapper however: if an O/R mapper relies on a cache, these kind of operations are likely not supported because they make it impossible to track whether an entity is actually removed from the DB and thus can be removed from the cache. Fetching all entities to update with an expression into memory first. Similar to the previous point: it is more efficient to update a set of entities directly with a single UPDATE query using an expression instead of fetching the entities into memory first and then updating the entities in a loop, and afterwards saving them. It might however be a compromise you don't want to take as it is working around the idea of having an object graph in memory which is manipulated and instead makes the code fully aware there's a RDBMS somewhere. Conclusion Performance tuning is almost always about compromises and making choices. It's also about knowing where to look and how the systems in play behave and should behave. The four steps I provided should help you stay focused on the real problem and lead you towards the solution. Knowing how to optimally use the systems participating in your own code (.NET framework, O/R mapper, RDBMS, network/services) is key for success as well as knowing what's going on inside the application you built. I hope you'll find this guide useful in tracking down performance problems and dealing with them in a useful way.  

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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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  • 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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  • 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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  • 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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  • 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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  • Chaining IQueryables together

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

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  • JPA Entity Manager resource handling

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

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  • How to do a timestamp comparison with JPA query?

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

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

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

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

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

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

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

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