Parameterized types - the <> expressions that can be used in Java as of JDK 5 are not just for collections. I find myself frequently using them in APIs I design. They really do let you write things which are more generic in the non-Java sense of the word - and the result is more reusable code, which means less code overall, which means fewer bugs and things to test. The verbosity, and some of the weirdness of type-erasure are less than ideal, but used right, the benefits are worth the complexity. The standard (and somewhere recommended) naming convention for parameterized types is to use a single-letter name. That works fine in signatures that have only one such type. But in practice, single-letter names make code less self-describing, and if you're defining a class with more than one parameterized type, it can be confusing and hard to read. People other than me will have to call, understand and maintain my code - the more self-describing I can make it, the better. So I am looking for a naming convention that makes it obvious that something is a parameterized type, but allows for descriptive names. I am wondering if anybody else has run into this problem, and if there is any emerging consensus on naming generics. Do you work on a project that uses generics a lot? If so, what do you do? Here's an example. At the moment, I'm writing a generic (in both senses) class which simply limits the number of threads which can access some resource. It's basically a wrapper around a Semaphore which uses a Runnable-like object to ensure that the Semaphore is accessed correctly, and does some non-blocking statistic gathering about thread contention. So to access the scarce resource, you pass in a ResourceAccessor: public interface ResourceAccessor { public Result run (ProtectedResource resource, Argument argument); } The problem is that, when somebody looks at this interface, they will instantly get the idea that there are really classes they need to go find, which are called ProtectedResource, Argument and Result - and of course, no such classes exist - these are just names for generic types. The standard-naming-convention is worse: public interface ResourceAccessor { public S run (T resource, R argument); } Here, nobody could possibly figure out what on earth this class is for without extensive documentation - this is a really horrible idea. So I've concluded that the standard recommendations for generic type names are simply wrong for any non-trivial usage (I.e. Collection is fine, since there is one type and Collections are well-understood). You simply can't do this on a non-collection code structure you have invented, or people will just be confused and not use it. The best suggestion I've heard thus far is using $ as a prefix: public interface ResourceAccessor <$ProtectedResource, $Argument, $Result> { public $Result run ($ProtectedResource resource, $Argument argument); } I don't find this pretty, but I don't have any better ideas, and at least it makes it crystal-clear that there is something different about these names. Any thoughts? What do you do in this situation?

In the first and second parts of this series I talked about some of the new features like enum and concurrency support that have been added in commons-lang 3. In this article, I am going to talk about a new package 'org.apache.commons.lang3.text.translate' which has been added in commons-lang 3. This package is added to fix problems in the design and implementation of the StringEscapeUtils class which exists in versions prior to 3.0. To make it clearer, let's first talk about the purpose of StringEscapeUtils class and the problems it had prior to version 3. Purpose of StringEscapeUtils StringEscapeUtils is a utility class which escapes and unescapes String for Java, JavaScript, HTML, XML, and SQL. For example, @Test public void test_StringEscapeUtils() { assertEquals("\\\\\\n\\t\\r", StringEscapeUtils.escapeJava("\\\n\t\r")); // escapes the Java String assertEquals("\\\n\t\r",StringEscapeUtils.unescapeJava("\\\\\\n\\t\\r")); //unescapes the Java String assertEquals("I didn\\'t say \\\"you to run!\\\"",StringEscapeUtils.escapeJavaScript("I didn't say \"you to run!\""));//escapes the Javascript assertEquals("<xml>", StringEscapeUtils.escapeXml(""));//escapes the xml } Problems with StringEscapeUtils There were a lot of problems in the StringEscapeUtils implementation prior to version3. Some of these were: The implementation was not extensible. Let's take an example of escapeJava, suppose we want to add support in the escapeJava method that it should start escaping single quotes. To add such support we would have to change the existing class code and another if condition which if satisfied will escape single quotes. So, the API was breaking the open-closed principle i.e. a class should be open for extension and closed for modification. It was not symmetric i.e. original should be equal to unescape(escape(original)) but it was not the case. StringEscapeUtils.escapeHtml() escapes multibyte characters like Chinese, Japanese etc. Issue 339 @Test public void testEscapeHiragana() { // Some random Japanese unicode characters String original = "\u304B\u304C\u3068"; String escaped = StringEscapeUtils.escapeHtml(original); assertEquals(original, escaped); } StringEscapeUtils.escapeHtml incorrectly converts unicode characters above U+00FFFF into 2 characters. Issue 480 @Test public void testEscapeHtmlHighUnicode() throws java.io.UnsupportedEncodingException { byte[] data = new byte[] { (byte) 0xF0, (byte) 0x9D, (byte) 0x8D,(byte) 0xA2 }; String original = new String(data, "UTF8"); String escaped = StringEscapeUtils.escapeHtml(original); assertEquals(original, escaped); } StringEscaper.escapeXml() escapes characters > 0x7f . Issue 66 @Test public void shouldNotEscapeValuesGreaterThan0x7f() { assertEquals("XML should not escape >0x7f values", "\u00A1",StringEscapeUtils.escapeXml("\u00A1")); } Solution -- Rewritten StringEscapeUtils In version 3.0, StringEscapeUtils is completely rewritten to fix all the bugs associated with this class and to provide a way for the user to customize the behavior of its methods. They have moved all the logic present in the StringEscapeUtils to the classes in the package 'org.apache.commons.lang3.text.translate'. Let's take an example of escapeJava function in StringEscapeUtils, escapeJava function does not contain any business logic, it just calls the translate method on CharSequenceTranslator reference. What they did can be best understood by looking at the code below public static final CharSequenceTranslator ESCAPE_JAVA = new AggregateTranslator(new LookupTranslator( new String[][] { {"\"", "\\\""}, {"\\", "\\\\"}, }),new LookupTranslator(EntityArrays.JAVA_CTRL_CHARS_ESCAPE()),UnicodeEscaper.outsideOf(32, 0x7f)); and in the escapeJava method public static final String escapeJava(String input) { return ESCAPE_JAVA.translate(input); } A constant of type CharSequenceTranslator was assigned an AggregateTranslator object. AggregateTranslator can take an array of translators, and it iterates over each of them. The LookupTranslator replaces the string at zeroth index with the string at the first index. UnicodeEscaper translates values outside the given range to unicode values. As you can see, you can very easily write your own escape methods. For example, if you want to add the support of escaping &, you can do it like this public static final CharSequenceTranslator ESCAPE_JAVA = new LookupTranslator( new String[][] { {"\"", "\\\""}, {"\\", "\\\\"}, }).with(new LookupTranslator( new String[][]{ {"&", "&"}, {"<", "<"} )).with( new LookupTranslator(EntityArrays.JAVA_CTRL_CHARS_ESCAPE()) ).with( UnicodeEscaper.outsideOf(32, 0x7f) ); StringEscapeUtils.escapeSql has been removed from the API as it was misleading developers to not use PreparedStatement.This method was not of much use as it was only escaping single quotes.

this post describes how to configure the jetty-maven-plugin and the jetty servlet container to run a web application that uses a data source and requires users to log in, which are the basic requirements of most web applications. i use jetty in development because it’s fast and easy to work with. why jetty? well, because it’s much faster then the websphere as i normally use and it really well supports fast (or shall i say agile? ) development thanks to its fast turnaround. and because it’s simply cool to type bash$ svn checkout http://example.com/repo/trunk/mywebapp bash$ cd mywebapp bash$ mvn jetty:run bash$ firefox http://localhost:8080/mywebapp and to be able to immediatelly log into and interact with the application. however it should be noted that jetty isn’t a full-featured javaee server and thus may not be always usable. project setup general configuration you need to add the jetty plugin to your pom.xml : 4.0.0 com.example mywebapp war ... ... org.mortbay.jetty maven-jetty-plugin 6.1.0 3 ... ... ... as you can see, i’m using jetty 6.1.0. defining a datasource let’s assume that the application uses a datasource configured at the server and accesses it normally via jndi. then we must define a reference to the data source in src/main/webapp/ web-inf/web.xml : ... ... ... jdbc/lmsdb javax.sql.datasource container shareable next we need to describe the datasource to jetty. there are multiple ways to do that, i’ve chosen to do so in src/main/webapp/ web-inf/jetty-env.xml : jdbc/lmsdb lmsdb myuser secret db.toronto.ca.ibm.com 3711 notice that the class used is db2simpledatasource and not a jdbc driver. that is, of course, because we need a datasource, not a driver. the jetty wiki pages also contain examples of datasource configuration for other dbs . finally we must make the corresponding jdbc implementation available to jetty by adding it to the plugin’s dependencies in the pom.xml : org.mortbay.jetty maven-jetty-plugin 6.1.0 <... com.ibm.db2 db2jcc 9.7 jar system ${basedir}/../lms.sharedlibraries/db2/db2jcc.jar com.ibm.db2 db2jcc_license_cisuz 9.7 jar system ${basedir}/../lms.sharedlibraries/db2/db2jcc_license_cisuz.jar please do not scorn me for using system-scoped dependencies , sometimes that is unfortunatelly the most feasible way. enabling security and configuring an authentication mechanism we would like to limit access to the application only to the authenticated users in the admin role with the exception of pages under public/. therefore we declare the appropriate security constraints in web.xml: ... authorizedusers all urls /* admin publicaccess public pages /public/* basic learning@ibm mini person feed management administrator access admin ... beware that jetty doesn’t support https out of the box and thus if you will add the data constraint confidential to any resource, you will automatically get http 403 forbidden no matter what you do. that’s why i’ve commented it out above. it is possible to enable ssl in jetty but i didn’t want to bother with certificate generation etc. next we need to tell jetty how to authenticate users. this is done via realms and we will use the simplest, file-based one. again there are multiple ways to configure it, for example in the pom.xml : org.mortbay.jetty maven-jetty-plugin 6.1.0 3 learning@ibm mini person feed management src/test/resources/jetty-users.properties ... the name must match exactly the realm-name in web.xml. you then define the users and their passwords and roles in the declared file, in this case in src/test/resources/ jetty-users.properties : user=psw,admin the format of the file is username=password[,role1,role2,...]. when you download jetty, you will find a fine example of using jaas with a file-based back-end for authentication and authorization under examples/test-jaas-webapp (invoke mvn jetty:run from the folder and go to http://localhost:8080/jetty-test-jaas/). however it seems that jaas causes an additional overhead visible as a few-seconds delay when starting the server so it might be preferrable not to use it. conclusion with jetty it’s easy to enable security and create a data source, which are the basic requirements of most web applications. anybody can then very easily run the application to test and develop it. development is where jetty really shines provided that you don’t need any feature it doesn’t have. when troubleshooting, you may want to tell jetty to log at the debug level with mvn -ddebug .. or to log requests , which can be also configured in the jetty-env.xml. beware that this post describes configuration for jetty 6.1.0. it can be different in other versions and it certainly is different in jetty 7. from http://theholyjava.wordpress.com/2010/09/10/jetty-maven-plugin-running-a-webapp-with-a-datasource-and-security/

Why do we keep instance variables private? We don’t want other classes to depend on them. Moreover it gives the flexibility to change a variable’s type or implementation on a whim or an impulse. Why, then programmers automatically add or override getters and setters to their objects, exposing their private variables as if they were public? Accessor methods Accessors (also known as getters and setters) are methods that let you read and write the value of an instance variable of an object. public class AccessorExample { private String attribute; public String getAttribute() { return attribute; } public void setAttribute(String attribute) { this.attribute = attribute; } } Why Accessors? There are actually many good reasons to consider using accessors rather than directly exposing fields of a class Getter and Setter make API more stable. For instance, consider a field public in a class which is accessed by other classes. Now, later on, you want to add any extra logic while getting and setting the variable. This will impact the existing client that uses the API. So any changes to this public field will require a change to each class that refers it. On the contrary, with accessor methods, one can easily add some logic like cache some data, lazily initialize it later. Moreover, one can fire a property changed event if the new value is different from the previous value. All this will be seamless to the class that gets the value using accessor method. Should I have Accessor Methods for all my fields? Fields can be declared public for package-private or private nested class. Exposing fields in these classes produces less visual clutter compare to accessor-method approach, both in the class definition and in the client code that uses it. If a class is package-private or is a private nested class, there is nothing inherently wrong with exposing its data fields—assuming they do an adequate job of describing the abstraction provided by the class. Such code is restricted to the package where the class is declared, while the client code is tied to class internal representation. We can change it without modifying any code outside that package. Moreover, in the case of a private nested class, the scope of the change is further restricted to the enclosing class. Another example of a design that uses public fields is JavaSpace entry objects. Ken Arnold described the process they went through to decide to make those fields public instead of private with get and set methods here Now this sometimes makes people uncomfortable because they've been told not to have public fields; that public fields are bad. And often, people interpret those things religiously. But we're not a very religious bunch. Rules have reasons. And the reason for the private data rule doesn't apply in this particular case. It is a rare exception to the rule. I also tell people not to put public fields in their objects, but exceptions exist. This is an exception to the rule, because it is simpler and safer to just say it is a field. We sat back and asked: Why is the rule thus? Does it apply? In this case it doesn't. Private fields + Public accessors == encapsulation Consider the example below public class A { public int a; } Usually, this is considered bad coding practice as it violates encapsulation. The alternate approach is public class A { private int a; public void setA(int a) { this.a =a; } public int getA() { return this.a; } } It is argued that this encapsulates the attribute. Now is this really encapsulation? The fact is, Getters/setters have nothing to do with encapsulation. Here the data isn't more hidden or encapsulated than it was in a public field. Other objects still have intimate knowledge of the internals of the class. Changes made to the class might ripple out and enforce changes in dependent classes. Getter and setter in this way are generally breaking encapsulation. A truly well-encapsulated class has no setters and preferably no getters either. Rather than asking a class for some data and then compute something with it, the class should be responsible for computing something with its data and then return the result. Consider an example below, public class Screens { private Map screens = new HashMap(); public Map getScreens() { return screens; } public void setScreens(Map screens) { this.screens = screens; } // remaining code here } If we need to get a particular screen, we do code like below, Screen s = (Screen)screens.get(screenId); There are things worth noticing here.... The client needs to get an Object from the Map and casting it to the right type. Moreover, the worst is that any client of the Map has the power to clear it which may not be the case we usually want. An alternative implementation of the same logic is: public class Screens { private Map screens = new HashMap(); public Screen getById(String id) { return (Screen) screens.get(id); } // remaining code here } Here the Map instance and the interface at the boundary (Map) are hidden. Getters and Setters are highly Overused Creating private fields and then using the IDE to automatically generate getters and setters for all these fields is almost as bad as using public fields. One reason for the overuse is that in an IDE it’s just now a matter of few clicks to create these accessors. The completely meaningless getter/setter code is at times longer than the real logic in a class and you will read these functions many times even if you don't want to. All fields should be kept private, but with setters only when they make sense which makes object Immutable. Adding an unnecessary getter reveals internal structure, which is an opportunity for increased coupling. To avoid this, every time before adding the accessor, we should analyse if we can encapsulate the behaviour instead. Let’s take another example, public class Money { private double amount; public double getAmount() { return amount; } public void setAmount(double amount) { this.amount = amount; } //client Money pocketMoney = new Money(); pocketMoney.setAmount(15d); double amount = pocketMoney.getAmount(); // we know its double pocketMoney.setAmount(amount + 10d); } With the above logic, later on, if we assume that double is not a right type to use and should use BigDecimal instead, then the existing client that uses this class also breaks. Let’s restructure the above example, public class Money { private BigDecimal amount; public Money(String amount) { this.amount = new BigDecimal(amount); } public void add(Money toAdd) { amount = amount.add(toAdd.amount); } // client Money balance1 = new Money("10.0"); Money balance2 = new Money("6.0"); balance1.add(balance2); } Now instead of asking for a value, the class has a responsibility to increase its own value. With this approach, the change request for any other datatype in future requires no change in the client code. Here not only the data is encapsulated but also the data which is stored, or even the fact that it exists at all. Conclusions Use of accessors to restrict direct access to field variable is preferred over the use of public fields, however, making getters and setter for each and every field is overkill. It also depends on the situation though, sometimes you just want a dumb data object. Accessors should be added to a field where they're really required. A class should expose larger behaviour which happens to use its state, rather than a repository of state to be manipulated by other classes. More Reading http://c2.com/cgi/wiki?TellDontAsk http://c2.com/cgi/wiki?AccessorsAreEvil Effective Java - See more at http://muhammadkhojaye.blogspot.co.uk/2010/10/getter-setter-to-use-or-not-to-use.html

Information can travel in very different ways between the parts of an applications, which could be tricky when these parts are distributed on different tiers, or times. The Data Transfer Object pattern prescribes to use a first-class citizen like a class to model the data used in the communication. This pattern was originally meant to aid logic distribution: coarse grained interfaces, which are ideal for remote interaction, must return more data in each call to reduce the number of messages sent over the network and their overhead. This kind of objects is built with the goal of be easy to serialize and send over the wire. In the PHP world, the communication is usually either from server to server, or even targets the same server: PHP is a lot different from other languages which are used for application distribution. Commonly the serialization and unserialization of objects are accomplished by the same codebase, which has a short life (the time of an HTTP request) and stores data with the serialization mechanism to maintain state between different requests. As a result, the usual dependencies discourse, which prescribes to use a Data Mapper between the Domain Model and the DTOs, do not apply; to the point that sometimes even domain model objects are serialized directly (an ORM like Doctrine 2 allows you to do so). There are more differences with classic Java DTOs which we'll see in this article. Implementation The definition of Data Transfer Object talks about communication between different processes: subsequent executions of the same PHP script are indeed different processes (or, when the same process is reused, it does not share any variable space with the previous executions). PHP is peculiar also from the implementation point of view: a DTO may not even be an object, since an ordinary or multidimensional array will do the same job in many cases. However, an object implementation is necessary to take advantage of object handlers, where the structure of data is not hierarchical but involves a connected graph of objects or recursive data structures. This implementation choice is much more clear than using arrays and variable references, which can be tricky in PHP. Basically, a DTO is the object which we have been told to never write: it has getters and setters to expose allof its properties, while providing nearly no encapsulation nor business logic. It's a data structure, like a Value Object (which was its name in certain Java literature). But it is not immutable nor it carries the semantic meaning of Value Objects. Use cases Data Transfer Objects come handy in many use cases. In their simplest implementation, they are used for returning multiple values from a method. Their further evolution involves then serialization mechanisms; of course both the provenience and the destination of a Data Transfer Object need to be a PHP environment, a fact which opens different scenarios from classical Java distribution ones, that involve clients well. For example a Data Transfer object, easily serializable, can be stored in caches (memcache) or sessions ($_SESSION). Other use cases regard databases: according to Fowler, Record Sets like PDOStatement can be defined as the Data Transfer Objects of relational databases. There are even more scenarios for Data Transfer Object, like clients which return a serialized data structure, or their usage for breaking dependencies between different layers: a Controller may populate a DTO and pass it to the View. Serialization As you may have experience while using var_dump() in PHP, the objects of a Domain Model are usually interconnected in a complex graph; DTOs isolate a subset of the graph and make it easy to serialize, even by omitting part of the information. Thus, serialization of domain object needs some complex infrastructure (lazy loading proxies which are discarded on serialization and must be re-initialized during the merge with a current object graph); sometimes is by far easier to extract data in a DTO, especially when you do not have particular libraries at your disposal. Once you have an isolated object graph, PHP will handle serialization by itself, even without marker interfaces; it will simply include all the public, protected and private properties (this behavior can be modified by specifying a __sleep() method). Example This running code shows a small domain model composed of the User and Group classes, and how a DTO for the User class allows to serialize a User without pulling in its Groups, for example for quickly storing it in a cache. _name; } public function setName($name) { $this->_name = $name; } /** * @return string */ public function getRole() { return $this->_role; } public function setRole($role) { $this->_role = $role; } public function addGroup(Group $group) { $this->_group = $group; } public function getGroups() { return $this->_groups; } } /** * Another Domain Model class. */ class Group { private $_name; /** * @return string */ public function getName() { return $this->_name; } public function setName($name) { $this->_name = $name; } } /** * The Data Transfer Object for User. * It stores the mandatory data for a particular use case - for example ignoring the groups, * and ensuring easy serialization. */ class UserDTO { /** * In more complex implementations, the population of the DTO can be the responsibilty * of an Assembler object, which would also break any dependency between User and UserDTO. */ public function __construct(User $user) { $this->_name = $user->getName(); $this->_role = $user->getRole(); } public function getName() { return $this->_name; } public function getRole() { return $this->_role; } // there are no setters because this use cases does not require modification of data // however, in general DTOs do not need to be immutable. } // client code $user = new User(); $user->setName('Giorgio'); $user->setRole('Author'); $user->addGroup(new Group('Authors')); $user->addGroup(new Group('Editors')); // many more groups $dto = new UserDTO($user); // this value is what will be stored in the session, or in a cache... var_dump(serialize($dto));

Recently I attended a local rheinJUG meeting in Düsseldorf. While the topic of the session was Eclipse e4, the night’s sponsor itemis provided some handouts on Xtext which got me very interested. The reason is that currently at work we are developing a mobile Java application (J9, CDC/Foundation 1.1 on Windows CE6) for which we needed an easy to use and reliable way for configuring navigation through the application. In a previous iteration we had – mostly because of time constraints – hard coded most of the navigational paths, but this time the app is more complex and doing that again was not really an option. First we thought about an XML based configuration, but this seemed to be a hassle to write (and read) and also would mean we would have to pay the price of parsing it on every application startup. Enter Xtext: An Eclipse based framework/library for building text based DSLs. In short, you just provide a grammar description of a new DSL to suit your needs and with – literally – just a few mouse clicks you are provided with a content-assist, syntax-highlight, outline-view-enabled Eclipse editor and optionally a code generator based on that language. Getting started: Sample Grammar There is a nice tutorial provided as part of the Xtext documentation, but I believe it might be beneficial to provide another example of how to put a DSL to good use. I will not go into every step in great detail, because setting up Xtext is Eclipse 3.6 Helios is just a matter of putting an Update Site URL in, and the New Project wizard provided makes the initial setup a snap. I assume, you have already set up Eclipse and Xtext and created a new Xtext project including a generator project (activate the corresponding checkbox when going through the wizard). In this post I am assuming a project name of com.danielschneller.navi.dsl and a file extension of .navi. When finished we will have the infrastructure ready for editing, parsing and generating code based on files like these: navigation rules for MyApplication mappings { map permission AdminPermission to "privAdmin" map permission DataAccessPermission to "privData" map coordinate Login to "com.danielschneller.myapp.gui.login.LoginController" in "com.danielschneller.myapp.login" map coordinate LoginFailed to "com.danielschneller.myapp.gui.login.LoginFailedController" in "com.danielschneller.myapp.login" map coordinate MainMenu to "com.danielschneller.myapp.gui.menu.MainMenuController" in "com.danielschneller.myapp.menu" map coordinate UserAdministration to "com.danielschneller.myapp.gui.admin.UserAdminController" in "com.danielschneller.myapp.admin" map coordinate DataLookup to "com.danielschneller.myapp.gui.lookup.LookupController" in "com.danielschneller.myapp.lookup" } navigations { define navigation USER_LOGON_FAILED define navigation USER_LOGON_SUCCESS define navigation OK define navigation BACK define navigation ADMIN define navigation DATA_LOOKUP } navrules { from Login on navigation USER_LOGON_FAILED go to LoginFailed on navigation USER_LOGON_SUCCESS go to MainMenu from LoginFailed on navigation OK go to Login from MainMenu on navigation ADMIN go to UserAdministration with AdminPermission on navigation DATA_LOOKUP go to DataLookup with DataAccessPermission from UserAdministration on navigation BACK go to MainMenu from DataLookup on navigation BACK go to MainMenu } As you can see it is a nice little language for defining coordinates in an application, meaning a specific GUI for a certain task and the possible navigation paths between them. Optionally a navigation path can be tagged to require one or more permissions to work. So for example one possible navigation path shown in the above sample is from the applications main menu, identified by the identifier MainMenu and represented in code by the com.danielschneller.myapp.gui.menu.MainMenuController class in the com.danielschneller.myapp.menu OSGi bundle to a GUI identified as DataLookup, implemented by com.danielschneller.myapp.gui.lookup.LookupController in the com.danielschneller.myapp.lookup bundle. For this path to be taken, the application must request the DataLookup navigation path and the currently logged in user be assigned the DataAccessPermission. What exactly that means is not the focus of this tutorial, suffice it to say that we somehow need to get the information contained in this specialized language into our Java application in some shape or form that can be evaluated at runtime. In the following example all information will be transformed into a HashMap based data structure. For our little mobile application this has several advantages over the XML option mentioned earlier: No XML parsing necessary on application startup, saving some performance Validation of the navigation rules ahead of time, preventing parse errors at runtime No libraries needed to access the information – by putting everything in a simple HashMap we do not have to rely on any non-standard classes whatsoever First thing I did when I started with Xtext was define a sample input file such as the one above. Then – following its general structure – I began to extract a formal grammar for it. Of course, the first draft of the sample data was not perfect, over the course of a few iterations I refined some of the syntax, but in the end this is the grammar definition I came up with. It is heavily commented to allow you to copy it out and still leave the documentation intact: grammar com.danielschneller.navi.NavigationRules with org.eclipse.xtext.common.Terminals generate navigationRules "http://com.danielschneller/fw/funkmde/navi/NavigationRules" /* * The top level entry point for the file. * "Root" is just a name as good as any, but * makes the meaning quite clear. */ Root: // first thing in the file is a "keyword", // followed by an attribute that will be // accessible as "name" later and allow // definition of an ID type of thing. 'navigation rules for' name=ID // after the keyword and "name" attribute // three sections follow, each assigned // to an attribute for later reference // (called "mappingdefs", "transitiondefs" // and "ruledefs"). // Their types are defined later in the file. mappingsdefs=Mappings transitiondefs=TransitionDefinitions ruledefs=NavigationRules // semicolon ends the definition of "Root" ; // mappings section >>>>>>>>>>>>>>>>>>>>>>>>>>>>> /* * Definition of the "Mappings" type used in * the "Root" type. */ Mappings: // first the keyword "mappings" is expected, // then an open curly 'mappings' '{' // after that a collection of "Mapping"s is // expected. The "+=" means that they will // all be collected in a collection type element // called "mappings" for future reference. // The "+" at the end means "at least one, but // more is just fine". (mappings+=Mapping)+ // finally the "Mappings" type requires a closing // curly brace. '}' // semicolon ends the definition of "Mappings" ; /* * Definition of a single "Mapping", those we are * collecting in the "mappings" attribute of the * "Mappings" type. */ Mapping: // each mapping starts with the keyword "map" // and is followed by an element of type "MappingSpec" 'map' MappingSpec ; /* * Definition of a "MappingSpec" element. This is * actually just a "parent type" for two more specific * kinds of "MappingSpec": */ MappingSpec: // no keywords are defined here, a "MappingSpec" // can be either a "PermissionMappingSpec" or a // "CoordinateMappingSpec". Any of these will be // fine where a "MappingSpec" is asked for. PermissionMappingSpec | CoordinateMappingSpec ; /* * Definition of a "PermissionMappingSpec" element. */ PermissionMappingSpec: // first the keyword "permission" is required. // then a "name" attribute is expected of type ID. // Following the name the "to" keyword is expected, // followed by a string that is stored in the "value" // attribute 'permission' name=ID 'to' value=STRING ; /* * Definition of a "CoordinateMappingSpec" element. * The definition is very similar to the "PermissionMappingSpec" * but has more attributes. */ CoordinateMappingSpec: // first the keyword "coordinate", then an ID stored as "name", // the keyword "to", followed by a string stored as "controllername", // next the keyword "in" and finally another string, memorized as // "bundleid" 'coordinate' name=ID 'to' controllername=STRING 'in' bundleid=STRING ; // <<<<<<<<<<<<<<<<<<<<<<<<<<<<< mappings section // >>>>>>>>>>>>>>>>>>>>>>>>>>>>> navigations section /* * Definition of the "TransitionDefinitions" type used in * the "Root" type. */ TransitionDefinitions: // first, this element is introduced with the "navigations" // keyword, followed by an open curly brace. 'navigations' '{' // after that a collection of "TransitionDefinition"s is // expected. The "+=" means that they will // all be collected in a collection type element // called "transitions" for future reference. // The "+" at the end means "at least one, but // more is just fine". (transitions+=TransitionDefinition)+ // the element ends with a closing curly brace '}' ; /* * Definition of a "TransitionDefinition" element. This * one is very simple. */ TransitionDefinition: // the keyword "define navigation" is required first, // then a "name" attribute of type ID is expected. 'define navigation' name=ID ; // <<<<<<<<<<<<<<<<<<<<<<<<<<<<< navigations section // >>>>>>>>>>>>>>>>>>>>>>>>>>>>> navrules section /* * Definition of the "NavigationRules" element. */ NavigationRules: // Element starts with the keywords "navrules" and // open curly. 'navrules' '{' // collection attribute called "rules", consisting // of one or more occurrences of a "Rule" element. (rules+=Rule)+ // element finishes with a closing curly keyword '}' ; /* * Definition of a "Rule" element as used in the "NavigationRules" * element. */ Rule: // first the "from" keyword, then a reference to one of the // coordinate mappings defined earlier. This time no new // definition of a coordinate is required, but one of those // that have been listed before. So the type here is put in // square brackets 'from' source=[CoordinateMappingSpec] // following the source specification, one or more "Destination" // type elements are expected, collected in a collection attribute // named "destinations" (destinations+=Destination)+ ; /* * Definition of a "Destination" type. These are collected * in a "Rule". */ Destination: // first comes an "on navigation" keyword. After that a // reference to one of the Transition elements defined // in the "navigations" section is required and stored // in the "transition" attribute. // after that follows a "go to" keyword and a reference // to a coordinate mapping, stored in the "target" attribute. // finally - as with the "destinations" collection attribute // in the "Rule" element - a "permissions" collection is // defined to store none or more (*) "PermissionReference" // elements. 'on navigation' transition=[TransitionDefinition] 'go to' target=[CoordinateMappingSpec] (permissions+=PermissionReference)* ; /* * Definition of a "PermissionReference" type. This is used * in the "permissions" collection of a "Destination". */ PermissionReference: // first, a "with" keyword is expected. After that a // "permission" attribute stores a reference to one of // the previously defined permission mappings from the // "mappings" section. 'with' permission=[PermissionMappingSpec] ; // <<<<<<<<<<<<<<<<<<<<<<<<<<<<< navrules section This is what XText can digest and create an editor plugin and outline view for. Just save this as navigationRules.xtext – when you created the XText project in Eclipse using the wizard it should have been prepared for you. Copying and pasting this into a .xtext file in Eclipse will provide you with syntax highlighting, code completion and syntax checking, making it easy to play around with grammar files. Once done, right click the .mwe2 file lying next to the grammar file in the Package Explorer view and select Run As MWE2 Workflow from the context menu. This will take a moment and generate several classes, both in the current (XText) project and the accompanying ...ui project. Next, right click the Xtext project and select Run As Eclipse Application from the context menu. This will bring up another Eclipse instance with the newly created support for navigation rules files (with a .navi suffix) installed. To try it out, just create a new project and in that a new file. Make sure its name ends in .navi. When asked, make sure to accept adding the Xtext nature to the project. You will be presented with a new, empty editor that already has an error marker in it. This is because according to our grammar definition, an empty file does not comply to all the rules we specified. Try hitting the code-completion shortcut (Ctrl-Space) twice and see what happens: The first code-completion fills in the navigation rules for part. According to the grammar this is the only valid text at the beginning of a file, so it is automatically inserted. Hitting Ctrl-Space again will tell you that now you need a Name of type ID. Just go ahead and try out the completion. It will help you create a syntactically sound navigation rules file. Notice that the Problems View tells you what is currently wrong. Also notice, that one you reach a part where references are expected by the grammar (e. g. when defining source and destination coordinates in a navigation rule) you will get suggestions based on what you entered earlier. This is what the whole sample from above looks like in the editor: While you are still fleshing out and fine tuning your grammar definitions, you will probably close this Eclipse instance and reopen it, once you repeated the Run As MWE2 Workflow steps in the main instance. In the long run I suggest you create a Feature and an Update Site project to allow easier distribution and updates of the intermediate iterations. Generating Code Now, as we have a complete Xtext DSL defined and in place let’s have a look at the Code Generation side of things. This part is completely optional: You are free to include the necessary Xtext libraries into your applications runtime (although they seem to be numerous) and just use them to dynamically load and parse .navi files on-the-fly. This would probably be a good idea if you were writing an Eclipse based application anyway. However, when targeting a very limited platform like JavaME this option is not viable. Instead we will now create a code generator that provides a transformation from the DSL syntax into more classic Java terms – specifically we will create a HashMap based data structure that carries all the same information, but in Java terms. This is a sample of what the generated output is going to look like: public class NaviRules { private Map navigationRules = new Hashtable(); // ... public NaviRules() { NaviDestination naviDest; // ========== From Login (com.danielschneller.myapp.gui.login.LoginController) // ========== On USER_LOGON_FAILED // ========== To LoginFailed (com.danielschneller.myapp.gui.login.LoginFailedController in com.danielschneller.myapp.login) naviDest = new NaviDestination(); naviDest.action = "USER_LOGON_FAILED"; naviDest.targetClassname = "com.danielschneller.myapp.gui.login.LoginFailedController"; naviDest.targetBundleId = "com.danielschneller.myapp.login"; store("com.danielschneller.myapp.gui.login.LoginController", naviDest); // ========== On USER_LOGON_SUCCESS // ========== To MainMenu (com.danielschneller.myapp.gui.menu.MainMenuController in com.danielschneller.myapp.menu) naviDest = new NaviDestination(); naviDest.action = "USER_LOGON_SUCCESS"; naviDest.targetClassname = "com.danielschneller.myapp.gui.menu.MainMenuController"; naviDest.targetBundleId = "com.danielschneller.myapp.menu"; store("com.danielschneller.myapp.gui.login.LoginController", naviDest); // ============================================================================= // ========== From LoginFailed (com.danielschneller.myapp.gui.login.LoginFailedController) // ========== On OK // ========== To Login (com.danielschneller.myapp.gui.login.LoginController in com.danielschneller.myapp.login) naviDest = new NaviDestination(); naviDest.action = "OK"; naviDest.targetClassname = "com.danielschneller.myapp.gui.login.LoginController"; naviDest.targetBundleId = "com.danielschneller.myapp.login"; store("com.danielschneller.myapp.gui.login.LoginFailedController", naviDest); // .... and so on ... } } The support class NaviDestination is omitted but is generally just a value holder struct type class. When creating the Xtext project using the wizard earlier we created a third Eclipse project, ending in ...generator. Its src folder contains three subdirectories called model, templates and workflow. Put the sample .navi file into the model directory. It will serve as the input for the generator. Create the first template Code generation is based on templates. Xtext leverages the Xpand template engine. In the templates directory create a new Xpand template using the context menu. Call it NaviRules.xpt, open it and insert the following: «REM» import the namespace defined in our DSL model «ENDREM» «IMPORT navigationRules» «REM» Define a template called "main" for elements of type "Root". The minus sign at the end takes care of not adding a newline at the end of it. «ENDREM» «DEFINE main FOR Root-» «ENDDEFINE» As there is only one instance of a Root element in a navigation rules file, this will be the main entry point - hence the name. There is no need to call it main, but it seems fitting. Now between the DEFINE and ENDDEFINE insert what is to be generated: As shown above, we need a new Java source file called NaviRules.java: ... «DEFINE main FOR Root-» «FILE "NaviRules.java"-» «ENDFILE-» «ENDDEFINE» ... Again, the contents to be generated is put in between the FILE and ENDFILE brackets. Anything not enclosed in «» will be used verbatim in the output file. So first of all, put in the static parts of the Java file. What I did was first write the source for a single navigation rule by hand, made sure it compiled and then copied over the relevant parts into the template piece by piece: ... «FILE "NaviRules.java"-» import java.util.*; public class NaviRules { public static class NaviDestination { String action; List requiredPermissions = new ArrayList(); String targetClassname; String targetBundleId; NaviDestination() {}; public final List getRequiredPermissions() { return new ArrayList(requiredPermissions); } // let Eclipse generate getters, setters, // equals and hashCode methods for this } private Map navigationRules = new Hashtable(); «ENDFILE-» ... Now, this is nothing special so far. To fill in the elements from the navigation rules DSL file put in the following: ... private Map navigationRules = new Hashtable(); public NaviRules() { NaviDestination naviDest; «REM» Iterate all elements in the "rules" collection attribute of the "ruledefs" attribute of the "Root" element. Call each iterated element (which is of type "Rule") "rule" and expand the "ruletmpl" template for it here. «ENDREM» «FOREACH ruledefs.rules AS r»«EXPAND ruletmpl FOR r»«ENDFOREACH» } ... In the class constructor we first define a local variable naviDest of the previously declared type. Then - as the comment states - the FOREACH instruction will iterate over all Rule type elements. This might not seem to be completely obvious at first. Remember at this point in the template the current scope is the "Root" element from the navigation rules file. It has an attribute called ruledefs as per the grammer definition. This attribute is of type NavigationRules which in turn has a collection attribute called rules, containing of Rule type objects. Inside the loop the current element can then be adressed by the template variable name r. The loop body (between FOREACH and ENDFOREACH) contains another Xpand instruction to expand a template called ruletmpl which will be declared next. Don't worry, even though this is a little difficult at first - switching contexts between the Java and the template scopes is made significantly easier in Eclipse, because the Xpand template editor will syntax color (static parts are blue) and also assist you with code completion inside the Xpand template parts. Ctrl-Spacing your way through it will make things more obvious than they are when reading an example. Now for the ruletmpl template. Place it below the ENDDEFINE statement belonging to the main template: ... «ENDFILE-» «ENDDEFINE» «DEFINE ruletmpl FOR Rule-» // ========== From «source.name» («source.controllername») «FOREACH destinations AS d»«EXPAND destTmpl(source) FOR d»«ENDFOREACH» // ============================================================================= «ENDDEFINE» You see the same idea used again: Static parts that get transferred into the output file 1:1 and Xpand statements that fill in data from the navigation rules definition file. In this case you see references to the attributes of the Rule element. As per the FOREACH instruction in the previous template, the one at hand will be repeated for every instance of Rule in our source file. Inside this definition the current scope is that of Rule, so with «source.name» the name attribute of the CoordinateMappingSpec object referenced as source in a Rule is taken first, then the controllername attribute likewise. Next up another FOREACH loop iterates the one or more possible Destinations of each Rule. Instead of just applying a template (destTmpl) for every Destination we also pass in the corresponding CoordinateMappingSpec stored in the source attribute of the Rule. This is then used in the following template: ... «DEFINE destTmpl(CoordinateMappingSpec source) FOR Destination-» // ========== On «transition.name» // ========== To «target.name» («target.controllername» in «target.bundleid») naviDest = new NaviDestination(); naviDest.action = "«transition.name»"; naviDest.targetClassname = "«target.controllername»"; naviDest.targetBundleId = "«target.bundleid»"; «FOREACH permissions AS p»«EXPAND permTmpl FOR p»«ENDFOREACH» store("«source.controllername»", naviDest); «ENDDEFINE» «DEFINE permTmpl FOR PermissionReference-» naviDest.requiredPermissions.add("«permission.value»"); «ENDDEFINE» In this innermost templates the attributes of the CoordinateMappingSpec objects source and target are accessed and put into place to be assigned to the members a NaviDestination Java object instance per Destination. There is only one more (very simple) template for the PermissionReference elements. With this, the Xpand file is complete. Set Up The Generator Workflow The wizard initially created a NavigationRulesGenerator.mwe2 file in the workflow folder. Open it and replace its contents with the following: module workflow.NavigationRulesGenerator import org.eclipse.emf.mwe.utils.* var targetDir = "src-gen" var fileEncoding = "Cp1252" var modelPath = "src/model" Workflow { component = org.eclipse.xtext.mwe.Reader { path = modelPath // this class has been generated by the xtext generator register = com.danielschneller.navi.NavigationRulesStandaloneSetup {} load = { slot = "root" type = "Root" } } component = org.eclipse.xpand2.Generator { metaModel = org.eclipse.xtend.typesystem.emf.EmfRegistryMetaModel {} expand = "templates::NaviRules::main FOREACH root" outlet = { path = targetDir } fileEncoding = fileEncoding } } The most interesting parts of this workflow file are the load section in the Reader component and the expand and outlet sections in the Generator component: The first one will connect a so-called slot with the Root element from our navigation rules. The second one will trigger the evaluation of the main template in the NaviRules.xpt file in the templates folder and feed any Root instances it finds in the *.navi files from the src/model (modelPath) into it. Now it is time for some actual generation. Run the generator workflow Right click the MWE2 file you just edited and select the Run As MWE2 Workflow command from the context menu. The Eclipse console will show this output: 0 [main] DEBUG org.eclipse.xtext.mwe.Reader - Resource Pathes : [src/model] 431 [main] DEBUG xt.validation.ResourceValidatorImpl - Syntax check OK! Resource: file:/Users/ds/ws/ws36_xtext/com.danielschneller.navi.dsl.generator/src/model/MyApp.navi 1013 [main] INFO org.eclipse.xpand2.Generator - Written 1 files to outlet [default](src-gen) 1014 [main] INFO .emf.mwe2.runtime.workflow.Workflow - Done. Then have a look at the newly generated contents of the src-gen source folder. If everything went alright, you should find a fresh NaviRules.java file placed there, based on the contents of your navigation rules file and the Xpand templates. Try and make some changes to the template, then re-run the workflow. You will see the changes reflected in the generated source file. Generate a second source File In the templates directory add another Xpand template file Navigation.xpt with the following content: «IMPORT navigationRules»; «DEFINE main FOR Root-» «FILE "Navigation.java"-» public final class Navigation { «FOREACH ruledefs.rules.destinations.transition.collect(e|e.name).toSet().sortBy(e|e) AS t»«EXPAND actionTmpl FOR t»«ENDFOREACH» private final String name; private Navigation(String aName) { name = aName; } public String getName() { return name; } } «ENDFILE-» «ENDDEFINE» «DEFINE actionTmpl FOR String-» /** Constant for Navigation «this» */ public static final Navigation «this» = new Navigation("«this»"); «ENDDEFINE» This is a template for a type-safe enumeration that can be used in Java 1.4 - remember I had to do this for JavaME. Notice the FOREACH loop in this case. It demonstrates that not only simple iterations are possible, but that Xpand allows more complex operations as well. In this case it will collect the names of all the navigation transitions from all the Destinations in the navigation rules. These are of type String. They are made unique by converting them to a Set datastructure and then finally sorted in their natural order. The resulting list of sorted strings is then iterated, each one - called t - is passed to the actionTmpl template. It is very simple, just placing the string itself («this») into a single line of Java source code. Of course, strictly speaking this is a rather complicated procedure to get the same information we could also have taken from the TransitionDefinitions element in the rules definition. However I think it serves as a nice example for additional Xpand capabilities. For a full description of its possibilities, have a look at the Xpand Reference in the Eclipse documentation. To use the new template, add another section to the MWE2 workflow definition: component = org.eclipse.xpand2.Generator { metaModel = org.eclipse.xtend.typesystem.emf.EmfRegistryMetaModel {} expand = "templates::Navigation::main FOREACH root" outlet = { path = targetDir } fileEncoding = fileEncoding } Running it again will produce a slightly different output, making clear that two files have been generated. This is what comes out in the src-gen folder as Navigation.java: public final class Navigation { /** Constant for Navigation ADMIN */ public static final Navigation ADMIN = new Navigation("ADMIN"); /** Constant for Navigation BACK */ public static final Navigation BACK = new Navigation("BACK"); /** Constant for Navigation DATA_LOOKUP */ public static final Navigation DATA_LOOKUP = new Navigation("DATA_LOOKUP"); /** Constant for Navigation OK */ public static final Navigation OK = new Navigation("OK"); ... More... This was just about my first experiments with Xtext. I am sure there is plenty more to be done with it. For more reading, please have a look at this very nice Getting started with Xtext tutorial by Peter Friese of Itemis. From http://www.danielschneller.com/2010/08/code-generation-with-xtext.html

'Impedance mismatch'. No two words encompass the troubles, headaches and quirks most developers face when attempting to link applications to relational databases (RDBMS). But lets face it, object orientated designs aren't going away anytime soon from mainstream languages and neither are the relational storage systems used in most applications. One side works with objects, while the other with tables. Resolving these differences -- or as its technically referred to 'object/relational impedance mismatch' -- can result in substantial overhead, which in turn can materialize into poor application performance. In Java, the Java Persistence API (JPA) is one of the most popular mechanisms used to bridge the gap between objects (i.e. the Java language) and tables (i.e. relational databases). Though there are other mechanisms that allow Java applications to interact with relational databases -- such as JDBC and JDO -- JPA has gained wider adoption due to its underpinnings: Object Relational Mapping (ORM). ORM's gain in popularity is due precisely to it being specifically designed to address the interaction between object and tables. In the case of JPA, there is a standard body charged with setting its course, a process which has given way to several JPA implementations, among the three most popular you will find: EclipseLink (evolved from TopLink), Hibernate and OpenJPA. But even though all three are based on the same standard, ORM being such a deep and complex topic, beyond core functionality each implementation has differences ranging from configuration to optimization techniques. What I will do next is explain a series of topics related to optimizing an application's use of the JPA, using and comparing each of the previous JPA implementations. While JPA is capable of automatically creating relational tables and can work with a series of relational database vendors, I will part from having pre-existing data deployed on a MySQL relational database, in addition to relying on the Spring framework to facilitate the use of the JPA. This will not only make it a fairer comparison, but also make the described techniques appealing to a wider audience, since performance issues become a serious concern once you have a large volume of data, in addition to MySQL and Spring being a common choice due to their community driven (i.e. open-source) roots. See the source code/application section at the end for instructions on setting up the application code discussed in the remainder of the sections. Download the Source Code associated with this article (~45 MB) The basics: Metrics In order to establish JPA performance levels in an application, it's vital to first obtain a series of metrics related to a JPA implementation's inner workings. These include things like: What are the actual queries being performed against a RDBMS? How long does each query take? Are queries being performed constantly against the RDBMS or is a cache being used? These metrics will be critical to our performance analysis, since they will shed light on the underlying operations performed by a JPA implementation and in the process show the effectiveness or ineffectiveness of certain techniques. In this area you will find the first differences among implementations, and I'm not talking about metric results, but actually how to obtain these metrics. To kick things off, I will first address the topic of logging. By default, all three JPA implementations discussed here -- EclipseLink, Hibernate and OpenJPA -- log the query performed against a RDBMS, which will be an advantage in determining if the queries performed by an ORM are optimal for a particular relational data model. Nevertheless, tweaking the logging level of a JPA implementation further can be helpful for one of two things: Getting even more details from the underlying operations made by a JPA -- which can be turned off by default (e.g. database connection details) -- or getting no logging information at all -- which can benefit a production system's performance. Logging in JPA implementations is managed through one of several logging frameworks, such as Apache Commons Logging or Log4J. This requires the presence of such libraries in an application. Logging configuration of a JPA implementation is mostly done through a value in an application's persistence.xml file or in some cases, directly in a logging framework's configuration files. The following table describes JPA logging configuration parameters: Large table, so here's an external link In addition to the information obtained through logging, there is another set of JPA performance metrics which require different steps to be obtained. One of these metrics is the time it takes to perform a query. Even though some JPA implementations provide this information using certain configurations, some do not. Even so, I opted to use a separate approach and apply it to all three JPA implementations in question. After all, time metrics measured in milliseconds can be skewed in certain ways depending on start and end time criteria. So to measure query times, I will use Aspects with the aid of the Spring framework. Aspects will allow us to measure the time it takes a method containing a query to be executed, without mixing the timing logic with the actual query logic -- the last feature of which is the whole purpose of using Aspects. Further discussing Aspects would go beyond the scope of performance, so next I will concentrate on the Aspect itself. I advise you to look over the accompanying source code, Aspects and Spring Aspects for more details on these topics and their configuration. The following Aspect is used for measuring execution times in query methods. package com.webforefront.aop;import org.apache.commons.lang.time.StopWatch;import org.apache.commons.logging.Log;import org.apache.commons.logging.LogFactory;import org.aspectj.lang.ProceedingJoinPoint;import org.aspectj.lang.annotation.Around;import org.aspectj.lang.annotation.Pointcut;import org.aspectj.lang.annotation.Aspect;@Aspectpublic class DAOInterceptor { private Log log = LogFactory.getLog(DAOInterceptor.class); @Around("execution(* com.webforefront.jpa.service..*.*(..))") public Object logQueryTimes(ProceedingJoinPoint pjp) throws Throwable { StopWatch stopWatch = new StopWatch(); stopWatch.start(); Object retVal = pjp.proceed(); stopWatch.stop(); String str = pjp.getTarget().toString(); log.info(str.substring(str.lastIndexOf(".")+1, str.lastIndexOf("@")) + " - " + pjp.getSignature().getName() + ": " + stopWatch.getTime() + "ms"); return retVal; } The main part of the Aspect is the @Around annotation. The value assigned to this last annotation indicates to execute the aspect method -- logQueryTimes -- each time a method belonging to a class in the com.webforefront.jpa.service package is executed -- this last package is where all our application's JPA query methods will reside. The logic performed by the logQueryTimes aspect method is tasked with calculating the execution time and outputting it as logging information using Apache Commons Logging. Another set of important JPA metrics is related to statistics beyond those provided by standard logging. The statistics I'm referring to are things related to caches, sessions and transactions. Since the JPA standard doesn't dictate any particular approach to statistics, each JPA implementation also varies in the type and way it collects statistics. Both Hibernate and OpenJPA have their own statistics class, where as EclipseLink relies on a Profiler to gather similar metrics. Since I'm already relying on Aspects, I will also use an Aspect to obtain statistics both prior and after the execution of a JPA query method. The following Aspect obtains statistics for an application relying on Hibernate. package com.webforefront.aop;import org.hibernate.stat.Statistics;import org.hibernate.SessionFactory;import org.aspectj.lang.ProceedingJoinPoint;import org.aspectj.lang.annotation.Around;import org.aspectj.lang.annotation.Aspect;import org.springframework.beans.factory.annotation.Autowired;import javax.persistence.EntityManagerFactory;import org.hibernate.ejb.HibernateEntityManagerFactory;import org.apache.commons.logging.Log;import org.apache.commons.logging.LogFactory;@Aspectpublic class CacheHibernateInterceptor { private Log log = LogFactory.getLog(DAOInterceptor.class); @Autowired private EntityManagerFactory entityManagerFactory; @Around("execution(* com.webforefront.jpa.service..*.*(..))") public Object log(ProceedingJoinPoint pjp) throws Throwable { HibernateEntityManagerFactory hbmanagerfactory = (HibernateEntityManagerFactory) entityManagerFactory; SessionFactory sessionFactory = hbmanagerfactory.getSessionFactory(); Statistics statistics = sessionFactory.getStatistics(); String str = pjp.getTarget().toString(); statistics.setStatisticsEnabled(true); log.info(str.substring(str.lastIndexOf(".")+1, str.lastIndexOf("@")) + " - " + pjp.getSignature().getName() + ": (Before call) " + statistics); Object result = pjp.proceed(); log.info(str.substring(str.lastIndexOf(".")+1, str.lastIndexOf("@")) + " - " + pjp.getSignature().getName() + ": (After call) " + statistics); return result; } } Notice the similar structure to the prior timing Aspect, except in this case the logging output contains values that belong to the Statistics Hibernate class obtained via the application's EntityManagerFactory. The next Aspect is used to obtain statistics for an application relying on OpenJPA. package com.webforefront.aop;import org.apache.openjpa.datacache.CacheStatistics;import org.apache.openjpa.persistence.OpenJPAEntityManagerFactory;import org.apache.openjpa.persistence.OpenJPAPersistence;import org.aspectj.lang.ProceedingJoinPoint;import org.aspectj.lang.annotation.Around;import org.aspectj.lang.annotation.Aspect;import org.springframework.beans.factory.annotation.Autowired;import javax.persistence.EntityManagerFactory;import org.apache.commons.logging.Log;import org.apache.commons.logging.LogFactory;@Aspectpublic class CacheOpenJPAInterceptor { private Log log = LogFactory.getLog(DAOInterceptor.class); @Autowired private EntityManagerFactory entityManagerFactory; @Around("execution(* com.webforefront.jpa.service..*.*(..))") public Object log(ProceedingJoinPoint pjp) throws Throwable { OpenJPAEntityManagerFactory ojpamanagerfactory = OpenJPAPersistence.cast(entityManagerFactory); CacheStatistics statistics = ojpamanagerfactory.getStoreCache().getStatistics(); String str = pjp.getTarget().toString(); log.info(str.substring(str.lastIndexOf(".")+1, str.lastIndexOf("@")) + " - " + pjp.getSignature().getName() + ": (Before call) Statistics [start time=" + statistics.start() + ",read count=" + statistics.getReadCount() + ",hit count=" + statistics.getHitCount() +",write count=" + statistics.getWriteCount() + ",total read count=" + statistics.getTotalReadCount() + ",total hit count=" + statistics.getTotalHitCount() +",total write count=" + statistics.getTotalWriteCount()); Object result = pjp.proceed(); log.info(str.substring(str.lastIndexOf(".")+1, str.lastIndexOf("@")) + " - " + pjp.getSignature().getName() + ": (After call) Statistics [start time=" + statistics.start() + ",read count=" + statistics.getReadCount() + ",hit count=" + statistics.getHitCount() +",write count=" + statistics.getWriteCount() + ",total read count=" + statistics.getTotalReadCount() + ",total hit count=" + statistics.getTotalHitCount() +",total write count=" + statistics.getTotalWriteCount()); return result; } } Once again, notice the similar Aspect structure to the previous Aspect which relies on an application's EntityManagerFactory. In this case, the logging output contains values that belong to the CacheStatistics OpenJPA class. Since OpenJPA does not enable statistics by default, you will need to add the following two properties to an application's persistence.xml file: The first property ensures statistics are gathered, while the second property is used to indicate the gathering of statistics take place on a single JVM. NOTE: The value "true(EnableStatistics=true)" also enables caching in addition to statistics. Since EclipseLink doesn't have any particular statistics class and relies on a Profiler to determine advanced metrics, the simplest way to obtain similar statistics to those of Hibernate and OpenJPA is through the Profiler itself. To active EclipseLink's Profiler you just need to add the following property to an application's persistence.xml file: . By doing so, the EclipseLink Profiler output's several metrics on each JPA query method execution as logging information. Now that you know how to obtain several metrics from all three JPA implementations and understand they will be obtained as fairly as possible for all three providers, it's time to put each JPA implementation to the test along with several performance techniques. JPQL queries, weaving and class transformations Lets start by making a query that retrieves data belonging to a pre-existing RDBMS table named "Master". The "Master" table contains over 17,000 records belonging to baseball players. To simplify matters, I will create a Java class named "Player" and map it to the "Master" table in order to retrieve the records as objects. Next, relying on the Spring framework's JpaTemplate functionality, I will setup a query to retrieve all "Player" objects, with the query taking the following form: getJpaTemplate().find("select e from Player e"); See the accompanying source code for more details on this last process. Next, I deploy the application using each of the three JPA implementations on Apache Tomcat, doing so separately, as well as starting and stopping the server on each deployment to ensure fair results. These are the results of doing so on a 64-bit Ubuntu-4GB RAM box, using Java 1.6: All player objects - 17,468 records Time Query Hibernate 3558 ms select player0_.lahmanID as lahmanID0_, player0_.nameFirst as nameFirst0_, player0_.nameLast as nameLast0_ from Master player0_ EclipseLink (Run-time weaver - Spring ReflectiveLoadTimeWeaver weaver ) 3215 ms SELECT lahmanID, nameLast, nameFirst FROM Master EclipseLink (Build-time weaving) 3571 ms SELECT lahmanID, nameLast, nameFirst FROM Master EclipseLink (No weaving) 3996 ms SELECT lahmanID, nameLast, nameFirst FROM Master OpenJPA (Build-time enhanced classes) 5998 ms SELECT t0.lahmanID, t0.nameFirst, t0.nameLast FROM Master t0 OpenJPA (Run-time enhanced classes- OpenJPA enhancer) 6136 ms SELECT t0.lahmanID, t0.nameFirst, t0.nameLast FROM Master t0 OpenJPA (Non enhanced classes) 7677 ms SELECT t0.lahmanID, t0.nameFirst, t0.nameLast FROM Master t0 As you can observe, the queries performed by each JPA implementation are fairly similar, with two of them using a shortcut notation (e.g. t0 and player0 for the table named 'Master'). This syntax variation though has minimal impact on performance, since directly querying an RDBMS using any of these notation variations shows identical results. However, the query times made through several JPA implementations using distinct parameters vary considerably. One important factor leading to this time difference is due to how each implementation handles JPA entities. Lets start with the OpenJPA implementation which had the poorest times. OpenJPA can execute an enhancement process on Java entities (e.g. in this case the 'Player' class). This enhancement process can be performed when the entities are built, at run-time or foregone altogether. As you can observe, foregoing entity enhancement altogether in OpenJPA produced the longest query times. Where as enhancing entities at either build-time or run-time produced relatively better results, with the former beating out the latter. By default, OpenJPA expects entities to be enhanced. This means you will either need to explicitly configure an application to support unenhanced classes by adding the following: ...property to an application's persistence.xml file or enhance classes at build-time or at run-time relying on the OpenJPA enhancer, otherwise an application relying on OpenJPA will throw an error. Given these OpenJPA results, the remaining OpenJPA tests will be based on build-time enhanced entity classes. For more on the topic of OpenJPA enhancement, refer to the OpenJPA documentation in addition to consulting the accompanying source code for this article. You may be wondering what exactly constitutes OpenJPA enhancement ? OpenJPA entity enhancement is a processing step applied to the bytecode generated by the Java compiler which adds JPA specific instructions to provide optimal runtime performance, these instructions can include things like flexible lazy loading and dirty read tracking. So why doesn't Hibernate or EclipseLink enhance entities ? In short, Hibernate and EclipseLink also enhance JPA entites, they just don't outright call it 'enhancement'. EclipseLink calls this 'enhancement' process by the more technical term: weaving. Similar to OpenJPA's enhancement process, weaving in EclipseLink can take place at either build-time (a.k.a. static weaving), run-time or forgone altogether. As you can observe in the results, all of EclipseLink's tests present smaller variations compared to OpenJPA. The longest EclipseLink variation involved not using weaving. If you think about it, this is rather logical given that the purpose of weaving consists of altering Java byte code for the purpose of adding optimized JPA instructions that include lazy loading, change tracking, fetch groups and internal optimizations. For the EclipseLink tests using weaving, both build-time and run-time weaving present better results. For build-time weaving, I used EclipseLink's library along with an Apache Ant task, where as for run-time weaving, I used the Spring framework's ReflectiveLoadTimeWeaver. I can only assume the slightly better performance of using run-time weaving over build-time weaving in EclipseLink was due to the fact of using a weaver integrated with the Spring framework, which in turn could result in better JPA optimizations designed for Spring applications. Nevertheless, considering the test result of forgoing weaving altogether, weaving does not appear to be a major performance impact when using EclipseLink, ceteris paribus. By default, EclipseLink expects run-time weaving to be enabled, otherwise you will receive an error in the form 'Cannot apply class transformer without LoadTimeWeaver specified'. This means that for cases using build-time weaving or no weaving at all, you will need to explicitly indicate this behavior. In order to disable EclipseLink weaving you will need to either configure an application's EntityManagerFactory Spring bean with: ... or add the .... ...property to an application's persistence.xml file. To indicate an application's entities are built using build-time weaving, substitute the previous property's "false" value with "static". To configure the default run-time weaver expected by EclipseLink, add the following: ...property to an application's EntityManagerFactory Spring bean. Given these EclipseLink results, the remaining EclipseLink tests will be based on run-time weaving provided by the Spring framework. For more on the topic of EclipseLink weaving, refer to the EclipseLink documentation at http://wiki.eclipse.org/Introduction_to_EclipseLink_Application_Development_(ELUG)#Using_Weaving, in addition to consulting the accompanying source code for this article. Hibernate doesn't require neither enhancing JPA entities or weaving. For this reason, there is only one test result. This not only makes Hibernate simpler to setup, but judging by its only test result -- which clock's in at second place with respect to all other tests -- Hibernate's performance ranks high compared to its counterparts. However, in what I would consider Hibernate's equivalent to OpenJPA's enhancement process or EclipseLink's weaving, you will find a series of Hibernate properties. For example, Hibernate has properties such as hibernate.default_batch_fetch_size designed to optimize lazy loading. As you might recall, among the purposes of both OpenJPA's enhancement process and EclipseLink's weaving are the optimization of lazy loading. So where as OpenJPA and EclipseLink require a separate and monolithic step -- at build-time or run-time -- to achieve JPA optimization techniques, Hibernate falls back to the use of granular properties specified in an application's persistence.xml file. Nevertheless, given that Hibernate's default behavior proved to be on par with the best query times, I didn't feel a need to further explore with these Hibernate properties. To get another sense of the times and mapping procedures of each JPA implementation, I will make more selective queries based on a Player object's first name and last name. These are the results of performing a query for all Player objects whose first name is John and a query for all Player objects whose last name in Smith. All player objects whose first name is John - 472 records Time Query EclipseLink 1265 ms SELECT lahmanID, nameLast, nameFirst FROM Master WHERE (nameFirst = ?) Hibernate 613 ms select player0_.lahmanID as lahmanID0_, player0_.nameFirst as nameFirst0_, player0_.nameLast as nameLast0_ from Master player0_ where player0_.nameFirst=? OpenJPA 1643 ms SELECT t0.lahmanID, t0.nameFirst, t0.nameLast FROM Master t0 WHERE (t0.nameFirst = ?) [params=?] All player objects whose last name is Smith - 146 records Time Query EclipseLink 986 ms SELECT lahmanID, nameLast, nameFirst FROM Master WHERE (nameLastt = ?) Hibernate 537 ms select player0_.lahmanID as lahmanID0_, player0_.nameFirst as nameFirst0_, player0_.nameLast as nameLast0_ from Master player0_ where player0_.nameLast=? OpenJPA 1452 ms SELECT t0.lahmanID, t0.nameFirst, t0.nameLast FROM Master t0 WHERE (t0.nameLast = ?) [params=?] These test results tell a slightly different story,with all three JPA implementations presenting substantial time differences amongst one another. At a lower record count, Hibernate's out-of-the-box configuration resulted in almost twice as fast queries as its closest competitor and almost three times faster queries than its other competitor. To get an even broader sense of the times and mapping procedures of each JPA implementation, I will make a query on a single Player object based on its id. These are the results of performing such a query. Single player object whose ID is 777- 1 record Time Query EclipseLink 521 ms SELECT lahmanID, nameLast, nameFirst FROM Master WHERE (lahmanID = ?) Hibernate 157 ms select player0_.lahmanID as lahmanID0_0_, player0_.nameFirst as nameFirst0_0_, player0_.nameLast as nameLast0_0_ from Master player0_ where player0_.lahmanID=? OpenJPA 1052 ms SELECT t0.nameFirst, t0.nameLast FROM Master t0 WHERE t0.lahmanID = ? [params=?] With the exception of the faster query times -- due to it being a query for a single Player object -- the times between JPA implementations are practically in proportion to the queries used for extracting multiple Player objects by first and last name. This will do it as far as test queries are concerned. However, a word of caution is in order when discussing these topics on optimization/enhancement/weaving. Even though the previous tests consisted of querying over 17,000 records and confirm clear advantages of using one provider and technique over another, they are still one dimensional, since they're based on read operations performed on a single object type and a single RDBMS table. JPA can perform a large array of operations that also include updating, writing and deleting RDBMS records, not to mention the execution of more elaborate queries that can span multiple objects and tables. In addition, RDBMS themselves can have influencing factors (e.g. indexes) over JPA query times. So all this said, it's not too far fetched to think the use of OpenJPA entity enhancement, EclipseLink weaving or Hibernate properties, could have varying degrees -- either beneficial or detrimental -- depending on the queries (i.e. multi-table, multi-object) and type of JPA operation (i.e. read, write, update, delete) involved. Next, I will describe one of the most popular techniques used to boost performance in JPA applications. Caches A cache allows data to remain closer to an application's tier without constantly polling an RDBMS for the same data. I entitled the section in plural -- caches -- because there can be several caches involved in an application using JPA. This of course doesn't mean you have to configure or use all the caches provided by an application relying on JPA, but properly configuring caches can go a long way toward enhancing an application's JPA performance. So lets start by analyzing what it's each JPA implementation offers in its out-of-the-box state in terms of caching. The following table illustrates tests done by simply invoking the previous JPA queries for a second and third consecutive time, without stopping the server. Note that the same process of deploying a single application at once was used, in addition to the server being re-started on each set of tests. Query / Implementation EclipseLink Hibernate OpenJPA All records (1st time) 3215 ms 3558 ms 5998 ms All records (2nd time) 507 ms 272 ms 521 ms All records (3rd time) 439 ms 218 ms 263 ms First name (1st time) 1265 ms 613 ms 1643 ms First name (2nd time) 151 ms 115 ms 239 ms First name (3rd time) 154 ms 101 ms 227 ms Last name (1st time) 986 ms 537 ms 1452 ms Last name (2nd time) 41 ms 41 ms 112 ms Last name (3rd time) 65 ms 38 ms 117 ms By ID (1st time) 521 ms 157 ms 1052 ms By ID (2nd time) 1 ms 6 ms 3 ms By ID (3rd time) 1 ms 3 ms 3 ms As you can observe, on both the second and third invocation all the queries show substantial improvements with respect to the first invocation. The primary cause for these improvements is unequivocally due to the use of a cache. But what type of cache exactly ? Could it be an RDBMS's own caching engine ? JPA ? Spring ? Or some other variation ?. In order to shed some light on cache usage, the following table illustrates the cache statistics generated on each of the previous JPA queries. Query / Impleme)ntation EclipseLink Hibernate OpenJPA All records (2nd time) number of objects=17468, total time=506, local time=506, row fetch=65, object building=328, cache=112, sql execute=47, objects/second=34521, sessions opened=2, sessions closed=2, connections obtained=2, statements prepared=2, statements closed=2, second level cache puts=0, second level cache hits=0, second level cache misses=0, entities loaded=34936, queries executed to database=2, query cache puts=0, query cache hits=0, query cache misses=0 N/A All records (3rd time) number of objects=17468, total time=435, local time=435, profiling time=1, row fetch=28, object building=323, cache=106, logging=1, sql execute=27, objects/second=40156, sessions opened=3, sessions closed=3, connections obtained=3, statements prepared=3, statements closed=3, second level cache puts=0, second level cache hits=0, second level cache misses=0, entities loaded=52404, queries executed to database=3, query cache puts=0, query cache hits=0, query cache misses=0 N/A First name (2nd time) number of objects=472, total time=148, local time=148, row fetch=27, object building=106, cache=7, logging=1, sql execute=3, objects/second=3189, sessions opened=2, sessions closed=2, connections obtained=2, statements prepared=2, statements closed=2, second level cache puts=0, second level cache hits=0, second level cache misses=0, entities loaded=944, queries executed to database=2, query cache puts=0, query cache hits=0, query cache misses=0 N/A First name (3rd time) number of objects=472, total time=152, local time=152, row fetch=20, object building=121, cache=7, sql execute=3, objects/second=3105, sessions opened=3, sessions closed=3 connections obtained=3, statements prepared=3, statements closed=3, second level cache puts=0, second level cache hits=0, second level cache misses=0, entities loaded=1416, queries executed to database=3, query cache puts=0, query cache hits=0, query cache misses=0 N/A Last name (2nd time) number of objects=146, total time=40, local time=40, row fetch=7, object building=27, cache=2, logging=1, sql execute=3, objects/second=3650, sessions opened=2, sessions closed=2, connections obtained=2, statements prepared=2, statements closed=2, second level cache puts=0, second level cache hits=0, second level cache misses=0, entities loaded=292, queries executed to database=2, query cache puts=0, query cache hits=0, query cache misses=0 N/A Last name (3rd time) number of objects=146, total time=63, local time=63, profiling time=1, row fetch=6, object building=19, cache=5, sql prepare=1, sql execute=23, objects/second=2317, sessions opened=3, sessions closed=3, connections obtained=3, statements prepared=3, statements closed=3, second level cache puts=0, second level cache hits=0, second level cache misses=0, entities loaded=438 queries executed to database=3, query cache puts=0, query cache hits=0, query cache misses=0 N/A By ID (2nd time) number of objects=1, total time=1, local time=1, time/object=1, objects/second=1000, sessions opened=2, sessions closed=2, connections obtained=2, statements prepared=2, statements closed=2, second level cache puts=0, second level cache hits=0, second level cache misses=0, entities loaded=2, queries executed to database=0, query cache puts=0, query cache hits=0, query cache misses=0 N/A By ID (3rd time) number of objects=1, total time=1, local time=1, time/object=1, objects/second=1000, sessions opened=3, sessions closed=3, connections obtained=3, statements prepared=3, statements closed=3, second level cache puts=0, second level cache hits=0, second level cache misses=0, entities loaded=3, queries executed to database=0, query cache puts=0, query cache hits=0, query cache misses=0 N/A Notice the statistics generated by each JPA implementation are different. EclipseLink reports a single cache statistic, OpenJPA doesn't even report statistics unless a cache is enabled -- see previous section on metrics for details on this behavior -- and Hibernate reports two cache related statistics: second level cache and query cache. At this juncture, if you look at the test results and statistics for the second and third invocation, something won't add up. How is it that OpenJPA's test results came out faster when caching is disabled by default ? An how about Hibernate returning 0's on its cache related statistics, even when its test results came out faster ? The reason for this performance increase is due to RDBMS caching. On the first query, the RDBMS needs to read data from its own file system (i.e. perform an I/O operation), on subsequent requests the data is present in RDBMS memory (i.e. its cache) making the entire JPA query much faster. A closer look at the Hibernate statistics field 'queries executed to the database' can confirm this. Notice that on every second query it shows 2 and on every third query it shows 3, meaning the data was read directly from the database. NOTE: The only exception to this occurs when a query is made on a single entity (i.e. by id), I will address this shortly. Next, lets start breaking down the caches you will encounter when using JPA applications. The JPA 2.0 standard defines two types of caches: A first level cache and a second level cache. The first level cache or EntityManager cache is used to properly handle JPA transactions. A first level cache only exist for the duration of the EntityManager. With the exception of long lived operations performed against a RDBMS, JPA EntityManager's are short lived and are created & destroyed per request or per transaction. In this case, given the nature of the queries, first level caches are cleared on every query. A second level cache on the other hand is a broader cache that can be used across transactions and users. This makes a JPA second level cache more powerful, since it can avoid constantly polling an RDBMS for the same data. But even though the JPA 2.0 standard now addresses second level cache features, this was not the case in JPA 1.0. In the 1.0 version of the JPA standard only a first level cache was addressed, leaving the door completely open on the topic of a second level cache. This created a fragmented approach to caching in JPA implementations, which even now as JPA 2.0 compliant implementations emerge, some non-standard features continue to be part of certain implementations given the value they provide to JPA caching in general. So as I move forward, bear in mind that just like previous JPA topics, each JPA implementation can have its own particular way of dealing with second level caching. I will start with OpenJPA, which has the least amount of proprietary caching options. To enable OpenJPA caching (i.e. second level caching) you need to declare the following two properties in an application's persistence.xml file: The first property ensures caching and statistics are activated, while the second property is used to indicate caching take place on a single JVM. The following results and statistics were obtained with OpenJPA's second level cache enabled. Query with OpenJPA caching Time Statistics Time without statistics All records (2nd time) 420 ms read count=34936, hit count=17468, write count=17468, total read count=34936, total hit count=17468, total write count=17468 347 ms All records (3rd time) 254 ms read count=52404, hit count=34936, write count=17468, total read count=52404, total hit count=34936, total write count=17468 230 ms First name (2nd time) 125 ms read count=944, hit count=472, write count=472, total read count=944, total hit count=472, total write count=472 127 ms First name (3rd time) 114 ms read count=1416, hit count=944, write count=472, total read count=1416, total hit count=944, total write count=472 132 ms Last name (2nd time) 63 ms read count=292, hit count=146, write count=146, total read count=292, total hit count=146, total write count=146 53 ms Last name (3rd time) 49 ms read count=438, hit count=292, write count=146, total read count=438, total hit count=292, total write count=146 50 ms By ID (2nd time) 5 ms read count=2, hit count=1, write count=1, total read count=2, total hit count=1, total write count=1 1 ms By ID (3rd time) 4 ms read count=3, hit count=2, write count=1, total read count=3, total hit count=2, total write count=1 1 ms As these test results illustrate, executing subsequent JPA queries with OpenJPA's second level cache produce superior results. Another important behavior illustrated in some of these test cases is that by simply disabling statistics -- and still using the second level cache -- query times improve even more. The OpenJPA statistics also demonstrate how the cache is being used. Notice that on each subsequent query the statistics field 'hit count' is duplicated, which means data is being read from the cache (i.e. a hit). Also notice the statistics field 'write count' remains static, which means data is only written once from the RDBMS to the cache. This is pretty basic functionality for a second level cache. On certain occasions a need may arise to interact directly with a cache. These interactions can range from prohibiting an entity from being cached, assigning a particular amount of memory to a cache, forcing an entity to always be cached, flushing all the data contained in a cache, or even plugging-in a third party caching solution to provide a more robust strategy, among other things. The JPA 2.0 standard provides a very basic feature set in terms of second level caching through javax.persistence.Cache. Upon consulting this interface, you'll realize it only provides four methods charged with verifying the presence of entities and evicting them. This feature set not only proves to be limited, but also cumbersome since it can only be leveraged programmatically (i.e. through an API). In this sense, and as I've already mentioned, JPA implementations have provided a series of features ranging from persistence.xml properties to Java annotations related to second level caching. OpenJPA offers several of these second level caching features, including a separate and supplemental cache called a 'query cache' which can further improve JPA performance. For such cases, I will point you directly to OpenJPA's cache documentation available at http://openjpa.apache.org/builds/apache-openjpa-2.1.0-SNAPSHOT/docs/manual/ref_guide_caching.html#ref_guide_cache_query so you can try these parameters for yourself on the accompanying application source code. Hibernate just like OpenJPA has its second level cache disabled. To enable Hibernate's second level cache you need to add the following properties to an application's persistence.xml file: Its worth mentioning that Hibernate has integral support for other second level caches. The previous properties displayed how to enable the HashtableCacheProvider cache -- the simplest of the integral second level caches -- but Hibernate also provides support for five additional caches, which include: EHCache, OSCache, SwarmCache, JBoss cache 1 and JBoss cache 2, all of which provide distinct features, albeit require additional configuration. Besides these properties, Hibernate also requires that each JPA entity be declared with a caching strategy. In this case, since the Person entity is read only, a caching strategy like the following would be used: Similar to OpenJPA, Hibernate also offers several second level caching features through proprietary annotations and configurations, as well as support for the separate and supplemental cache called a 'query cache' which can further improve JPA performance. For such cases, I will also point you directly to Hibernate's cache documentation available at http://docs.jboss.org/hibernate/core/3.3/reference/en/html/performance.html#performance-cache so you can try these parameters for yourself on the accompanying application source code. Unlike OpenJPA and Hibernate, EclipseLink's second level cache is enabled by default, therefore there is no need to provide any additional configuration. However, similar to its counterparts, EclipseLink also has a series of proprietary second level cache features which can enhance JPA performance. You can find more information on these features by consulting EclipseLink's cache documentation available at: http://wiki.eclipse.org/Introduction_to_Cache_(ELUG) With this we bring our discussion on object relational mapping performance with JPA to a close. I hope you found the various tests and metrics presented here a helpful aid in making decisions about your own JPA applications. In addition, don't forget you can rely on the accompanying source code to try out several JPA variations more ad-hoc to your circumstances. About the author Daniel Rubio is an independent technology consultant specializing in enterprise and web-based software. He blogs regularly on these and other software areas at http://www.webforefront.com. He's also authored and co-authored three books on Java technology. Source code/Application installation * Install MySQL on your workstation (Tested on MySQL 5.1.37-64 bits) - http://dev.mysql.com/downloads/ * Install data set on MySQL - Go to http://www.baseball-databank.org/ and click on the link titled 'Database in MySQL form'. This will download a zipped file with a series of MySQL data structures containing baseball statistics. First create a MySQL database to host the data using the command: 'mysqladmin -p create jpaperformance'. This will create a database named 'jpaperformance'. Next, load the baseball statistics using the following command: 'mysql -p -D jpaperformance < BDB-sql-2009-11-25.sql' where 'BDB-sql-2009-11.25.sql' represents the unzipped SQL script obtained by extracting the zip file you dowloaded. * Create JPA application WARs - The download includes source code, library dependencies and an Ant build file. This includes all three JPA implementations Hibernate 3.5.3, EclipseLink 2.1 and OpenJPA 2.1. To build the JPA Hibernate WAR - ant hibernate To build the JPA EclipseLink WAR - ant eclipselink To build the JPA OpenJPA WAR - ant openjpa All builds are placed under the dist/ directories. * Deploy to Tomcat 6.0.26 - Copy the MySQL Java driver and Spring Tomcat Weaver -- included in the download directory 'tomcat_jar_deps' -- to Apache Tomcat's /lib directory. - Copy each JPA application WAR to Apache Tomcat's /webapps directory, as needed. * Deployment URL's http://localhost:8080/hibernate/hibernate/home ( Query all Player objects ) http://localhost:8080/eclipselink/eclipselink/home ( Query all Player objects ) http://localhost:8080/openjpa/openjpa/home ( Query all Player objects ) http://localhost:8080/hibernate/hibernate/firstname/ ( Query Player objects by first name) http://localhost:8080/eclipselink/eclipselink/firstname/ ( Query Player objects by first name) http://localhost:8080/openjpa/openjpa/firstname/ ( Query Player objects by first name) http://localhost:8080/hibernate/hibernate/lastname/ ( Query Player objects by last name) http://localhost:8080/eclipselink/eclipselink/lastname/ ( Query Player objects by last name) http://localhost:8080/openjpa/openjpa/lastname/ ( Query Player objects by last name) http://localhost:8080/hibernate/hibernate/playerid/ (Query Player by id) http://localhost:8080/eclipselink/eclipselink/playerid/ ( Query Player by id) http://localhost:8080/openjpa/openjpa/playerid/ ( Query Player by id)

The intent of the Metadata Mapping pattern is to express implementation details, related a particular domain and Domain Model, as metadata of a general purpose library. In the sense intended here, metadata is related to the persistence operations (transferring objects back and forth from a database). These metadata is usually fed to a general purpose object-relational mapper. Technically the term metadata is plural (of metadatum, data about data), but it is commonly used as an uncountable noun. Why expressing metadata Object-relational mapping is a difficult task to automate, prone to lots of potential bugs and undefined behaviors; expressing the domain-related peculiarities as metadata means that you are able to code only one ORM, and not have to repeat the same work in many custom Data Mappers, which are very boring to write and can't be transported out a specific application. Custom Data Mappers were a cleaner solution for Domain Models with regard to employing Active Records, and they are advocated for example in Zend Framework books like Keith's Pope one. They are finally becoming obsolete thanks to the power of a declarative approach like this pattern, which tools like Doctrine 2 are based on. Historicacally, Hibernate from JBoss was the first Data Mapper implemented as a generic ORM (it is a Java product). Doctrine 2 is the most famous PHP implementation, and it is in beta at the time of this writing. The metadata we'd like to tell to an ORM are for example: which classes should be persisted at all. Optional names for the tables (it can use the class names.) Which fields form the primary key. The types of the different columns, particularly important in a loosely typed language like PHP. Which collaborators have to be persisted and via what means: foreign keys and additional association tables. The metadata should usually not consist of code: non-standard behavior shouldn't be contained in them, as in general all the behavior like ineritance strategies and conversion of relationships is extracted in the generic ORM. Thus there are different formats we can use in place of PHP code: XML, annotations, YAML, INI... Different approaches There are two approaches to Metadata Mapping pattern, described by Fowler in his original book. The first one is code generation: the metadata is processed to generate the source code of the mapping classes, for example a Data Mapper for every entity or aggregate root of your model (one for User, one for BlogPost, and so on). The ORM would theoretically not be necessary in production if the generation is complete enough. Doctrine 1 used this approach in part, but it generated also the PHP code of the domain model itself from the Yaml mapping, as subclasses of Doctrine_Record. Still, Doctrine 1 was necessary to instantiate those classes and the solution wasn't so clean. Doctrine 2 is very different in architecture and goals. The second approach is called reflective program, and consists in interpreting the mapping at runtime in the ORM's code, to open up correctly the objects via reflection (or a standard interface) and putting them in the database. The converse can happen: objects can be recreated from the union of metadata and database tables. How it is used The reflective solution is the common one nowadays, and Doctrine 2 borrows it from Hibernate in its own design. Reflection is used to access the private fields to persist. Some critics point out speed problems of this technique, but keep in mind that your ORM is communicating with an external process or database machine at the same time of using reflection: it probably won't count much in the benchmark. Doctrine 2 however takes optimization seriously to the point that metadata internal classes (accessed very often) present an Api with public properties instead of methods to avoid every overhead in a crucial part (hydration of objects with data retrieved from the database). An advantage of generated code is that it would be easier to debug, but it is usually a pain to maintain: every time you evolve or refactor a domain class you have to regenerate the Mapper classes. You can't customize this code either, because you would lost your changes at the regeneration time. Advantages and (few) disadvantages Of course we lose some expressiveness by specifying metadata instead of a programmatical behavior like the source code of a custom Data Mapper. But we gain very much: a fully tested ORM, like Doctrine 2 in the PHP case, with only some lines of added metadata to keep in sync with the rest of the code base. Declarative approaches trading off completeness of functionalities (the absent ones are not used very often anyway) for developers time. But there are other advantages, such as the generation (and migration) of the database schema based on the metadata, and also of the proxy classes. Ideally, the metadata mapping is the only point of strong coupling of your Domain Model with an external adapter, the ORM. It is of course part of the infrastructure, so keep it under version control along with the code! Adding and removing fields or relationships, changing keys or refactoring is much easier because you do it declaratively instead of refactoring a specific mapper class. Note that automated refactoring tools are not to be trusted here: for example they usually ignore the mapping when you change a field name. So grep is your best ally. Examples The sample code of this article will present the different ways of specifying metadata for Doctrine 2, the most high-tech PHP ORM. The performance of the different methods are equivalent, since the metadata are read only one time into native PHP objects and then cached. Metadata is a vast subject since all the different persistence implementations have to be driven by it, but we will look more at the types of metadata specification we can use instead of all the different metadata instances, which are best described in conjunction with the single features (for example, the inheritance patterns articles contain the description of the metadata related to subclassing.) The simplest way to express metadata mapping in Doctrine 2 is via annotations, embedded in the docblocks and ignored from anything but the ORM: Don't be alarmed by the size: this mapping does much more than the annotations example's one. A third way to specify metadata is via YAML, a format widely used in symfony-related software: --- # Doctrine.Tests.ORM.Mapping.User.dcm.yml Doctrine\Tests\ORM\Mapping\User: type: entity table: cms_users id: id: type: integer generator: strategy: AUTO fields: name: type: string length: 50 oneToOne: address: targetEntity: Address joinColumn: name: address_id referencedColumnName: id oneToMany: phonenumbers: targetEntity: Phonenumber mappedBy: user cascade: cascadePersist manyToMany: groups: targetEntity: Group joinTable: name: cms_users_groups joinColumns: user_id: referencedColumnName: id inverseJoinColumns: group_id: referencedColumnName: id lifecycleCallbacks: prePersist: [ doStuffOnPrePersist, doOtherStuffOnPrePersistToo ] postPersist: [ doStuffOnPostPersist ]

A few weeks ago, a co-worker sent me interesting email after talking with the Zoompf CEO at JSConf. One interesting tip mentioned was how we querystring the version on our scripts and css. Apparently this doesn't always cache the way we expected it would (some proxies will never cache an asset if it has a querystring). The recommendation is to rev the filename itself. This article explains how we implemented a "cache busting" system in our application with Maven and the UrlRewriteFilter. We originally used querystring in our implementation, but switched to filenames after reading Souders' recommendation. That part was figured out by my esteemed colleague Noah Paci. Our Requirements Make the URL include a version number for each static asset URL (JS, CSS and SWF) that serves to expire a client's cache of the asset. Insert the version number into the application so the version number can be included in the URL. Use a random version number when in development mode (based on running without a packaged war) so that developers will not need to clear their browser cache when making changes to static resources. The random version number should match the production version number formats which is currently: x.y-SNAPSHOT-revisionNumber When running in production, the version number/cachebust is computed once (when a Filter is initialized). In development, a new cachebust is computed on each request. In our app, we're using Maven, Spring and JSP, but the latter two don't really matter for the purposes of this discussion. Implementation Steps 1. First we added the buildnumber-maven-plugin to our project's pom.xml so the build number is calculated from SVN. org.codehaus.mojo buildnumber-maven-plugin 1.0-beta-4 validate create false false javasvn 2. Next we used the maven-war-plugin to add these values to our WAR's MANIFEST.MF file. maven-war-plugin 2.0.2 true ${project.version} ${buildNumber} ${timestamp} 3. Then we configured a Filter to read the values from this file on startup. If this file doesn't exist, a default version number of "1.0-SNAPSHOT-{random}" is used. Otherwise, the version is calculated as ${project.version}-${buildNumber}. private String buildNumber = null; ... @Override public void initFilterBean() throws ServletException { try { InputStream is = servletContext.getResourceAsStream("/META-INF/MANIFEST.MF"); if (is == null) { log.warn("META-INF/MANIFEST.MF not found."); } else { Manifest mf = new Manifest(); mf.read(is); Attributes atts = mf.getMainAttributes(); buildNumber = atts.getValue("Implementation-Version") + "-" + atts.getValue("Implementation-Build"); log.info("Application version set to: " + buildNumber); } } catch (IOException e) { log.error("I/O Exception reading manifest: " + e.getMessage()); } } ... // If there was a build number defined in the war, then use it for // the cache buster. Otherwise, assume we are in development mode // and use a random cache buster so developers don't have to clear // their browswer cache. requestVars.put("cachebust", buildNumber != null ? buildNumber : "1.0-SNAPSHOT-" + new Random().nextInt(100000)); 4. We then used the "cachebust" variable and appended it to static asset URLs as indicated below. The injection of /v/[CACHEBUSTINGSTRING]/(assets|compressed) eventually has to map back to the actual asset (that does not include the two first elements of the URI). The application must remove these two elements to map back to the actual asset. To do this, we use the UrlRewriteFilter. The UrlRewriteFilter is used (instead of Apache's mod_rewrite) so when developers run locally (using mvn jetty:run) they don't have to configure Apache. 5. In our application, "/compressed/" is mapped to wro4j's WroFilter. In order to get UrlRewriteFilter and WroFilter to work with this setup, the WroFilter has to accept FORWARD and REQUEST dispatchers. rewriteFilter /* WebResourceOptimizer /compressed/* FORWARD REQUEST Once this was configured, we added the following rules to our urlrewrite.xml to allow rewriting of any assets or compressed resource request back to its "correct" URL. ^/v/[0-9A-Za-z_.\-]+/assets/(.*)$ /assets/$1 ^/v/[0-9A-Za-z_.\-]+/compressed/(.*)$ /compressed/$1 /compressed/** /compressed/$1 Of course, you can also do this in Apache. This is what it might look like in your vhost.d file: RewriteEngine on RewriteLogLevel 0! RewriteLog /srv/log/apache22/app_rewrite_log RewriteRule ^/v/[.A-Za-z0-9_-]+/assets/(.*) /assets/$1 [PT] RewriteRule ^/v/[.A-Za-z0-9_-]+/compressed/(.*) /compressed/$1 [PT] Whether it's a good idea to implement this in Apache or using the UrlRewriteFilter is up for debate. If we're able to do this with the UrlRewriteFilter, the benefit of doing this at all in Apache is questionable, especially since it creates a duplicate of code. From http://raibledesigns.com/rd/entry/versioning_static_assets_with_urlrewritefilter

The Identity Map pattern is a Map implementation related to a Data Mapper usage. A map in the computer science sense is also called dictionary, or associative array; although in PHP associative arrays are very powerful, this kind of Map can be implemented as an object to present a specific interface to client code. The purpose of an Identity Map in the Data Mapper context is to keep a list of all the references to the in-memory domain objects that has been reconstituted by the Data Mapper internal mechanism, or are somehow managed by the Data Mapper itself (for example they have been scheduled for persistence). The Identity Map solves the problem of multiple loading of objects, which leads to performance issues and inconsistencies like two different objects with different states (but whose identity is the same, since they have for example an equal user id) that has to be stored in the back end. Ideally has a reference to every single object of the domain (that contains state, and thus is managed by the Data Mapper instead of being created by infrastructure or domain factories), in practice it is an array of references to the loaded objects. PHP implementation In PHP, the Identity Map is not unique troughout the whole application, but it is an object whose scope is limited to the single HTTP request (and so for example different requests have different Identity Maps which can become inconsistent with each other.) This limited scope, which is part of the nature of PHP and its scalable architecture, requires careful handling of objects that have been detached from the Data Mapper. In general, you can serialize or store in a cache domain objects for performance boost or simplification of business logic. You have, however, the obligation of reattaching a domain object to the Data Mapper with a special method (in Doctrine 2 EntityManager::merge()) to subsequently persist it, so that it can be reinserted in the Identity Map instead of being considered new or being duplicated. Remember that here duplication is more an issue of consistency than performance: a Data Mapper which accepts two different objects that points to the same place in the data store is not reliable. In fact, an Identity Map is a fundamental part of a non-naive Data Mapper: before recreating an entity the mapper looks for it in the Identity Map, to check if it is already available. Only if the object is not there, the Data Mapper creates a new one and inserts it in the Map for later reuse. Thus the Identity Map bridges the gap between the storage and the memory, keeping track of which parts of the object graph have been brought in memory and which are still on disks or external database machines, since we are forced by the technology to actually reconstitute a very small part of the application state in the form of objects (to be able to work on them). This approach is particularly suited to PHP's shared nothing mentality: there are other solutions for languages like Java and C#, like keeping the whole object graph in memory (some gigabytes) and dealing with persistence by taking a periodical snapshot of the graph, which is then freezed and stored on slower-but-larger memories like disks or SSD. In Doctrine 2 From the technical point of view, the Identity Map is an object or an associative array, with a single instance that exists for the entire request. This data structure is composed by the Entity Manager (the Facade of the Data Mapper) or by a Unit of Work, or even by some internal class of the Data Mapper. Even when an object is reconstituted as part of a query and not requested by its primary key, the loader class has to extract a unique identifier for the domain object and ask the Identity Map. In the sample code we will see at the end of this article, Doctrine 2 choice has been to keep the Identity Map as a private property (a multidimensional associative array) of the Unit of Work, which has a set of public methods available to access the Map to act as the unique Facade for the internal code. The tipical key used for the indexing is a combination of the class name of the domain object and of its unique identifier (usually the primary key used in storage, reduced to a serialized value if constituted by multiple fields.) This indexing implementation is generic enough to deal with most of the use cases, even with inheritance strategies. Another supplemental indexing is based on the spl_object_hash() function result, which returns a unique identifier for every in-memory object; this indexing is used to quickly check if an object originated from somewhere is in the Identity Map, without extracting its identifier and class name. The sample code is part of the Unit of Work of Doctrine 2. I cut all the aspects which did not involve its internal Identity Map as we have already described it in its own article. * @author Guilherme Blanco * @author Jonathan Wage * @author Roman Borschel * @internal This class contains highly performance-sensitive code. */ class UnitOfWork implements PropertyChangedListener { //... /** * The identity map that holds references to all managed entities that have * an identity. The entities are grouped by their class name. * Since all classes in a hierarchy must share the same identifier set, * we always take the root class name of the hierarchy. * * @var array */ private $_identityMap = array(); /** * Map of all identifiers of managed entities. * Keys are object ids (spl_object_hash). * * @var array */ private $_entityIdentifiers = array(); /** * INTERNAL: * Registers an entity in the identity map. * Note that entities in a hierarchy are registered with the class name of * the root entity. * * @ignore * @param object $entity The entity to register. * @return boolean TRUE if the registration was successful, FALSE if the identity of * the entity in question is already managed. */ public function addToIdentityMap($entity) { $classMetadata = $this->_em->getClassMetadata(get_class($entity)); $idHash = implode(' ', $this->_entityIdentifiers[spl_object_hash($entity)]); if ($idHash === '') { throw new \InvalidArgumentException("The given entity has no identity."); } $className = $classMetadata->rootEntityName; if (isset($this->_identityMap[$className][$idHash])) { return false; } $this->_identityMap[$className][$idHash] = $entity; if ($entity instanceof NotifyPropertyChanged) { $entity->addPropertyChangedListener($this); } return true; } /** * INTERNAL: * Removes an entity from the identity map. This effectively detaches the * entity from the persistence management of Doctrine. * * @ignore * @param object $entity * @return boolean */ public function removeFromIdentityMap($entity) { $oid = spl_object_hash($entity); $classMetadata = $this->_em->getClassMetadata(get_class($entity)); $idHash = implode(' ', $this->_entityIdentifiers[$oid]); if ($idHash === '') { throw new \InvalidArgumentException("The given entity has no identity."); } $className = $classMetadata->rootEntityName; if (isset($this->_identityMap[$className][$idHash])) { unset($this->_identityMap[$className][$idHash]); $this->_entityStates[$oid] = self::STATE_DETACHED; return true; } return false; } /** * Checks whether an entity is registered in the identity map of this UnitOfWork. * * @param object $entity * @return boolean */ public function isInIdentityMap($entity) { $oid = spl_object_hash($entity); if ( ! isset($this->_entityIdentifiers[$oid])) { return false; } $classMetadata = $this->_em->getClassMetadata(get_class($entity)); $idHash = implode(' ', $this->_entityIdentifiers[$oid]); if ($idHash === '') { return false; } return isset($this->_identityMap[$classMetadata->rootEntityName][$idHash]); } }

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/