This is the forth of a series of articles about Persistence with Spring. This article will focus on the configuration and implementation of the persistence layer with Spring 3.1, JPA and Spring Data. For a step by step introduction about setting up the Spring context using Java based configuration and the basic Maven pom for the project, see this article. The Persistence with Spring series: Part 1 – The Persistence Layer with Spring 3.1 and Hibernate Part 3 – The Persistence Layer with Spring 3.1 and JPA Part 5 – Transaction configuration with JPA and Spring 3.1 No More DAO implementations As I discussed in a previous post, the DAO layer usually consists of a lot of boilerplate code that can and should be simplified. The advantages of such a simplification are many fold: a decrease in the number of artifacts that need to be defined and maintained, simplification and consistency of data access patterns and consistency of configuration. Spring Data takes this simplification one step forward and makes it possible to remove the DAO implementations entirely – the interface of the DAO is now the only artifact that need to be explicitly defined. The Spring Data managed DAO In order to start leveraging the Spring Data programming model with JPA, a DAO interface needs to extend the JPA specific Repository interface - JpaRepository – in Spring’s interface hierarchy. This will enable Spring Data to find this interface and automatically create an implementation for it. Also, by extending the interface we get most if not all relevant CRUD generic methods for standard data access available in the DAO. Defining custom access method and queries As discussed, by implementing one of the Repository interfaces, the DAO will already have some basic CRUD methods (and queries) defined and implemented. To define more specific access methods, Spring JPA supports quite a few options – you can either simply define a new method in the interface, or you can provide the actual JPQ query by using the @Query annotation. A third option to define custom queries is to make use of JPA Named Queries, but this has the disadvantage that it either involves XML or burdening the domain class with the queries. In addition to these, Spring Data introduces a more flexible and convenient API, similar to the JPA Criteria API, only more readable and reusable. The advantages of this API will become more pronounced when dealing with a large number of fixed queries that could potentially be more concisely expressed through a smaller number of reusable blocks that keep occurring in different combinations. Automatic Custom Queries When Spring Data creates a new Repository implementation, it analyzes all the methods defined by the interfaces and tries to automatically generate queries from the method name. While this has limitations, it is a very powerful and elegant way of defining new custom access methods with very little effort. For example, if the managed entity has a name field (and the Java Bean standard getter and setter for that field), defining the findByName method in the DAO interface will automatically generate the correct query: public interface IFooDAO extends JpaRepository< Foo, Long >{ Foo findByName( final String name ); } This is a relatively simple example; a much larger set of keywords is supported by query creation mechanism. In the case that the parser cannot match the property with the domain object field, the following exception is thrown: java.lang.IllegalArgumentException: No property nam found for type class org.rest.model.Foo Manual Custom Queries In addition to deriving the query from the method name, a custom query can be manually specified with the method level @Query annotation. For even more fine grained control over the creation of queries, such as using named parameters or modifying existing queries, the reference is a good place to start. Spring Data transaction configuration The actual implementation of the Spring Data managed DAO – SimpleJpaRepository – uses annotations to define and configure transactions. A read only @Transactional annotation is used at the class level, which is then overridden for the non read-only methods. The rest of the transaction semantics are default, but these can be easily overridden manually per method. Exception Translation without the template One of the responsibilities of Spring ORM templates (JpaTemplate, HibernateTemplate) is exception translation – translating JPA exceptions – which tie the API to JPA – to Spring’s DataAccessException hierarchy. Without the template to do that, exception translation can still be enabled by annotating the DAOs with the @Repository annotation. That, coupled with a Spring bean postprocessor will advice all @Repository beans with all the implementations of PersistenceExceptionTranslator found in the Container – to provide exception translation without using the template. The fact that exception translation is indeed active can easily be verified with an integration test: @Test( expected = DataAccessException.class ) public void whenAUniqueConstraintIsBroken_thenSpringSpecificExceptionIsThrown(){ String name = "randomName"; this.service.save( new Foo( name ) ); this.service.save( new Foo( name ) ); } Exception translation is done through proxies; in order for Spring to be able to create proxies around the DAO classes, these must not be declared final. Spring Data Configuration To activate the Spring JPA repository support, the jpa namespace is defined and used to specify the package where to DAO interfaces are located: At this point, there is no equivalent Java based configuration – support for it is however in the works. The Spring Java or XML configuration The JPA configuration with Spring 3.1 has already been carefully discussed in the previous article of this series. Spring Data also takes advantage of the Spring support for the JPA @PersistenceContext annotation which it uses to wire the EntityManager into the Spring factory bean responsible with creating the actual DAO implementations – JpaRepositoryFactoryBean. In addition to the already discussed configuration, there is one last missing piece – including the Spring Data XML configuration in the overall persistence configuration: @Configuration @EnableTransactionManagement @ImportResource( "classpath*:*springDataConfig.xml" ) public class PersistenceJPAConfig{ ... } The Maven configuration In addition to the Maven configuration for JPA defined in a previous article, the spring-data-jpa dependency is addeed: org.springframework.data spring-data-jpa 1.0.2.RELEASE Conclusion This article covered the configuration and implementation of the persistence layer with Spring 3.1, JPA 2 and Spring JPA (part of the Spring Data umbrella project), using both XML and Java based configuration. The various method of defining more advanced custom queries are discussed, as well as configuration with the new jpa namespace and transactional semantics. The final result is a new and elegant take on data access with Spring, with almost no actual implementation work. You can check out the full implementation in the github project. From the originalThe Persistence Layer with Spring Data JPA of the Persistence with Spring series

In my previous post we saw how to compress data consisting of very long runs of repeating elements. This type of compression is known as “run-length encoding” and can be very handy when transferring data with no loss. The problem is that the data must follow a specific format. Thus the string “aaaaaaaabbbbbbbb” can be compressed as “a8b8”. Now a string with length 16 can be compressed as a string with length 4, which is 25% of its initial length without loosing any information. There will be a problem in case the characters (elements) were dispersed in a different way. What would happen if the characters are the same, but they don’t form long runs? What if the string was “abababababababab”? The same length, the same characters, but we cannot use run-length encoding! Indeed using this algorithm we’ll get at best the same string. In this case, however, we can see another fact. The string consists of too many repeating elements, although not arranged one after another. We can compress this string with a bitmap. This means that we can save the positions of the occurrences of a given element with a sequence of bits, which can be easily converted into a decimal value. In the example above the string “abababababababab” can be compressed as “1010101010101010”, which is 43690 in decimals, and even better AAAA in hexadecimal. Thus the long string can be compressed. When decompressing (decoding) the message we can convert again from decimal/hexadecimal into binary and match the occurrences of the characters. Well, the example above is too simple, but let’s say only one of the characters is repeating and the rest of the string consists of different characters like this: “abacadaeafagahai”. Then we can use bitmap only for the character “a” – “1010101010101010” and compress it as “AAAA bcdefghi”. As you can see all the example strings are exactly 16 characters and that is a limitation. To use bitmaps with variable length of the data is a bit tricky and it is not always easy (if possible) to decompress it. Basically bitmap compression saves the positions of an element that is repeated very often in the message! In the other hand bitmap compression is not only applicable on strings. We can compress also arrays, objects or any kind of data. The example from my previous post is very suitable. Then we had to transfer a large array from a server to the client (browser) using JSON. The data then was very suitable for “run-length encoding”. Now let’s assume we have the same data – a set of different years, which this time are dispersed in a different way. $data = array( 0 => 1991, 1 => 1992, 2 => 1993, 3 => 1994, 4 => 1991, 5 => 1992, 6 => 1993, 7 => 1992, 8 => 1991, 9 => 1991, 10 => 1991, 11 => 1992, 12 => 1992, 13 => 1991, 14 => 1991, 15 => 1992, ... ); The JSON will encoded message will be the following (a simple but yet very large javascript array). [1991,1992,1993,1994,1991,1992,1993,1992,1991,1991,1991,1992,1992,1991,1991,1992, ...] However if we use bitmap compression we’ll get a “shorter” array. $data = array( 0 => array(1991, '1000100011100110'), 1 => array(1992, '0100010100011001'), 2 => array(1993, '0010001000000000'), 3 => array(1994, '0001000000000000'), ); Now the JSON is: [[1991,"1000100011100110"],[1992,"0100010100011001"],[1993,"0010001000000000"],[1994,"0001000000000000"]] It is obvious that the compression ratio is getting better and better as the uncompressed data grows. In fact, most of us know bitmap compression from images, because this algorithm is largely used for image compression. We can imagine how successful it can be when compressing black and white images (as black and white can be represented as 0 and 1s). Actually it is used for more than two colors (256 for instance) and again the level of compression is very high. Implementation The following implementation on PHP aims only to illustrate the bitmap compressing algorithm. As we know this algorithm can be applicable for any kind of data structures. // too many repeating "a" characters $msg = 'aazahalavaatalawacamaahakafaaaqaaaiauaacaaxaauaxaaaaaapaayatagaaoafaawayazavaaaazaaabararaaaaakakaaqaarazacajaazavanazaaaeanaaoajauaaaaaxalaraaapabataaavaaab'; function bitmap($message) { $i = 0; $bits = $rest = ''; while ($v = $message[$i]) { if ($v == 'a') { $bits .= '1'; } else { $bits .= '0'; $rest .= $v; } $i++; } return number_format(bindec($bits), 0, '.', '') . $rest;; } echo bitmap($msg); // uncompressed: acaaaaadaaaabalaaeaaaaganaaxakaavawamaasavajawaaaayaauaaadalanagaeaeamaarafalaazaaaiasaanaahaaazaraxaalaahaaawaaajasamahaajaakarapanaakaoakaanawalaacamauaamaal // compressed: 152299251941730035874325065523548237677352452096zhlvtlwcmhkfqiucxuxpytgofwyzvzbrrkkqrzcjzvnzenojuxlrpbtvb Application This algorithm is very useful when there is an element in our data that repeats very often, so you need to investigate the nature of the data you want to compress. Actually because of this fact this algorithm is used for image compression as PNG8 or GIF. Source: http://www.stoimen.com/blog/2012/01/16/computer-algorithms-data-compression-with-bitmaps/

Using open as a context manager is a great way to ensure your file handles are closed properly and is becoming common: with open('/some/path', 'w') as f: f.write('something') The issue is that even if you mock out the call to open it is the returned object that is used as a context manager (and has __enter__ and __exit__ called). Using MagicMock from the mock library, we can mock out context managers very simply. However, mocking open is fiddly enough that a helper function is useful. Here mock_open creates and configures a MagicMock that behaves as a file context manager. from mock import inPy3k, MagicMock if inPy3k: file_spec = ['_CHUNK_SIZE', '__enter__', '__eq__', '__exit__', '__format__', '__ge__', '__gt__', '__hash__', '__iter__', '__le__', '__lt__', '__ne__', '__next__', '__repr__', '__str__', '_checkClosed', '_checkReadable', '_checkSeekable', '_checkWritable', 'buffer', 'close', 'closed', 'detach', 'encoding', 'errors', 'fileno', 'flush', 'isatty', 'line_buffering', 'mode', 'name', 'newlines', 'peek', 'raw', 'read', 'read1', 'readable', 'readinto', 'readline', 'readlines', 'seek', 'seekable', 'tell', 'truncate', 'writable', 'write', 'writelines'] else: file_spec = file def mock_open(mock=None, data=None): if mock is None: mock = MagicMock(spec=file_spec) handle = MagicMock(spec=file_spec) handle.write.return_value = None if data is None: handle.__enter__.return_value = handle else: handle.__enter__.return_value = data mock.return_value = handle return mock >>> m = mock_open() >>> with patch('__main__.open', m, create=True): ... with open('foo', 'w') as h: ... h.write('some stuff') ... >>> m.assert_called_once_with('foo', 'w') >>> m.mock_calls [call('foo', 'w'), call().__enter__(), call().write('some stuff'), call().__exit__(None, None, None)] >>> handle = m() >>> handle.write.assert_called_once_with('some stuff') And for reading files, using a StringIO to represent the file handle: >>> from StringIO import StringIO >>> m = mock_open(data=StringIO('foo bar baz')) >>> with patch('__main__.open', m, create=True): ... with open('foo') as h: ... result = h.read() ... >>> m.assert_called_once_with('foo') >>> assert result == 'foo bar baz' Note that the StringIO will only be used for the data if open is used as a context manager. If you just configure and use mocks they will work whichever way open is used. This helper function will be built into mock 0.9. Source: http://www.voidspace.org.uk/python/weblog/arch_d7_2012_01_07.shtml

This is an excerpt from the upcoming Manning book about Big Data. Big Data Principles and Best Practices of Scalable Realtime Data Systems By Nathan Marz and Samuel E. Ritchie Thrift is a widely used project that originated at Facebook. It can be used for making language-neutral RPC servers, but developers use it for its schema-creation capabilities. In this article based on chapter 2, author Nathan Marz discusses workhorses of Thrift—the struct and union type definitions—and Thrift’s built-in mechanisms for evolving a schema over time. You may also be interested in… Thrift is a widely used project that originated at Facebook. It can be used for making language-neutral RPC servers, but developers use it for its schema-creation capabilities. The workhorses of Thrift are the struct and union type definitions, and Thrift has built-in mechanisms for evolving a schema over time. Orginally Authored by Nathan Marz and Samuel E. Ritchie Structs The following code shows how to define a struct using the Thrift Interface Definition Language (IDL). Defining a struct is like defining a class in an object-oriented language: you specify all the data the object contains. The difference is that a Thrift struct only contains data and doesn't specify any extra behavior for the object. Fields in a struct can be: Primitive types like strings, ints, longs, and doubles. In the Thrift IDL, these are referred to as string, i32, i64, and double, respectively. Collections of other types. Thrift supports list, map, and set. Another Thrift struct or union. struct Person { 1: string twitter_username; 2: string full_name; 3: list interests; } The following code listing shows how to serialize a struct with Java. As you can see, we're using ArrayList, a native Java data structure, as part of the Person object. List interests = new ArrayList() {{ add("hadoop"); add("nosql"); }; Person person = new Person("joesmith", "Joe Smith", interests); TSerializer serializer = new TSerializer(); byte[] serialized = serializer.serialize(person); Here's how to deserialize a Person object in Python. When the object is deserialized, it will be using native Python data structures for any collection types. person = Person() deserialize(person, serialized_bytes) Fields in structs can be defined as being either required or optional. If a field is defined as required, than a value for that field must be provided or else Thrift will give an error upon serialization or deserialization. If a field is optional, the value will be null if not provided. You should always declare fields as being either required or optional. The following code listing shows how to define a struct containing required and optional fields. struct Tweet { 1: required string text; 2: required i64 id; 3: required i64 timestamp; 4: required Person person; 5: optional i64 response_to_tweet_id; Unions You can also define unions in Thrift. A union is a struct that must have exactly one field set. Unions are useful for representing polymorphic data. The following listing shows how to define a "PersonID" using a Thrift union that can be one of many different kinds of identifiers. union PersonID { 1: string email; 2: i64 facebook_id; 3: i64 twitter_id; } Evolving a schema Thrift is designed so that schemas can be evolved over time. The key to evolving Thrift schemas over time is the numeric identifiers used for every field. Those ids are used to identify fields in their serialized form. When you want to change the schema but still be backward compatible with existing data, you must obey the following rules. Fields may be renamed. This is because the serialized form of an object uses the field ids to identify fields, not the names. Fields may be removed, but you must be sure never to reuse that field id. When deserializing, Thrift will skip over any fields that don't match an id it's expecting. So the data for that field will just be ignored in the existing data. If you were to reuse that field id, Thrift will try to deserialize that old data into your new field which will lead to either invalid or incorrect data. Only optional fields can be added to existing structs. You can't add required fields because existing data won't have that field and will not be deserializable. Note that this point does not apply to unions since unions have no notion of required and optional fields. Summary In a relational database, the schema language is part of the database system and is integrated with how the database stores and processes that data. In the Big Data world, you use your own serialization framework that's separate from the storage and processing pieces. You get the flexibility to fine-tune this component to work exactly as needed to fit your data model. There are a few different open source serialization frameworks available, namely Thrift, Protocol Buffers, and Avro. We discussed our favorite, Apache Thrift, because it’s mature and supports most languages, but you could use any of these tools for defining a schema. Here are some other Manning titles you might be interested in: MongoDB in Action Kyle Banker RabbitMQ in Action Alvaro Videla and Jason J.W. Williams Hadoop in Action Chuck Lam Last updated: January 11, 2012

in the graph database space, there are two types of traversal engines: local and distributed. local traversal engines are typically for single-machine graph databases and are used for real-time production applications. distributed traversal engines are typically for multi-machine graph databases and are used for batch processing applications. this divide is quite sharp in the community, but there is nothing that prevents the unification of both models. a discussion of this divide and its unification is presented in this post. local traversal engines in a local traversal engine, there is typically a single processing agent that obeys a program. the agent is called a traverser and the program it follows is called a path description . in gremlin , a friend-of-a-friend path description is defined as such: g.v(1).oute('friend').inv.oute('friend').inv when this path description is interpreted by a traverser over a graph, an instance of the description is realized as actual paths in the graph that match that description. for example, the gremlin traverser starts at vertex 1 and then steps to its outgoing friend -edges. next, it will move to the head/target vertices of those edges (i.e. vertices 3 and 4). after that, it will go to the friend -edges of those vertices and finally, to the head of the previous edges (i.e. vertices 6 and 7). in this way, a single traverser is following all the paths that are exposed with each new atomic graph operation (i.e. each new step after a .). the abstract syntax being: step.step.step . what is returned by this friend-of-a-friend path description, on the example graph diagrammed, is vertices 6 and 7. in many situations, its not the end of the path that is desired, but some side-effect of the traversal. for example, as the traverser walks it can update some global data structure such as a ranking of the vertices. this idea is presented in the path description below, where the oute.inv path is looped over 1000 times. each time a vertex is traversed over, the map m is updated. this global map m maintains keys that are vertices and values that denote the number of times that each vertex has been touched ( groupcount ‘s behavior). m = [:] g.v(1).oute.inv.groupcount(m).loop(3){it.loops < 1000} the local traversal engine pattern is abstractly diagrammed on the right, where a single traverser is obeying some path description ( a.b.c ) and in doing so, moving around on a graph and updating a global data structure (the red boxed map). given the need for traversers to move from element to element, graph databases of this form tend to support strong data locality by means of a direct-reference graph data structure (i.e. vertices have pointers to edges and edges to vertices). a few examples of such graph databases include neo4j , orientdb , and dex . distributed traversal engines in a distributed traversal engine, a traversal is represented as a flow of messages between the elements of the graph. generally, each element (e.g. vertex) is operating independently of the other elements. each element is seen as its own processor with its own (usually homogenous) program to execute. elements communicate with each other via message passing . when no more messages have been passed, the traversal is complete and the results of the traversal are typically represented as a distributed data structure over the elements. graph databases of this nature tend to use the bulk synchronous parallel model of distributed computing. each step is synchronized in a manner analogous to a clock cycle in hardware. instances of this model include agrapa , pregel , trinity , and goldenorb . an example of distributed graph traversing is now presented using a ranking algorithm in java. [ note : in this example, edges are not first class citizens. this is typical of the state of the art in distributed traversal engines. they tend to be for single-relational, unlabeled-edge graphs.] public void evaluatestep(int step) { if(!this.inbox.isempty() && step < 1000) { this.rank = this.rank + this.inbox.size(); for(vertex vertex : this.adjacentvertices()) { for(int i=0; i

1. Overview This is the second of a series of articles about Persistence with Spring. The previous article discussed setting up the persistence layer with Spring 3.1 and Hibernate, without using templates. This article will focus on simplifying the Data Access Layer by using a single, generified DAO, which will result in elegant data access, with no unnecessary clutter. Yes, in Java. The Persistence with Spring series: Part 1 – The Persistence Layer with Spring 3.1 and Hibernate Part 3 – The Persistence Layer with Spring 3.1 and JPA Part 4 – The Persistence Layer with Spring Data JPA Part 5 – Transaction configuration with JPA and Spring 3.1 2. The DAO mess Most production codebases have some kind of DAO layer. Usually the implementation ranges from a raw class with no inheritance to some kind of generified class, but one thing is consistent – there is always more then one. Most likely, there are as many DAOs as there are entities in the system. Also, depending on the level of generics involved, the actual implementations can vary from heavily duplicated code to almost empty, with the bulk of the logic grouped in an abstract class. 2.1. A Generic DAO Instead of having multiple implementations – one for each entity in the system – a single parametrized DAO can be used in such a way that it still takes full advantage of the type safety provided by generics. Two implementations of this concept are presented next, one for a Hibernate centric persistence layer and the other focusing on JPA. These implementation are by no means complete – only some data access methods are included, but they can be easily be made more thorough. 2.2. The Abstract Hibernate DAO public abstract class AbstractHibernateDAO< T extends Serializable > { private Class< T > clazz; @Autowired SessionFactory sessionFactory; public void setClazz( Class< T > clazzToSet ){ this.clazz = clazzToSet; } public T findOne( Long id ){ return (T) this.getCurrentSession().get( this.clazz, id ); } public List< T > findAll(){ return this.getCurrentSession() .createQuery( "from " + this.clazz.getName() ).list(); } public void save( T entity ){ this.getCurrentSession().persist( entity ); } public void update( T entity ){ this.getCurrentSession().merge( entity ); } public void delete( T entity ){ this.getCurrentSession().delete( entity ); } public void deleteById( Long entityId ){ T entity = this.getById( entityId ); this.delete( entity ); } protected Session getCurrentSession(){ return this.sessionFactory.getCurrentSession(); } } The DAO uses the Hibernate API directly, without relying on any Spring templates (such as HibernateTemplate). Using of templates, as well as management of the SessionFactory which is autowired in the DAO were covered in the previous post of the series. 2.3. The Abstract JPA DAO public abstract class AbstractJpaDAO< T extends Serializable > { private Class< T > clazz; @PersistenceContext EntityManager entityManager; public void setClazz( Class< T > clazzToSet ){ this.clazz = clazzToSet; } public T findOne( Long id ){ return this.entityManager.find( this.clazz, id ); } public List< T > findAll(){ return this.entityManager.createQuery( "from " + this.clazz.getName() ) .getResultList(); } public void save( T entity ){ this.entityManager.persist( entity ); } public void update( T entity ){ this.entityManager.merge( entity ); } public void delete( T entity ){ this.entityManager.remove( entity ); } public void deleteById( Long entityId ){ T entity = this.getById( entityId ); this.delete( entity ); } } Similar to the Hibernate DAO implementation, the Java Persistence API is used here directly, again not relying on the now deprecated Spring JpaTemplate. 2.4. The Generic DAO Now, the actual implementation of the generic DAO is as simple as it can be – it contains no logic. Its only purpose is to be injected by the Spring container in a service layer (or in whatever other type of client of the Data Access Layer): @Repository @Scope( BeanDefinition.SCOPE_PROTOTYPE ) public class GenericJpaDAO< T extends Serializable > extends AbstractJpaDAO< T > implements IGenericDAO< T >{ // } @Repository @Scope( BeanDefinition.SCOPE_PROTOTYPE ) public class GenericHibernateDAO< T extends Serializable > extends AbstractHibernateDAO< T > implements IGenericDAO< T >{ // } First, note that the generic implementation is itself parametrized – allowing the client to choose the correct parameter in a case by case basis. This will mean that the clients gets all the benefits of type safety without needing to create multiple artifacts for each entity. Second, notice the prototype scope of these generic DAO implementation. Using this scope means that the Spring container will create a new instance of the DAO each time it is requested (including on autowiring). That will allow a service to use multiple DAOs with different parameters for different entities, as needed. The reason this scope is so important is due to the way Spring initializes beans in the container. Leaving the generic DAO without a scope would mean using the default singleton scope, which would lead to a single instance of the DAO living in the container. That would obviously be majorly restrictive for any kind of more complex scenario. 3. The Service There is now a single DAO to be injected by Spring; also, the Class needs to be specified: @Service class FooService implements IFooService{ IGenericDAO< Foo > dao; @Autowired public void setDao( IGenericDAO< Foo > daoToSet ){ this.dao = daoToSet; this.dao.setClazz( Foo.class ); } // ... } Spring autowires the new DAO insteince using setter injection so that the implementation can be customized with the Class object. After this point, the DAO is fully parametrized and ready to be used by the service. 4. Conclusion This article discussed the simplification of the Data Access Layer by providing a single, reusable implementation of a generic DAO. This implementation was presented in both a Hibernate and a JPA based environment. The result is a streamlined persistence layer, with no unnecessary clutter. For a step by step introduction about setting up the Spring context using Java based configuration and the basic Maven pom for the project, see this article. The next article of the Persistence with Spring series will focus on setting up the DAL layer with Spring 3.1 and JPA. In the meantime, you can check out the full implementation in the github project. If you read this far, you should follow me on twitter here.

Joda-Time provides an alternative to the Date and Calendar classes currently provided in Java SE. Since they are provided in a separate library JAXB does not provide a default mapping for these classes. We can supply the necessary mapping via XmlAdapters. In this post we will cover the following Joda-Time types: DateTime, DateMidnight, LocalDate, LocalTime, LocalDateTime. Java Model The following domain model will be used for this example: package blog.jodatime; import javax.xml.bind.annotation.XmlRootElement; import javax.xml.bind.annotation.XmlType; import org.joda.time.DateMidnight; import org.joda.time.DateTime; import org.joda.time.LocalDate; import org.joda.time.LocalDateTime; import org.joda.time.LocalTime; @XmlRootElement @XmlType(propOrder={ "dateTime", "dateMidnight", "localDate", "localTime", "localDateTime"}) public class Root { private DateTime dateTime; private DateMidnight dateMidnight; private LocalDate localDate; private LocalTime localTime; private LocalDateTime localDateTime; public DateTime getDateTime() { return dateTime; } public void setDateTime(DateTime dateTime) { this.dateTime = dateTime; } public DateMidnight getDateMidnight() { return dateMidnight; } public void setDateMidnight(DateMidnight dateMidnight) { this.dateMidnight = dateMidnight; } public LocalDate getLocalDate() { return localDate; } public void setLocalDate(LocalDate localDate) { this.localDate = localDate; } public LocalTime getLocalTime() { return localTime; } public void setLocalTime(LocalTime localTime) { this.localTime = localTime; } public LocalDateTime getLocalDateTime() { return localDateTime; } public void setLocalDateTime(LocalDateTime localDateTime) { this.localDateTime = localDateTime; } } XmlAdapters Since Joda-Time and XML Schema both represent data and time information according to ISO 8601 the implementation of the XmlAdapters is quite trivial. DateTimeAdapter package blog.jodatime; import javax.xml.bind.annotation.adapters.XmlAdapter; import org.joda.time.DateTime; public class DateTimeAdapter extends XmlAdapter{ public DateTime unmarshal(String v) throws Exception { return new DateTime(v); } public String marshal(DateTime v) throws Exception { return v.toString(); } } DateMidnightAdapter package blog.jodatime; import javax.xml.bind.annotation.adapters.XmlAdapter; import org.joda.time.DateMidnight; public class DateMidnightAdapter extends XmlAdapter { public DateMidnight unmarshal(String v) throws Exception { return new DateMidnight(v); } public String marshal(DateMidnight v) throws Exception { return v.toString(); } } LocalDateAdapter package blog.jodatime; import javax.xml.bind.annotation.adapters.XmlAdapter; import org.joda.time.LocalDate; public class LocalDateAdapter extends XmlAdapter{ public LocalDate unmarshal(String v) throws Exception { return new LocalDate(v); } public String marshal(LocalDate v) throws Exception { return v.toString(); } } LocalTimeAdapter package blog.jodatime; import javax.xml.bind.annotation.adapters.XmlAdapter; import org.joda.time.LocalTime; public class LocalTimeAdapter extends XmlAdapter { public LocalTime unmarshal(String v) throws Exception { return new LocalTime(v); } public String marshal(LocalTime v) throws Exception { return v.toString(); } } LocalDateTimeAdapter package blog.jodatime; import javax.xml.bind.annotation.adapters.XmlAdapter; import org.joda.time.LocalDateTime; public class LocalDateTimeAdapter extends XmlAdapter{ public LocalDateTime unmarshal(String v) throws Exception { return new LocalDateTime(v); } public String marshal(LocalDateTime v) throws Exception { return v.toString(); } } Registering the XmlAdapters We will use the @XmlJavaTypeAdapters annotation to register the Joda-Time types at the package level. This means that whenever these types are found on a field/property on a class within this package the XmlAdapter will automatically be applied. @XmlJavaTypeAdapters({ @XmlJavaTypeAdapter(type=DateTime.class, value=DateTimeAdapter.class), @XmlJavaTypeAdapter(type=DateMidnight.class, value=DateMidnightAdapter.class), @XmlJavaTypeAdapter(type=LocalDate.class, value=LocalDateAdapter.class), @XmlJavaTypeAdapter(type=LocalTime.class, value=LocalTimeAdapter.class), @XmlJavaTypeAdapter(type=LocalDateTime.class, value=LocalDateTimeAdapter.class) }) package blog.jodatime; import javax.xml.bind.annotation.adapters.XmlJavaTypeAdapter; import javax.xml.bind.annotation.adapters.XmlJavaTypeAdapters; import org.joda.time.DateMidnight; import org.joda.time.DateTime; import org.joda.time.LocalDate; import org.joda.time.LocalDateTime; import org.joda.time.LocalTime; Demo To run the following demo you will need the Joda-Time jar on your classpath. It can be obtained here: http://sourceforge.net/projects/joda-time/files/joda-time/ package blog.jodatime; import javax.xml.bind.JAXBContext; import javax.xml.bind.Marshaller; import org.joda.time.DateMidnight; import org.joda.time.DateTime; import org.joda.time.LocalDate; import org.joda.time.LocalDateTime; import org.joda.time.LocalTime; public class Demo { public static void main(String[] args) throws Exception { Root root = new Root(); root.setDateTime(new DateTime(2011, 5, 30, 11, 2, 30, 0)); root.setDateMidnight(new DateMidnight(2011, 5, 30)); root.setLocalDate(new LocalDate(2011, 5, 30)); root.setLocalTime(new LocalTime(11, 2, 30)); root.setLocalDateTime(new LocalDateTime(2011, 5, 30, 11, 2, 30)); JAXBContext jc = JAXBContext.newInstance(Root.class); Marshaller marshaller = jc.createMarshaller(); marshaller.setProperty(Marshaller.JAXB_FORMATTED_OUTPUT, true); marshaller.marshal(root, System.out); } } Output The following is the output from our demo code: 2011-05-30T11:02:30.000-04:00 2011-05-30T00:00:00.000-04:00 2011-05-30 11:02:30.000 2011-05-30T11:02:30.000 From http://blog.bdoughan.com/2011/05/jaxb-and-joda-time-dates-and-times.html

In November 1995, while working as Lead software architect at Hughes Aircraft Of Canada Philippe Kruchten published a paper entitled: "Architectural Blueprints—The “4+1” View Model of Software Architecture". The intent was to come up with a mechanism to separate the different aspects of a software system into different views of the system. Why? Because different stakeholders always have different interest in a software system. Some aspects of a system are relevant to the Developers; others are relevant to System administrators. The Developers want to know about things like classes; System administrators want to know about deployment, hardware and network configurations and don't care about classes. Similar points can be made for Testers, Project Managers and Customers. Kruchten thought it made sense to decompose architecture into distinct views so stakeholders could get what they wanted. In total there were 5 views in his approach but he decided to call it 4 + 1. We'll discuss why it's called 4 + 1 later! But first, let's have a look at each of the different views. The logical view This contains information about the various parts of the system. In UML the logical view is modelled using Class, Object, State machine and Interaction diagrams (e.g Sequence diagrams). It's relevance is really to developers. The process view This describes the concurrent processes within the system. It encompasses some non-functional requirements such as performance and availability. In UML, Activity diagrams - which can be used to model concurrent behaviour - are used to model the process view. The development view The development view focusses on software modules and subsystems. In UML, Package and Component diagrams are used to model the development view. The physical view The physical view describes the physical deployment of the system. For example, how many nodes are used and what is deployed on what node. Thus, the physical view concerns some non-functional requirements such as scalability and availability. In UML, Deployment diagrams are used to model the physical view. The use case view This view describes the functionality of the system from the perspective from outside world. It contains diagrams describing what the system is supposed to do from a black box perspective. This view typically contains Use Case diagrams. All other views use this view to guide them. Why is it called the 4 + 1 instead of just 5? Well this is because of the special significance the use case view has. When all other views are finished, it's effectively redundant. However, all other views would not be possible without it. It details the high levels requirements of the system. The other views detail how those requirements are realised. 4 + 1 came before UML It's important to remember the 4 + 1 approach was put forward two years before the first the introduction of UML which did not manifest in its first guise until 1997. UML is how most enterprise architectures are modelled and the 4 + 1 approach still plays a relevance to UML today. UML 2.0 has 13 different types of diagrams - each diagram type can be categorised into one of the 4 + 1 views. UML is 4 + 1 friendly! So is it important? The 4 + 1 approach isn't just about satisfying different stakeholders. It makes modelling easier to do because it makes it easier to organise. A typical project will contain numerous diagrams of the various types. For example, a project may contain a few hundred sequence diagrams and several class diagrams. Grouping diagrams of similar types and purpose means there is an emphasis in separating concerns. Sure isn't it just the same with Java? Grouping Java classes of similar purpose and related responsibilities into packages means organisation is better. Similarly, grouping different components into different jar files means organisation is better. Modelling tools will usually support the 4 + 1 approach and this means projects will have templates for how to split the various types of diagrams. In a company when projects follow industry standard templates again it means things are better organised. The 4 + 1 approach also provides a way for architects to be able to prioritise modelling concerns. It is rare that a project will have enough time to model every single diagram possible for an architecture. Architects can prioritise different views. For example, for a business domain intensive project it would make sense to prioritise the logical view. In a project with high concurrency and complex timing it would make sense to ensure the process view gets ample time. Similarly, the 4 + 1 approach makes it possible for stakeholders to get the parts of the model that are relevant to them. References: Architectural Blueprints—The “4+1” View Model of Software Architecture Paper http://www.cs.ubc.ca/~gregor/teaching/papers/4+1view-architecture.pdf Learning UML 2.0 by Russ Miles & Kim Hamilton. O'Reilly From http://dublintech.blogspot.com/2011/05/41-view-model-of-software-architecture.html

A collection of short how-to's for enabling JMX in several popular Java technologies. Continuing our journey with JMX (see: ...JMX for human beings) we will learn how to enable JMX support (typically statistics and monitoring capabilities) in some popular frameworks. Most of this information can be found on project's home pages, but I decided to collect it with few the addition of some useful tips. Hibernate (with Spring support) Exposing Hibernate statistics with JMX is pretty simple, however some nasty workarounds are requires when JPA API is used to obtain underlying SessionFactory class JmxLocalContainerEntityManagerFactoryBean() extends LocalContainerEntityManagerFactoryBean { override def createNativeEntityManagerFactory() = { val managerFactory = super.createNativeEntityManagerFactory() registerStatisticsMBean(managerFactory) managerFactory } def registerStatisticsMBean(managerFactory: EntityManagerFactory) { managerFactory match { case impl: EntityManagerFactoryImpl => val mBean = new StatisticsService(); mBean.setStatisticsEnabled(true) mBean.setSessionFactory(impl.getSessionFactory); val name = new ObjectName("org.hibernate:type=Statistics,application=spring-pitfalls") ManagementFactory.getPlatformMBeanServer.registerMBean(mBean, name); case _ => } } } Note that I have created a subclass of Springs built-in LocalContainerEntityManagerFactoryBean. By overriding createNativeEntityManagerFactory() method I can access EntityManagerFactory and by trying to downcast it to org.hibernate.ejb.EntityManagerFactoryImpl we were able to register Hibernate Mbean. One more thing has left. Obviously we have to use our custom subclass instead of org.springframework.orm.jpa.LocalContainerEntityManagerFactoryBean. Also, in order to collect the actual statistics instead of just seeing zeroes all the way down we must set the hibernate.generate_statistics flag. @Bean def entityManagerFactoryBean() = { val entityManagerFactoryBean = new JmxLocalContainerEntityManagerFactoryBean() entityManagerFactoryBean.setDataSource(dataSource()) entityManagerFactoryBean.setJpaVendorAdapter(jpaVendorAdapter()) entityManagerFactoryBean.setPackagesToScan("com.blogspot.nurkiewicz") entityManagerFactoryBean.setJpaPropertyMap( Map( "hibernate.hbm2ddl.auto" -> "create", "hibernate.format_sql" -> "true", "hibernate.ejb.naming_strategy" -> classOf[ImprovedNamingStrategy].getName, "hibernate.generate_statistics" -> true.toString ).asJava ) entityManagerFactoryBean } Here is a sample of what can we expect to see in JvisualVM (don't forget to install all plugins!): In addition we get a nice Hibernate logging: HQL: select generatedAlias0 from Book as generatedAlias0, time: 10ms, rows: 20 EhCache Monitoring caches is very important, especially in application where you expect values to generally be present there. I tend to query the database as often as needed to avoid unnecessary method arguments or local caching. Everything to make code as simple as possible. However this approach only works when caching on the database layer works correctly. Similar to Hibernate, enabling JMX monitoring in EhCache is a two-step process. First you need to expose provided MBean in MBeanServer: @Bean(initMethod = "init", destroyMethod = "dispose") def managementService = new ManagementService(ehCacheManager(), platformMBeanServer(), true, true, true, true, true) @Bean def platformMBeanServer() = ManagementFactory.getPlatformMBeanServer def ehCacheManager() = ehCacheManagerFactoryBean.getObject @Bean def ehCacheManagerFactoryBean = { val ehCacheManagerFactoryBean = new EhCacheManagerFactoryBean ehCacheManagerFactoryBean.setShared(true) ehCacheManagerFactoryBean.setCacheManagerName("spring-pitfalls") ehCacheManagerFactoryBean } Note that I explicitly set CacheManager name. This is not required but this name is used as part of the Mbean name and a default one contains hashCode value, which is not very pleasant. The final touch is to enable statistics on a cache basis: Now we can happily monitor various caching characteristics of every cache separately: As we can see the percentage of cache misses increases. Never a good thing. If we don't enable cache statistics, enabling JMX is still a good idea since we get a lot of management operations for free, including flushing and clearing caches (useful during debugging and testing). Quartz scheduler In my humble opinion Quartz scheduler is very underestimated library, but I will write an article about it on its own. This time we will only learn how to monitor it via JMX. Fortunately it's as simple as adding: org.quartz.scheduler.jmx.export=true To quartz.properties file. The JMX support in Quartz could have been slightly broader, but still one can query e.g. which jobs are currently running. By the way the new major version of Quartz (2.x) brings very nice DSL-like support for scheduling: val job = newJob(classOf[MyJob]) val trigger = newTrigger(). withSchedule( repeatSecondlyForever() ). startAt( futureDate(30, SECOND) ) scheduler.scheduleJob(job.build(), trigger.build()) Apache Commons DBCP Apache Commons DBCP is the most reasonable JDBC pooling library I came across. There is also c3p0, but it doesn't seem like it's actively developed any more. Tomcat JDBC Connection Pool looked promising, but since it's bundled in Tomcat, your JDBC drivers can no longer be packaged in WAR. The only problem with DBCP is that it does not support JMX. At all (see this two and a half year old issue). Fortunately this can be easily worked around. Besides we will learn how to use Spring built-in JMX support. Looks like the standard BasicDataSource has all what we need, all we have to do is to expose existing metrics via JMX. With Spring it is dead-simple – just subclass BasicDataSource and add @ManagedAttribute annotation over desired attributes: @ManagedResource class ManagedBasicDataSource extends BasicDataSource { @ManagedAttribute override def getNumActive = super.getNumActive @ManagedAttribute override def getNumIdle = super.getNumIdle @ManagedAttribute def getNumOpen = getNumActive + getNumIdle @ManagedAttribute override def getMaxActive: Int= super.getMaxActive @ManagedAttribute override def setMaxActive(maxActive: Int) { super.setMaxActive(maxActive) } @ManagedAttribute override def getMaxIdle = super.getMaxIdle @ManagedAttribute override def setMaxIdle(maxIdle: Int) { super.setMaxIdle(maxIdle) } @ManagedAttribute override def getMinIdle = super.getMinIdle @ManagedAttribute override def setMinIdle(minIdle: Int) { super.setMinIdle(minIdle) } @ManagedAttribute override def getMaxWait = super.getMaxWait @ManagedAttribute override def setMaxWait(maxWait: Long) { super.setMaxWait(maxWait) } @ManagedAttribute override def getUrl = super.getUrl @ManagedAttribute override def getUsername = super.getUsername } Here are few data source metrics going crazy during load-test: JMX support in the Spring framework itself is pretty simple. As you have seen above exposing arbitrary attribute or operation is just a matter of adding an annotation. You only have to remember about enabling JMX support using either XML or Java (also see: SPR-8943 : Annotation equivalent to with @Configuration): or: @Bean def annotationMBeanExporter() = new AnnotationMBeanExporter() This article wasn't particularly exciting. However, the knowledge of JMX metrics will enable us to write simple yet fancy dashboards in no time. Stay tuned! From http://nurkiewicz.blogspot.com/2011/12/enabling-jmx-in-hibernate-ehcache-qurtz.html

Among all cool new features introduced by HTML5, the possibility of caching web pages for offline use is definitely one of my favorites. Today, I’m glad to show you how you can create a page that will be available for offline browsing. Getting started View Demo Download files 1 – Add HTML5 doctype The first thing to do is create a valid HTML5 document. The HTML5 doctype is easier to remember than ones used for xhtml: ... Create a file named index.html, or get the example files from my CSS3 media queries article to use as a basis for this tutorial. In case you need it, the full HTML5 specs are available on the W3C website. 2 – Add .htaccess support The file we’re going to create to cache our web page is called a manifest file. Before creating it, we first have to add a directive to the .htaccess file (assuming your server is Apache). Open the .htaccess file, which is located on your website root, and add the following code: AddType text/cache-manifest .manifest This directive makes sure that every .manifest file is served as text/cache-manifest. If the file isn’t, then the whole manifest will have no effect and the page will not be available offline. 3 – Create the manifest file Now, things are going to be more interesting as we create a manifest file. Create a new file and save it as offline.manifest. Then, paste the following code in it. I’ll explain it later. CACHE MANIFEST #This is a comment CACHE index.html style.css image.jpg image-med.jpg image-small.jpg notre-dame.jpg Right now, you have a perfectly working manifest file. The way it works is very simple: After the CACHE declaration, you have to list each files you want to make available offline. That’s enough for caching a simple web page like the one from my example, but HTML5 caching has other interesting possibilities. For example, consider the following manifest file: CACHE MANIFEST #This is a comment CACHE index.html style.css NETWORK: search.php login.php FALLBACK: /api offline.html Like in the example manifest file, we have a CACHE declaration that caches index.html and style.css. But we also have the NETWORK declaration, which is used to specify files that shouldn’t be cached, such as a login page. The last declaration is FALLBACK. This declaration allows you to redirect the user to a particular file (in this example, offline.html) if a resource (/api) isn’t available offline. 4 – Link your manifest file to the html document Now, both your manifest file and your main html document are ready. The only thing you still have to do is to link the manifest file to the html document. Doing this is easy: simply add the manifest attribute to the html element as shown below: 5 – Test it Once done, you’re ready to go. If you visit your index.html file with Firefox 3.5+, you should see a banner like this one: Other browser I’ve tested (Chrome, Safari, Android and iPhone) do not warn about the file caching, and the file is automatically cached. Below you’ll find the browser compatibility of this technique: As usual Internet Explorer does not support it. IE: No support Firefox: 3.5+ Safari: 4.0+ Chrome: 5.0+ Opera: 10.6+ iPhone: 2.1+ Android: 2.0+ Source: http://www.catswhocode.com/blog/how-to-create-offline-html5-web-apps-in-5-easy-steps

this is an big overhaul of one of my tutorials on making and moving shapes on an html5 canvas. this new tutorial is vastly cleaner than my old one, but if you still want to see that one or are looking for the concept of a “ghost context” you can find that one here. this tutorial will show you how to create a simple data structure for shapes on an html5 canvas and how to have them be selectable. the finished canvas will look like this: this article’s code is written primarily to be easy to understand. we’ll be going over a few things that are essential to interactive apps such as games (drawing loop, hit testing), and in later tutorials i will probably turn this example into a small game of some kind. i will try to accommodate javascript beginners but this introduction does expect at least a rudimentary understanding of js. not every piece of code is explained in the text, but almost every piece of code is thoroughly commented! the html5 canvas a canvas is made by using the tag in html: this text is displayed if your browser does not support html5 canvas. a canvas isn’t smart: it’s just a place for drawing pixels. if you ask it to draw something it will execute the drawing command and then immediately forget everything about what it has just drawn. this is sometimes referred to as an immediate drawing surface, as contrasted with svg as a retained drawing surface, since svg keeps a reference to everything drawn. because we have no such references, we have to keep track ourselves of all the things we want to draw (and re-draw) each frame. canvas also has no built-in way of dealing with animation. if you want to make something you’ve drawn move, you have to clear the entire canvas and redraw all of the objects with one or more of them moved. and you have to do it often, of course, if you want a semblance of animation or motion. so we’ll need to add: code for keeping track of objects code for keeping track of canvas state code for mouse events code for drawing the objects as they are made and move around keeping track of what we draw to keep things simple for this example we will start with a shape class to represent rectangular objects. javascript doesn’t technically have classes, but that isn’t a problem because javascript programmers are very good at playing pretend. functionally (well, for our example) we are going to have a shape class and create shape instances with it. what we are really doing is defining a function named shape and adding functions to shape’s prototype. you can make new instances of the function shape and all instances will share the functions defined on shape’s prototype. if you’ve never encountered prototypes in javascript before or if the above sounds confusing to you, i highly recommend reading crockford’s javascript: the good parts . the book is an intermediate overview of javascript that gives a good understanding of why programmers choose to create objects in different ways, why certain conventions are frowned upon, and just what makes javascript so different. here’s our shape constructor and one of the two prototype methods, which are comparable to a class instance methods: // constructor for shape objects to hold data for all drawn objects. // for now they will just be defined as rectangles. function shape(x, y, w, h, fill) { // this is a very simple and unsafe constructor. // all we're doing is checking if the values exist. // "x || 0" just means "if there is a value for x, use that. otherwise use 0." this.x = x || 0; this.y = y || 0; this.w = w || 1; this.h = h || 1; this.fill = fill || '#aaaaaa'; } // draws this shape to a given context shape.prototype.draw = function(ctx) { ctx.fillstyle = this.fill; ctx.fillrect(this.x, this.y, this.w, this.h); } keeping track of canvas state we’re going to have a second class/function called canvasstate. we’re only going to make one instance of this class and it will hold all of the state in this tutorial that is not associated with shapes themselves. canvasstate is going to foremost need a reference to the canvas and a few other field for convenience. we’re also going to compute and save the border and padding (if there is any) so that we can get accurate mouse coordinates. in the canvasstate constructor we will also have a collection of state relating to the objects on the canvas and the current status of dragging. we’ll make an array of shapes, a flag “dragging” that will be true while we are dragging, a field to keep track of which object is selected and a “valid” flag that will be set to false will cause the canvas to clear everything and redraw. is going to need an array of shapes to keep track of whats been drawn so far. i’m going to add a bunch of variables for keeping track of the drawing and mouse state. i already added boxes[] to keep track of each object, but we’ll also need a var for the canvas, the canvas’ 2d context (where wall drawing is done), whether the mouse is dragging, width/height of the canvas, and so on. we’ll also want to make a second canvas, for selection purposes, but i’ll talk about that later. function canvasstate(canvas) { // ... // i removed some setup code to save space // see the full source at the end // **** keep track of state! **** this.valid = false; // when set to true, the canvas will redraw everything this.shapes = []; // the collection of things to be drawn this.dragging = false; // keep track of when we are dragging // the current selected object. // in the future we could turn this into an array for multiple selection this.selection = null; this.dragoffx = 0; // see mousedown and mousemove events for explanation this.dragoffy = 0; mouse events we’ll add events for mousedown, mouseup, and mousemove that will control when an object starts and stops dragging. we’ll also disable the selectstart event, which stops double-clicking on canvas from accidentally selecting text on the page. finally we’ll add a double-click event that will create a new shape and add it to the canvasstate’s list of shapes. the mousedown event begins by calling getmouse on our canvasstate to return the x and y position of the mouse. we then iterate through the list of shapes to see if any of them contain the mouse position. we go through them backwards because they are drawn forwards, and we want to select the one that appears topmost, so we must find the potential shape that was drawn last. if we find the shape we save the offset, save that shape as our selection, set dragging to true and set the valid flag to false. already we’ve used most of our state! finally if we didn’t find any objects we need to see if there was a selection saved from last time. if there is we should clear it. since we clicked on nothing, we obviously didn’t click on the already-selected object! clearing the selection means we will have to clear the canvas and redraw everything without the selection ring, so we set the valid flag to false. // ... // (we are still in the canvasstate constructor) // this is an example of a closure! // right here "this" means the canvasstate. but we are making events on the canvas itself, // and when the events are fired on the canvas the variable "this" is going to mean the canvas! // since we still want to use this particular canvasstate in the events we have to save a reference to it. // this is our reference! var mystate = this; //fixes a problem where double clicking causes text to get selected on the canvas canvas.addeventlistener('selectstart', function(e) { e.preventdefault(); return false; }, false); // up, down, and move are for dragging canvas.addeventlistener('mousedown', function(e) { var mouse = mystate.getmouse(e); var mx = mouse.x; var my = mouse.y; var shapes = mystate.shapes; var l = shapes.length; for (var i = l-1; i >= 0; i--) { if (shapes[i].contains(mx, my)) { var mysel = shapes[i]; // keep track of where in the object we clicked // so we can move it smoothly (see mousemove) mystate.dragoffx = mx - mysel.x; mystate.dragoffy = my - mysel.y; mystate.dragging = true; mystate.selection = mysel; mystate.valid = false; return; } } // havent returned means we have failed to select anything. // if there was an object selected, we deselect it if (mystate.selection) { mystate.selection = null; mystate.valid = false; // need to clear the old selection border } }, true); the mousemove event checks to see if we have set the dragging flag to true. if we have it gets the current mouse positon and moves the selected object to that position, remembering the offset of where we were grabbing it. if the dragging flag is false the mousemove event does nothing. canvas.addeventlistener('mousemove', function(e) { if (mystate.dragging){ var mouse = mystate.getmouse(e); // we don't want to drag the object by its top-left corner, // we want to drag from where we clicked. // thats why we saved the offset and use it here mystate.selection.x = mouse.x - mystate.dragoffx; mystate.selection.y = mouse.y - mystate.dragoffy; mystate.valid = false; // something's dragging so we must redraw } }, true); the mouseup event is simple, all it has to do is update the canvasstate so that we are no longer dragging! so once you lift the mouse, the mousemove event is back to doing nothing. canvas.addeventlistener('mouseup', function(e) { mystate.dragging = false; }, true); the double click event we’ll use to add more shapes to our canvas. it calls addshape on the canvasstate with a new instance of shape. all addshape does is add the argument to the list of shapes in the canvasstate. // double click for making new shapes canvas.addeventlistener('dblclick', function(e) { var mouse = mystate.getmouse(e); mystate.addshape(new shape(mouse.x - 10, mouse.y - 10, 20, 20, 'rgba(0,255,0,.6)')); }, true); there are a few options i implemented, what the selection ring looks like and how often we redraw. setinterval simply calls our canvasstate’s draw method. our interval of 30 means that we call the draw method every 30 milliseconds. // **** options! **** this.selectioncolor = '#cc0000'; this.selectionwidth = 2; this.interval = 30; setinterval(function() { mystate.draw(); }, mystate.interval); } drawing now we’re set up to draw every 30 milliseconds, which will allow us to continuously update the canvas so it appears like the shapes we drag are smoothly moving around. however, drawing doesn’t just mean drawing the shapes over and over; we also have to clear the canvas on every draw. if we don’t clear it, dragging will look like the shape is making a solid line because none of the old shape-positions will go away. because of this, we clear the entire canvas before each draw frame. this can get expensive, and we only want to draw if something has actually changed within our framework, which is why we have the “valid” flag in our canvasstate. after everything is drawn the draw method will set the valid flag to true. then, ocne we do something like adding a new shape or trying to drag a shape, the state will get invalidated and draw() will clear, redraw all objects, and set the valid flag again. // while draw is called as often as the interval variable demands, // it only ever does something if the canvas gets invalidated by our code canvasstate.prototype.draw = function() { // if our state is invalid, redraw and validate! if (!this.valid) { var ctx = this.ctx; var shapes = this.shapes; this.clear(); // ** add stuff you want drawn in the background all the time here ** // draw all shapes var l = shapes.length; for (var i = 0; i < l; i++) { var shape = shapes[i]; // we can skip the drawing of elements that have moved off the screen: if (shape.x > this.width || shape.y > this.height || shape.x + shape.w < 0 || shape.y + shape.h < 0) return; shapes[i].draw(ctx); } // draw selection // right now this is just a stroke along the edge of the selected shape if (this.selection != null) { ctx.strokestyle = this.selectioncolor; ctx.linewidth = this.selectionwidth; var mysel = this.selection; ctx.strokerect(mysel.x,mysel.y,mysel.w,mysel.h); } // ** add stuff you want drawn on top all the time here ** this.valid = true; } } we go through all of shapes[] and draw each one in order. this will give the nice appearance of later shapes looking as if they are on top of earlier shapes. after all the shapes are drawn, a selection handle (if there is a selection) gets drawn around the shape that this.selection references. if you wanted a background (like a city) or a foreground (like clouds), one way to add them is to put them before or after the main two drawing bits. there are often better ways though, like using multiple canvases or a css background-image, but we won’t go over that here. getting mouse coordinates on canvas getting good mouse coordinates is a little tricky on canvas. you could use offsetx/y and layerx/y, but layerx/y is deprecated in webkit (chrome and safari) and firefox does not have offsetx/y. the most bulletproof way to get the correct mouse position is shown below. you have to walk up the tree adding the offsets together. then you must add any padding or border to the offset. finally, to fix coordinate problems when you have fixed-position elements on the page (like the wordpress admin bar or a stumbleupon bar) you must add the ’s offsettop and offsetleft. then you simply subtract that offset from the e.pagex/y values and you’ll get perfect coordinates in almost every possible situation. // creates an object with x and y defined, // set to the mouse position relative to the state's canvas // if you wanna be super-correct this can be tricky, // we have to worry about padding and borders canvasstate.prototype.getmouse = function(e) { var element = this.canvas, offsetx = 0, offsety = 0, mx, my; // compute the total offset if (element.offsetparent !== undefined) { do { offsetx += element.offsetleft; offsety += element.offsettop; } while ((element = element.offsetparent)); } // add padding and border style widths to offset // also add the offsets in case there's a position:fixed bar offsetx += this.stylepaddingleft + this.styleborderleft + this.htmlleft; offsety += this.stylepaddingtop + this.stylebordertop + this.htmltop; mx = e.pagex - offsetx; my = e.pagey - offsety; // we return a simple javascript object (a hash) with x and y defined return {x: mx, y: my}; } there are a few little methods i added that are not shown, such as shape’s method to see if a point is inside its bounds. you can see and download the full demo source here . now that we have a basic structure down, it is easy to write code that handles more complex shapes, like paths or images or video. rotation and scaling these things takes a bit more work, but is quite doable with the canvas and our selection method is already set up to deal with them. if you would like to see this code enhanced in future posts (or have any fixes), let me know. source: http://simonsarris.com/blog/510-making-html5-canvas-useful

This Post is a continuation of my series on Google Guava, this time covering Guava Cache. Guava Cache offers more flexibility and power than either a HashMap or ConcurrentHashMap, but is not as heavy as using EHCache or Memcached (or robust for that matter, as Guava Cache operates solely in memory). The Cache interface has methods you would expect to see like ‘get’, and ‘invalidate’. A method you won’t find is ‘put’, because Guava Cache is ‘self-populating’, values that aren’t present when requested are fetched or calculated, then stored. This means a ‘get’ call will never return null. In all fairness, the previous statement is not %100 accurate. There is another method ‘asMap’ that exposes the entries in the cache as a thread safe map. Using ‘asMap’ will result in not having any of the self loading operations performed, so calls to ‘get’ will return null if the value is not present (What fun is that?). Although this is a post about Guava Cache, I am going to spend the bulk of the time talking about CacheLoader and CacheBuilder. CacheLoader specifies how to load values, and CacheBuilder is used to set the desired features and actually build the cache. CacheLoader CacheLoader is an abstract class that specifies how to calculate or load values, if not present. There are two ways to create an instance of a CacheLoader: Extend the CacheLoader class Use the static factory method CacheLoader.from If you extend CacheLoader you need to override the V load(K key) method, instructing how to generate the value for a given key. Using the static CacheLoader.from method you build a CacheLoader either by supplying a Function or Supplier interface. When supplying a Function object, the Function is applied to the key to calculate or retrieve the results. Using a Supplier interface the value is obtained independent of the key. CacheBuilder The CacheBuilder is used to construct cache instances. It uses the fluent style of building and gives you the option of setting the following properties on the cache: Cache Size limit (removals use a LRU algorithm) Wrapping keys in WeakReferences (Strong references used by default for keys) Wrapping values in either WeakReferences or SoftReferences (Strong references used by default) Time to expire entires after last access Time based expiration of entries after being written or updated Setting a RemovalListener that can recieve events once an entry is removed from the cache Concurrency Level of the cache (defaults to 4) The concurrency level option is used to partition the table internally such that updates can occur without contention. The ideal setting would be the maximum number of threads that could potentially access the cache at one time. Here is an example of a possible usage scenario for Guava Cache. public class PersonSearchServiceImpl implements SearchService> { public PersonSearchServiceImpl(SampleLuceneSearcher luceneSearcher, SampleDBService dbService) { this.luceneSearcher = luceneSearcher; this.dbService = dbService; buildCache(); } @Override public List search(String query) throws Exception { return cache.get(query); } private void buildCache() { cache = CacheBuilder.newBuilder().expireAfterWrite(10, TimeUnit.MINUTES) .maximumSize(1000) .build(new CacheLoader>() { @Override public List load(String queryKey) throws Exception { List ids = luceneSearcher.search(queryKey); return dbService.getPersonsById(ids); } }); } } In this example, I am setting the cache entries to expire after 10 minutes of being written or updated in the cache, with a maximum amount of 1,000 entires. Note the usage of CacheLoader on line 15. RemovalListener The RemovalListener will receive notification of an item being removed from the cache. These notifications could be from manual invalidations or from a automatic one due to time expiration or garbage collection. The RemovalListener parameters can be set to listen for specific type. To receive notifications for any key or value set them to use Object. It should be noted here that a RemovalListener will receive a RemovalNotification object that implements the Map.Entry interface. The key or value could be null if either has already been garbage collected. Also the key and value object will be strong references, regardless of the type of references used by the cache. CacheStats There is also a very useful class CacheStats that can be retrieved via a call to Cache.stats(). The CacheStats object can give insight into the effectiveness and performance of your cache by providing statistics such as: hit count miss count total load timme total requests CacheStats provides many other counts in addition to the ones listed above. Conclusion The Guava Cache presents some very compelling functionality. The decision to use a Guava Cache really comes down to the tradeoff between memory availability/usage versus increases in performance. I have added a unit test CacheTest demonstrating the usages discussed here. As alway comments and suggestions are welcomed. Thanks for your time. Resources Guava Project Home Cache API Source Code for blog series From http://codingjunkie.net/google-guava-cache/

A string[] array and use a LINQ query expression to order its contents alphabetically. Note that we are ordering the strings, not the letters in the strings. using System; using System.Linq; class Program { static void Main() { string[] a = new string[] {"Indonesian","Korean","Japanese","English","German"}; var sort = from s in a orderby s select s; foreach (string c in sort) { Console.WriteLine(c); } } } /*OUTPUT English German Indonesian Japanese Korean */ Eclipse IDE and Eclipse

One of NoSQL's goals: handle previously-unthinkable amounts of data. One of unthinkable-amounts-of-data's problems: previously-improbable events become extremely probable, precisely because the set of interactions is so large. Flip a coin a hundred times, and you're not likely to get 50 heads in a row. But flip it a few trillion times, and you probably will find some 50-heads streaks. So NoSQL's performance strength is also its mathematical weakness. This order of scale can result in lots of problems, but one of the most common is consistency -- the C in ACID -- clearly a fundamental desideratum for any database system, but in principle much harder to acheive for NoSQL databases than for others. Emerging database technologies have forced developers and computer scientists to define more exactly what kind of consistency is really needed, for any given application. Two years ago, ACM (the Association for Computing Machinery) published an extremely helpful examination of the attenuated notion of consistency called 'eventual consistency'. Their summary: Data inconsistency in large-scale reliable distributed systems must be tolerated for two reasons: improving read and write performance under highly concurrent conditions; and handling partition cases where a majority model would render part of the system unavailable even though the nodes are up and running. The article surveys technical solutions as well as user considerations that might soften the undesirability of anything less than perfect, instantaneous consistency. It's not long (4 pages plus pictures), and explains some deep database issues quite clearly. On the more practical side of the problem: Russell Brown recently gave a talk at the NoSQL Exchange 2011 on exactly this topic. More specifically, he showed how some distributed systems (Riak in particular) try to minimize conflicts, and suggested some ways to reconcile conflicts automatically using smart semantic techniques. Check out the NoSQL Exchange page for Russell's talk here, which includes an embedded video. But read the ACM article first for a broader overview, since Russell launches into technical details pretty quickly.

Let me first define what we mean by circular dependency in OOAD terms vis-a-vis Java. Suppose we have a class called A which has class B’s Object. (in UML terms A HAS B). at the same time we class B is also composed of Object of class A (in UML terms B HAS A). obviously this represents circular dependency because while creating the object of A, the compiler must know the size of B... on the other hand while creating object of B, the compiler must know the size of A. this is something like egg vs. chicken problem... this may be possible in real life situation as well. for example suppose a multi storied building has a lift. so in the UML terms, the building HAS lift... but at the same time, suppose, while constructing the lift object, we need to give it the information about the building object to access various functionalities of the Building class... for example, suppose the speed of the lift is set depending on the number of floors of the Building... hence while constructing the Lift object it must access the functionalities of the Building object which will give the number of floors the building has got...hence in UML terms the lift HAS building... so this is a sure case of circular dependency... in real java code it will look something as follows: public class Building { private Lift lift; private int floor; public Building(){ lift = new Lift(); setFloor(15); } public int getFloor(){ return floor; } public void setFloor(int floor){ this.floor = floor; } }//end of class building //class Lift public class Lift { private Building building; private int Speed; public Lift(){ building = new Building(); setSpeed(); } public void setSpeed(){ if (building.getFloor()>20){ //one set of functionalities //may be the the speed of the lift will be more this.Speed = 10; } else { //different set of functionalities //may be the speed of the lift will be less this.Speed = 5; } } public int getSpeed(){ return Speed; } }//end of class Lift As it becomes clear from the above code, that while creating the Building object it will create the Lift object, and while creating the Lift object, it will try to create a Building object to access some of its functionalities... So, ultimately it will go out of memory and we get a StackOverflow runtime exception... So how do we handle this problem in Java? We actually tackle this problem by declaring an IBuildingProxy interface and by deriving our Building class from that... the lift class, instead of Having Building object, it Has IBuildingProxy... the source code of the solution looks like the following... public interface IBuildingProxy { int getFloor(); void setFloor(int floor); } public class Building implements IBuildingProxy{ private Lift lift; private int floor; public Building(){ lift = new Lift(this); setFloor(15); } public int getFloor(){ return floor; } public void setFloor(int floor){ this.floor = floor; } } public class Lift { private IBuildingProxy building; private int Speed; public Lift(Building b){ this.building = b; setSpeed(); } private void setSpeed(){ if (building.getFloor()>20){ / /one set of functionalities //may be the the speed of the lift will be more this.Speed = 10; } else { //different set of functionalities //may be the speed of the lift will be less this.Speed = 5; } } public int getSpeed(){ return Speed; } } public class CircularDependencyTest { public static void main(String[] args){ Building b = new Building(); Lift l = new Lift(b); } } So whats the principle behind such work around... It will be clear soon... As it becomes clear from the code that Building HAS Lift... That is not a problem... Now when it comes to solve the part that Lift HAS Building, instead of the Building object, we have created an IBuildingProxy interface and we pass it to the Lift class... what it essentially means, that the building class knows the memory requirement to initialize the Lift object, and as the Lift class HAS just a proxy interface of the Building, it does not have to care for the Building's memory requirement... and that solves the problem... Hope this discussion becomes helpful for Java learners...

In a classical model of recommendation system, there are "users" and "items". User has associated metadata (or content) such as age, gender, race and other demographic information. Items also has its metadata such as text description, price, weight ... etc. On top of that, there are interaction (or transaction) between user and items, such as userA download/purchase movieB, userX give a rating 5 to productY ... etc. Now given all the metadata of user and item, as well as their interaction over time, can we answer the following questions ... What is the probability that userX purchase itemY ? What rating will userX give to itemY ? What is the top k unseen items that should be recommended to userX ? Content-based Approach In this approach, we make use of the metadata to categorize user and item and then match them at the category level. One example is to recommend jobs to candidates, we can do a IR/text search to match the user's resume with the job descriptions. Another example is to recommend an item that is "similar" to the one that the user has purchased. Similarity is measured according to the item's metadata and various distance function can be used. The goal is to find k nearest neighbors of the item we know the user likes. Collaborative Filtering Approach In this approach, we look purely at the interactions between user and item, and use that to perform our recommendation. The interaction data can be represented as a matrix. Notice that each cell represents the interaction between user and item. For example, the cell can contain the rating that user gives to the item (in the case the cell is a numeric value), or the cell can be just a binary value indicating whether the interaction between user and item has happened. (e.g. a "1" if userX has purchased itemY, and "0" otherwise. The matrix is also extremely sparse, meaning that most of the cells are unfilled. We need to be careful about how we treat these unfilled cells, there are 2 common ways ... Treat these unknown cells as "0". Make them equivalent to user giving a rate "0". This may or may not be a good idea depends on your application scenarios. Guess what the missing value should be. For example, to guess what userX will rate itemA given we know his has rate on itemB, we can look at all users (or those who is in the same age group of userX) who has rate both itemA and itemB, then compute an average rating from them. Use the average rating of itemA and itemB to interpolate userX's rating on itemA given his rating on itemB. User-based Collaboration Filter In this model, we do the following Find a group of users that is “similar” to user X Find all movies liked by this group that hasn’t been seen by user X Rank these movies and recommend to user X This introduces the concept of user-to-user similarity, which is basically the similarity between 2 row vectors of the user/item matrix. To compute the K nearest neighbor of a particular users. A naive implementation is to compute the "similarity" for all other users and pick the top K. Different similarity functions can be used. Jaccard distance function is defined as the number of intersections of movies that both users has seen divided by the number of union of movies they both seen. Pearson similarity is first normalizing the user's rating and then compute the cosine distance. There are two problems with this approach Compare userX and userY is expensive as they have millions of attributes Find top k similar users to userX require computing all pairs of userX and userY Location Sensitive Hashing and Minhash To resolve problem 1, we approximate the similarity using a cheap estimation function, called minhash. The idea is to find a hash function h() such that the probability of h(userX) = h(userY) is proportion to the similarity of userX and userY. And if we can find 100 of h() function, we can just count the number of such function where h(userX) = h(userY) to determine how similar userX is to userY. The idea is depicted as follows ... It will be expensive to permute the rows if the number of rows is large. Remember that the purpose of h(c1) is to return row number of the first row that is 1. So we can scan each row of c1 to see if it is 1, if so we apply a function newRowNum = hash(rowNum) to simulate a permutation. Take the minimum of the newRowNum seen so far. As an optimization, instead of doing one column at a time, we can do it a row at the time, the algorithm is as follows To solve problem 2, we need to avoid computing all other users' similarity to userX. The idea is to hash users into buckets such that similar users will be fall into the same bucket. Therefore, instead of computing all users, we only compute the similarity of those users who is in the same bucket of userX. The idea is to horizontally partition the column into b bands, each with r rows. By pick the parameter b and r, we can control the likelihood (function of similarity) that they will fall into the same bucket in at least one band. Item-based Collaboration Filter If we transpose the user/item matrix and do the same thing, we can compute the item to item similarity. In this model, we do the following ... Find the set of movies that user X likes (from interaction data) Find a group of movies that is similar to these set of movies that we know user X likes Rank these movies and recommend to user X It turns out that computing item-based collaboration filter has more benefit than computing user to user similarity for the following reasons ... Number of items typically smaller than number of users While user's taste will change over time and hence the similarity matrix need to be updated more frequent, item to item similarity tends to be more stable and requires less update. Singular Value Decomposition If we look back at the matrix, we can see the matrix multiplication is equivalent to mapping an item from the item space to the user space. In other words, if we view each of the existing item as an axis in the user space (notice, each user is a vector of their rating on existing items), then multiplying a new item with the matrix gives the same vector like the user. So we can then compute a dot product with this projected new item with user to determine its similarity. It turns out that this is equivalent to map the user to the item space and compute a dot product there. In other words, multiply the matrix is equivalent to mapping between item space and user space. Now lets imagine there is a hidden concept space in between. Instead of jumping directly from user space to item space, we can think of jumping from user space to a concept space, and then to the item space. Notice that here we first map the user space to the concept space and also map the item space to the concept space. Then we match both user and item at the concept space. This is a generalization of our recommender. We can use SVD to factor the matrix into 2 parts. Let P be the m by n matrix (m rows and n columns). P = UDV where U is an m by m matrix, each column represents the eigenvectors of P*transpose(P). And V is an n by n matrix with each row represents the eigenvector of transpose(P)*P. D is a diagonal matrix containing eigenvalues of P*transpose(P), or transpose(P)*P. In other words, we can decompose P into U*squareroot(D) and squareroot(D)*V. Notice that D can be thought as the strength of each "concept" in the concept space. And the value is order in terms of their magnitude in decreasing order. If we remove some of the weakest concept by making them zero, we reduce the number of non-zero elements in D, which effective generalize the concept space (make them focus in the important concepts). Calculate SVD decomposition for matrix with large dimensions is expensive. Fortunately, if our goal is to compute an SVD approximation (with k diagonal non-zero value), we can use the random projection mechanism as describer here. Association Rule Based In this model, we use the market/basket association rule algorithm to discover rule like ... {item1, item2} => {item3, item4, item5} We represent each user as a basket and each viewing as an item (notice that we ignore the rating and use a binary value). After that we use association rule mining algorithm to detect frequent item set and the association rules. Then for each user, we match the user's previous viewing items to the set of rules to determine what other movies should we recommend. Evaluate the recommender After we have a recommender, how do we evaluate the performance of it ? The basic idea is to use separate the data into the training set and the test set. For the test set, we remove certain user-to-movies interaction (change certain cells from 1 to 0) and pretending the user hasn't seen the item. Then we use the training set to train a recommender and then fit the test set (with removed interaction) to the recommender. The performance is measured by how much overlap between the recommended items with the one that we have removed. In other words, a good recommender should be able to recover the set of items that we have removed from the test set. Leverage tagging information on items In some cases, items has explicit tags associated with them (we can considered the tags is a user-annotated concept space added to the items). Consider each item is described with a vector of tags. Now user can also be auto-tagged based on the items they have interacted. For example, if userX purchase itemY which is tagged with Z1, and Z2. Then user will increase her tag Z1 and Z2 in her existing tag vector. We can use a time decay mechanism to update the user's tag vector as follows ... current_user_tag = alpha * item_tag + (1 - alpha) * prev_user_tag To recommend an item to the user, we simply need to calculate the top k items by computing the dot product (ie: cosine distance) of the user tag vector and the item tag vector. Source: http://horicky.blogspot.com/2011/09/recommendation-engine.html

How often have we all heard that “batching” will increase latency? As someone with a passion for low-latency systems this surprises me. In my experience when batching is done correctly, not only does it increase throughput, it can also reduce average latency and keep it consistent. Well then, how can batching magically reduce latency? It comes down to what algorithm and data structures are employed. In a distributed environment we are often having to batch up messages/events into network packets to achieve greater throughput. We also employ similar techniques in buffering writes to storage to reduce the number of IOPS. That storage could be a block device backed file-system or a relational database. Most IO devices can only handle a modest number of IO operations per second, so it is best to fill those operations efficiently. Many approaches to batching involve waiting for a timeout to occur and this will by its very nature increase latency. The batch can also get filled before the timeout occurs making the latency even more unpredictable. Figure 1. Figure 1. above depicts decoupling the access to an IO device, and therefore the contention for access to it, by introducing a queue like structure to stage the messages/events to be sent and a thread doing the batching for writing to the device. The Algorithm An approach to batching uses the following algorithm in Java pseudo code: public final class NetworkBatcher implements Runnable { private final NetworkFacade network; private final Queue queue; private final ByteBuffer buffer; public NetworkBatcher(final NetworkFacade network, final int maxPacketSize, final Queue queue) { this.network = network; buffer = ByteBuffer.allocate(maxPacketSize); this.queue = queue; } @Override public void run() { while (!Thread.currentThread().isInterrupted()) { while (null == queue.peek()) { employWaitStrategy(); // block, spin, yield, etc. } Message msg; while (null != (msg = queue.poll())) { if (msg.size() > buffer.remaining()) { sendBuffer(); } buffer.put(msg.getBytes()); } sendBuffer(); } } private void sendBuffer() { buffer.flip(); network.send(buffer); buffer.clear(); } } Basically, wait for data to become available and as soon as it is, send it right away. While sending a previous message or waiting on new messages, a burst of traffic may arrive which can all be sent in a batch, up to the size of the buffer, to the underlying resource. This approach can use ConcurrentLinkedQueue which provides low-latency and avoid locks. However it has an issue in not creating back pressure to stall producing/publishing threads if they are outpacing the batcher whereby the queue could grow out of control because it is unbounded. I’ve often had to wrap ConcurrentLinkedQueue to track its size and thus create back pressure. This size tracking can add 50% to the processing cost of using this queue in my experience. This algorithm respects the single writer principle and can often be employed when writing to a network or storage device, and thus avoid lock contention in third party API libraries. By avoiding the contention we avoid the J-Curve latency profile normally associated with contention on resources, due to the queuing effect on locks. With this algorithm, as load increases, latency stays constant until the underlying device is saturated with traffic resulting in a more "bathtub" profile than the J-Curve. Let’s take a worked example of handling 10 messages that arrive as a burst of traffic. In most systems traffic comes in bursts and is seldom uniformly spaced out in time. One approach will assume no batching and the threads write to device API directly as in Figure 1. above. The other will use a lock free data structure to collect the messages plus a single thread consuming messages in a loop as per the algorithm above. For the example let’s assume it takes 100µs to write a single buffer to the network device as a synchronous operation and have it acknowledged. The buffer will ideally be less than the MTU of the network in size when latency is critical. Many network sub-systems are asynchronous and support pipelining but we will make the above assumption to clarify the example. If the network operation is using a protocol like HTTP under REST or Web Services then this assumption matches the underlying implementation. Best (µs) Average (µs) Worst (µs) Packets Sent Serial 100 500 1,000 10 Smart Batching 100 150 200 1-2 The absolute lowest latency will be achieved if a message is sent from the thread originating the data directly to the resource, if the resource is un-contended. The table above shows what happens when contention occurs and a queuing effect kicks in. With the serial approach 10 individual packets will have to be sent and these typically need to queue on a lock managing access to the resource, therefore they get processed sequentially. The above figures assume the locking strategy works perfectly with no perceivable overhead which is unlikely in a real application. For the batching solution it is likely all 10 packets will be picked up in first batch if the concurrent queue is efficient, thus giving the best case latency scenario. In the worst case only one message is sent in the first batch with the other nine following in the next. Therefore in the worst case scenario one message has a latency of 100µs and the following 9 have a latency of 200µs thus giving a worst case average of 190µs which is significantly better than the serial approach. This is one good example when the simplest solution is just a bit too simple because of the contention. The batching solution helps achieve consistent low-latency under burst conditions and is best for throughput. It also has a nice effect across the network on the receiving end in that the receiver has to process fewer packets and therefore makes the communication more efficient both ends. Most hardware handles data in buffers up to a fixed size for efficiency. For a storage device this will typically be a 4KB block. For networks this will be the MTU and is typically 1500 bytes for Ethernet. When batching, it is best to understand the underlying hardware and write batches down in ideal buffer size to be optimally efficient. However keep in mind that some devices need to envelope the data, e.g. the Ethernet and IP headers for network packets so the buffer needs to allow for this. There will always be an increased latency from a thread switch and the cost of exchange via the data structure. However there are a number of very good non-blocking structures available using lock-free techniques. For the Disruptor this type of exchange can be achieved in as little as 50-100ns thus making the choice of taking the smart batching approach a no brainer for low-latency or high-throughput distributed systems. This technique can be employed for many problems and not just IO. The core of the Disruptor uses this technique to help rebalance the system when the publishers burst and outpace the EventProcessors. The algorithm can be seen inside the BatchEventProcessor. Note: For this algorithm to work the queueing structure must handle the contention better than the underlying resource. Many queue implementations are extremely poor at managing contention. Use science and measure before coming to a conclusion. Batching with the Disruptor The code below shows the same algorithm in action using the Disruptor's EventHandler mechanism. In my experience, this is a very effective technique for handling any IO device efficiently and keeping latency low when dealing with load or burst traffic. public final class NetworkBatchHandler implements EventHander { private final NetworkFacade network; private final ByteBuffer buffer; public NetworkBatchHandler(final NetworkFacade network, final int maxPacketSize) { this.network = network; buffer = ByteBuffer.allocate(maxPacketSize); } public void onEvent(Message msg, long sequence, boolean endOfBatch) throws Exception { if (msg.size() > buffer.remaining()) { sendBuffer(); } buffer.put(msg.getBytes()); if (endOfBatch) { sendBuffer(); } } private void sendBuffer() { buffer.flip(); network.send(buffer); buffer.clear(); } } The endOfBatch parameter greatly simplifies the handling of the batch compared to the double loop in the algorithm above. I have simplified the examples to illustrate the algorithm. Clearly error handling and other edge conditions need to be considered. Separation of IO from Work Processing There is another very good reason to separate the IO from the threads doing the work processing. Handing off the IO to another thread means the worker thread, or threads, can continue processing without blocking in a nice cache friendly manner. I've found this to be critical in achieving high-performance throughput. If the underlying IO device or resource becomes briefly saturated then the messages can be queued for the batcher thread allowing the work processing threads to continue. The batching thread then feeds the messages to the IO device in the most efficient way possible allowing the data structure to handle the burst and if full apply the necessary back pressure, thus providing a good separation of concerns in the workflow. Conclusion So there you have it. Smart Batching can be employed in concert with the appropriate data structures to achieve consistent low-latency and maximum throughput. From http://mechanical-sympathy.blogspot.com/2011/10/smart-batching.html

as a user interface fan, i could not miss the development with html 5. so the goal of this post is to walk through a graphic application that uses javascript and html 5. we will see through examples one way (among others) to develop this kind of project. application overview tools the html 5 page data gathering cards loading & cache handling cards display mouse management state storage animations handling multi-devices conclusion to go further application overview we will produce an application that will let us display a magic the gathering ©(courtesy of www.wizards.com/magic ) cards collection. users will be able to scroll and zoom using the mouse (like bing maps, for example). you can see the final result here: http://bolaslenses.catuhe.com the project source files can be downloaded here: http://www.catuhe.com/msdn/bolaslenses.zip cards are stored on windows azure storage and use the azure content distribution network ( cdn : a service that deploys data near the final users) in order to achieve maximum performances. an asp.net service is used to return cards list (using json format). tools to write our application, we will use visual studio 2010 sp1 with web standards update . this extension adds intellisense support in html 5 page (which is a really important thing ). so, our solution will contain an html 5 page side by side with .js files (these files will contain javascript scripts). about debug, it is possible to set a breakpoint directly in the .js files under visual studio. it is also possible to use the developer bar of internet explorer 9 (use f12 key to display it). debug with visual studio 2010 debug with internet explorer 9 (f12/developer bar) so, we have a modern developer environment with intellisense and debug support. therefore, we are ready to start and first of all, we will write the html 5 page. the html 5 page our page will be built around an html 5 canvas which will be used to draw the cards: cards scanned by mwshq team magic the gathering official site : http://www.wizards.com/magic bolas lenses your browser does not support html5 canvas. loading data... if we dissect this page, we can note that it is divided into two parts: the header part with the title, the logo and the special mentions the main part (section) holds the canvas and the tooltips that will display the status of the application. there is also a hidden image ( backimage ) used as source for not yet loaded cards. to build the layout of the page, a style sheet ( full.css ) is applied. style sheets are a mechanism used to change the tags styles (in html, a style defines the entire display options for a tag): html, body { height: 100%; } body { background-color: #888888; font-size: .85em; font-family: "segoe ui, trebuchet ms" , verdana, helvetica, sans-serif; margin: 0; padding: 0; color: #696969; } a:link { color: #034af3; text-decoration: underline; } a:visited { color: #505abc; } a:hover { color: #1d60ff; text-decoration: none; } a:active { color: #12eb87; } header, footer, nav, section { display: block; } table { width: 100%; } header, #header { position: relative; margin-bottom: 0px; color: #000; padding: 0; } #title { font-weight: bold; color: #fff; border: none; font-size: 60px !important; vertical-align: middle; margin-left: 70px } #legal { text-align: right; color: white; font-size: 14px; width: 50%; position: absolute; top: 15px; right: 10px } #leftheader { width: 50%; vertical-align: middle; } section { margin: 20px 20px 20px 20px; } #maincanvas{ border: 4px solid #000000; } #cardscount { font-weight: bolder; font-size: 1.1em; } .tooltip { position: absolute; bottom: 5px; color: black; background-color: white; margin-right: auto; margin-left: auto; left: 35%; right: 35%; padding: 5px; width: 30%; text-align: center; border-radius: 10px; -webkit-border-radius: 10px; -moz-border-radius: 10px; box-shadow: 2px 2px 2px #333333; } #bolaslogo { width: 64px; height: 64px; } #picturecell { float: left; width: 64px; margin: 5px 5px 5px 5px; vertical-align: middle; } thus, this sheet is responsible for setting up the following display: style sheets are powerful tools that allow an infinite number of displays. however, they are sometimes complicated to setup (for example if a tag is affected by a class, an identifier and its container). to simplify this setup, the development bar of internet explorer 9 is particularly useful because we can use it to see styles hierarchy that is applied to a tag. for example let’s take a look at the waittext tooltip with the development bar. to do this, you must press f12 in internet explorer and use the selector to choose the tooltip: once the selection is done, we can see the styles hierarchy: thus, we can see that our div received its styles from the body tag and the . tooltip entry of the style sheet. with this tool, it becomes possible to see the effect of each style (which can be disabled). it is also possible to add new style on the fly. another important point of this window is the ability to change the rendering mode of internet explorer 9. indeed, we can test how, for example, internet explorer 8 will handle the same page. to do this, go to the [ browser mode ] menu and select the engine of internet explorer 8. this change will especially impact our tooltip as it uses border-radius (rounded edge) and box-shadow that are features of css 3: internet explorer 9 internet explorer 8 our page provides a graceful degradation as it still works (with no annoying visual difference) when the browser does not support all the required technologies. now that our interface is ready, we will take a look at the data source to retrieve the cards to display. the server provides the cards list using json format on this url: http://bolaslenses.catuhe.com/ home/listofcards/?colorstring=0 it takes one parameter ( colorstring ) to select a specific color (0 = all). when developing with javascript, there is a good reflex to have (reflex also good in other languages too, but really important in javascript): one must ask whether what we want to develop has not been already done in an existing framework. indeed, there is a multitude of open source projects around javascript. one of them is jquery which provides a plethora of convenient services. thus, in our case to connect to the url of our server and get the cards list, we could go through a xmlhttprequest and have fun to parse the returned json. or we can use jquery . so we will use the getjson function which will take care of everything for us: function getlistofcards() { var url = "http://bolaslenses.catuhe.com/home/listofcards/?jsoncallback=?"; $.getjson(url, { colorstring: "0" }, function (data) { listofcards = data; $("#cardscount").text(listofcards.length + " cards displayed"); $("#waittext").slidetoggle("fast"); }); } as we can see, our function stores the cards list in the listofcards variable and calls two jquery functions: text that change the text of a tag slidetoggle that hides (or shows) a tag by animating its height the listofcards list contains objects whose format is: id : unique identifier of the card path : relative path of the card (without the extension) it should be noted that the url of the server is called with the “ ?jsoncallback=? ” suffix. indeed, ajax calls are constrained in terms of security to connect only to the same address as the calling script. however, there is a solution called jsonp that will allow us to make a concerted call to the server (which of course must be aware of the operation). and fortunately, jquery can handle it all alone by just adding the right suffix. once we have our cards list, we can set up the pictures loading and caching. cards loading & cache handling the main trick of our application is to draw only the cards effectively visible on the screen. the display window is defined by a zoom level and an offset (x, y) in the overall system. var visucontrol = { zoom : 0.25, offsetx : 0, offsety : 0 }; the overall system is defined by 14819 cards that are spread over 200 columns and 75 rows. also, we must be aware that each card is available in three versions: high definition: 480x680 without compression (.jpg suffix) medium definition: 240x340 with standard compression (.50.jpg suffix) low definition: 120x170 with strong compression (.25.jpg suffix) thus, depending on the zoom level, we will load the correct version to optimize networks transfer. to do this we will develop a function that will give an image for a given card. this function will be configured to download a certain level of quality. in addition it will be linked with lower quality level to return it if the card for the current level is not yet uploaded: function imagecache(substr, replacementcache) { var extension = substr; var backimage = document.getelementbyid("backimage"); this.load = function (card) { var localcache = this; if (this[card.id] != undefined) return; var img = new image(); localcache[card.id] = { image: img, isloaded: false }; currentdownloads++; img.onload = function () { localcache[card.id].isloaded = true; currentdownloads--; }; img.onerror = function() { currentdownloads--; }; img.src = "http://az30809.vo.msecnd.net/" + card.path + extension; }; this.getreplacementfromlowercache = function (card) { if (replacementcache == undefined) return backimage; return replacementcache.getimageforcard(card); }; this.getimageforcard = function(card) { var img; if (this[card.id] == undefined) { this.load(card); img = this.getreplacementfromlowercache(card); } else { if (this[card.id].isloaded) img = this[card.id].image; else img = this.getreplacementfromlowercache(card); } return img; }; } an imagecache is built by giving the associated suffix and the underlying cache. here you can see two important functions: load : this function will load the right picture and will store it in a cache (the msecnd.net url is the azure cdn address of the cards) getimageforcard : this function returns the card picture from the cache if already loaded. otherwise it requests the underlying cache to return its version (and so on) so to handle our 3 levels of caches, we have to declare three variables: var imagescache25 = new imagecache(".25.jpg"); var imagescache50 = new imagecache(".50.jpg", imagescache25); var imagescachefull = new imagecache(".jpg", imagescache50); selecting the right cover is only depending on zoom: function getcorrectimagecache() { if (visucontrol.zoom <= 0.25) return imagescache25; if (visucontrol.zoom <= 0.8) return imagescache50; return imagescachefull; } to give a feedback to the user, we will add a timer that will manage a tooltip that indicates the number of images currently loaded: function updatestats() { var stats = $("#stats"); stats.html(currentdownloads + " card(s) currently downloaded."); if (currentdownloads == 0 && statsvisible) { statsvisible = false; stats.slidetoggle("fast"); } else if (currentdownloads > 1 && !statsvisible) { statsvisible = true; stats.slidetoggle("fast"); } } setinterval(updatestats, 200); again we note the use of jquery to simplify animations. we will now discuss the display of cards. cards display to draw our cards, we need to actually fill the canvas using its 2d context (which exists only if the browser supports html 5 canvas): var maincanvas = document.getelementbyid("maincanvas"); var drawingcontext = maincanvas.getcontext('2d'); the drawing will be made by processlistofcards function (called 60 times per second): function processlistofcards() { if (listofcards == undefined) { drawwaitmessage(); return; } maincanvas.width = document.getelementbyid("center").clientwidth; maincanvas.height = document.getelementbyid("center").clientheight; totalcards = listofcards.length; var localcardwidth = cardwidth * visucontrol.zoom; var localcardheight = cardheight * visucontrol.zoom; var effectivetotalcardsinwidth = colscount * localcardwidth; var rowscount = math.ceil(totalcards / colscount); var effectivetotalcardsinheight = rowscount * localcardheight; initialx = (maincanvas.width - effectivetotalcardsinwidth) / 2.0 - localcardwidth / 2.0; initialy = (maincanvas.height - effectivetotalcardsinheight) / 2.0 - localcardheight / 2.0; // clear clearcanvas(); // computing of the viewing area var initialoffsetx = initialx + visucontrol.offsetx * visucontrol.zoom; var initialoffsety = initialy + visucontrol.offsety * visucontrol.zoom; var startx = math.max(math.floor(-initialoffsetx / localcardwidth) - 1, 0); var starty = math.max(math.floor(-initialoffsety / localcardheight) - 1, 0); var endx = math.min(startx + math.floor((maincanvas.width - initialoffsetx - startx * localcardwidth) / localcardwidth) + 1, colscount); var endy = math.min(starty + math.floor((maincanvas.height - initialoffsety - starty * localcardheight) / localcardheight) + 1, rowscount); // getting current cache var imagecache = getcorrectimagecache(); // render for (var y = starty; y < endy; y++) { for (var x = startx; x < endx; x++) { var localx = x * localcardwidth + initialoffsetx; var localy = y * localcardheight + initialoffsety; // clip if (localx > maincanvas.width) continue; if (localy > maincanvas.height) continue; if (localx + localcardwidth < 0) continue; if (localy + localcardheight < 0) continue; var card = listofcards[x + y * colscount]; if (card == undefined) continue; // get from cache var img = imagecache.getimageforcard(card); // render try { if (img != undefined) drawingcontext.drawimage(img, localx, localy, localcardwidth, localcardheight); } catch (e) { $.grep(listofcards, function (item) { return item.image != img; }); } } }; // scroll bars drawscrollbars(effectivetotalcardsinwidth, effectivetotalcardsinheight, initialoffsetx, initialoffsety); // fps computefps(); } this function is built around many key points: if the cards list is not yet loaded, we display a tooltip indicating that download is in progress: var pointcount = 0; function drawwaitmessage() { pointcount++; if (pointcount > 200) pointcount = 0; var points = ""; for (var index = 0; index < pointcount / 10; index++) points += "."; $("#waittext").html("loading...please wait" + points); subsequently, we define the position of the display window (in terms of cards and coordinates), then we proceed to clean the canvas: function clearcanvas() { maincanvas.width = document.body.clientwidth - 50; maincanvas.height = document.body.clientheight - 140; drawingcontext.fillstyle = "rgb(0, 0, 0)"; drawingcontext.fillrect(0, 0, maincanvas.width, maincanvas.height); } then we browse the cards list and call the drawimage function of the canvas context. the current image is provided by the active cache (depending on the zoom): // get from cache var img = imagecache.getimageforcard(card); // render try { if (img != undefined) drawingcontext.drawimage(img, localx, localy, localcardwidth, localcardheight); } catch (e) { $.grep(listofcards, function (item) { return item.image != img; }); we also have to draw the scroll bar with the roundedrectangle function that uses quadratic curves: function roundedrectangle(x, y, width, height, radius) { drawingcontext.beginpath(); drawingcontext.moveto(x + radius, y); drawingcontext.lineto(x + width - radius, y); drawingcontext.quadraticcurveto(x + width, y, x + width, y + radius); drawingcontext.lineto(x + width, y + height - radius); drawingcontext.quadraticcurveto(x + width, y + height, x + width - radius, y + height); drawingcontext.lineto(x + radius, y + height); drawingcontext.quadraticcurveto(x, y + height, x, y + height - radius); drawingcontext.lineto(x, y + radius); drawingcontext.quadraticcurveto(x, y, x + radius, y); drawingcontext.closepath(); drawingcontext.stroke(); drawingcontext.fill(); } function drawscrollbars(effectivetotalcardsinwidth, effectivetotalcardsinheight, initialoffsetx, initialoffsety) { drawingcontext.fillstyle = "rgba(255, 255, 255, 0.6)"; drawingcontext.linewidth = 2; // vertical var totalscrollheight = effectivetotalcardsinheight + maincanvas.height; var scaleheight = maincanvas.height - 20; var scrollheight = maincanvas.height / totalscrollheight; var scrollstarty = (-initialoffsety + maincanvas.height * 0.5) / totalscrollheight; roundedrectangle(maincanvas.width - 8, scrollstarty * scaleheight + 10, 5, scrollheight * scaleheight, 4); // horizontal var totalscrollwidth = effectivetotalcardsinwidth + maincanvas.width; var scalewidth = maincanvas.width - 20; var scrollwidth = maincanvas.width / totalscrollwidth; var scrollstartx = (-initialoffsetx + maincanvas.width * 0.5) / totalscrollwidth; roundedrectangle(scrollstartx * scalewidth + 10, maincanvas.height - 8, scrollwidth * scalewidth, 5, 4); } and finally, we need to compute the number of frames per second: function computefps() { if (previous.length > 60) { previous.splice(0, 1); } var start = (new date).gettime(); previous.push(start); var sum = 0; for (var id = 0; id < previous.length - 1; id++) { sum += previous[id + 1] - previous[id]; } var diff = 1000.0 / (sum / previous.length); $("#cardscount").text(diff.tofixed() + " fps. " + listofcards.length + " cards displayed"); } drawing cards relies heavily on the browser's ability to speed up canvas rendering. for the record, here are the performances on my machine with the minimum zoom level (0.05): browser fps internet explorer 9 30 firefox 5 30 chrome 12 17 ipad (with a zoom level of 0.8) 7 windows phone mango (with a zoom level of 0.8) 20 (!!) the site even works on mobile phones and tablets as long as they support html 5. here we can see the inner power of html 5 browsers that can handle a full screen of cards more than 30 times per second! mouse management to browse our cards collection, we have to manage the mouse (including its wheel). for the scrolling, we'll just handle the onmouvemove , onmouseup and onmousedown events. onmouseup and onmousedown events will be used to detect if the mouse is clicked or not: var mousedown = 0; document.body.onmousedown = function (e) { mousedown = 1; getmouseposition(e); previousx = posx; previousy = posy; }; document.body.onmouseup = function () { mousedown = 0; }; the onmousemove event is connected to the canvas and used to move the view: var previousx = 0; var previousy = 0; var posx = 0; var posy = 0; function getmouseposition(eventargs) { var e; if (!eventargs) e = window.event; else { e = eventargs; } if (e.offsetx || e.offsety) { posx = e.offsetx; posy = e.offsety; } else if (e.clientx || e.clienty) { posx = e.clientx; posy = e.clienty; } } function onmousemove(e) { if (!mousedown) return; getmouseposition(e); mousemovefunc(posx, posy, previousx, previousy); previousx = posx; previousy = posy; } this function (onmousemove) calculates the current position and provides also the previous value in order to move the offset of the display window: function move(posx, posy, previousx, previousy) { currentaddx = (posx - previousx) / visucontrol.zoom; currentaddy = (posy - previousy) / visucontrol.zoom; } mousehelper.registermousemove(maincanvas, move); note that jquery also provides tools to manage mouse events. for the management of the wheel, we will have to adapt to different browsers that do not behave the same way on this point: function wheel(event) { var delta = 0; if (event.wheeldelta) { delta = event.wheeldelta / 120; if (window.opera) delta = -delta; } else if (event.detail) { /** mozilla case. */ delta = -event.detail / 3; } if (delta) { wheelfunc(delta); } if (event.preventdefault) event.preventdefault(); event.returnvalue = false; } we can see that everyone does what he wants :). the function to register with this event is: mousehelper.registerwheel = function (func) { wheelfunc = func; if (window.addeventlistener) window.addeventlistener('dommousescroll', wheel, false); window.onmousewheel = document.onmousewheel = wheel; }; and we will use this function to change the zoom with the wheel: // mouse mousehelper.registerwheel(function (delta) { currentaddzoom += delta / 500.0; }); finally we will add a bit of inertia when moving the mouse (and the zoom) to give some kind of smoothness: // inertia var inertia = 0.92; var currentaddx = 0; var currentaddy = 0; var currentaddzoom = 0; function doinertia() { visucontrol.offsetx += currentaddx; visucontrol.offsety += currentaddy; visucontrol.zoom += currentaddzoom; var effectivetotalcardsinwidth = colscount * cardwidth; var rowscount = math.ceil(totalcards / colscount); var effectivetotalcardsinheight = rowscount * cardheight var maxoffsetx = effectivetotalcardsinwidth / 2.0; var maxoffsety = effectivetotalcardsinheight / 2.0; if (visucontrol.offsetx < -maxoffsetx + cardwidth) visucontrol.offsetx = -maxoffsetx + cardwidth; else if (visucontrol.offsetx > maxoffsetx) visucontrol.offsetx = maxoffsetx; if (visucontrol.offsety < -maxoffsety + cardheight) visucontrol.offsety = -maxoffsety + cardheight; else if (visucontrol.offsety > maxoffsety) visucontrol.offsety = maxoffsety; if (visucontrol.zoom < 0.05) visucontrol.zoom = 0.05; else if (visucontrol.zoom > 1) visucontrol.zoom = 1; processlistofcards(); currentaddx *= inertia; currentaddy *= inertia; currentaddzoom *= inertia; // epsilon if (math.abs(currentaddx) < 0.001) currentaddx = 0; if (math.abs(currentaddy) < 0.001) currentaddy = 0; } this kind of small function does not cost a lot to implement, but adds a lot to the quality of user experience. state storage also to provide a better user experience, we will save the display window’s position and zoom. to do this, we will use the service of localstorage (which saves pairs of keys / values for the long term (the data is retained after the browser is closed) and only accessible by the current window object): function saveconfig() { if (window.localstorage == undefined) return; // zoom window.localstorage["zoom"] = visucontrol.zoom; // offsets window.localstorage["offsetx"] = visucontrol.offsetx; window.localstorage["offsety"] = visucontrol.offsety; } // restore data if (window.localstorage != undefined) { var storedzoom = window.localstorage["zoom"]; if (storedzoom != undefined) visucontrol.zoom = parsefloat(storedzoom); var storedoffsetx = window.localstorage["offsetx"]; if (storedoffsetx != undefined) visucontrol.offsetx = parsefloat(storedoffsetx); var storedoffsety = window.localstorage["offsety"]; if (storedoffsety != undefined) visucontrol.offsety = parsefloat(storedoffsety); } animations to add even more dynamism to our application we will allow our users to double-click on a card to zoom and focus on it. our system should animate three values: the two offsets (x, y) and the zoom. to do this, we will use a function that will be responsible of animating a variable from a source value to a destination value with a given duration: var animationhelper = function (root, name) { var paramname = name; this.animate = function (current, to, duration) { var offset = (to - current); var ticks = math.floor(duration / 16); var offsetpart = offset / ticks; var tickscount = 0; var intervalid = setinterval(function () { current += offsetpart; root[paramname] = current; tickscount++; if (tickscount == ticks) { clearinterval(intervalid); root[paramname] = to; } }, 16); }; }; the use of this function is: // prepare animations parameters var zoomanimationhelper = new animationhelper(visucontrol, "zoom"); var offsetxanimationhelper = new animationhelper(visucontrol, "offsetx"); var offsetyanimationhelper = new animationhelper(visucontrol, "offsety"); var speed = 1.1 - visucontrol.zoom; zoomanimationhelper.animate(visucontrol.zoom, 1.0, 1000 * speed); offsetxanimationhelper.animate(visucontrol.offsetx, targetoffsetx, 1000 * speed); offsetyanimationhelper.animate(visucontrol.offsety, targetoffsety, 1000 * speed); the advantage of the animationhelper function is that it is able to animate as many parameters as you wish (and that only with the settimer function!) handling multi-devices finally we will ensure that our page can also be seen on tablets pc and even on phones. to do this, we will use a feature of css 3: the media-queries . with this technology, we can apply style sheets according to some queries such as a specific display size: here we see that if the screen width is less than 480 pixels, the following style sheet will be added: #legal { font-size: 8px; } #title { font-size: 30px !important; } #waittext { font-size: 12px; } #bolaslogo { width: 48px; height: 48px; } #picturecell { width: 48px; } finally we will ensure that our page can also be seen on tablets pc and even on phones. to do this, we will use a feature of css 3: #legal { font-size: 8px; } #title { font-size: 30px !important; } #waittext { font-size: 12px; } #bolaslogo { width: 48px; height: 48px; } #picturecell { width: 48px; } conclusion html 5 / css 3 / javascript and visual studio 2010 allow to develop portable and efficient solutions (within the limits of browsers that support html 5 of course) with some great features such as hardware accelerated rendering. this kind of development is also simplified by the use of frameworks like jquery. also, i am especially fan of javascript that turns out to be a very powerful dynamic language. of course, c# or vb.net developers have to change theirs reflexes but for the development of web pages it's worth. in conclusion, i think that the best to be convinced is to try! to go further internet explorer test drive: http://ie.microsoft.com/testdrive/ internet explorer 9 guide for developer : http://msdn.microsoft.com/en-us/ie/ff468705 w3c site for html 5 : http://dev.w3.org/html5/spec/overview.html internet explorer site : http://msdn.microsoft.com/en-us/ie/aa740469 about the author david catuhe is a developer evangelist for microsoft france in charge of user experience development tools (from xaml to directx/xna and html5). he defines himself as a geek and likes coding all that refer to graphics. before working for microsoft, he founded a company that developed a realtime 3d engine written with directx ( www.vertice.fr ). source: http://blogs.msdn.com/b/eternalcoding/archive/2011/07/25/feedback-of-a-graphic-development-using-html5-amp-javascript.aspx

i guess, i’m writing this post after a long time. this time, i’m writing about apache tika api that a friend of mine and i tried out to extract/retrieve metadata information from audio files supported by it – .mp3, .aiff, .au, .midi, .wav. to make it clear, here’s a screenshot of the information shown by windows vista about an audio file: we wanted to extract this using java and with googling, found that apache tika would help. we needed this metadata to index audio files for it to be searchable in a search application that we’re building using apache lucene . here’s a sample java program that extracts metadata from an mp3 file: package singz.samples.search.audio.metadata; import java.io.file; import java.io.fileinputstream; import java.io.filenotfoundexception; import java.io.ioexception; import java.io.inputstream; import org.apache.tika.exception.tikaexception; import org.apache.tika.metadata.metadata; import org.apache.tika.parser.parsecontext; import org.apache.tika.parser.parser; import org.apache.tika.parser.mp3.mp3parser; import org.xml.sax.contenthandler; import org.xml.sax.saxexception; import org.xml.sax.helpers.defaulthandler; /** * @author singaram subramanian * extract metadata of an audio file using apache tika api * */ public class audiometadataextractordemo { public static void main(string[] args) { // this audio file has metadata embedded in xmp (extensible metadata platform) standard // created by adobe systems inc. xmp standardizes the definition, creation, and // processing of extensible metadata. string audiofileloc = "c:\\pop\\backstreetboys_showmethemeaningofbeinglonely.mp3"; try { inputstream input = new fileinputstream(new file(audiofileloc)); contenthandler handler = new defaulthandler(); metadata metadata = new metadata(); parser parser = new mp3parser(); parsecontext parsectx = new parsecontext(); parser.parse(input, handler, metadata, parsectx); input.close(); // list all metadata string[] metadatanames = metadata.names(); for(string name : metadatanames){ system.out.println(name + ": " + metadata.get(name)); } // retrieve the necessary info from metadata // names - title, xmpdm:artist etc. - mentioned below may differ based // on the standard used for processing and storing standardized and/or // proprietary information relating to the contents of a file. system.out.println("title: " + metadata.get("title")); system.out.println("artists: " + metadata.get("xmpdm:artist")); system.out.println("genre: " + metadata.get("xmpdm:genre")); } catch (filenotfoundexception e) { e.printstacktrace(); } catch (ioexception e) { e.printstacktrace(); } catch (saxexception e) { e.printstacktrace(); } catch (tikaexception e) { e.printstacktrace(); } } } maven pom xml 4.0.0 singz.samples.search.audio audiometadataextractor 0.0.1 jar audiometadataextractor http://maven.apache.org utf-8 org.apache.tika tika-core 0.10 org.apache.tika tika-parsers 0.10 output xmpdm:releasedate: 2001 xmpdm:audiochanneltype: stereo xmpdm:album: top 100 pop author: backstreet boys xmpdm:artist: backstreet boys channels: 2 xmpdm:audiosamplerate: 44100 xmpdm:logcomment: eng xmpdm:tracknumber: 04 version: mpeg 3 layer iii version 1 xmpdm:composer: null xmpdm:audiocompressor: mp3 title: show me the meaning of being lonely samplerate: 44100 xmpdm:genre: pop content-type: audio/mpeg title: show me the meaning of being lonely artists: backstreet boys genre: pop about apache tika http://tika.apache.org/index.html “the apache tika™ toolkit detects and extracts metadata and structured text content from various documents using existing parser libraries.” http://www.lucidimagination.com/devzone/technical-articles/content-extraction-tika#article.tika “apache tika is a content type detection and content extraction framework. tika provides a general application programming interface that can be used to detect the content type of a document and also parse textual content and metadata from several document formats. tika does not try to understand the full variety of different document formats by itself but instead delegates the real work to various existing parser libraries such as apache poi for microsoft formats, pdfbox for adobe pdf, neko html for html etc. the grand idea behind tika is that it offers a generic interface for parsing multiple formats. the tika api hides the technical differences of the various parser implementations. this means that you don’t have to learn and consume one api for every format you use but can instead use a single api – the tika api. internally tika usually delegates the parsing work to existing parsing libraries and adapts the parse result so that client applications can easily manage variety of formats. tika aims to be efficient in using available resources (mainly ram) while parsing. the tika api is stream oriented so that the parsed source document does not need to be loaded into memory all at once but only as it is needed. ultimately, however, the amount of resources consumed is mandated by the parser libraries that tika uses. at the time of writing this, tika supports directly around 30 document formats. see list of supported document formats . the list of supported document formats is not limited by tika in any way. in the simplest case you can add support for new document formats by implementing a thin adapter that that implements the parser interface for the new document format.” about xmp standard http://en.wikipedia.org/wiki/extensible_metadata_platform “the adobe extensible metadata platform ( xmp ) is a standard, created by adobe systems inc. , for processing and storing standardized and proprietary information relating to the contents of a file. xmp standardizes the definition, creation, and processing of extensible metadata . serialized xmp can be embedded into a significant number of popular file formats, without breaking their readability by non-xmp-aware applications. embedding metadata avoids many problems that occur when metadata is stored separately. xmp is used in pdf , photography and photo editing applications. xmp can be used in several file formats such as pdf , jpeg , jpeg 2000 , jpeg xr , gif , png , html , tiff , adobe illustrator , psd , mp3 , mp4 , audio video interleave , wav , rf64 , audio interchange file format , postscript , encapsulated postscript , and proposed for djvu . in a typical edited jpeg file, xmp information is typically included alongside exif and iptc information interchange model data.” from http://singztechmusings.wordpress.com/2011/10/17/how-to-retrieveextract-metadata-information-from-audio-files-using-java-and-apache-tika-api/

Every now and then some OOP purist comes and tells us that getters and setters are evil, because they break encapsulation. And you should never, ever use getters and setters because this is a sign of a bad design and leads to maintainability nightmares. Well, don’t worry, because those people are wrong. Not completely wrong of course, because getters and setters can break encapsulation, but in the usual scenario for regular business projects they don’t. What is the purpose of encapsulation? First, to hide how exactly an object performs its job. And to protect the internal data of an object, so that no external object can violate its state space. In other words, only the object knows which combination of field values is valid and which isn’t. Exposing fields to the outside world can leave the object in inconsistent state. For example what if you could change the backing array in an ArrayList, without setting the size field? The ArrayList instance will be inconsistent and will be violating its contract. So no getter and setter for the array list internal array. But the majority of objects for which people generate getters and setters are simple data holders. They don’t have any rules to enforce on their state, the state space consists of all possible combinations of values, and furthermore – there is nothing they can do with that data. And before you call me “anemic”, it doesn’t matter if you are doing “real OOP” with domain-driven design, where you have business logic & state in the same object, or you are doing fat service layer + anemic objects. Why it doesn’t matter? Because even in domain-driven projects you have DTOs. And DTOs are simply data holders, which need getters and setters. Another thing is that in many cases your object state is public anyway. Tools use reflection to make use of objects – view technologies use EL to access objects, ORMs use reflection to persist your entities, jackson uses reflection to serialize your objects to JSON, jasper reports uses reflection to get details from its model, etc. Virtually anything you do in the regular project out there requires data being passed outside of the application: to the user, to the database, to the printer, as a result of an API call. And you have to know what that data is. In EL you have ${foo.bar} – with, or without a getter, you consume that field. In an ORM you need to know what database types to use. In the documentation of your JSON API you should specify the structure (another topic here is whether rest-like services need documentation). The overall point here is that you win nothing by not having getters and setters on your data holder objects. Their internal state is public anyway, and it has to be. And any change in those fields means a change has to be made in other places. Change is something people fear – “you will have to change it everywhere in your project” .. well, yeah, you have, because it has changed. If you change the structure of an address from String to an Address class, it’s likely that you should revisit all places it is used and split it there as well. If you change a double to BigDecimal you’d better go and fix all your calculations. Another point – the above examples emphasized on reading the data. However, you must set that data somehow. You have roughly 3 options – constructor, builder, setters. A constructor with 15 arguments is obviously not an option. A builder for every object is just too verbose. So we use setters, because it is more practical and more readable. And that’s the main point here – setters and getters are practical when used on data holder objects. I have supported quite big projects that had a lot of setters and getters, and I had absolutely no problem with that. In fact, tracing “who sets that data” is the same as “where did this object (that encapsulates its data) came from”. And yes, in an ideal OO world you wouldn’t need data holders / DTOs, and there will be no flow of data in the system. But in the real world there is. To conclude – be careful with setters and getters on non-data-only objects. Encapsulation is a real thing. When designing a library, a component or some base frameworks in your project – don’t simply generate getters and setters. But for the regular data object – don’t worry, there’s no evil in that. From http://techblog.bozho.net/?p=621