Using the SqlDependency Class is a good way to make your data driven application (whether it be Web or Windows Forms) more efficient by removing the need to constantly re-query your database checking for data changes. For the purposes of this article we'll be discussing using SqlDependencywith SQL 2005, but I'm sure it works the same, or very similar with SQL 2008. Using SqlDependency to monitor for data changed relies on two things from SQL Server, Service Broker. A Service Broker allows the sending of asynchronous messages to and from your database. The second item it relies on Queues, your Service Broker will use your Queue to create loose coupling between the sender and receiver. The sender can send its message to the Queue and move on, and relies on the Service Broker to make sure whatever message was put into the Queue is processed and a response is sent back to the sender (your application). For this article I’m using a sample database I created named SqlDependencyExample and a table named Employee whick has this structure: CREATE TABLE [dbo].[EmployeeList]( [EmployeeID] [int] IDENTITY(1,1) NOT NULL, [FirstName] [varchar](25) NOT NULL, [LastName] [varchar](25) NOT NULL, [PhoneNumber] [varchar](13) NOT NULL) ON [PRIMARY] We will be using a stored procedure for querying, but this can also be done with a regular text query in your code. Here’s the stored procedure CREATE PROCEDURE uspGetEmployeeInformationASBEGIN -- Insert statements for procedure here SELECT EmployeeID, FirstName, LastName, PhoneNumber FROM dbo.EmployeeListEND NOTE: Notice in the stored procedure we have dbo in the table name, when using dependency to monitor data changes you have to have the table in the format [owner].[Tablename] of it can cause unwanted results, so to avoid that just use the said format. First thing we need to do is create your Service Broker and Queue so we can send messages back & forth between our database. In my example I have a simple table that holds employee names & phone numbers. This is how we create the Service Broker & Queue (My database name is SqlDependencyExample but change that with your database name (We also need to give our SQL user permission to access it) USING [SqlDependencyExample]CREATE QUEUE NewEmployeeAddedQueue;CREATE SERVICE NewEmployeeAddedService ON QUEUE NewEmployeeAddedQueue([http://schemas.microsoft.com/sql/notifications/postquerynotification]); GRANT SUBSCRIBE QUERY NOTIFICATIONS TO SqlDependencyExampleUser; Now we move on to the fun part, the code (I know that's what you're waiting for). Before this can work we need to check and make sure the connected user has the proper permissions for the notifications. We can do this with creating a simple method CheckUserPermissions which uses the SqlClientPermissions Class to check the permissions of the currently connected user. So here’s the simple method for accomplishing this: private bool CheckUserPermissions(){ try { SqlClientPermission permissions = new SqlClientPermission(PermissionState.Unrestricted); //if we cann Demand() it will throw an exception if the current user //doesnt have the proper permissions permissions.Demand(); return true; } catch { return false; } One thing to know, SqlDependency relies on the OnChangeEventHandler Delegate which handles the SqlDependency.OnChange Event, which fires when any notification is received by any of the commands for the SqlDependency object. Now for getting the employee list, in this method we will query our EmployeeList table to get the employees information. We will also set the OnChange event of our SqlDependency object so that it can let us know when data has changed in our table and re-populate it with the latest employee list. Before trying to access our database we will call the method CheckUserPermissions method we created earlier to make sure the current user has the proper permissions, if not we display a message otherwise we move on to getting the employee list and populating a ListView with the ID, first & last name of the employee and their phone number. Here’s the GetEmployeeList method, which expects a parameter of type ListView (which is what will be displaying our employee list) /// /// method for querying our database to get an employee list/// /// the ListView we want to display the employee list inprivate void GetEmployeeList(ListView lview){ //the connection string to your database string connString = "YourConnectionString"; //the name of our stored procedure string proc = "uspGetEmployeeInformation"; //first we need to check that the current user has the proper permissions, //otherwise display the error if (!CheckUserPermissions()) MessageBox.Show("An error has occurred when checking permissions"); //clear our ListView so the data isnt doubled up lview.Items.Clear(); //in case we have dependency running we need to go a head and stop it, then //restart it SqlDependency.Stop(connString); SqlDependency.Start(connString); using (SqlConnection sqlConn = new SqlConnection(connString)) { using (SqlCommand sqlCmd = new SqlCommand()) { sqlCmd.Connection = sqlConn; sqlCmd.Connection.Open(); //tell our command object what to execute sqlCmd.CommandType = CommandType.StoredProcedure; sqlCmd.CommandText = proc; sqlCmd.Notification = null; SqlDependency dependency = new SqlDependency(sqlCmd); dependency.OnChange += new OnChangeEventHandler(dependency_OnDataChangedDelegate); sqlConn.Open(); using (SqlDataReader reader = sqlCmd.ExecuteReader()) { while (reader.Read()) { ListViewItem lv = new ListViewItem(); lv.Text = reader.GetInt32(0).ToString(); lv.SubItems.Add(reader.GetString(1)); lv.SubItems.Add(reader.GetString(2)); lv.SubItems.Add(reader.GetString(3)); lview.Items.Add(lv); } } } } Notice we set the OnChangeEventHandler of our SqlDependency object to dependency_OnDataChangedDelegate, this is the method that will re-query our table and send notifications when the data has changed in our EmployeeList table. In this method we invoke the work onto the main UI thread, this will help us avoid the dreaded cross—thread exception when we go to re-populate the ListView control when any notifications are sent to our application. Since our method (GetEmployeeList) required a parameter we cannot use the standard MethodInvoker delegate (as this cannot accept parameters). So what we will do is create our own Delegate which can accept our parameter. Here’s the delegate (very simple): private delegate void getEmployeeListDelegate(ListView lv); Our dependency_OnDataChangedDelegate requires a SqlNotificationEventArgs in the signature. Here we check the control being used to make sure InvokeRequired is true, if no then we use Invoke to invoke the work onto the main UI thread for re-populating our ListView, otherwise we just call our method to re-query: private void dependency_OnDataChangedDelegate(object sender, SqlNotificationEventArgs e){ //avoid a cross-thread exception (since this will run asynchronously) //we will invoke it onto the main UI thread if (listView1.InvokeRequired) listView1.Invoke(new getEmployeeListDelegate(GetEmployeeList), listView1); else GetEmployeeList(listView1); //this example is for only a single notification so now we remove the //event handler from the dependency object SqlDependency dependency = sender as SqlDependency; dependency.OnChange -= new OnChangeEventHandler(dependency_OnDataChangedDelegate);} NOTE: One thing to remember is you have to stop the dependency from querying your database when your form is closed, to do this use the forms FormClosing event to stop the work in the form you are using the dependency work in. That’s how you use the SqlDependency Class for monitoring data changes in your database without having to use something like a timer control to re-query at certain intervals. Thanks for reading and happy coding :)

The Unit of Work pattern is one of the most complex moving parts of Object-Relational Mappers, and usually of Data Mappers in general. A Unit of Work is a component (for us, an object with collaborators) which keeps track of the new, modified and deleted domain objects whose changes have to be reflected in the data store. At at the end of a transaction the Unit of Work, if used correctly, is capable of producing a list of changes to perform on the data store, solving concurrency or consistency problems, and avoiding too many redundant queries in the relational case or a chatty communication in the schemaless one. As I've already said, the Unit of Work pattern is usually not employed alone but as part of a Data Mapper, which provides a different interface to the internal client code and mixes up this pattern with several other ones. The minimum transaction that a PHP Unit of Work performs is usually an HTTP request, or a session composed by more than one request in case the domain objects can be saved in an intermediate store (like $_SESSION or a cache of any kind). Being able to serialize objects in a store and reattaching them to the Unit of Work during subsequent requests is not a trivial problem. Advantages The power of a Unit of Work resides in the fact that the actual database transaction is only performed (and kept opened) when the commit() method of the Unit of Work is called, while until that moment there is ideally no use of the database connection. This paradigm is called batch update. Objects stored in a Unit of Work have usually an associated state, like: new (which correspondes to INSERT queries during the batch update) clean (no SQL queries have to be issued since the object has been retrieved and not modified) dirty (UPDATE queries) removed (DELETE queries) There are different strategies for detecting changes to the object graph. The simplest strategy is comparing objects with a clean copy kept in memory (while it is usually not performance-wise to compare them with the database.) A more complex solution is having a specific interface which is implemented by the objects, so that they can manage their state and declare they are dirty or have to be removed. This implementation choice introduces a dependency from the domain layer to the infrastructure one, thus I prefer heavier approaches like the former, which is equivalent to generate a diff with your source control system of choice, but on the object graph instead of a codebase: the source files are not responsible for diffing themselves. Furthermore, the Unit of Work decoupling from the database state introduces an upper level of management, that makes us able to rollback changes if some constraint are not satisfied, or the computation has produced an error. In PHP, the client code can simply throw the object graph away, and the partial Unit of Work changeset is forgotten in the next requests. Issues While decoupling the object graph from the data store to perform custom computations is a comfortable possibility for the client code, at the same time it can be an issue that introduces stale data. The more the objects are kept in the Unit of Work, the more the data store is prone to external concurrent modifications inconsistent with the in-memory graph (for example updating fields with different values than the ones modified in this very session.) Either a optimistic or pessimistic locking mechanism has to be introduced when the scope of the object graph is longer than the few seconds necessary of producing an HTTP response, or even less than that when the traffic is higher. Injecting the Unit of Work in the domain objects so that they can track their state can be problematic and too much an invasion of the domain layer. Usually the problem is solved the other way around: when the objects are passed to the Object-Relational Mapper (almost always implemented as a Data Mapper and not as an Active Record), it delegates part of the logic to the Unit of Work, which is a first-class citizen and can be tested independently from the other components of the library. The alternative to the inherent complexity of the Unit of Work pattern is saving an object at the moment it is updated. This solution is problematic because either the client code has to explicitly call save() methods, or queries (read modification to the data store in case of non-relational model) have to be performed at the very time of an atomic change, for instance issuing multiple UPDATE statements, one for every time a field is modified. Example The sample code of this article is the internal API of Doctrine 2. The actual Unit of Work code is dependent on the strategy adopted to detect changes to domain objects, but the interface exposed to the Entity Manager is always the same and should provide a panoramic of an Unit of Work's responsibilities and features. In this implementation, the methods persist() and remove() are used to introduce new objects to the Unit of Work or to schedule something for deletion from the database, while commit() executes a batch update on demand. * @author Guilherme Blanco * @author Jonathan Wage * @author Roman Borschel * @internal This class contains highly performance-sensitive code. */ class UnitOfWork implements PropertyChangedListener { /** * An entity is in MANAGED state when its persistence is managed by an EntityManager. */ const STATE_MANAGED = 1; /** * An entity is new if it has just been instantiated (i.e. using the "new" operator) * and is not (yet) managed by an EntityManager. */ const STATE_NEW = 2; /** * A detached entity is an instance with a persistent identity that is not * (or no longer) associated with an EntityManager (and a UnitOfWork). */ const STATE_DETACHED = 3; /** * A removed entity instance is an instance with a persistent identity, * associated with an EntityManager, whose persistent state has been * deleted (or is scheduled for deletion). */ const STATE_REMOVED = 4; /** * Commits the UnitOfWork, executing all operations that have been postponed * up to this point. The state of all managed entities will be synchronized with * the database. * * The operations are executed in the following order: * * 1) All entity insertions * 2) All entity updates * 3) All collection deletions * 4) All collection updates * 5) All entity deletions * */ public function commit() { // Compute changes done since last commit. $this->computeChangeSets(); if ( ! ($this->_entityInsertions || $this->_entityDeletions || $this->_entityUpdates || $this->_collectionUpdates || $this->_collectionDeletions || $this->_orphanRemovals)) { return; // Nothing to do. } if ($this->_orphanRemovals) { foreach ($this->_orphanRemovals as $orphan) { $this->remove($orphan); } } // Raise onFlush if ($this->_evm->hasListeners(Events::onFlush)) { $this->_evm->dispatchEvent(Events::onFlush, new Event\OnFlushEventArgs($this->_em)); } // Now we need a commit order to maintain referential integrity $commitOrder = $this->_getCommitOrder(); $conn = $this->_em->getConnection(); $conn->beginTransaction(); try { if ($this->_entityInsertions) { foreach ($commitOrder as $class) { $this->_executeInserts($class); } } if ($this->_entityUpdates) { foreach ($commitOrder as $class) { $this->_executeUpdates($class); } } // Extra updates that were requested by persisters. if ($this->_extraUpdates) { $this->_executeExtraUpdates(); } // Collection deletions (deletions of complete collections) foreach ($this->_collectionDeletions as $collectionToDelete) { $this->getCollectionPersister($collectionToDelete->getMapping()) ->delete($collectionToDelete); } // Collection updates (deleteRows, updateRows, insertRows) foreach ($this->_collectionUpdates as $collectionToUpdate) { $this->getCollectionPersister($collectionToUpdate->getMapping()) ->update($collectionToUpdate); } // Entity deletions come last and need to be in reverse commit order if ($this->_entityDeletions) { for ($count = count($commitOrder), $i = $count - 1; $i >= 0; --$i) { $this->_executeDeletions($commitOrder[$i]); } } $conn->commit(); } catch (Exception $e) { $this->_em->close(); $conn->rollback(); throw $e; } // Take new snapshots from visited collections foreach ($this->_visitedCollections as $coll) { $coll->takeSnapshot(); } // Clear up $this->_entityInsertions = $this->_entityUpdates = $this->_entityDeletions = $this->_extraUpdates = $this->_entityChangeSets = $this->_collectionUpdates = $this->_collectionDeletions = $this->_visitedCollections = $this->_scheduledForDirtyCheck = $this->_orphanRemovals = array(); } /** * Computes the changes that happened to a single entity. * * Modifies/populates the following properties: * * {@link _originalEntityData} * If the entity is NEW or MANAGED but not yet fully persisted (only has an id) * then it was not fetched from the database and therefore we have no original * entity data yet. All of the current entity data is stored as the original entity data. * * {@link _entityChangeSets} * The changes detected on all properties of the entity are stored there. * A change is a tuple array where the first entry is the old value and the second * entry is the new value of the property. Changesets are used by persisters * to INSERT/UPDATE the persistent entity state. * * {@link _entityUpdates} * If the entity is already fully MANAGED (has been fetched from the database before) * and any changes to its properties are detected, then a reference to the entity is stored * there to mark it for an update. * * {@link _collectionDeletions} * If a PersistentCollection has been de-referenced in a fully MANAGED entity, * then this collection is marked for deletion. * * @param ClassMetadata $class The class descriptor of the entity. * @param object $entity The entity for which to compute the changes. */ public function computeChangeSet(Mapping\ClassMetadata $class, $entity) { // ... } /** * Computes all the changes that have been done to entities and collections * since the last commit and stores these changes in the _entityChangeSet map * temporarily for access by the persisters, until the UoW commit is finished. */ public function computeChangeSets() { // ... } /** * Schedules an entity for insertion into the database. * If the entity already has an identifier, it will be added to the identity map. * * @param object $entity The entity to schedule for insertion. */ public function scheduleForInsert($entity) { $oid = spl_object_hash($entity); if (isset($this->_entityUpdates[$oid])) { throw new \InvalidArgumentException("Dirty entity can not be scheduled for insertion."); } if (isset($this->_entityDeletions[$oid])) { throw new \InvalidArgumentException("Removed entity can not be scheduled for insertion."); } if (isset($this->_entityInsertions[$oid])) { throw new \InvalidArgumentException("Entity can not be scheduled for insertion twice."); } $this->_entityInsertions[$oid] = $entity; if (isset($this->_entityIdentifiers[$oid])) { $this->addToIdentityMap($entity); } } /** * Schedules an entity for being updated. * * @param object $entity The entity to schedule for being updated. */ public function scheduleForUpdate($entity) { $oid = spl_object_hash($entity); if ( ! isset($this->_entityIdentifiers[$oid])) { throw new \InvalidArgumentException("Entity has no identity."); } if (isset($this->_entityDeletions[$oid])) { throw new \InvalidArgumentException("Entity is removed."); } if ( ! isset($this->_entityUpdates[$oid]) && ! isset($this->_entityInsertions[$oid])) { $this->_entityUpdates[$oid] = $entity; } } /** * INTERNAL: * Schedules an entity for deletion. * * @param object $entity */ public function scheduleForDelete($entity) { $oid = spl_object_hash($entity); if (isset($this->_entityInsertions[$oid])) { if ($this->isInIdentityMap($entity)) { $this->removeFromIdentityMap($entity); } unset($this->_entityInsertions[$oid]); return; // entity has not been persisted yet, so nothing more to do. } if ( ! $this->isInIdentityMap($entity)) { return; // ignore } $this->removeFromIdentityMap($entity); if (isset($this->_entityUpdates[$oid])) { unset($this->_entityUpdates[$oid]); } if ( ! isset($this->_entityDeletions[$oid])) { $this->_entityDeletions[$oid] = $entity; } } /** * Checks whether an entity is scheduled for insertion, update or deletion. * * @param $entity * @return boolean */ public function isEntityScheduled($entity) { $oid = spl_object_hash($entity); return isset($this->_entityInsertions[$oid]) || isset($this->_entityUpdates[$oid]) || isset($this->_entityDeletions[$oid]); } public function persist($entity) { $visited = array(); $this->_doPersist($entity, $visited); } /** * Saves an entity as part of the current unit of work. * This method is internally called during save() cascades as it tracks * the already visited entities to prevent infinite recursions. * * NOTE: This method always considers entities that are not yet known to * this UnitOfWork as NEW. * * @param object $entity The entity to persist. * @param array $visited The already visited entities. */ private function _doPersist($entity, array &$visited) { $oid = spl_object_hash($entity); if (isset($visited[$oid])) { return; // Prevent infinite recursion } $visited[$oid] = $entity; // Mark visited $class = $this->_em->getClassMetadata(get_class($entity)); $entityState = $this->getEntityState($entity, self::STATE_NEW); switch ($entityState) { case self::STATE_MANAGED: // Nothing to do, except if policy is "deferred explicit" if ($class->isChangeTrackingDeferredExplicit()) { $this->scheduleForDirtyCheck($entity); } break; case self::STATE_NEW: if (isset($class->lifecycleCallbacks[Events::prePersist])) { $class->invokeLifecycleCallbacks(Events::prePersist, $entity); } if ($this->_evm->hasListeners(Events::prePersist)) { $this->_evm->dispatchEvent(Events::prePersist, new LifecycleEventArgs($entity, $this->_em)); } $idGen = $class->idGenerator; if ( ! $idGen->isPostInsertGenerator()) { $idValue = $idGen->generate($this->_em, $entity); if ( ! $idGen instanceof \Doctrine\ORM\Id\AssignedGenerator) { $this->_entityIdentifiers[$oid] = array($class->identifier[0] => $idValue); $class->setIdentifierValues($entity, $idValue); } else { $this->_entityIdentifiers[$oid] = $idValue; } } $this->_entityStates[$oid] = self::STATE_MANAGED; $this->scheduleForInsert($entity); break; case self::STATE_DETACHED: throw new \InvalidArgumentException( "Behavior of persist() for a detached entity is not yet defined."); case self::STATE_REMOVED: // Entity becomes managed again if ($this->isScheduledForDelete($entity)) { unset($this->_entityDeletions[$oid]); } else { //FIXME: There's more to think of here... $this->scheduleForInsert($entity); } break; default: throw ORMException::invalidEntityState($entityState); } $this->_cascadePersist($entity, $visited); } /** * Deletes an entity as part of the current unit of work. * * @param object $entity The entity to remove. */ public function remove($entity) { $visited = array(); $this->_doRemove($entity, $visited); } /** * Deletes an entity as part of the current unit of work. * * This method is internally called during delete() cascades as it tracks * the already visited entities to prevent infinite recursions. * * @param object $entity The entity to delete. * @param array $visited The map of the already visited entities. * @throws InvalidArgumentException If the instance is a detached entity. */ private function _doRemove($entity, array &$visited) { // ... } }

Data Mapper is one of the most advanced persistence-related patterns: an implementation of a Data Mapper stores objects (in general a whole object graph) in a database, and decouples the object model from the backend data representation, moving objects back and forth from the data store without introducing hardcode dependencies towards it. The database back end used by most of the implementations are usually relational: Object-relational mappers are some of the widely used tools today (and are even fading in some areas where different types of database are preferred.) Dependencies The interfaces for a Data Mapper can be put in the domain layer, but actual implementatons are in the category of infrastructure adapters and should be kept out of it, to promote the reuse and testing of domain layer classes without the need for a database back end or driver to be present. When this pattern is employed in an application, there are no more dependencies from the domain layer to external components, and no subclassing like in the Active Record case. Domain entities and value objects become Plain Old PHP Objects which do not extend anything (extends keyword) and do not need to reflect any database schema (if they are saved in a relational db), ensuring the maximum freedom of modelling to the developers. Different kind of implementations Early implementations of Data Mapper did not store an inner reference to a database connection or object that represent the link with the data store; in this case, result sets or some kind of raw data are passed to the Data Mapper, which reconstitutes the objects and encapsulates the process. Currently it is preferred to put all the references to the database as internals of the Data Mapper implementation (or in an abstraction layer under it). Anyway the Data Mapper hides as much as possible, like the type of the database and related knowledge, from the client code (domain layer or an upper one). The interface of the modern Data Mappers become from store() (insert and update) and remove() to one that comprehends also find() methods or a more complex system of querying; the implementation of querying is out of the scope of this pattern, but can be mixed up with it easily. A distinction in the implementations of Data Mapper is in their scope. A Data Mapper can be specific to a particular Entity/Aggregate Root (single class or class with composed objects), or a generic implementation can be customized with metadata (annotations, XML configuration) to work with different classes. Generic implementations are usually very complex, and specific ones may become much more easy to code due to simplifications. However, generic Data Mappers are prone to reuse and present less bugs than the project-specific ones, which were the only alternative in the last years. Issues The difficulties in implementing such a pattern are clear. Given a transaction, like an http request, the mapper has to keep track of the changed objects, and generate automatically the right DML queries to issue (SELECT, UPDATE, DELETE), in the right order and without leaving out any part of the modified data, avoid duplicating rows or update ones that do not exist anymore. This is a case of simple interface and complex implementation. To avoid breaking encapsulation, implementations usually employ reflection to access private fields of the object to store or that the mapper is reconstituting. Other possible choices for the data access are specific constructors for reconstitution or specific interfaces for domain mapping, but this solution still breaks encapsulation by providing to the client code methods that are not meant to be called, or fields that should not even be seen out of the objects but are actually accessed. This results in a unclear Api which may promote dependencies on persistence-related items. Providing metadata breaks encapsulation too, of course, but at least it is kept in the immediate so that it can change with the domain classes. Annotations are the preferred mean to specify metadata such as column names or relationships, and in PHP they are hidden in the docblock comments so that when the Data Mapper is not used they are just ignored. Data Mapper does not provide a total illusion (abstraction) of an in-memory collection of objects: the knowledge that there is some kind of external data store scatters into the application upper layers. Moreover, eventually some particular issue of the storage leaks into the object part of the application. As an example, consider the performance of queries, which is often the object of discussion when using object-relational mappers. Usually not all the object graph is instantiated as it may be very large; tuning how large the instantiated part will be is a trade-off which depends on the underlying database. Furthermore, generated queries may result very inefficient to the point that much of the client code must hint the joins to perform via the Api. Examples The generic Data Mapper Doctrine 2 (now in beta) is one of the few implementations in PHP of this pattern. As we've seen before in this article, specific implementations are dependent on the domain layer, so they are usually not reusable. A working copy of Doctrine 2 would be too large in size for inclusion in this post, so we are only analyzing the interface that most of the client code would see: the Entity Manager (name borrowed from Hibernate and JPA, since Java application used Data Mappers for years before this pattern has seen adoption from PHP ones.) The Entity Manager is not a domain specific interface, but other patterns like the Repository one can then compose the mapper to provid segregated interfaces for a particular class (aggregate root). As always, I have removed the less interesting methods or code to show the Api, and expanded the comments. * @author Guilherme Blanco * @author Jonathan Wage * @author Roman Borschel */ class EntityManager { /** * Flushes all changes to objects that have been queued up to now to the database. * This effectively synchronizes the in-memory state of managed objects with the * database. * No query is executed before this method is called from client code. * * @throws Doctrine\ORM\OptimisticLockException If a version check on an entity that * makes use of optimistic locking fails. */ public function flush() { $this->_errorIfClosed(); $this->_unitOfWork->commit(); } /** * Finds an Entity by its identifier. * This method is often combined with query-oriented ones. * * @param string $entityName the class name * @param mixed $identifier usually primary key * @param int $lockMode * @param int $lockVersion * @return object */ public function find($entityName, $identifier, $lockMode = LockMode::NONE, $lockVersion = null) { return $this->getRepository($entityName)->find($identifier, $lockMode, $lockVersion); } /** * Tells the EntityManager to make an instance managed and persistent. * * The entity will be entered into the database at or before transaction * commit or as a result of the flush operation. * * NOTE: The persist operation always considers entities that are not yet known to * this EntityManager as NEW. Do not pass detached entities to the persist operation. * * @param object $object The instance to make managed and persistent. */ public function persist($entity) { if ( ! is_object($entity)) { throw new \InvalidArgumentException(gettype($entity)); } $this->_errorIfClosed(); $this->_unitOfWork->persist($entity); } /** * Removes an entity instance. * * A removed entity will be removed from the database at or before transaction commit * or as a result of the flush operation. * * @param object $entity The entity instance to remove. */ public function remove($entity) { if ( ! is_object($entity)) { throw new \InvalidArgumentException(gettype($entity)); } $this->_errorIfClosed(); $this->_unitOfWork->remove($entity); } /** * Refreshes the persistent state of an entity from the database, * overriding any local changes that have not yet been persisted. * * @param object $entity The entity to refresh. */ public function refresh($entity) { if ( ! is_object($entity)) { throw new \InvalidArgumentException(gettype($entity)); } $this->_errorIfClosed(); $this->_unitOfWork->refresh($entity); } /** * Determines whether an entity instance is managed in this EntityManager. * * @param object $entity * @return boolean TRUE if this EntityManager currently manages the given entity, FALSE otherwise. */ public function contains($entity) { return $this->_unitOfWork->isScheduledForInsert($entity) || $this->_unitOfWork->isInIdentityMap($entity) && ! $this->_unitOfWork->isScheduledForDelete($entity); } /** * Factory method to create EntityManager instances. * * @param mixed $conn An array with the connection parameters or an existing * Connection instance. * @param Configuration $config The Configuration instance to use. * @param EventManager $eventManager The EventManager instance to use. * @return EntityManager The created EntityManager. */ public static function create($conn, Configuration $config, EventManager $eventManager = null); }

I just installed TPTP into my Eclipse 3.5 under Ubuntu 9.04 and tried to profile a class. The Profile Configuration opened with a red warning reading “the launch requires at least one data collector to be selected“. Clicking the configuration’s Monitor tab reveals a more detailed error (and nothing to select): IWATO435E An error occured when connecting to the host. A quick check of the error log (Window – Show View – Other… – General – Error Log) reveals the cause: RAServer generated the following output: [Error Stream]:ACServer: error while loading shared libraries: /home/jholy/development/tools/eclipse-ide/pulse2-2.4.2/Common/plugins/org.eclipse.tptp.platform.ac.linux_ia32_4.4.202.v201002100300/agent_controller/bin/../lib/libtptpUtils.so.4: file too short Checking the content of the lib/ folder revealed an interesting thing: -rw-r–r– 1 jholy jholy 17 2010-02-16 23:16 libtptpUtils.so -rw-r–r– 1 jholy jholy 21 2010-02-16 23:16 libtptpUtils.so.4 -rwxr-xr-x 1 jholy jholy 100K 2010-02-16 23:16 libtptpUtils.so.4.5.0 As also the content of the two small files suggests (they contain a name of the corresponding file with a longer name), the *.so and *.so.4 files should have been links but the installer failed to create them. Solution List all files in the lib/ folder, you will see that there are many real files like libtptpUtils.so.4.5.0 and libxerces-c.so.26.0 and many should-be-links files. The solution is, of course, to replace all those files that shoud be links with actual links. For me the solution was: $ cd .../plugins/org.eclipse.tptp.platform.ac.linux_ia32_4.4.202.v201002100300/agent_controller/lib # Move out the files that are OK lib$ mkdir tmp lib$ mv libswt-* libcbe.so tmp/ # Fix the links lib$ for FILE in `ls *.so`; do ln -sf "${FILE}.4.5.0" $FILE; ln -sf "${FILE}.4.5.0" "${FILE}.4"; done # Move the correct files back lib$ mv tmp/* . lib$ rmdir tmp # Fix links for files with *.26 instead of *.4.5.0 lib$ ln -sf libxerces-c.so.26.0 libxerces-c.so.26 lib$ ln -sf libxerces-c.so.26.0 libxerces-c.so lib$ ln -sf libxerces-depdom.so.26.0 libxerces-depdom.so.26 lib$ ln -sf libxerces-depdom.so.26.0 libxerces-depdom.so lib$ rm libxerces-depdom.so.4 libxerces-c.so.4 # Done! Try to open the profile configuration now, the IWATO435E should have disappeared and you should be able to select a data collector. If not, restart Eclipse, try again, check the error log. My environment Ubuntu 9.04 Eclipse 3.5 TPTP – see above From http://theholyjava.wordpress.com/2010/05/13/eclipse-profile-configuration-the-launch-requires-at-least-one-data-collector/

This article shows 2 ways to convert Java Map to String. Approach 1: simple, lightweight – produces query string like output, but restrictive. Approach 2: uses Java XML bean serialization, more robust but produces overly verbose output. Approach 1: Map to query string format Approach 1 converts a map to a query-string like output. Here’s what an output looks like: name1=value1&name2=value2 Full Code: import java.io.UnsupportedEncodingException; import java.net.URLDecoder; import java.net.URLEncoder; import java.util.HashMap; import java.util.Map; public class MapUtil { public static String mapToString(Map map) { StringBuilder stringBuilder = new StringBuilder(); for (String key : map.keySet()) { if (stringBuilder.length() > 0) { stringBuilder.append("&"); } String value = map.get(key); try { stringBuilder.append((key != null ? URLEncoder.encode(key, "UTF-8") : "")); stringBuilder.append("="); stringBuilder.append(value != null ? URLEncoder.encode(value, "UTF-8") : ""); } catch (UnsupportedEncodingException e) { throw new RuntimeException("This method requires UTF-8 encoding support", e); } } return stringBuilder.toString(); } public static Map stringToMap(String input) { Map map = new HashMap(); String[] nameValuePairs = input.split("&"); for (String nameValuePair : nameValuePairs) { String[] nameValue = nameValuePair.split("="); try { map.put(URLDecoder.decode(nameValue[0], "UTF-8"), nameValue.length > 1 ? URLDecoder.decode( nameValue[1], "UTF-8") : ""); } catch (UnsupportedEncodingException e) { throw new RuntimeException("This method requires UTF-8 encoding support", e); } } return map; } } Example usage code Map map = new HashMap(); map.put("color", "red"); map.put("symbols", "{,=&*?}"); map.put("empty", ""); String output = MapUtil.mapToString(map); Map parsedMap = MapUtil.stringToMap(output); for (String key : map.keySet()) { Assert.assertEquals(parsedMap.get(key), map.get(key)); } Output with Approach 1: symbols=%7B%2C%3D%26*%3F%7D&color=red∅= Caveat Only supports String keys and values. Due to the nature of serialization, null keys and values are not supported. Null will be converted to an empty String. This is because there is no way to distinguish between a null and an empty String in the serialized form. If you need support for null keys and values, use java.beans.XMLEncoder as shown below. Approach 2: Java Bean XMLEncoder: Map to String Java provides XMLEncoder and XMLDecoder classes as part of the java.beans package as a standard way to serialize and deserialize objects. This Map map = new HashMap(); map.put("color", "red"); map.put("symbols", "{,=&*?}"); map.put("empty", ""); ByteArrayOutputStream bos = new ByteArrayOutputStream(); XMLEncoder xmlEncoder = new XMLEncoder(bos); xmlEncoder.writeObject(map); xmlEncoder.flush(); String serializedMap = bos.toString() System.output.println(serializedMap); Output with Approach 2 The serialized value is shown below. As you can see this is more verbose, but can accommodate different data types and null keys and values. symbols {,=&*?} color red empty symbols {,=&*?} color red empty Java Bean XMLDecoder: String to Map XMLDecoder xmlDecoder = new XMLDecoder(new ByteArrayInputStream(serializedMap.getBytes())); Map parsedMap = (Map) xmlDecoder.readObject(); for (String key : map.keySet()) { Assert.assertEquals(parsedMap.get(key), map.get(key)); } Summary While Java provides a standard (and overly verbose) way to serialize and deserialize objects, this articles discusses an alternative lightweight way to convert a Java Map to String and back. If you are serializing a map with non-null String keys and values, then you should be able to use this alternative way, otherwise use the Java bean serialization. From http://www.vineetmanohar.com/2010/05/07/2-ways-to-convert-java-map-to-string

The Table Data Gateway pattern is the object-oriented equivalent of a relational table. In fact, this pattern's intent is to encapsulate the full interaction with a database table, holding all the logic specific to this particular implementation of the back end. In the majority of cases, a Table Data Gateway deals with a relational model, having a 1:1 relationship with the main tables of the database. Minor tables may not need a specific class, or can be managed via Table Data Gateways of tables that link them with foreign keys (for example entities introduced to store M:N relationships are usually not first-class citizens.) In the relational implementation, the Table Data Gateway handles all SQL queries, presenting a domain-specific interface when a class is coded for a specific table, or a generic one when a generic implementation is reused throughout different applications. The difference between the two APIs may be something like findBy($field, $value) (generic) versus findByPrice($price) (domain-specific). Note that in PHP magic methods are often used to implement domain-specific interfaces without code generation: a __call() implementation can catch the various findBy*() method and throw exceptions if the methods is not applicable. Related patterns Although, the concept of table is already correlated with a relational model (and it does not hold when the back end is an object-oriented database or one of the key-value stores so trendy today), this pattern is named Gateway because it is a specialization of the Gateway category of pattern, which decouple an object graph (or any in-memory structures) from external infrastructure like databases, web services, filesystems and so on. In fact, there is an alternate name for this pattern: Data Access Object (or DAO for friends). Although if I was pedantic I would highlight the differences between the implementations of DAOs and Table Data Gateway, their intent is really the same and there are differences in an individual pattern implementations that are greater than the ones between the different patterns. There's no clear demarcation line between the two. Another related pattern is the Table Module one. Table Data Gateway does not work against, but with a Table Module, providing a separation of concerns: the first object takes the rows out of the database, while the second performs in-memory operation on them (generally by composing the Table Data Gateway or its results). The in-memory operations of a Table Module are easier to test, but the SQL-based operations of the Table Data Gateway are pushed on the database side: there is a trade-off between the logic should be kept in each class. When used in isolation, the Table Data Gateway is also a Factory for also for Row Data Gateways or Active Records, both again implemented with generic or domain-specific interfaces. Many frameworks and first-generation PHP ORMs based on Active Record are also based on Table Data Gateway to provide a collection-level access to the objects stored as rows. In the context of Active Record, the only alternative to a Table Data Gateway to handle operations like find() is to place static methods on the Active Record class, with all the testability and dishonest API issues that ensue. Both Zend Framework and Doctrine 1.x represent tables as first-class objects. Examples Zend Framework's component Zend_Db, which is explored in the sample code, provides always generic implementations of Zend_Db_Table, and the possibility of optional subclassing (to add domain-specific methods). It is not recommend to expose the API of Table Data Gateway in front-end code, but it's a simple solution when the business logic does not warrant a full-featured Domain Model. Even when working with a Domain Model, and before the introduction of generic Data Mappers for PHP, the Table Data Gateway can be used in a composition solution (wrapped) to craft a simple API for a domain-specific Data Mapper, resulting in decoupling from the database. As I've written earlier, the sample code is taken from the Zend_Db_Table class of Zend Framework (actually from its parent abstract class, Zend_Db_Table_Abstract). I've enriched the docblock comments and left out all the methods not part of the main API (most of getters and setters for configuration and protecte|private members). $value) { switch ($key) { case self::ADAPTER: $this->_setAdapter($value); break; case self::DEFINITION: $this->setDefinition($value); break; case self::DEFINITION_CONFIG_NAME: $this->setDefinitionConfigName($value); break; case self::SCHEMA: $this->_schema = (string) $value; break; case self::NAME: $this->_name = (string) $value; break; case self::PRIMARY: $this->_primary = (array) $value; break; case self::ROW_CLASS: $this->setRowClass($value); break; case self::ROWSET_CLASS: $this->setRowsetClass($value); break; case self::REFERENCE_MAP: $this->setReferences($value); break; case self::DEPENDENT_TABLES: $this->setDependentTables($value); break; case self::METADATA_CACHE: $this->_setMetadataCache($value); break; case self::METADATA_CACHE_IN_CLASS: $this->setMetadataCacheInClass($value); break; case self::SEQUENCE: $this->_setSequence($value); break; default: // ignore unrecognized configuration directive break; } } return $this; } /** * Inserts a new row. * The data structure is as generic as possible. The list of columns is * known by configuration. * $this->_db is a light abstraction over PDO, which already encapsulates * most of the SQL. Database abstraction is not a banal task and segregating * the functionalities in different classes is very helpful. * * @param array $data Column-value pairs. * @return mixed The primary key of the row inserted. */ public function insert(array $data) { $this->_setupPrimaryKey(); /** * Zend_Db_Table assumes that if you have a compound primary key * and one of the columns in the key uses a sequence, * it's the _first_ column in the compound key. */ $primary = (array) $this->_primary; $pkIdentity = $primary[(int)$this->_identity]; /** * If this table uses a database sequence object and the data does not * specify a value, then get the next ID from the sequence and add it * to the row. We assume that only the first column in a compound * primary key takes a value from a sequence. */ if (is_string($this->_sequence) && !isset($data[$pkIdentity])) { $data[$pkIdentity] = $this->_db->nextSequenceId($this->_sequence); } /** * If the primary key can be generated automatically, and no value was * specified in the user-supplied data, then omit it from the tuple. */ if (array_key_exists($pkIdentity, $data) && $data[$pkIdentity] === null) { unset($data[$pkIdentity]); } /** * INSERT the new row. */ $tableSpec = ($this->_schema ? $this->_schema . '.' : '') . $this->_name; $this->_db->insert($tableSpec, $data); /** * Fetch the most recent ID generated by an auto-increment * or IDENTITY column, unless the user has specified a value, * overriding the auto-increment mechanism. */ if ($this->_sequence === true && !isset($data[$pkIdentity])) { $data[$pkIdentity] = $this->_db->lastInsertId(); } /** * Return the primary key value if the PK is a single column, * else return an associative array of the PK column/value pairs. */ $pkData = array_intersect_key($data, array_flip($primary)); if (count($primary) == 1) { reset($pkData); return current($pkData); } return $pkData; } /** * Updates existing rows. * Again we see generic data structures, not tied to PDO * or to particular adapters. * * @param array $data Column-value pairs. * @param array|string $where An SQL WHERE clause, or an array of SQL WHERE clauses. * @return int The number of rows updated. */ public function update(array $data, $where) { $tableSpec = ($this->_schema ? $this->_schema . '.' : '') . $this->_name; return $this->_db->update($tableSpec, $data, $where); } /** * Deletes existing rows. * * @param array|string $where SQL WHERE clause(s). * @return int The number of rows deleted. */ public function delete($where) { $tableSpec = ($this->_schema ? $this->_schema . '.' : '') . $this->_name; return $this->_db->delete($tableSpec, $where); } /** * Fetches rows by primary key. The argument specifies one or more primary * key value(s). To find multiple rows by primary key, the argument must * be an array. * * This method accepts a variable number of arguments. If the table has a * multi-column primary key, the number of arguments must be the same as * the number of columns in the primary key. To find multiple rows in a * table with a multi-column primary key, each argument must be an array * with the same number of elements. * * The find() method always returns a Rowset object, even if only one row * was found. * * @param mixed $key The value(s) of the primary keys. * @return Zend_Db_Table_Rowset_Abstract Row(s) matching the criteria. * @throws Zend_Db_Table_Exception */ public function find() { $this->_setupPrimaryKey(); $args = func_get_args(); $keyNames = array_values((array) $this->_primary); if (count($args) < count($keyNames)) { require_once 'Zend/Db/Table/Exception.php'; throw new Zend_Db_Table_Exception("Too few columns for the primary key"); } if (count($args) > count($keyNames)) { require_once 'Zend/Db/Table/Exception.php'; throw new Zend_Db_Table_Exception("Too many columns for the primary key"); } $whereList = array(); $numberTerms = 0; foreach ($args as $keyPosition => $keyValues) { $keyValuesCount = count($keyValues); // Coerce the values to an array. // Don't simply typecast to array, because the values // might be Zend_Db_Expr objects. if (!is_array($keyValues)) { $keyValues = array($keyValues); } if ($numberTerms == 0) { $numberTerms = $keyValuesCount; } else if ($keyValuesCount != $numberTerms) { require_once 'Zend/Db/Table/Exception.php'; throw new Zend_Db_Table_Exception("Missing value(s) for the primary key"); } $keyValues = array_values($keyValues); for ($i = 0; $i < $keyValuesCount; ++$i) { if (!isset($whereList[$i])) { $whereList[$i] = array(); } $whereList[$i][$keyPosition] = $keyValues[$i]; } } $whereClause = null; if (count($whereList)) { $whereOrTerms = array(); $tableName = $this->_db->quoteTableAs($this->_name, null, true); foreach ($whereList as $keyValueSets) { $whereAndTerms = array(); foreach ($keyValueSets as $keyPosition => $keyValue) { $type = $this->_metadata[$keyNames[$keyPosition]]['DATA_TYPE']; $columnName = $this->_db->quoteIdentifier($keyNames[$keyPosition], true); $whereAndTerms[] = $this->_db->quoteInto( $tableName . '.' . $columnName . ' = ?', $keyValue, $type); } $whereOrTerms[] = '(' . implode(' AND ', $whereAndTerms) . ')'; } $whereClause = '(' . implode(' OR ', $whereOrTerms) . ')'; } // issue ZF-5775 (empty where clause should return empty rowset) if ($whereClause == null) { $rowsetClass = $this->getRowsetClass(); if (!class_exists($rowsetClass)) { require_once 'Zend/Loader.php'; Zend_Loader::loadClass($rowsetClass); } return new $rowsetClass(array('table' => $this, 'rowClass' => $this->getRowClass(), 'stored' => true)); } return $this->fetchAll($whereClause); } /** * Fetches a new blank row (not from the database). * Thanks to the metadata, a new Row Data Gateway can be created. This * if a Factory Method. The dynamic nature of PHP makes configuring the * subclass for the Row Data Gateway as simple as defining a string. * * @param array $data OPTIONAL data to populate in the new row. * @param string $defaultSource OPTIONAL flag to force default values into new row * @return Zend_Db_Table_Row_Abstract */ public function createRow(array $data = array(), $defaultSource = null) { $cols = $this->_getCols(); $defaults = array_combine($cols, array_fill(0, count($cols), null)); // nothing provided at call-time, take the class value if ($defaultSource == null) { $defaultSource = $this->_defaultSource; } if (!in_array($defaultSource, array(self::DEFAULT_CLASS, self::DEFAULT_DB, self::DEFAULT_NONE))) { $defaultSource = self::DEFAULT_NONE; } if ($defaultSource == self::DEFAULT_DB) { foreach ($this->_metadata as $metadataName => $metadata) { if (($metadata['DEFAULT'] != null) && ($metadata['NULLABLE'] !== true || ($metadata['NULLABLE'] === true && isset($this->_defaultValues[$metadataName]) && $this->_defaultValues[$metadataName] === true)) && (!(isset($this->_defaultValues[$metadataName]) && $this->_defaultValues[$metadataName] === false))) { $defaults[$metadataName] = $metadata['DEFAULT']; } } } elseif ($defaultSource == self::DEFAULT_CLASS && $this->_defaultValues) { foreach ($this->_defaultValues as $defaultName => $defaultValue) { if (array_key_exists($defaultName, $defaults)) { $defaults[$defaultName] = $defaultValue; } } } $config = array( 'table' => $this, 'data' => $defaults, 'readOnly' => false, 'stored' => false ); $rowClass = $this->getRowClass(); if (!class_exists($rowClass)) { require_once 'Zend/Loader.php'; Zend_Loader::loadClass($rowClass); } $row = new $rowClass($config); $row->setFromArray($data); return $row; } }

The architectural pattern I'd like to talk about in this article is the overly famous Domain Model. An application's Domain Model is simply defined as an object graph created from domain-specific classes; when present, a Domain Model is the core of the application, where all the business logic resides. This object graph is employed by upper layers of an application which present it to the user. The metaphor for this methodology In software development, the term domain (or business domain) is an umbrella for the area the application is built in, and that it will serve. The new domains we encounter as we move to new projects are one of the most interesting points of software development, where we are constantly embracing new fields and gaining knowledge. Given a domain such as a particular industry (chemical, electronics) or business (air travelling, e-commerce), the point of connection of an application with these activities is its model. A model is an abstract representation of the reality of the domain, which captures its interesting and relevant aspects. The practice of modelling is not a specific trait of software development (in particular model-driven development), but it is a more general scientifical process. For example, everyone who works in the field of information technology knows the voltage/current relationships for simple components such as resistors and capacitors (Ohm's law and current derivative of the voltage). The specific domain here is electronics, and this model is named lumped component model, essentially because it lets a designer connect isolated one-port (two terminals) components to build his desired circuit. This model is a simplification of much more complex models of reality: the Maxwell equations and the propagation of electromagnetic fields; the lumped component model is valid whenever the frequency of the voltage/current signals in the circuit is low, so that the wavelengths of these signals are far greater than the dimensions of the circuit (if that goes over your head, don't worry, it's the field of electrical engineers.) When designers consider larger circuits, such as a transmission line, this model ceases to give correct results and more general ones must be employed. The domain is almost the same, but the model serves a different purpose and has to be necessarily different from the one used in small scale circuits. This complex example is here only to show that given a domain, there is no single model for it, but there are many possible ones which may adapt more or less reliably to the goals of an application. Starting from a modelling phase and deep understanding of the domain are key points of Domain-Driven Design, one of the ascending methodologies for developing complex enterprise software. Software models While there are standard mathematical models for many domains in the scientific world, software developers usually build a tailored one in every different application, performing an analysis of the domain (or at least they should.) The result of the modelling can comprehend document or diagrams, but the most powerful artifact is an executable model. Object-oriented programming is a almost perfect paradigm when it comes to modelling the real world, and lets the developers construct a Domain Model in the form of a set of classes. In a correct implementation of a Domain Model, these classes should be behaviorally complete: they must encapsulate their data as much as possible and expose a set of methods, while avoiding their usage as dumb data containers. The bread and butter of a Domain Model are the classical example of User, Post, Forum, Group, PrivateMessage classes, which are usually in a one to one relationship with database tables. But the Domain Model is not limited to these Entity classes: it also "comprehends" ValueObjects (modelization of domain-specific data types) and various kinds of Services. Every class that encapsulates business logic is welcome, so that this logic is not duplicated in upper layers, which are the primary clients of the Domain Model. Dependencies and purity Another key trait of the classes included in the Domain Model is the absence of external dependencies, like a library to store in the data contained in the objects in a database. The code artifact in a Domain Model are either interfaces, or Plain Old Php Objects (classes which do not extend any external abstract superclass.) Active Record approaches should be avoided because not only a relational database is an infrastructure detail not included in the Domain Model itself, but the very concept of persistence is abstracted away. As far as the clients of the Domain Model are concerned, the state and behavior of the application are represented by an in-memory object graph, whose methods expose functionalities and which client code can play with. There are no dependencies from a Domain Model towards infrastructure classes, because these dependencies must be inverted. The resulting system is an instance of the hexagonal architecture, where the Domain Model defines ports (interfaces) and infrastructure can be chosen to provide adapters for these ports (implementations in the form of classes extraneous to the model). The implementaton of non-invasive persistence is the subject of the Data Mapper pattern, which will be treated later in this series, but every kind of service implementation which communicate with the outside of the core object graph (databases, network, filesystem) is only defined as a contract in the Domain Model. Persistence is almost always dealt with a library in other object-oriented languages, now also in PHP with a non-invasive ORM such as Doctrine 2. Nothing obstructs the developers from implementing a specific Data Mapper by hand, but it's a very repetitive and prone to errors task. While in origin simpler, invasive patterns such as Active Record could be used in a Domain Model, nowadays with Data Mapper availables it is considered an hack. Sample Returning to the subject of the Domain Model as the core of an application, the diffused opinion is that the more complex the business logic and the data involved, the more the application benefits from a rich Domain Model. Thus, this pattern should not be used in small-sized applications where there is no much more logic than CRUD screens for data containers, which unfortunately were a target for PHP in the last ten years. I hope PHP keeps evolving to finally break in the enterprise segment, where this pattern is most valuable. Due to the size and scope of this article, I am forced to keep the sample code short. Forgive me if you think that you can achieve the same functionality with fewer lines of code, but this pattern is about architecture and should highlight the separation of concerns between classes more than the KISS principle. Another problem with code samples in modelling is that you have to actually know the domain well to follow the discussion. For this reason I chose a webmail system for this example. _sender; } /** * Do we need setters and getters? Every field should be * analyzed. If we can keep it private and inaccessible, * it's usually better. */ public function setSender($sender) { $this->_sender = $sender; } /** * @return string */ public function getRecipient() { return $this->_recipient; } public function setRecipient($recipient) { $this->_recipient = $recipient; } /** * @return string */ public function getSubject() { return $this->_subject; } public function setSubject($subject) { $this->_subject = $subject; } /** * @return string */ public function getText() { return $this->_text; } public function setText($text) { $this->_text = $text; } public function __toString() { return $this->_subject . ' > ' . substr($this->_text, 0, 20) . '...'; } public function reply() { $reply = new Email(); $reply->setRecipient($this->_sender); $reply->setSender($this->_recipient); $reply->setSubject('Re: ' . $this->_subject); $reply->setText($this->_sender . " wrote:\n" . $this->_text); return $reply; } } /** * Interface for a service. This is part of the Domain Model, * implementations will be plugged in depending on the environment. */ interface EmailRepository { /** * @return array * @TypeOf(Email) */ public function getEmailsFor($recipient); } // client code $mail = new Email(); $mail->setSender("[email protected]"); $mail->setRecipient("[email protected]"); $mail->setSubject('Hello'); $mail->setText('This is a test of an Email object, which is part of our Domain Model.'); echo $mail, "\n"; $reply = $mail->reply(); echo $reply, "\n";

while recently fiddling with some more runtime generic type extraction for deployit , i was caught out by some unexpected behaviour by the reflection api. a check of the javadocs quickly revealed that i had once again been too hasty in relying on "common sense". still, the case seems sufficiently unintuitive to merit discussion. in this case, the issue centres on the interplay between class.gettypeparameters and parameterizedtype . the gist of the code looks something like: interface spying {} // small class hierarchy class person {} class professional extends person {} class agent extends professional {} class assassin extends professional {} class bystander extends person {} ... person jbond = new agent(); system.out.println("generic superclass type argument: " + trygetsuperclassgenerictypeparam(jbond)); person joepublic = new bystander(); system.out.println("generic superclass type argument: " + trygetsuperclassgenerictypeparam(joepublic)); person oddjob = new assassin(); system.out.println("generic superclass type argument: " + trygetsuperclassgenerictypeparam(oddjob)); ... type trygetsuperclassgenerictypeparam(object obj) { class clazz = obj.getclass(); class superclass = clazz.getsuperclass(); // elvis would be preferred, but for the sake of clarity... if (superclass.gettypeparameters().length > 0) { return ((parameterizedtype) clazz.getgenericsuperclass()).getactualtypearguments()[0]; } else { return null; } } so...what happens? trygetsuperclassgenerictypeparam is where the action happens. it seems fairly straightforward: see if the object's superclass is generic (i.e. takes type parameters) and, if so, cast its type representation to parameterizedtype to extract the actual value for the type parameter. if the superclass is not generic, simply return null. when this code is run, the first two invocations of trygetsuperclassgenerictypeparam result in the expected: generic superclass type argument: interface spying generic superclass type argument: null what about the third one? well, given the fact that we've omitted to specify a generic type parameter for professional we might assume 1 that we'd also get null. the actual output, however, is: exception in thread "main" java.lang.classcastexception: java.lang.class cannot be cast to java.lang.reflect.parameterizedtype at trygetsuperclassgenerictypeparam(...) huh? in order to figure out what's going on here, let's have a look at the javadoc for class.gettypeparameters: returns an array of typevariable objects that represent the type variables declared by the generic declaration represented by this genericdeclaration object, in declaration order. returns an array of length 0 if the underlying generic declaration declares no type variables. in other words, this is returning class-level information about the declaration of, in our case, the professional class, which of course does have a type parameter. however, if we look at class.getgenericsuperclass 2 , which we invoke next, we find that it: returns the type representing the direct superclass of the entity [...] represented by this class. if the superclass is a parameterized type, the type object returned must accurately reflect the actual type parameters used in the source code. here, the information returned is specific to the actual declaration of the class, which may (or may not, as in our case) specify type paramaters for its superclass. and therein lies the problem: professional.class.gettypearguments looks at the declaration of the professional class, discovering a type argument, whereas assassin.class.getgenericsuperclass looks at the occurrence of professional in the declaration of assassin and discovers no type parameters. hence, it returns a class rather than a parameterizedtype and blows up our code. ergo to cut a long story short: if an object's superclass has type arguments as determined by class.gettypearguments that does not mean that object.getclass().getgenericsuperclass() will be a parameterizedtype. footnotes read "i assumed" it's a pity that class.getgenericsignature , which determines the "generic or not" behaviour of class.getgenericsuperclass, is private, native and undocumented. from http://blog.xebia.com/2010/04/22/when-a-class-with-type-parameters-is-not-a-parameterized-class-a-java-generics-puzzler/

For readability’s sake, it’s almost always a good idea to replace magic numbers and string literals with constants. That’s all good, but it can take a bit of time to refactor these to constants, especially strings or parts of strings. For example, in the code below we want to refactor “shovel and spade” to a private static final String called TOOLS. To do that manually would take some time. It goes even slower if we only want to extract “spade” to a constant because we first have to convert the string to a concatenation. String tools = "shovel and spade"; ... String otherTools = "shovel and spade"; Luckily, Eclipse has a couple of ways to instantly convert literals to constants. Coupled with tools to speed up string selection and to pick out part of a string, you have the ability to create a constant in about 2 seconds flat. I’ll discuss all these features below. Extract a constant from a string/number There are 2 ways to extract a constant, the one uses a quick fix and the other a refactoring. I’ll show the quick fix method first and then the refactoring and discuss the (small) differences between the two. The example uses a string, but everything is true for numbers as well. Follow these steps to use the quick fix: First select the string. The fastest way is to place the cursor on the string and press Alt+Shift+Up (Select Enclosing Element; a nifty shortcut that I discuss in Select strings and methods with a single keystroke). After selecting the string, press Ctr+1 (Quick Fix) and then select Extract to constant. Eclipse will do the following: (a) Create a private final static variable of type String with a default name, (b) replace all occurrences of that string with the constant and (c) place the cursor on the constant’s declaration to give you a chance to change the name, type and visibility of the variable using placeholders that you can Tab through. Once you’re happy with the constant details, press Enter to go back to the line on which you initiated the quick fix. Here’s a short video with an example of using quick fix. We’ll extract a constant (called TOOLS) from a string literal (“shovel and spade”) that’s used in two places. Note: You can use Tab to move from one placeholder to another and pressing Enter will get you back to your original line. The other way to extract a constant is by using the Extract Constant refactoring. Again, select the string, then select Refactor > Extract Constant… (Alt+T, A) from the application menu. A dialog appears prompting you for the constant’s name, its visibility and whether to replace all occurrences of the string with the constant. After you’ve entered the details, press Enter and you’ll have your constant defined. Here’s a short video with an example using refactoring. We’ll use the same example as above. The differences between the two? Not much, the biggest difference being when you enter the details of the constant (ie. before the change is made or after). The refactoring dialog also provides an option to add the qualifying type name before the constant’s usage, but most of time this is redundant. I’d recommend using the quick fix, unless you’re more comfortable with dialogs. BTW, you can assign custom keyboard shortcuts to either command by mapping either Quick Assist – Extract Constant or the command Extract Constant. Pick out part of a string Sometimes you’ll want to break up a string into multiple parts and convert one of those parts into a constant. Eclipse can do this automatically. Select the part of the string you want to pick out (don’t worry about quotes), press Ctrl+1 and choose Pick out selected part of String. Eclipse will convert that part into a string with quotes, concatenate it to the rest of the string and select it. You can then use any of the Extract Constant tools above. Here’s an example of how to use this feature. Notice how the string’s already selected so we can use the Extract Constant quick fix immediately. Related Tips Select entire strings and methods in Eclipse with a single keystroke Convert string concatenations into StringBuilder or MessageFormat calls with Eclipse’s Quick Fix How to manage keyboard shortcuts in Eclipse and why you should Join/split if statements and rearrange expressions using Eclipse Quick Fix More tips on using quick fixes and making editing faster.

Do you use Jetty and need to change the default setting for browser cache control? Have a look at the init-param element named cacheControl in webdefault.xml. Here’s the default configuration for the version of Jetty I use. Note the element is commented. To enable and configure browser cache control, uncomment and edit the param-value as appropriate. The following example instructs the browser to disable all caching. cacheControl no-store,no-cache,must-revalidate For information on Cache-Control, see RFC 2616, Section 14.9. From http://codeaweso.me/2009/09/jetty-browser-cache-control/

When a hard drive crashes, you can lose all your data. Corrupt hard drives happen out of the blue and for seemingly no good reason. If your hard drive fails, what can you do? One option is to call a hard drive recovery company. If your data is worth a lot of money to you, you can pay a forensic computer company to get the data off your hard drive. Before you write a check though, try a little Do-It-Yourself first. What is going on inside the hard drive is a bunch of little platters spinning at high speed. When data is accessed or written to the disk, a little head (sort of like on a record player) moves to the right spot and does it's magic. The space between the head and the platter is very very tiny. Freezing the hard drive will shrink the head and the platter ever so slightly, often allowing you to read data. Here is how I got the data off of a failed hard drive. Remove the hard drive from the computer. Place the hard drive inside of a zip top freezer bag. (don't buy a cheap bag.) Place the wrapped hard drive inside of ANOTHER zip top freezer bag. (yes, you need to do this) (see figure 1 below) Place the double wrapped hard drive in the coldest part of your freezer. Leave the hard drive in the freezer for 12 hours at least. You want it good and cold! (see figure 2 below) Once very chilled, install the hard drive in your computer and start pulling off data. Begin with the most valuable data. At some point, the hard drive will fail again. When it does, mark the last successfully copied data, pull out the hard drive, double wrap it again and stick it in the Chill Chest for another 12 hours. You may need to do this a number of times to get all the data you want, or until the hard drive stops working completely. Double Wrapped Hard Drive Hard Drive in the Freezer

Some of the Spock Framework 0.4 features are starting to see the light of day, with the Data Tables being explained last week in a nice blog post from Peter Niederwieser. One of the new features that I had not seen before is the new advanced @Unroll usage. Mixed with Data Tables, it produces some very cool results, and it can still be used with 0.3 style specs as well. Here's the juice: JUnit Integration and @Unroll Spock is built on JUnit, and has always had good IDE support without any effort from you as a user. For the most part, the IDEs just think Spock is another unit test. Here's the a Spock spec for the new Data Tables feature and how it shows up in an IDE. import spock.lang.* class TableTest extends Specification { def "maximum of two numbers"() { expect: Math.max(a, b) == c where: a | b | c 3 | 7 | 7 5 | 4 | 5 9 | 9 | 9 } } The assertion will be run 3 times: once for each row in the data table. And JUnit faithfully reports the method name correctly, even when the method names has a space in it: The problem with data driven tests and xUnit is poor error location. When a test fails you will receive an error stating which method is the culprit... but what if the method runs an assertion across 50 or 60 pieces of data? The cause of a failure is almost never clear with data driven tests. At it's worst you have to step through several iterations of code waiting for an exception. Good tests have a clear point of failure, but good tests also do not repeat themselves with boilerplate. This is exactly why Spock has the @Unroll annotation. As a test author you get to write one concise unit test, and JUnit does the work of reporting results that help you isolate failures. Consider the same test method with the @Unroll annotation and the accompanying IDE output. @Unroll def "maximum of two numbers"() { expect: Math.max(a, b) == c where: a | b | c 3 | 7 | 7 5 | 4 | 5 9 | 9 | 9 } When executed, JUnit sees three test methods instead of one: one for each row in the data table: The end result for you as a test writer is accurate failure resolution. You can pinpoint exactly which row failed. This feature is available in Spock 0.3 and you can use it today. What is new in 0.4 is the ability to change the test name dynamically. Here is a full @Unroll annotation that changes the method name: @Unroll("maximum of #a and #b is #c") def "maximum of two numbers"() { expect: Math.max(a, b) == c where: a | b | c 3 | 7 | 7 5 | 4 | 5 9 | 9 | 9 } Notice the #variable syntax in the annotation parameter. The # produces a sort of GString-like variable substitution that lets you bind columns from your data table into your test name. The annotation parameter references #a, #b, and #c, which aligns with the data table definition of a | b | c. Check out the IDE output: Previously, the test name was just the iteration number within the test. The new @Unroll parameter allows you to make the test name much more meaningful. Your tests will improve because failures become more descriptive. Unrolled failure messages before simply had the iteration name embedded in them, while now they can have meaningful data that you prescribe. My favorite part of playing with the new @Unroll was to see the default value of the parameter within the Spock source code: java.lang.String value() default "#featureName[#iterationCount]"; Talk about eating your own dog food... the default value is a test name template, just like you could have written in your own test. Makes you wonder what other variables are in scope, huh? Spock snapshot builds for 0.4 are available at: http://m2repo.spockframework.org. Get it before the link breaks. From http://hamletdarcy.blogspot.com

This article shows you step by step how to cache your entire tomcat web application with Squid reverse Proxy without writing any Java code. What is Squid Squid is a free proxy server for HTTP, HTTPS and FTP which saves bandwidth and increases response time by caching frequently requested web pages. While squid can be used as a proxy server when users try to download pages from the internet, it can be also used as a reverse-proxy by putting squid between the user and your webapp. All user requests first hit Squid. If the requested page already exists in Squid’s cache it is served directly from the cache without hitting your Webapp. If the page does not exist in Squid’s cache, it is fetched from your web application and stored in the cache for future requests. Squid reduces hits to your server by caching response pages. You don’t have to worry about building page level caching in every application that your write, Squid takes care of that part. When should I use Squid Ideally you should use Squid for pages which have a high ratio of reads to writes. In other words, a page that changes less frequently but is accessed very often. Here are some scenarios: A dynamical web page which displays news and is updated once an hour, and receives hundreds of hits during the hour A static web page accessed freqently. Squid can give performance boost by caching frequently accessed static web pages in memory When should I not use Squid In most cases, if the request URL is the only factor which determines the response then you can safely use Squid. See more specific examples below: If the entire apps is very dynamic in nature, and the validity of pages changes immediately. Squid is not suitable for apps which require login. This unfortunately is a large number of applications. Such applications need to resort to back end caching, for example use other caching frameworks like Ehcache to cache re-usable page fragments and/or cache database queries and/or other performance bottlenecks. Apps which heavily use browser cookies. Squid relies on URLs to cache pages. If the page served is computed from URLs + cookies, then you should not cache those pages in Squid. How does the overall setup work Apache Squid Tomcat architecture Apache receives requests on port 80. Apache calls Squid with the request. Squid checks its cache to see if it has the response cached from before. If yes and if the response is not expired, it returns the cached response.In this case: Squid will write the following header to the response X-Cache: HIT from www.vineetmanohar.com X-Cache: HIT from www.vineetmanohar.com If the response is not found in Squid’s cache, squid will make a call to Tomcat on port 8082. Tomcat’s proxy connector is listening on this port. It processes the request and sends the response back to Squid. Squid saves the response in its cache, unless caching is disabled for that URL. Squid returns the final response to Apache which sends the response back to the user. What if I don’t want to use Apache Using Apache is not required to use Squid. You can run Squid on port 80, and point your users directly to Squid. If that is the case, skip section one and directly jump to section 2 below. Step 1/3: Apache Httpd Config If you are using Apache as a front end, you need to instruct Apache to forward requests to Squid at port 3128. See the following code snippet. Change the server name and paths to reflect your real values. Apache config file: /etc/httpd/conf/httpd.conf ServerName www.vineetmanohar.com DocumentRoot /home/webadmin/www.vineetmanohar.com/html # forward requests to squid running on port 3128 ProxyPass / http://localhost:3128/ ProxyPassReverse / http://localhost:3128/ /etc/httpd/conf/httpd.conf ServerName www.vineetmanohar.com DocumentRoot /home/webadmin/www.vineetmanohar.com/html # forward requests to squid running on port 3128 ProxyPass / http://localhost:3128/ ProxyPassReverse / http://localhost:3128/ In addition to the above, you also need mod_proxy installed. If you see the following in your httpd.conf, you probably already have mod_proxy installed. If you first need to install mod_proxy LoadModule proxy_module modules/mod_proxy.so LoadModule proxy_http_module modules/mod_proxy_http.so LoadModule proxy_module modules/mod_proxy.so LoadModule proxy_http_module modules/mod_proxy_http.so Step 2/3: Squid Config First make sure that Squid is installed on your server. You can download Squid from here. The squid config file on Linux/Unix is located at this location /etc/squid/squid.conf /etc/squid/squid.conf The config file is pretty long. Follow these instructions and set the values appropriately. 1. # leave the port to 3128 2. http_port 3128 3. 4. # how much memory cache do you want? depends on how much memory you have on the machine 5. cache_mem 200 MB 6. 7. # what's the biggest page that you want stored in memory. If you home page is 100 KB and 8. # you want it stored in memory, you may set it to a number bigger than that. 9. maximum_object_size_in_memory 100 KB 10. 11. # how much disk cache do you want. It is 6400 MB in the following example, change it as per 12. # your needs. Make sure you have that much disk space free. 13. cache_dir ufs /var/spool/squid 6400 16 256 14. 15. # this is probably the most important config section. Here you can configure the cache life for 16. # each URL pattern. 17. 18. # Time is in minutes 19. # 1 day = 1440, 2 days = 2880, 7 days = 10080, 28 days = 40320 20. 21. # do not cache url1 22. refresh_pattern ^http://127.0.0.1:8082/url1/ 0 20% 0 23. 24. # cache url2 for 1 day 25. refresh_pattern ^http://127.0.0.1:8082/url2/ 1440 20% 1440 override-expire override-lastmod reload-into-ims ignore-reload 26. 27. # cache css for 7 days 28. refresh_pattern ^http://127.0.0.1:8082/css 10080 20% 10080 override-expire override-lastmod reload-into-ims ignore-reload 29. 30. # by default cache the whole website for 1 minute 31. refresh_pattern ^http://127.0.0.1:8082/ 0 20% 0 override-expire override-lastmod reload-into-ims ignore-reload 32. 33. # how long should the errors should be cached for. For example 404s, HTTP 500 errors 34. negative_ttl 0 seconds 35. 36. # On which host does tomcat run. Set 127.0.0.1 for localhost 37. httpd_accel_host 127.0.0.1 38. 39. # this is the proxy port as defined in Tomcat server.xml. By default it is "8082" 40. httpd_accel_port 8082 41. 42. # set this to "on". Read more documentation if you want to change this. 43. httpd_accel_single_host on 44. 45. # To access Squid stats via the manager interface, you need to enter a password here 46. cachemgr_passwd your_clear_text_password all 47. 48. # Say "off" if you want the query string to appear in the squid logs. 49. strip_query_terms off # leave the port to 3128 http_port 3128 # how much memory cache do you want? depends on how much memory you have on the machine cache_mem 200 MB # what's the biggest page that you want stored in memory. If you home page is 100 KB and # you want it stored in memory, you may set it to a number bigger than that. maximum_object_size_in_memory 100 KB # how much disk cache do you want. It is 6400 MB in the following example, change it as per # your needs. Make sure you have that much disk space free. cache_dir ufs /var/spool/squid 6400 16 256 # this is probably the most important config section. Here you can configure the cache life for # each URL pattern. # Time is in minutes # 1 day = 1440, 2 days = 2880, 7 days = 10080, 28 days = 40320 # do not cache url1 refresh_pattern ^http://127.0.0.1:8082/url1/ 0 20% 0 # cache url2 for 1 day refresh_pattern ^http://127.0.0.1:8082/url2/ 1440 20% 1440 override-expire override-lastmod reload-into-ims ignore-reload # cache css for 7 days refresh_pattern ^http://127.0.0.1:8082/css 10080 20% 10080 override-expire override-lastmod reload-into-ims ignore-reload # by default cache the whole website for 1 minute refresh_pattern ^http://127.0.0.1:8082/ 0 20% 0 override-expire override-lastmod reload-into-ims ignore-reload # how long should the errors should be cached for. For example 404s, HTTP 500 errors negative_ttl 0 seconds # On which host does tomcat run. Set 127.0.0.1 for localhost httpd_accel_host 127.0.0.1 # this is the proxy port as defined in Tomcat server.xml. By default it is "8082" httpd_accel_port 8082 # set this to "on". Read more documentation if you want to change this. httpd_accel_single_host on # To access Squid stats via the manager interface, you need to enter a password here cachemgr_passwd your_clear_text_password all # Say "off" if you want the query string to appear in the squid logs. strip_query_terms off Step 3/3: Tomcat Config Make sure that the HTTP Proxy Connector is defined in TOMCAT_HOME/conf/server.xml. If needed, see additional documentation on Tomcat proxy connector. Squid Manager Interface You can access the Squid config and stats via the Squid Manger HTTP interface. Make sure that the “cachemgr.cgi” file which ships with squid installation is in your cgi-bin directory. More documentation on setting that up here. Once you’ve set it up, you can access the cache manager via this URL: http:///cgi-bin/cachemgr.cgi http:///cgi-bin/cachemgr.cgi To continue enter the following values: Cache host: localhost Cache port: 3128 Manager name: manager Password: Cache host: localhost Cache port: 3128 Manager name: manager Password: Store Directory Stats shows you how much disk space is used by the disk cache. Cache Client List show you the cache HIT/MISS ratio as %. You should monitor this frequently and tune your cache to get a higher hit %. Reload Squid Config without restarting Edit the squid config using “vi” or your favorite editor vi /etc/squid/squid.conf vi /etc/squid/squid.conf Once you are done editing, reload the new config without restarting Squid /usr/sbin/squid -k reconfigure /usr/sbin/squid -k reconfigure Clearing Squid Cache To clear Squid cache: 1) Set the memory cache to 4 MB (or a lower number) cache_mem 8 MB cache_mem 8 MB 2) Set the disk cache to 8 MB (or a lower number). The disk cache must be higher that the memory cache. cache_dir ufs /var/spool/squid 20 16 256 cache_dir ufs /var/spool/squid 20 16 256 3) Reload squid config without restart as described in the previous section 4) You may need to wait a few hours for the cache to get cleared. Once the cache is clear, you may restore the previous cache sizes and reload the new config again. You can monitor the cache size through the Squid Manager HTTP interface. Bypassing Squid If for some reason you need to bypass Squid, reconfigure Apache to directly send requests to Tomcat. Edit the Apache config file /etc/httpd/conf/httpd.conf # forward requests directly to Tomcat's proxy connector running on port 8082 ProxyPass / http://localhost:8082/ ProxyPassReverse / http://localhost:8082/ # forward requests directly to Tomcat's proxy connector running on port 8082 ProxyPass / http://localhost:8082/ ProxyPassReverse / http://localhost:8082/ You will need to restart Apache after making this change. /etc/init.d/httpd restart Conclusion Squid is a very powerful tool for caching. It is not for all applications. Please examine the need of your application and use squid appropriately. I’ve used squid for several years for caching the output from a Java data mashup application and am very satisfied with the ease of use and benefits. Hope you found this tutorial useful. Feel free to post a comment or share your experience with squid. References Squid official website From http://www.vineetmanohar.com

For almost a year now, the idea of "NoSQL" has been spreading due to the demand for relational database alternatives. Maybe the biggest motivation behind NoSQL is scalability. Relational databases don't lend themselves well to the kind of horizontal scalability that's required for large-scale social networking or cloud applications, and ORMs can abstract away impedance mismatch only so much. In other cases, companies just don't need as many of the complex features and rigid schemas provided by relational databases. Most people are not suggesting that we all ditch the RDBMS, in fact, many companies don't really need to switch. Relational databases will probably be necessary for many applications years and years from now. In essence, NoSQL is a movement that aims to reexamine the way we structure data and draw attention to innovation in hopes of finding the solution to the next generation's data persistence problems. Here are some of the better known open source data stores/models labeled as "NoSQL": CouchDB- Document Store Maps keys to data It provides a RESTful JSON API and is written in Erlang You can upload functions to index data and then you can call those functions Has a very simple REST interface Provides an innovative replication strategy - nodes can reconnect, sync, and reconcile differences after being disconnected for long periods of time Enables new distributed types of applications and data MongoDB - Document Store Free-form key-value-like data store with good performance Powerful, expansive query model Usability rivals that of Redis Good for complex data storage needs. Production-quality sharding capabilities Neo4j - GraphDB Disk-based Has a restricted, single-threaded model for graph traversal Has optional layers to expose Neo4j as an RDF store Can handle graphs of several billion nodes, relationships, or properties on a single machine Released under a dual license - free for non-commercial use Apache Hbase - Wide Column Store/Column Families Built on top of Hadoop, which has functionality similar to Google's GFS and MapReduce systems Hadoop's HDFS provides a mechanism that reliably stores and organizes large amounts of data Random access performance is on par with MySQL Has a high performance Thrift gateway Cascading source and sink modules Redis - Key Value/Tuple Store Provides a rich API and does more operations in memory, using disk only periodically. It's extremely fast Lets you append a value to the end of a list of items that's already been stored on a key. Has atomic operations, making it a best-of-breed tally server. Memcached - Key Value/Tuple Store High-performance, distributed memory object caching Free and open source Generic and agnostic to the objects/strings it caches It's all in-memory data Simple yet elegant design enables easy development and deployment Language neutral caching scheme. Most of the large properties on the web are using it now, except for Microsoft Project Voldemort - Eventually Consistent Key Value Store Used by LinkedIn Handles server failure transparently Pluggable serialization supports rich keys and values including lists and tuples with named fields Supports common serialization frameworks including Protocol Buffers, Thrift, and Java Serialization Data items are versioned Supports pluggable data placement strategies Memory caching and the storage system are combined Tokyo Cabinet and Tokyo Tyrant - Key Value/Tuple Store Supports hashtable mode, b-tree mode, and table mode It's fast and straightforward Good for small to medium-sized amounts of data that require rapid updating and can be easily modeled in terms of keys and values Cassandra - Wide Column Store/Column Families First developed by Facebook SuperColumns can turn a simple key-value architecture into an architecture that handles sorted lists, based on an index specified by the user. Can scale from one node to several thousand nodes clustered in different data centers. Can be tuned for more consistency or availability Smooth node replacement if one goes down ____ Some other well known NoSQL-style data stores that are closed source include Google BigTable and Amazon SimpleDB. GigaSpaces is a popular space-based Grid solution that has NoSQL qualities. Check out this informative post on NoSQL patterns.

Learn the Abstract Factory Design Pattern with easy Java source code examples as James Sugrue continues his design patterns tutorial series, Design Patterns Uncovered

A while ago I wrote about some rules of thumb that I'd been taught by my colleagues with respect to software development and I was reminded of one of them – don't put anything in the session – during a presentation my colleague Luca Grulla gave at our client on scaling applications by making use of the infrastructure of the web. The problem with putting state in the session is that it means that requests from a specific user have to be tied to a specific server i.e. we have to use a sticky session/session affinity. This reduces our ability to scale our system horizontally (scale out) i.e. by adding more servers to handle requests. If, for example, we have a small amount of users (whose first request went to the same server) making a lot of requests (perhaps through AJAX calls) then we may quickly put one of our servers under load while the others are sitting there idle. In addition we have increased complexity around our deployment process. If we want to do an incremental deployment of a new version of our website across some of our servers then we need to ensure that we create a copy of any sessions on those servers and copy them to the ones we're not updating so that any users still on the system don't experience loss of data. There are no doubts products which can allow us to do this more easily but it seems to me to be an unnecessary product in the first place since we can just design our application to not rely on the session. As I understand it the web was designed to be stateless i.e. each request is independent and all the information is contained within that request and the idea of the session was only something which was added in later on. How does the way we code change if we don't use the session? One thing we've often used the session for on projects that I've worked on is to store the current state of a form that the user is filling in. When they've completed the form then we would probably store some representation of what they've entered in a database. If we don't use the session then we need to store this intermediate data somewhere and include a key to load it in the request. On the project I'm working on at the moment we're storing that data in a database but then clearing out that data every other day since it's not needed once the user has completed the form. An alternative perhaps could be to store it in a cache since in reality all we have is a key/value pair which we need to keep for a relatively short amount of time. Advantages/disadvantages of this approach The disadvantage of this approach is that we have to make more reads and writes to the database to deal with this temporary data. Apart from the advantages I outlined initially, we are also more protected if a server handling a user's request goes down. If we were using the session to store intermediate state then that information would be lost and they would have to start over. In the approach we've using this isn't a problem and when the request is sent to another server we can still query the database and get whatever data the user had already saved. As with most things there's a trade off to be made but in this case it seems a fair one to me. Alternative approaches I've come across some alternative approaches where we avoid using the session but don't store intermediate state in a database. One way is to store that state in hidden fields on the form and another is to send it in the request parameters. Neither of these approaches seem particularly clean to me and they give the user an easier way to change the intermediate data in ways that the form might not allow them to do. From my experience our server side code becomes more complicated since we're always writing all of the data entered so far back into the page. In addition the url becomes a complete mess with the second approach. From http://www.markhneedham.com

There are at least four methods that can be used in different combinations to make the process of setting up a complete development environment a lot less painful.

Develop a web-based app based on technologies in the JEE6 specs such as Enterprise Java Beans 3.1 and JPA with the help of NetBeans IDE 6.8.

One nice feature in JUnit 4 is that of Parameterized Tests, which let you do data-driven testing in JUnit with a minimum of fuss. It's easy enough, and very useful, to set up basic data-driven tests by defining your test data directly in your Java class. But what if you want to get your test data from somewhere else? In this article, we look at how to obtain test data from an Excel spreadsheet. Parameterized tests allow data-driven tests in JUnit. That is, rather than having different of test cases that explore various aspects of your class's (or your application's) behavior, you define sets of input parameters and expected results, and test how your application (or, more often, one particular component) behaves. Data-driven tests are great for applications involving calculations, for testing ranges, boundary conditions and corner cases. In JUnit, a typical parameterized test might look like this: @RunWith(Parameterized.class) public class PremiumTweetsServiceTest { private int numberOfTweets; private double expectedFee; @Parameters public static Collection data() { return Arrays.asList(new Object[][] { { 0, 0.00 }, { 50, 5.00 }, { 99, 9.90 }, { 100, 10.00 }, { 101, 10.08 }, { 200, 18}, { 499, 41.92 }, { 500, 42 }, { 501, 42.05 }, { 1000, 67 }, { 10000, 517 }, }); } public PremiumTweetsServiceTest(int numberOfTweets, double expectedFee) { super(); this.numberOfTweets = numberOfTweets; this.expectedFee = expectedFee; } @Test public void shouldCalculateCorrectFee() { PremiumTweetsService premiumTweetsService = new PremiumTweetsService(); double calculatedFees = premiumTweetsService.calculateFeesDue(numberOfTweets); assertThat(calculatedFees, is(expectedFee)); } } The test class has member variables that correspond to input values (numberOfTweets) and expected results (expectedFee). The @RunWith(Parameterzed.class) annotation gets JUnit to inject your test data into instances of your test class, via the constructor. The test data is provided by a method with the @Parameters annotation. This method needs to return a collection of arrays, but beyond that you can implement it however you want. In the above example, we just create an embedded array in the Java code. However, you can also get it from other sources. To illustrate this point, I wrote a simple class that reads in an Excel spreadsheet and provides the data in it in this form: @RunWith(Parameterized.class) public class DataDrivenTestsWithSpreadsheetTest { private double a; private double b; private double aTimesB; @Parameters public static Collection spreadsheetData() throws IOException { InputStream spreadsheet = new FileInputStream("src/test/resources/aTimesB.xls"); return new SpreadsheetData(spreadsheet).getData(); } public DataDrivenTestsWithSpreadsheetTest(double a, double b, double aTimesB) { super(); this.a = a; this.b = b; this.aTimesB = aTimesB; } @Test public void shouldCalculateATimesB() { double calculatedValue = a * b; assertThat(calculatedValue, is(aTimesB)); } } The Excel spreadsheet contains multiplication tables in three columns: The SpreadsheetData class uses the Apache POI project to load data from an Excel spreadsheet and transform it into a list of Object arrays compatible with the @Parameters annotation. I've placed the source code, complete with unit-test examples on BitBucket. For the curious, the SpreadsheetData class is shown here: public class SpreadsheetData { private transient Collection data = null; public SpreadsheetData(final InputStream excelInputStream) throws IOException { this.data = loadFromSpreadsheet(excelInputStream); } public Collection getData() { return data; } private Collection loadFromSpreadsheet(final InputStream excelFile) throws IOException { HSSFWorkbook workbook = new HSSFWorkbook(excelFile); data = new ArrayList(); Sheet sheet = workbook.getSheetAt(0); int numberOfColumns = countNonEmptyColumns(sheet); List rows = new ArrayList(); List rowData = new ArrayList(); for (Row row : sheet) { if (isEmpty(row)) { break; } else { rowData.clear(); for (int column = 0; column < numberOfColumns; column++) { Cell cell = row.getCell(column); rowData.add(objectFrom(workbook, cell)); } rows.add(rowData.toArray()); } } return rows; } private boolean isEmpty(final Row row) { Cell firstCell = row.getCell(0); boolean rowIsEmpty = (firstCell == null) || (firstCell.getCellType() == Cell.CELL_TYPE_BLANK); return rowIsEmpty; } /** * Count the number of columns, using the number of non-empty cells in the * first row. */ private int countNonEmptyColumns(final Sheet sheet) { Row firstRow = sheet.getRow(0); return firstEmptyCellPosition(firstRow); } private int firstEmptyCellPosition(final Row cells) { int columnCount = 0; for (Cell cell : cells) { if (cell.getCellType() == Cell.CELL_TYPE_BLANK) { break; } columnCount++; } return columnCount; } private Object objectFrom(final HSSFWorkbook workbook, final Cell cell) { Object cellValue = null; if (cell.getCellType() == Cell.CELL_TYPE_STRING) { cellValue = cell.getRichStringCellValue().getString(); } else if (cell.getCellType() == Cell.CELL_TYPE_NUMERIC) { cellValue = getNumericCellValue(cell); } else if (cell.getCellType() == Cell.CELL_TYPE_BOOLEAN) { cellValue = cell.getBooleanCellValue(); } else if (cell.getCellType() ==Cell.CELL_TYPE_FORMULA) { cellValue = evaluateCellFormula(workbook, cell); } return cellValue; } private Object getNumericCellValue(final Cell cell) { Object cellValue; if (DateUtil.isCellDateFormatted(cell)) { cellValue = new Date(cell.getDateCellValue().getTime()); } else { cellValue = cell.getNumericCellValue(); } return cellValue; } private Object evaluateCellFormula(final HSSFWorkbook workbook, final Cell cell) { FormulaEvaluator evaluator = workbook.getCreationHelper() .createFormulaEvaluator(); CellValue cellValue = evaluator.evaluate(cell); Object result = null; if (cellValue.getCellType() == Cell.CELL_TYPE_BOOLEAN) { result = cellValue.getBooleanValue(); } else if (cellValue.getCellType() == Cell.CELL_TYPE_NUMERIC) { result = cellValue.getNumberValue(); } else if (cellValue.getCellType() == Cell.CELL_TYPE_STRING) { result = cellValue.getStringValue(); } return result; } } Data-driven testing is a great way to test calculation-based applications more thoroughly. In a real-world application, this Excel spreadsheet could be provided by the client or the end-user with the business logic encoded within the spreadsheet. (The POI library handles numerical calculations just fine, though it seems to have a bit of trouble with calculations using dates). In this scenario, the Excel spreadsheet becomes part of your acceptance tests, and helps to define your requirements, allows effective test-driven development of the code itself, and also acts as part of your acceptance tests. From http://weblogs.java.net/blog/johnsmart

This example shows you how to edit an XML template to fill in the data to generate an output XML file. /* This is the template */ /* Output XML file Empire Burlesque Bob Dylan USA Columbia 10.9 1985 Still Got the Blues Gary More UK Virgin Records 10.2 1990 */ import com.ximpleware.*; import java.io.*; public class editTemplate { public static void main(String[] args) throws Exception { VTDGen vg = new VTDGen(); FileOutputStream fos = new FileOutputStream("new_cd.xml"); AutoPilot ap = new AutoPilot(); ap.selectXPath("/CATALOG/CD"); if (vg.parseFile("cd_Template.xml", false)) { VTDNav vn = vg.getNav(); ap.bind(vn); if (ap.evalXPath() == -1) { System.out.println("XPath eval failed"); System.exit(0); } fillTemplate(vn, "Empire Burlesque", "Bob Dylan", "USA", "Columbia", 10.90, 1985); if (ap.evalXPath() == -1) { System.out.println("XPath eval failed"); System.exit(0); } fillTemplate(vn, "Still Got the Blues", "Gary More", "UK", "Virgin Records", 10.20, 1990); // dump out the XML fos.write(vn.getXML().getBytes()); fos.close(); } } public static void fillTemplate(VTDNav vn, String title, String artist, String country, String company, double price, int year) throws NavException { int i = -1; if (vn.toElement(VTDNav.FIRST_CHILD)) { vn.overWrite(vn.getText(), title.getBytes()); vn.toElement(VTDNav.NEXT_SIBLING); vn.overWrite(vn.getText(), artist.getBytes()); vn.toElement(VTDNav.NEXT_SIBLING); vn.overWrite(vn.getText(), country.getBytes()); vn.toElement(VTDNav.NEXT_SIBLING); vn.overWrite(vn.getText(), company.getBytes()); vn.toElement(VTDNav.NEXT_SIBLING); vn.overWrite(vn.getText(), (price + "").getBytes()); vn.toElement(VTDNav.NEXT_SIBLING); vn.overWrite(vn.getText(), (year + "").getBytes()); } vn.toElement(VTDNav.PARENT); } }