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  • Approaches for Content-based Item Recommendations

    - by PartlyCloudy
    Hello, I'm currently developing an application where I want to group similar items. Items (like videos) can be created by users and also their attributes can be altered or extended later (like new tags). Instead of relying on users' preferences as most collaborative filtering mechanisms do, I want to compare item similarity based on the items' attributes (like similar length, similar colors, similar set of tags, etc.). The computation is necessary for two main purposes: Suggesting x similar items for a given item and for clustering into groups of similar items. My application so far is follows an asynchronous design and I want to decouple this clustering component as far as possible. The creation of new items or the addition of new attributes for an existing item will be advertised by publishing events the component can then consume. Computations can be provided best-effort and "snapshotted", which means that I'm okay with the best result possible at a given point in time, although result quality will eventually increase. So I am now searching for appropriate algorithms to compute both similar items and clusters. At important constraint is scalability. Initially the application has to handle a few thousand items, but later million items might be possible as well. Of course, computations will then be executed on additional nodes, but the algorithm itself should scale. It would also be nice if the algorithm supports some kind of incremental mode on partial changes of the data. My initial thought of comparing each item with each other and storing the numerical similarity sounds a little bit crude. Also, it requires n*(n-1)/2 entries for storing all similarities and any change or new item will eventually cause n similarity computations. Thanks in advance! UPDATE tl;dr To clarify what I want, here is my targeted scenario: User generate entries (think of documents) User edit entry meta data (think of tags) And here is what my system should provide: List of similar entries to a given item as recommendation Clusters of similar entries Both calculations should be based on: The meta data/attributes of entries (i.e. usage of similar tags) Thus, the distance of two entries using appropriate metrics NOT based on user votings, preferences or actions (unlike collaborative filtering). Although users may create entries and change attributes, the computation should only take into account the items and their attributes, and not the users associated with (just like a system where only items and no users exist). Ideally, the algorithm should support: permanent changes of attributes of an entry incrementally compute similar entries/clusters on changes scale something better than a simple distance table, if possible (because of the O(n²) space complexity)

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  • How would you structure your entity model for storing arbitrary key/value data with different data t

    - by Nathan Ridley
    I keep coming across scenarios where it will be useful to store a set of arbitrary data in a table using a per-row key/value model, rather than a rigid column/field model. The problem is, I want to store the values with their correct data type rather than converting everything to a string. This means I have to choose either a single table with multiple nullable columns, one for each data type, or a set of value tables, one for each data type. I'm also unsure as to whether I should use full third normal form and separate the keys into a separate table, referencing them via a foreign key from the value table(s), or if it would be better to keep things simple and store the string keys in the value table(s) and accept the duplication of strings. Old/bad: This solution makes adding additional values a pain in a fluid environment because the table needs to be modified regularly. MyTable ============================ ID Key1 Key2 Key3 int int string date ---------------------------- 1 Value1 Value2 Value3 2 Value4 Value5 Value6 Single Table Solution This solution allows simplicity via a single table. The querying code still needs to check for nulls to determine which data type the field is storing. A check constraint is probably also required to ensure only one of the value fields contains non-nulll data. DataValues ============================================================= ID RecordID Key IntValue StringValue DateValue int int string int string date ------------------------------------------------------------- 1 1 Key1 Value1 NULL NULL 2 1 Key2 NULL Value2 NULL 3 1 Key3 NULL NULL Value3 4 2 Key1 Value4 NULL NULL 5 2 Key2 NULL Value5 NULL 6 2 Key3 NULL NULL Value6 Multiple-Table Solution This solution allows for more concise purposing of each table, though the code needs to know the data type in advance as it needs to query a different table for each data type. Indexing is probably simpler and more efficient because there are less columns that need indexing. IntegerValues =============================== ID RecordID Key Value int int string int ------------------------------- 1 1 Key1 Value1 2 2 Key1 Value4 StringValues =============================== ID RecordID Key Value int int string string ------------------------------- 1 1 Key2 Value2 2 2 Key2 Value5 DateValues =============================== ID RecordID Key Value int int string date ------------------------------- 1 1 Key3 Value3 2 2 Key3 Value6 How do you approach this problem? Which solution is better? Also, should the key column be separated into a separate table and referenced via a foreign key or be should it be kept in the value table and bulk updated if for some reason the key name changes?

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  • Authenticating users in iPhone app

    - by Myron
    I'm developing an HTTP api for our web application. Initially, the primary consumer of the API will be an iPhone app we're developing, but I'm designing this with future uses in mind (such as mobile apps for other platforms). I'm trying to decide on the best way to authenticate users so they can access their accounts from the iPhone. I've got a design that I think works well, but I'm no security expert, so I figured it would be good to ask for feedback here. The design of the user authentication has 3 primary goals: Good user experience: We want to allow users to enter their credentials once, and remain logged in indefinitely, until they explicitly log out. I would have considered OAuth if not for the fact that the experience from an iPhone app is pretty awful, from what I've heard (i.e. it launches the login form in Safari, then tells the user to return to the app when authentication succeeds). No need to store the user creds with the app: I always hate the idea of having the user's password stored in either plain text or symmetrically encrypted anywhere, so I don't want the app to have to store the password to pass it to the API for future API requests. Security: We definitely don't need the intense security of a banking app, but I'd obviously like this to be secure. Overall, the API is REST-inspired (i.e. treating URLs as resources, and using the HTTP methods and status codes semantically). Each request to the API must include two custom HTTP headers: an API Key (unique to each client app) and a unique device ID. The API requires all requests to be made using HTTPS, so that the headers and body are encrypted. My plan is to have an api_sessions table in my database. It has a unique constraint on the API key and unique device ID (so that a device may only be logged into a single user account through a given app) as well as a foreign key to the users table. The API will have a login endpoint, which receives the username/password and, if they match an account, logs the user in, creating an api_sessions record for the given API key and device id. Future API requests will look up the api_session using the API key and device id, and, if a record is found, treat the request as being logged in under the user account referenced by the api_session record. There will also be a logout API endpoint, which deletes the record from the api_sessions table. Does anyone see any obvious security holes in this?

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  • passing back answers in prolog

    - by AhmadAssaf
    i have this code than runs perfectly .. returns a true .. when tracing the values are ok .. but its not returning back the answer .. it acts strangely when it ends and always return empty list .. uninstantiated variable .. test :- extend(4,12,[4,3,1,2],[[1,5],[3,4],[6]],_ExtendedBins). %printing basic information about the extend(NumBins,Capacity,RemainingNumbers,BinsSoFar,_ExtendedBins) :- getNumberofBins(BinsSoFar,NumberOfBins), msort(RemainingNumbers,SortedRemaining),nl, format("Current Number of Bins is :~w\n",[NumberOfBins]), format("Allowed Capacity is :~w\n",[Capacity]), format("maximum limit in bin is :~w\n",[NumBins]), format("Trying to fit :~w\n\n",[SortedRemaining]), format("Possible Solutions :\n\n"), fitElements(NumBins,NumberOfBins, Capacity,SortedRemaining,BinsSoFar,[]). %this is were the creation for possibilities will start %will check first if the number of bins allowed is less than then %we create a new list with all the possible combinations %after that we start matching to other bins with capacity constraint fitElements(NumBins,NumberOfBins, Capacity,RemainingNumbers,Bins,ExtendedBins) :- ( NumberOfBins < NumBins -> print('Creating new set: '); print('Sorry, Cannot create New Sets')), createNewList(Capacity,RemainingNumbers,Bins,ExtendedBins). createNewList(Capacity,RemainingNumbers,Bins,ExtendedBins) :- createNewList(Capacity,RemainingNumbers,Bins,[],ExtendedBins), print(ExtendedBins). createNewList(0,Bins,Bins,ExtendedBins,ExtendedBins). createNewList(_,[],_,ExtendedBins,ExtendedBins). createNewList(Capacity,[Element|Rest],Bins,Temp,ExtendedBins) :- conjunct_to_list(Element,ListedElement), append(ListedElement,Temp,NewList), sumlist(NewList,Sum), (Sum =< Capacity, append(ListedElement,ExtendedBins,Result); Capacity = 0), createNewList(Capacity,Rest,Bins,NewList,Result). fit(0,[],ExtendedBins,ExtendedBins). fit(Capacity,[Element|Rest],Bin,ExtendedBins) :- conjunct_to_list(Element,Listed), append(Listed,Bin,NewBin), sumlist(NewBin,Sum), (Sum =< Capacity -> fit(Capacity,Rest,NewBin,ExtendedBins); Capacity = 0, append(NewBin,ExtendedBins,NewExtendedBins), print(NewExtendedBins), fit(0,[],NewBin,ExtendedBins)). %get the number of bins provided getNumberofBins(List,NumberOfBins) :- getNumberofBins(List,0,NumberOfBins). getNumberofBins([],NumberOfBins,NumberOfBins). getNumberofBins([_List|Rest],TempCount,NumberOfBins) :- NewCount is TempCount + 1, %calculate the count getNumberofBins(Rest,NewCount,NumberOfBins). %recursive call %Convert set of terms into a list - used when needed to append conjunct_to_list((A,B), L) :- !, conjunct_to_list(A, L0), conjunct_to_list(B, L1), append(L0, L1, L). conjunct_to_list(A, [A]). Greatly appreciate the help

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  • How to see if type is instance of a class in Haskell?

    - by Raekye
    I'm probably doing this completely wrong (the unhaskell way); I'm just learning so please let me know if there's a better way to approach this. Context: I'm writing a bunch of tree structures. I want to reuse my prettyprint function for binary trees. Not all trees can use the generic Node/Branch data type though; different trees need different extra data. So to reuse the prettyprint function I thought of creating a class different trees would be instances of: class GenericBinaryTree a where is_leaf :: a -> Bool left :: a -> a node :: a -> b right :: a -> a This way they only have to implement methods to retrieve the left, right, and current node value, and prettyprint doesn't need to know about the internal structure. Then I get down to here: prettyprint_helper :: GenericBinaryTree a => a -> [String] prettyprint_helper tree | is_leaf tree = [] | otherwise = ("{" ++ (show (node tree)) ++ "}") : (prettyprint_subtree (left tree) (right tree)) where prettyprint_subtree left right = ((pad "+- " "| ") (prettyprint_helper right)) ++ ((pad "`- " " ") (prettyprint_helper left)) pad first rest = zipWith (++) (first : repeat rest) And I get the Ambiguous type variable 'a0' in the constraint: (Show a0) arising from a use of 'show' error for (show (node tree)) Here's an example of the most basic tree data type and instance definition (my other trees have other fields but they're irrelevant to the generic prettyprint function) data Tree a = Branch (Tree a) a (Tree a) | Leaf instance GenericBinaryTree (Tree a) where is_leaf Leaf = True is_leaf _ = False left (Branch left node right) = left right (Branch left node right) = right node (Branch left node right) = node I could have defined node :: a -> [String] and deal with the stringification in each instance/type of tree, but this feels neater. In terms of prettyprint, I only need a string representation, but if I add other generic binary tree functions later I may want the actual values. So how can I write this to work whether the node value is an instance of Show or not? Or what other way should I be approaching this problem? In an object oriented language I could easily check whether a class implements something, or if an object has a method. I can't use something like prettyprint :: Show a => a -> String Because it's not the tree that needs to be showable, it's the value inside the tree (returned by function node) that needs to be showable. I also tried changing node to Show b => a -> b without luck (and a bunch of other type class/preconditions/whatever/I don't even know what I'm doing anymore).

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  • Another Marketing Conference, part two – the afternoon

    - by Roger Hart
    In my previous post, I’ve covered the morning sessions at AMC2012. Here’s the rest of the write-up. I’ve skipped Charles Nixon’s session which was a blend of funky futurism and professional development advice, but you can see his slides here. I’ve also skipped the Google presentation, as it was a little thin on insight. 6 – Brand ambassadors: Getting universal buy in across the organisation, Vanessa Northam Slides are here This was the strongest enforcement of the idea that brand and campaign values need to be delivered throughout the organization if they’re going to work. Vanessa runs internal communications at e-on, and shared her experience of using internal comms to align an organization and thereby get the most out of a campaign. She views the purpose of internal comms as: “…to help leaders, to communicate the purpose and future of an organization, and support change.” This (and culture) primes front line staff, which creates customer experience and spreads brand. You ensure a whole organization knows what’s going on with both internal and external comms. If everybody is aligned and informed, if everybody can clearly articulate your brand and campaign goals, then you can turn everybody into an advocate. Alignment is a powerful tool for delivering a consistent experience and message. The pathological counter example is the one in which a marketing message goes out, which creates inbound customer contacts that front line contact staff haven’t been briefed to handle. The NatWest campaign was again mentioned in this context. The good example was e-on’s cheaper tariff campaign. Building a groundswell of internal excitement, and even running an internal launch meant everyone could contribute to a good customer experience. They found that meter readers were excited – not a group they’d considered as obvious in providing customer experience. But they were a group that has a lot of face-to-face contact with customers, and often were asked questions they may not have been briefed to answer. Being able to communicate a simple new message made it easier for them, and also let them become a sales and marketing asset to the organization. 7 – Goodbye Internet, Hello Outernet: the rise and rise of augmented reality, Matt Mills I wasn’t going to write this up, because it was essentially a sales demo for Aurasma. But the technology does merit some discussion. Basically, it replaces QR codes with visual recognition, and provides a simple-looking back end for attaching content. It’s quite sexy. But here’s my beef with it: QR codes had a clear visual language – when you saw one you knew what it was and what to do with it. They were clunky, but they had the “getting started” problem solved out of the box once you knew what you were looking at. However, they fail because QR code reading isn’t native to the platform. You needed an app, which meant you needed to know to download one. Consequentially, you can’t use QR codes with and ubiquity, or depend on them. This means marketers, content providers, etc, never pushed them, and they remained and awkward oddity, a minority sport. Aurasma half solves problem two, and re-introduces problem one, making it potentially half as useful as a QR code. It’s free, and you can apparently build it into your own apps. Add to that the likelihood of it becoming native to the platform if it takes off, and it may have legs. I guess we’ll see. 8 – We all need to code, Helen Mayor Great title – good point. If there was anybody in the room who didn’t at least know basic HTML, and if Helen’s presentation inspired them to learn, that’s fantastic. However, this was a half hour sales pitch for a basic coding training course. Beyond advocating coding skills it contained no useful content. Marketers may also like to consider some of these resources if they’re looking to learn code: Code Academy – free interactive tutorials Treehouse – learn web design, web dev, or app dev WebPlatform.org – tutorials and documentation for web tech  11 – Understanding our inner creativity, Margaret Boden This session was the most theoretical and probably least actionable of the day. It also held my attention utterly. Margaret spoke fluently, fascinatingly, without slides, on the subject of types of creativity and how they work. It was splendid. Yes, it raised a wry smile whenever she spoke of “the content of advertisements” and gave an example from 1970s TV ads, but even without the attempt to meet the conference’s theme this would have been thoroughly engaging. There are, Margaret suggested, three types of creativity: Combinatorial creativity The most common form, and consisting of synthesising ideas from existing and familiar concepts and tropes. Exploratory creativity Less common, this involves exploring the limits and quirks of a particular constraint or style. Transformational creativity This is uncommon, and arises from finding a way to do something that the existing rules would hold to be impossible. In essence, this involves breaking one of the constraints that exploratory creativity is composed from. Combinatorial creativity, she suggested, is particularly important for attaching favourable ideas to existing things. As such is it probably worth developing for marketing. Exploratory creativity may then come into play in something like developing and optimising an idea or campaign that now has momentum. Transformational creativity exists at the edges of this exploration. She suggested that products may often be transformational, but that marketing seemed unlikely to in her experience. This made me wonder about Listerine. Crucially, transformational creativity is characterised by there being some element of continuity with the strictures of previous thinking. Once it has happened, there may be  move from a revolutionary instance into an explored style. Again, from a marketing perspective, this seems to chime well with the thinking in Youngme Moon’s book: Different Talking about the birth of Modernism is visual art, Margaret pointed out that transformational creativity has historically risked a backlash, demanding what is essentially an education of the market. This is best accomplished by referring back to the continuities with the past in order to make the new familiar. Thoughts The afternoon is harder to sum up than the morning. It felt less concrete, and was troubled by a short run of poor presentations in the middle. Mainly, I found myself wrestling with the internal comms issue. It’s one of those things that seems astonishingly obvious in hindsight, but any campaign – particularly any large one – is doomed if the people involved can’t believe in it. We’ve run things here that haven’t gone so well, of course we have; who hasn’t? I’m not going to air any laundry, but people not being informed (much less aligned) feels like a common factor. It’s tough though. Managing and anticipating information needs across an organization of any size can’t be easy. Even the simple things like ensuring sales and support departments know what’s in a product release, and what messages go with it are easy to botch. The thing I like about framing this as a brand and campaign advocacy problem is that it makes it likely to get addressed. Better is always sexier than less-worse. Any technical communicator who’s ever felt crowded out by a content strategist or marketing copywriter  knows this – increasing revenue gets a seat at the table far more readily than reducing support costs, even if the financial impact is identical. So that’s it from AMC. The big thought-provokers were social buying behaviour and eliciting behaviour change, and the value of internal communications in ensuring successful campaigns and continuity of customer experience. I’ll be chewing over that for a while, and I’d definitely return next year.      

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  • LLBLGen Pro v3.1 released!

    - by FransBouma
    Yesterday we released LLBLGen Pro v3.1! Version 3.1 comes with new features and enhancements, which I'll describe briefly below. v3.1 is a free upgrade for v3.x licensees. What's new / changed? Designer Extensible Import system. An extensible import system has been added to the designer to import project data from external sources. Importers are plug-ins which import project meta-data (like entity definitions, mappings and relational model data) from an external source into the loaded project. In v3.1, an importer plug-in for importing project elements from existing LLBLGen Pro v3.x project files has been included. You can use this importer to create source projects from which you import parts of models to build your actual project with. Model-only relationships. In v3.1, relationships of the type 1:1, m:1 and 1:n can be marked as model-only. A model-only relationship isn't required to have a backing foreign key constraint in the relational model data. They're ideal for projects which have to work with relational databases where changes can't always be made or some relationships can't be added to (e.g. the ones which are important for the entity model, but are not allowed to be added to the relational model for some reason). Custom field ordering. Although fields in an entity definition don't really have an ordering, it can be important for some situations to have the entity fields in a given order, e.g. when you use compound primary keys. Field ordering can be defined using a pop-up dialog which can be opened through various ways, e.g. inside the project explorer, model view and entity editor. It can also be set automatically during refreshes based on new settings. Command line relational model data refresher tool, CliRefresher.exe. The command line refresh tool shipped with v2.6 is now available for v3.1 as well Navigation enhancements in various designer elements. It's now easier to find elements like entities, typed views etc. in the project explorer from editors, to navigate to related entities in the project explorer by right clicking a relationship, navigate to the super-type in the project explorer when right-clicking an entity and navigate to the sub-type in the project explorer when right-clicking a sub-type node in the project explorer. Minor visual enhancements / tweaks LLBLGen Pro Runtime Framework Entity creation is now up to 30% faster and takes 5% less memory. Creating an entity object has been optimized further by tweaks inside the framework to make instantiating an entity object up to 30% faster. It now also takes up to 5% less memory than in v3.0 Prefetch Path node merging is now up to 20-25% faster. Setting entity references required the creation of a new relationship object. As this relationship object is always used internally it could be cached (as it's used for syncing only). This increases performance by 20-25% in the merging functionality. Entity fetches are now up to 20% faster. A large number of tweaks have been applied to make entity fetches up to 20% faster than in v3.0. Full WCF RIA support. It's now possible to use your LLBLGen Pro runtime framework powered domain layer in a WCF RIA application using the VS.NET tools for WCF RIA services. WCF RIA services is a Microsoft technology for .NET 4 and typically used within silverlight applications. SQL Server DQE compatibility level is now per instance. (Usable in Adapter). It's now possible to set the compatibility level of the SQL Server Dynamic Query Engine (DQE) per instance of the DQE instead of the global setting it was before. The global setting is still available and is used as the default value for the compatibility level per-instance. You can use this to switch between CE Desktop and normal SQL Server compatibility per DataAccessAdapter instance. Support for COUNT_BIG aggregate function (SQL Server specific). The aggregate function COUNT_BIG has been added to the list of available aggregate functions to be used in the framework. Minor changes / tweaks I'm especially pleased with the import system, as that makes working with entity models a lot easier. The import system lets you import from another LLBLGen Pro v3 project any entity definition, mapping and / or meta-data like table definitions. This way you can build repository projects where you store model fragments, e.g. the building blocks for a customer-order system, a user credential model etc., any model you can think of. In most projects, you'll recognize that some parts of your new model look familiar. In these cases it would have been easier if you would have been able to import these parts from projects you had pre-created. With LLBLGen Pro v3.1 you can. For example, say you have an Oracle schema called CRM which contains the bread 'n' butter customer-order-product kind of model. You create an entity model from that schema and save it in a project file. Now you start working on another project for another customer and you have to use SQL Server. You also start using model-first development, so develop the entity model from scratch as there's no existing database. As this customer also requires some CRM like entity model, you import the entities from your saved Oracle project into this new SQL Server targeting project. Because you don't work with Oracle this time, you don't import the relational meta-data, just the entities, their relationships and possibly their inheritance hierarchies, if any. As they're now entities in your project you can change them a bit to match the new customer's requirements. This can save you a lot of time, because you can re-use pre-fab model fragments for new projects. In the example above there are no tables yet (as you work model first) so using the forward mapping capabilities of LLBLGen Pro v3 creates the tables, PK constraints, Unique Constraints and FK constraints for you. This way you can build a nice repository of model fragments which you can re-use in new projects.

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  • SQL SERVER – Introduction to SQL Server 2014 In-Memory OLTP

    - by Pinal Dave
    In SQL Server 2014 Microsoft has introduced a new database engine component called In-Memory OLTP aka project “Hekaton” which is fully integrated into the SQL Server Database Engine. It is optimized for OLTP workloads accessing memory resident data. In-memory OLTP helps us create memory optimized tables which in turn offer significant performance improvement for our typical OLTP workload. The main objective of memory optimized table is to ensure that highly transactional tables could live in memory and remain in memory forever without even losing out a single record. The most significant part is that it still supports majority of our Transact-SQL statement. Transact-SQL stored procedures can be compiled to machine code for further performance improvements on memory-optimized tables. This engine is designed to ensure higher concurrency and minimal blocking. In-Memory OLTP alleviates the issue of locking, using a new type of multi-version optimistic concurrency control. It also substantially reduces waiting for log writes by generating far less log data and needing fewer log writes. Points to remember Memory-optimized tables refer to tables using the new data structures and key words added as part of In-Memory OLTP. Disk-based tables refer to your normal tables which we used to create in SQL Server since its inception. These tables use a fixed size 8 KB pages that need to be read from and written to disk as a unit. Natively compiled stored procedures refer to an object Type which is new and is supported by in-memory OLTP engine which convert it into machine code, which can further improve the data access performance for memory –optimized tables. Natively compiled stored procedures can only reference memory-optimized tables, they can’t be used to reference any disk –based table. Interpreted Transact-SQL stored procedures, which is what SQL Server has always used. Cross-container transactions refer to transactions that reference both memory-optimized tables and disk-based tables. Interop refers to interpreted Transact-SQL that references memory-optimized tables. Using In-Memory OLTP In-Memory OLTP engine has been available as part of SQL Server 2014 since June 2013 CTPs. Installation of In-Memory OLTP is part of the SQL Server setup application. The In-Memory OLTP components can only be installed with a 64-bit edition of SQL Server 2014 hence they are not available with 32-bit editions. Creating Databases Any database that will store memory-optimized tables must have a MEMORY_OPTIMIZED_DATA filegroup. This filegroup is specifically designed to store the checkpoint files needed by SQL Server to recover the memory-optimized tables, and although the syntax for creating the filegroup is almost the same as for creating a regular filestream filegroup, it must also specify the option CONTAINS MEMORY_OPTIMIZED_DATA. Here is an example of a CREATE DATABASE statement for a database that can support memory-optimized tables: CREATE DATABASE InMemoryDB ON PRIMARY(NAME = [InMemoryDB_data], FILENAME = 'D:\data\InMemoryDB_data.mdf', size=500MB), FILEGROUP [SampleDB_mod_fg] CONTAINS MEMORY_OPTIMIZED_DATA (NAME = [InMemoryDB_mod_dir], FILENAME = 'S:\data\InMemoryDB_mod_dir'), (NAME = [InMemoryDB_mod_dir], FILENAME = 'R:\data\InMemoryDB_mod_dir') LOG ON (name = [SampleDB_log], Filename='L:\log\InMemoryDB_log.ldf', size=500MB) COLLATE Latin1_General_100_BIN2; Above example code creates files on three different drives (D:  S: and R:) for the data files and in memory storage so if you would like to run this code kindly change the drive and folder locations as per your convenience. Also notice that binary collation was specified as Windows (non-SQL). BIN2 collation is the only collation support at this point for any indexes on memory optimized tables. It is also possible to add a MEMORY_OPTIMIZED_DATA file group to an existing database, use the below command to achieve the same. ALTER DATABASE AdventureWorks2012 ADD FILEGROUP hekaton_mod CONTAINS MEMORY_OPTIMIZED_DATA; GO ALTER DATABASE AdventureWorks2012 ADD FILE (NAME='hekaton_mod', FILENAME='S:\data\hekaton_mod') TO FILEGROUP hekaton_mod; GO Creating Tables There is no major syntactical difference between creating a disk based table or a memory –optimized table but yes there are a few restrictions and a few new essential extensions. Essentially any memory-optimized table should use the MEMORY_OPTIMIZED = ON clause as shown in the Create Table query example. DURABILITY clause (SCHEMA_AND_DATA or SCHEMA_ONLY) Memory-optimized table should always be defined with a DURABILITY value which can be either SCHEMA_AND_DATA or  SCHEMA_ONLY the former being the default. A memory-optimized table defined with DURABILITY=SCHEMA_ONLY will not persist the data to disk which means the data durability is compromised whereas DURABILITY= SCHEMA_AND_DATA ensures that data is also persisted along with the schema. Indexing Memory Optimized Table A memory-optimized table must always have an index for all tables created with DURABILITY= SCHEMA_AND_DATA and this can be achieved by declaring a PRIMARY KEY Constraint at the time of creating a table. The following example shows a PRIMARY KEY index created as a HASH index, for which a bucket count must also be specified. CREATE TABLE Mem_Table ( [Name] VARCHAR(32) NOT NULL PRIMARY KEY NONCLUSTERED HASH WITH (BUCKET_COUNT = 100000), [City] VARCHAR(32) NULL, [State_Province] VARCHAR(32) NULL, [LastModified] DATETIME NOT NULL, ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA); Now as you can see in the above query example we have used the clause MEMORY_OPTIMIZED = ON to make sure that it is considered as a memory optimized table and not just a normal table and also used the DURABILITY Clause= SCHEMA_AND_DATA which means it will persist data along with metadata and also you can notice this table has a PRIMARY KEY mentioned upfront which is also a mandatory clause for memory-optimized tables. We will talk more about HASH Indexes and BUCKET_COUNT in later articles on this topic which will be focusing more on Row and Index storage on Memory-Optimized tables. So stay tuned for that as well. Now as we covered the basics of Memory Optimized tables and understood the key things to remember while using memory optimized tables, let’s explore more using examples to understand the Performance gains using memory-optimized tables. I will be using the database which i created earlier in this article i.e. InMemoryDB in the below Demo Exercise. USE InMemoryDB GO -- Creating a disk based table CREATE TABLE dbo.Disktable ( Id INT IDENTITY, Name CHAR(40) ) GO CREATE NONCLUSTERED INDEX IX_ID ON dbo.Disktable (Id) GO -- Creating a memory optimized table with similar structure and DURABILITY = SCHEMA_AND_DATA CREATE TABLE dbo.Memorytable_durable ( Id INT NOT NULL PRIMARY KEY NONCLUSTERED Hash WITH (bucket_count =1000000), Name CHAR(40) ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_AND_DATA) GO -- Creating an another memory optimized table with similar structure but DURABILITY = SCHEMA_Only CREATE TABLE dbo.Memorytable_nondurable ( Id INT NOT NULL PRIMARY KEY NONCLUSTERED Hash WITH (bucket_count =1000000), Name CHAR(40) ) WITH (MEMORY_OPTIMIZED = ON, DURABILITY = SCHEMA_only) GO -- Now insert 100000 records in dbo.Disktable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Disktable(Name) VALUES('sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END -- Do the same inserts for Memory table dbo.Memorytable_durable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Memorytable_durable VALUES(@i_t, 'sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END -- Now finally do the same inserts for Memory table dbo.Memorytable_nondurable and observe the Time Taken DECLARE @i_t bigint SET @i_t =1 WHILE @i_t<= 100000 BEGIN INSERT INTO dbo.Memorytable_nondurable VALUES(@i_t, 'sachin' + CONVERT(VARCHAR,@i_t)) SET @i_t+=1 END The above 3 Inserts took 1.20 minutes, 54 secs, and 2 secs respectively to insert 100000 records on my machine with 8 Gb RAM. This proves the point that memory-optimized tables can definitely help businesses achieve better performance for their highly transactional business table and memory- optimized tables with Durability SCHEMA_ONLY is even faster as it does not bother persisting its data to disk which makes it supremely fast. Koenig Solutions is one of the few organizations which offer IT training on SQL Server 2014 and all its updates. Now, I leave the decision on using memory_Optimized tables on you, I hope you like this article and it helped you understand  the fundamentals of IN-Memory OLTP . Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL Tagged: Koenig

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  • SQL – Migrate Database from SQL Server to NuoDB – A Quick Tutorial

    - by Pinal Dave
    Data is growing exponentially and every organization with growing data is thinking of next big innovation in the world of Big Data. Big data is a indeed a future for every organization at one point of the time. Just like every other next big thing, big data has its own challenges and issues. The biggest challenge associated with the big data is to find the ideal platform which supports the scalability and growth of the data. If you are a regular reader of this blog, you must be familiar with NuoDB. I have been working with NuoDB for a while and their recent release is the best thus far. NuoDB is an elastically scalable SQL database that can run on local host, datacenter and cloud-based resources. A key feature of the product is that it does not require sharding (read more here). Last week, I was able to install NuoDB in less than 90 seconds and have explored their Explorer and Admin sections. You can read about my experiences in these posts: SQL – Step by Step Guide to Download and Install NuoDB – Getting Started with NuoDB SQL – Quick Start with Admin Sections of NuoDB – Manage NuoDB Database SQL – Quick Start with Explorer Sections of NuoDB – Query NuoDB Database Many SQL Authority readers have been following me in my journey to evaluate NuoDB. One of the frequently asked questions I’ve received from you is if there is any way to migrate data from SQL Server to NuoDB. The fact is that there is indeed a way to do so and NuoDB provides a fantastic tool which can help users to do it. NuoDB Migrator is a command line utility that supports the migration of Microsoft SQL Server, MySQL, Oracle, and PostgreSQL schemas and data to NuoDB. The migration to NuoDB is a three-step process: NuoDB Migrator generates a schema for a target NuoDB database It loads data into the target NuoDB database It dumps data from the source database Let’s see how we can migrate our data from SQL Server to NuoDB using a simple three-step approach. But before we do that we will create a sample database in MSSQL and later we will migrate the same database to NuoDB: Setup Step 1: Build a sample data CREATE DATABASE [Test]; CREATE TABLE [Department]( [DepartmentID] [smallint] NOT NULL, [Name] VARCHAR(100) NOT NULL, [GroupName] VARCHAR(100) NOT NULL, [ModifiedDate] [datetime] NOT NULL, CONSTRAINT [PK_Department_DepartmentID] PRIMARY KEY CLUSTERED ( [DepartmentID] ASC ) ) ON [PRIMARY]; INSERT INTO Department SELECT * FROM AdventureWorks2012.HumanResources.Department; Note that I am using the SQL Server AdventureWorks database to build this sample table but you can build this sample table any way you prefer. Setup Step 2: Install Java 64 bit Before you can begin the migration process to NuoDB, make sure you have 64-bit Java installed on your computer. This is due to the fact that the NuoDB Migrator tool is built in Java. You can download 64-bit Java for Windows, Mac OSX, or Linux from the following link: http://java.com/en/download/manual.jsp. One more thing to remember is that you make sure that the path in your environment settings is set to your JAVA_HOME directory or else the tool will not work. Here is how you can do it: Go to My Computer >> Right Click >> Select Properties >> Click on Advanced System Settings >> Click on Environment Variables >> Click on New and enter the following values. Variable Name: JAVA_HOME Variable Value: C:\Program Files\Java\jre7 Make sure you enter your Java installation directory in the Variable Value field. Setup Step 3: Install JDBC driver for SQL Server. There are two JDBC drivers available for SQL Server.  Select the one you prefer to use by following one of the two links below: Microsoft JDBC Driver jTDS JDBC Driver In this example we will be using jTDS JDBC driver. Once you download the driver, move the driver to your NuoDB installation folder. In my case, I have moved the JAR file of the driver into the C:\Program Files\NuoDB\tools\migrator\jar folder as this is my NuoDB installation directory. Now we are all set to start the three-step migration process from SQL Server to NuoDB: Migration Step 1: NuoDB Schema Generation Here is the command I use to generate a schema of my SQL Server Database in NuoDB. First I go to the folder C:\Program Files\NuoDB\tools\migrator\bin and execute the nuodb-migrator.bat file. Note that my database name is ‘test’. Additionally my username and password is also ‘test’. You can see that my SQL Server database is running on my localhost on port 1433. Additionally, the schema of the table is ‘dbo’. nuodb-migrator schema –source.driver=net.sourceforge.jtds.jdbc.Driver –source.url=jdbc:jtds:sqlserver://localhost:1433/ –source.username=test –source.password=test –source.catalog=test –source.schema=dbo –output.path=/tmp/schema.sql The above script will generate a schema of all my SQL Server tables and will put it in the folder C:\tmp\schema.sql . You can open the schema.sql file and execute this file directly in your NuoDB instance. You can follow the link here to see how you can execute the SQL script in NuoDB. Please note that if you have not yet created the schema in the NuoDB database, you should create it before executing this step. Step 2: Generate the Dump File of the Data Once you have recreated your schema in NuoDB from SQL Server, the next step is very easy. Here we create a CSV format dump file, which will contain all the data from all the tables from the SQL Server database. The command to do so is very similar to the above command. Be aware that this step may take a bit of time based on your database size. nuodb-migrator dump –source.driver=net.sourceforge.jtds.jdbc.Driver –source.url=jdbc:jtds:sqlserver://localhost:1433/ –source.username=test –source.password=test –source.catalog=test –source.schema=dbo –output.type=csv –output.path=/tmp/dump.cat Once the above command is successfully executed you can find your CSV file in the C:\tmp\ folder. However, you do not have to do anything manually. The third and final step will take care of completing the migration process. Migration Step 3: Load the Data into NuoDB After building schema and taking a dump of the data, the very next step is essential and crucial. It will take the CSV file and load it into the NuoDB database. nuodb-migrator load –target.url=jdbc:com.nuodb://localhost:48004/mytest –target.schema=dbo –target.username=test –target.password=test –input.path=/tmp/dump.cat Please note that in the above script we are now targeting the NuoDB database, which we have already created with the name of “MyTest”. If the database does not exist, create it manually before executing the above script. I have kept the username and password as “test”, but please make sure that you create a more secure password for your database for security reasons. Voila!  You’re Done That’s it. You are done. It took 3 setup and 3 migration steps to migrate your SQL Server database to NuoDB.  You can now start exploring the database and build excellent, scale-out applications. In this blog post, I have done my best to come up with simple and easy process, which you can follow to migrate your app from SQL Server to NuoDB. Download NuoDB I strongly encourage you to download NuoDB and go through my 3-step migration tutorial from SQL Server to NuoDB. Additionally here are two very important blog post from NuoDB CTO Seth Proctor. He has written excellent blog posts on the concept of the Administrative Domains. NuoDB has this concept of an Administrative Domain, which is a collection of hosts that can run one or multiple databases.  Each database has its own TEs and SMs, but all are managed within the Admin Console for that particular domain. http://www.nuodb.com/techblog/2013/03/11/getting-started-provisioning-a-domain/ http://www.nuodb.com/techblog/2013/03/14/getting-started-running-a-database/ Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: NuoDB

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  • SPARC T4-4 Beats 8-CPU IBM POWER7 on TPC-H @3000GB Benchmark

    - by Brian
    Oracle's SPARC T4-4 server delivered a world record TPC-H @3000GB benchmark result for systems with four processors. This result beats eight processor results from IBM (POWER7) and HP (x86). The SPARC T4-4 server also delivered better performance per core than these eight processor systems from IBM and HP. Comparisons below are based upon system to system comparisons, highlighting Oracle's complete software and hardware solution. This database world record result used Oracle's Sun Storage 2540-M2 arrays (rotating disk) connected to a SPARC T4-4 server running Oracle Solaris 11 and Oracle Database 11g Release 2 demonstrating the power of Oracle's integrated hardware and software solution. The SPARC T4-4 server based configuration achieved a TPC-H scale factor 3000 world record for four processor systems of 205,792 QphH@3000GB with price/performance of $4.10/QphH@3000GB. The SPARC T4-4 server with four SPARC T4 processors (total of 32 cores) is 7% faster than the IBM Power 780 server with eight POWER7 processors (total of 32 cores) on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 36% better in price performance compared to the IBM Power 780 server on the TPC-H @3000GB Benchmark. The SPARC T4-4 server is 29% faster than the IBM Power 780 for data loading. The SPARC T4-4 server is up to 3.4 times faster than the IBM Power 780 server for the Refresh Function. The SPARC T4-4 server with four SPARC T4 processors is 27% faster than the HP ProLiant DL980 G7 server with eight x86 processors on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 52% faster than the HP ProLiant DL980 G7 server for data loading. The SPARC T4-4 server is up to 3.2 times faster than the HP ProLiant DL980 G7 for the Refresh Function. The SPARC T4-4 server achieved a peak IO rate from the Oracle database of 17 GB/sec. This rate was independent of the storage used, as demonstrated by the TPC-H @3000TB benchmark which used twelve Sun Storage 2540-M2 arrays (rotating disk) and the TPC-H @1000TB benchmark which used four Sun Storage F5100 Flash Array devices (flash storage). [*] The SPARC T4-4 server showed linear scaling from TPC-H @1000GB to TPC-H @3000GB. This demonstrates that the SPARC T4-4 server can handle the increasingly larger databases required of DSS systems. [*] The SPARC T4-4 server benchmark results demonstrate a complete solution of building Decision Support Systems including data loading, business questions and refreshing data. Each phase usually has a time constraint and the SPARC T4-4 server shows superior performance during each phase. [*] The TPC believes that comparisons of results published with different scale factors are misleading and discourages such comparisons. Performance Landscape The table lists the leading TPC-H @3000GB results for non-clustered systems. TPC-H @3000GB, Non-Clustered Systems System Processor P/C/T – Memory Composite(QphH) $/perf($/QphH) Power(QppH) Throughput(QthH) Database Available SPARC Enterprise M9000 3.0 GHz SPARC64 VII+ 64/256/256 – 1024 GB 386,478.3 $18.19 316,835.8 471,428.6 Oracle 11g R2 09/22/11 SPARC T4-4 3.0 GHz SPARC T4 4/32/256 – 1024 GB 205,792.0 $4.10 190,325.1 222,515.9 Oracle 11g R2 05/31/12 SPARC Enterprise M9000 2.88 GHz SPARC64 VII 32/128/256 – 512 GB 198,907.5 $15.27 182,350.7 216,967.7 Oracle 11g R2 12/09/10 IBM Power 780 4.1 GHz POWER7 8/32/128 – 1024 GB 192,001.1 $6.37 210,368.4 175,237.4 Sybase 15.4 11/30/11 HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 8/64/128 – 512 GB 162,601.7 $2.68 185,297.7 142,685.6 SQL Server 2008 10/13/10 P/C/T = Processors, Cores, Threads QphH = the Composite Metric (bigger is better) $/QphH = the Price/Performance metric in USD (smaller is better) QppH = the Power Numerical Quantity QthH = the Throughput Numerical Quantity The following table lists data load times and refresh function times during the power run. TPC-H @3000GB, Non-Clustered Systems Database Load & Database Refresh System Processor Data Loading(h:m:s) T4Advan RF1(sec) T4Advan RF2(sec) T4Advan SPARC T4-4 3.0 GHz SPARC T4 04:08:29 1.0x 67.1 1.0x 39.5 1.0x IBM Power 780 4.1 GHz POWER7 05:51:50 1.5x 147.3 2.2x 133.2 3.4x HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 08:35:17 2.1x 173.0 2.6x 126.3 3.2x Data Loading = database load time RF1 = power test first refresh transaction RF2 = power test second refresh transaction T4 Advan = the ratio of time to T4 time Complete benchmark results found at the TPC benchmark website http://www.tpc.org. Configuration Summary and Results Hardware Configuration: SPARC T4-4 server 4 x SPARC T4 3.0 GHz processors (total of 32 cores, 128 threads) 1024 GB memory 8 x internal SAS (8 x 300 GB) disk drives External Storage: 12 x Sun Storage 2540-M2 array storage, each with 12 x 15K RPM 300 GB drives, 2 controllers, 2 GB cache Software Configuration: Oracle Solaris 11 11/11 Oracle Database 11g Release 2 Enterprise Edition Audited Results: Database Size: 3000 GB (Scale Factor 3000) TPC-H Composite: 205,792.0 QphH@3000GB Price/performance: $4.10/QphH@3000GB Available: 05/31/2012 Total 3 year Cost: $843,656 TPC-H Power: 190,325.1 TPC-H Throughput: 222,515.9 Database Load Time: 4:08:29 Benchmark Description The TPC-H benchmark is a performance benchmark established by the Transaction Processing Council (TPC) to demonstrate Data Warehousing/Decision Support Systems (DSS). TPC-H measurements are produced for customers to evaluate the performance of various DSS systems. These queries and updates are executed against a standard database under controlled conditions. Performance projections and comparisons between different TPC-H Database sizes (100GB, 300GB, 1000GB, 3000GB, 10000GB, 30000GB and 100000GB) are not allowed by the TPC. TPC-H is a data warehousing-oriented, non-industry-specific benchmark that consists of a large number of complex queries typical of decision support applications. It also includes some insert and delete activity that is intended to simulate loading and purging data from a warehouse. TPC-H measures the combined performance of a particular database manager on a specific computer system. The main performance metric reported by TPC-H is called the TPC-H Composite Query-per-Hour Performance Metric (QphH@SF, where SF is the number of GB of raw data, referred to as the scale factor). QphH@SF is intended to summarize the ability of the system to process queries in both single and multiple user modes. The benchmark requires reporting of price/performance, which is the ratio of the total HW/SW cost plus 3 years maintenance to the QphH. A secondary metric is the storage efficiency, which is the ratio of total configured disk space in GB to the scale factor. Key Points and Best Practices Twelve Sun Storage 2540-M2 arrays were used for the benchmark. Each Sun Storage 2540-M2 array contains 12 15K RPM drives and is connected to a single dual port 8Gb FC HBA using 2 ports. Each Sun Storage 2540-M2 array showed 1.5 GB/sec for sequential read operations and showed linear scaling, achieving 18 GB/sec with twelve Sun Storage 2540-M2 arrays. These were stand alone IO tests. The peak IO rate measured from the Oracle database was 17 GB/sec. Oracle Solaris 11 11/11 required very little system tuning. Some vendors try to make the point that storage ratios are of customer concern. However, storage ratio size has more to do with disk layout and the increasing capacities of disks – so this is not an important metric in which to compare systems. The SPARC T4-4 server and Oracle Solaris efficiently managed the system load of over one thousand Oracle Database parallel processes. Six Sun Storage 2540-M2 arrays were mirrored to another six Sun Storage 2540-M2 arrays on which all of the Oracle database files were placed. IO performance was high and balanced across all the arrays. The TPC-H Refresh Function (RF) simulates periodical refresh portion of Data Warehouse by adding new sales and deleting old sales data. Parallel DML (parallel insert and delete in this case) and database log performance are a key for this function and the SPARC T4-4 server outperformed both the IBM POWER7 server and HP ProLiant DL980 G7 server. (See the RF columns above.) See Also Transaction Processing Performance Council (TPC) Home Page Ideas International Benchmark Page SPARC T4-4 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Sun Storage 2540-M2 Array oracle.com OTN Disclosure Statement TPC-H, QphH, $/QphH are trademarks of Transaction Processing Performance Council (TPC). For more information, see www.tpc.org. SPARC T4-4 205,792.0 QphH@3000GB, $4.10/QphH@3000GB, available 5/31/12, 4 processors, 32 cores, 256 threads; IBM Power 780 QphH@3000GB, 192,001.1 QphH@3000GB, $6.37/QphH@3000GB, available 11/30/11, 8 processors, 32 cores, 128 threads; HP ProLiant DL980 G7 162,601.7 QphH@3000GB, $2.68/QphH@3000GB available 10/13/10, 8 processors, 64 cores, 128 threads.

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  • Using the BAM Interceptor with Continuation

    - by Charles Young
    Originally posted on: http://geekswithblogs.net/cyoung/archive/2014/06/02/using-the-bam-interceptor-with-continuation.aspxI’ve recently been resurrecting some code written several years ago that makes extensive use of the BAM Interceptor provided as part of BizTalk Server’s BAM event observation library.  In doing this, I noticed an issue with continuations.  Essentially, whenever I tried to configure one or more continuations for an activity, the BAM Interceptor failed to complete the activity correctly.   Careful inspection of my code confirmed that I was initializing and invoking the BAM interceptor correctly, so I was mystified.  However, I eventually found the problem.  It is a logical error in the BAM Interceptor code itself. The BAM Interceptor provides a useful mechanism for implementing dynamic tracking.  It supports configurable ‘track points’.  These are grouped into named ‘locations’.  BAM uses the term ‘step’ as a synonym for ‘location’.   Each track point defines a BAM action such as starting an activity, extracting a data item, enabling a continuation, etc.  Each step defines a collection of track points. Understanding Steps The BAM Interceptor provides an abstract model for handling configuration of steps.  It doesn’t, however, define any specific configuration mechanism (e.g., config files, SSO, etc.)  It is up to the developer to decide how to store, manage and retrieve configuration data.  At run time, this configuration is used to register track points which then drive the BAM Interceptor. The full semantics of a step are not immediately clear from Microsoft’s documentation.  They represent a point in a business activity where BAM tracking occurs.  They are named locations in the code.  What is less obvious is that they always represent either the full tracking work for a given activity or a discrete fragment of that work which commences with the start of a new activity or the continuation of an existing activity.  The BAM Interceptor enforces this by throwing an error if no ‘start new’ or ‘continue’ track point is registered for a named location. This constraint implies that each step must marked with an ‘end activity’ track point.  One of the peculiarities of BAM semantics is that when an activity is continued under a correlated ID, you must first mark the current activity as ‘ended’ in order to ensure the right housekeeping is done in the database.  If you re-start an ended activity under the same ID, you will leave the BAM import tables in an inconsistent state.  A step, therefore, always represents an entire unit of work for a given activity or continuation ID.  For activities with continuation, each unit of work is termed a ‘fragment’. Instance and Fragment State Internally, the BAM Interceptor maintains state data at two levels.  First, it represents the overall state of the activity using a ‘trace instance’ token.  This token contains the name and ID of the activity together with a couple of state flags.  The second level of state represents a ‘trace fragment’.   As we have seen, a fragment of an activity corresponds directly to the notion of a ‘step’.  It is the unit of work done at a named location, and it must be bounded by start and end, or continue and end, actions. When handling continuations, the BAM Interceptor differentiates between ‘root’ fragments and other fragments.  Very simply, a root fragment represents the start of an activity.  Other fragments represent continuations.  This is where the logic breaks down.  The BAM Interceptor loses state integrity for root fragments when continuations are defined. Initialization Microsoft’s BAM Interceptor code supports the initialization of BAM Interceptors from track point configuration data.  The process starts by populating an Activity Interceptor Configuration object with an array of track points.  These can belong to different steps (aka ‘locations’) and can be registered in any order.  Once it is populated with track points, the Activity Interceptor Configuration is used to initialise the BAM Interceptor.  The BAM Interceptor sets up a hash table of array lists.  Each step is represented by an array list, and each array list contains an ordered set of track points.  The BAM Interceptor represents track points as ‘executable’ components.  When the OnStep method of the BAM Interceptor is called for a given step, the corresponding list of track points is retrieved and each track point is executed in turn.  Each track point retrieves any required data using a call back mechanism and then serializes a BAM trace fragment object representing a specific action (e.g., start, update, enable continuation, stop, etc.).  The serialised trace fragment is then handed off to a BAM event stream (buffered or direct) which takes the appropriate action. The Root of the Problem The logic breaks down in the Activity Interceptor Configuration.  Each Activity Interceptor Configuration is initialised with an instance of a ‘trace instance’ token.  This provides the basic metadata for the activity as a whole.  It contains the activity name and ID together with state flags indicating if the activity ID is a root (i.e., not a continuation fragment) and if it is completed.  This single token is then shared by all trace actions for all steps registered with the Activity Interceptor Configuration. Each trace instance token is automatically initialised to represent a root fragment.  However, if you subsequently register a ‘continuation’ step with the Activity Interceptor Configuration, the ‘root’ flag is set to false at the point the ‘continue’ track point is registered for that step.   If you use a ‘reflector’ tool to inspect the code for the ActivityInterceptorConfiguration class, you can see the flag being set in one of the overloads of the RegisterContinue method.    This makes no sense.  The trace instance token is shared across all the track points registered with the Activity Interceptor Configuration.  The Activity Interceptor Configuration is designed to hold track points for multiple steps.  The ‘root’ flag is clearly meant to be initialised to ‘true’ for the preliminary root fragment and then subsequently set to false at the point that a continuation step is processed.  Instead, if the Activity Interceptor Configuration contains a continuation step, it is changed to ‘false’ before the root fragment is processed.  This is clearly an error in logic. The problem causes havoc when the BAM Interceptor is used with continuation.  Effectively the root step is no longer processed correctly, and the ultimate effect is that the continued activity never completes!   This has nothing to do with the root and the continuation being in the same process.  It is due to a fundamental mistake of setting the ‘root’ flag to false for a continuation before the root fragment is processed. The Workaround Fortunately, it is easy to work around the bug.  The trick is to ensure that you create a new Activity Interceptor Configuration object for each individual step.  This may mean filtering your configuration data to extract the track points for a single step or grouping the configured track points into individual steps and the creating a separate Activity Interceptor Configuration for each group.  In my case, the first approach was required.  Here is what the amended code looks like: // Because of a logic error in Microsoft's code, a separate ActivityInterceptorConfiguration must be used // for each location. The following code extracts only those track points for a given step name (location). var trackPointGroup = from ResolutionService.TrackPoint tp in bamActivity.TrackPoints                       where (string)tp.Location == bamStepName                       select tp; var bamActivityInterceptorConfig =     new Microsoft.BizTalk.Bam.EventObservation.ActivityInterceptorConfiguration(activityName); foreach (var trackPoint in trackPointGroup) {     switch (trackPoint.Type)     {         case TrackPointType.Start:             bamActivityInterceptorConfig.RegisterStartNew(trackPoint.Location, trackPoint.ExtractionInfo);             break; etc… I’m using LINQ to filter a list of track points for those entries that correspond to a given step and then registering only those track points on a new instance of the ActivityInterceptorConfiguration class.   As soon as I re-wrote the code to do this, activities with continuations started to complete correctly.

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  • The Shift: how Orchard painlessly shifted to document storage, and how it’ll affect you

    - by Bertrand Le Roy
    We’ve known it all along. The storage for Orchard content items would be much more efficient using a document database than a relational one. Orchard content items are composed of parts that serialize naturally into infoset kinds of documents. Storing them as relational data like we’ve done so far was unnatural and requires the data for a single item to span multiple tables, related through 1-1 relationships. This means lots of joins in queries, and a great potential for Select N+1 problems. Document databases, unfortunately, are still a tough sell in many places that prefer the more familiar relational model. Being able to x-copy Orchard to hosters has also been a basic constraint in the design of Orchard. Combine those with the necessity at the time to run in medium trust, and with license compatibility issues, and you’ll find yourself with very few reasonable choices. So we went, a little reluctantly, for relational SQL stores, with the dream of one day transitioning to document storage. We have played for a while with the idea of building our own document storage on top of SQL databases, and Sébastien implemented something more than decent along those lines, but we had a better way all along that we didn’t notice until recently… In Orchard, there are fields, which are named properties that you can add dynamically to a content part. Because they are so dynamic, we have been storing them as XML into a column on the main content item table. This infoset storage and its associated API are fairly generic, but were only used for fields. The breakthrough was when Sébastien realized how this existing storage could give us the advantages of document storage with minimal changes, while continuing to use relational databases as the substrate. public bool CommercialPrices { get { return this.Retrieve(p => p.CommercialPrices); } set { this.Store(p => p.CommercialPrices, value); } } This code is very compact and efficient because the API can infer from the expression what the type and name of the property are. It is then able to do the proper conversions for you. For this code to work in a content part, there is no need for a record at all. This is particularly nice for site settings: one query on one table and you get everything you need. This shows how the existing infoset solves the data storage problem, but you still need to query. Well, for those properties that need to be filtered and sorted on, you can still use the current record-based relational system. This of course continues to work. We do however provide APIs that make it trivial to store into both record properties and the infoset storage in one operation: public double Price { get { return Retrieve(r => r.Price); } set { Store(r => r.Price, value); } } This code looks strikingly similar to the non-record case above. The difference is that it will manage both the infoset and the record-based storages. The call to the Store method will send the data in both places, keeping them in sync. The call to the Retrieve method does something even cooler: if the property you’re looking for exists in the infoset, it will return it, but if it doesn’t, it will automatically look into the record for it. And if that wasn’t cool enough, it will take that value from the record and store it into the infoset for the next time it’s required. This means that your data will start automagically migrating to infoset storage just by virtue of using the code above instead of the usual: public double Price { get { return Record.Price; } set { Record.Price = value; } } As your users browse the site, it will get faster and faster as Select N+1 issues will optimize themselves away. If you preferred, you could still have explicit migration code, but it really shouldn’t be necessary most of the time. If you do already have code using QueryHints to mitigate Select N+1 issues, you might want to reconsider those, as with the new system, you’ll want to avoid joins that you don’t need for filtering or sorting, further optimizing your queries. There are some rare cases where the storage of the property must be handled differently. Check out this string[] property on SearchSettingsPart for example: public string[] SearchedFields { get { return (Retrieve<string>("SearchedFields") ?? "") .Split(new[] {',', ' '}, StringSplitOptions.RemoveEmptyEntries); } set { Store("SearchedFields", String.Join(", ", value)); } } The array of strings is transformed by the property accessors into and from a comma-separated list stored in a string. The Retrieve and Store overloads used in this case are lower-level versions that explicitly specify the type and name of the attribute to retrieve or store. You may be wondering what this means for code or operations that look directly at the database tables instead of going through the new infoset APIs. Even if there is a record, the infoset version of the property will win if it exists, so it is necessary to keep the infoset up-to-date. It’s not very complicated, but definitely something to keep in mind. Here is what a product record looks like in Nwazet.Commerce for example: And here is the same data in the infoset: The infoset is stored in Orchard_Framework_ContentItemRecord or Orchard_Framework_ContentItemVersionRecord, depending on whether the content type is versionable or not. A good way to find what you’re looking for is to inspect the record table first, as it’s usually easier to read, and then get the item record of the same id. Here is the detailed XML document for this product: <Data> <ProductPart Inventory="40" Price="18" Sku="pi-camera-box" OutOfStockMessage="" AllowBackOrder="false" Weight="0.2" Size="" ShippingCost="null" IsDigital="false" /> <ProductAttributesPart Attributes="" /> <AutoroutePart DisplayAlias="camera-box" /> <TitlePart Title="Nwazet Pi Camera Box" /> <BodyPart Text="[...]" /> <CommonPart CreatedUtc="2013-09-10T00:39:00Z" PublishedUtc="2013-09-14T01:07:47Z" /> </Data> The data is neatly organized under each part. It is easy to see how that document is all you need to know about that content item, all in one table. If you want to modify that data directly in the database, you should be careful to do it in both the record table and the infoset in the content item record. In this configuration, the record is now nothing more than an index, and will only be used for sorting and filtering. Of course, it’s perfectly fine to mix record-backed properties and record-less properties on the same part. It really depends what you think must be sorted and filtered on. In turn, this potentially simplifies migrations considerably. So here it is, the great shift of Orchard to document storage, something that Orchard has been designed for all along, and that we were able to implement with a satisfying and surprising economy of resources. Expect this code to make its way into the 1.8 version of Orchard when that’s available.

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  • FTP Publishing with the new Windows Azure Release

    - by Harish Ranganathan
    There is a good chance you might have stumbled upon the new Windows Azure Release that we made on June 6th.  Scott Guthrie’s Post quite summarizes the overall new features. One of my favorite features is the Windows Azure Websites and the ability to do publish files to Azure using your FTP Client. Windows Azure Websites offers low cost (free upto 10 websites) web hosting where you can deploy any website that can run on IIS 7.0, quickly. The earlier releases of Azure SDKs and the Azure platform support .NET 3.5 & above for running your applications.  This was a constraint for many since there are/were a lot of ASP.NET 2.0 applications built over time and simply to put it on Azure, many of you were skeptical to migrate it to .NET 4. Windows Azure Websites offer the flexibility of running IIS 7.0 supported .NET Versions which means you can run .NET 1.1, 2.0, 3.5 and .NET 4.  Not just that! You can also run classic ASP Applications. Windows Azure Websites don’t need you to go through the complexity of adding the Cloud Project Template and then publishing the Configuration Files.  Lets take a step by step understanding of Websites and publishing using FTP. I downloaded the Club Website Starter Kit from http://www.asp.net/downloads/starter-kits/club It also requires a database and I downloaded the SQL Scripts and created a SQL Server Database called Club. This installs a Web Site Project Template.  Note that I am running Windows 8 Release Preview and Visual Studio 2012 RC.  After installing the template, select File – New – Website and don’t forget to choose the Framework version as .NET 2.0 You can see the “Club Website Starter Kit” .  Once you select the Website gets created.  You would encounter a warning indicating that the Club Website Starter Kit uses SQL Express and the recommended database is LocalDB Express.  Click ok to continue.  Once the Website is created open up the Web.config and locate the “ClubSiteDB” connection string.  By default, it points to a SQL Express Database.  Instead configure it to use your local SQL Server. Also, open up Global.asax and comment out the following line if (!Roles.RoleExists("Administrators")) Roles.CreateRole("Administrators"); There seems to be an issue in the code that doesn’t create the role.  Post that, hit CTRL+F5 and you should be able to see the Website Running, as below So, now we have the Club Starter Kit site up running locally.  Moving to Azure Visit http://manage.windowsazure.com/ and sign up for a trial account.  This allows you to host up to 10 websites for free and a host of other benefits.  The free Websites can be extended to an year without any charge.  Once you have signed up, sign in to the portal using the Live ID used for sign up. After signing in, you would be presented with the “All Items” listing page which lists, Websites, Cloud Services, Databases etc.,  If this is the first time, you wouldn’t find anything. Click on the “Websites” link from the left menu.  Click on “New” in the bottom and it should show up a dialog.  In the same, select Website and click on “Quick Create” and in the URL Textbox, specify “MyFirstDemo” and click the “Create Web Site” link below. It should take a few seconds to create the Website.  Once the Website is created, click on the listing and it should open up the Dashboard.  Since we haven’t done anything yet, there shouldn’t be any statistics Click on the “Download publish profile” link in the right bottom.  This file has the FTP publishing settings. Also, if you scroll down you can see the FTP URL for this site.  It should typically start ftp://waws-xxxx-xxx-xxxx In the downloaded publish profile file, you can also find the ftp URL.  Pick the following from this file publishUrl (the 2nd one, the one that features after publishMethod =”FTP”) and the userName and userPWD that follows. Note that we have everything required to publish the files.  But since the Club Starter Kit uses Databases, we need to have the Database running on SQL Azure.  Go back to the Main Menu and click on “New” in the bottom but this time select “SQL Database” and provide “Club” as Database name for “Quick Create” If this is the first time a Server would be created.  Otherwise, it would pickup the existing server name. Once the database is created, you can use the SQL Azure Migration Wizard http://sqlazuremw.codeplex.com/ and provide the credentials to connect to local database and then the SQL Azure database for migrating the “Club” database.  The migration wizard UI hasn’t changed much and is the same as explained by me in one my posts earlier http://geekswithblogs.net/ranganh/archive/2009/09/29/taking-your-northwind-database-to-sql-azure-and-binding-it.aspx Once the database is migrated, come back to the main screen and click on the Database base in the Azure Management Portal.  It opens up the dashboard of the database.  Click on “Show connection Strings” and it would popup a list of connection string formats.  Choose the ADO.NET connection string and after editing the password with the password that you provided when creating the database server in the Azure Portal, paste it into the config file of the Club Starter Kit Website.  Just to reiterate, the connection string key is ClubSiteDB. Try running the Website once to ensure that the application though running locally could connect to the SQL Database running on Azure. Once you are able to run the website successfully, we are all set to do the FTP Publishing. Download your favorite FTP tool.  I use http://filezilla-project.org/ In the Host Textbox, paste the FTP URL that you picked up from the publish profile file and also paste the username and password.  Click on “QuickConnect”.  If everything is fine, you should be able to connect to the remote server.  If it is successfully connected, you can see the wwwroot folder of the Website, running in Azure Make sure on the “Local Site” in the left, you choose the path to the folder of your Website.  Open up the Website folder on the left such that it lists all the files and folders inside.  Select all of them and click select “Upload” or simply drag and drop all the files to the root folder that is listed above.  Once the publishing is done, you should be able to hit the SiteURL that you can find the dashboard page of the website.  In our case, it would be http://MyFirstDemo.azurewebsites.net That’s it, we have now done FTP publishing in Azure and that too we are running a .NET 2.0 Website on Azure. Cheers !!!

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  • Magento hosting on a budget

    - by spa
    I have to do a setup for Magento. My constraint is primarily ease of setup and fault tolerance/fail over. Furthermore costs are an issue. I have three identical physical servers to get the job done. Each server node has an i7 quad core, 16GB RAM, and 2x3TB HD in a software RAID 1 configuration. Each node runs Ubuntu 12.04. right now. I have an additional IP address which can be routed to any of these nodes. The Magento shop has max. 1000 products, 50% of it are bundle products. I would estimate that max. 100 users are active at once. This leads me to the conclusion, that performance is not top priority here. My first setup idea One node (lb) runs nginx as a load balancer. The additional IP is used with domain name and routed to this node by default. Nginx distributes the load equally to the other two nodes (shop1, shop2). Shop1 and shop2 are configured equally: each server runs Apache2 and MySQL. The Mysqls are configured with master/slave replication. My failover strategy: Lb fails = Route IP to shop1 (MySQL master), continue. Shop1 fails = Lb will handle that automatically, promote MySQL slave on shop2 to master, reconfigure Magento to use shop2 for writes, continue. Shop2 fails = Lb will handle that automatically, continue. Is this a sane strategy? Has anyone done a similar setup with Magento? My second setup idea Another way to do it would be to use drbd for storing the MySQL data files on shop1 and shop2. I understand that in this scenario only one node/MySQL instance can be active and the other is used as hot standby. So in case shop1 fails, I would start up MySQL on shop2, route the IP to shop2, and continue. I like that as the MySQL setup is easier and the nodes can be configured 99% identical. So in this case the load balancer becomes useless and I have a spare server. My third setup idea The third way might be master-master replication of MySQL databases. However, in my optinion this might be tricky, as Magento isn't build for this scenario (e.g. conflicting ids for new rows). I would not do that until I have heard of a working example. Could you give me an advice which route to follow? There seems not one "good" way to do it. E.g. I read blog posts which describe a MySQL master/slave setup for Magento, but elsewhere I read, that data might get duplicated when the slave lags behind the master (e.g. when an order is placed, a customer might get created twice). I'm kind of lost here.

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  • SQL SERVER – Using expressor Composite Types to Enforce Business Rules

    - by pinaldave
    One of the features that distinguish the expressor Data Integration Platform from other products in the data integration space is its concept of composite types, which provide an effective and easily reusable way to clearly define the structure and characteristics of data within your application.  An important feature of the composite type approach is that it allows you to easily adjust the content of a record to its ultimate purpose.  For example, a record used to update a row in a database table is easily defined to include only the minimum set of columns, that is, a value for the key column and values for only those columns that need to be updated. Much like a class in higher level programming languages, you can also use the composite type as a way to enforce business rules onto your data by encapsulating a datum’s name, data type, and constraints (for example, maximum, minimum, or acceptable values) as a single entity, which ensures that your data can not assume an invalid value.  To what extent you use this functionality is a decision you make when designing your application; the expressor design paradigm does not force this approach on you. Let’s take a look at how these features are used.  Suppose you want to create a group of applications that maintain the employee table in your human resources database. Your table might have a structure similar to the HumanResources.Employee table in the AdventureWorks database.  This table includes two columns, EmployeID and rowguid, that are maintained by the relational database management system; you cannot provide values for these columns when inserting new rows into the table. Additionally, there are columns such as VacationHours and SickLeaveHours that you might choose to update for all employees on a monthly basis, which justifies creation of a dedicated application. By creating distinct composite types for the read, insert and update operations against this table, you can more easily manage this table’s content. When developing this application within expressor Studio, your first task is to create a schema artifact for the database table.  This process is completely driven by a wizard, only requiring that you select the desired database schema and table.  The resulting schema artifact defines the mapping of result set records to a record within the expressor data integration application.  The structure of the record within the expressor application is a composite type that is given the default name CompositeType1.  As you can see in the following figure, all columns from the table are included in the result set and mapped to an identically named attribute in the default composite type. If you are developing an application that needs to read this table, perhaps to prepare a year-end report of employees by department, you would probably not be interested in the data in the rowguid and ModifiedDate columns.  A typical approach would be to drop this unwanted data in a downstream operator.  But using an alternative composite type provides a better approach in which the unwanted data never enters your application. While working in expressor  Studio’s schema editor, simply create a second composite type within the same schema artifact, which you could name ReadTable, and remove the attributes corresponding to the unwanted columns. The value of an alternative composite type is even more apparent when you want to insert into or update the table.  In the composite type used to insert rows, remove the attributes corresponding to the EmployeeID primary key and rowguid uniqueidentifier columns since these values are provided by the relational database management system. And to update just the VacationHours and SickLeaveHours columns, use a composite type that includes only the attributes corresponding to the EmployeeID, VacationHours, SickLeaveHours and ModifiedDate columns. By specifying this schema artifact and composite type in a Write Table operator, your upstream application need only deal with the four required attributes and there is no risk of unintentionally overwriting a value in a column that does not need to be updated. Now, what about the option to use the composite type to enforce business rules?  If you review the composition of the default composite type CompositeType1, you will note that the constraints defined for many of the attributes mirror the table column specifications.  For example, the maximum number of characters in the NationaIDNumber, LoginID and Title attributes is equivalent to the maximum width of the target column, and the size of the MaritalStatus and Gender attributes is limited to a single character as required by the table column definition.  If your application code leads to a violation of these constraints, an error will be raised.  The expressor design paradigm then allows you to handle the error in a way suitable for your application.  For example, a string value could be truncated or a numeric value could be rounded. Moreover, you have the option of specifying additional constraints that support business rules unrelated to the table definition. Let’s assume that the only acceptable values for marital status are S, M, and D.  Within the schema editor, double-click on the MaritalStatus attribute to open the Edit Attribute window.  Then click the Allowed Values checkbox and enter the acceptable values into the Constraint Value text box. The schema editor is updated accordingly. There is one more option that the expressor semantic type paradigm supports.  Since the MaritalStatus attribute now clearly specifies how this type of information should be represented (a single character limited to S, M or D), you can convert this attribute definition into a shared type, which will allow you to quickly incorporate this definition into another composite type or into the description of an output record from a transform operator. Again, double-click on the MaritalStatus attribute and in the Edit Attribute window, click Convert, which opens the Share Local Semantic Type window that you use to name this shared type.  There’s no requirement that you give the shared type the same name as the attribute from which it was derived.  You should supply a name that makes it obvious what the shared type represents. In this posting, I’ve overviewed the expressor semantic type paradigm and shown how it can be used to make your application development process more productive.  The beauty of this feature is that you choose when and to what extent you utilize the functionality, but I’m certain that if you opt to follow this approach your efforts will become more efficient and your work will progress more quickly.  As always, I encourage you to download and evaluate expressor Studio for your current and future data integration needs. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: CodeProject, Pinal Dave, PostADay, SQL, SQL Authority, SQL Documentation, SQL Query, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

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  • SQL University: What and why of database refactoring

    - by Mladen Prajdic
    This is a post for a great idea called SQL University started by Jorge Segarra also famously known as SqlChicken on Twitter. It’s a collection of blog posts on different database related topics contributed by several smart people all over the world. So this week is mine and we’ll be talking about database testing and refactoring. In 3 posts we’ll cover: SQLU part 1 - What and why of database testing SQLU part 2 - What and why of database refactoring SQLU part 3 - Tools of the trade This is a second part of the series and in it we’ll take a look at what database refactoring is and why do it. Why refactor a database To know why refactor we first have to know what refactoring actually is. Code refactoring is a process where we change module internals in a way that does not change that module’s input/output behavior. For successful refactoring there is one crucial thing we absolutely must have: Tests. Automated unit tests are the only guarantee we have that we haven’t broken the input/output behavior before refactoring. If you haven’t go back ad read my post on the matter. Then start writing them. Next thing you need is a code module. Those are views, UDFs and stored procedures. By having direct table access we can kiss fast and sweet refactoring good bye. One more point to have a database abstraction layer. And no, ORM’s don’t fall into that category. But also know that refactoring is NOT adding new functionality to your code. Many have fallen into this trap. Don’t be one of them and resist the lure of the dark side. And it’s a strong lure. We developers in general love to add new stuff to our code, but hate fixing our own mistakes or changing existing code for no apparent reason. To be a good refactorer one needs discipline and focus. Now we know that refactoring is all about changing inner workings of existing code. This can be due to performance optimizations, changing internal code workflows or some other reason. This is a typical black box scenario to the outside world. If we upgrade the car engine it still has to drive on the road (preferably faster) and not fly (no matter how cool that would be). Also be aware that white box tests will break when we refactor. What to refactor in a database Refactoring databases doesn’t happen that often but when it does it can include a lot of stuff. Let us look at a few common cases. Adding or removing database schema objects Adding, removing or changing table columns in any way, adding constraints, keys, etc… All of these can be counted as internal changes not visible to the data consumer. But each of these carries a potential input/output behavior change. Dropping a column can result in views not working anymore or stored procedure logic crashing. Adding a unique constraint shows duplicated data that shouldn’t exist. Foreign keys break a truncate table command executed from an application that runs once a month. All these scenarios are very real and can happen. With the proper database abstraction layer fully covered with black box tests we can make sure something like that does not happen (hopefully at all). Changing physical structures Physical structures include heaps, indexes and partitions. We can pretty much add or remove those without changing the data returned by the database. But the performance can be affected. So here we use our performance tests. We do have them, right? Just by adding a single index we can achieve orders of magnitude performance improvement. Won’t that make users happy? But what if that index causes our write operations to crawl to a stop. again we have to test this. There are a lot of things to think about and have tests for. Without tests we can’t do successful refactoring! Fixing bad code We all have some bad code in our systems. We usually refer to that code as code smell as they violate good coding practices. Examples of such code smells are SQL injection, use of SELECT *, scalar UDFs or cursors, etc… Each of those is huge code smell and can result in major code changes. Take SELECT * from example. If we remove a column from a table the client using that SELECT * statement won’t have a clue about that until it runs. Then it will gracefully crash and burn. Not to mention the widely unknown SELECT * view refresh problem that Tomas LaRock (@SQLRockstar on Twitter) and Colin Stasiuk (@BenchmarkIT on Twitter) talk about in detail. Go read about it, it’s informative. Refactoring this includes replacing the * with column names and most likely change to application using the database. Breaking apart huge stored procedures Have you ever seen seen a stored procedure that was 2000 lines long? I have. It’s not pretty. It hurts the eyes and sucks the will to live the next 10 minutes. They are a maintenance nightmare and turn into things no one dares to touch. I’m willing to bet that 100% of time they don’t have a single test on them. Large stored procedures (and functions) are a clear sign that they contain business logic. General opinion on good database coding practices says that business logic has no business in the database. That’s the applications part. Refactoring such behemoths requires writing lots of edge case tests for the stored procedure input/output behavior and then start to refactor it. First we split the logic inside into smaller parts like new stored procedures and UDFs. Those then get called from the master stored procedure. Once we’ve successfully modularized the database code it’s best to transfer that logic into the applications consuming it. This only leaves the stored procedure with common data manipulation logic. Of course this isn’t always possible so having a plethora of performance and behavior unit tests is absolutely necessary to confirm we’ve actually improved the codebase in some way.   Refactoring is not a popular chore amongst developers or managers. The former don’t like fixing old code, the latter can’t see the financial benefit. Remember how we talked about being lousy at estimating future costs in the previous post? But there comes a time when it must be done. Hopefully I’ve given you some ideas how to get started. In the last post of the series we’ll take a look at the tools to use and an example of testing and refactoring.

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  • Automating deployments with the SQL Compare command line

    - by Jonathan Hickford
    In my previous article, “Five Tips to Get Your Organisation Releasing Software Frequently” I looked at how teams can automate processes to speed up release frequency. In this post, I’m looking specifically at automating deployments using the SQL Compare command line. SQL Compare compares SQL Server schemas and deploys the differences. It works very effectively in scenarios where only one deployment target is required – source and target databases are specified, compared, and a change script is automatically generated and applied. But if multiple targets exist, and pressure to increase the frequency of releases builds, this solution quickly becomes unwieldy.   This is where SQL Compare’s command line comes into its own. I’ve put together a PowerShell script that loops through the Servers table and pulls out the server and database, these are then passed to sqlcompare.exe to be used as target parameters. In the example the source database is a scripts folder, a folder structure of scripted-out database objects used by both SQL Source Control and SQL Compare. The script can easily be adapted to use schema snapshots.     -- Create a DeploymentTargets database and a Servers table CREATE DATABASE DeploymentTargets GO USE DeploymentTargets GO CREATE TABLE [dbo].[Servers]( [id] [int] IDENTITY(1,1) NOT NULL, [serverName] [nvarchar](50) NULL, [environment] [nvarchar](50) NULL, [databaseName] [nvarchar](50) NULL, CONSTRAINT [PK_Servers] PRIMARY KEY CLUSTERED ([id] ASC) ) GO -- Now insert your target server and database details INSERT INTO dbo.Servers ( serverName , environment , databaseName) VALUES ( N'myserverinstance' , N'myenvironment1' , N'mydb1') INSERT INTO dbo.Servers ( serverName , environment , databaseName) VALUES ( N'myserverinstance' , N'myenvironment2' , N'mydb2') Here’s the PowerShell script you can adapt for yourself as well. # We're holding the server names and database names that we want to deploy to in a database table. # We need to connect to that server to read these details $serverName = "" $databaseName = "DeploymentTargets" $authentication = "Integrated Security=SSPI" #$authentication = "User Id=xxx;PWD=xxx" # If you are using database authentication instead of Windows authentication. # Path to the scripts folder we want to deploy to the databases $scriptsPath = "SimpleTalk" # Path to SQLCompare.exe $SQLComparePath = "C:\Program Files (x86)\Red Gate\SQL Compare 10\sqlcompare.exe" # Create SQL connection string, and connection $ServerConnectionString = "Data Source=$serverName;Initial Catalog=$databaseName;$authentication" $ServerConnection = new-object system.data.SqlClient.SqlConnection($ServerConnectionString); # Create a Dataset to hold the DataTable $dataSet = new-object "System.Data.DataSet" "ServerList" # Create a query $query = "SET NOCOUNT ON;" $query += "SELECT serverName, environment, databaseName " $query += "FROM dbo.Servers; " # Create a DataAdapter to populate the DataSet with the results $dataAdapter = new-object "System.Data.SqlClient.SqlDataAdapter" ($query, $ServerConnection) $dataAdapter.Fill($dataSet) | Out-Null # Close the connection $ServerConnection.Close() # Populate the DataTable $dataTable = new-object "System.Data.DataTable" "Servers" $dataTable = $dataSet.Tables[0] #For every row in the DataTable $dataTable | FOREACH-OBJECT { "Server Name: $($_.serverName)" "Database Name: $($_.databaseName)" "Environment: $($_.environment)" # Compare the scripts folder to the database and synchronize the database to match # NB. Have set SQL Compare to abort on medium level warnings. $arguments = @("/scripts1:$($scriptsPath)", "/server2:$($_.serverName)", "/database2:$($_.databaseName)", "/AbortOnWarnings:Medium") # + @("/sync" ) # Commented out the 'sync' parameter for safety, write-host $arguments & $SQLComparePath $arguments "Exit Code: $LASTEXITCODE" # Some interesting variations # Check that every database matches a folder. # For example this might be a pre-deployment step to validate everything is at the same baseline state. # Or a post deployment script to validate the deployment worked. # An exit code of 0 means the databases are identical. # # $arguments = @("/scripts1:$($scriptsPath)", "/server2:$($_.serverName)", "/database2:$($_.databaseName)", "/Assertidentical") # Generate a report of the difference between the folder and each database. Generate a SQL update script for each database. # For example use this after the above to generate upgrade scripts for each database # Examine the warnings and the HTML diff report to understand how the script will change objects # #$arguments = @("/scripts1:$($scriptsPath)", "/server2:$($_.serverName)", "/database2:$($_.databaseName)", "/ScriptFile:update_$($_.environment+"_"+$_.databaseName).sql", "/report:update_$($_.environment+"_"+$_.databaseName).html" , "/reportType:Interactive", "/showWarnings", "/include:Identical") } It’s worth noting that the above example generates the deployment scripts dynamically. This approach should be problem-free for the vast majority of changes, but it is still good practice to review and test a pre-generated deployment script prior to deployment. An alternative approach would be to pre-generate a single deployment script using SQL Compare, and run this en masse to multiple targets programmatically using sqlcmd, or using a tool like SQL Multi Script.  You can use the /ScriptFile, /report, and /showWarnings flags to generate change scripts, difference reports and any warnings.  See the commented out example in the PowerShell: #$arguments = @("/scripts1:$($scriptsPath)", "/server2:$($_.serverName)", "/database2:$($_.databaseName)", "/ScriptFile:update_$($_.environment+"_"+$_.databaseName).sql", "/report:update_$($_.environment+"_"+$_.databaseName).html" , "/reportType:Interactive", "/showWarnings", "/include:Identical") There is a drawback of running a pre-generated deployment script; it assumes that a given database target hasn’t drifted from its expected state. Often there are (rightly or wrongly) many individuals within an organization who have permissions to alter the production database, and changes can therefore be made outside of the prescribed development processes. The consequence is that at deployment time, the applied script has been validated against a target that no longer represents reality. The solution here would be to add a check for drift prior to running the deployment script. This is achieved by using sqlcompare.exe to compare the target against the expected schema snapshot using the /Assertidentical flag. Should this return any differences (sqlcompare.exe Exit Code 79), a drift report is outputted instead of executing the deployment script.  See the commented out example. # $arguments = @("/scripts1:$($scriptsPath)", "/server2:$($_.serverName)", "/database2:$($_.databaseName)", "/Assertidentical") Any checks and processes that should be undertaken prior to a manual deployment, should also be happen during an automated deployment. You might think about triggering backups prior to deployment – even better, automate the verification of the backup too.   You can use SQL Compare’s command line interface along with PowerShell to automate multiple actions and checks that you need in your deployment process. Automation is a practical solution where multiple targets and a higher release cadence come into play. As we know, with great power comes great responsibility – responsibility to ensure that the necessary checks are made so deployments remain trouble-free.  (The code sample supplied in this post automates the simple dynamic deployment case – if you are considering more advanced automation, e.g. the drift checks, script generation, deploying to large numbers of targets and backup/verification, please email me at [email protected] for further script samples or if you have further questions)

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  • Inheritance Mapping Strategies with Entity Framework Code First CTP5: Part 2 – Table per Type (TPT)

    - by mortezam
    In the previous blog post you saw that there are three different approaches to representing an inheritance hierarchy and I explained Table per Hierarchy (TPH) as the default mapping strategy in EF Code First. We argued that the disadvantages of TPH may be too serious for our design since it results in denormalized schemas that can become a major burden in the long run. In today’s blog post we are going to learn about Table per Type (TPT) as another inheritance mapping strategy and we'll see that TPT doesn’t expose us to this problem. Table per Type (TPT)Table per Type is about representing inheritance relationships as relational foreign key associations. Every class/subclass that declares persistent properties—including abstract classes—has its own table. The table for subclasses contains columns only for each noninherited property (each property declared by the subclass itself) along with a primary key that is also a foreign key of the base class table. This approach is shown in the following figure: For example, if an instance of the CreditCard subclass is made persistent, the values of properties declared by the BillingDetail base class are persisted to a new row of the BillingDetails table. Only the values of properties declared by the subclass (i.e. CreditCard) are persisted to a new row of the CreditCards table. The two rows are linked together by their shared primary key value. Later, the subclass instance may be retrieved from the database by joining the subclass table with the base class table. TPT Advantages The primary advantage of this strategy is that the SQL schema is normalized. In addition, schema evolution is straightforward (modifying the base class or adding a new subclass is just a matter of modify/add one table). Integrity constraint definition are also straightforward (note how CardType in CreditCards table is now a non-nullable column). Another much more important advantage is the ability to handle polymorphic associations (a polymorphic association is an association to a base class, hence to all classes in the hierarchy with dynamic resolution of the concrete class at runtime). A polymorphic association to a particular subclass may be represented as a foreign key referencing the table of that particular subclass. Implement TPT in EF Code First We can create a TPT mapping simply by placing Table attribute on the subclasses to specify the mapped table name (Table attribute is a new data annotation and has been added to System.ComponentModel.DataAnnotations namespace in CTP5): public abstract class BillingDetail {     public int BillingDetailId { get; set; }     public string Owner { get; set; }     public string Number { get; set; } } [Table("BankAccounts")] public class BankAccount : BillingDetail {     public string BankName { get; set; }     public string Swift { get; set; } } [Table("CreditCards")] public class CreditCard : BillingDetail {     public int CardType { get; set; }     public string ExpiryMonth { get; set; }     public string ExpiryYear { get; set; } } public class InheritanceMappingContext : DbContext {     public DbSet<BillingDetail> BillingDetails { get; set; } } If you prefer fluent API, then you can create a TPT mapping by using ToTable() method: protected override void OnModelCreating(ModelBuilder modelBuilder) {     modelBuilder.Entity<BankAccount>().ToTable("BankAccounts");     modelBuilder.Entity<CreditCard>().ToTable("CreditCards"); } Generated SQL For QueriesLet’s take an example of a simple non-polymorphic query that returns a list of all the BankAccounts: var query = from b in context.BillingDetails.OfType<BankAccount>() select b; Executing this query (by invoking ToList() method) results in the following SQL statements being sent to the database (on the bottom, you can also see the result of executing the generated query in SQL Server Management Studio): Now, let’s take an example of a very simple polymorphic query that requests all the BillingDetails which includes both BankAccount and CreditCard types: projects some properties out of the base class BillingDetail, without querying for anything from any of the subclasses: var query = from b in context.BillingDetails             select new { b.BillingDetailId, b.Number, b.Owner }; -- var query = from b in context.BillingDetails select b; This LINQ query seems even more simple than the previous one but the resulting SQL query is not as simple as you might expect: -- As you can see, EF Code First relies on an INNER JOIN to detect the existence (or absence) of rows in the subclass tables CreditCards and BankAccounts so it can determine the concrete subclass for a particular row of the BillingDetails table. Also the SQL CASE statements that you see in the beginning of the query is just to ensure columns that are irrelevant for a particular row have NULL values in the returning flattened table. (e.g. BankName for a row that represents a CreditCard type) TPT ConsiderationsEven though this mapping strategy is deceptively simple, the experience shows that performance can be unacceptable for complex class hierarchies because queries always require a join across many tables. In addition, this mapping strategy is more difficult to implement by hand— even ad-hoc reporting is more complex. This is an important consideration if you plan to use handwritten SQL in your application (For ad hoc reporting, database views provide a way to offset the complexity of the TPT strategy. A view may be used to transform the table-per-type model into the much simpler table-per-hierarchy model.) SummaryIn this post we learned about Table per Type as the second inheritance mapping in our series. So far, the strategies we’ve discussed require extra consideration with regard to the SQL schema (e.g. in TPT, foreign keys are needed). This situation changes with the Table per Concrete Type (TPC) that we will discuss in the next post. References ADO.NET team blog Java Persistence with Hibernate book a { text-decoration: none; } a:visited { color: Blue; } .title { padding-bottom: 5px; font-family: Segoe UI; font-size: 11pt; font-weight: bold; padding-top: 15px; } .code, .typeName { font-family: consolas; } .typeName { color: #2b91af; } .padTop5 { padding-top: 5px; } .padTop10 { padding-top: 10px; } p.MsoNormal { margin-top: 0in; margin-right: 0in; margin-bottom: 10.0pt; margin-left: 0in; line-height: 115%; font-size: 11.0pt; font-family: "Calibri" , "sans-serif"; }

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  • I see no LOBs!

    - by Paul White
    Is it possible to see LOB (large object) logical reads from STATISTICS IO output on a table with no LOB columns? I was asked this question today by someone who had spent a good fraction of their afternoon trying to work out why this was occurring – even going so far as to re-run DBCC CHECKDB to see if any corruption had taken place.  The table in question wasn’t particularly pretty – it had grown somewhat organically over time, with new columns being added every so often as the need arose.  Nevertheless, it remained a simple structure with no LOB columns – no TEXT or IMAGE, no XML, no MAX types – nothing aside from ordinary INT, MONEY, VARCHAR, and DATETIME types.  To add to the air of mystery, not every query that ran against the table would report LOB logical reads – just sometimes – but when it did, the query often took much longer to execute. Ok, enough of the pre-amble.  I can’t reproduce the exact structure here, but the following script creates a table that will serve to demonstrate the effect: IF OBJECT_ID(N'dbo.Test', N'U') IS NOT NULL DROP TABLE dbo.Test GO CREATE TABLE dbo.Test ( row_id NUMERIC IDENTITY NOT NULL,   col01 NVARCHAR(450) NOT NULL, col02 NVARCHAR(450) NOT NULL, col03 NVARCHAR(450) NOT NULL, col04 NVARCHAR(450) NOT NULL, col05 NVARCHAR(450) NOT NULL, col06 NVARCHAR(450) NOT NULL, col07 NVARCHAR(450) NOT NULL, col08 NVARCHAR(450) NOT NULL, col09 NVARCHAR(450) NOT NULL, col10 NVARCHAR(450) NOT NULL, CONSTRAINT [PK dbo.Test row_id] PRIMARY KEY CLUSTERED (row_id) ) ; The next script loads the ten variable-length character columns with one-character strings in the first row, two-character strings in the second row, and so on down to the 450th row: WITH Numbers AS ( -- Generates numbers 1 - 450 inclusive SELECT TOP (450) n = ROW_NUMBER() OVER (ORDER BY (SELECT 0)) FROM master.sys.columns C1, master.sys.columns C2, master.sys.columns C3 ORDER BY n ASC ) INSERT dbo.Test WITH (TABLOCKX) SELECT REPLICATE(N'A', N.n), REPLICATE(N'B', N.n), REPLICATE(N'C', N.n), REPLICATE(N'D', N.n), REPLICATE(N'E', N.n), REPLICATE(N'F', N.n), REPLICATE(N'G', N.n), REPLICATE(N'H', N.n), REPLICATE(N'I', N.n), REPLICATE(N'J', N.n) FROM Numbers AS N ORDER BY N.n ASC ; Once those two scripts have run, the table contains 450 rows and 10 columns of data like this: Most of the time, when we query data from this table, we don’t see any LOB logical reads, for example: -- Find the maximum length of the data in -- column 5 for a range of rows SELECT result = MAX(DATALENGTH(T.col05)) FROM dbo.Test AS T WHERE row_id BETWEEN 50 AND 100 ; But with a different query… -- Read all the data in column 1 SELECT result = MAX(DATALENGTH(T.col01)) FROM dbo.Test AS T ; …suddenly we have 49 LOB logical reads, as well as the ‘normal’ logical reads we would expect. The Explanation If we had tried to create this table in SQL Server 2000, we would have received a warning message to say that future INSERT or UPDATE operations on the table might fail if the resulting row exceeded the in-row storage limit of 8060 bytes.  If we needed to store more data than would fit in an 8060 byte row (including internal overhead) we had to use a LOB column – TEXT, NTEXT, or IMAGE.  These special data types store the large data values in a separate structure, with just a small pointer left in the original row. Row Overflow SQL Server 2005 introduced a feature called row overflow, which allows one or more variable-length columns in a row to move to off-row storage if the data in a particular row would otherwise exceed 8060 bytes.  You no longer receive a warning when creating (or altering) a table that might need more than 8060 bytes of in-row storage; if SQL Server finds that it can no longer fit a variable-length column in a particular row, it will silently move one or more of these columns off the row into a separate allocation unit. Only variable-length columns can be moved in this way (for example the (N)VARCHAR, VARBINARY, and SQL_VARIANT types).  Fixed-length columns (like INTEGER and DATETIME for example) never move into ‘row overflow’ storage.  The decision to move a column off-row is done on a row-by-row basis – so data in a particular column might be stored in-row for some table records, and off-row for others. In general, if SQL Server finds that it needs to move a column into row-overflow storage, it moves the largest variable-length column record for that row.  Note that in the case of an UPDATE statement that results in the 8060 byte limit being exceeded, it might not be the column that grew that is moved! Sneaky LOBs Anyway, that’s all very interesting but I don’t want to get too carried away with the intricacies of row-overflow storage internals.  The point is that it is now possible to define a table with non-LOB columns that will silently exceed the old row-size limit and result in ordinary variable-length columns being moved to off-row storage.  Adding new columns to a table, expanding an existing column definition, or simply storing more data in a column than you used to – all these things can result in one or more variable-length columns being moved off the row. Note that row-overflow storage is logically quite different from old-style LOB and new-style MAX data type storage – individual variable-length columns are still limited to 8000 bytes each – you can just have more of them now.  Having said that, the physical mechanisms involved are very similar to full LOB storage – a column moved to row-overflow leaves a 24-byte pointer record in the row, and the ‘separate storage’ I have been talking about is structured very similarly to both old-style LOBs and new-style MAX types.  The disadvantages are also the same: when SQL Server needs a row-overflow column value it needs to follow the in-row pointer a navigate another chain of pages, just like retrieving a traditional LOB. And Finally… In the example script presented above, the rows with row_id values from 402 to 450 inclusive all exceed the total in-row storage limit of 8060 bytes.  A SELECT that references a column in one of those rows that has moved to off-row storage will incur one or more lob logical reads as the storage engine locates the data.  The results on your system might vary slightly depending on your settings, of course; but in my tests only column 1 in rows 402-450 moved off-row.  You might like to play around with the script – updating columns, changing data type lengths, and so on – to see the effect on lob logical reads and which columns get moved when.  You might even see row-overflow columns moving back in-row if they are updated to be smaller (hint: reduce the size of a column entry by at least 1000 bytes if you hope to see this). Be aware that SQL Server will not warn you when it moves ‘ordinary’ variable-length columns into overflow storage, and it can have dramatic effects on performance.  It makes more sense than ever to choose column data types sensibly.  If you make every column a VARCHAR(8000) or NVARCHAR(4000), and someone stores data that results in a row needing more than 8060 bytes, SQL Server might turn some of your column data into pseudo-LOBs – all without saying a word. Finally, some people make a distinction between ordinary LOBs (those that can hold up to 2GB of data) and the LOB-like structures created by row-overflow (where columns are still limited to 8000 bytes) by referring to row-overflow LOBs as SLOBs.  I find that quite appealing, but the ‘S’ stands for ‘small’, which makes expanding the whole acronym a little daft-sounding…small large objects anyone? © Paul White 2011 email: [email protected] twitter: @SQL_Kiwi

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  • JPA 2.1 Schema Generation (TOTD #187)

    - by arungupta
    This blog explained some of the key features of JPA 2.1 earlier. Since then Schema Generation has been added to JPA 2.1. This Tip Of The Day (TOTD) will provide more details about this new feature in JPA 2.1. Schema Generation refers to generation of database artifacts like tables, indexes, and constraints in a database schema. It may or may not involve generation of a proper database schema depending upon the credentials and authorization of the user. This helps in prototyping of your application where the required artifacts are generated either prior to application deployment or as part of EntityManagerFactory creation. This is also useful in environments that require provisioning database on demand, e.g. in a cloud. This feature will allow your JPA domain object model to be directly generated in a database. The generated schema may need to be tuned for actual production environment. This usecase is supported by allowing the schema generation to occur into DDL scripts which can then be further tuned by a DBA. The following set of properties in persistence.xml or specified during EntityManagerFactory creation controls the behaviour of schema generation. Property Name Purpose Values javax.persistence.schema-generation-action Controls action to be taken by persistence provider "none", "create", "drop-and-create", "drop" javax.persistence.schema-generation-target Controls whehter schema to be created in database, whether DDL scripts are to be created, or both "database", "scripts", "database-and-scripts" javax.persistence.ddl-create-script-target, javax.persistence.ddl-drop-script-target Controls target locations for writing of scripts. Writers are pre-configured for the persistence provider. Need to be specified only if scripts are to be generated. java.io.Writer (e.g. MyWriter.class) or URL strings javax.persistence.ddl-create-script-source, javax.persistence.ddl-drop-script-source Specifies locations from which DDL scripts are to be read. Readers are pre-configured for the persistence provider. java.io.Reader (e.g. MyReader.class) or URL strings javax.persistence.sql-load-script-source Specifies location of SQL bulk load script. java.io.Reader (e.g. MyReader.class) or URL string javax.persistence.schema-generation-connection JDBC connection to be used for schema generation javax.persistence.database-product-name, javax.persistence.database-major-version, javax.persistence.database-minor-version Needed if scripts are to be generated and no connection to target database. Values are those obtained from JDBC DatabaseMetaData. javax.persistence.create-database-schemas Whether Persistence Provider need to create schema in addition to creating database objects such as tables, sequences, constraints, etc. "true", "false" Section 11.2 in the JPA 2.1 specification defines the annotations used for schema generation process. For example, @Table, @Column, @CollectionTable, @JoinTable, @JoinColumn, are used to define the generated schema. Several layers of defaulting may be involved. For example, the table name is defaulted from entity name and entity name (which can be specified explicitly as well) is defaulted from the class name. However annotations may be used to override or customize the values. The following entity class: @Entity public class Employee {    @Id private int id;    private String name;     . . .     @ManyToOne     private Department dept; } is generated in the database with the following attributes: Maps to EMPLOYEE table in default schema "id" field is mapped to ID column as primary key "name" is mapped to NAME column with a default VARCHAR(255). The length of this field can be easily tuned using @Column. @ManyToOne is mapped to DEPT_ID foreign key column. Can be customized using JOIN_COLUMN. In addition to these properties, couple of new annotations are added to JPA 2.1: @Index - An index for the primary key is generated by default in a database. This new annotation will allow to define additional indexes, over a single or multiple columns, for a better performance. This is specified as part of @Table, @SecondaryTable, @CollectionTable, @JoinTable, and @TableGenerator. For example: @Table(indexes = {@Index(columnList="NAME"), @Index(columnList="DEPT_ID DESC")})@Entity public class Employee {    . . .} The generated table will have a default index on the primary key. In addition, two new indexes are defined on the NAME column (default ascending) and the foreign key that maps to the department in descending order. @ForeignKey - It is used to define foreign key constraint or to otherwise override or disable the persistence provider's default foreign key definition. Can be specified as part of JoinColumn(s), MapKeyJoinColumn(s), PrimaryKeyJoinColumn(s). For example: @Entity public class Employee {    @Id private int id;    private String name;    @ManyToOne    @JoinColumn(foreignKey=@ForeignKey(foreignKeyDefinition="FOREIGN KEY (MANAGER_ID) REFERENCES MANAGER"))    private Manager manager;     . . . } In this entity, the employee's manager is mapped by MANAGER_ID column in the MANAGER table. The value of foreignKeyDefinition would be a database specific string. A complete replay of Linda's talk at JavaOne 2012 can be seen here (click on CON4212_mp4_4212_001 in Media). These features will be available in GlassFish 4 promoted builds in the near future. JPA 2.1 will be delivered as part of Java EE 7. The different components in the Java EE 7 platform are tracked here. JPA 2.1 Expert Group has released Early Draft 2 of the specification. Section 9.4 and 11.2 provide all details about Schema Generation. The latest javadocs can be obtained from here. And the JPA EG would appreciate feedback.

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  • Deleting multiple objects in a AWS S3 bucket with s3curl.pl?

    - by user183394
    I have been trying to use the AWS "official" command line tool s3curl.pl to test out the recently announced multi-object delete. Here is what I have done: First, I tested out the s3curl.pl with a set of credentials without a hitch: $ s3curl.pl --id=s3 -- http://testbucket-0.s3.amazonaws.com/|xmllint --format - % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 884 0 884 0 0 4399 0 --:--:-- --:--:-- --:--:-- 5703 <?xml version="1.0" encoding="UTF-8"?> <ListBucketResult xmlns="http://s3.amazonaws.com/doc/2006-03-01/"> <Name>testbucket-0</Name> <Prefix/> <Marker/> <MaxKeys>1000</MaxKeys> <IsTruncated>false</IsTruncated> <Contents> <Key>file_1</Key> <LastModified>2012-03-22T17:08:17.000Z</LastModified> <ETag>"ee0e521a76524034aaa5b331842a8b4e"</ETag> <Size>400000</Size> <Owner> <ID>e6d81ea69572270e58d3814ab674df8c8f1fd5d502669633a4951bdd5185f7f4</ID> <DisplayName>zackp</DisplayName> </Owner> <StorageClass>STANDARD</StorageClass> </Contents> <Contents> <Key>file_2</Key> <LastModified>2012-03-22T17:08:19.000Z</LastModified> <ETag>"6b32cbf8219a59690a9f69ba6ff3f590"</ETag> <Size>600000</Size> <Owner> <ID>e6d81ea69572270e58d3814ab674df8c8f1fd5d502669633a4951bdd5185f7f4</ID> <DisplayName>zackp</DisplayName> </Owner> <StorageClass>STANDARD</StorageClass> </Contents> </ListBucketResult> Then, I following the s3curl.pl's usage instructions: s3curl.pl --help Usage /usr/local/bin/s3curl.pl --id friendly-name (or AWSAccessKeyId) [options] -- [curl-options] [URL] options: --key SecretAccessKey id/key are AWSAcessKeyId and Secret (unsafe) --contentType text/plain set content-type header --acl public-read use a 'canned' ACL (x-amz-acl header) --contentMd5 content_md5 add x-amz-content-md5 header --put <filename> PUT request (from the provided local file) --post [<filename>] POST request (optional local file) --copySrc bucket/key Copy from this source key --createBucket [<region>] create-bucket with optional location constraint --head HEAD request --debug enable debug logging common curl options: -H 'x-amz-acl: public-read' another way of using canned ACLs -v verbose logging Then, I tried the following, and always got back error. I would appreciated it very much if someone could point out where I made a mistake? $ s3curl.pl --id=s3 --post multi_delete.xml -- http://testbucket-0.s3.amazonaws.com/?delete <?xml version="1.0" encoding="UTF-8"?> <Error><Code>SignatureDoesNotMatch</Code><Message>The request signature we calculated does not match the signature you provided. Check your key and signing method.</Message><StringToSignBytes>50 4f 53 54 0a 0a 0a 54 68 75 2c 20 30 35 20 41 70 72 20 32 30 31 32 20 30 30 3a 35 30 3a 30 38 20 2b 30 30 30 30 0a 2f 7a 65 74 74 61 72 2d 74 2f 3f 64 65 6c 65 74 65</StringToSignBytes><RequestId>707FBE0EB4A571A8</RequestId><HostId>mP3ZwlPTcRqARQZd6gU4UvBrxGBNIVa0VVe5p0rqGmq5hM65RprwcG/qcXe+pmDT</HostId><SignatureProvided>edkNGuugiSFe0ku4eGzkh8kYgHw=</SignatureProvided><StringToSign>POST Thu, 05 Apr 2012 00:50:08 +0000 The file multi_delete.xml contains the following: cat multi_delete.xml <?xml version="1.0" encoding="UTF-8"?> <Delete> <Quiet>true</Quiet> <Object> <Key>file_1</Key> <VersionId> </VersionId>> </Object> <Object> <Key>file_2</Key> <VersionId> </VersionId> </Object> </Delete> Thanks for any help! --Zack

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  • FOSUserBundle override mapping to remove need for username

    - by musoNic80
    I want to remove the need for a username in the FOSUserBundle. My users will login using an email address only and I've added real name fields as part of the user entity. I realised that I needed to redo the entire mapping as described here. I think I've done it correctly but when I try to submit the registration form I get the error: "Only field names mapped by Doctrine can be validated for uniqueness." The strange thing is that I haven't tried to assert a unique constraint to anything in the user entity. Here is my full user entity file: <?php // src/MyApp/UserBundle/Entity/User.php namespace MyApp\UserBundle\Entity; use FOS\UserBundle\Model\User as BaseUser; use Doctrine\ORM\Mapping as ORM; use Symfony\Component\Validator\Constraints as Assert; /** * @ORM\Entity * @ORM\Table(name="depbook_user") */ class User extends BaseUser { /** * @ORM\Id * @ORM\Column(type="integer") * @ORM\GeneratedValue(strategy="AUTO") */ protected $id; /** * @ORM\Column(type="string", length=255) * * @Assert\NotBlank(message="Please enter your first name.", groups={"Registration", "Profile"}) * @Assert\MaxLength(limit="255", message="The name is too long.", groups={"Registration", "Profile"}) */ protected $firstName; /** * @ORM\Column(type="string", length=255) * * @Assert\NotBlank(message="Please enter your last name.", groups={"Registration", "Profile"}) * @Assert\MaxLength(limit="255", message="The name is too long.", groups={"Registration", "Profile"}) */ protected $lastName; /** * @ORM\Column(type="string", length=255) * * @Assert\NotBlank(message="Please enter your email address.", groups={"Registration", "Profile"}) * @Assert\MaxLength(limit="255", message="The name is too long.", groups={"Registration", "Profile"}) * @Assert\Email(groups={"Registration"}) */ protected $email; /** * @ORM\Column(type="string", length=255, name="email_canonical", unique=true) */ protected $emailCanonical; /** * @ORM\Column(type="boolean") */ protected $enabled; /** * @ORM\Column(type="string") */ protected $salt; /** * @ORM\Column(type="string") */ protected $password; /** * @ORM\Column(type="datetime", nullable=true, name="last_login") */ protected $lastLogin; /** * @ORM\Column(type="boolean") */ protected $locked; /** * @ORM\Column(type="boolean") */ protected $expired; /** * @ORM\Column(type="datetime", nullable=true, name="expires_at") */ protected $expiresAt; /** * @ORM\Column(type="string", nullable=true, name="confirmation_token") */ protected $confirmationToken; /** * @ORM\Column(type="datetime", nullable=true, name="password_requested_at") */ protected $passwordRequestedAt; /** * @ORM\Column(type="array") */ protected $roles; /** * @ORM\Column(type="boolean", name="credentials_expired") */ protected $credentialsExpired; /** * @ORM\Column(type="datetime", nullable=true, name="credentials_expired_at") */ protected $credentialsExpiredAt; public function __construct() { parent::__construct(); // your own logic } /** * @return string */ public function getFirstName() { return $this->firstName; } /** * @return string */ public function getLastName() { return $this->lastName; } /** * Sets the first name. * * @param string $firstname * * @return User */ public function setFirstName($firstname) { $this->firstName = $firstname; return $this; } /** * Sets the last name. * * @param string $lastname * * @return User */ public function setLastName($lastname) { $this->lastName = $lastname; return $this; } } I've seen various suggestions about this but none of the suggestions seem to work for me. The FOSUserBundle docs are very sparse about what must be a very common request.

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  • Spring 3 simple extentionless url mappings with annotation-based mapping - impossible?

    - by caerphilly
    Hi, I'm using Spring 3, and trying to set up a simple web-app using annotations to define controller mappings. This seems to be incredibly difficult without peppering all the urls with *.form or *.do Because part of the site needs to be password protected, these urls are all under /secure. There is a <security-constraint> in the web.xml protecting everything under that root. I want to map all the Spring controllers to /secure/app/. Example URLs would be: /secure/app/landingpage /secure/app/edit/customer/{id} each of which I would handle with an appropriate jsp/xml/whatever. So, in web.xml I have this: <servlet> <servlet-name>dispatcher</servlet-name> <servlet-class>org.springframework.web.servlet.DispatcherServlet</servlet-class> <load-on-startup>1</load-on-startup> </servlet> <servlet-mapping> <servlet-name>dispatcher</servlet-name> <url-pattern>/secure/app/*</url-pattern> </servlet-mapping> And in despatcher-servlet.xml I have this: <context:component-scan base-package="controller" /> In the Controller package I have a controller class: package controller; import org.springframework.stereotype.Controller; import org.springframework.web.bind.annotation.RequestMapping; import org.springframework.web.bind.annotation.RequestMethod; import org.springframework.web.servlet.ModelAndView; import javax.servlet.http.HttpServletRequest; @Controller @RequestMapping("/secure/app/main") public class HomePageController { public HomePageController() { } @RequestMapping(method = RequestMethod.GET) public ModelAndView getPage(HttpServletRequest request) { ModelAndView mav = new ModelAndView(); mav.setViewName("main"); return mav; } } Under /WEB-INF/jsp I have a "main.jsp", and a suitable view resolver set up to point to this. I had things working when mapping the despatcher using *.form, but can't get anything working using the above code. When Spring starts up it appears to map everything correctly: 13:22:36,762 INFO main annotation.DefaultAnnotationHandlerMapping:399 - Mapped URL path [/secure/app/main] onto handler [controller.HomePageController@2a8ab08f] I also noticed this line, which looked suspicious: 13:25:49,578 DEBUG main servlet.DispatcherServlet:443 - No HandlerMappings found in servlet 'dispatcher': using default And at run time any attempt to view /secure/app/main just returns a 404 error in Tomcat, with this log output: 13:25:53,382 DEBUG http-8080-1 servlet.DispatcherServlet:842 - DispatcherServlet with name 'dispatcher' determining Last-Modified value for [/secure/app/main] 13:25:53,383 DEBUG http-8080-1 servlet.DispatcherServlet:850 - No handler found in getLastModified 13:25:53,390 DEBUG http-8080-1 servlet.DispatcherServlet:690 - DispatcherServlet with name 'dispatcher' processing GET request for [/secure/app/main] 13:25:53,393 WARN http-8080-1 servlet.PageNotFound:962 - No mapping found for HTTP request with URI [/secure/app/main] in DispatcherServlet with name 'dispatcher' 13:25:53,393 DEBUG http-8080-1 servlet.DispatcherServlet:677 - Successfully completed request So... Spring maps a URL, and then "forgets" about that mapping a second later? What is going on? Thanks.

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  • How can I best share Ant targets between projects?

    - by Rob Hruska
    Is there a well-established way to share Ant targets between projects? I have a solution currently, but it's a bit inelegant. Here's what I'm doing so far. I've got a file called ivy-tasks.xml hosted on a server on our network. This file contains, among other targets, boilerplate tasks for managing project dependencies with Ivy. For example: <project name="ant-ivy-tasks" default="init-ivy" xmlns:ivy="antlib:org.apache.ivy.ant"> ... <target name="ivy-download" unless="skip.ivy.download"> <mkdir dir="${ivy.jar.dir}"/> <echo message="Installing ivy..."/> <get src="http://repo1.maven.org/maven2/org/apache/ivy/ivy/${ivy.install.version}/ivy-${ivy.install.version}.jar" dest="${ivy.jar.file}" usetimestamp="true"/> </target> <target name="ivy-init" depends="ivy-download" description="-> Defines ivy tasks and loads global settings"> <path id="ivy.lib.path"> <fileset dir="${ivy.jar.dir}" includes="*.jar"/> </path> <taskdef resource="org/apache/ivy/ant/antlib.xml" uri="antlib:org.apache.ivy.ant" classpathref="ivy.lib.path"/> <ivy:settings url="http://myserver/ivy/settings/ivysettings-user.xml"/> </target> ... </project> The reason this file is hosted is because I don't want to: Check the file into every project that needs it - this will result in duplication, making maintaining the targets harder. Have my build.xml depend on checking out a project from source control - this will make the build have more XML at the top-level just to access the file. What I do with this file in my projects' build.xmls is along the lines of: <property name="download.dir" location="download"/> <mkdir dir="${download.dir}"/> <echo message="Downloading import files to ${download.dir}"/> <get src="http://myserver/ivy/ivy-tasks.xml" dest="${download.dir}/ivy-tasks.xml" usetimestamp="true"/> <import file="${download.dir}/ivy-tasks.xml"/> The "dirty" part about this is that I have to do the above steps outside of a target, because the import task must be at the top-level. Plus, I still have to include this XML in all of the build.xml files that need it (i.e. there's still some amount of duplication). On top of that, there might be additional situations where I might have common (non-Ivy) tasks that I'd like imported. If I were to provide these tasks using Ivy's dependency management I'd still have problems, since by the time I'd have resolved the dependencies I would have to be inside of a target in my build.xml, and unable to import (due to the constraint mentioned above). Is there a better solution for what I'm trying to accomplish?

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  • Ninject 2 and MVC 2.0

    - by theouteredge
    I've updated a project to VS2010 and MVC2 from VS2008 and MVC1. I'm having problems with Ninject not finding controllers within Areas Here is my global.asax.cs file: namespace Website { // Note: For instructions on enabling IIS6 or IIS7 classic mode, // visit http://go.microsoft.com/?LinkId=9394801 public class MvcApplication : NinjectHttpApplication { public static StandardKernel NinjectKernel; public static void RegisterRoutes(RouteCollection routes) { routes.IgnoreRoute("{resource}.axd/{*pathInfo}"); routes.MapRoute( "Balance", "Balance/{action}/{month}/{year}", new { controller = "Balance", action = "Index", month = DateTime.Now.Month, year = DateTime.Now.Year } ); routes.MapRoute( "Default", // Route name "{controller}/{action}/{id}", // URL with parameters new { controller = "Login", action = "Index", id = "" } // Parameter defaults ); } /* protected void Application_Start() { AreaRegistration.RegisterAllAreas(); RegisterRoutes(RouteTable.Routes); // initializes the NHProfiler so you can see what is going on with your queries HibernatingRhinos.Profiler.Appender.NHibernate.NHibernateProfiler.Initialize(); } */ protected override void OnApplicationStarted() { RegisterRoutes(RouteTable.Routes); AreaRegistration.RegisterAllAreas(); RegisterAllControllersIn(Assembly.GetExecutingAssembly()); } protected void Application_Error(object sender, EventArgs e) { var errorService = NinjectKernel.Get<IErrorLogService>(); errorService.LogError(HttpContext.Current.Server.GetLastError().GetBaseException(), "AppSite"); } protected override IKernel CreateKernel() { if (NinjectKernel == null) { NinjectKernel = new StandardKernel(new ServiceModule()); } return NinjectKernel; } } public class ServiceModule : NinjectModule { public override void Load() { Bind<IHelper>().To<Helper>().InRequestScope(); Bind<IErrorLogService>().To<ErrorLogService>(); Bind<INHSessionFactory>().To<NHSessionFactory>().InSingletonScope(); Bind<ISessionFactory>().ToMethod(ctx => ctx.Kernel.Get<INHSessionFactory>().GetSessionFactory()) .InSingletonScope(); Bind<INHSession>().To<NHSession>(); Bind<ISession>().ToMethod(ctx => ctx.Kernel.Get<INHSession>().GetSession()); } } } Accessing controllers within the /Controllers folder works OK, but accessing controllers within a /Areas/Member/Controller throws the following error: Server Error in '/' Application. Cannot be null Parameter name: service Description: An unhandled exception occurred during the execution of the current web request. Please review the stack trace for more information about the error and where it originated in the code. Exception Details: System.ArgumentNullException: Cannot be null Parameter name: service Source Error: An unhandled exception was generated during the execution of the current web request. Information regarding the origin and location of the exception can be identified using the exception stack trace below. Stack Trace: [ArgumentNullException: Cannot be null Parameter name: service] Ninject.ResolutionExtensions.GetResolutionIterator(IResolutionRoot root, Type service, Func`2 constraint, IEnumerable`1 parameters, Boolean isOptional, Boolean isUnique) +193 Ninject.Web.Mvc.NinjectControllerFactory.GetControllerInstance(RequestContext requestContext, Type controllerType) +41 System.Web.Mvc.DefaultControllerFactory.CreateController(RequestContext requestContext, String controllerName) +66 System.Web.Mvc.MvcHandler.ProcessRequestInit(HttpContextBase httpContext, IController& controller, IControllerFactory& factory) +124 System.Web.Mvc.MvcHandler.BeginProcessRequest(HttpContextBase httpContext, AsyncCallback callback, Object state) +50 System.Web.Mvc.MvcHandler.BeginProcessRequest(HttpContext httpContext, AsyncCallback callback, Object state) +48 System.Web.Mvc.MvcHandler.System.Web.IHttpAsyncHandler.BeginProcessRequest(HttpContext context, AsyncCallback cb, Object extraData) +16 System.Web.CallHandlerExecutionStep.System.Web.HttpApplication.IExecutionStep.Execute() +8771488 System.Web.HttpApplication.ExecuteStep(IExecutionStep step, Boolean& completedSynchronously) +184 Version Information: Microsoft .NET Framework Version:4.0.30128; ASP.NET Version:4.0.30128.1 The Url for this request is /Member/Controller/, If I change the Url too /Controller the controller fires but I get an error that the system cannot find the View in the path /Views When it should be looking in /Area/Members/Views I have either done something wrong in the upgrade or I'm missing something bt I just can't figure out what. I've been trying to figure this out for 3 days...

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