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  • Pessimistic locking is not working with Query API

    - by Reddy
    List esns=session.createQuery("from Pool e where e.status=:status "+ "order by uuid asc") .setString("status", "AVAILABLE") .setMaxResults(n) .setLockMode("e", LockMode.PESSIMISTIC_WRITE) .list(); I have the above query written, however it is not generating for update query and simultaneous updates are happening. I am using 3.5.2 version and it has a bug in Criteria API, is the same bug present in query API as well or I am doing something wrong?

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  • Alternatives to Pessimistic Locking in Cluster Applications

    - by amphibient
    I am researching alternatives to database-level pessimistic locking to achieve transaction isolation in a cluster of Java applications going against the same database. Synchronizing concurrent access in the application tier is clearly not a solution in the present configuration because the same database transaction can be invoked from multiple JVMs concurrently. Currently, we are subject to occasional race conditions which, due to the optimistic locking we have in place via Hibernate, cause a StaleObjectStateException exception and data loss. I have a moderately large transaction within the scope of my refactoring project. Let's describe it as updating one top-level table row and then making various related inserts and/or updates to several of its child entities. I would like to insure exclusive access to the top-level table row and all of the children to be affected but I would like to stay away from pessimistic locking at the database level for performance reasons mostly. We use Hibernate for ORM. Does it make sense to start a single (perhaps synchronous) message queue application into which this method could be moved to insure synchronized access as opposed to each cluster node using its own, which is a clear race condition hazard? I am mentioning this approach even though I am not confident in it because both the top-level table row and its children could also be updated from other system calls, not just the mentioned transaction. So I am seeking to design a solution where the top-level table row and its children will all somehow be pseudo-locked (exclusive transaction isolation) but at the application and not the database level. I am open to ideas and suggestions, I understand this is not a very cut and dried challenge.

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  • Crash when checking BOF property of pessimistic locked ADO recordset

    - by Patrick
    Bit of an odd one for you: I've got two connections to a database, on one I've opened a _RecordsetPtr with a pessimistic lock. I can no longer send an UPDATE command on the other connection. I can send a SELECT command on the second connection and data is returned. If I use a read only lock then there are no problems however when I use a pessimistic lock on the second connection as well I can check the State == adStateOpen but the program hangs when I test the BOF property! If I don't test the BOF property and try to call moveNext on the second connection the software hangs If I do neither of these I am able to access the data via the second connection but trying to access the data from the first connection causes the software to hang. Any one seen anything similar as I'm a bit stuck? EDIT : it wasn't hanging, someone had put a 30 minute timeout on the connection and I wasn't waiting that long while testing...

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  • optimistic and pessimistic locks

    - by billmce
    Working on my first php/Codeigniter project and I’ve scoured the ‘net for information on locking access to editing data and haven’t found very much information. I expect it to be a fairly regular occurrence for 2 users to attempt to edit the same form simultaneously. My experience (in the stateful world of BBx, filePro, and other RAD apps) is that the data being edited is locked using a pessimistic lock—one user has access to the edit form at the time. The second user basically has to wait for the first to finish. I understand this can be done using Ajax sending XMLHttpRequests to maintain a ‘lock’ database. The php world, lacking state, seems to prefer optimistic locking. If I understand it correctly it works like this: both users get to access the data and they each record a ‘before changes’ version of the data. Before saving their changes, the data is once again retrieved and compared the ‘before changes’ version. If the two versions are identical then the users changes are written. If they are different; the user is shown what has changed since he/she started editing and some mechanism is added to resolve the differences—or the user is shown a ‘Sorry, try again’ message. I’m interested in any experience people here have had with implementing both pessimistic and optimistic locking. If there are any libraries, tools, or ‘how-to’s available I’m appreciate a link. Thanks

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  • How to specify pessimistic lock with Criteria API?

    - by Reddy
    I am retrieving a list of objects in hibernate using Criteria API. However I need lock on those objects as another thread executing at the same time will get the exact objects and only one of the thread will succeed in absence of a pessimistic lock. I tried like below, but it is not working. List esns=session.createCriteria(Reddy_Pool.class) .add(Restrictions.eq("status", "AVAILABLE")) .add(Restrictions.eq("name", "REDDY2")) .addOrder(Order.asc("id")) .setMaxResults(n) .setLockMode(LockMode.PESSIMISTIC_WRITE) //not working at all .list();

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  • Nhibernate setting query time out period for commands and pessimistic locking

    - by Nagesh
    I wish to specify a specific command timeout (or LOCK_TIMEOUT) for an SQL and once this time out is reached an exception (or alert) has to be raised in nHibernate. The following is an example pseudo-code what I have written: using (var session = sessionFactory.OpenSession()) { using (var sqlTrans = session.BeginTransaction()) { ICriteria criteria = session.CreateCriteria(typeof(Foo)); criteria.SetTimeout(5); //Here is the specified command timout, eg: property SqlCommand.CommandTimeout Foo fooObject = session.Load<Foo>(primaryKeyIntegerValue, LockMode.Force); session.SaveOrUpdate(fooObject); sqlTrans.Commit(); } } In SQL server we used to achieve this using the following SQL: BEGIN TRAN SET LOCK_TIMEOUT 500 SELECT * FROM Foo WITH (UPDLOCK, ROWLOCK) WHERE PrimaryKeyID = 1000001 If PrimaryKeyID row would have locked in other transaction the following error message is being shown by SQL Server: Msg 1222, Level 16, State 51, Line 3 Lock request time out period exceeded Similarly I wish to show a lock time out or command time out information using nHibernate. Please help me to achieve this. Thanks in advance for your help.

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  • "SELECT ... FOR UPDATE" not working for Hibernate and MySQL

    - by Andres Rodriguez
    Hi, We have a system in which we must use pessimistic locking in one entity. We are using hibernate, so we use LockMode.UPGRADE. However, it does not lock. The tables are InnoDB We have checked that locking works correctly in the database (5.0.32), so this bug http://bugs.mysql.com/bug.php?id=18184 seems to be no problem. We have checked that datasource includes the autoCommit = false parameter. We have checked that the SQL hibernate (version 3.2) generates includes the " FOR UPDATE". Thanks,

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  • Why avoid pessimistic locking in a version control system?

    - by raven
    Based on a few posts I've read concerning version control, it seems people think pessimistic locking in a version control system is a bad thing. Why? I understand that it prevents one developer from submitting a change while another has the file checked out, but so what? If your code files are so big that you constantly have more than one person working on them at the same time, I submit that you should reorganize your code. Break it up into smaller functional units. Integration of concurrent code changes is a tedious and error-prone process even with the tools a good version control system provides to make it easier. I think it should be avoided if at all possible. So, why is pessimistic locking discouraged?

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  • SQL SERVER – Concurrency Basics – Guest Post by Vinod Kumar

    - by pinaldave
    This guest post is by Vinod Kumar. Vinod Kumar has worked with SQL Server extensively since joining the industry over a decade ago. Working on various versions from SQL Server 7.0, Oracle 7.3 and other database technologies – he now works with the Microsoft Technology Center (MTC) as a Technology Architect. Let us read the blog post in Vinod’s own voice. Learning is always fun when it comes to SQL Server and learning the basics again can be more fun. I did write about Transaction Logs and recovery over my blogs and the concept of simplifying the basics is a challenge. In the real world we always see checks and queues for a process – say railway reservation, banks, customer supports etc there is a process of line and queue to facilitate everyone. Shorter the queue higher is the efficiency of system (a.k.a higher is the concurrency). Every database does implement this using checks like locking, blocking mechanisms and they implement the standards in a way to facilitate higher concurrency. In this post, let us talk about the topic of Concurrency and what are the various aspects that one needs to know about concurrency inside SQL Server. Let us learn the concepts as one-liners: Concurrency can be defined as the ability of multiple processes to access or change shared data at the same time. The greater the number of concurrent user processes that can be active without interfering with each other, the greater the concurrency of the database system. Concurrency is reduced when a process that is changing data prevents other processes from reading that data or when a process that is reading data prevents other processes from changing that data. Concurrency is also affected when multiple processes are attempting to change the same data simultaneously. Two approaches to managing concurrent data access: Optimistic Concurrency Model Pessimistic Concurrency Model Concurrency Models Pessimistic Concurrency Default behavior: acquire locks to block access to data that another process is using. Assumes that enough data modification operations are in the system that any given read operation is likely affected by a data modification made by another user (assumes conflicts will occur). Avoids conflicts by acquiring a lock on data being read so no other processes can modify that data. Also acquires locks on data being modified so no other processes can access the data for either reading or modifying. Readers block writer, writers block readers and writers. Optimistic Concurrency Assumes that there are sufficiently few conflicting data modification operations in the system that any single transaction is unlikely to modify data that another transaction is modifying. Default behavior of optimistic concurrency is to use row versioning to allow data readers to see the state of the data before the modification occurs. Older versions of the data are saved so a process reading data can see the data as it was when the process started reading and not affected by any changes being made to that data. Processes modifying the data is unaffected by processes reading the data because the reader is accessing a saved version of the data rows. Readers do not block writers and writers do not block readers, but, writers can and will block writers. Transaction Processing A transaction is the basic unit of work in SQL Server. Transaction consists of SQL commands that read and update the database but the update is not considered final until a COMMIT command is issued (at least for an explicit transaction: marked with a BEGIN TRAN and the end is marked by a COMMIT TRAN or ROLLBACK TRAN). Transactions must exhibit all the ACID properties of a transaction. ACID Properties Transaction processing must guarantee the consistency and recoverability of SQL Server databases. Ensures all transactions are performed as a single unit of work regardless of hardware or system failure. A – Atomicity C – Consistency I – Isolation D- Durability Atomicity: Each transaction is treated as all or nothing – it either commits or aborts. Consistency: ensures that a transaction won’t allow the system to arrive at an incorrect logical state – the data must always be logically correct.  Consistency is honored even in the event of a system failure. Isolation: separates concurrent transactions from the updates of other incomplete transactions. SQL Server accomplishes isolation among transactions by locking data or creating row versions. Durability: After a transaction commits, the durability property ensures that the effects of the transaction persist even if a system failure occurs. If a system failure occurs while a transaction is in progress, the transaction is completely undone, leaving no partial effects on data. Transaction Dependencies In addition to supporting all four ACID properties, a transaction might exhibit few other behaviors (known as dependency problems or consistency problems). Lost Updates: Occur when two processes read the same data and both manipulate the data, changing its value and then both try to update the original data to the new value. The second process might overwrite the first update completely. Dirty Reads: Occurs when a process reads uncommitted data. If one process has changed data but not yet committed the change, another process reading the data will read it in an inconsistent state. Non-repeatable Reads: A read is non-repeatable if a process might get different values when reading the same data in two reads within the same transaction. This can happen when another process changes the data in between the reads that the first process is doing. Phantoms: Occurs when membership in a set changes. It occurs if two SELECT operations using the same predicate in the same transaction return a different number of rows. Isolation Levels SQL Server supports 5 isolation levels that control the behavior of read operations. Read Uncommitted All behaviors except for lost updates are possible. Implemented by allowing the read operations to not take any locks, and because of this, it won’t be blocked by conflicting locks acquired by other processes. The process can read data that another process has modified but not yet committed. When using the read uncommitted isolation level and scanning an entire table, SQL Server can decide to do an allocation order scan (in page-number order) instead of a logical order scan (following page pointers). If another process doing concurrent operations changes data and move rows to a new location in the table, the allocation order scan can end up reading the same row twice. Also can happen if you have read a row before it is updated and then an update moves the row to a higher page number than your scan encounters later. Performing an allocation order scan under Read Uncommitted can cause you to miss a row completely – can happen when a row on a high page number that hasn’t been read yet is updated and moved to a lower page number that has already been read. Read Committed Two varieties of read committed isolation: optimistic and pessimistic (default). Ensures that a read never reads data that another application hasn’t committed. If another transaction is updating data and has exclusive locks on data, your transaction will have to wait for the locks to be released. Your transaction must put share locks on data that are visited, which means that data might be unavailable for others to use. A share lock doesn’t prevent others from reading but prevents them from updating. Read committed (snapshot) ensures that an operation never reads uncommitted data, but not by forcing other processes to wait. SQL Server generates a version of the changed row with its previous committed values. Data being changed is still locked but other processes can see the previous versions of the data as it was before the update operation began. Repeatable Read This is a Pessimistic isolation level. Ensures that if a transaction revisits data or a query is reissued the data doesn’t change. That is, issuing the same query twice within a transaction cannot pickup any changes to data values made by another user’s transaction because no changes can be made by other transactions. However, this does allow phantom rows to appear. Preventing non-repeatable read is a desirable safeguard but cost is that all shared locks in a transaction must be held until the completion of the transaction. Snapshot Snapshot Isolation (SI) is an optimistic isolation level. Allows for processes to read older versions of committed data if the current version is locked. Difference between snapshot and read committed has to do with how old the older versions have to be. It’s possible to have two transactions executing simultaneously that give us a result that is not possible in any serial execution. Serializable This is the strongest of the pessimistic isolation level. Adds to repeatable read isolation level by ensuring that if a query is reissued rows were not added in the interim, i.e, phantoms do not appear. Preventing phantoms is another desirable safeguard, but cost of this extra safeguard is similar to that of repeatable read – all shared locks in a transaction must be held until the transaction completes. In addition serializable isolation level requires that you lock data that has been read but also data that doesn’t exist. Ex: if a SELECT returned no rows, you want it to return no. rows when the query is reissued. This is implemented in SQL Server by a special kind of lock called the key-range lock. Key-range locks require that there be an index on the column that defines the range of values. If there is no index on the column, serializable isolation requires a table lock. Gets its name from the fact that running multiple serializable transactions at the same time is equivalent of running them one at a time. Now that we understand the basics of what concurrency is, the subsequent blog posts will try to bring out the basics around locking, blocking, deadlocks because they are the fundamental blocks that make concurrency possible. Now if you are with me – let us continue learning for SQL Server Locking Basics. 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, Technology Tagged: Concurrency

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  • Master-slave vs. peer-to-peer archictecture: benefits and problems

    - by Ashok_Ora
    Normal 0 false false false EN-US X-NONE X-NONE Almost two decades ago, I was a member of a database development team that introduced adaptive locking. Locking, the most popular concurrency control technique in database systems, is pessimistic. Locking ensures that two or more conflicting operations on the same data item don’t “trample” on each other’s toes, resulting in data corruption. In a nutshell, here’s the issue we were trying to address. In everyday life, traffic lights serve the same purpose. They ensure that traffic flows smoothly and when everyone follows the rules, there are no accidents at intersections. As I mentioned earlier, the problem with typical locking protocols is that they are pessimistic. Regardless of whether there is another conflicting operation in the system or not, you have to hold a lock! Acquiring and releasing locks can be quite expensive, depending on how many objects the transaction touches. Every transaction has to pay this penalty. To use the earlier traffic light analogy, if you have ever waited at a red light in the middle of nowhere with no one on the road, wondering why you need to wait when there’s clearly no danger of a collision, you know what I mean. The adaptive locking scheme that we invented was able to minimize the number of locks that a transaction held, by detecting whether there were one or more transactions that needed conflicting eyou could get by without holding any lock at all. In many “well-behaved” workloads, there are few conflicts, so this optimization is a huge win. If, on the other hand, there are many concurrent, conflicting requests, the algorithm gracefully degrades to the “normal” behavior with minimal cost. We were able to reduce the number of lock requests per TPC-B transaction from 178 requests down to 2! Wow! This is a dramatic improvement in concurrency as well as transaction latency. The lesson from this exercise was that if you can identify the common scenario and optimize for that case so that only the uncommon scenarios are more expensive, you can make dramatic improvements in performance without sacrificing correctness. So how does this relate to the architecture and design of some of the modern NoSQL systems? NoSQL systems can be broadly classified as master-slave sharded, or peer-to-peer sharded systems. NoSQL systems with a peer-to-peer architecture have an interesting way of handling changes. Whenever an item is changed, the client (or an intermediary) propagates the changes synchronously or asynchronously to multiple copies (for availability) of the data. Since the change can be propagated asynchronously, during some interval in time, it will be the case that some copies have received the update, and others haven’t. What happens if someone tries to read the item during this interval? The client in a peer-to-peer system will fetch the same item from multiple copies and compare them to each other. If they’re all the same, then every copy that was queried has the same (and up-to-date) value of the data item, so all’s good. If not, then the system provides a mechanism to reconcile the discrepancy and to update stale copies. So what’s the problem with this? There are two major issues: First, IT’S HORRIBLY PESSIMISTIC because, in the common case, it is unlikely that the same data item will be updated and read from different locations at around the same time! For every read operation, you have to read from multiple copies. That’s a pretty expensive, especially if the data are stored in multiple geographically separate locations and network latencies are high. Second, if the copies are not all the same, the application has to reconcile the differences and propagate the correct value to the out-dated copies. This means that the application program has to handle discrepancies in the different versions of the data item and resolve the issue (which can further add to cost and operation latency). Resolving discrepancies is only one part of the problem. What if the same data item was updated independently on two different nodes (copies)? In that case, due to the asynchronous nature of change propagation, you might land up with different versions of the data item in different copies. In this case, the application program also has to resolve conflicts and then propagate the correct value to the copies that are out-dated or have incorrect versions. This can get really complicated. My hunch is that there are many peer-to-peer-based applications that don’t handle this correctly, and worse, don’t even know it. Imagine have 100s of millions of records in your database – how can you tell whether a particular data item is incorrect or out of date? And what price are you willing to pay for ensuring that the data can be trusted? Multiple network messages per read request? Discrepancy and conflict resolution logic in the application, and potentially, additional messages? All this overhead, when all you were trying to do was to read a data item. Wouldn’t it be simpler to avoid this problem in the first place? Master-slave architectures like the Oracle NoSQL Database handles this very elegantly. A change to a data item is always sent to the master copy. Consequently, the master copy always has the most current and authoritative version of the data item. The master is also responsible for propagating the change to the other copies (for availability and read scalability). Client drivers are aware of master copies and replicas, and client drivers are also aware of the “currency” of a replica. In other words, each NoSQL Database client knows how stale a replica is. This vastly simplifies the job of the application developer. If the application needs the most current version of the data item, the client driver will automatically route the request to the master copy. If the application is willing to tolerate some staleness of data (e.g. a version that is no more than 1 second out of date), the client can easily determine which replica (or set of replicas) can satisfy the request, and route the request to the most efficient copy. This results in a dramatic simplification in application logic and also minimizes network requests (the driver will only send the request to exactl the right replica, not many). So, back to my original point. A well designed and well architected system minimizes or eliminates unnecessary overhead and avoids pessimistic algorithms wherever possible in order to deliver a highly efficient and high performance system. If you’ve every programmed an Oracle NoSQL Database application, you’ll know the difference! /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;}

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  • How to implement an offline reader writer lock

    - by Peter Morris
    Some context for the question All objects in this question are persistent. All requests will be from a Silverlight client talking to an app server via a binary protocol (Hessian) and not WCF. Each user will have a session key (not an ASP.NET session) which will be a string, integer, or GUID (undecided so far). Some objects might take a long time to edit (30 or more minutes) so we have decided to use pessimistic offline locking. Pessimistic because having to reconcile conflicts would be far too annoying for users, offline because the client is not permanently connected to the server. Rather than storing session/object locking information in the object itself I have decided that any aggregate root that may have its instances locked should implement an interface ILockable public interface ILockable { Guid LockID { get; } } This LockID will be the identity of a "Lock" object which holds the information of which session is locking it. Now, if this were simple pessimistic locking I'd be able to achieve this very simply (using an incrementing version number on Lock to identify update conflicts), but what I actually need is ReaderWriter pessimistic offline locking. The reason is that some parts of the application will perform actions that read these complex structures. These include things like Reading a single structure to clone it. Reading multiple structures in order to create a binary file to "publish" the data to an external source. Read locks will be held for a very short period of time, typically less than a second, although in some circumstances they could be held for about 5 seconds at a guess. Write locks will mostly be held for a long time as they are mostly held by humans. There is a high probability of two users trying to edit the same aggregate at the same time, and a high probability of many users needing to temporarily read-lock at the same time too. I'm looking for suggestions as to how I might implement this. One additional point to make is that if I want to place a write lock and there are some read locks, I would like to "queue" the write lock so that no new read locks are placed. If the read locks are removed withing X seconds then the write lock is obtained, if not then the write lock backs off; no new read-locks would be placed while a write lock is queued. So far I have this idea The Lock object will have a version number (int) so I can detect multi-update conflicts, reload, try again. It will have a string[] for read locks A string to hold the session ID that has a write lock A string to hold the queued write lock Possibly a recursion counter to allow the same session to lock multiple times (for both read and write locks), but not sure about this yet. Rules: Can't place a read lock if there is a write lock or queued write lock. Can't place a write lock if there is a write lock or queued write lock. If there are no locks at all then a write lock may be placed. If there are read locks then a write lock will be queued instead of a full write lock placed. (If after X time the read locks are not gone the lock backs off, otherwise it is upgraded). Can't queue a write lock for a session that has a read lock. Can anyone see any problems? Suggest alternatives? Anything? I'd appreciate feedback before deciding on what approach to take.

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  • The case of the phantom ADF developer (and other yarns)

    - by Chris Muir
    A few years of ADF experience means I see common mistakes made by different developers, some I regularly make myself.  This post is designed to assist beginners to Oracle JDeveloper Application Development Framework (ADF) avoid a common ADF pitfall, the case of the phantom ADF developer [add Scooby-Doo music here]. ADF Business Components - triggers, default table values and instead of views. Oracle's JDeveloper tutorials help with the A-B-Cs of ADF development, typically built on the nice 'n safe demo schema provided by with the Oracle database such as the HR demo schema. However it's not too long until ADF beginners, having built up some confidence from learning with the tutorials and vanilla demo schemas, start building ADF Business Components based upon their own existing database schema objects.  This is where unexpected problems can sneak in. The crime Developers may encounter a surprising error at runtime when editing a record they just created or updated and committed to the database, based on their own existing tables, namely the error: JBO-25014: Another user has changed the row with primary key oracle.jbo.Key[x] ...where X is the primary key value of the row at hand.  In a production environment with multiple users this error may be legit, one of the other users has updated the row since you queried it.  Yet in a development environment this error is just plain confusing.  If developers are isolated in their own database, creating and editing records they know other users can't possibly be working with, or all the other developers have gone home for the day, how is this error possible? There are no other users?  It must be the phantom ADF developer! [insert dramatic music here] The following picture is what you'll see in the Business Component Browser, and you'll receive a similar error message via an ADF Faces page: A false conclusion What can possibly cause this issue if it isn't our phantom ADF developer?  Doesn't ADF BC implement record locking, locking database records when the row is modified in the ADF middle-tier by a user?  How can our phantom ADF developer even take out a lock if this is the case?  Maybe ADF has a bug, maybe ADF isn't implementing record locking at all?  Shouldn't we see the error "JBO-26030: Failed to lock the record, another user holds the lock" as we attempt to modify the record, why do we see JBO-25014? : Let's verify that ADF is in fact issuing the correct SQL LOCK-FOR-UPDATE statement to the database. First we need to verify ADF's locking strategy.  It is determined by the Application Module's jbo.locking.mode property.  The default (as of JDev 11.1.1.4.0 if memory serves me correct) and recommended value is optimistic, and the other valid value is pessimistic. Next we need a mechanism to check that ADF is issuing the LOCK statements to the database.  We could ask DBAs to monitor locks with OEM, but optimally we'd rather not involve overworked DBAs in this process, so instead we can use the ADF runtime setting –Djbo.debugoutput=console.  At runtime this options turns on instrumentation within the ADF BC layer, which among a lot of extra detail displayed in the log window, will show the actual SQL statement issued to the database, including the LOCK statement we're looking to confirm. Setting our locking mode to pessimistic, opening the Business Components Browser of a JSF page allowing us to edit a record, say the CHARGEABLE field within a BOOKINGS record where BOOKING_NO = 1206, upon editing the record see among others the following log entries: [421] Built select: 'SELECT BOOKING_NO, EVENT_NO, RESOURCE_CODE, CHARGEABLE, MADE_BY, QUANTITY, COST, STATUS, COMMENTS FROM BOOKINGS Bookings'[422] Executing LOCK...SELECT BOOKING_NO, EVENT_NO, RESOURCE_CODE, CHARGEABLE, MADE_BY, QUANTITY, COST, STATUS, COMMENTS FROM BOOKINGS Bookings WHERE BOOKING_NO=:1 FOR UPDATE NOWAIT[423] Where binding param 1: 1206  As can be seen on line 422, in fact a LOCK-FOR-UPDATE is indeed issued to the database.  Later when we commit the record we see: [441] OracleSQLBuilder: SAVEPOINT 'BO_SP'[442] OracleSQLBuilder Executing, Lock 1 DML on: BOOKINGS (Update)[443] UPDATE buf Bookings>#u SQLStmtBufLen: 210, actual=62[444] UPDATE BOOKINGS Bookings SET CHARGEABLE=:1 WHERE BOOKING_NO=:2[445] Update binding param 1: N[446] Where binding param 2: 1206[447] BookingsView1 notify COMMIT ... [448] _LOCAL_VIEW_USAGE_model_Bookings_ResourceTypesView1 notify COMMIT ... [449] EntityCache close prepared statement ....and as a result the changes are saved to the database, and the lock is released. Let's see what happens when we use the optimistic locking mode, this time to change the same BOOKINGS record CHARGEABLE column again.  As soon as we edit the record we see little activity in the logs, nothing to indicate any SQL statement, let alone a LOCK has been taken out on the row. However when we save our records by issuing a commit, the following is recorded in the logs: [509] OracleSQLBuilder: SAVEPOINT 'BO_SP'[510] OracleSQLBuilder Executing doEntitySelect on: BOOKINGS (true)[511] Built select: 'SELECT BOOKING_NO, EVENT_NO, RESOURCE_CODE, CHARGEABLE, MADE_BY, QUANTITY, COST, STATUS, COMMENTS FROM BOOKINGS Bookings'[512] Executing LOCK...SELECT BOOKING_NO, EVENT_NO, RESOURCE_CODE, CHARGEABLE, MADE_BY, QUANTITY, COST, STATUS, COMMENTS FROM BOOKINGS Bookings WHERE BOOKING_NO=:1 FOR UPDATE NOWAIT[513] Where binding param 1: 1205[514] OracleSQLBuilder Executing, Lock 2 DML on: BOOKINGS (Update)[515] UPDATE buf Bookings>#u SQLStmtBufLen: 210, actual=62[516] UPDATE BOOKINGS Bookings SET CHARGEABLE=:1 WHERE BOOKING_NO=:2[517] Update binding param 1: Y[518] Where binding param 2: 1205[519] BookingsView1 notify COMMIT ... [520] _LOCAL_VIEW_USAGE_model_Bookings_ResourceTypesView1 notify COMMIT ... [521] EntityCache close prepared statement Again even though we're seeing the midtier delay the LOCK statement until commit time, it is in fact occurring on line 412, and released as part of the commit issued on line 419.  Therefore with either optimistic or pessimistic locking a lock is indeed issued. Our conclusion at this point must be, unless there's the unlikely cause the LOCK statement is never really hitting the database, or the even less likely cause the database has a bug, then ADF does in fact take out a lock on the record before allowing the current user to update it.  So there's no way our phantom ADF developer could even modify the record if he tried without at least someone receiving a lock error. Hmm, we can only conclude the locking mode is a red herring and not the true cause of our problem.  Who is the phantom? At this point we'll need to conclude that the error message "JBO-25014: Another user has changed" is somehow legit, even though we don't understand yet what's causing it. This leads onto two further questions, how does ADF know another user has changed the row, and what's been changed anyway? To answer the first question, how does ADF know another user has changed the row, the Fusion Guide's section 4.10.11 How to Protect Against Losing Simultaneous Updated Data , that details the Entity Object Change-Indicator property, gives us the answer: At runtime the framework provides automatic "lost update" detection for entity objects to ensure that a user cannot unknowingly modify data that another user has updated and committed in the meantime. Typically, this check is performed by comparing the original values of each persistent entity attribute against the corresponding current column values in the database at the time the underlying row is locked. Before updating a row, the entity object verifies that the row to be updated is still consistent with the current state of the database.  The guide further suggests to make this solution more efficient: You can make the lost update detection more efficient by identifying any attributes of your entity whose values you know will be updated whenever the entity is modified. Typical candidates include a version number column or an updated date column in the row.....To detect whether the row has been modified since the user queried it in the most efficient way, select the Change Indicator option to compare only the change-indicator attribute values. We now know that ADF BC doesn't use the locking mechanism at all to protect the current user against updates, but rather it keeps a copy of the original record fetched, separate to the user changed version of the record, and it compares the original record against the one in the database when the lock is taken out.  If values don't match, be it the default compare-all-columns behaviour, or the more efficient Change Indicator mechanism, ADF BC will throw the JBO-25014 error. This leaves one last question.  Now we know the mechanism under which ADF identifies a changed row, what we don't know is what's changed and who changed it? The real culprit What's changed?  We know the record in the mid-tier has been changed by the user, however ADF doesn't use the changed record in the mid-tier to compare to the database record, but rather a copy of the original record before it was changed.  This leaves us to conclude the database record has changed, but how and by who? There are three potential causes: Database triggers The database trigger among other uses, can be configured to fire PLSQL code on a database table insert, update or delete.  In particular in an insert or update the trigger can override the value assigned to a particular column.  The trigger execution is actioned by the database on behalf of the user initiating the insert or update action. Why this causes the issue specific to our ADF use, is when we insert or update a record in the database via ADF, ADF keeps a copy of the record written to the database.  However the cached record is instantly out of date as the database triggers have modified the record that was actually written to the database.  Thus when we update the record we just inserted or updated for a second time to the database, ADF compares its original copy of the record to that in the database, and it detects the record has been changed – giving us JBO-25014. This is probably the most common cause of this problem. Default values A second reason this issue can occur is another database feature, default column values.  When creating a database table the schema designer can define default values for specific columns.  For example a CREATED_BY column could be set to SYSDATE, or a flag column to Y or N.  Default values are only used by the database when a user inserts a new record and the specific column is assigned NULL.  The database in this case will overwrite the column with the default value. As per the database trigger section, it then becomes apparent why ADF chokes on this feature, though it can only specifically occur in an insert-commit-update-commit scenario, not the update-commit-update-commit scenario. Instead of trigger views I must admit I haven't double checked this scenario but it seems plausible, that of the Oracle database's instead of trigger view (sometimes referred to as instead of views).  A view in the database is based on a query, and dependent on the queries complexity, may support insert, update and delete functionality to a limited degree.  In order to support fully insertable, updateable and deletable views, Oracle introduced the instead of view, that gives the view designer the ability to not only define the view query, but a set of programmatic PLSQL triggers where the developer can define their own logic for inserts, updates and deletes. While this provides the database programmer a very powerful feature, it can cause issues for our ADF application.  On inserting or updating a record in the instead of view, the record and it's data that goes in is not necessarily the data that comes out when ADF compares the records, as the view developer has the option to practically do anything with the incoming data, including throwing it away or pushing it to tables which aren't used by the view underlying query for fetching the data. Readers are at this point reminded that this article is specifically about how the JBO-25014 error occurs in the context of 1 developer on an isolated database.  The article is not considering how the error occurs in a production environment where there are multiple users who can cause this error in a legitimate fashion.  Assuming none of the above features are the cause of the problem, and optimistic locking is turned on (this error is not possible if pessimistic locking is the default mode *and* none of the previous causes are possible), JBO-25014 is quite feasible in a production ADF application if 2 users modify the same record. At this point under project timelines pressure, the obvious fix for developers is to drop both database triggers and default values from the underlying tables.  However we must be careful that these legacy constructs aren't used and assumed to be in place by other legacy systems.  Dropping the database triggers or default value that the existing Oracle Forms  applications assumes and requires to be in place could cause unexpected behaviour and bugs in the Forms application.  Proficient software engineers would recognize such a change may require a partial or full regression test of the existing legacy system, a potentially costly and timely exercise, not ideal. Solving the mystery once and for all Luckily ADF has built in functionality to deal with this issue, though it's not a surprise, as Oracle as the author of ADF also built the database, and are fully aware of the Oracle database's feature set.  At the Entity Object attribute level, the Refresh After Insert and Refresh After Update properties.  Simply selecting these instructs ADF BC after inserting or updating a record to the database, to expect the database to modify the said attributes, and read a copy of the changed attributes back into its cached mid-tier record.  Thus next time the developer modifies the current record, the comparison between the mid-tier record and the database record match, and JBO-25014: Another user has changed" is no longer an issue. [Post edit - as per the comment from Oracle's Steven Davelaar below, as he correctly points out the above solution will not work for instead-of-triggers views as it relies on SQL RETURNING clause which is incompatible with this type of view] Alternatively you can set the Change Indicator on one of the attributes.  This will work as long as the relating column for the attribute in the database itself isn't inadvertently updated.  In turn you're possibly just masking the issue rather than solving it, because if another developer turns the Change Indicator back on the original issue will return.

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  • High Performance Storage Systems for SQL Server

    Rod Colledge turns his pessimistic mindset to storage systems, and describes the best way to configure the storage systems of SQL Servers for both performance and reliability. Even Rod gets a glint in his eye when he then goes on to describe the dazzling speed of solid-state storage, though he is quick to identify the risks.

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  • High Performance Storage Systems for SQL Server

    Rod Colledge turns his pessimistic mindset to storage systems, and describes the best way to configure the storage systems of SQL Servers for both performance and reliability. Even Rod gets a glint in his eye when he then goes on to describe the dazzling speed of solid-state storage, though he is quick to identify the risks....Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • Reliable Storage Systems for SQL Server

    By validating the IO path before commissioning the production database system, and performing ongoing validation through page checksums and DBCC checks, you can hopefully avoid data corruption altogether, or at least nip it in the bud. If corruption occurs, then you have to take the right decisions fast to deal with it. Rod Colledge explains how a pessimistic mindset can be an advantage

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  • Reliable Storage Systems for SQL Server

    By validating the IO path before commissioning the production database system, and performing ongoing validation through page checksums and DBCC checks, you can hopefully avoid data corruption altogether, or at least nip it in the bud. If corruption occurs, then you have to take the right decisions fast to deal with it. Rod Colledge explains how a pessimistic mindset can be an advantage

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  • Google search does not show sub-pages from my website

    - by Chang
    My website appears in Google search, but only the first page. Of course I have sub-pages linked from the first page, but the sub-pages do not show in Google search. Not in Yahoo, not in Bing. What should I do? It has been three years that sub-pages do not show. (I tried searching site:mydomain.com and pressed 'repeat the search with the omitted results included' link) What would you suspect the reason? My website addresses were like xxx.php?yy=zzz etc, etc, so I changed it to /yy/zzz using mod_rewrite. I thought it might be (X)HTML standard violations, so now I changed it. I hope Google will soon have my entire website, but I am a little bit pessimistic. Do you have any thought?

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  • Google search does not show sub-pages from my website

    - by user5679
    My website appears in Google search, but only the first page. Of course I have sub-pages linked from the first page, but the sub-pages do not show in Google search. Not in Yahoo, not in Bing. What should I do? It has been three years that sub-pages do not show. (I tried searching site:mydomain.com and pressed 'repeat the search with the omitted results included' link) What would you suspect the reason? My website addresses were like xxx.php?yy=zzz etc, etc, so I changed it to /yy/zzz using mod_rewrite. I thought it might be (X)HTML standard violations, so now I changed it. I hope Google will soon have my entire website, but I am a little bit pessimistic. Do you have any thought?

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  • Coherence Data Guarantees for Data Reads - Basic Terminology

    - by jpurdy
    When integrating Coherence into applications, each application has its own set of requirements with respect to data integrity guarantees. Developers often describe these requirements using expressions like "avoiding dirty reads" or "making sure that updates are transactional", but we often find that even in a small group of people, there may be a wide range of opinions as to what these terms mean. This may simply be due to a lack of familiarity, but given that Coherence sits at an intersection of several (mostly) unrelated fields, it may be a matter of conflicting vocabularies (e.g. "consistency" is similar but different in transaction processing versus multi-threaded programming). Since almost all data read consistency issues are related to the concept of concurrency, it is helpful to start with a definition of that, or rather what it means for two operations to be concurrent. Rather than implying that they occur "at the same time", concurrency is a slightly weaker statement -- it simply means that it can't be proven that one event precedes (or follows) the other. As an example, in a Coherence application, if two client members mutate two different cache entries sitting on two different cache servers at roughly the same time, it is likely that one update will precede the other by a significant amount of time (say 0.1ms). However, since there is no guarantee that all four members have their clocks perfectly synchronized, and there is no way to precisely measure the time it takes to send a given message between any two members (that have differing clocks), we consider these to be concurrent operations since we can not (easily) prove otherwise. So this leads to a question that we hear quite frequently: "Are the contents of the near cache always synchronized with the underlying distributed cache?". It's easy to see that if an update on a cache server results in a message being sent to each near cache, and then that near cache being updated that there is a window where the contents are different. However, this is irrelevant, since even if the application reads directly from the distributed cache, another thread update the cache before the read is returned to the application. Even if no other member modifies a cache entry prior to the local near cache entry being updated (and subsequently read), the purpose of reading a cache entry is to do something with the result, usually either displaying for consumption by a human, or by updating the entry based on the current state of the entry. In the former case, it's clear that if the data is updated faster than a human can perceive, then there is no problem (and in many cases this can be relaxed even further). For the latter case, the application must assume that the value might potentially be updated before it has a chance to update it. This almost aways the case with read-only caches, and the solution is the traditional optimistic transaction pattern, which requires the application to explicitly state what assumptions it made about the old value of the cache entry. If the application doesn't want to bother stating those assumptions, it is free to lock the cache entry prior to reading it, ensuring that no other threads will mutate the entry, a pessimistic approach. The optimistic approach relies on what is sometimes called a "fuzzy read". In other words, the application assumes that the read should be correct, but it also acknowledges that it might not be. (I use the qualifier "sometimes" because in some writings, "fuzzy read" indicates the situation where the application actually sees an original value and then later sees an updated value within the same transaction -- however, both definitions are roughly equivalent from an application design perspective). If the read is not correct it is called a "stale read". Going back to the definition of concurrency, it may seem difficult to precisely define a stale read, but the practical way of detecting a stale read is that is will cause the encompassing transaction to roll back if it tries to update that value. The pessimistic approach relies on a "coherent read", a guarantee that the value returned is not only the same as the primary copy of that value, but also that it will remain that way. In most cases this can be used interchangeably with "repeatable read" (though that term has additional implications when used in the context of a database system). In none of cases above is it possible for the application to perform a "dirty read". A dirty read occurs when the application reads a piece of data that was never committed. In practice the only way this can occur is with multi-phase updates such as transactions, where a value may be temporarily update but then withdrawn when a transaction is rolled back. If another thread sees that value prior to the rollback, it is a dirty read. If an application uses optimistic transactions, dirty reads will merely result in a lack of forward progress (this is actually one of the main risks of dirty reads -- they can be chained and potentially cause cascading rollbacks). The concepts of dirty reads, fuzzy reads, stale reads and coherent reads are able to describe the vast majority of requirements that we see in the field. However, the important thing is to define the terms used to define requirements. A quick web search for each of the terms in this article will show multiple meanings, so I've selected what are generally the most common variations, but it never hurts to state each definition explicitly if they are critical to the success of a project (many applications have sufficiently loose requirements that precise terminology can be avoided).

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  • Reliable Storage Systems for SQL Server

    By validating the IO path before commissioning the production database system, and performing ongoing validation through page checksums and DBCC checks, you can hopefully avoid data corruption altogether, or at least nip it in the bud. If corruption occurs, then you have to take the right decisions fast to deal with it. Rod Colledge explains how a pessimistic mindset can be an advantage...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • SQLAuthority News – Community Tech Days – December 11, 2010

    - by pinaldave
    Community Tech Days are very close on December 11. The venue details are as following: H K Hall, H K College Campus, Near Handloom House, Opp. Natraj Cinema, Ashram Road, Ahmedabad – 380009 Click here to Registration for the event. Please read the announcement details here. I will be speaking on following session. Best Database Practice for SharePoint Server This session will be very unique. I will be starting with a bit pessimistic talk about how one cannot many things in SQL Server when SharePoint Server is installed. I will go over in the details for the reasons for the same. Right after this 5 minutes I am going to show few things to attendees which they can apply right away to their database and instantly get the performance. I am going to share the easy scripts with them online right away and if they run the same on their SharePoint Database, they will get the performance right out of the box right away – I Promise! This is the same session I presented at SharePoint Conference and I have received excellent feedback on the same subject. Join us! Reference: Pinal Dave (http://blog.SQLAuthority.com)   Filed under: MVP, Pinal Dave, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority Author Visit, SQLAuthority News, T SQL, Technology

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  • Insurance Outlook: Just Right of Center

    - by Chuck Johnston Admin
    On Tuesday June 21st, PwC lead a session at the International Insurance Society meeting in Toronto focused on the opportunity in insurance.  The scenarios focusing on globalization, regulation and new areas of insurance opportunity were well defined and thought provoking, but the most interesting part of the session was the audience participation. PwC used a favorite strategic planning tool of mine, scenario planning, to highlight the important financial, political, social and technological dimensions that impact the insurance industry. Using wireless polling keypads, the audience was able to participate in scoring a range of possibilities across each dimension using a 1 to 5 ranking; 1 being generally negative or highly pessimistic scenarios and 5 being very positive or more confident scenarios. The results were then displayed on a screen with a line or "center" in the middle. "Left of center" was defined as being highly cautious and conservative, while "right of center" was defined as a more optimistic outlook for the industry's future. This session was attended by insurance carriers' senior leadership, leading insurance academics, senior regulators, and the occasional insurance technology executive. In general, the average answer fell just right of center, i.e. a little more positive or optimistic than center. Three years ago, after the 2008 financial crisis, I suspect the answers would have skewed more sharply to the left of center. This sense that things are generally getting better for insurers and that there is the potential for positive change pervaded the conference. There is still caution and concern around economic factors, regulation (especially the potential pitfalls of regulatory convergence with banking) and talent management, but in general, the industry outlook is more positive than it's been in several years. Chuck Johnston is vice president of industry strategy, Oracle Insurance. 

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  • Is it a bad idea to release software on the night before Christmas?

    - by Conor
    We're about to go live with a new version of our system. It's getting really close to Christmas. I work in a very small company. Everyone will be on leave or sporadically available over the next week or so. I've argued with my boss that this is very risky and that we should go live in the new year when everyone is back and when we can provide full support. He is unflinching - he argues that we need to go live sooner - so that we can get new users and more revenue which we need. The number of new users will be minor amount over the next week or so. There has been a decent amount of system testing performed on the system. However a new live system, in my experience, needs a lot of care and attention in the first few days. Am I being pessimistic or realistic? Update - January: The system did not go live over Christmas. Ongoing system testing revealed various problems. So no support issues to deal with. Still preparing for release...

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  • What are jQuery best practices regarding Ajax convenience methods and error handling?

    - by JonathanHayward
    Let's suppose, for an example, that I want to partly clone Gmail's interface with jQuery Ajax and implement periodic auto-saving as well as sending. And in particular, let us suppose that I care about error handling, expecting network and other errors, and instead of just being optimistic I want sensible handling of different errors. If I use the "low-level" feature of $.ajax() then it's clear how to specify an error callback, but the convenience methods of $.get(), $.post(), and .load() do not allow an error callback to be specified. What are the best practices for pessimistic error handling? Is it by registering a .ajaxError() with certain wrapped sets, or an introspection-style global error handler in $.ajaxSetup()? What would the relevant portions of code look like to initiate an autosave so that a "could not autosave" type warning is displayed if an attempted autosave fails, and perhaps a message that is customized to the type of error? Thanks,

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