Utility Method for Spring REST public static HttpServletRequest getCurrentRequest() { RequestAttributes requestAttributes = RequestContextHolder.getRequestAttributes(); Assert.state(requestAttributes != null, "Could not find current request via RequestContextHolder"); Assert.isInstanceOf(ServletRequestAttributes.class, requestAttributes); HttpServletRequest servletRequest = ((ServletRequestAttributes) requestAttributes).getRequest(); Assert.state(servletRequest != null, "Could not find current HttpServletRequest"); return servletRequest; } Sometimes it’s easier to get the underlying Servlet request to get some headers or variables. final String userIpAddress = getCurrentRequest().getRemoteAddr(); final String userAgent = getCurrentRequest().getHeader("user-agent"); This is used in the simple REST service using HTTP Post verb @ the awesome CloudFoundry: (Source) Tool for Creating Your Test JSON. Spring Boot Documentation

What is CXF? Apache CXF is an open source services framework. CXF helps you build and develop services using frontend programming APIs, like JAX-WS and JAX-RS. These services can speak a variety of protocols such as SOAP, XML/HTTP, RESTful HTTP, or CORBA and work over a variety of transports such as HTTP, JMS etc. How CXF Works? As you can see here and here, how cxf service calls are processed,most of the functionality in the Apache CXF runtime is implemented by interceptors. Every endpoint created by the Apache CXF runtime has potential interceptor chains for processing messages. The interceptors in the these chains are responsible for transforming messages between the raw data transported across the wire and the Java objects handled by the endpoint’s implementation code. Interceptors in CXF When a CXF client invokes a CXF server, there is an outgoing interceptor chain for the client and an incoming chain for the server. When the server sends the response back to the client, there is an outgoing chain for the server and an incoming one for the client. Additionally, in the case of SOAPFaults, a CXF web service will create a separate outbound error handling chain and the client will create an inbound error handling chain. The interceptors are organized into phases to ensure that processing happens on the proper order.Various phases involved during the Interceptor chains are listed in CXF documentation here. Adding your custom Interceptor involves extending one of the Abstract Intereceptor classes that CXF provides, and providing a phase when that interceptor should be invoked. AbstractPhaseInterceptor class - This abstract class provides implementations for the phase management methods of the PhaseInterceptor interface. The AbstractPhaseInterceptor class also provides a default implementation of the handleFault() method. Developers need to provide an implementation of the handleMessage() method. They can also provide a different implementation for the handleFault() method. The developer-provided implementations can manipulate the message data using the methods provided by the generic org.apache.cxf.message.Message interface. For applications that work with SOAP messages, Apache CXF provides an AbstractSoapInterceptor class. Extending this class provides the handleMessage() method and the handleFault() method with access to the message data as an org.apache.cxf.binding.soap.SoapMessage object. SoapMessage objects have methods for retrieving the SOAP headers, the SOAP envelope, and other SOAP metadata from the message. Below piece of code will show, how we can add a Custom Object as Header to an outgoing request – Spring Configuration - Interceptor :- public class SoapHeaderInterceptor extends AbstractSoapInterceptor { public SoapHeaderInterceptor() { super(Phase.POST_LOGICAL); } @Override public void handleMessage(SoapMessage message) throws Fault { List headers = message.getHeaders(); TestHeader testHeader = new TestHeader(); JAXBElement testHeaders = new ObjectFactory() .createTestHeader(testHeader); try { Header header = new Header(testHeaders.getName(), testHeader, new JAXBDataBinding(TestHeader.class)); headers.add(header); message.put(Header.HEADER_LIST, headers); } catch (JAXBException e) { e.printStackTrace(); } }

After attending Sam Newman’s microservice talks at Geecon last week I started to think more about what is most likely an essential feature of service-oriented/microservice platforms for monitoring, reporting and diagnostics: correlation ids. Correlation ids allow distributed tracing within complex service oriented platforms, where a single request into the application can often be dealt with by multiple downstream service. Without the ability to correlate downstream service requests it can be very difficult to understand how requests are being handled within your platform. I’ve seen the benefit of correlation ids in several recent SOA projects I have worked on, but as Sam mentioned in his talks, it’s often very easy to think this type of tracing won’t be needed when building the initial version of the application, but then very difficult to retrofit into the application when you do realise the benefits (and the need for!). I’ve not yet found the perfect way to implement correlation ids within a Java/Spring-based application, but after chatting to Sam via email he made several suggestions which I have now turned into a simple project using Spring Boot to demonstrate how this could be implemented. Why? During both of Sam’s Geecon talks he mentioned that in his experience correlation ids were very useful for diagnostic purposes. Correlation ids are essentially an id that is generated and associated with a single (typically user-driven) request into the application that is passed down through the stack and onto dependent services. In SOA or microservice platforms this type of id is very useful, as requests into the application typically are ‘fanned out’ or handled by multiple downstream services, and a correlation id allows all of the downstream requests (from the initial point of request) to be correlated or grouped based on the id. So called ‘distributed tracing’ can then be performed using the correlation ids by combining all the downstream service logs and matching the required id to see the trace of the request throughout your entire application stack (which is very easy if you are using a centralised logging framework such as logstash) The big players in the service-oriented field have been talking about the need for distributed tracing and correlating requests for quite some time, and as such Twitter have created their open source Zipkin framework (which often plugs into their RPC framework Finagle), and Netflix has open-sourced their Karyon web/microservice framework, both of which provide distributed tracing. There are of course commercial offering in this area, one such product being AppDynamics, which is very cool, but has a rather hefty price tag. Creating a proof-of-concept in Spring Boot As great as Zipkin and Karyon are, they are both relatively invasive, in that you have to build your services on top of the (often opinionated) frameworks. This might be fine for some use cases, but no so much for others, especially when you are building microservices. I’ve been enjoying experimenting with Spring Boot of late, and this framework builds on the much known and loved (at least by me :-) ) Spring framework by providing lots of preconfigured sensible defaults. This allows you to build microservices (especially ones that communicate via RESTful interfaces) very rapidly. The remainder of this blog pos explains how I implemented a (hopefully) non-invasive way of implementing correlation ids. Goals Allow a correlation id to be generated for a initial request into the application Enable the correlation id to be passed to downstream services, using as method that is as non-invasive into the code as possible Implementation I have created two projects on GitHub, one containing an implementation where all requests are being handled in a synchronous style (i.e. the traditional Spring approach of handling all request processing on a single thread), and also one for when an asynchronous (non-blocking) style of communication is being used (i.e., using the Servlet 3 asynchronous support combined with Spring’s DeferredResult and Java’s Futures/Callables). The majority of this article describes the asynchronous implementation, as this is more interesting: Spring Boot asynchronous (DeferredResult + Futures) communication correlation id Github repo The main work in both code bases is undertaken by the CorrelationHeaderFilter, which is a standard Java EE Filter that inspects the HttpServletRequest header for the presence of a correlationId. If one is found then we set a ThreadLocal variable in the RequestCorrelation Class (discussed later). If a correlation id is not found then one is generated and added to the RequestCorrelation Class: public class CorrelationHeaderFilter implements Filter { //... @Override public void doFilter(ServletRequest servletRequest, ServletResponse servletResponse, FilterChain filterChain) throws IOException, ServletException { final HttpServletRequest httpServletRequest = (HttpServletRequest) servletRequest; String currentCorrId = httpServletRequest.getHeader(RequestCorrelation.CORRELATION_ID_HEADER); if (!currentRequestIsAsyncDispatcher(httpServletRequest)) { if (currentCorrId == null) { currentCorrId = UUID.randomUUID().toString(); LOGGER.info("No correlationId found in Header. Generated : " + currentCorrId); } else { LOGGER.info("Found correlationId in Header : " + currentCorrId); } RequestCorrelation.setId(currentCorrId); } filterChain.doFilter(httpServletRequest, servletResponse); } //... private boolean currentRequestIsAsyncDispatcher(HttpServletRequest httpServletRequest) { return httpServletRequest.getDispatcherType().equals(DispatcherType.ASYNC); } The only thing is this code that may not instantly be obvious is the conditional check currentRequestIsAsyncDispatcher(httpServletRequest), but this is here to guard against the correlation id code being executed when the Async Dispatcher thread is running to return the results (this is interesting to note, as I initially didn’t expect the Async Dispatcher to trigger the execution of the filter again?) Here is the RequestCorrelation Class, which contains a simple ThreadLocal static variable to hold the correlation id for the current Thread of execution (set via the CorrelationHeaderFilter above) public class RequestCorrelation { public static final String CORRELATION_ID = "correlationId"; private static final ThreadLocal id = new ThreadLocal(); public static String getId() { return id.get(); } public static void setId(String correlationId) { id.set(correlationId); } } Once the correlation id is stored in the RequestCorrelation Class it can be retrieved and added to downstream service requests (or data store access etc) as required by calling the static getId() method within RequestCorrelation. It is probably a good idea to encapsulate this behaviour away from your application services, and you can see an example of how to do this in a RestClient Class I have created, which composes Spring’s RestTemplate and handles the setting of the correlation id within the header transparently from the calling Class. @Component public class CorrelatingRestClient implements RestClient { private RestTemplate restTemplate = new RestTemplate(); @Override public String getForString(String uri) { String correlationId = RequestCorrelation.getId(); HttpHeaders httpHeaders = new HttpHeaders(); httpHeaders.set(RequestCorrelation.CORRELATION_ID, correlationId); LOGGER.info("start REST request to {} with correlationId {}", uri, correlationId); //TODO: error-handling and fault-tolerance in production ResponseEntity response = restTemplate.exchange(uri, HttpMethod.GET, new HttpEntity(httpHeaders), String.class); LOGGER.info("completed REST request to {} with correlationId {}", uri, correlationId); return response.getBody(); } } //... calling Class public String exampleMethod() { RestClient restClient = new CorrelatingRestClient(); return restClient.getForString(URI_LOCATION); //correlation id handling completely abstracted to RestClient impl } Making this work for asynchronous requests… The code included above works fine when you are handling all of your requests synchronously, but it is often a good idea in a SOA/microservice platform to handle requests in a non-blocking asynchronous manner. In Spring this can be achieved by using the DeferredResult Class in combination with the Servlet 3 asynchronous support. The problem with using ThreadLocal variables within the asynchronous approach is that the Thread that initially handles the request (and creates the DeferredResult/Future) will not be the Thread doing the actual processing. Accordingly, a bit of glue code is needed to ensure that the correlation id is propagated across the Threads. This can be achieved by extending Callable with the required functionality: (don’t worry if example Calling Class code doesn’t look intuitive – this adaption between DeferredResults and Futures is a necessary evil within Spring, and the full code including the boilerplate ListenableFutureAdapter is in my GitHub repo): public class CorrelationCallable implements Callable { private String correlationId; private Callable callable; public CorrelationCallable(Callable targetCallable) { correlationId = RequestCorrelation.getId(); callable = targetCallable; } @Override public V call() throws Exception { RequestCorrelation.setId(correlationId); return callable.call(); } } //... Calling Class @RequestMapping("externalNews") public DeferredResult externalNews() { return new ListenableFutureAdapter<>(service.submit(new CorrelationCallable<>(externalNewsService::getNews))); } And there we have it – the propagation of correlation id regardless of the synchronous/asynchronous nature of processing! You can clone the Github report containing my asynchronous example, and execute the application by running mvn spring-boot:run at the command line. If you access http://localhost:8080/externalNews in your browser (or via curl) you will see something similar to the following in your Spring Boot console, which clearly demonstrates a correlation id being generated on the initial request, and then this being propagated through to a simulated external call (have a look in the ExternalNewsServiceRest Class to see how this has been implemented): [nio-8080-exec-1] u.c.t.e.c.w.f.CorrelationHeaderFilter : No correlationId found in Header. Generated : d205991b-c613-4acd-97b8-97112b2b2ad0 [pool-1-thread-1] u.c.t.e.c.w.c.CorrelatingRestClient : start REST request to http://localhost:8080/news with correlationId d205991b-c613-4acd-97b8-97112b2b2ad0 [nio-8080-exec-2] u.c.t.e.c.w.f.CorrelationHeaderFilter : Found correlationId in Header : d205991b-c613-4acd-97b8-97112b2b2ad0 [pool-1-thread-1] u.c.t.e.c.w.c.CorrelatingRestClient : completed REST request to http://localhost:8080/news with correlationId d205991b-c613-4acd-97b8-97112b2b2ad0 Conclusion I’m quite happy with this simple prototype, and it does meet the two goals I listed above. Future work will include writing some tests for this code (shame on me for not TDDing!), and also extend this functionality to a more realistic example. I would like to say a massive thanks to Sam, not only for sharing his knowledge at the great talks at Geecon, but also for taking time to respond to my emails. If you’re interested in microservices and related work I can highly recommend Sam’s Microservice book which is available in Early Access at O’Reilly. I’ve enjoyed reading the currently available chapters, and having implemented quite a few SOA projects recently I can relate to a lot of the good advice contained within. I’ll be following the development of this book with keen interest! If you have any comments or thoughts then please do share them via the comment below, or feel free to get in touch via the usual mechanisms! References I used Tomasz Nurkiewicz’s excellent blog several times for learning how best to wire up all of the DeferredResult/Future code in Spring: http://www.nurkiewicz.com/2013/03/deferredresult-asynchronous-processing.html

Clients and VMs and VPNs, Oh My! As regular readers of this blog may be aware, I recently hung up my technical evangelist hat, and made the jump back into full-time consulting. Consistent with best practices, I decided that when working with a new client, the best course of action would be to set up a new virtual machine to keep all of the development environment, tools, and files isolated from anything on my host machine, which helps minimize the risk that installing the latest bleeding-edge tools (which are good to have to stay ahead of the learning curve) don't endanger the work I'm doing for the client. With my current client, I need to be able to access files, servers, and tools on their remote network, which they enable via the Cisco AnyConnect VPN client software. So far, so good. I had no trouble at all installing and connecting with this software from my laptop over my FiOS connection. Just like being at the office. The Tricky Part Unfortunately, the VPN connection does not pass through to the virtual machine I set up, using client Hyper-V on Windows 8.1 (update 1). Which is interesting, because while I was onsite recently, when I connected to the LAN directly via cable, that connection would pass through to the VM. But since I'm not a networking geek, I'll leave that to others to explain. So, the next step was to try installing the VPN client software in the VM itself. But it was not to be. The client software installs fine, but I found that when I tried to connect, I'd get the following error message: OK, so now what? Well, truth be told, since I didn't have time to troubleshoot this immediately, I set the problem aside for a while, which can be a good way to let your brain work on the problem while you're doing other things. Or sometimes, you get lucky...this was one of those times. Basic or Enhanced? By good fortune, this morning, I ran across a brief blog post by Osama Mourad (No, not the same person who runs one of the CMAP Special Interest Groups), which suggested that connecting the VPN was possible "if connected to the VM using Hyper-V Manager." A bit cryptic, but it gave me hope that it was at least possible. Here's where luck comes in. I was trying to see if there was a different way to connect to the VM from Hyper-V Manager, when I noticed that if I did not have the VM session window full-screen, there is an icon at the end of the toolbar that looks like this: That button switches the VM session from Enhanced Session Mode (the default in newer versions of Hyper-V), which uses a Remote Desktop Connection to interact with the VM, to Basic Session Mode, which provides simple screen, keyboard, and mouse redirection. And beautifully, it turns out that in Basic Session Mode, connecting the VPN works just fine. And once connected, you can switch back to Enhanced Session Mode, and the VPN will remain connected. Conclusion Using a virtual machine is a good practice for keeping your client environment isolated from your day-to-day experiments or bleeding edge tools, etc. And it also has the advantage of making the environment portable. You can store the VM files on a portable drive, or copy them from one machine to another if you need to migrate systems. But along with the convenience comes the occasional head-scratcher or stumbling block. I hope that this post will help anyone else who runs into this particular issue resolve their problem. You can learn more about Enhanced Session Mode from this TechNet article. My thanks to Osama for the clue that helped me track down the solution.

Introduction When I am looking for a forum post related to SQL Server, one of the junior professional is asking how to use a DO…WHILE loop is MS SQL Server. Several people wrote their opinion related to it. Everyone is saying to use WHILE loop and some of them suggesting with T-SQL structure of CURSOR with WHILE LOOP. Obviously, when a junior professional is learning MS SQL server, the question in mind arises: is there DO… WHILE, REPEAT … UNTIL loop present in MS SQL Server as there is in C or C++ etc? No one is answering directly on the forum whether we can use DO… WHILE or REPEAT … UNTIL in MS SQL Server or NOT. If yes, how can we implement them? DO… WHILE in MS SQL Sever First we look at the algorithm of DO… WHILE. SET X = 1 DO PRINT X SET X = X + 1 WHILE X <= 10 Now we try to implement it in MS SQL Server. DECLARE @X INT=1; WAY: --> Here the DO statement PRINT @X; SET @X += 1; IF @X<=10 GOTO WAY; --> Here the WHILE @X<=1 REPEAT… UNTIL First we look at the algorithm of REPEAT... UNTIL SET X = 1 REPEAT PRINT X SET X = X + 1 UNTIL X > 10 Now we try to implement it in MS SQL Server DECLARE @X INT = 1; WAY: -- Here the REPEAT statement PRINT @X; SET @X += 1; IFNOT(@X >1 0) GOTO WAY; -- Here the UNTIL @X>10 So we see that it is possible, but a little complicated… So most developers prefer the WHILE loop in MS SQL Server.

For doing SSL with SOAP, there’s a few things you need to setup. C:\Program Files (x86)\SmartBear\soapUI-Pro-4.5.1\jre\lib\security Also did it at the main jre at C:\Program Files (x86)\Java\jre7\lib\security keytool -genkey -alias svs -keyalg RSA -keystore keystore.jks -keysize 2048 git config --global core.autocrlf true javax.net.ssl.trustStore=<> javax.net.ssl.trustStorePassword=<> If these properties are not set, the default ones will be picked up from your the default location.[$JAVA_HOME/lib/security/jssecacerts, $JAVA_HOME/lib/security/cacerts] To view the contents of keystore file, use: keytool -list -v -keystore file.keystore -storepass changeit To debug the ssl handshake process and view the certificates, set the VM parameter -Djavax.net.debug=all keytool -genkey -keyalg RSA -alias selfsigned -keystore keystore.jks -storepass changeit -validity 360 -keysize 2048 -Djava.net.preferIPv4Stack=true added to soapui.bat C:\Program Files (x86)\SmartBear\SoapUI-4.6.3\bin -Djavax.net.debug=ssl,trustmanager http://docs.oracle.com/cd/E19509-01/820-3503/ggfgo/index.html http://www.sslshopper.com/article-most-common-java-keytool-keystore-commands.html http://ianso.blogspot.com/2009/12/building-ws-security-enabled-soap.html http://javarevisited.blogspot.com/2012/09/difference-between-truststore-vs-keyStore-Java-SSL.html http://javarevisited.blogspot.com/2012/03/add-list-certficates-java-keystore.html http://www.ibm.com/developerworks/java/library/j-jws17/index.html http://www.coderanch.com/t/223027/Web-Services/java/SOAP-HTTPS-SSL http://ruchirawageesha.blogspot.in/2010/07/how-to-create-clientserver-keystores.html http://stackoverflow.com/questions/11001102/how-to-programmatically-set-the-sslcontext-of-a-jax-ws-client http://busylog.net/ssl-java-keytool-soap-and-eclipse/ http://www.sslshopper.com/article-how-to-create-a-self-signed-certificate-using-java-keytool.html openssl s_client -showcerts -host webservices-cert.storedvalue.com -port 443 keytool -keystore clientkeystore -genkey -alias client wsdl2java.bat -uri my.wsdl -o svsproj -p com.agilemobiledeveloper.service -d xmlbeans -t -ss -ssi -sd -g -ns2p System.setProperty("javax.net.ssl.keyStore", keystore.jks"); System.setProperty("javax.net.ssl.keyStorePassword", "changeit"); System.setProperty("javax.net.ssl.trustStore", "clientkeystore"); System.setProperty("javax.net.ssl.trustStorePassword", "changeit"); setx -m JAVA_HOME "C:\Program Files\Java\jdk1.7.0_51″ setx -m javax.net.ssl.keyStore "keystore.jks"); setx -m javax.net.ssl.keyStorePassword "changeit"); setx -m javax.net.ssl.trustStore "keystore.jks"); setx -m javax.net.ssl.trustStorePassword "passwordislong");

We care a lot about the stuff that goes around Solr and Elasticsearch in our client’s infrastructure. One area that seems to always be being reinvented for-better-or-worse is the data ETL/data ingest path from data source X to the search engine. One tool we’ve enjoyed using for basic ETL these days is Apache Camel. Camel is an extremely feature-rich Java data integration framework for wiring up just about anything to anything else. And by anything I mean anything: file system, databases, HTTP, search engines, twitter, IRC, etc. One area I initially struggled with with Camel was exactly how to test my code. Lets say I have defined a simple Camel route like this: from("file:inbox") .unmarshall(csv) // parse as CSV .split() // now we're operating on individual CSV lines .bean("customTransformation") // do some random operation on the CSV line .to("solr://localhost:8983/solr/collection1/update") Great! Now if you’ve gotten into Camel testing, you may know there’s something called “AdviceWith“. What is this interesting sounding thing? Well I think its a way of saying “take these routes and muck with them” — stub out this, intercept that and don’t forward, etc. Exactly the kind of slicing and dicing I’d like to do in my unit tests! I definitely recommend reading up on the docs, but here’s the real step-by-step built around where you’re probably going to get stuck (cause its where I got stuck!) getting AdviceWith to work for your tests. 1. Use CamelTestSupport Ok most importantly, we need to actually define a test that uses CamelTestSupport. CamelTestSupport automatically creates and starts our camel context for us. public class ItGoesToSolrTest extends CamelTestSupport { ... } 2. Specify the route builder we’re testing In our test, we need to tell CamelTestSupport where it can access its routes: @Override protected RouteBuilder createRouteBuilder() { return new MyProductionRouteBuilder(); } 3. Specify any beans we’d like to register Its probably the case that you’re using Java beans with Camel. If you’re using the bean integration and referring to beans by name in your camel routes, you’ll need to register those names with an instance of your class. @Override protected Context createJndiContext() throws Exception { JndiContext context = new JndiContext(); context.bind("customTransformation", new CustomTransformation()); return context; } 4. Monkey with our production routes using advice with Second we need to actually use the AdviceWithRouteBuilder before each test: @Before public void mockEndpoints() throws Exception { AdviceWithRouteBuilder mockSolr = new AdviceWithRouteBuilder() { @Override public void configure() throws Exception { // mock the for testing interceptSendToEndpoint("solr://localhost:8983/solr/collection1/update") .skipSendToOriginalEndpoint() .to("mock:catchSolrMessages"); } }) context.getRouteDefinition(1). .adviceWith(context, mockSolr); } There’s a couple things to notice here: In configure we simply snag an endpoint (in this case Solr) and then we have complete freedom to do whatever we want. In this case, we’re rewiring it to a mock endpoint we can use for testing. Notice how we get a route definition by index (in this case 1) to snag the route we’re testing and that we’d like to monkey with. This is how I’ve seen it in most Camel examples, and its hard to guess how Camel is going to assign some index to your route. A better way would be to give our route definition a name: from(“file:inbox”) .routeId(“csvToSolrRoute”) .unmarshall(csv) // parse as CSV then we can refer to this name when retrieving our route: context.getRouteDefinition("csvToSolrRoute"). .adviceWith(context, mockSolr); 5. Tell CamelTestSupport you want to manually start/stop camel One problem you will run into with the normal tutorials is that CamelTestSupport may start routes before your mocks have taken hold. Thus your mocked routes won’t be part of what CamelTestSupport has actually started. You’ll be pulling your hair out wondering why Camel insists on attempting to forward documents to an actual Solr instance and not your test endpoint. To take matters into your own hands, luckily CamelTestSupport comes to the rescue with a simple method you need to override to communicate your intent to manually start/stop the camel context: @Override public boolean isUseAdviceWith() { return true; } Then in your test, you’ll need to be sure to do: @Test public void foo() { context.start(); // tests! context.stop(); } 6. Write a test! Now you’re equipped to try out a real test! @Test public void testWithRealFile() { MockEndpoint mockSolr = getMockEndpoint("mock:catchSolrMessages"); File testCsv = getTestfile(); context.start(); mockSolr.expectedMessageCount(1); FileUtils.copyFile(testCsv, "inbox"); mockSolr.assertIsSatisfied(); context.stop(); } And that’s just scratching the surface of Camel’s testing capabilities. Check out the camel docs for information on stimulating endpoints directly with the ProducerTemplate thus letting you avoid using real files — and all kinds of goodies. Anyway, hopefully my experiences with AdviceWith can help you get it up and running in your tests! I’d love to hear about your experiences or any tips I’m missing either in the comments or [via email][5]. If you’d love to utilize Solr or Elasticsearch for search and analytics, but can’t figure out how to integrate them with your data infrastructure — contact us! Maybe there’s a camel recipe we could cook up for you that could do just the trick.

Cloudfoundry's java buildpack is supporting some popular jvm based applications. This article is oriented to the audiences already with experience of cloudfoundry/heroku buildpack who want to have more understanding of how buildpack and cloudfoundry works internally. cf push app -p app.war -b build-pack-url The above command demonstrates the usage of pushing a war file to cloudfoundry by using a custom buildpack (E.g. https://github.com/cloudfoundry/java-buildpack). However, what exactly happens inside, or how cloudfoundry bootstrap the war file with tomcat? There are three contracts phase that bridge communication between buildpack and cloudfoundry. The three phases are detect, compile and release, which are three ruby shell scripts: Java buildpack has multiple sub components, while each of them has all of these three phases (E.g. tomcat is one of the sub components, while it contained another layer of sub components). Detect Phase: detect phase is to check whether a particular buildpack/component applies to the deployed application. Take the war file example, tomcat applies only when https://github.com/cloudfoundry/java-buildpack/blob/master/lib/java_buildpack/container/tomcat.rb is true: def supports? web_inf? && !JavaBuildpack::Util::JavaMainUtils.main_class(@application) end The above code means, the tomcat applies when the application has a WEB-INF folder andthisisnot a main class bootstrapped application. Compile Phase: Compile phase would be the major/comprehensive work for a customized buildpack, while it is trying to build a file system on a lxc container. Take the example of our war application and tomcat example. In https://github.com/cloudfoundry/java-buildpack/blob/master/lib/java_buildpack/container/tomcat/tomcat_instance.rb def compile download(@version, @uri) { |file| expand file } link_to(@application.root.children, root) @droplet.additional_libraries << tomcat_datasource_jar if tomcat_datasource_jar.exist? @droplet.additional_libraries.link_to web_inf_lib end def expand(file) with_timing "Expanding Tomcat to #{@droplet.sandbox.relative_path_from(@droplet.root)}" do FileUtils.mkdir_p @droplet.sandbox shell "tar xzf #{file.path} -C #{@droplet.sandbox} --strip 1 --exclude webapps 2>&1" @droplet.copy_resources end The above code is all about preparing the tomcat and link the application files, so the application files will be available for the tomcat classpath. Before going to the code, we have to understand the working directory when the above code executes: . => working directory .app => @application, contains the extracted war archive .buildpack/tomcat => @droplet.sandbox .buildpack/jdk .buildpack/other needed components Inside compile method: download method will download tomcat binary file (specified here: https://github.com/cloudfoundry/java-buildpack/blob/master/config/tomcat.yml), and then extract the archive file to @droplet.sandbox directory. Then copy the resources folder's files to https://github.com/cloudfoundry/java-buildpack/tree/master/resources/tomcat/conf to @droplet.sandbox/conf Symlink the @droplet.sandbox/webapps/ROOT to .app/ Symlink additional libraries (comes from other component rather than application) to the WEB-INF/lib Note: All the symlinks use relative path, since when the container deployed to DEA, the absolute paths would be different. RELEASE PHASE: Release phase is to setup instructions of how to start tomcat. Look at the code in :https://github.com/cloudfoundry/java-buildpack/blob/master/lib/java_buildpack/container/tomcat.rb def command @droplet.java_opts.add_system_property 'http.port', '$PORT' [ @droplet.java_home.as_env_var, @droplet.java_opts.as_env_var, "$PWD/#{(@droplet.sandbox + 'bin/catalina.sh').relative_path_from(@droplet.root)}", 'run' ].flatten.compact.join(' ') end The above code does: Add java system properties http.port (referenced in tomcat server.xml) with environment properties ($PORT), this is the port on the DEA bridging to the lxc container already setup when the container was provisioned. instruction of how to run the tomcat Eg. "./bin/catalina.sh run"

Cyclop is a web-based tool for querying Cassandra databases with features like syntax highlighting and query completion.

Spring Boot is a brand new application framework from Spring. It allows fabulously quick development and rapid prototyping (even including CLI). One of its main features is to work from single "uber jar" file. By "uber jar" I mean that all dependencies, even an application server like Tomcat or Jetty are packed into a single file. In that we can start web application by typing java -jar application.jar The only thing we're missing is the managing script. And now I want to dive into that topic. Of course to do anything more than starting our application we need to know its PID. Spring Boot has a solution named ApplicationPidListener. To use it we need to tell SpringApplication we want to include this listener. And there are to ways to achieve that. Easiest way it to create file META-INF/spring.factories containing lines: org.springframework.context.ApplicationListener=\ org.springframework.boot.actuate.system.ApplicationPidListener Second way allows us to customize listener by specifying own name or location for PID file. public class Application { public static void main(String[] args) { SpringApplication springApplication = new SpringApplication(Application.class); springApplication.addListeners( new ApplicationPidListener("app.pid")); springApplication.run(args); } } Now, when we already have our PID file we need bash script providing standard operations like stop, start, restart and status checking. Below you can find simple script solving that challenge. Of course remember to customize highlighted lines :) #!/bin/sh JARFile="application.jar" PIDFile="application.pid" SPRING_OPTS="-DLOG_FILE=application.log" function check_if_pid_file_exists { if [ ! -f $PIDFile ] then echo "PID file not found: $PIDFile" exit 1 fi } function check_if_process_is_running { if ps -p $(print_process) > /dev/null then return 0 else return 1 fi } function print_process { echo $(<"$PIDFile") } case "$1" in status) check_if_pid_file_exists if check_if_process_is_running then echo $(print_process)" is running" else echo "Process not running: $(print_process)" fi ;; stop) check_if_pid_file_exists if ! check_if_process_is_running then echo "Process $(print_process) already stopped" exit 0 fi kill -TERM $(print_process) echo -ne "Waiting for process to stop" NOT_KILLED=1 for i in {1..20}; do if check_if_process_is_running then echo -ne "." sleep 1 else NOT_KILLED=0 fi done echo if [ $NOT_KILLED = 1 ] then echo "Cannot kill process $(print_process)" exit 1 fi echo "Process stopped" ;; start) if [ -f $PIDFile ] && check_if_process_is_running then echo "Process $(print_process) already running" exit 1 fi nohup java $SPRING_OPTS -jar $JARFile & echo "Process started" ;; restart) $0 stop if [ $? = 1 ] then exit 1 fi $0 start ;; *) echo "Usage: $0 {start|stop|restart|status}" exit 1 esac exit 0 I'm sure that there are a lot of possibilities to tune that script, so comments are welcomed :)

Earlier I had blogged about using Scala with Spring Boot and how the combination just works. There was one issue with the previous approach though - the only way to run the earlier configuration was to build the project into a jar file and run the jar file. ./gradlew build java -jar build/libs/spring-boot-scala-web-0.1.0.jar Spring boot comes with a gradle based plugin which should have allowed the project to run with a "gradle bootRun" command, this unfortunately gives an error for scala based projects. A good workaround is to use sbt for building and running Spring-boot based projects. The catch though is that with gradle and maven, the versions of the dependencies would have been managed through a parent pom, now these have to be explicitly specified. This is how a sample sbt build file with the dependencies spelled out looks: name := "spring-boot-scala-web" version := "1.0" scalaVersion := "2.10.4" sbtVersion := "0.13.1" seq(webSettings : _*) libraryDependencies ++= Seq( "org.springframework.boot" % "spring-boot-starter-web" % "1.0.2.RELEASE", "org.springframework.boot" % "spring-boot-starter-data-jpa" % "1.0.2.RELEASE", "org.webjars" % "bootstrap" % "3.1.1", "org.webjars" % "jquery" % "2.1.0-2", "org.thymeleaf" % "thymeleaf-spring4" % "2.1.2.RELEASE", "org.hibernate" % "hibernate-validator" % "5.0.2.Final", "nz.net.ultraq.thymeleaf" % "thymeleaf-layout-dialect" % "1.2.1", "org.hsqldb" % "hsqldb" % "2.3.1", "org.springframework.boot" % "spring-boot-starter-tomcat" % "1.0.2.RELEASE" % "provided", "javax.servlet" % "javax.servlet-api" % "3.0.1" % "provided" ) libraryDependencies ++= Seq( "org.apache.tomcat.embed" % "tomcat-embed-core" % "7.0.53" % "container", "org.apache.tomcat.embed" % "tomcat-embed-logging-juli" % "7.0.53" % "container", "org.apache.tomcat.embed" % "tomcat-embed-jasper" % "7.0.53" % "container" ) Here I am also using xsbt-web-plugin which is plugin for building scala web applications. xsbt-web-plugin also comes with commands to start-up tomcat or jetty based containers and run the applications within these containers, however I had difficulty in getting these to work. What worked is the runMain command to start up the Spring-boot main program through sbt: runMain mvctest.SampleWebApplication and xsbt-web-plugin allows the project to be packaged as a war file using the "package" command, this war deploys and runs without any issues in a standalone tomcat container. Here is a github project with these changes: https://github.com/bijukunjummen/spring-boot-scala-web.git

The Open Session in View (OSIV) pattern gives rise to different opinions in the Java development community. Let's go over OSIV and some of the pros and cons of this pattern. The problem The problem that OSIV solves is a mismatch between the Hibernate concept of session and it's lifecycle and the way that many server-side view technologies work. In a typical Java frontend application the service layer starts by querying some of the data needed to build the view. The remaining data needed can be lazy-loaded later, with the condition that the Hibernate session remains open - and there lies the problem. Between the moment that the service layer method finishes it's execution and the moment that the view is rendered, Hibernate has already committed the transaction and closed the session. When the view tries to lazy load the extra data that it needs, if finds the Hibernate session closed, causing a LazyInitializationException. The OSIV solution OSIV tackles this problem by ensuring that the Hibernate session is kept open all the way up to the rendering of the view - hence the name of the pattern. Because the session is kept open, no more LazyInitializationExceptions occur. The session or entity manager is kept open by means of a filter that is added to the request processing chain. In the case of JPA the OpenEntityManagerInViewFilter will create an entity manager at the beginning of the request, and then bind it to the request thread. The service layer will then be executed and the business transaction committed or rolled back, but the transaction manager will not remove the entity manager from the thread after the commit. When the view rendering starts, the transaction manager will then check if there is already an entity manager binded to the thread, and if so use it instead of creating a new one. After the request is processed, the filter will then unbind the entity manager from the thread. The end result is that the same entity manager used to commit the business transaction was kept around in the request thread, allowing the view rendering code to lazy load the needed data. Going back to the original problem Let's step back a moment and go back to the initial problem: the LazyInitializationException. Is this exception really a problem? This exception can also be seen as a warning sign of a wrongly written query in the service layer. When building a view and it's backing services, the developer knows upfront what data is needed, and can make sure that the needed data is loaded before the rendering starts. Several relation types such as one-to-many use lazy-loading by default, but that default setting can be overridden if needed at query time using the following syntax: select p FROM Person p left join fetch p.invoices This means that the lazy loading can be turned off on a case by case basis depending on the data needed by the view. OSIV in projects I've worked In projects I have worked that used OSIV, we could see via query logging that the database was getting hit with a high number of SQL queries, sometimes to the point that developers had to turn off the Hibernate SQL logging. The performance of these application was impacted, but it was kept manageable using second-level caches, and due to the fact that these where intranet-based applications with a limited number of users. Pros of OSIV The main advantage of OSIV is that it makes working with ORM and the database more transparent: Less queries need to be manually written Less awareness is required about the Hibernate session and how to solve LazyInitializationExceptions. Cons of OSIV OSIV seems to be easy to misuse and can accidentally introduce N+1 performance problems in the application. On projects I've worked OSIV did not work out well in the long-term. The alternative of writing custom queries that eager fetch data depending on the use case is manageable and turned out well in other projects I've worked. Alternatives to OSIV Besides the application-level solution of writing custom queries to pre-fetch the needed data, there are other framework-level aproaches to OSIV. The Seam Framework was built by some of the same developers as Hibernate , and solves the problem by introducing the notion of conversation. Can you let me know in the comments bellow your thoughts and experiences with OSIV, thanks for reading.

wanted to go through some of the basic tenets, the technical terminology related to java ee. for many people, java ee/j2ee still mean servlets, jsps or maybe struts at best. no offence or pun intended! this is not a java ee 'bible' by any means. i am not capable enough of writing such a thing! so let us line up the 'keywords' related to java ee and then look at them one by one java ee java ee apis (specifications) containers services multitiered applications components let's try to elaborate on the above mentioned points. ok. so what is java ee? 'ee' stands for enterprise edition. that essentially makes java ee - java enterprise edition. if i had to summarize java ee in a couple of sentences, it would go something like this "java ee is a platform which defines 'standard specifications/apis' which are then implemented by vendors and used for development of enterprise (distributed, 'multi-tired', robust) 'applications'. these applications are composed of modules or 'components' which use java ee 'containers' as their run-time infrastructure." what is this 'standardized platform' based upon? what does it constitute? the platform revolves around 'standard' specifications or apis . think of these as contracts defined by a standard body e.g. enterprise java beans (ejb), java persistence api (jpa), java message service (jms) etc. these contracts/specifications/apis are implemented by different vendors e.g. glassfish, oracle weblogic, apache tomee etc alright. what about containers? containers can be visualized as 'virtual/logical partitions' . each container supports a subset of the apis/specifications defined by the java ee platform they provide run-time 'services' to the 'applications' which they host the java ee specification lists 4 types of containers ejb container web container application client container applet container java ee containers i am not going to dwell into details of these containers in this post. services?? well, 'services' are nothing but a result of the vendor implementations of the standard 'specifications' (mentioned above). examples of specifications are - jersey for jax-rs (restful services), tyrus (web sockets), eclipselink (jpa), weld (cdi) etc. the 'container' is the interface between the deployed application ('service' consumer) and the application server. here is a list of 'services' which are rendered by the 'container' to the underlying 'components' (this is not an exhaustive list) persistence - offered by the java persistence api (jpa) which drives object relational mapping (orm) and an abstraction for the database operations. messaging - the java message service (jms) provides asynchronous messaging between disparate parts of your applications. contexts & dependency injection - cdi provides loosely coupled and type safe injection of resources. web services - jaxrs and jaxws provide support for rest and soap style services respectively transaction - provided by the java transaction api (jta) implementation what is a typical java ee 'application'? what does it comprise of? applications are composed of different ' components ' which in turn are supported by their corresponding ' container ' supported 'component' types are: enterprise applications - make use of the specifications like ejb, jms, jpa etc and are executed within an ejb container web applications - they leverage the servlet api, jsp, jsf etc and are supported by a web container application client - executed in client side. they need an application client container which has a set of supported libraries and executes in a java se environment. applets - these are gui applications which execute in a web browser. how are java ee applications structured? as far as java ee 'application' architecture is concerned, they generally tend follow the n-tier model consisting of client tier, server tier and of course the database (back end) tier client tier - consists of web browsers or gui (swing, java fx) based clients. web browsers tend to talk to the 'web components' on the server tier while the gui clients interact directly with the 'business' layer within the server tier server tier - this tier comprises of the dynamic web components (jsp, jsf, servlets) and the business layer driven by ejbs, jms, jpa, jta specifications. database tier - contains 'enterprise information systems' backed by databases or even legacy data repositories. generic 3-tier java ee application architecture java ee - bare bones, basics.... as quickly and briefly as i possibly could. that's all for now! :-) stay tuned for more java ee content, specifically around the latest and greatest version of the java ee platform --> java ee 7 happy reading!

It is always recommended to use Spring properties with Mule, to externalize any configuration parameters (URLs, ports, user names, passwords, etc.). For example, the Acme APIfrom my previous post connects to an external database. So instead of hard-coding connectivity options inside my application code, I would create a properties file, e.g. acme.properties, as follows: acme.jdbc.host=acmedb acme.jdbc.port=3306 acme.jdbc.database=acmeProducts acme.jdbc.user=WileECoyote acme.jdbc.password=GeeWhizz Obviously, as a developer, I would use a test instance of Acme database to test my application. I’d commit the code to the version control system, including the properties file. Then my application would begin its journey from the automated build system to the Dev environment, to QA, Pre-Prod, and finally Prod – and fail to deploy on production because it wouldn’t be able to connect to the test database! Or even worse, it would connect to the test database and use it and no one would notice the problem until customers placed $0 order for an Acme widget which would normally cost $1000, all because the test database didn’t contain actual prices! Sure, I could just follow the recommendations on our web site and create multiple sets of properties, e.g. acme.dev.properties, acme.qa.properties, acme.prod.properties etc. But instead of solving the problem, it would create a few new ones. First, those properties must still be packaged within the application. Needless to say, IT guys would never give me the credentials for the production database, so I’d have to provide instructions for them on how to modify the properties file AFTER the application is deployed on the prod platform. Second, if (or rather WHEN) any of those properties will need to be changed (for example, the production DB is migrated to a new server), the whole process has to be repeated. And don’t forget about passwords and other sensitive data that should never appear in the code as open text and have to be encrypted. It seems like every single customer I’ve worked with has this problem. And there was no convincing solution until one of our customers told me about an application called Zuul. As the description on the Zuul web site says, “Zuul is a free, open source web application which can be used to centralize and manage configuration for your internal applications. It enables your operations team to control changes and your developers a centralized place to organize settings.” Of course, I couldn’t resist the urge to download it and try it out with Mule. The installation and configuration of the Zuul server was pretty straightforward. After all, Zuul is a standard web application, so I just deployed it to my local Tomcat instance, alongside with MMC which was already deployed on it. I configured the database settings to point to my local MySQL instance. For the LDAP server I used OpenLDAP. I had to download and install the Unlimited Strength JCE Policy Files. Then I started Tomcat and opened the Zuul URL in my browser and logged in as administrator. The first task is to create my environments. Navigating to Administration->Environments menu, I see three environments, prod, qa, and dev, which Zuul creates by default. Just what I need! Moreover, the prod environment is red – which means, only someone with Administrator privileges can mess with it. And while we are in the Administration screen, let’s create a new encryption key for our password values. Administration->Key Management, then click on Create New... button and populate the form: And now we can create our properties. Select Settings->Create New, give it a name, e.g. AcmeProperties. On the next screen, you’re given the option to create a new properties set from scratch, or to upload an existing properties file. Since we already have acme.properties for our dev environment, let’s just use it. Select dev environment on the left tab, then click Upload File button: Upload acme.properties and you’ll see the following screen: Now we can encrypt the database password. Just make sure the correct key is selected, then click Edit and select Encrypt. To finish the server setup, we replicate this set of properties on the qa and prod environments. Select qa tab, then click Copy Existing, then in the Search text box type dev. Your properties set "/dev/AcmeProperties.properties" will be highlighted. Click Copy button and now you have the identical set of properties in qa. Repeat the process for the prod environment. Change properties values on each environment accordingly. This concludes the server setup procedure. In the next post, I will show you how to configure Mule to use Zuul properties management. UPDATE: Zuul can be downloaded at http://www.devnull.org/zuul

This post was originally written by Vlad Lesin for the MySQL Performance Blog. Percona Server 5.6.11-60.3 introduces a new “log archiving” feature. Percona XtraBackup 2.1.5 supports “apply archived logs.” What does it mean and how it can be used? Percona products propose three kinds of incremental backups. The first is full scan of data files and comparison the data with backup data to find some delta. This approach provides a history of changes and saves disk space by storing only data deltas. But the disadvantage is a full-data file scan that adds load to the disk subsystem. The second kind of incremental backup avoids extra disk load during data file scans. The idea is in reading only changed data pages. The information about what specific pages were changed is provided by the server itself which writes files with the information during work. It’s a good alternative but changed-pages tracking adds some small load. And Percona XtraBackup’s delta reading leads to non-sequential disk io. This is good alternative but there is one more option. The Innodb engine has a data log. It writes all operations which modify database pages to log files. This log is used in the case of unexpected server terminating to recover data. The Innodb log consists of the several log files which are filled sequentially in circular. The idea is to save those files somewhere and apply all modifications from archived logs to backup data files. The disadvantage of this approach is in using extra disk space. The advantage is there is no need to do an “explicit” backup on the host server. A simple script could sit and wait for logs to appear then scp/netcat them over to another machine. But why not use good-old replication? Maybe replication does not have such performance as logs recovering but it is more controlled and well-known. Archived logs allows you to do any number of things with them from just storing them to doing periodic log applying. You can not recover from a ‘DROP TABLE’, etc with replication. But with this framework one could maintain the idea of “point in time” backups. So the “archived logs” feature is one more option to organize incremental backups. It is not widely used as it was issued not so far and there is not A good understanding of how it works and how it can be used. We are open to any suggestions about its suggest improvements and use cases. The subject of this post is to describe how it works in depth. As log archiving is closely tied with innodb redo logs the internals of redo logs will be covered too. This post would be useful not only for DBA but also for Software Engineers because not only common principles are considered but the specific code too, and knowledge from this post can be used for further MySQL code exploring and patching. What is the innodb log and how it is written? Let’s remember what are innodb logs, why they are written, what they are used for. The Innodb engine has buffer pool. This is a cache of database pages. Any changes are done on page in buffer pool, then page is considered as “dirty,” which means it must be flushed, and pushed to the flush list which is processed periodically by special thread. If pages are not flushed to disk and server is terminated unexpectedly the changes will be lost. To avoid this innodb writes changes to redo log and recover data from redo log during start. This technique allows to delay buffer pool pages flushing. It can increase performance because several changes of one page can be accumulated in memory and then flushed by one io. Except that flushed pages can be grouped to decrease the number of non-sequential io’s. But the down-side of this approach is time for data recovering. Let’s consider how this log is stored, generated and used for data recovering. Log files Redo log consists of a several log files which are treated as a circular buffer. The number and the size of log files can be configured. Each log file has a header. The description of this header can be found in “storage/innobase/include/log0log.h” by “LOG_GROUP_ID” keyword. Each log file contains log records. Redo log records are written sequentially by log blocks of OS_FILE_LOG_BLOCK_SIZE size which is equal to 512 bytes by default and can be changed with innodb option. Each record has its LSN. LSN is a “Log Sequence Number” – the number of bytes written to log from the log creation to the certain log record. Each block consists of header, trailer and log records. Log blocks Let’s consider log block header. The first 4 bytes of the header is log block number. The block number is very similar as LSN but LSN is measured in bytes and block number is measured by OS_FILE_LOG_BLOCK_SIZE. Here is the simple formula how LSN is converted to block number: return(((ulint) (lsn / OS_FILE_LOG_BLOCK_SIZE) & 0x3FFFFFFFUL) + 1); This formula can be found in log_block_convert_lsn_to_no() function. The next two bytes is the number of bytes used in the block. The next two bytes is the offset of the first MTR log record in this block. What is MTR will be described below. Currently it can be considered as a synonym of bunch of log records which are gathered together as a description of some logical operation. For example it can be a group of log records for inserting new row to some table. This field is used when there are records of several MTR’s in one block. The next four bytes is a checkpoint number. The trailer is four bytes of log block checksum. The above description can be found in “storage/innobase/include/log0log.h” by “LOG_BLOCK_HDR_NO” keyword. Before writing to disk log blocks must be somehow formed and stored. And the question is: How log blocks are stored in memory and on disk? Where log blocks are stored before flushing to disk and how they are written and flushed? Global log object and log buffer The answer to the first part of the question is log buffer. Server holds very important global object log_sys in memory. It contains a lot of useful information about logging state. Log buffer is pointed by log_sys->buf pointer which is initialized in log_init(). I would highlight the following log_sys fields that are used for work with log buffer and flushing: log_sys->buf_size – the size of log buffer, can be set with innodb-log-buffer-size variable, the default value is 8M; log_sys->buf_free – the offset from which the next log record will be written; log_sys->max_buf_free – if log_sys->buf_free is greater then this value log buffer must be flushed, see log_free_check(); log_sys->buf_next_to_write – the offset of the next log record to write to disk; log_sys->write_lsn – the LSN up to which log is written; log_sys->flushed_to_disk_lsn – the LSN up to which log is written and flushed; log_sys->lsn – the last LSN in log buffer; So log_sys->buf_next_to_write is between 0 and log_sys->buf_free, log_sys->write_lsn is equal or less log_sys->lsn, log_sys->flushed_to_disk_lsn is less or equal to log_sys->write_lsn. The relationships for those fields can be easily traced with debugged by setting up watchpoints. Ok, we have log buffer, but how do log records come to this buffer? Where log records come from? Innodb has special objects that allow you to gather redo log records for some operations in one bunch before writing them to log buffer. These objects are called “mini-transactions” and corresponding functions and data types have “mtr” prefix in the code. The objects itself are described in mtr_t “c” structure. The most interesting fields of this structure are the following: mtr_t::log – contains log records for the mini-transaction, mtr_t::memo – contains pointers to pages which are changed or locked by the mini-transaction, it is used to push pages to flush list and release locks after logs records are copied to log buffer in mtr_commit() (see mtr_memo_pop_all() called in mtr_commit()). mtr_start() function initializes an object of mtr_t type and mtr_commit() writes log records from mtr_t::log to log_sys->buf + log_sys->buf_free. So the typical sequence of any operation which changes data is the following: mtr_start(); // initialize mtr object some_ops... // operations on data which are logged in mtr_t::log mtr_commit(); // write logged operations from mtr_t::log to log buffer log_sys->buf page_cur_insert_rec_write_log() is a good example of how mtr records can be written and mtr::memo can be filled. The low-level function which writes data to log buffer is log_write_low(). This function is invoked inside of mtr_commit() and not only copy the log records from mtr_t object to log buffer log_sys->buf but also creates a new log blocks inside of log_sys->buf, fills their header, trailer, calculates checksum. So log buffer contains log blocks which are sequentially filled with log records which are grouped in “mini-transactions” which logically can be treated as some logical operation over data which consists of a sequence of mini-operations(log records). As log records are written sequentially in log buffer one mini-transaction and even one log record can be written in two neighbour blocks. That is why the header field which would contain the offset of the first MTR in the block is necessary to calculate the point from which log records parsing can be started. This field was described in 2.2. So we have a buffer of log blocks in a memory. How is data from this buffer written to disk? The mysql documentation says that this depends on innodb_flush_log_at_trx_commit option. There can be three cases depending on the value of this option. Let’s consider each of them. Writing log buffer to disk: innodb_flush_log_at_trx_commit is 1 or 2. The first two cases is when innodb_flush_log_at_trx_commit is 1 or 2. In these cases flush log records are written for 2 and flushed for 1 on each transaction commit. If innodb_flush_log_at_trx_commit is 2 log records are flushed periodically by special thread which will be considered later. The low-level function which writes log records from buffer to file is log_group_write_buf(). But in the most cases it is not called directly but it is called from more high level log_write_up_to(). For the current case the calling stack is the following: (trx_commit_in_memory() or trx_commit_complete_for_mysql() or trx_prepare() e.t.c)-> trx_flush_log_if_needed()-> trx_flush_log_if_needed_low()-> log_write_up_to()-> log_group_write_buf(). It is quite easy to find the higher levels of calling stack, just set up breakpoint on log_group_write_buf() and execute any sql query that modifies innodb data. For example for the simple “insert” sql query the higher levels of calling stack are the following: mysql_execute_command()-> trans_commit_stmt()-> ha_commit_trans()-> TC_LOG_DUMMY::commit()-> ha_commit_low()-> innobase_commit()-> trx_commit_complete_for_mysql()-> trx_flush_log_if_needed()-> ... . log_io_complete() callback is invoked when i/o is finished for log files (see fil_aio_wait()). log_io_complete() flushes log files if this is not forbidden by innodb_flush_method or innodb_flush_log_at_trx_commit options. Writing log buffer to disk: innodb_flush_log_at_trx_commit is equal to 0 The third case is when innodb_flush_log_at_trx_commit is equal to 0. For this case log buffer is NOT written to disk on transaction commit, it is written and flushed periodically by separate thread “srv_master_thread”. If innodb_flush_log_at_trx_commit = 0 log files are flushed in the same thread by the same calls. The calling stack is the following: srv_master_thread()-> (srv_master_do_active_tasks() or srv_master_do_idle_tasks() or srv_master_do_shutdown_tasks())-> srv_sync_log_buffer_in_background()-> log_buffer_sync_in_background()->log_write_up_to()->... . Special cases for logs flushing While log_io_complete() do flushing depending on innodb_flush_log_at_trx_commit value among others log_write_up_to() has it’s own flushing criteria. This is flush_to_disk function argument. So it is possible to force log files flushing even if innodb_flush_log_at_trx_commit = 0. Here are examples of such cases: 1) buf_flush_write_block_low() Each page contains information about the last applied LSN(buf_flush_write_block_low::newest_modification), each log record is a description of change on certain page. Imagine we flushed some changed pages but log records for these pages were not flushed and server goes down. After starting the server some pages will have the newest modifications, but some of them were not flushed and the correspondent log records are lost too. We will have inconsistent database in this case. That is why log records must be flushed before the pages they refer. 2) srv_sync_log_buffer_in_background() As it was described above this function is called periodically by special thread and forces flushing. 3) log_checkpoint() When checkpoint is made log files must be reliably flushed. 4) The special handlerton innobase_flush_logs() which can be called through ha_flush_logs() from mysql server. For example ha_flush_logs() is called from MYSQL_BIN_LOG::reset_logs() when “RESET MASTER” or “RESET SLAVE” are executed. 5) srv_master_do_shutdown_tasks() – on shutdown, ha_innobase::create() – on table creating, ha_innobase::delete_table() – on table removing, innobase_drop_database() – on all database tables removing, innobase_rename_table() – on table rename e.t.c If log files are treated as circular buffer what happens when the buffer is overflown? Briefly. Innodb has a mechanism which allows you to avoid overflowing. It is called “checkpoints.” The checkpoint is a state when log files are synchronized with data files. In this case there is no need to keep the history of changes before checkpoint because all pages with the last modifications LSN less or equal to checkpoint LSN are flushed and the log files space from the last written LSN to the last checkpoint LSN can be reused. We will not describe a checkpoint process here because it is a separate interesting subject. The only thing we need to know is when checkpoint happens all pages with modification LSN less or equal to checkpoint LSN are reliably flushed. How archived logs are written by server. So the log contains information about page changes. But as we said, log files are the circular buffer. This means that they occupy fixed disk size and the oldest records can be rewritten by the newest ones as there are points when data files are synchronized with log files called checkpoints and there is no need to store the previous history of log records to guarantee database consistency. The idea is to save somewhere all log records to have the possibility of applying them to backuped data to have some kind if incremental backup. For example if we want to have an archive of log records. As log consists of log files it is reasonable to store log records in such files too, and these files are called “archived logs.” Archived log files are written to the directory which can be set with special innodb option. Each file has the same size as innodb log size and the suffix of each archived file is the LSN from which it is started. As well as log writing system log archiving system stores its data in global log_sys object. Here are the most valuable fields in log_sys from my point of view: log_sys->archive_buf, log_sys->archive_buf_size – logs archive buffer and its size, log records are copied from log buffer log_sys->buf to this buffer before writing to disk; log_sys->archiving_phase – the current phase of log archiving: LOG_ARCHIVE_READ when log records are being copied from log_sys->buf to log_sys->archive_buf, LOG_ARCHIVE_WRITE when log_sys->archive_buf is being written to disk; log_sys->archived_lsn – the LSN to which log files are written; log_sys->next_archived_lsn – the LSN to which write operations was invoked but not yet finished; log_sys->max_archived_lsn_age – the maximum difference between log_sys->lsn and log_sys->archived_lsn, if this difference exceeds the log are being archived synchronously, i.e. the difference is decreased; log_sys->archive_lock – this is rw-lock which is used for synchronizing LOG_ARCHIVE_WRITE and LOG_ARCHIVE_READ phases, it is x-locked on LOG_ARCHIVE_WRITE phase. So how is data copied from log_sys->buf to log_sys->archived_buf? log_archive_do() is used for this. It is not only set the proper state for archived log fields in log_sys but also invokes log_group_read_log_seg() with corresponding arguments which not only copy data from log buffer to archived log buffer but also invokes asynchronous write operation for archived log buffer. log_archive_do() can wait until io operations are finished using log_sys->archive_lock if corresponding function parameter is set. The main question is on what circumstances log_archive_do() is invoked, i.e. when log records are being written to archived log files. The first call stack is the following: log_free_check()-> log_check_margins()-> log_archive_margin()-> log_archive_do(). Here is text of log_free_check() with comments: /*********************************************************************// Checks if there is need for a log buffer flush or a new checkpoint, and does this if yes. Any database operation should call this when it has modified more than about 4 pages. NOTE that this function may only be called when the OS thread owns no synchronization objects except the dictionary mutex. */ UNIV_INLINE void log_free_check(void) /*================*/ { #ifdef UNIV_SYNC_DEBUG ut_ad(sync_thread_levels_empty_except_dict()); #endif /* UNIV_SYNC_DEBUG */ if (log_sys->check_flush_or_checkpoint) { log_check_margins(); } } log_sys->check_flush_or_checkpoint is set when there is no enough free space in log buffer or it is time to do checkpoint or any other bound case. log_archive_margin() is invoked only if the limit if the difference between log_sys->lsn and log_sys->archived_lsn is exceeded. Let’s refer to this difference as archived lsn age. One more call log_archive_do() is from log_open() when archived lsn age exceeds some limit. log_open() is called on each mtr_commit(). And for this case archived logs are written synchronously. The next synchronous call is from log_archive_all() during shutdown. Summarizing all above archived logs begins to be written when the log buffer is full enough to be written or when checkpoint happens or when the server is in the process of shut down. And there is no any delay between writing to archive log buffer and writing to disk. I mean there is no way to say that archived logs must be written once a second as it is possible for redo logs with innodb_flush_log_at_trx_commit = 0. As soon as data is copied to the buffer the write operation is invoked immediately for this buffer. Archived log buffer is not filled on each mtr_commit() so it does not slow down the usual logging process. The exception is when there are a lot of io operations what can be the reason of archive log age is too big. The result of big archive log age is the synchronous archived logs writing during mtr_commit(). Memory to memory copying is quite fast operation that is why the data is copied to archived log buffer and is written to disk asynchronously minimizing delays which can be caused by logs archiving. PS: Here is another call stack for writing archived log buffer to archived log files: log_io_complete()->log_io_complete_archive()->log_archive_check_completion_low()->log_archive_groups(). I propose to explore this stack yourself. Logs recovery process, how it is started and works inside. Archived logs applying. So we discovered how innodb redo logging works, and how redo logs are archived. And the last uncovered thing is how recovery works and how archived logs are applied. These two processes are very similar – that is why they are discussed in one section of this post. The story begins with innobase_start_or_create_for_mysql() which is invoked from innobase_init(). The following trident in innobase_start_or_create_for_mysql() can be used to search the relevant code: if (create_new_db) { ... } else if (srv_archive_recovery) { ... } else { ... } The second condition and the last one is the place from which archived logs applying and innodb logs recovery processes correspondingly start. These two blocks wrap two pairs of functions: recv_recovery_from_archive_start() recv_recovery_from_archive_finish() and recv_recovery_from_checkpoint_start() recv_recovery_from_checkpoint_finish() And all the magic happens in these pairs. As well as global log_sys object for redo logging there is global recv_sys object for innodb recovery and archived logs applying. It is created and initialized in recv_sys_create() and recv_sys_init() functions correspondingly. The following fields if recv_sys object are the most important from my point: recv_sys->limit_lsn – the LSN up to which recovery should be made, this value is initialized with the maximum value of uint64_t(see #define LSN_MAX) for the recovery process and with certain value which is passed as an argument of recv_recovery_from_archive_start() function and can be set via xtrabackup option for log applying; recv_sys->parse_start_lsn – the LSN from which logs parsing is started, for the the logs recovery this value equals to the last checkpoint LSN, for logs applying this is last applied LSN; recv_sys->scanned_lsn – the LSN up to which log files are scanned; recv_sys->recovered_lsn – the LSN up to which log records are applied, this value <= recv_sys->scanned_lsn; The first thing that must be done for starting recovery process is to find out the point in log files where the recovery must be started from. This is the last checkpoint LSN. recv_find_max_checkpoint() proceed this. As we can see in log_group_checkpoint() the following code writes checkpoint info into two places in the first log file depending on the checkpoint number: /* We alternate the physical place of the checkpoint info in the first log file */ if ((log_sys->next_checkpoint_no & 1) == 0) { write_offset = LOG_CHECKPOINT_1; } else { write_offset = LOG_CHECKPOINT_2; } So recv_find_max_checkpoint() reads checkpoint info from both places and selects the latest checkpoint. The same idea is applied for logs, too, but the last applied LSN instead of last checkpoint LSN must be found. Here is the call stack for reading last applied LSN: innobase_start_or_create_for_mysql()-> open_or_create_data_files()-> fil_read_first_page(). The last applied LSN is stored in the first page of data files in (min|max)_flushed_lsn fields(see FIL_PAGE_FILE_FLUSH_LSN offset). These values are written in fil_write_flushed_lsn_to_data_files() function on server shutdown. So the main difference between logs applying and recovery process at this stage is the manner of calculating LSN from which log records will be read. For logs applying the last flushed LSN is used but for recovery process it is the last checkpoint LSN. Why does this difference take place? Logs can be applied periodically. Assume we gather archived logs and apply them once an hour to have fresh backup. After applying the previous bunch of log files there can be unfinished transactions. For the recovery process any unfinished transactions are rolled back to have consistent db state at server starting. But for the logs applying process there is no need to roll back them because any unfinished transactions can be finished during the next logs applying. After calculating the start LSN the sequence of actions is the same for both recovering and applying. The next step is reading and parsing log records. See recv_group_scan_log_recs() which is invoked from recv_recovery_from_checkpoint_start_func() for logs recovering and recv_recovery_from_archive_start()->log_group_recover_from_archive_file() for logs applying. The first we read log records to some buffer and then invoke recv_scan_log_recs() to parse them. recv_scan_log_recs() checks each log block on consistency(checksum + comparing the log block number written in log block with log block number calculated from log block LSN) and other edge cases and copy it to parsing buffer recv_sys->buf with recv_sys_add_to_parsing_buf() function. The parsing buffer is then parsed by recv_parse_log_recs(). Log records are stored in hash table recv_sys->addr_hash. The key for this hash table is calculated basing on space id and page number pair. This pair refers to the page to which log records must be applied. The value of the hash table is object of recv_addr_t type. recv_addr_t type contains rec_list field which is the list of log records for applying to the (space id, page num) page (see recv_add_to_hash_table(). After parsing and storing log record in hash table recv_sys->addr_hash log records are applied. The function which is responsible for log records applying is recv_apply_hashed_log_recs(). It is invoked from recv_scan_log_recs() if there is no enough memory to store log records and at the end of recovering/applying process. For each element of recv_sys->addr_hash, i.e. for each DB page which must be changed with log records recv_recover_page() is invoked. It can be invoked as from recv_apply_hashed_log_recs() in the case if page is already in buffer pool of from buf_page_io_complete() on io completion, i.e. just after page was read from storage. Applying log records on page read completion is necessary and very convenient. Assume log records have not yet applied as we had enough memory to store the whole recovery log records. But we want for example to boot DB dictionary. I this case any records that concern to the pages of the dictionary will be applied to those pages just after reading them from storage to buffer pool. The function which applies log records to the certain page is recv_recover_page_func(). It gets the list of log records for the certain page from recv_sys->addr_hash hash table, for each element of this list it compares the lsn of last page changes with the LSN of the record, and if the former is greater the later it applies log record to the page. After applying all log records from archived logs xtrabackup writes last applied LSN to (min|max)_flushed LSN fields of each data file and finishes execution. The logs recovery process rollbacks all unfinished transactions unless this is forbidden with innodb-force-recovery parameter. Conclusion We covered the processes of redo logs writing and recovery in depth. These are very important processes as they provide data consistency on crashes. These two processes became a base for logs archiving and applying features. As log records can describe any data changes the idea is to store these records somewhere and then apply them to backups for organizing some kind of incremental backup. The features were implemented a short time ago and currently they are not widely used. So if you have something to say about them you are welcome to comment for discussion.

Camel is very often used in distributed environments for accessing remote resources. Remote services may fail for various reasons and periods. For services that are temporarily unavailable and recoverable after short period of time, a retry strategy may help. But some services can fail or hang for longer period of time making the calling application unresponsive and slow. A good strategy to prevent from cascading failures and exhaustion of critical resources is the Circuit Breaker pattern described by Michael Nygard in the Release It! book. Circuit Breaker is a stateful pattern that wraps the failure-prone resource and monitors for errors. Initially the Circuit Breaker is in closed state and passes all calls to the wrapped resource. When the failures reaches a certain threshold, the circuit moves to open state where it returns error to the caller without actually calling the wrapped resource. This prevents from overloading the already failing resource. While at this state, we need a mechanism to detect whether the failures are over and start calling the protected resource. This is where the third state called half-open comes into play. This state is reached after a certain time following the last failure. At this state, the calls are passed through to the protected resource, but the result of the call is important. If the call is successful, it is assumed that the protected resource has recovered and the circuit is moved into closed state, and if the call fails, the timeout is reset, and the circuit is moved back to open state where all calls are rejected. Here is the state diagram of Circuit Breaker from Martin Fowler's post: How Circuit Breaker is implemented in Camel? Circuit Breaker is available in the latest snapshot version of Camel as a Load balancer policy. Camel Load Balancer already has policies for Round Robin, Random, Failover, etc. and now also CircuiBreaker policy. Here is an example load balancer that uses Circuit Breaker policy with threshold of 2 errors and halfOpenAfter timeout of 1 second. Notice also that this policy applies only to errors caused by MyCustomException. new RouteBuilder() { public void configure() { from("direct:start").loadBalance() .circuitBreaker(2, 1000L, MyCustomException.class) .to("mock:result"); } }; And here is the same example using Spring XML DSL: MyCustomException

Here is how I enable remote debugging with WebLogic Server (11g) and Eclipse IDE. (Actually the java option is for any JVM, just the instruction here is WLS specific.) 1. Edit /bin/setDomainEnv.sh file and add this on top: JAVA_OPTIONS="$JAVA_OPTIONS -Xrunjdwp:transport=dt_socket,address=8000,server=y,suspend=y" The suspend=y will start your server and wait for you to connect with IDE before continue. If you don't want this, then set to suspend=n instead. 2. Start/restart your WLS with /bin/startWebLogic.sh 3. Once WLS is running, you may connect to it using Eclipse IDE. Go to Menu: Run > Debug Configuration ... > Remote Java Application and create a new entry. Ensure your port number is matching to what you used above. Read more java debugging options here: http://www.oracle.com/technetwork/java/javase/tech/vmoptions-jsp-140102.html#DebuggingOptions

Welcome to part 5 of “Be a lazy but a productive android developer” series. If you are a lazy Android developer and looking for image loading library, which could help you to load image(s) asynchronously without writing a logic for downloading and caching images then this article is for you. This series so far: Part 1: We looked at RoboGuice, a dependency injection library by which we can reduce the boiler plate code, save time and there by achieve productivity during Android app development. Part 2: We saw and explored about Genymotion, which is a rocket speed emulator and super-fast emulator as compared to native emulator. And we can use Genymotion while developing apps and can quickly test apps and there by can achieve productivity. Part 3: We understood and explored about JSON Parsing libraries (GSON and Jackson), using which we can increase app performance, we can decrease boilerplate code and there by can optimize productivity. Part 4: We talked about Card UI and explored card library, also created a basic card and simple card list demo. In this part In this part, we are going to talk about some image libraries using which we can load image(s) asynchronously, can cache images and also can download images into the local storage. Required features for loading images Almost every android app has a need to load remote images. While loading remote images, we have to take care of below things: Image loading process must be done in background (i.e. asynchronously) to avoid blocking UI main thread. Image recycling image should be done. Image should be displayed once its loaded successfully. Images should be cached in local memory for the later use. If remote image gets failed (due to network connection or bad url or any other reasons) to load then it should be managed perfectly for avoiding duplicate requests to load the same again, instead it should load if and only if net connection is available. Memory management should be done efficiently. In short, we have to write a code to manage each and every aspects of image loading but there are some awesome libraries available, using which we can load/download image asynchronously. We just have to call the load image method and success/failure callbacks. Asynchronous image loading Consider a case where we are having 50 images and 50 titles and we try to load all the images/text into the listview, it won’t display anything until all the images get downloaded. Here Asynchronous image loading process comes in picture. Asynchronous image loading is nothing but a loading process which happens in background so that it doesn’t block main UI thread and let user to play with other loaded data on the screen. Images will be getting displayed as and when it gets downloaded from background threads. Asynchronous image loading libraries Nostra’s Universal Image loader – https://github.com/nostra13/Android-Universal-Image-Loader Picasso – http://square.github.io/picasso/ UrlImageViewHelper by Koush Volley - By Android team members @ Google Novoda’s Image loader – https://github.com/novoda/ImageLoader Let’s have a look at examples using Picasso and Universal Image loader libraries. Example 1: Nostra’s Universal Image loader Step 1: Initialize ImageLoader configuration ? public class MyApplication extends Application{ @Override public void onCreate() { // TODO Auto-generated method stub super.onCreate(); // Create global configuration and initialize ImageLoader with this configuration ImageLoaderConfiguration config = new ImageLoaderConfiguration.Builder(getApplicationContext()).build(); ImageLoader.getInstance().init(config); } } Step 2: Declare application class inside Application tag in AndroidManifest.xml file ? Step 3: Load image and display into ImageView ? ImageLoader.getInstance().displayImage(objVideo.getThumb(), holder.imgVideo); Now, Universal Image loader also provides a functionality to implement success/failure callback to check whether image loading is failed or successful. ? ImageLoader.getInstance().displayImage(photoUrl, imgView, new ImageLoadingListener() { @Override public void onLoadingStarted(String arg0, View arg1) { // TODO Auto-generated method stub findViewById(R.id.EL3002).setVisibility(View.VISIBLE); } @Override public void onLoadingFailed(String arg0, View arg1, FailReason arg2) { // TODO Auto-generated method stub findViewById(R.id.EL3002).setVisibility(View.GONE); } @Override public void onLoadingComplete(String arg0, View arg1, Bitmap arg2) { // TODO Auto-generated method stub findViewById(R.id.EL3002).setVisibility(View.GONE); } @Override public void onLoadingCancelled(String arg0, View arg1) { // TODO Auto-generated method stub findViewById(R.id.EL3002).setVisibility(View.GONE); } }); Example 2: Picasso Image loading straight way: ? Picasso.with(context).load("http://postimg.org/image/wjidfl5pd/").into(imageView); Image re-sizing: ? Picasso.with(context) .load(imageUrl) .resize(100, 100) .centerCrop() .into(imageView) Example 3: UrlImageViewHelper library It’s an android library that sets an ImageView’s contents from a url, manages image downloading, caching, and makes your coffee too. UrlImageViewHelper will automatically download and manage all the web images and ImageViews. Duplicate urls will not be loaded into memory twice. Bitmap memory is managed by using a weak reference hash table, so as soon as the image is no longer used by you, it will be garbage collected automatically. Image loading straight way: ? UrlImageViewHelper.setUrlDrawable(imgView, "http://yourwebsite.com/image.png"); Placeholder image when image is being downloaded: ? UrlImageViewHelper.setUrlDrawable(imgView, "http://yourwebsite.com/image.png", R.drawable.loadingPlaceHolder); Cache images for a minute only: ? UrlImageViewHelper.setUrlDrawable(imgView, "http://yourwebsite.com/image.png", null, 60000); Example 4: Volley library Yes Volley is a library developed and being managed by some android team members at Google, it was announced by Ficus Kirkpatrick during the last I/O. I wrote an article about Volley library 10 months back , read it and give it a try if you haven’t used it yet. Let’s look at an example of image loading using Volley. Step 1: Take a NetworkImageView inside your xml layout. ? Step 2: Define a ImageCache class Yes you are reading title perfectly, we have to define an ImageCache class for initializing ImageLoader object. ? public class BitmapLruCache extends LruCache implements ImageLoader.ImageCache { public BitmapLruCache() { this(getDefaultLruCacheSize()); } public BitmapLruCache(int sizeInKiloBytes) { super(sizeInKiloBytes); } @Override protected int sizeOf(String key, Bitmap value) { return value.getRowBytes() * value.getHeight() / 1024; } @Override public Bitmap getBitmap(String url) { return get(url); } @Override public void putBitmap(String url, Bitmap bitmap) { put(url, bitmap); } public static int getDefaultLruCacheSize() { final int maxMemory = (int) (Runtime.getRuntime().maxMemory() / 1024); final int cacheSize = maxMemory / 8; return cacheSize; } } Step 3: Create an ImageLoader object and load image Create an ImageLoader object and initialize it with ImageCache object and RequestQueue object. ? ImageLoader.ImageCache imageCache = new BitmapLruCache(); ImageLoader imageLoader = new ImageLoader(Volley.newRequestQueue(context), imageCache); Step 4: Load an image into ImageView ? NetworkImageView imgAvatar = (NetworkImageView) findViewById(R.id.imgDemo); imageView.setImageUrl(url, imageLoader); Which library to use? Can you decide which library you would use? Let us know which and what are the reasons? Selection of the library is always depends on the requirement. Let’s look at the few fact points about each library so that you would able to compare exactly and can take decision. Picasso: It’s just a one liner code to load image using Picasso. No need to initialize ImageLoader and to prepare a singleton instance of image loader. Picasso allows you to specify exact target image size. It’s useful when you have memory pressure or performance issues, you can trade off some image quality for speed. Picasso doesn’t provide a way to prepare and store thumbnails of local images. Sometimes you need to check image loading process is in which state, loading, finished execution, failed or cancelled image loading. Surprisingly It doesn’t provide a callback functionality to check any state. “fetch()” dose not pass back anything. “get()” is for synchronously read, and “load()” is for asynchronously draw a view. Universal Image loader (UIL): It’s the most popular image loading library out there. Actually, it’s based on the Fedor Vlasov’s project which was again probably a very first complete solution and also a most voted answer (for the image loading solution) on Stackoverflow. UIL library is better in documentation and even there’s a demo example which highlights almost all the features. UIL provides an easy way to download image. UIL uses builders for customization. Almost everything can be configured. UIL doesn’t not provide a way to specify image size directly you want to load into a view. It uses some rules based on the size of the view. Indirectly you can do it by mentioning ImageSize argument in the source code and bypass the view size checking. It’s not as flexible as Picasso. Volley: It’s officially by Android dev team, Google but still it’s not documented. It’s just not an image loading library only but an asynchronous networking library Developer has to define ImageCache class their self and has to initialize ImageLoader object with RequestQueue and ImageCache objects. So now I am sure now you can be able to compare libraries. Choosing library is a bit difficult talk because it always depends on the requirement and type of projects. If the project is large then you should go for Picasso or Universal Image loader. If the project is small then you can consider to use Volley librar, because Volley isn’t an image loading library only but it tries to solve a more generic solution.). I suggest you to start with Picasso. If you want more control and customization, go for UIL. Read more: http://blog.bignerdranch.com/3177-solving-the-android-image-loading-problem-volley-vs-picasso/ http://stackoverflow.com/questions/19995007/local-image-caching-solution-for-android-square-picasso-vs-universal-image-load https://plus.google.com/103583939320326217147/posts/bfAFC5YZ3mq Hope you liked this part of “Lazy android developer: Be productive” series. Till the next part, keep exploring image loading libraries mentioned above and enjoy!

Was just setting up new JavaScript project based on Grunt. I scaffolded the project template by Yeoman with usage of angular-fullstack generator. I decided to try MEAN stack without MongoDB for my new project (DB isn't needed). Next step was integrating Require.JS and configuring measurement of code coverage on client and server by Instanbul. When was this all done I was wondering if it is possible to measure code coverage by Protractor end-to-end testing. After quick search I found that Ryan Bridges recently released grunt-protractor-coverage plugin. Interesting coincidence. So I decided to try it and can confirm that it's working fine with mentioned stack. Configuration was smooth and Ryan fixed small issue very promptly. It's based on grunt-protractor-runner plugin. I created separate Grunt configuration file just for this purpose not to mess around with normal build. I had also problems to run 'makeReport' task of grunt-istanbul plugin for two different directories (Mocha server side code coverage measurement is using same task). So here is the Grunt flow. First we need to copy non JavaScript files into target directory. It needs to be in separate directory because JavaScript files will need to be instrumented. copy: { coverageE2E: { files: [{ expand: true, dot: true, cwd: '', dest: '/app', src: [ '*.{ico,png,txt}', '.htaccess', 'bower_components/**/*', 'images/**/*', 'fonts/**/*', 'views/**/*', 'styles/**/*', ] }] }, }, Next step is instrumentation of the code. It is needed for gathering coverage stats. Each line is decorated by special instructions that helps during measurement. Pay attention to fact that we are instrumenting server and client side code. Instrumented code is placed into target directory represented by placeholder . instrument: { files: ['server/**/*.js', 'app/scripts/**/*.js'], options: { lazy: true, basePath: '/' } }, Next we start Express from target directory. express: { options: { port: process.env.PORT || 9000 }, coverageE2E: { options: { script: '/lib/server.js', debug: true } }, }, And the protractor_coverage task of grunt-protractor-coverage plugin. Configuration should be the same as for grunt-protractor-runner plugin. protractor_coverage: { options: { configFile: 'test/protractor/protractorConf.js', // Default config file keepAlive: true, // If false, the grunt process stops when the test fails. noColor: false, // If true, protractor will not use colors in its output. coverageDir: '', args: {} }, chrome: { options: { args: { baseUrl: 'http://localhost:3000/', // Arguments passed to the command 'browser': 'chrome' } } }, }, Last step is generation of coverage report. makeReport: { src: '/*.json', options: { type: 'html', dir: '/reports', print: 'detail' } }, Finally, this is grunt task gathering all steps. grunt.registerTask('default', [ 'clean:coverageE2E', 'copy:coverageE2E', 'instrument', 'express:coverageE2E', 'protractor_coverage:chrome', 'makeReport' ]); EDIT: Notice that following Github link was changed to branch, because project structure was significantly changed: Source code for this project can be found on GitHub. To run this Grunt configuration file: grunt --gruntfile Gruntfile-e2e.js For running end to end Protractor test you have to have webdriver-manager running. See Protractor documentation.

I’m going to start by discussing the Spring WebMVC configuration to compile and run Java without an IDE.