Exposing a REST based endpoint for a Spring Boot application or for that matter a straight Spring MVC application is straightforward, the following is a controller exposing an endpoint to create an entity based on the content POST'ed to it: @RestController @RequestMapping("/rest/hotels") public class RestHotelController { .... @RequestMapping(method=RequestMethod.POST) public Hotel create(@RequestBody @Valid Hotel hotel) { return this.hotelRepository.save(hotel); } } Internally Spring MVC uses a component called a HttpMessageConverter to convert the Http request to an object representation and back. A set of default converters are automatically registered which supports a whole range of different resource representation formats - json, xml for instance. Now, if there is a need to customize the message converters in some way, Spring Boot makes it simple. As an example consider if the POST method in the sample above needs to be little more flexible and should ignore properties which are not present in the Hotel entity - typically this can be done by configuring the Jackson ObjectMapper, all that needs to be done with Spring Boot is to create a new HttpMessageConverter bean and that would end up overriding all the default message converters, this way: @Bean public MappingJackson2HttpMessageConverter mappingJackson2HttpMessageConverter() { MappingJackson2HttpMessageConverter jsonConverter = new MappingJackson2HttpMessageConverter(); ObjectMapper objectMapper = new ObjectMapper(); objectMapper.configure(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES, false); jsonConverter.setObjectMapper(objectMapper); return jsonConverter; } This works well for a Spring-Boot application, however for straight Spring MVC applications which do not make use of Spring-Boot, configuring a custom converter is a little more complicated - the default converters are not registered by default and an end user has to be explicit about registering the defaults: @Configuration public class WebConfig extends WebMvcConfigurationSupport { @Bean public MappingJackson2HttpMessageConverter customJackson2HttpMessageConverter() { MappingJackson2HttpMessageConverter jsonConverter = new MappingJackson2HttpMessageConverter(); ObjectMapper objectMapper = new ObjectMapper(); objectMapper.configure(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES, false); jsonConverter.setObjectMapper(objectMapper); return jsonConverter; } @Override public void configureMessageConverters(List> converters) { converters.add(customJackson2HttpMessageConverter()); super.addDefaultHttpMessageConverters(); } } Here WebMvcConfigurationSupport provides a way to more finely tune the MVC tier configuration of a Spring based application. In the configureMessageConverters method, the custom converter is being registered and then an explicit call is being made to ensure that the defaults are registered also. A little more work than for a Spring-Boot based application.

Apache Camel 2.14 is being released later this month. There is a slight holdup due some Apache infrastructure issue which is being worked on. This blog post is to talk about one of the new functions we have added to this release. Thanks to Lauri Kimmel who donated a camel-metrics component, we integrated with the excellent codehale metrics library. So I took this component one step further and integrated it with the Camel routes so we have additional metrics about the route performances using codehale metrics. This allows end users to seamless feed Camel routing information together with existing data they are gathering using codehale metrics. Also take note we have a lot of existing metrics from camel-core which of course is still around. What codehale brings to the table is that they have additional statistical data which we do not have in camel-core. To use the codehale metics all you need to do is: add camel-metrics component enable route metrics in XML or Java code To enable in XML you declare a as shown below: &;t;bean id="metricsRoutePolicyFactory" class="org.apache.camel.component.metrics. routepolicy.MetricsRoutePolicyFactory"/> And doing so in Java code is easy as well by calling this method on your CamelContext. context.addRoutePolicyFactory(new MetricsRoutePolicyFactory()); Now performance metrics is only useable if you have a way of displaying them, and for that you can use hawtio. Notice you can use any kind of monitoring tooling which can integrate with JMX, as the metrics is available over JMX. The actual data is 100% codehale json format, where a piece of the data is shown in the figure below. Sample of the route metrics JSON data The next release of hawtio supports Camel 2.14 and automatic detects if you have enabled route metrics and if so, then shows a sub, where the information can be seen in real time in a graphical charts. hawtio have detected that we have route metrics enabled, and shows a sub tab where we can see the data in real time The screenshot above is from the new camel-example-servlet-rest-tomcat which we ship out of the box. This example demonstrates another new functionality in Camel 2.14 which is the Rest DSL (I will do a blog about that later). This example enables the route metrics out of the box, so what I did was to deploy this example together with hawtio (the hawtio-default WAR) in Apache Tomcat 8. With hawtio you can also build custom dashboards, so here at the end I have put together a dashboard with various screens from hawtio to have a custom view of a Camel application. hawtio dashboard with Camel route and metrics as well control panel to control the route(s), and the logs in the bottom.

JSF is not what we often think it is. It's also a framework that can be somewhat tricky to debug, especially when first encountered. In this post let's go over on why that is and provide some JSF debugging techniques. We will go through the following topics: JSF is not what we often think The difficulties of JSF debugging How to debug JSF systematically How JSF Works - The JSF lifecycle Debugging an Ajax request from browser to server and back Debugging the JSF frontend Javascript code Final thoughts - alternatives? (questions to the reader) JSF is not what we often think JSF looks on first look like an enterprise Java/XML frontend framework, but under the hood it really isn't. It's really a polyglot Java/Javascript framework, where the client Javascript part is non-neglectable and also important to understand it. It also has good support for direct HTML/CSS use. JSF developers are on ocasion already polyglot developers, whose primary language is Java but still need to use ocasionally Javascript. The difficulties of JSF debugging When comparing JSF to GWT and AngularJS in a previous post, I found that the (most often used) approach that the framework takes of abstracting HTML and CSS from the developer behind XML adds to the difficulty of debugging, because it creates an extra level of indirection. A more direct approach of using HTML/CSS directly is also possible, but it seems enterprise Java developers tend to stick to XML in most cases, because it's a more familiar technology. Also another problem is that the client side Javascript part of the framework/libraries is not very well documented, and it's often important to understand what is going on. The only way to debug JSF systematically When first encountering JSF, I first tried to approach it from a Java, XML and documentation only. While I could do a part of the work that way, there where frequent situations where that approach was really not sufficient. The conclusion that I got to is that in order to be able to debug JSF applications effectively, an understanding of the following is needed: HTML CSS Javascript HTTP Chrome Dev Tools, Firebug or equivalent The JSF Lifecycle This might sound surprising to developers that work mostly in Java/XML, but this web-centric approach to debugging JSF is the only way that I managed to tackle many requirements that needed some significant component customization, or to be able to fix certain bugs. Let’s start by understanding the inner workings of JSF, so that we can debug it better. The JSF take on MVC The way JSF approaches MVC is that the whole 3 components reside on the server side: The Model is a tree of plain Java objects The View is a server side template defined in XML that is read to build an in-memory view definition The Controller is a Java servlet, that receives each request and processes them through a series of steps The browser is assumed to be simply a rendering engine for the HTML generated at server side. Ajax is achieved by submitting parts of the page for server processing, and requesting a server to ‘repaint’ only portions of the screen, without navigating away from the page. The JSF Lifecycle Once an HTTP request reaches the backend, it gets caught by the JSF Controller that will then process it. The request goes through a series of phases known as the JSF lifecycle, which is essential to understand how JSF works: Design Goals of the JSF Lifecycle The whole point of the lifecycle is to manage MVC 100% on the server side, using the browser as a rendering platform only. The initial idea was to decouple the rendering platform from the server-side UI component model, in order to allow to replace HTML with alternative markup languages by swapping the Render Response phase. This was in the early 2000's when HTML could be soon replaced by XML-based alternatives (that never came to be), and then HTML5 came along. Also browsers where much more qwirkier than what they are today, and the idea of cross-browser Javascript libraries was not widespread. So let’s go through each phase and see how to debug it if needed, starting in the browser. Let's base ourselves in a simple example that uses an Ajax request. A JSF 2 Hello World Example The following is a minimal JSF 2 page, that receives an input text from the user, sends the text via an Ajax request to the backend and refreshes only an output label: JSF 2.2 Hello World Example The page looks like this: Following one Ajax request - to the server and back Let’s click submit in order to trigger the Ajax request, and use the Chrome Dev Tools Network tab (right click and inspect any element on the page).What goes over the wire? This is what we see in the Form Data section of the request: j_idt8:input: Hello World javax.faces.ViewState: -2798727343674530263:954565149304692491 javax.faces.source: j_idt8:j_idt9 javax.faces.partial.event: click javax.faces.partial.execute: j_idt8:j_idt9 j_idt8:input javax.faces.partial.render: j_idt8:output javax.faces.behavior.event: action javax.faces.partial.ajax:true This request says: The new value of the input field is "Hello World", send me a new value for the output field only, and don't navigate away from this page. Let's see how this can be read from the request. As we can see, the new values of the form are submitted to the server, namely the “Hello World” value. This is the meaning of the several entries: javax.faces.ViewState identifies the view from which the request was made. The request is an Ajax request, as indicated by the flag javax.faces.partial.ajax, The request was triggered by a click as defined in javax.faces.partial.event. But what are those j_ strings ? Those are space separated generated identifiers of HTML elements. For example this is how we can see what is the page element corresponding to j_idt8:input, using the Chrome Dev Tools: There are also 3 extra form parameters that use these identifiers, that are linked to UI components: javax.faces.source: The identifier of the HTML element that originated this request, in this case the Id of the submit button. javax.faces.execute: The list of identifiers of the elements whose values are sent to the server for processing, in this case the input text field. javax.faces.render: The list of identifiers of the sections of the page that are to be ‘repainted', in this case the output field only. But what happens when the request hits the server ? JSF lifecycle - Restore View Phase Once the request reaches the server, the JSF controller will inspect the javax.faces.ViewState and identify to which view it refers. It will then build or restore a Java representation of the view, that is somehow similar to the document definition in the browser side. The view will be attached to the request and used throughout. There is usually little need to debug this phase during application development. JSF Lifecycle - Apply Request Values The JSF Controller will then apply to the view widgets the new values received via the request. The values might be invalid at this point. Each JSF component gets a call to it’s decode method in this phase. This method will retrieve the submitted value for the widget in question from the HTTP request and store it on the widget itself. To debug this, let’s put a breakpoint in the decode method of the HtmlInputText class, to see the value “Hello World”: Notice the conditional breakpoint using the HTML clientId of the field we want. This would allow to quickly debug only the decoding of the component we want, even in a large page with many other similar widgets. Next after decoding is the validation phase. JSF Lifecycle - Process Validations In this phase, validations are applied and if the value is found to be in error (for example a date is invalid), then the request bypasses Invoke Application and goes directly to Render Response phase. To debug this phase, a similar breakpoint can be put on method processValidators, or in the validators themselves if you happen to know which ones or if they are custom. JSF Lifecycle - Update Model In this phase, we know all the submitted values where correct. JSF can now update the view model by applying the new values received in the requests to the plain Java objects in the view model. This phase can be debugged by putting a breakpoint in the processUpdates method of the component in question, eventually using a similar conditional breakpoint to break only on the component needed. JSF Lifecycle - Invoke Application This is the simplest phase to debug. The application now has an updated view model, and some logic can be applied on it. This is where the action listeners defined in the XML view definition (the 'action' properties and the listener tags) are executed. JSF Lifecycle - Render Response This is the phase that I end up debugging the most: why is the value not being displayed as we expect it, etc, it all can be found here. In this phase the view and the new model values will be transformed from Java objects into HTML, CSS and eventually Javascript and sent back over the wire to the browser. This phase can be debugged using breakpoints in the encodeBegin, encodeChildren and encodeEnd methods of the component in question. The components will either render themselves or delegate rendering to aRenderer class. Back in the browser It was a long trip, but we are back where we started! This is how the response generated by JSF looks once received in the browser: -8188482707773604502:6956126859616189525> What the Javascript part of the framework will do is to take the contents of the partial response, update by update. Using the Id of the update, the client side JSF callback will search for a component with that Id, delete it from the document and replace it with the new updated version. In this case, "Hello World" will show up on the label next to the Input text field! And so thats how JSF works under the hood. But what about if we need to debug the Javascript part of the framework? Debugging the JSF Javascript Code The Chrome Dev Tools can help debug the client part. For example let’s say that we want to halt the client when an Ajax request is triggered. We need to go to the sources tab, add an XHR (Ajax) breakpoint and trigger the browser action. The debugger will stop and the call stack can be examined: For some frameworks like Primefaces, the Javascript sources might be minified (non human-readable) because they are optimized for size. To solve this, download the source code of the library and do a non minified build of the jar. There are usually instructions for this, otherwise check the project poms. This will install in your Maven repository a jar with non minified sources for debugging. The UI Debug tag: The ui:debug tag allows to view a lot of debugging information using a keyboard shortcut, see here for further details. Final Thoughts JSF is very popular in the enterprise Java world, and it handles a lot of problems well, specially if the UI designers take into account the possibilities of the widget library being used. The problem is that there are usually feature requests that force us to dig deeper into the widgets internal implementation in order to customize them, and this requires HTML, CSS, Javascript and HTTP plus JSF lifecycle knowledge. Is polyglot an alternative? We can wonder that if developers have to know a fair amount about web technologies in order to be able to debug JSF effectively, then it would be simpler to build enterprise front ends (just the client part) using those technologies directly instead. It's possible that a polyglot approach of a Java backend plus a Javascript-only frontend could be proved effective in a nearby future, specially using some sort of a client side MVC framework like Angular. This would require learning more Javascript, (have a look at Javascript for Java developers post if curious), but this is already often necessary to do custom widget development in JSF anyway. Conclusions and some questions to the reader Thanks for reading, please take a moment to share your thoughts on these matters on the comments bellow: do you believe polyglot development (Java/Javascript) is a viable alternative in general, and in your workplace in particular? Did you find one of the GWT-based frameworks (plain GWT, Vaadin, Errai), or the Play Framework to be easier to use and of better productivity?

I’ve been working on migrating some batch jobs for Podcastpedia.org to Spring Batch. Before, these jobs were developed in my own kind of way, and I thought it was high time to use a more “standardized” approach. Because I had never used Spring with java configuration before, I thought this were a good opportunity to learn about it, by configuring the Spring Batch jobs in java. And since I am all into trying new things with Spring, why not also throw Spring Boot into the boat… Before you begin with this tutorial I recommend you read first Spring’s Getting started – Creating a Batch Service, because the structure and the code presented here builds on that original. 1. What I’ll build So, as mentioned, in this post I will present Spring Batch in the context of configuring it and developing with it some batch jobs for Podcastpedia.org. Here’s a short description of the two jobs that are currently part of the Podcastpedia-batch project: addNewPodcastJob reads podcast metadata (feed url, identifier, categories etc.) from a flat file transforms (parses and prepares episodes to be inserted with Http Apache Client) the data and in the last step, insert it to the Podcastpedia database and inform the submitter via emailabout it notifyEmailSubscribersJob – people can subscribe to their favorite podcasts on Podcastpedia.orgvia email. For those who did it is checked on a regular basis (DAILY, WEEKLY, MONTHLY) if new episodes are available, and if they are the subscribers are informed via email about those; read from database, expand read data via JPA, re-group it and notify subscriber via email Source code: The source code for this tutorial is available on GitHub – Podcastpedia-batch. Note: Before you start I also highly recommend you read the Domain Language of Batch, so that terms like “Jobs”, “Steps” or “ItemReaders” don’t sound strange to you. 2. What you’ll need A favorite text editor or IDE JDK 1.7 or later Maven 3.0+ 3. Set up the project The project is built with Maven. It uses Spring Boot, which makes it easy to create stand-alone Spring based Applications that you can “just run”. You can learn more about the Spring Boot by visiting theproject’s website. 3.1. Maven build file Because it uses Spring Boot it will have the spring-boot-starter-parent as its parent, and a couple of other spring-boot-starters that will get for us some libraries required in the project: pom.xml of the podcastpedia-batch project 4.0.0 org.podcastpedia.batch podcastpedia-batch 0.1.0 1.1.6.RELEASE 1.7 org.springframework.boot spring-boot-starter-parent 1.1.6.RELEASE org.springframework.boot spring-boot-starter-batch org.springframework.boot spring-boot-starter-data-jpa org.apache.httpcomponents httpclient 4.3.5 org.apache.httpcomponents httpcore 4.3.2 org.apache.velocity velocity 1.7 org.apache.velocity velocity-tools 2.0 org.apache.struts struts-core rome rome 1.0 rome rome-fetcher 1.0 org.jdom jdom 1.1 xerces xercesImpl 2.9.1 mysql mysql-connector-java 5.1.31 org.springframework.boot spring-boot-starter-freemarker org.springframework.boot spring-boot-starter-remote-shell javax.mail mail javax.mail mail 1.4.7 javax.inject javax.inject 1 org.twitter4j twitter4j-core [4.0,) org.springframework.boot spring-boot-starter-test maven-compiler-plugin org.springframework.boot spring-boot-maven-plugin Note: One big advantage of using the spring-boot-starter-parent as the project’s parent is that you only have to upgrade the version of the parent and it will get the “latest” libraries for you. When I started the project spring boot was in version 1.1.3.RELEASE and by the time of finishing to write this post is already at 1.1.6.RELEASE. 3.2. Project directory structure I structured the project in the following way: └── src └── main └── java └── org └── podcastpedia └── batch └── common └── jobs └── addpodcast └── notifysubscribers Note: the org.podcastpedia.batch.jobs package contains sub-packages having specific classes to particular jobs. the org.podcastpedia.batch.jobs.common package contains classes used by all the jobs, like for example the JPA entities that both the current jobs require. 4. Create a batch Job configuration I will start by presenting the Java configuration class for the first batch job: package org.podcastpedia.batch.jobs.addpodcast; import org.podcastpedia.batch.common.configuration.DatabaseAccessConfiguration; import org.podcastpedia.batch.common.listeners.LogProcessListener; import org.podcastpedia.batch.common.listeners.ProtocolListener; import org.podcastpedia.batch.jobs.addpodcast.model.SuggestedPodcast; import org.springframework.batch.core.Job; import org.springframework.batch.core.Step; import org.springframework.batch.core.configuration.annotation.EnableBatchProcessing; import org.springframework.batch.core.configuration.annotation.JobBuilderFactory; import org.springframework.batch.core.configuration.annotation.StepBuilderFactory; import org.springframework.batch.item.ItemProcessor; import org.springframework.batch.item.ItemReader; import org.springframework.batch.item.ItemWriter; import org.springframework.batch.item.file.FlatFileItemReader; import org.springframework.batch.item.file.LineMapper; import org.springframework.batch.item.file.mapping.BeanWrapperFieldSetMapper; import org.springframework.batch.item.file.mapping.DefaultLineMapper; import org.springframework.batch.item.file.transform.DelimitedLineTokenizer; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration; import org.springframework.context.annotation.Import; import org.springframework.core.io.ClassPathResource; import com.mysql.jdbc.exceptions.jdbc4.MySQLIntegrityConstraintViolationException; @Configuration @EnableBatchProcessing @Import({DatabaseAccessConfiguration.class, ServicesConfiguration.class}) public class AddPodcastJobConfiguration { @Autowired private JobBuilderFactory jobs; @Autowired private StepBuilderFactory stepBuilderFactory; // tag::jobstep[] @Bean public Job addNewPodcastJob(){ return jobs.get("addNewPodcastJob") .listener(protocolListener()) .start(step()) .build(); } @Bean public Step step(){ return stepBuilderFactory.get("step") .chunk(1) //important to be one in this case to commit after every line read .reader(reader()) .processor(processor()) .writer(writer()) .listener(logProcessListener()) .faultTolerant() .skipLimit(10) //default is set to 0 .skip(MySQLIntegrityConstraintViolationException.class) .build(); } // end::jobstep[] // tag::readerwriterprocessor[] @Bean public ItemReader reader(){ FlatFileItemReader reader = new FlatFileItemReader(); reader.setLinesToSkip(1);//first line is title definition reader.setResource(new ClassPathResource("suggested-podcasts.txt")); reader.setLineMapper(lineMapper()); return reader; } @Bean public LineMapper lineMapper() { DefaultLineMapper lineMapper = new DefaultLineMapper(); DelimitedLineTokenizer lineTokenizer = new DelimitedLineTokenizer(); lineTokenizer.setDelimiter(";"); lineTokenizer.setStrict(false); lineTokenizer.setNames(new String[]{"FEED_URL", "IDENTIFIER_ON_PODCASTPEDIA", "CATEGORIES", "LANGUAGE", "MEDIA_TYPE", "UPDATE_FREQUENCY", "KEYWORDS", "FB_PAGE", "TWITTER_PAGE", "GPLUS_PAGE", "NAME_SUBMITTER", "EMAIL_SUBMITTER"}); BeanWrapperFieldSetMapper fieldSetMapper = new BeanWrapperFieldSetMapper(); fieldSetMapper.setTargetType(SuggestedPodcast.class); lineMapper.setLineTokenizer(lineTokenizer); lineMapper.setFieldSetMapper(suggestedPodcastFieldSetMapper()); return lineMapper; } @Bean public SuggestedPodcastFieldSetMapper suggestedPodcastFieldSetMapper() { return new SuggestedPodcastFieldSetMapper(); } /** configure the processor related stuff */ @Bean public ItemProcessor processor() { return new SuggestedPodcastItemProcessor(); } @Bean public ItemWriter writer() { return new Writer(); } // end::readerwriterprocessor[] @Bean public ProtocolListener protocolListener(){ return new ProtocolListener(); } @Bean public LogProcessListener logProcessListener(){ return new LogProcessListener(); } } The @EnableBatchProcessing annotation adds many critical beans that support jobs and saves us configuration work. For example you will also be able to @Autowired some useful stuff into your context: a JobRepository (bean name “jobRepository”) a JobLauncher (bean name “jobLauncher”) a JobRegistry (bean name “jobRegistry”) a PlatformTransactionManager (bean name “transactionManager”) a JobBuilderFactory (bean name “jobBuilders”) as a convenience to prevent you from having to inject the job repository into every job, as in the examples above a StepBuilderFactory (bean name “stepBuilders”) as a convenience to prevent you from having to inject the job repository and transaction manager into every step The first part focuses on the actual job configuration: @Bean public Job addNewPodcastJob(){ return jobs.get("addNewPodcastJob") .listener(protocolListener()) .start(step()) .build(); } @Bean public Step step(){ return stepBuilderFactory.get("step") .chunk(1) //important to be one in this case to commit after every line read .reader(reader()) .processor(processor()) .writer(writer()) .listener(logProcessListener()) .faultTolerant() .skipLimit(10) //default is set to 0 .skip(MySQLIntegrityConstraintViolationException.class) .build(); } The first method defines a job and the second one defines a single step. As you’ve read in The Domain Language of Batch, jobs are built from steps, where each step can involve a reader, a processor, and a writer. In the step definition, you define how much data to write at a time (in our case 1 record at a time). Next you specify the reader, processor and writer. 5. Spring Batch processing units Most of the batch processing can be described as reading data, doing some transformation on it and then writing the result out. This mirrors somehow the Extract, Transform, Load (ETL) process, in case you know more about that. Spring Batch provides three key interfaces to help perform bulk reading and writing: ItemReader, ItemProcessor and ItemWriter. 5.1. Readers ItemReader is an abstraction providing the mean to retrieve data from many different types of input: flat files, xml files, database, jms etc., one item at a time. See the Appendix A. List of ItemReaders and ItemWriters for a complete list of available item readers. In the Podcastpedia batch jobs I use the following specialized ItemReaders: 5.1.1. FlatFileItemReader which, as the name implies, reads lines of data from a flat file that typically describe records with fields of data defined by fixed positions in the file or delimited by some special character (e.g. Comma). This type of ItemReader is being used in the first batch job, addNewPodcastJob. The input file used is named suggested-podcasts.in, resides in the classpath (src/main/resources) and looks something like the following: FEED_URL; IDENTIFIER_ON_PODCASTPEDIA; CATEGORIES; LANGUAGE; MEDIA_TYPE; UPDATE_FREQUENCY; KEYWORDS; FB_PAGE; TWITTER_PAGE; GPLUS_PAGE; NAME_SUBMITTER; EMAIL_SUBMITTER http://www.5minutebiographies.com/feed/; 5minutebiographies; people_society, history; en; Audio; WEEKLY; biography, biographies, short biography, short biographies, 5 minute biographies, five minute biographies, 5 minute biography, five minute biography; https://www.facebook.com/5minutebiographies;https://twitter.com/5MinuteBios; ; Adrian Matei; [email protected] http://notanotherpodcast.libsyn.com/rss; NotAnotherPodcast; entertainment; en; Audio; WEEKLY; Comedy, Sports, Cinema, Movies, Pop Culture, Food, Games; https://www.facebook.com/notanotherpodcastusa;https://twitter.com/NAPodcastUSA;https://plus.google.com/u/0/103089891373760354121/posts; Adrian Matei; [email protected] As you can see the first line defines the names of the “columns”, and the following lines contain the actual data (delimited by “;”), that needs translating to domain objects relevant in the context. Let’s see now how to configure the FlatFileItemReader: @Bean public ItemReader reader(){ FlatFileItemReader reader = new FlatFileItemReader(); reader.setLinesToSkip(1);//first line is title definition reader.setResource(new ClassPathResource("suggested-podcasts.in")); reader.setLineMapper(lineMapper()); return reader; } You can specify, among other things, the input resource, the number of lines to skip, and a line mapper. 5.1.1.1. LineMapper The LineMapper is an interface for mapping lines (strings) to domain objects, typically used to map lines read from a file to domain objects on a per line basis. For the Podcastpedia job I used the DefaultLineMapper, which is two-phase implementation consisting of tokenization of the line into a FieldSet followed by mapping to item: @Bean public LineMapper lineMapper() { DefaultLineMapper lineMapper = new DefaultLineMapper(); DelimitedLineTokenizer lineTokenizer = new DelimitedLineTokenizer(); lineTokenizer.setDelimiter(";"); lineTokenizer.setStrict(false); lineTokenizer.setNames(new String[]{"FEED_URL", "IDENTIFIER_ON_PODCASTPEDIA", "CATEGORIES", "LANGUAGE", "MEDIA_TYPE", "UPDATE_FREQUENCY", "KEYWORDS", "FB_PAGE", "TWITTER_PAGE", "GPLUS_PAGE", "NAME_SUBMITTER", "EMAIL_SUBMITTER"}); BeanWrapperFieldSetMapper fieldSetMapper = new BeanWrapperFieldSetMapper(); fieldSetMapper.setTargetType(SuggestedPodcast.class); lineMapper.setLineTokenizer(lineTokenizer); lineMapper.setFieldSetMapper(suggestedPodcastFieldSetMapper()); return lineMapper; } the DelimitedLineTokenizer splits the input String via the “;” delimiter. if you set the strict flag to false then lines with less tokens will be tolerated and padded with empty columns, and lines with more tokens will simply be truncated. the columns names from the first line are set lineTokenizer.setNames(...); and the fieldMapper is set (line 14) Note: The FieldSet is an “interface used by flat file input sources to encapsulate concerns of converting an array of Strings to Java native types. A bit like the role played by ResultSet in JDBC, clients will know the name or position of strongly typed fields that they want to extract.“ 5.1.1.2. FieldSetMapper The FieldSetMapper is an interface that is used to map data obtained from a FieldSet into an object. Here’s my implementation which maps the fieldSet to the SuggestedPodcast domain object that will be further passed to the processor: public class SuggestedPodcastFieldSetMapper implements FieldSetMapper { @Override public SuggestedPodcast mapFieldSet(FieldSet fieldSet) throws BindException { SuggestedPodcast suggestedPodcast = new SuggestedPodcast(); suggestedPodcast.setCategories(fieldSet.readString("CATEGORIES")); suggestedPodcast.setEmail(fieldSet.readString("EMAIL_SUBMITTER")); suggestedPodcast.setName(fieldSet.readString("NAME_SUBMITTER")); suggestedPodcast.setTags(fieldSet.readString("KEYWORDS")); //some of the attributes we can map directly into the Podcast entity that we'll insert later into the database Podcast podcast = new Podcast(); podcast.setUrl(fieldSet.readString("FEED_URL")); podcast.setIdentifier(fieldSet.readString("IDENTIFIER_ON_PODCASTPEDIA")); podcast.setLanguageCode(LanguageCode.valueOf(fieldSet.readString("LANGUAGE"))); podcast.setMediaType(MediaType.valueOf(fieldSet.readString("MEDIA_TYPE"))); podcast.setUpdateFrequency(UpdateFrequency.valueOf(fieldSet.readString("UPDATE_FREQUENCY"))); podcast.setFbPage(fieldSet.readString("FB_PAGE")); podcast.setTwitterPage(fieldSet.readString("TWITTER_PAGE")); podcast.setGplusPage(fieldSet.readString("GPLUS_PAGE")); suggestedPodcast.setPodcast(podcast); return suggestedPodcast; } } 5.2. JdbcCursorItemReader In the second job, notifyEmailSubscribersJob, in the reader, I only read email subscribers from a single database table, but further in the processor a more detailed read(via JPA) is executed to retrieve all the new episodes of the podcasts the user subscribed to. This is a common pattern employed in the batch world. Follow this link for more Common Batch Patterns. For the initial read, I chose the JdbcCursorItemReader, which is a simple reader implementation that opens a JDBC cursor and continually retrieves the next row in the ResultSet: @Bean public ItemReader notifySubscribersReader(){ JdbcCursorItemReader reader = new JdbcCursorItemReader(); String sql = "select * from users where is_email_subscriber is not null"; reader.setSql(sql); reader.setDataSource(dataSource); reader.setRowMapper(rowMapper()); return reader; } Note I had to set the sql, the datasource to read from and a RowMapper. 5.2.1. RowMapper The RowMapper is an interface used by JdbcTemplate for mapping rows of a Result’set on a per-row basis. My implementation of this interface, , performs the actual work of mapping each row to a result object, but I don’t need to worry about exception handling: public class UserRowMapper implements RowMapper { @Override public User mapRow(ResultSet rs, int rowNum) throws SQLException { User user = new User(); user.setEmail(rs.getString("email")); return user; } } 5.2. Writers ItemWriter is an abstraction that represents the output of a Step, one batch or chunk of items at a time. Generally, an item writer has no knowledge of the input it will receive next, only the item that was passed in its current invocation. The writers for the two jobs presented are quite simple. They just use external services to send email notifications and post tweets on Podcastpedia’s account. Here is the implementation of the ItemWriterfor the first job – addNewPodcast: package org.podcastpedia.batch.jobs.addpodcast; import java.util.Date; import java.util.List; import javax.inject.Inject; import javax.persistence.EntityManager; import org.podcastpedia.batch.common.entities.Podcast; import org.podcastpedia.batch.jobs.addpodcast.model.SuggestedPodcast; import org.podcastpedia.batch.jobs.addpodcast.service.EmailNotificationService; import org.podcastpedia.batch.jobs.addpodcast.service.SocialMediaService; import org.springframework.batch.item.ItemWriter; import org.springframework.beans.factory.annotation.Autowired; public class Writer implements ItemWriter{ @Autowired private EntityManager entityManager; @Inject private EmailNotificationService emailNotificationService; @Inject private SocialMediaService socialMediaService; @Override public void write(List items) throws Exception { if(items.get(0) != null){ SuggestedPodcast suggestedPodcast = items.get(0); //first insert the data in the database Podcast podcast = suggestedPodcast.getPodcast(); podcast.setInsertionDate(new Date()); entityManager.persist(podcast); entityManager.flush(); //notify submitter about the insertion and post a twitt about it String url = buildUrlOnPodcastpedia(podcast); emailNotificationService.sendPodcastAdditionConfirmation( suggestedPodcast.getName(), suggestedPodcast.getEmail(), url); if(podcast.getTwitterPage() != null){ socialMediaService.postOnTwitterAboutNewPodcast(podcast, url); } } } private String buildUrlOnPodcastpedia(Podcast podcast) { StringBuffer urlOnPodcastpedia = new StringBuffer( "http://www.podcastpedia.org"); if (podcast.getIdentifier() != null) { urlOnPodcastpedia.append("/" + podcast.getIdentifier()); } else { urlOnPodcastpedia.append("/podcasts/"); urlOnPodcastpedia.append(String.valueOf(podcast.getPodcastId())); urlOnPodcastpedia.append("/" + podcast.getTitleInUrl()); } String url = urlOnPodcastpedia.toString(); return url; } } As you can see there’s nothing special here, except that the write method has to be overriden and this is where the injected external services EmailNotificationService and SocialMediaService are used to inform via email the podcast submitter about the addition to the podcast directory, and if a Twitter page was submitted a tweet will be posted on the Podcastpedia’s wall. You can find detailed explanation on how to send email via Velocity and how to post on Twitter from Java in the following posts: How to compose html emails in Java with Spring and Velocity How to post to Twittter from Java with Twitter4J in 10 minutes 5.3. Processors ItemProcessor is an abstraction that represents the business processing of an item. While theItemReader reads one item, and the ItemWriter writes them, the ItemProcessor provides access to transform or apply other business processing. When using your own Processors you have to implement the ItemProcessor interface, with its only method O process(I item) throws Exception, returning a potentially modified or a new item for continued processing. If the returned result is null, it is assumed that processing of the item should not continue. While the processor of the first job requires a little bit of more logic, because I have to set the etag andlast-modified header attributes, the feed attributes, episodes, categories and keywords of the podcast: public class SuggestedPodcastItemProcessor implements ItemProcessor { private static final int TIMEOUT = 10; @Autowired ReadDao readDao; @Autowired PodcastAndEpisodeAttributesService podcastAndEpisodeAttributesService; @Autowired private PoolingHttpClientConnectionManager poolingHttpClientConnectionManager; @Autowired private SyndFeedService syndFeedService; /** * Method used to build the categories, tags and episodes of the podcast */ @Override public SuggestedPodcast process(SuggestedPodcast item) throws Exception { if(isPodcastAlreadyInTheDirectory(item.getPodcast().getUrl())) { return null; } String[] categories = item.getCategories().trim().split("\\s*,\\s*"); item.getPodcast().setAvailability(org.apache.http.HttpStatus.SC_OK); //set etag and last modified attributes for the podcast setHeaderFieldAttributes(item.getPodcast()); //set the other attributes of the podcast from the feed podcastAndEpisodeAttributesService.setPodcastFeedAttributes(item.getPodcast()); //set the categories List categoriesByNames = readDao.findCategoriesByNames(categories); item.getPodcast().setCategories(categoriesByNames); //set the tags setTagsForPodcast(item); //build the episodes setEpisodesForPodcast(item.getPodcast()); return item; } ...... } the processor from the second job uses the ‘Driving Query’ approach, where I expand the data retrieved from the Reader with another “JPA-read” and I group the items on podcasts with episodes so that it looks nice in the emails that I am sending out to subscribers: @Scope("step") public class NotifySubscribersItemProcessor implements ItemProcessor { @Autowired EntityManager em; @Value("#{jobParameters[updateFrequency]}") String updateFrequency; @Override public User process(User item) throws Exception { String sqlInnerJoinEpisodes = "select e from User u JOIN u.podcasts p JOIN p.episodes e WHERE u.email=?1 AND p.updateFrequency=?2 AND" + " e.isNew IS NOT NULL AND e.availability=200 ORDER BY e.podcast.podcastId ASC, e.publicationDate ASC"; TypedQuery queryInnerJoinepisodes = em.createQuery(sqlInnerJoinEpisodes, Episode.class); queryInnerJoinepisodes.setParameter(1, item.getEmail()); queryInnerJoinepisodes.setParameter(2, UpdateFrequency.valueOf(updateFrequency)); List newEpisodes = queryInnerJoinepisodes.getResultList(); return regroupPodcastsWithEpisodes(item, newEpisodes); } ....... } Note: If you’d like to find out more how to use the Apache Http Client, to get the etag and last-modifiedheaders, you can have a look at my post – How to use the new Apache Http Client to make a HEAD request 6. Execute the batch application Batch processing can be embedded in web applications and WAR files, but I chose in the beginning the simpler approach that creates a standalone application, that can be started by the Java main() method: package org.podcastpedia.batch; //imports ...; @ComponentScan @EnableAutoConfiguration public class Application { private static final String NEW_EPISODES_NOTIFICATION_JOB = "newEpisodesNotificationJob"; private static final String ADD_NEW_PODCAST_JOB = "addNewPodcastJob"; public static void main(String[] args) throws BeansException, JobExecutionAlreadyRunningException, JobRestartException, JobInstanceAlreadyCompleteException, JobParametersInvalidException, InterruptedException { Log log = LogFactory.getLog(Application.class); SpringApplication app = new SpringApplication(Application.class); app.setWebEnvironment(false); ConfigurableApplicationContext ctx= app.run(args); JobLauncher jobLauncher = ctx.getBean(JobLauncher.class); if(ADD_NEW_PODCAST_JOB.equals(args[0])){ //addNewPodcastJob Job addNewPodcastJob = ctx.getBean(ADD_NEW_PODCAST_JOB, Job.class); JobParameters jobParameters = new JobParametersBuilder() .addDate("date", new Date()) .toJobParameters(); JobExecution jobExecution = jobLauncher.run(addNewPodcastJob, jobParameters); BatchStatus batchStatus = jobExecution.getStatus(); while(batchStatus.isRunning()){ log.info("*********** Still running.... **************"); Thread.sleep(1000); } log.info(String.format("*********** Exit status: %s", jobExecution.getExitStatus().getExitCode())); JobInstance jobInstance = jobExecution.getJobInstance(); log.info(String.format("********* Name of the job %s", jobInstance.getJobName())); log.info(String.format("*********** job instance Id: %d", jobInstance.getId())); System.exit(0); } else if(NEW_EPISODES_NOTIFICATION_JOB.equals(args[0])){ JobParameters jobParameters = new JobParametersBuilder() .addDate("date", new Date()) .addString("updateFrequency", args[1]) .toJobParameters(); jobLauncher.run(ctx.getBean(NEW_EPISODES_NOTIFICATION_JOB, Job.class), jobParameters); } else { throw new IllegalArgumentException("Please provide a valid Job name as first application parameter"); } System.exit(0); } } The best explanation for SpringApplication-, @ComponentScan- and @EnableAutoConfiguration-magic you get from the source – Getting Started – Creating a Batch Service: “The main() method defers to the SpringApplication helper class, providing Application.class as an argument to its run() method. This tells Spring to read the annotation metadata from Application and to manage it as a component in the Spring application context. The @ComponentScan annotation tells Spring to search recursively through theorg.podcastpedia.batchpackage and its children for classes marked directly or indirectly with Spring’s @Component annotation. This directive ensures that Spring finds and registers BatchConfiguration, because it is marked with @Configuration, which in turn is a kind of @Component annotation. The @EnableAutoConfiguration annotation switches on reasonable default behaviors based on the content of your classpath. For example, it looks for any class that implements the CommandLineRunner interface and invokes its run() method.” Execution construction steps: the JobLauncher, which is a simple interface for controlling jobs, is retrieved from the ApplicationContext. Remember this is automatically made available via the@EnableBatchProcessing annotation. now based on the first parameter of the application (args[0]), I will retrieve the correspondingJob from the ApplicationContext then the JobParameters are prepared, where I use the current date - .addDate("date", new Date()), so that the job executions are always unique. once everything is in place, the job can be executed: JobExecution jobExecution = jobLauncher.run(addNewPodcastJob, jobParameters); you can use the returned jobExecution to gain access to BatchStatus, exit code, or job name and id. Note: I highly recommend you read and understand the Meta-Data Schema for Spring Batch. It will also help you better understand the Spring Batch Domain objects. 6.1. Running the application on dev and prod environments To be able to run the Spring Batch / Spring Boot application on different environments I make use of the Spring Profiles capability. By default the application runs with development data (database). But if I want the job to use the production database I have to do the following: provide the following environment argument -Dspring.profiles.active=prod have the production database properties configured in the application-prod.properties file in the classpath, right besides the default application.properties file Summary In this tutorial we’ve learned how to configure a Spring Batch project with Spring Boot and Java configuration, how to use some of the most common readers in batch processing, how to configure some simple jobs, and how to start Spring Batch jobs from a main method. Note: As I mentioned, I am fairly new to Spring Batch, and especially to Spring Boot and Spring Configuration with Java, so if you see any potential for improvement (code, job design etc.) please make a pull request or leave a comment below. Thanks a lot.

This article lists some of the best practices that would be useful for developers while they are coding with AngularJS. These are out of my own experiences while working on AngularJS and do not warranty the entire list. I am sure there can be more to this list and thus, request my readers to suggest/comment such that they could be added to the list below. Found some of the following pages which presents a set of good practices you would want to refer. Thanks to the readers for the valuable contribution. AngularJS Style Guide App Structure Best practices Initialization One should try and place the

In an earlier post I showed how easy it is to integrate Hystrix into a Spring Boot application. Now I’m going to show you a neat trick which combines the health indicator endpoint in Spring Boot and the metrics provided by Hystrix. Hystrix has a built-in system to query the metrics that drive the framework. For example you can query the metrics of each command such as the mean execution time or whether the circuit breaker for that command has tripped. And it’s that last one that is very interesting to the health indicator of your application. Most production environments have a dashboard that show the health of an application’s instances. If the circuitbreaker has tripped, your application is essentially in an unhealthy state. The circuit breaker mechanism will ensure that failures won’t cascade, but in a clustered environment you’d want that server removed from the pool or at least have a general indication that something is wrong. Spring Boot’s health endpoints works by querying various indicators. Like most things in Spring Boot, indicators are only active if there are components that can be checked. For example, if you have a datasource, an indicator will become active checking the state of that datasource. The same thing happens with NoSQL or AMQP connections. A simple implementation with Hystrix, which I’ll show in a minute, could be that when there is a tripped circuitbreaker in the system, the health of the application might be ‘out of service’. This is actually very easy to do. You just need to add a bean in your configurations returning an implementation of AbstractHealthIndicator: class HystrixMetricsHealthIndicator extends AbstractHealthIndicator { @Override protected void doHealthCheck(Health.Builder builder) throws Exception { def breakers = [] HystrixCommandMetrics.instances.each { def breaker = HystrixCircuitBreaker.Factory.getInstance(it.commandKey) def breakerOpen = breaker.open?:false if(breakerOpen) { breakers << it.commandGroup.name() + "::" + it.commandKey.name() } } breakers ? builder.outOfService().withDetail("openCircuitBreakers", breakers) : builder.up() } } Whenever a circuitbreaker gets tripped, the health endpoint will return the state of the application as OUT_OF_SERVICE and will also return the name of the open circuit breakers (the command key and the group it’s in). Now, this implementation can go a whole lot further. For example, you can add a new state to the health indication, for example UNSTABLE. This will however require you to change to order of the health aggregator, as Spring Boot will aggregate all the indicators and show a single application state. The new state needs to be fit in the existing order of states (DOWN > OUT_OF_SERVICE > UP > UNKNOWN). In the case of UNSTABLE, it would probably be between OUT_OF_SERVICE and UP. I can also think of a use-case in which the tripping of certain circuit breakers may be more critical than others, in which case the state of the application might really become OUT_OF_SERVICE. In that case you might decide to remove the instance from the pool of available instances (in a clustered environment) or restart the server. Or you can automate the process :). The last use case I’ll discuss is when your application is slow or is getting hammered by requests, which can be detected by Hystrix as well. In this case, you can introduce yet another state STRUGGLING, which would logically be between UNSTABLE and UP. In this case you can automate a process that starts up another instance and add it automatically to the pool. You can also see this the other way around, adding a state UNUSED which is on the same level as UP. This might indicate you have too many instances running and can possible shutdown that node (if it’s not the only one), or that you need to take a look at the load-balancing. As you can see, with such mechanisms it becomes possible to create a self-regulating instance pool, creating and removing instances as it goes. The health indicators in Spring Boot are an invaluable tool for DevOps teams and show how versatile Spring Boot actually is. UPDATE: Normally, if you want to alter the order in which statuses are aggregated, you can use a property in your application.properties like health.status.order = DOWN,OUT_OF_SERVICE,UNSTABLE,STRUGGLING,UP,UNKNOWN as documented. However, if you’re using the YAML-style properties, you’re out of luck, as there’s an annoying bugthat’s restricting you from using this feature. So if you’re using YAML properties, you’ll have to configure the HealthAggregator yourself. Luckily, this isn’t that hard, just add this bean to your application context: @Bean HealthAggregator healthAggregator() { def healthAggregator = new OrderedHealthAggregator(); healthAggregator.setStatusOrder(["DOWN", "OUT_OF_SERVICE", "UNSTABLE", "UP", "UNKNOWN"]); return healthAggregator; } Why they didn’t use the @EnableConfigurationProperties in the HealthIndicatorAutoConfiguration is a mystery to me, as this would have solved the issue. Perhaps I’ll do it myself and make a pull request.

Overview : Recently, I was working on a project which uses a REST services layer to communicate with the client application (GWT application). So I have spent a lot of to time to figure out how to secure the REST services with Spring Security. This article describes the solution I found, and I have implemented. I hope that this solution will be helpful to someone and will save a much valuable time. The solution : In a normal web application, whenever a secured resource is accessed Spring Security check the security context for the current user and will decide either to forward him to login page (if the user is not authenticated), or to forward him to the resource not authorised page (if he doesn’t have the required permissions). In our scenario this is different, because we don’t have pages to forward to, we need to adapt and override Spring Security to communicate using HTTP protocols status only, below I liste the things to do to make Spring Security works best : The authentication is going to be managed by the normal form login, the only difference is that the response will be on JSON along with an HTTP status which can either code 200 (if the autentication passed) or code 401 (if the authentication failed) ; Override the AuthenticationFailureHandler to return the code 401 UNAUTHORIZED ; Override the AuthenticationSuccessHandler to return the code 20 OK, the body of the HTTP response contain the JSON data of the current authenticated user ; Override the AuthenticationEntryPoint to always return the code 401 UNAUTHORIZED. This will override the default behavior of Spring Security which is forwarding the user to the login page if he don’t meet the security requirements, because on REST we don’t have any login page ; Override the LogoutSuccessHandler to return the code 20 OK ; Like a normal web application secured by Spring Security, before accessing a protected service, it is mandatory to first authenticate by submitting the password and username to the Login URL. Note: The following solution requires Spring Security in version minimum 3.2. Overriding the AuthenticationEntryPoint : Class extends org.springframework.security.web.AuthenticationEntryPoint, and implements only one method, which sends response error (with 401 status code) in cause of unauthorized attempt. @Component public class HttpAuthenticationEntryPoint implements AuthenticationEntryPoint { @Override public void commence(HttpServletRequest request, HttpServletResponse response, AuthenticationException authException) throws IOException { response.sendError(HttpServletResponse.SC_UNAUTHORIZED, authException.getMessage()); } } Overriding the AuthenticationSuccessHandler : The AuthenticationSuccessHandler is responsible of what to do after a successful authentication, by default it will redirect to an URL, but in our case we want it to send an HTTP response with data. @Component public class AuthSuccessHandler extends SavedRequestAwareAuthenticationSuccessHandler { private static final Logger LOGGER = LoggerFactory.getLogger(AuthSuccessHandler.class); private final ObjectMapper mapper; @Autowired AuthSuccessHandler(MappingJackson2HttpMessageConverter messageConverter) { this.mapper = messageConverter.getObjectMapper(); } @Override public void onAuthenticationSuccess(HttpServletRequest request, HttpServletResponse response, Authentication authentication) throws IOException, ServletException { response.setStatus(HttpServletResponse.SC_OK); NuvolaUserDetails userDetails = (NuvolaUserDetails) authentication.getPrincipal(); User user = userDetails.getUser(); userDetails.setUser(user); LOGGER.info(userDetails.getUsername() + " got is connected "); PrintWriter writer = response.getWriter(); mapper.writeValue(writer, user); writer.flush(); } } Overriding the AuthenticationFailureHandler : The AuthenticationFaillureHandler is responsible of what to after a failed authentication, by default it will redirect to the login page URL, but in our case we just want it to send an HTTP response with the 401 UNAUTHORIZED code. @Component public class AuthFailureHandler extends SimpleUrlAuthenticationFailureHandler { @Override public void onAuthenticationFailure(HttpServletRequest request, HttpServletResponse response, AuthenticationException exception) throws IOException, ServletException { response.setStatus(HttpServletResponse.SC_UNAUTHORIZED); PrintWriter writer = response.getWriter(); writer.write(exception.getMessage()); writer.flush(); } } Overriding the LogoutSuccessHandler : The LogoutSuccessHandler decide what to do if the user logged out successfully, by default it will redirect to the login page URL, because we don’t have that I did override it to return an HTTP response with the 20 OK code. @Component public class HttpLogoutSuccessHandler implements LogoutSuccessHandler { @Override public void onLogoutSuccess(HttpServletRequest request, HttpServletResponse response, Authentication authentication) throws IOException { response.setStatus(HttpServletResponse.SC_OK); response.getWriter().flush(); } } Spring security configuration : This is the final step, to put all what we did together, I prefer using the new way to configure Spring Security which is with Java no XML, but you can easily adapt this configuration to XML. @Configuration @EnableWebSecurity public class WebSecurityConfig extends WebSecurityConfigurerAdapter { private static final String LOGIN_PATH = ApiPaths.ROOT + ApiPaths.User.ROOT + ApiPaths.User.LOGIN; @Autowired private NuvolaUserDetailsService userDetailsService; @Autowired private HttpAuthenticationEntryPoint authenticationEntryPoint; @Autowired private AuthSuccessHandler authSuccessHandler; @Autowired private AuthFailureHandler authFailureHandler; @Autowired private HttpLogoutSuccessHandler logoutSuccessHandler; @Bean @Override public AuthenticationManager authenticationManagerBean() throws Exception { return super.authenticationManagerBean(); } @Bean @Override public UserDetailsService userDetailsServiceBean() throws Exception { return super.userDetailsServiceBean(); } @Bean public AuthenticationProvider authenticationProvider() { DaoAuthenticationProvider authenticationProvider = new DaoAuthenticationProvider(); authenticationProvider.setUserDetailsService(userDetailsService); authenticationProvider.setPasswordEncoder(new ShaPasswordEncoder()); return authenticationProvider; } @Override protected void configure(AuthenticationManagerBuilder auth) throws Exception { auth.authenticationProvider(authenticationProvider()); } @Override protected AuthenticationManager authenticationManager() throws Exception { return super.authenticationManager(); } @Override protected void configure(HttpSecurity http) throws Exception { http.csrf().disable() .authenticationProvider(authenticationProvider()) .exceptionHandling() .authenticationEntryPoint(authenticationEntryPoint) .and() .formLogin() .permitAll() .loginProcessingUrl(LOGIN_PATH) .usernameParameter(USERNAME) .passwordParameter(PASSWORD) .successHandler(authSuccessHandler) .failureHandler(authFailureHandler) .and() .logout() .permitAll() .logoutRequestMatcher(new AntPathRequestMatcher(LOGIN_PATH, "DELETE")) .logoutSuccessHandler(logoutSuccessHandler) .and() .sessionManagement() .maximumSessions(1); http.authorizeRequests().anyRequest().authenticated(); } } This was a sneak peak at the overall configuration, I attached in this article a Github repository containing a sample project https://github.com/imrabti/gwtp-spring-security. I hope this will help some of you developers struggling to figure out a solution, please feel free to ask any questions, or post any enhancements that can make this solution better.

Making your application resilient to failure can seem like a daunting task. Those who read “Release It!” know how many aspects there can be to making your application ready for the apocalypse. Luckily we live in a world where a lot of software needs such resilience and where there are companies who are willing to share their solutions. Enter what Netflix has created: Hystrix. Hystrix is a Java library aimed towards making integration points less susceptible to failures and mitigating the impact a failure might have on your application. It provides the means to incorporate bulkheads, circuit breakers and metrics into your framework. Those not familiar with these concepts should read the book I mentioned earlier. For example, a circuit breaker makes sure that if a certain integration point is having trouble, your application will not be affected. If for example a integration point takes 20 seconds to reply instead of the normal 50ms, you can configure a circuit breaker that trips if 10 calls within 10 seconds take longer than 5 seconds. When tripped, you can configure a quick fallback or fail fast. Hystrix has an elegant solution for this. Every command to an external integration point should get wrapped in a HystrixCommand. HystrixCommand provide support for circuit breakers, timeouts, fallbacks and other disaster recovery methods. So instead of directly calling the integration point, you’ll call a command that in turn calls the integration point. Hystrix also allows you to choose whether you want to do this synchronously or asynchronously (returning a Future). One of the really nice things about Hystrix is that it also has support for metrics and even has a nice dashboard to show those metrics. I can almost imagine that every development team has this on the dashboard next to the Hudson/Jenkins monitor in the near future, just because it’s so trivial to incorporate. Now, creating a new subclass for each and every distinct call to an integration endpoint may seems like a lot of work. It is, but the reasoning behind this is that incorporating Hystrix in your application should be explicit. However, if you really don’t like this, Hystrix also supports Spring AOP and has a aspect that does most of the work for you, using a contributed module (javanica). The only thing you need to do is annotate the methods you want covered by Hystrix. Whenever I see decent Spring integration, I now immediately look at Spring Boot support. Hystrix doesn’t have autoconfiguration for Spring Boot yet, but it’s really easy to implement. I used the annotation/aspect approach because I’m lazy and I like the transparency of going down this path. First you need to add a couple of dependencies. Here’s what you need in Gradle: compile("com.netflix.hystrix:hystrix-javanica:1.3.16") compile("com.netflix.hystrix:hystrix-metrics-event-stream:1.3.16") Then you need to create a configuration for Hystrix. I opted to create the configuration just like any other autoconfiguration module in Spring Boot (an @Configuration annotated class and a class describing the configuration properties). I also used conditional beans so that the . /** * {@link EnableAutoConfiguration Auto-configuration} for Hystrix. * * @author Lieven Doclo */ @Configuration @EnableConfigurationProperties(HystrixProperties) @ConditionalOnExpression("\${hystrix.enabled:true}") class HystrixConfiguration { @Autowired HystrixProperties hystrixProperties; @Bean @ConditionalOnClass(HystrixCommandAspect) HystrixCommandAspect hystrixCommandAspect() { new HystrixCommandAspect(); } @Bean @ConditionalOnClass(HystrixMetricsStreamServlet) @ConditionalOnExpression("\${hystrix.streamEnabled:false}") public ServletRegistrationBean hystrixStreamServlet(){ new ServletRegistrationBean(new HystrixMetricsStreamServlet(), hystrixProperties.streamUrl); } } /** * Configuration properties for Hystrix. * * @author Lieven Doclo */ @ConfigurationProperties(prefix = "hystrix", ignoreUnknownFields = true) class HystrixProperties { boolean enabled = true boolean streamEnabled = false String streamUrl = "/hystrix.stream" } In short, if you add this to your Spring Boot application, Hystrix will be automatically integrated in your application. As you might have seen, I’ve also added some configuration properties. I added support for the event stream that powers the dashboard and which is only activated if you add hystrix.streamEnabled = true to your application.properties. The URL through which the stream is served is also configurable (but has a sensible default). If you want, you can disable Hystrix as a whole by adding hystrix.enabled = false to your application.properties. This code is actually ready to be put into Spring Boot’s autoconfigure module :). Two simple classes and two simple dependencies and your code is ready for the apocalypse. Doesn’t seem like a bad deal to me. Hystrix has a lot more to offer than I touched in this article (command aggregation, reactive calls through events, …). If your application has a lot of integration points, certainly have a look at this library. Your application may be stable, but that doesn’t mean that all the REST services you’re calling are.

i am eclipse user. so when i wanted to analyze my code with pmd, i needed to use “pmd for eclipse” plugin. this plugin used to be very buggy, which was enhanced in later versions (currently 4.0.3). but the performance is really bad sometimes. especially when you are dealing with relatively big codebase and have option “check code after saving” on. ecplise-pmd plugin so when i realized that there is new alternative pmd plugin called eclipse-pmd out there i evaluated it immediately with great happiness. installation uses modern eclipse marketplace method. you just need to go “help” -> “eclipse marketplace…” and search for “eclipse-pmd” . than hit “install” and follow instructions. after installation i was a little bit confused because i didn’t find any configuration options it general settings ( “window” -> “preferences” ). i discovered that you need to turn on pmd for each project separately. which make sense, because you can have different rule set per project. so to turn it on, right click on project -> “preferences” -> “pmd” (there would be two pmd sections if you didn’t uninstall old pmd plugin) -> “enable pmd for this project” -> “add…” . now you should pick a location of pmd ruleset file. unlike old pmd plugin, eclipse-pmd don’t import ruleset. it is using ruleset file directly. this is very handy, because typically you want to have it in source control. when you pull changes to ruleset file from source control system, they are applied without re-import (re-import was needed for old pmd plugin). problem can be when you (or your team) don’t have existing ruleset. i would suggest to start with full ruleset and exclude rules you don’t want to use. your ruleset would evolve anyway, so starting with most restrictive (default) deck make perfect sense for me. unfortunately eclipse-pmd plugin doesn’t provide option to generate ruleset file. so i created full ruleset for pmd 5.1.1 (5.1.1 is pmd version not plugin version) . i have to admit that it was created with help of old pmd plugin. you can see that i literally included all the rule categories. i would suggest to specify your set this way and exclude/configure rules explicitly as needed. here is link to pmd site that explains how to customize your ruleset . this approach can be handy when pmd version will be updated. new rules can appear in category and they will be automatically included into your ruleset when you are listing categories, not rules individually. but you have to keep eye on new rules/categories when updating pmd version anyway, because categories often change with new pmd version. so now we should have rulset configured and working. here are some screen shots of rules in action: when you hover over left side panel warning: when you hover over problematic snippet: when you do quick fix on problematic snippet: generating suppress warning annotation for pmd rules is very nice feature. it also provide quick fixes for some rules. take a look at its change log site for full list. these pmd warning sometimes clash with eclipse native warnings, so there is possibility to make them more visible. go to “window” -> “preferences” -> “general” -> “editors” -> “text editors” -> “annotations” and find “pmd violations” . here you can configure your own style of highlighting pmd issues. this is mine: to explore full feature list of this plugin take a look at its change log site. there are some features in old plugin i miss though. for example i would appreciate some quick link or full description of the rule. short description provided is sometimes not enough. i encourage you to take a look at full pmd rule description if you are not sure what’s source of the problem. you will learn a lot about java language itself or about libraries you are using. quick links would help a lot in such case. also some rules doesn’t use code highlighting (only side panel markers). it is sometimes hard to distinguish between compiler and pmd issues. this is problem for me because our team doesn’t use javadoc warnings but i do. so i get a lot of javadoc warnings from code written by teammates. and sometimes i can miss pmd issue because it is lost in javadoc warnings. (fortunately svn commit is rejected if i forget to fix some rule). conclusion this plugin enhanced my eclipse workflow. no more disruptions because of endless “checking code…” processing by old plugin.

Apparently writing a URL shortener service is the new "Hello, world!" in the IoT/microservice/era world. It all started with A URL shortener service in 45 lines of Scala - neat piece of Scala, flavoured with Spray and Redis for storage. This was quickly followed with A url shortener service in 35 lines of Clojure and even URL Shortener in 43 lines of Haskell. So my inner anti-hipster asked: how long would it be in Java? But not plain Java, for goodness' sake. Spring Boot with Spring Data Redis are a good starting point. All we need is a simple controller handling GET and POST: import com.google.common.hash.Hashing; import org.apache.commons.validator.routines.UrlValidator; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.boot.SpringApplication; import org.springframework.data.redis.core.StringRedisTemplate; import org.springframework.http.*; import org.springframework.web.bind.annotation.*; import javax.servlet.http.*; import java.nio.charset.StandardCharsets; @org.springframework.boot.autoconfigure.EnableAutoConfiguration @org.springframework.stereotype.Controller public class UrlShortener { public static void main(String[] args) { SpringApplication.run(UrlShortener.class, args); } @Autowired private StringRedisTemplate redis; @RequestMapping(value = "/{id}", method = RequestMethod.GET) public void redirect(@PathVariable String id, HttpServletResponse resp) throws Exception { final String url = redis.opsForValue().get(id); if (url != null) resp.sendRedirect(url); else resp.sendError(HttpServletResponse.SC_NOT_FOUND); } @RequestMapping(method = RequestMethod.POST) public ResponseEntity save(HttpServletRequest req) { final String queryParams = (req.getQueryString() != null) ? "?" + req.getQueryString() : ""; final String url = (req.getRequestURI() + queryParams).substring(1); final UrlValidator urlValidator = new UrlValidator(new String[]{"http", "https"}); if (urlValidator.isValid(url)) { final String id = Hashing.murmur3_32().hashString(url, StandardCharsets.UTF_8).toString(); redis.opsForValue().set(id, url); return new ResponseEntity<>("http://mydomain.com/" + id, HttpStatus.OK); } else return new ResponseEntity<>(HttpStatus.BAD_REQUEST); } } The code is nicely self-descriptive and is functionally equivalent to a version in Scala. I didn't try to it squeeze too much to keep line count as short as possible, code above is quite typical with few details: I don't normally use wildcard imports I don't use fully qualified class names (I wanted to save one import line, I admit) I surround if/else blocks with braces I almost never use field injection, ugliest brother in inversion of control family. Instead I would go for constructor to allow testing with mocked Redis: private final StringRedisTemplate redis; @Autowired public UrlShortener(StringRedisTemplate redis) { this.redis = redis; } The thing I struggled the most was... obtaining the original, full URL. Basically I needed everything after .com or port. No bloody way (neither servlets, nor Spring MVC), hence the awkward getQueryString() fiddling. You can use the service as follows - creating shorter URL: $ curl -vX POST localhost:8080/https://www.google.pl/search?q=tomasz+nurkiewicz > POST /https://www.google.pl/search?q=tomasz+nurkiewicz HTTP/1.1 > User-Agent: curl/7.30.0 > Host: localhost:8080 > Accept: */* > < HTTP/1.1 200 OK < Server: Apache-Coyote/1.1 < Content-Type: text/plain;charset=ISO-8859-1 < Content-Length: 28 < Date: Sat, 23 Aug 2014 20:47:40 GMT < http://mydomain.com/50784f51 Redirecting through shorter URL: $ curl -v localhost:8080/50784f51 > GET /50784f51 HTTP/1.1 > User-Agent: curl/7.30.0 > Host: localhost:8080 > Accept: */* > < HTTP/1.1 302 Found < Server: Apache-Coyote/1.1 < Location: https://www.google.pl/search?q=tomasz+nurkiewicz < Content-Length: 0 < Date: Sat, 23 Aug 2014 20:48:00 GMT < For completeness, here is a build file in Gradle (maven would work as well), skipped in all previous solutions: buildscript { repositories { mavenLocal() maven { url "http://repo.spring.io/libs-snapshot" } mavenCentral() } dependencies { classpath 'org.springframework.boot:spring-boot-gradle-plugin:1.1.5.RELEASE' } } apply plugin: 'java' apply plugin: 'spring-boot' sourceCompatibility = '1.8' repositories { mavenLocal() maven { url 'http://repository.codehaus.org' } maven { url 'http://repo.spring.io/milestone' } mavenCentral() } dependencies { compile "org.springframework.boot:spring-boot-starter-web:1.1.5.RELEASE" compile "org.springframework.boot:spring-boot-starter-redis:1.1.5.RELEASE" compile 'com.google.guava:guava:17.0' compile 'org.apache.commons:commons-lang3:3.3.2' compile 'commons-validator:commons-validator:1.4.0' compile 'org.apache.tomcat.embed:tomcat-embed-el:8.0.9' compile "org.aspectj:aspectjrt:1.8.1" runtime "cglib:cglib-nodep:3.1" } tasks.withType(GroovyCompile) { groovyOptions.optimizationOptions.indy = true } task wrapper(type: Wrapper) { gradleVersion = '2.0' } Actually also 42 lines... That's the whole application, no XML, no descriptors, not setup. I don't treat this exercise as just a dummy code golf for shortest, most obfuscated working code. URL shortener web service with Redis back-end is an interesting showcase of syntax and capabilities of a given language and ecosystem. Much more entertaining then a bunch of algorithmic problems, e.g. found in Rosetta code. Also it's a good bare minimum template for writing a REST service. One important feature of original Scala implementation, that was somehow silently forgotten in all implementations, including this one, is that it's non-blocking. Both HTTP and Redis access is event-driven (reactive, all right, I said it), thus I suppose it can handle tens of thousands of clients simultaneously. This can't be achieved with blocking controllers backed by Tomcat. But still you have to admit such a service written in Java (not even Java 8!) is surprisingly concise, easy to follow and straightforward - none of the other solutions are that readable (this is of course subjective). Waiting for others!

Learn How to Enable Swagger in your Spring Boot Web Application

Once we know something, we find it hard to imagine what it was like not to know it. - Chip & Dan Heath, Authors of Made to Stick, Switch Update: I have recently published a book on ELK stack titled - Learning ELK Stack , more details can be found here. What is the ELK stack ? The ELK stack is ElasticSearch, Logstash and Kibana. These three provide a fully working real-time data analytics tool for getting wonderful information sitting on your data. ElasticSearch ElasticSearch,built on top of Apache Lucene, is a search engine with focus on real-time analysis of the data, and is based on the RESTful architecture. It provides standard full text search functionality and powerful search based on query. ElasticSearch is document-oriented/based and you can store everything you want as JSON. This makes it powerful, simple and flexible. Logstash Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use.In ELK Stack logstash plays an important role in shipping the log and indexing them later which can be supplied to Elastic Search. Kibana Kibana is a user friendly way to view, search and visualize your log data, which will present the data stored from Logstash into ElasticSearch, in a very customizable interface with histogram and other panels which provides real-time analysis and search of data you have parsed into ElasticSearch. How Do I Get It ? http://www.elasticsearch.org/overview/elkdownloads/ How Do They Work Together ? Logstash is essentially a pipelining tool. In a basic, centralized installation a logstash agent, known as the shipper, will read input from one to many input sources and output that text wrapped in a JSON message to a broker. Typically Redis, the broker, caches the messages until another logstash agent, known as the collector, picks them up, and sends them to another output. In the common example this output is Elasticsearch, where the messages will be indexed and stored for searching. The Elasticsearch store is accessed via the Kibana web application which allows you to visualize and search through the logs. The entire system is scalable. Many different shippers may be running on many different hosts, watching log files and shipping the messages off to a cluster of brokers. Then many collectors can be reading those messages and writing them to an Elasticsearch cluster. (E)lasticSearch (L)ogstash (K)ibana (The ELK Stack) How Do I Fetch Useful Information Out of Logs? Fetching useful information from logs is one of the most important part of this stack and is being done in logstash using its grok filters and a set of input , filter and output plugins which helps to scale this functionality for taking various kinds of inputs ( file,tcp, udp, gemfire, stdin, unix, web sockets and even IRC and twitter and many more) , filter them using (groks,grep,date filters etc.) and finally write ouput to ElasticSearch,redis,email,HTTP,MongoDB,Gemfire , Jira , Google Cloud Storage etc. A Bit More About Log Stash Filters Transforming the logs as they go through the pipeline is possible as well using filters. Either on the shipper or collector, whichever suits your needs better. As an example, an Apache HTTP log entry can have each element (request, response code, response size, etc) parsed out into individual fields so they can be searched on more seamlessly. Information can be dropped if it isn’t important. Sensitive data can be masked. Messages can be tagged. The list goes on. e.g. input { file { path => ["var/log/apache.log"] type => "saurzcode_apache_logs" } } filter { grok { match => ["message","%{COMBINEDAPACHELOG}"] } } output{ stdout{} } Above example takes input from an apache log file applies a grok filter with %{COMBINEDAPACHELOG}, which will index apache logs information on fields and finally output to Standard Output Console. Writing Grok Filters Writing grok filters and fetching information is the only task that requires some serious efforts and if done properly will give you great insights in to your data like Number of Transations performed over time, Which type of products have most hits etc. Below links will help you a lot in writing grok filters and test them with ease - Grok Debugger http://grokdebug.herokuapp.com/ Grok Patterns Lookup https://github.com/elasticsearch/logstash/tree/v1.4.2/patterns References http://www.elasticsearch.org/overview/ http://logstash.net/ http://rashidkpc.github.io/Kibana/about.html

Hello, how are you? Today we will talk about situations in which the use of the JPA/Hibernate is not recommended. Which alternatives do we have outside the JPA world? What we will talk about: JPA/Hibernate problems Solutions to some of the JPA/Hibernate problems Criteria for choosing the frameworks described here Spring JDBC Template MyBatis Sormula sql2o Take a look at: jOOQ and Avaje Is a raw JDBC approach worth it? How can I choose the right framework? Final thoughts I have created 4 CRUDs in my github using the frameworks mentioned in this post, you will find the URL at the beginning of each page. I am not a radical that thinks that JPA is worthless, but I do believe that we need to choose the right framework for the each situation. If you do not know I wrote a JPA book (in Portuguese only) and I do not think that JPA is the silver bullet that will solve all the problems. I hope you like the post. [= JPA/Hibernate Problems There are times that JPA can do more harm than good. Below you will see the JPA/Hibernate problems and in the next page you will see some solutions to these problems: Composite Key: This, in my opinion, is the biggest headache of the JPA developers. When we map a composite key we are adding a huge complexity to the project when we need to persist or find a object in the database. When you use composite key several problems will might happen, and some of these problems could be implementation bugs. Legacy Database: A project that has a lot of business rules in the database can be a problem when wee need to invoke StoredProcedures or Functions. Artifact size: The artifact size will increase a lot if you are using the Hibernate implementation. The Hibernate uses a lot of dependencies that will increase the size of the generated jar/war/ear. The artifact size can be a problem if the developer needs to do a deploy in several remote servers with a low Internet band (or a slow upload). Imagine a project that in each new release it is necessary to update 10 customers servers across the country. Problems with slow upload, corrupted file and loss of Internet can happen making the dev/ops team to lose more time. Generated SQL: One of the JPA advantages is the database portability, but to use this portability advantage you need to use the JPQL/HQL language. This advantage can became a disadvantage when the generated query has a poor performance and it does not use the table index that was created to optimize the queries. Complex Query: That are projects that has several queries with a high level of complexity using database resources like: SUM, MAX, MIN, COUNT, HAVING, etc. If you combine those resources the JPA performance might drop and not use the table indexes, or you will not be able to use a specific database resource that could solve this problem. Framework complexity: To create a CRUD with JPA is very simples, but problems will appear when we start to use entities relationships, inheritance, cache, PersistenceUnit manipulation, PersistenceContext with several entities, etc. A development team without a developer with a good JPA experience will lose a lot of time with JPA ‘rules‘. Slow processing and a lot of RAM memory occupied: There are moments that JPA will lose performance at report processing, inserting a lot of entities or problems with a transaction that is opened for a long time. After reading all the problems above you might be thinking: “Is JPA good in doing anything?”. JPA has a lot of advantages that will not be detailed here because this is not the post theme, JPA is a tool that is indicated for a lot of situations. Some of the JPA advantages are: database portability, save a lot of the development time, make easier to create queries, cache optimization, a huge community support, etc. In the next page we will see some solutions for the problems detailed above, the solutions could help you to avoid a huge persistence framework refactoring. We will see some tips to fix or to workaround the problems described here. Solutions to Some of the JPA/Hibernate problems We need to be careful if we are thinking about removing the JPA of our projects. I am not of the developer type that thinks that we should remove a entire framework before trying to find a solution to the problems. Some times it is better to choose a less intrusive approach. Composite Key Unfortunately there is not a good solution to this problem. If possible, avoid the creation of tables with composite key if it is not required by the business rules. I have seen developers using composite keys when a simple key could be applied, the composite key complexity was added to the project unnecessarily. Legacy Databases The newest JPA version (2.1) has support to StoredProcedures and Functions, with this new resource will be easier to communicate with the database. If a JPA version upgrade is not possible I think that JPA is not the best solution to you. You could use some of the vendor resources, e.g. Hibernate, but you will lose database and implementations portability. Artifact Size An easy solution to this problem would be to change the JPA implementation. Instead of using the Hibernate implementation you could use the Eclipsellink, OpenJPA or the Batoo. A problem might appear if the project is using Hibernate annotation/resources; the implementation change will require some code refactoring. Generated SQL and Complexes Query The solution to these problems would be a resource named NativeQuery. With this resource you could have a simplified query or optimized SQL, but you will sacrifice the database portability. You could put your queries in a file, something like SEARCH_STUDENTS_ORACLE or SEARCH_STUDENTS_MYSQL, and in production environment the correct file would be accessed. The problem of this approach is that the same query must be written for every database. If we need to edit the SEARCH_STUDENTS query, it would be required to edit the oracle and mysql files. If your project is has only one database vendor the NativeQuery resource will not be a problem. The advantage of this hybrid approach (JPQL and NativeQuery in the same project) is the possibility of using the others JPA advantages. Slow Processing and Huge Memory Size This problem can be solved with optimized queries (with NativeQuery), query pagination and small transactions. Avoid using EJB with PersistenceContext Extended, this kind of context will consume more memory and processing of the server. There is also the possibility of getting an entity from database as a “read only” entity, e.g.: entity that will only be used in a report. To recover an entity in a “read only” state is not needed to open a transaction, take a look at the code below: String query = "select uai from Student uai"; EntityManager entityManager = entityManagerFactory.createEntityManager(); TypedQuery typedQuery = entityManager.createQuery(query, Student.class); List resultList = typedQuery.getResultList(); Notice that in the code above there is no opened transaction, all the returned entities will be detached (non monitored by the JPA). If you are using EJB mark your transaction as NOT_SUPPORTED or you could use @Transactional(readOnly=true). Complexity I would say that there is only one solution to this problem: to study. It will be necessary to read books, blogs, magazines or any other trustful source of JPA material. More study is equals to less doubts in JPA. I am not a developer that believes that JPA it is the only and the best solution to every problem, but there are moments that JPA is not the best to tool to use. You must be careful when deciding about a persistence framework change, usually a lot of classes are affected and a huge refactoring is needed. Several bugs may be caused by this refactoring. It is needed to talk with the project mangers about this refactoring and list all the positive and negative effects. In the next four pages we will see 4 persistence frameworks that can be used in our projects, but before we see the frameworks I will show how that I choose each framework. Criteria for Choosing the frameworks Described Here Maybe you will think: “why the framework X is not here?”. Below I will list the criteria applied for choosing the framework displayed here: Found in more than one source of research: we can find in forums people talking about a framework, but it is harder to find the same framework appearing in more than one forum. The most quoted frameworks were chosen. Quoted by different sources: Some frameworks that we found in the forums are indicated only by its committers. Some forums does not allow “self merchandise”, but some frameworks owners still doing it. Last update 01/05/2013: I have searched for frameworks that have been updated in this past year. Quick Hello World: Some frameworks I could not do a Hello World with less than 15~20min, and with some errors. To the tutorials found in this post I have worked 7 minutes in each framework: starting counting in its download until the first database insert. The frameworks that will be displayed in here has good methods and are easy to use. To make a real CRUD scenario we have a persistence model like below: A attribute with a name different of the column name: socialSecurityNumber —-> social_security_number A date attribute a ENUM attribute With this characteristics in a class we will see some problems and how the framework solve it. Spring JDBC Template One of the most famous frameworks that we can find to access the database data is the Spring JDBC Template. The code of this project can be found in here: https://github.com/uaihebert/SpringJdbcTemplateCrud The Sprint JDBC Template uses natives queries like below: As it is possible to see in the image above the query has a database syntax (I will be using MySQL). When we use a native SQL query it is possible to use all the database resources in an easy way. We need an instance of the object JDBC Template (used to execute the queries), and to create the JDBC Template object we need to set up a datasource: We can get the datasource now (thanks to the Spring injection) and create our JDBCTemplate: PS.: All the XML code above and the JDBCTemplate instantiation could be replace by Spring injection and with a code bootstrap, just do a little research about the Spring features. One thing that I did not liked is the INSERT statement with ID recover, it is very verbose: With the KeyHolder class we can recover the generated ID in the database, unfortunately we need a huge code to do it. The other CRUD functions are easier to use, like below: Notice that to execute a SQL query it is very simple and results in a populated object, thanks to the RowMapper. The RowMapper is the engine that the JDBC Template uses to make easier to populate a class with data from the database. Take a look at the RowMapper code below: The best news about the RowMapper is that it can be used in any query of the project. The developer that is responsible to write the logic that will populate the class data. To finish this page, take a look below in the database DELETE and the database UPDATE statement: About the Spring JDBC Template we can say: Has a good support: Any search in the Internet will result in several pages with tips and bug fixes. A lot of companies use it: several projects across the world use it Be careful with different databases for the same project: The native SQL can became a problem with your project run with different databases. Several queries will need to be rewritten to adapt all the project databases. Framework Knowledge: It is good to know the Spring basics, how it can be configured and used. To those that does not know the Spring has several modules and in your project it is possible to use only the JDBC Template module. You could keep all the other modules/frameworks of your project and add only the necessary to run the JDBC Template. MyBatis MyBatis (created with the name iBatis) is a very good framework that is used by a lot of developers. Has a lot of functionalities, but we will only see a few in this post. The code of this page can be found in here: https://github.com/uaihebert/MyBatisCrud To run your project with MyBatis you will need to instantiate a Session Factory. It is very easy and the documentation says that this factory can be static: When you run a project with MyBatis you just need to instantiate the Factory one time, that is why it is in a static code. The configuration XML (mybatis.xml) it is very simple and its code can be found below: The Mapper (an attribute inside the XML above) will hold information about the project queries and how to translate the database result into Java objects. It is possible to create a Mapper in XML or Interface. Let us see below the Mapper found in the file crud_query.xml: Notice that the file is easy to understand. The first configuration found is a ResultMap that indicates the query result type, and a result class was configured “uai.model.Customer”. In the class we have a attribute with a different name of the database table column, so we need to add a configuration to the ResultMap. All queries need a ID that will be used by MyBatis session. In the beginning of the file it is possible to see a namespace declared that works as a Java package, this package will wrap all the queries and the ResultMaps found in the XML file. We could also use a Interface+Annotation instead of the XML. The Mapper found in the crud_query.xml file could be translated in to a Interface like: Only the Read methods were written in the Interface to make the code smaller, but all the CRUD methods could be written in the Interface. Let us see first how to execute a query found in the XML file: The parsing of the object is automatically and the method is easy to read. To run the query all that is needed is to use the combination “namespace + query id” that we saw in the crud_query.xml code above. If the developer wants to use the Interface approach he could do like below: With the interface query mode we have a clean code and the developer will not need to instantiate the Interface, the session class of the MyBatis will do the work. If you want to update, delete or insert a record in the database the code is very easy: About MyBatis we could say: Excellent Documentation: Every time that I had a doubt I could answer it just by reading its site documentation Flexibility: Allowing XML or Interfaces+Annotations the framework gives a huge flexibility to the developer. Notice that if you choose the Interface approach the database portability will be harder, it is easier to choose which XML to send with the deploy artifact rather than an interface Integration: Has integration with Guice and Spring Dynamic Query: Allows to create queries in Runtime, like the JPA criteria. It is possible to add “IFs” to a query to decide which attribute will be used in the query Transaction: If your project is not using Guice of Spring you will need to manually control the transaction Sormula Sormula is a ORM OpenSource framework, very similar to the JPA/Hibernate. The code of the project in this page can be found in here: https://github.com/uaihebert/SormulaCrud Sormula has a class named Database that works like the JPA EntityManagerFactory, the Database class will be like a bridge between the database and your model classes. To execute the SQL actions we will use the Table class that works like the JPA EntityManager, but the Table class is typed. To run Sormula in a code you will need to create a Database instance: To create a Database instance all that we need is a Java Connection. To read data from the database is very easy, like below: You only need to create a Database instance and a Table instance to execute all kind of SQL actions. How can we map a class attribute name different from the database table column name? Take a look below: We can use annotations to do the database mapping in our classes, very close to the JPA style. To update, delete or create data in the database you can do like below: About Sormula we can say that: Has a good documentation Easy to set up It is not found in the maven repository, it will make harder to attach the source code if needed Has a lot of checked exceptions, you will need to do a try/catch for the invoked actions sql2o This framework works with native SQL and makes easier to transform database data into Java objects. The code of the project in this page can be found in here: https://github.com/uaihebert/sql2oCrud sql2o has a Connection class that is very easy to create: Notice that we have a static Sql2o object that will work like a Connection factory. To read the database data we would do something like: Notice that we have a Native SQL written, but we have named parameters. We are not using positional parameters like ‘?1′ but we gave a name to the parameter like ‘:id’. We can say that named parameters has the advantage that we will not get lost in a query with several parameters; when we forget to pass some parameter the error message will tell us the parameter name that is missing. We can inform in the query the name of the column with a different name, there is no need to create a Mapper/RowMapper. With the return type defined in the query we will not need to instantiate manually the object, sql2o will do it for us. If you want to update, delete or insert data in the database you can do like below: It is a “very easy to use” framework. About the sql2o we can say that: Easy to handle scalar query: the returned values of SUM, COUNT functions are easy to handle Named parameters in query: Will make easy to handle SQL with a lot of parameters Binding functions: bind is a function that will automatically populate the database query parameters through a given object, unfortunately it did not work in this project for a problem with the enum. I did not investigate the problem, but I think that it is something easy to handle Take a look at: jOOQ and Avaje jOOQ jOOQ it is a framework indicated by a lot of people, the users of this frameworks praise it in a lot of sites/forums. Unfortunately the jOOQ did not work in my PC because my database was too old, and I could not download other database when writing this post (I was in an airplane). I noticed that to use the jOOQ you will need to generated several jOOQ classes based in your model. jOOQ has a good documentation in the site and it details how to generate those classes. jOOQ is free to those that uses a free database like: MySQL, Postgre, etc. The paid jOOQ version is needed to those that uses paid databases like: Oracle, SQL Server, etc. www.jooq.org/ Avaje Is a framework quoted in several blogs/forums. It works with the ORM concept and it is easy to execute database CRUD actions. Problems that I found: Not well detailed documentation: its Hello World is not very detailed Configurations: it has a required properties configuration file with a lot of configurations, really boring to those that just want to do a Hello World A Enhancer is needed: enhancement is a method do optimize the class bytecode, but is hard to setup in the beginning and is mandatory to do before the Hello World www.avaje.org Is a Raw JDBC Approach Worth It? The advantages of JDBC are: Best performance: We will not have any framework between the persistence layer and the database. We can get the best performance with a raw JDBC Control over the SQL: The written SQL is the SQL that will be executed in the database, no framework will edit/update/generate the query SQL Native Resource: We could access all natives database resources without a problem, e.g.: functions, stored procedures, hints, etc The disadvantages are: Verbose Code: After receiving the database query result we need to instantiate and populate the object manually, invoking all the required “set” methods. This code will get worse if we have classes relationships like one-to-many. It will be very easy to find a while inside another while. Fragile Code: If a database table column changes its name it will be necessary to edit all the project queries that uses this column. Some project uses constants with the column name to help with this task, e.g. Customer.NAME_COLUMN, with this approach the table column name update would be easier. If a column is removed from the database all the project queries would be updated, even if you have a column constants. Complex Portability: If your project uses more than one database it would be necessary to have almost all queries written for each vendor. For any update in any query it would be necessary to update every vendor query, this could take a lot the time from the developers. I can see only one factor that would make me choose a raw JDBC approach almost instantly: Performance: If your project need to process thousands of transactions per minutes, need to be scalable and with a low memory usage this is the best choice. Usually median/huge projects has all this high performance requirements. It is also possible to have a hybrid solution to the projects; most of the project repository (DAO) will use a framework, and just a small part of it will use JDBC I do like JDBC a lot, I have worked and I still working with it. I just ask you to not think that JDBC is the silver bullet for every problem. If you know any other advantage/disadvantage that is not listed here, just tell me and I will add here with the credits going to you. [= How Can I Choose the Right Framework? We must be careful if you want to change JPA for other project or if you are just looking for other persistence framework. If the solutions in page 3 are not solving your problems the best solution is to change the persistence framework. What should you considerate before changing the persistence framework? Documentation: is the framework well documented? Is easy to understand how it works and can it answer most of your doubts? Community: has the framework an active community of users? Has a forum? Maintenance/Fix Bugs: Is the framework receiving commits to fix bugs or receiving new features? There are fix releases being created? With which frequency? How hard is to find a developer that knows about this framework? I believe that this is the most important issue to be considered. You could add to your project the best framework in the world but without developers that know how to operate it the framework will be useless. If you need to hire a senior developer how hard would be to find one? If you urgently need to hire someone that knows that unknown framework maybe this could be very difficult. Final Thoughts I will say it again: I do not think that JPA could/should be applied to every situation in every project in the world; I do no think that that JPA is useless just because it has disadvantages just like any other framework. I do not want you to be offended if your framework was not listed here, maybe the research words that I used to find persistence frameworks did not lead me to your framework. I hope that this post might help you. If your have any double/question just post it. [= See you soon! \o_

the article represents steps required to configure tomcat 8 with eclipse kepler. download tomcat 8 and place it within any local folder. download eclipse java ee kepler as of date, tomcat 8 is not supported in eclipse javeee kepler. however, you could add the tomcat 8 by doing following: go to the wtp downloads page, select the latest version (currently 3.6), and download the zip. here’s the current link . copy the all of the files in features and plugins directories of the downloaded wtp into the corresponding eclipse directories in your eclipse folder (overwriting the existing files). start eclipse and click on “servers” tab in the workbench. go ahead and try adding a new server. you would find option for tomcat 8 available for selection as shown below. after clicking finish, you would see a new server added with the name as “tomcat v8.0 server at localhost”. start the server. check http://localhost:8080 (provided you installed tomcat 8 and set http port as 8080) interestingly, you would not see the welcome page, but the 404 error page. to get rid of that, double click on ”tomcat v8.0 server at localhost”. in the window that opens up, select “use tomcat installation” and, change deploy path from wtpwebapps to webapps. look at the figure below. restart the server and access http://localhost:8080 . you are all set.

One of the things that one must get used to with a new language is dealing with new conventions. Now coming from mostly a Java and Ruby background, there is a notion of constructors and constructor chaining, basically when one constructor calls another one. So when it comes to Swift, there are a few rules around the methods used to create objects. Firstly, the language guide refers to constructors as initializers. So basically anytime you want to use the term constructor, use the term initializer when you are in Swift land. More specifically, constructor chaining is known as initializer delegation. Initializers fall into two categories, Designated and Convenience.Designated initializers are the primary initializers and are responsible for initializing all properties of a class. So in this case, the only initializer here is known as the Designated one. class Person { let name: String let age: Int init(name: String, age: Int) { self.name = name self.age = age } } Now if you want to add other initializers to delegate to the Designated one, they will be known as Convenience initializers. So let's add one that defaults the age. Convenience initializers have the keyword conveniencebefore them. class Person { let name: String let age: Int init(name: String, age: Int) { self.name = name self.age = age } convenience init(name: String) { self.init(name: name, age: 100) } } Convenience initializers need to delegate to another Convenienceinitializer or a Designated initializer. Here's an example of a Convenience initializer calling another Convenienceone. class Person { let name: String let age: Int init(name: String, age: Int) { self.name = name self.age = age } convenience init(name: String) { self.init(name: name, age: 30) } convenience init() { self.init(name: "Homer") } } If you wanted to, you could create another Designated initializer. Now we have two Designated initializers and two Convenience initializers. class Person { let name: String let age: Int init(name: String, age: Int) { self.name = name self.age = age } init(age: Int, name: String) { self.age = age self.name = name } convenience init(name: String) { self.init(name: name, age: 30) } convenience init() { self.init(name: "Homer") } } What about subclasses? Once again, with initializers, there are certain rules that need to be adhered to when subclassing. So Designated initializers must call otherDesignated initializers in their immediate parent class. So let's take a look at an example. We are adding a Student class as a subclass of the Person class. It will add a property student number. class Student : Person { let number: String init(name: String, age: Int, number: String) { self.number = number super.init(name: name, age: age) } } Here we are calling the Designated initializer in the Person class. If we tried to call one of the Convenience initializers in the Person class, a compile error would occur. Also note, we need to assign the number property before calling the initializer in the parent class as it is a requirement to ensure all properties are initialized in child classes before their respective parent initializer is called. Summary Basically in summary, as explained in the language guide, Designatedinitializers delegate up and Convenience initializers delegate across. I hope this article helped in some way to improve your understanding of initializers in Swift. References Swift Language Guide

I have a small Spring boot based application that uses a Postgres database as a datastore. I wanted to document the steps involved in deploying this sample application to Cloud Foundry. Some of the steps are described in the Spring Boot reference guide, however the guides do not sufficiently explain how to integrate with the datastore provided in a cloud based environment. Spring-cloud provides the glue to connect Spring based applications deployed on a Cloud to discover and connect to bound services, so the first step is to pull in the Spring-cloud libraries into the project with the following pom entries: org.springframework.cloud spring-cloud-spring-service-connector 1.0.0.RELEASE org.springframework.cloud spring-cloud-cloudfoundry-connector 1.0.0.RELEASE Once this dependency is pulled in, connecting to a bound service is easy, just define a configuration along these lines: @Configuration public class PostgresCloudConfig extends AbstractCloudConfig { @Bean public DataSource dataSource() { return connectionFactory().dataSource(); } } Spring-Cloud understands that the application is deployed on a specific Cloud(currently Cloud Foundry and Heroku by looking for certain characteristics of the deployed Cloud platform), discovers the bound services, recognizes that there is a bound service using which a Postgres based datasource can be created and returns the datasource as a Spring bean. This application can now deploy cleanly to a Cloud Foundry based Cloud. The sample application can be tried out in a version of Cloud Foundry deployed with bosh-lite, these are how the steps in my machine looks like once Cloud Foundry is up and running with bosh-lite: The following command creates a user provided service in Cloud Foundry: cf create-user-provided-service psgservice -p '{"uri":"postgres://postgres:[email protected]:5432/hotelsdb"}' Now, push the app, however don't start it up. We can do that once the service above is bound to the app: cf push spring-boot-mvc-test -p target/spring-boot-mvc-test-1.0.0-SNAPSHOT.war --no-start Bind the service to the app and restart the app: cf bind-service spring-boot-mvc-test psgservice cf restart spring-boot-mvc-test That is essentially it, Spring Cloud should ideally take over at the point and cleanly parse the credentials from the bound service which within Cloud Foundry translates to an environment variable called VCAP_SERVICES, and create the datasource from it. There is however an issue with this approach - once the datasource bean is created using spring-cloud approach, it does not work in a local environment anymore. The potential fix for this is to use Spring profiles, assume that there is a different "cloud" Spring profile available in Cloud environment where the Spring-cloud based datasource gets returned: @Profile("cloud") @Configuration public class PostgresCloudConfig extends AbstractCloudConfig { @Bean public DataSource dataSource() { return connectionFactory().dataSource(); } } and let Spring-boot auto-configuration create a datasource in the default local environment, this way the configuration works both local as well as in Cloud. Where does this "cloud" profile come from, it can be created using a ApplicationContextInitializer, and looks this way: public class SampleWebApplicationInitializer implementsApplicationContextInitializer { private static final Log logger = LogFactory.getLog(SampleWebApplicationInitializer.class); @Override public void initialize(AnnotationConfigEmbeddedWebApplicationContext applicationContext) { Cloud cloud = getCloud(); ConfigurableEnvironment appEnvironment = applicationContext.getEnvironment(); if (cloud!=null) { appEnvironment.addActiveProfile("cloud"); } logger.info("Cloud profile active"); } private Cloud getCloud() { try { CloudFactory cloudFactory = new CloudFactory(); return cloudFactory.getCloud(); } catch (CloudException ce) { return null; } } } This initializer makes use of the Spring-cloud's scanning capabilities to activate the "cloud" profile. One last thing which I wanted to try was to make my local behave like Cloud atleast in the eyes of Spring-Cloud and this can be done by adding in some environment variables using which Spring-Cloud makes the determination of the type of cloud where the application is deployed, the following is my startup script in local for the app to pretend as if it is deployed in Cloud Foundry: read -r -d '' VCAP_APPLICATION <<'ENDOFVAR' {"application_version":"1","application_name":"spring-boot-mvc-test","application_uris":[""],"version":"1.0","name":"spring-boot-mvc-test","instance_id":"abcd","instance_index":0,"host":"0.0.0.0","port":61008} ENDOFVAR export VCAP_APPLICATION=$VCAP_APPLICATION read -r -d '' VCAP_SERVICES <<'ENDOFVAR' {"postgres":[{"name":"psgservice","label":"postgresql","tags":["postgresql"],"plan":"Standard","credentials":{"uri":"postgres://postgres:[email protected]:5432/hotelsdb"}]} ENDOFVAR export VCAP_SERVICES=$VCAP_SERVICES mvn spring-boot:run This entire sample is available at this github location:https://github.com/bijukunjummen/spring-boot-mvc-test Conclusion Spring Boot along with Spring-Cloud project now provide an excellent toolset to create Spring-powered cloud ready applications, and hopefully these notes are useful in integrating Spring Boot with Spring-Cloud and using these for seamless local and Cloud deployments.

Spring Integration (SI) is a framework enabling a collection of individual applications to integrate together to deliver a business enterprise system. The framework is essentially a lightweight messaging system that enables spring based applications to communicate with one another and supports integration with external systems via declarative adaptors. It is based on the 'filters and pipes' design architecture. A key feature of it is that it achieves this integration in a minimally intrusive way. The framework is built on 3 main components: Messages Encapsulate the data to be transferred from one place to another. They comprise of a header (holds meta data such as message-id, timestamp, etc) and a payload (your data typically in the form of a POJO). Channels Provide a mechanism to transport messages from one endpoint to another. Represents the pipes in the pipes & filters architecture. SI offers two types of channels, namely Pollable and Subscribable Channels. The former rely on consumers to periodically check for messages whereas the latter is directly responsible for notifying registered consumers when messages become available. Endpoints Consumer/Producer of messages. Performs some action based on the payload. Endpoints come in various flavours, each performing a different function. These include Transformers (transform data), Routers (route data), Filters (filter data), Splitter (splits messages), Aggregator (aggregates group of messages into single message), Service Activator (connecting messages to Services) and Channel Adapters (connect channels to external applications). The basic idea behind the SI framework is that applications communicate with each other by sending/receiving messages. These messages would typically contain the information (payload) required by the next application in the process pipeline. The transport of messages from one application to another is performed by Channel components. The Endpoints perform some action based on the payload. This could be routing the messages to another endpoint or processing the payload itself. The objective of this post is to provide an introduction to Spring Integration. To help achieve this, I developed a sample application which will be discussed below. The source for this sample application is available at here. The project was built and run using spring-integration-4.0.0, maven 3.2.1 and jdk1.6. The main dependency is for the relevant spring-integration jar as declared in the pom.xml: org.springframework.integration spring-integration-stream 4.0.0.RC1 I ran the application using the maven exec plugin. This allows me to clean, package and run the application by invoking mvn clean package exec:java -P OnlineShop from the command line. Developing a sample application: Tabernus My goal as usual was to build something very simple which would help me to become familiar with key concepts of this framework and to this end I've knocked up a simple app which does not connect up individual systems but rather invokes methods on a POJO. Extending this to actual working applications shouldn't be too difficult. The scenario I'm going to model revolves around purchasing items from an online store (Tabernus). This store only sells 3 types of items: Books, Music CDs, and software. During a Sale, the owners have decided to apply different discounts based on the item type. In this instance books, music and software benefit from discounts of 5%, 10%, and 15% respectively. The following diagram shows our domain entities. The class diagram shows that a Customer can place an Order comprising of a number of OrderItems which are of type Book, MusicCD or Software. The problem I need to solve is to design a system which can interrogate each Order and apply the correct discount based on the item type. Subsequently it should be able to compute the total cost of the order once the discounts have been applied. To model this using Spring Integration we need the following pipeline The above diagram shows various components most of which can be divided into 2 categories, channels (blue cylinder shapes) and endpoints (rectangular boxes). The exception to this is the Poller component whose purpose is to enable the various endpoints to function correctly and discussion of it will be given later. We'll start off by briefly covering the various stages in this pipeline as indicated by the numbers in red. Following this we will delve deeper into how we build this pipeline using the SI framework. The pipeline is comprised of 6 major stages as reflected by the numbers in the diagram, The Gateway component represents the entry point to the messaging system. All new Orders will be submitted to this component which will in turn wrap them as messages and place them into the channel appropriately named ordersChannel. Using the Splitter component - each Order is decomposed into a collection of it's constituent OrderItem instances. Each of these is wrapped in a Message and placed in the orderItemsChannel. The Router component considers each OrderItem in turn and places it in the relevant channel, e.g. Book items will be placed in the bookItemsChannel etc. This allows us to consider the different item types separately. The ServiceActivator needs to consider messages within each of the 3 channels and calculate the correct discount based on the channel. After completing the calculation for each OrderItem, it will place the OrderItem in the processedItemsChannel. The Aggregator component will collect all OrderItem instances placed in the processedItemsChannel and reconstruct the original Order. This will subsequently be placed in the deliveriesChannel, which represents the end of the pipeline. The Poller Component is required to configure how often the various endpoints will interrogate their respective input channels for messages. To implement the pipeline shown above using the SI framework, we need to implement the various end points. configure the pipeline in an xml file (Shop.xml) - identifying the various channels and endpoints and how they wire up together. At this point I should mention that SI offers 2 approach to configuring your process pipeline, annotations based and xml. In this article I'll be using the latter. Let's start to look at some code. We'll consider each stage described above and show the java implementation of the endpoint and xml configuration required to wire up the components. Step 1 - Gateway To begin with, we need to implement the Client that will invoke the Gateway component to place the Order. The client (OnlineShop.java) is shown below, public class OnlineShop { public static void main(String[] args) { AbstractApplicationContext context = new ClassPathXmlApplicationContext("/META-INF/com/prodcod/shop.xml", OnlineShop.class); Shop shop = (Shop) context.getBean("shop"); final Order order = createOrder(); shop.placeOrder(order); context.close(); } The logic here is quite simple. The client creates a dummy Order and passes this as an argument when it invokes the placeOrder() method on the gateway component. The gateway component referred here as Shop is injected by Spring. The Gateway component looks like: // Gateway component public interface Shop { @Gateway(requestChannel="ordersChannel") void placeOrder(Order order); } As you can see, this is simply an interface, whose implementation will be provided by Spring when it is injected into the client application. This is achieved by the use of the @Gateway annotation which informs Spring that this is a Gateway component and it needs to provide the implementation. Additionally the annotation accepts an attribute, requestChannel which defines the channel on which the Order instance will be placed. The framework does this by simply wrapping our instance of Order within a Message instance and placing it in the channel, 'ordersChannel'. The Gateway component and the 'ordersChannel' are declared as follows in the file shop.xml Step 2 - Splitter The next end point is the Splitter component. Appropriately named, it's role is to take a single message containing a payload of a collection of items and splitting it into a number of messages, each of which contains a single element from the collection. In our case, we want to decompose the Order into it's constituent OrderItem instances. It does this by taking a Message containing the payload of Order from 'ordersChannel' and then processing it before sending messages (each containing an OrderItem instance) to the 'orderItemsChannel'. Our implementation of the splitter is called OrderSplitter and is defined as below, public class OrderSplitter extends AbstractMessageSplitter{ @Override protected Object splitMessage(Message message) { return ((Order)message.getPayload()).getOrderItems(); } } Implementing a splitter is quite easy and involves extending the AbstractMessageSplitter class and overriding the splitMessage() method. This simply takes a message containing the payload of Order and returns it's collection of OrderItems. Step 3 - Router Having decomposed the Order into it's constituent OrderItems, we now need to separate them into groups of Books, MusicCD, and Software. This is achieved using a router. Our implementation of the Router looks like, public class OrderItemRouter { public String routeOrder(OrderItem orderItem) { String channel = ""; if(isBook(orderItem)) { channel = "bookItemsChannel"; } else if(isMusic(orderItem)) { channel = "musicItemsChannel"; } else if(isSoftware(orderItem)) { channel = "softwareItemsChannel"; } return channel; } ..................... ..................... } Nothing too complicated here. For each OrderItem, the method routeOrder() will determine it's item type and return the name of the channel that this message should be sent to. The channel name is returned by the method. Spring will then ensure that the message containing the OrderItem is relayed to the named channel. The configuration for OrderItemRouter looks like, The config identifies that the class OrderItemRouter is a Router component which will consume messages from the orderItemsChannel. Further Spring needs to invoke the method routeOrder() which contains the logic to perform the routing. The channels for each item type are declared as follows Step 4 - ServiceActivator The next step is to calculate the discounted price for each item type and this is performed by a ServiceActivator component. This is implemented as follows public class Shopkeeper { private static final BigDecimal BOOK_DISCOUNT = new BigDecimal(0.05); private static final BigDecimal MUSIC_DISCOUNT = new BigDecimal(0.10); private static final BigDecimal SOFTWARE_DISCOUNT = new BigDecimal(0.15); /** * Performs discount on books * @param bookOrderItem OrderItem comprising of a book item * @return OrderItem with discount price newly calculated */ public OrderItem processBooks(OrderItem bookOrderItem){ final BigDecimal finalPrice = calculateDiscountedPrice(bookOrderItem, BOOK_DISCOUNT); bookOrderItem.setDiscountedPrice(finalPrice); return bookOrderItem; } /** * Performs discount on music * @param musicOrderItem OrderItem comprising of a music item * @return OrderItem with discount price newly calculated */ public OrderItem processMusic(OrderItem musicOrderItem){ final BigDecimal finalPrice = calculateDiscountedPrice(musicOrderItem, MUSIC_DISCOUNT); musicOrderItem.setDiscountedPrice(finalPrice); return musicOrderItem; } /** * Performs discount on software * @param softwareOrderItem OrderItem comprising of a book item * @return OrderItem with discount price newly calculated */ public OrderItem processSoftware(OrderItem softwareOrderItem){ final BigDecimal finalPrice = calculateDiscountedPrice(softwareOrderItem, SOFTWARE_DISCOUNT); softwareOrderItem.setDiscountedPrice(finalPrice); return softwareOrderItem; } } This class exposes 3 methods to compute the new discounted price for each item type. Each method returns the OrderItem instance with the new price. The ServiceActivator is configured as follows: This tells Spring that the Shopkeeper class is a ServiceActivator and will consume messages from any of the 3 channels defined in the input-channel attribute. When a message appears in one of these channels, Spring will invoke the appropriate method on the ServiceActivator class as specfied by the attribute method. Anything returned from all three methods will be placed in the processedItems channel, ready for the next step of the processing pipeline. Step 5 - Aggregator The final stage is to take the individual OrderItems with their newly computed discounted prices and reconstruct the Order. This is achieved using an aggregator. Our implementation of an aggregator is listed below public class OrderCompleter { public Order prepareDelivery(List orderItems) { final Order order = new Order(); order.setOrderItems(orderItems); return order; } } The aggregator exposes a method that takes a collection of OrderItem objects. These will come from the processedItems channel declared as Recall this is the output channel for the service activator class as discussed above. The aggregator is configured in the xml file as The configuration tells Spring that the aggregator component will consume messages from the processedItems channel. These will be processed by the method prepareDelivery on the class OrderCompleter. Any output from this class will be relayed to the channel-adaptor deliveries, which is declared as The stdout-channel-adapter component writes to the systems STDOUT output stream. Step 6 - Poller To complete the setup we have to configure a poller component. This is required to enable the channels to work correctly. All our channels are of a queue type and so their respective consumers need to know when to query them. This is achieved using a poller mechanism. It is configured in the following way In this case, we have declared a global poller (as indicated by the default attribute). This will be used by the various end points to determine when they should interrogate their respective input-channels for messages. The second attribute fixed-delay is used to configure the polling interval. Running the Application Building and running the app shows the following output: The logging shows that the Customer submitted an Order for 3 items, one of each type. All items cost £100 each. The Order was then split into 3 OrderItems each of which was routed to the correct processing channel based on the item type. The ServiceActivator (Shopkeeper) then calculated the discount for each item and this was set on the OrderItem instance. The OrderItems were then aggregated using the OrderCompleter class which displays the final discounted price of £270 to be paid by the Customer. Note that the messages are logged to be in different stages of the processing pipeline despite starting off in the same order. This completes the tutorial on the Spring Integration Framework. Any comments relating to corrections, omissions, etc are welcome.

Before some time I have written a post about Getting Started with Nhibernate and ASP.NET MVC –CRUD operations. It’s one of the most popular post blog post on my blog. I get lots of questions via email and other medium why you are not writing a post about Fluent Nhibernate and ASP.NET MVC. So I thought it will be a good idea to write a blog post about it. What is Fluent Nhibernate: Convection over configuration that is mantra of Fluent Hibernate If you have see the my blog post about Nhibernate then you might have found that we need to create xml mapping to map table. Fluent Nhibernate uses POCO mapping instead of XML mapping and I firmly believe in Convection over configuration that is why I like Fluent Nhibernate a lot. But it’s a matter of Personal Test and there is no strong argument why you should use Fluent Nhibernate instead of Nhibernate. Fluent Nhibernate is team Comprises of James Gregory, Paul Batum, Andrew Stewart and Hudson Akridge. There are lots of committers and it’s a open source project. You can find all more information about at following site. http://www.fluentnhibernate.org/ On this site you can find definition of Fluent Nhibernate like below. Fluent, XML-less, compile safe, automated, convention-based mappings for NHibernate. Get your fluent on. They have excellent getting started guide on following url. You can easily walk through it and learned it. https://github.com/jagregory/fluent-nhibernate/wiki/Getting-started ASP.NET MVC and Fluent Nhibernate: So all set it’s time to write a sample application. So from visual studio 2013 go to file – New Project and add a new web application project with ASP.NET MVC. Once you are done with creating a web project with ASP.NET MVC It’s time to add Fluent Nhibernate to Application. You can add Fluent Nhibernate to application via following NuGet Package. And you can install it with package manager console like following. Now we are done with adding a Fluent Nhibernate to it’s time to add a new database. Now I’m going to create a simple table called “Employee” with four columns Id, FirstName, LastName and Designation. Following is a script for that. CREATE TABLE [dbo].[Employee] ( [Id] INT NOT NULL PRIMARY KEY, [FirstName] NVARCHAR(50) NULL, [LastName] NVARCHAR(50) NULL, [Designation] NVARCHAR(50) NULL ) Now table is ready it’s time to create a Model class for Employee. So following is a code for that. namespace FluentNhibernateMVC.Models { public class Employee { public virtual int EmployeeId { get; set; } public virtual string FirstName { get; set; } public virtual string LastName { get; set; } public virtual string Designation{ get; set; } } } Now Once we are done with our model classes and other stuff now it’s time to create a Map class which map our model class to our database table as we know that Fluent Nhibernate is using POCO classes to map instead of xml mappings. Following is a map class for that. using FluentNHibernate.Mapping; namespace FluentNhibernateMVC.Models { public class EmployeeMap : ClassMap { public EmployeeMap() { Id(x => x.EmployeeId); Map(x => x.FirstName); Map(x => x.LastName); Map(x => x.Designation); Table("Employee"); } } } If you see Employee Map class carefully you will see that I have mapped Id column with EmployeeId in table and another things you can see I have written a table with “Employee” which will map Employee class to employee class. Now once we are done with our mapping class now it’s time to add Nhibernate Helper which will connect to SQL Server database. using FluentNHibernate.Cfg; using FluentNHibernate.Cfg.Db; using NHibernate; using NHibernate.Tool.hbm2ddl; namespace FluentNhibernateMVC.Models { public class NHibernateHelper { public static ISession OpenSession() { ISessionFactory sessionFactory = Fluently.Configure() .Database(MsSqlConfiguration.MsSql2008 .ConnectionString(@"Data Source=(LocalDB)\v11.0;AttachDbFilename=C:\data\Blog\Samples\FluentNhibernateMVC\FluentNhibernateMVC\FluentNhibernateMVC\App_Data\FNhibernateDemo.mdf;Integrated Security=True") .ShowSql() ) .Mappings(m => m.FluentMappings .AddFromAssemblyOf()) .ExposeConfiguration(cfg => new SchemaExport(cfg) .Create(false, false)) .BuildSessionFactory(); return sessionFactory.OpenSession(); } } } Now we are done with classes It’s time to scaffold our Controller for the application. Right click Controller folder and click on add new controller. It will ask for scaffolding items like following. I have selected MVC5 controller with read/write action. Once you click Add It will ask for controller name. Now our controller is ready. It’s time to write code for our actions. Listing: Following is a code for our listing action. public ActionResult Index() { using (ISession session = NHibernateHelper.OpenSession()) { var employees = session.Query().ToList(); return View(employees); } } You add view for listing via right click on View and Add View. That’s is you are done with listing of your database. I have added few records to database table to see whether its working or not. Following is a output as expected. Create: Now it’s time to create code and views for creating a Employee. Following is a code for action results. public ActionResult Create() { return View(); } // POST: Employee/Create [HttpPost] public ActionResult Create(Employee employee) { try { using (ISession session = NHibernateHelper.OpenSession()) { using (ITransaction transaction = session.BeginTransaction()) { session.Save(employee); transaction.Commit(); } } return RedirectToAction("Index"); } catch (Exception exception) { return View(); } } Here you can see one if for blank action and another for posting data and saving to SQL Server with HttpPost Attribute. You can add view for create via right click on view on action result like following. Now when you run this you will get output as expected. Once you click on create it will return back to listing screen like this. Edit: Now we are done with listing/adding employee it’s time to write code for editing/updating employee. Following is a code Action Methods. public ActionResult Edit(int id) { using (ISession session = NHibernateHelper.OpenSession()) { var employee = session.Get(id); return View(employee); } } // POST: Employee/Edit/5 [HttpPost] public ActionResult Edit(int id, Employee employee) { try { using (ISession session = NHibernateHelper.OpenSession()) { var employeetoUpdate = session.Get(id); employeetoUpdate.Designation = employee.Designation; employeetoUpdate.FirstName = employee.FirstName; employeetoUpdate.LastName = employee.LastName; using (ITransaction transaction = session.BeginTransaction()) { session.Save(employeetoUpdate); transaction.Commit(); } } return RedirectToAction("Index"); } catch { return View(); } } You can create edit view via following via right click on view. Now when you run application it will work as expected. Details: You can write details code like following. public ActionResult Details(int id) { using (ISession session = NHibernateHelper.OpenSession()) { var employee = session.Get(id); return View(employee); } } You can view via right clicking on view as following. Now when you run application following is a output as expected. Delete: Following is a code for deleting Employee one action method for confirmation of delete and another one for deleting data from employee table. public ActionResult Delete(int id) { using (ISession session = NHibernateHelper.OpenSession()) { var employee = session.Get(id); return View(employee); } } [HttpPost] public ActionResult Delete(int id, Employee employee) { try { using (ISession session = NHibernateHelper.OpenSession()) { using (ITransaction transaction = session.BeginTransaction()) { session.Delete(employee); transaction.Commit(); } } return RedirectToAction("Index"); } catch (Exception exception) { return View(); } } You can add view for delete via right click on view like following. When you run it’s running as expected. Once you click on delete it will delete employee. That’s it. You can see it’s very easy. Fluent Nhibernate supports POCO mapping without writing any complex xml mappings. You can find whole source code at GitHub at following location. https://github.com/dotnetjalps/FluentNhiberNateMVC Hope you like it. Stay tuned for more!!

You should by now have worked your way through Part 1: Getting Started with Ansible. If you haven't, go and do that now. In this article, I'll be demonstrating a very simple application deployment workflow, deploying an insanely simple node.js application from a github repository, and configuring it to start with supervisord, and be reverse-proxied with Nginx. As with last time, we'll be using Parallax as the starting point for this. I've actually gone through and put the config in there already (if you don't feel like doing it yourself ;) - name: Install all the packages and stuff required for a demobox hosts: demoboxes user: user sudo: yes roles: - redis - nginx - nodejs - zeromq # - deploy_thingy In the 9c818d0b8f version, you'll be able to see that I've created a new role, inventively called "deploy_thingy". **Updated** I've been recommended that my __template__ role be based on the output of ansible-galaxy init $rolename So I've recreated the __template__ role to be based on an ansible-galaxy role template. There's not that many changes, but it does include a new directory 'default/' containing the Galaxy metadata required if you wish to push back to the public galaxy role index. In an attempt to make creating new roles easier, I put a __template__ role into the file tree when I first created Parallax, so that all you do to create a new role is execute: cp -R __template__ new_role_name in the roles/ directory. . ├── files │ ├── .empty │ ├── thingy.nginx.conf │ └── thingy.super.conf ├── handlers │ ├── .empty │ └── main.yml ├── meta │ ├── .empty │ └── main.yml ├── tasks │ └── main.yml └── templates └── .empty In this role, we define some dependencies in meta/main.yml, there's two files in the files/ directory, and there's a set of tasks defined in tasks/main.yml. There's also some handlers defined in handlers/main.yml. Let's have a quick glance at the meta/main.yml file. --- dependencies: - { role: nodejs } - { role: nginx } This basically sets the requirement that this role, deploy_thingy depends on services installed by the roles: nginx and nodejs. Although these roles are explicitly stated to be installed in site.yml, this gives us a level of belt-and-braces configuration, in case the deploy_thingy role were ever included without the other two roles being explicitly stated, or if it were configured to run before its dependencies had explicitly been set to run. tasks/main.yml is simple. --- - name: Create directory under /srv for thingy file: path=/srv/thingy state=directory mode=755 - name: Git checkout from github git: repo=https://github.com/tomoconnor/shiny-octo-computing-machine.git dest=/srv/thingy - name: Drop Config for supervisord into the conf.d directory copy: src=thingy.super.conf dest=/etc/supervisor/conf.d/thingy.conf notify: reread supervisord - name: Drop Reverse Proxy Config for Nginx copy: src=thingy.nginx.conf dest=/etc/nginx/sites-enabled/thingy.conf notify: restart nginx We'll create somewhere for it to live, check the code out of my git repository [1], Then drop two config files in place, one to configure supervisor(d), and one to configure Nginx. Because the command to configure supervisor(d) and nginx change the configuration of those services, there are notify: handlers to reload the configuration, or restart the service. Let's have a quick peek at those handlers now: --- - name: reread supervisord shell: /usr/bin/supervisorctl reread && /usr/bin/supervisorctl update - name: restart nginx service: name=nginx state=restarted When the supervisor config changes (and we add something to /etc/supervisor/conf.d), we need to tell supervisord to re-read it's configuration files, at which point, it will see the new services, and then run supervisorctl update, which will set the state of the newly added items from 'available' to 'started'. When we change the nginx configuration, we'll hit nginx with a restart. It's possible to do softer actions, like reload here, but I've chosen service restart for simplicity. I've also changed the basic Ansible config, and configuration of roles/common/files/insecure_sudoers so that it will still ask you for a sudo password in light of some minor criticism. I've found that if you're developing Ansible playbooks on an isolated system, then there's no great harm in disabling SSH Host Key Checking (in ansible.cfg), similarly how there's no great problems in disabling sudo authentication, so it's effectively like NOPASSWD use. However, Micheil made a very good point that in live environments it's a bit dodgy to say the least. So I've commented those lines out of the playbook in Parallax, so that it should give users a reasonable level of basic security. At the end of the day, it's up to you how you use Parallax, and if you find that disabling security works for you, then fine. It's not like you haven't been warned. But I digress. The next thing to do is to edit site.yml, and ensure that the new role we've created gets mapped to a hostgroup in the play configuration. In the latest version of Parallax this is already done for you, but as long as the role name in the list matches the directory in roles/, it should be ready to go. Now if we run: ansible-playbook -k -K -i playbooks/example/hosts playbooks/example/site.yml It should go through the playbook, installing stuff, then finally do the git clone from github, deploy the configuration files, and trigger a reread of supervisord, and a restart of nginx. If I now test that it's working, with: curl -i http://192.168.20.151/ HTTP/1.1 200 OK Server: nginx/1.4.1 (Ubuntu) Date: Mon, 27 Jan 2014 14:51:29 GMT Content-Type: text/html; charset=utf-8 Content-Length: 170 Connection: keep-alive X-Powered-By: Express ETag: "1827834703" That X-Powered-By: Express line shows that Nginx is indeed working, and that the node.js application is running too. You can get more information about stuff that supervisord is controlling by running: sudo supervisorctl status on the target host. $ sudo supervisorctl status thingy RUNNING pid 19756, uptime 0:00:06 If the Nginx side is configured, but the node.js application isn't running, you'd get a HTTP 502 error, as follows: curl -i http://192.168.20.151/ HTTP/1.1 502 Bad Gateway Server: nginx/1.4.1 (Ubuntu) Date: Mon, 27 Jan 2014 14:59:34 GMT Content-Type: text/html Content-Length: 181 Connection: keep-alive So, that's it. A very simple guide to deploying a very simple application with Ansible. Of course, it should be obvious that you can deploy *anything* from a git repository, it really boils down to the configuration of supervisord. For that matter, it doesn't have to be supervisord. I consider configuring supervisord for process controlling to be outside of the scope of this article, but I might touch on it in future in more detail. Next up, Part 3: Ansible and Amazon Web Services. 1: It's really simple, and I'm not very node-savvy, so I'm sorry if it sucks.

This is a first post in a series of short code snippets that will present the configuration of Spring beans from XML to Java. XML: resource.loader=class class.resource.loader.class=org.apache.velocity.runtime.resource.loader.ClasspathResourceLoader Java @Bean public VelocityEngine velocityEngine() throws VelocityException, IOException{ VelocityEngineFactoryBean factory = new VelocityEngineFactoryBean(); Properties props = new Properties(); props.put("resource.loader", "class"); props.put("class.resource.loader.class", "org.apache.velocity.runtime.resource.loader." + "ClasspathResourceLoader"); factory.setVelocityProperties(props); return factory.createVelocityEngine(); }