I recently stumbled upon an article Synchronising Multithreaded Integration Tests on Captain Debug's Blog. That post emphasizes the problem of designing integration tests involving class under test running business logic asynchronously. This contrived example was given (I stripped some comments): public class ThreadWrapper { public void doWork() { Thread thread = new Thread() { @Override public void run() { System.out.println("Start of the thread"); addDataToDB(); System.out.println("End of the thread method"); } private void addDataToDB() { // Dummy Code... try { Thread.sleep(4000); } catch (InterruptedException e) { e.printStackTrace(); } } }; thread.start(); System.out.println("Off and running..."); } } This is only an example of common pattern where business logic is delegated to some asynchronous job pool we have no control over. Roger Hughes (the author) enumerates few techniques of testing such code, including: arbitrary ("long enough") sleep() in test method to make sure background logic finishes refactoring doWork() so that it accepts CountDownLatch and agrees to notify it when job is done making the method above package private and @VisibleForTesting only "The" solution - refactoring doWork() so that it accepts arbitrary Runnable. In test we can wrap this Runnable (decorator pattern) and wait for inner Runnable to complete Last solution is not bad but it changes the responsibilities of ThreadWrapper significantly. Now it's up to the caller to decide what kind of job should be executed asynchronously while previously ThreadWrapper was encapsulating business logic completely. I am not saying it's a bad design, but it's drastically different from original method. Awaitility Can we write a test without such a massive refactoring? First solution involves handy library called Awaitility. This library is not a silver bullet, it simply evaluates given condition periodically and makes sure it's fulfilled within given time. It's the kind of code you probably wrote once or twice - wrapped in a library with well designed API. So here is our initial approach: import static com.jayway.awaitility.Awaitility.await; import static java.util.concurrent.TimeUnit.SECONDS; //... await().atMost(10, SECONDS).until(recordInserted()); //... private Callable recordInserted() { return new Callable() { @Override public Boolean call() throws Exception { return dataExists(); } }; } I think there is nothing to explain here. dataExists() is simply a boolean method that initially returns false but will eventually return true once the background task (addDataToDB()) is done. In other words we assume that background task introduces some side effect and dataExists() can detect that side effect. BTW I happened to have JDK 8 with Lambda support installed and IntelliJ IDEA gives me this nice tooltip: Suddenly I get this Java 8-compatible alternative suggested: private Callable recordInserted() { return () -> dataExists(); } But there's more: Which transforms my code to: private Callable recordInserted() { return this::dataExists; } this:: prefix means that recordInsterted is a method of current object. Just as well we can say someDao::dataExists. Simply put this syntax turns method into a function object we can pass around (this process is called eta expansion in Scala). By now recordInsterted() method is no longer that needed so I can inline it and remove it completely: await().atMost(10, SECONDS).until(this::dataExists); I am not sure what I love more - the new lambda syntax or how IntelliJ IDEA takes pre-Java 8 code and retrofits it for me automatically (well, it's still a bit experimental, just reported IDEA-106670). I can run this intention in IntelliJ project-wide, Lambda-enabling my whole code base in seconds. Sweet! But back to original problem. Awaitility helps a lot by providing decent API and some handy features. I use it extensively in combination with FluentLenium. But periodically polling for state changes feels a bit like a workaround and still introduces minimal latency. But notice that running and synchronizing on asynchronous tasks is quite common and JDK already provides necessary facilities: Future abstraction! java.util.concurrent.Future To limit the scope of refactoring I will leave the original new Thread() approach for now and use SettableFuture from Guava. It is a Future implementation that allows triggering completion or failure at any time, from any thread (see DeferredResult - asynchronous processing in Spring MVC for more advanced usage). As you can see the changes are quite small: public class ThreadWrapper { public ListenableFuture doWork() { final SettableFuture future = SettableFuture.create(); Thread thread = new Thread() { @Override public void run() { addDataToDB() //... //last instruction future.set(null); } private void addDataToDB() { // Dummy Code... // ... } }; thread.start(); return future; } } doWork() now returns ListenableFuture with lifecycle controlled inside asynchronous task. We use Void but in reality you might want to return some asynchronous result instead. future.set(null) invocation in the end is crucial. It signals that future is fulfilled and all threads waiting for that future will be notified. Once again, in practice you would use e.g. Future and then instead of null we would say future.set(someInteger). Here null is just a placeholder for Void type. How does this help us? Test code can now rely on future completion: final ListenableFuture future = wrapper.doWork(); future.get(10, SECONDS); future.get() blocks until future is done (with timeout), i.e. until we call future.set(...). BTW I use ListenableFuture from Guava but Java 8 introduces equivalent and standard CompletableFuture - I will write about it soon. So, we are getting somewhere. Future is a useful abstraction for waiting and signalling completion of background jobs. But there is also one immense advantage of Future which are not taking, ekhm, advantage from - exception handling and propagation. Future.get() will block until future is complete and return asynchronous result or throw an exception initially thrown from our job. This is really useful for asynchronous tests. Currently if Thread.run() throws an exception it may or may not be logged or visible to us and future will never be completed. With Awaitility it's slightly better - it will timeout without any meaningful reason, which have to be tracked down manually in console/logs. But with minor modification our test is much more verbose: public void run() { try { addDataToDB() //... future.set(null); } catch (Exception e) { future.setException(e); } } If some exception occurs in asynchronous job, it will pop-up and be shown as JUnit/TestNG failure reason. (Listening)ExecutorService That's it. If addDataToDB() throws an exception it will not be lost. Instead our future.get() in test will re-throw that exception for us. Our test won't simply timeout leaving us with no clue what went wrong. Great, but do we really have to create this special SettableFuture instance, can't we just use existing libraries that already give us Future with correct underlying implementation? Of course! By this requires further refactoring: import com.google.common.util.concurrent.ListeningExecutorService; import com.google.common.util.concurrent.MoreExecutors; import java.util.concurrent.Executors; import java.util.concurrent.Future; public class ThreadWrapper { private final ListeningExecutorService executorService = MoreExecutors.listeningDecorator( Executors.newSingleThreadExecutor() ); public ListenableFuture doWork() { Runnable job = new Runnable() { @Override public void run() { //... } }; return executorService.submit(job); } } This is what you've all been waiting for. Don't start new Thread all the time, use thread pool! I actually went one step further by using ListeningExecutorService - an extension to ExecutorService that returns ListenableFuture instances (see why you want that). But the solution doesn't require this, I just spread good practices. As you can see Future instance is now created and managed for us. The test is exactly the same but production code is cleaner and more robust. MoreExecutors.sameThreadExecutor() The final trick I want to show you involves dependency injection. First let's externalize the creation of a thread pool from ThreadWrapper class: private final ListeningExecutorService executorService; public ThreadWrapper() { this(Executors.newSingleThreadExecutor()); } public ThreadWrapper(ExecutorService executorService) { this.executorService = MoreExecutors.listeningDecorator(executorService); } We can now optionally supply custom ExecutorService. This is good for various other reasons, but for us it opens brand new testing opportunity: MoreExecutors.sameThreadExecutor(). This time we modify our test slightly: final ThreadWrapper wrapper = new ThreadWrapper(MoreExecutors.sameThreadExecutor()); wrapper.doWork().get(); See how we pass custom ExecutorService? It's a very special implementation that doesn't really maintain thread pool of any kind. Every time you submit() some task to that "pool" it will be executed in the same thread in a blocking manner. This means that we no longer have asynchronous test, even though the production code wasn't changed that much! wrapper.doWork() will block until "background" job finishes. The extra call to get() is still needed to make sure exceptions are propagated, but is guaranteed to never block (because the job is already done). Using the same thread to execute asynchronous task instead of a thread pool might have an unexpected results if you somehow depend on thread-based properties, e.g. transactions, security, ThreadLocal. However if you use standard ThreadPoolExecutor with CallerRunsPolicy, JDK already behaves this way if thread pool is overflowed. So it's not that unusual. Summary Testing asynchronous code is hard, but you have options. Several options. But one conclusion that strikes me is the side effect of our efforts. We refactored original code in order to make it testable. But the final production code is not only testable, but also much better structured and robust. Surprisingly it's even source-code compatible with previous version as we barely changed return type from void to Future. It seems to be a rule - testable code is often better designed and implemented. Unit test is the first client code using our library. It naturally forces us to to think more about consumers, not the implementation.

I have been thinking about institutions that strive for change. Sometimes we call them communities or organizations, sometimes we call them alliances or parties. But whatever their nature, these institutions are usually led and managed by a small group of people. I see two kinds of leading groups: coalitions and councils. coalition A temporary alliance of distinct parties, persons, or states for joint action council A group elected or appointed as an advisory or legislative body Coalitions A coalition is a self-selecting team. The persons seek each other out because they want to be active agents for change, and by working together they can be more successful in achieving a common goal. In his change management books John Kotter referred to them as guiding coalitions. They are not elected. They are not appointed. They select each other because they want to. And they can even work undercover, because their goal is to influence, not to govern. The allied powers in World War II were a coalition. The Google founders were a coalition. The originators of the Stoos Network were a coalition. Councils A council is a group of representatives. These people also want to be active agents for change. But, their primary concern is to have buy-in from the larger group of people they are representing within the institute (community, organization, or party). The concept of democracy has led to many different versions of these councils. Sometimes we call them a government. Sometimes a committee. And everything has to be out in the open, because if it’s not, we call them cronies. Their goal is primarily to govern or advise the institute. The United Nations has a council. My former students society had a council. And many workplaces have management teams acting as councils. And you? If you have a group of people who all desire change, do you lead with a coalition or with a council? This is the big problem with some alliances and consortiums for change. They have directors who try to be both. It is a recipe for disaster. Maybe the best institutions have both: a coalition and a council. (image from Veni Markovski)

n this post i’m going to cover how to do job chaining in quartz versus obsidian scheduler . both are java job schedulers, but they have different approaches so i thought i’d highlight them here and give some guidance to users using both options. it’s very common when using a job scheduler to need to chain one job to another. chaining in this case refers to executing a specific job after a certain job completes (or maybe even fails). often we want to do this conditionally, or pass on data to the target job so it can receive it as input from the original job. we’ll start with demonstrating how to do this in quartz, which will take a fair bit of work. obsidian will come after since it’s so simple. chaining in quartz quartz is the most popular job scheduler out there, but unfortunately it doesn’t provide any way to give you chaining without you writing some code. quartz is a low-level library at heart, and it doesn’t try to solve these types of problems for you, which in my mind is unfortunate since it puts the onus on developers. but despite this, many teams still end up using quartz, so hopefully this is useful to some of you. i’m going to outline probably the most basic way to perform chaining. it will allow a job to chain to another, passing on its jobdatamap (for state). this is simpler than using listeners, which would require extra configuration, but if you want to take a look, check out this listener for a starting point. sample code this will rely on an abstract class that will provided basic flow and chaining functionality to any subclasses. it acts as a very simple template class. first, let’s create the abstract class that gives us chaining behaviour: import static org.quartz.jobbuilder.newjob; import static org.quartz.triggerbuilder.newtrigger; import org.quartz.*; import org.quartz.impl.*; public abstract class chainablejob implements job { private static final string chain_job_class = "chainedjobclass"; private static final string chain_job_name = "chainedjobname"; private static final string chain_job_group = "chainedjobgroup"; @override public void execute(jobexecutioncontext context) throws jobexecutionexception { // execute actual job code doexecute(context); // if chainjob() was called, chain the target job, passing on the jobdatamap if (context.getjobdetail().getjobdatamap().get(chain_job_class) != null) { try { chain(context); } catch (schedulerexception e) { e.printstacktrace(); } } } // actually schedule the chained job to run now private void chain(jobexecutioncontext context) throws schedulerexception { jobdatamap map = context.getjobdetail().getjobdatamap(); @suppresswarnings("unchecked") class jobclass = (class) map.remove(chain_job_class); string jobname = (string) map.remove(chain_job_name); string jobgroup = (string) map.remove(chain_job_group); jobdetail jobdetail = newjob(jobclass) .withidentity(jobname, jobgroup) .usingjobdata(map) .build(); trigger trigger = newtrigger() .withidentity(jobname + "trigger", jobgroup + "trigger") .startnow() .build(); system.out.println("chaining " + jobname); stdschedulerfactory.getdefaultscheduler().schedulejob(jobdetail, trigger); } protected abstract void doexecute(jobexecutioncontext context) throws jobexecutionexception; // trigger job chain (invocation waits for job completion) protected void chainjob(jobexecutioncontext context, class jobclass, string jobname, string jobgroup) { jobdatamap map = context.getjobdetail().getjobdatamap(); map.put(chain_job_class, jobclass); map.put(chain_job_name, jobname); map.put(chain_job_group, jobgroup); } } there’s a fair bit of code here, but it’s nothing too complicated. we create the basic flow for job chaining by creating an abstract class which calls a doexecute() method in the child class, then chains the job if it was requested by calling chainjob() . so how do we use it? check out the job below. it actually chains to itself to demonstrate that you can chain any job and that it can be conditional. in this case, we will chain the job to another instance of the same class if it hasn’t already been chained, and we get a true value from new random().nextboolean() . import java.util.*; import org.quartz.*; public class testjob extends chainablejob { @override protected void doexecute(jobexecutioncontext context) throws jobexecutionexception { jobdatamap map = context.getjobdetail().getjobdatamap(); system.out.println("executing " + context.getjobdetail().getkey().getname() + " with " + new linkedhashmap(map)); boolean alreadychained = map.get("jobvalue") != null; if (!alreadychained) { map.put("jobtime", new date().tostring()); map.put("jobvalue", new random().nextlong()); } if (!alreadychained && new random().nextboolean()) { chainjob(context, testjob.class, "secondjob", "secondjobgroup"); } } } the call to chainjob() at the end will result in the automatic job chaining behaviour in the parent class. note that this isn’t called immediately, but only executes after the job completes its doexecute() method. here’s a simple harness that demonstrates everything together: import org.quartz.*; import org.quartz.impl.*; public class test { public static void main(string[] args) throws exception { // start up scheduler stdschedulerfactory.getdefaultscheduler().start(); jobdetail job = jobbuilder.newjob(testjob.class) .withidentity("firstjob", "firstjobgroup").build(); // trigger our source job to triggers another trigger trigger = triggerbuilder.newtrigger() .withidentity("firstjobtrigger", "firstjobbtriggergroup") .startnow() .withschedule( simpleschedulebuilder.simpleschedule().withintervalinseconds(1) .repeatforever()).build(); stdschedulerfactory.getdefaultscheduler().schedulejob(job, trigger); thread.sleep(5000); // let job run a few times stdschedulerfactory.getdefaultscheduler().shutdown(); } } sample output executing firstjob with {} chaining secondjob executing secondjob with {jobvalue=5420204983304142728, jobtime=sat mar 02 15:19:29 pst 2013} executing firstjob with {} executing firstjob with {} chaining secondjob executing secondjob with {jobvalue=-2361712834083016932, jobtime=sat mar 02 15:19:31 pst 2013} executing firstjob with {} chaining secondjob executing secondjob with {jobvalue=7080718769449337795, jobtime=sat mar 02 15:19:32 pst 2013} executing firstjob with {} chaining secondjob executing secondjob with {jobvalue=7235143258790440677, jobtime=sat mar 02 15:19:33 pst 2013} executing firstjob with {} deficiencies well, we’re up and chaining, but there are some problems with this approach: it doesn’t integrate with a container like spring to use configured jobs. more code would be required. it forces you to know up front which jobs you want to chain, and write code for it. configuration is fixed, unless, once again, you write more code. no real-time changes (unless you write more code). a fair bit of code to maintain , and high likelihood you will have to expand it for more functionality. the theme here is that it’s doable, but it’s up to you to do the work to make it happen. obsidian avoids these problems by making chaining configurable, instead of it being a feature of the job itself. read on to find out how. chaining in obsidian in contrast to quartz, chaining in obsidian requires no code and no up-front knowledge of which jobs will chain or how you might want to chain them later. chaining is a form of configuration, and like all job-related configuration in obsidian, you can make live changes at any time without a build or any code at all. job configuration can use a native rest api or the web ui that’s included with obsidian. the following chaining features are available for free: no code and no redeploy to add or remove chains. you can chain specific configurations of job classes. you can chain only on certain states, including failure. chain conditionally based on source job saved state (equivalent to quartz’s jobdatamap), including multiple conditions. regexp/equals/greater than, etc. chain only when matching a schedule. check out the feature and ui documentation to find out more. now that we know what’s possible, let’s see an example. once you have your jobs configured , just create a new chain using the ui. rest api support will be here shortly but as of 1.5.1 chaining isn’t included in the api. if you need to script this right now, we can provide pointers . in the ui, it looks like the following: easy, huh? all configuration is stored in a database, so it’s easy to replicate it in various environments or to automate it via scripting. as a bonus, obsidian tracks and shows you all chaining state including what job triggered a chained job. it will even tell you why a job chain didn’t fire, whether it’s because the job status didn’t match, or one of your conditions didn’t. conclusion that summarizes how you can go about chaining in quartz and obsidian. quartz definitely has a minimalist approach, but that leaves developers with a lot of work to do. meanwhile, obsidian provides rich functionality out of the box to keep developers working on their own rich functionality, instead of the plumbing that so often seems to consume their time. if you have any suggestions or feature requests for obsidian, drop us a note by leaving a comment or by contacting us .

Many enterprise applications require batch processing to process billions of transactions every day. These big transaction sets have to be processed without performance problems. Spring Batch is a lightweight and robust batch framework to process these big data sets. Spring Batch offers ‘TaskletStep Oriented’ and ‘Chunk Oriented’ processing style. In this article, TaskletStep Oriented Processing Model is explained. Let us investigate fundamental Spring Batch components : Job : An entity that encapsulates an entire batch process. Step and Tasklets are defined under a Job Step : A domain object that encapsulates an independent, sequential phase of a batch job. JobInstance : Batch domain object representing a uniquely identifiable job run – it’s identity is given by the pair Job and JobParameters. JobParameters : Value object representing runtime parameters to a batch job. JobExecution : A JobExecution refers to the technical concept of a single attempt to run a Job. An execution may end in failure or success, but the JobInstance corresponding to a given execution will not be considered complete unless the execution completes successfully. JobRepository : An interface which responsible for persistence of batch meta-data entities. In the following sample, an in-memory repository is used via MapJobRepositoryFactoryBean. JobLauncher : An interface exposing run method, which launches and controls the defined jobs. TaskLet : An interface exposing execute method, which will be a called repeatedly until it either returns RepeatStatus.FINISHED or throws an exception to signal a failure. It is used when both readers and writers are not required as the following sample. Let us take a look how to develop Tasklet-Step Oriented Processing Model. Used Technologies : JDK 1.7.0_09 Spring 3.1.3 Spring Batch 2.1.9 Maven 3.0.4 STEP 1 : CREATE MAVEN PROJECT A maven project is created as below. (It can be created by using Maven or IDE Plug-in). STEP 2 : LIBRARIES Firstly, dependencies are added to Maven’ s pom.xml. 3.1.3.RELEASE 2.1.9.RELEASE org.springframework spring-core ${spring.version} org.springframework spring-context ${spring.version} org.springframework.batch spring-batch-core ${spring-batch.version} log4j log4j 1.2.16 maven-compiler-plugin(Maven Plugin) is used to compile the project with JDK 1.7 org.apache.maven.plugins maven-compiler-plugin 3.0 1.7 1.7 The following Maven plugin can be used to create runnable-jar, org.apache.maven.plugins maven-shade-plugin 2.0 package shade 1.7 1.7 com.onlinetechvision.exe.Application META-INF/spring.handlers META-INF/spring.schemas STEP 3 : CREATE SuccessfulStepTasklet TASKLET SuccessfulStepTasklet is created by implementing Tasklet Interface. It illustrates business logic in successful step. package com.onlinetechvision.tasklet; import org.apache.log4j.Logger; import org.springframework.batch.core.StepContribution; import org.springframework.batch.core.scope.context.ChunkContext; import org.springframework.batch.core.step.tasklet.Tasklet; import org.springframework.batch.repeat.RepeatStatus; /** * SuccessfulStepTasklet Class illustrates a successful job * * @author onlinetechvision.com * @since 27 Nov 2012 * @version 1.0.0 * */ public class SuccessfulStepTasklet implements Tasklet { private static final Logger logger = Logger.getLogger(SuccessfulStepTasklet.class); private String taskResult; /** * Executes SuccessfulStepTasklet * * @param StepContribution stepContribution * @param ChunkContext chunkContext * @return RepeatStatus * @throws Exception * */ @Override public RepeatStatus execute(StepContribution stepContribution, ChunkContext chunkContext) throws Exception { logger.debug("Task Result : " + getTaskResult()); return RepeatStatus.FINISHED; } public String getTaskResult() { return taskResult; } public void setTaskResult(String taskResult) { this.taskResult = taskResult; } } STEP 4 : CREATE FailedStepTasklet TASKLET FailedStepTasklet is created by implementing Tasklet Interface. It illustrates business logic in failed step. package com.onlinetechvision.tasklet; import org.apache.log4j.Logger; import org.springframework.batch.core.StepContribution; import org.springframework.batch.core.scope.context.ChunkContext; import org.springframework.batch.core.step.tasklet.Tasklet; import org.springframework.batch.repeat.RepeatStatus; /** * FailedStepTasklet Class illustrates a failed job. * * @author onlinetechvision.com * @since 27 Nov 2012 * @version 1.0.0 * */ public class FailedStepTasklet implements Tasklet { private static final Logger logger = Logger.getLogger(FailedStepTasklet.class); private String taskResult; /** * Executes FailedStepTasklet * * @param StepContribution stepContribution * @param ChunkContext chunkContext * @return RepeatStatus * @throws Exception * */ @Override public RepeatStatus execute(StepContribution stepContribution, ChunkContext chunkContext) throws Exception { logger.debug("Task Result : " + getTaskResult()); throw new Exception("Error occurred!"); } public String getTaskResult() { return taskResult; } public void setTaskResult(String taskResult) { this.taskResult = taskResult; } } STEP 5 : CREATE BatchProcessStarter CLASS BatchProcessStarter Class is created to launch the jobs. Also, it logs their execution results. A Completed Job Instance can not be restarted with the same parameter(s) because it already exists in job repository and JobInstanceAlreadyCompleteException is thrown with “A job instance already exists and is complete” description. It can be restarted with different parameter. In the following sample, different currentTime parameter is set in order to restart FirstJob. package com.onlinetechvision.spring.batch; import org.apache.log4j.Logger; import org.springframework.batch.core.Job; import org.springframework.batch.core.JobExecution; import org.springframework.batch.core.JobParametersBuilder; import org.springframework.batch.core.JobParametersInvalidException; import org.springframework.batch.core.launch.JobLauncher; import org.springframework.batch.core.repository.JobExecutionAlreadyRunningException; import org.springframework.batch.core.repository.JobInstanceAlreadyCompleteException; import org.springframework.batch.core.repository.JobRepository; import org.springframework.batch.core.repository.JobRestartException; /** * BatchProcessStarter Class launches the jobs and logs their execution results. * * @author onlinetechvision.com * @since 27 Nov 2012 * @version 1.0.0 * */ public class BatchProcessStarter { private static final Logger logger = Logger.getLogger(BatchProcessStarter.class); private Job firstJob; private Job secondJob; private Job thirdJob; private JobLauncher jobLauncher; private JobRepository jobRepository; /** * Starts the jobs and logs their execution results. * */ public void start() { JobExecution jobExecution = null; JobParametersBuilder builder = new JobParametersBuilder(); try { builder.addLong("currentTime", new Long(System.currentTimeMillis())); getJobLauncher().run(getFirstJob(), builder.toJobParameters()); jobExecution = getJobRepository().getLastJobExecution(getFirstJob().getName(), builder.toJobParameters()); logger.debug(jobExecution.toString()); getJobLauncher().run(getSecondJob(), builder.toJobParameters()); jobExecution = getJobRepository().getLastJobExecution(getSecondJob().getName(), builder.toJobParameters()); logger.debug(jobExecution.toString()); getJobLauncher().run(getThirdJob(), builder.toJobParameters()); jobExecution = getJobRepository().getLastJobExecution(getThirdJob().getName(), builder.toJobParameters()); logger.debug(jobExecution.toString()); builder.addLong("currentTime", new Long(System.currentTimeMillis())); getJobLauncher().run(getFirstJob(), builder.toJobParameters()); jobExecution = getJobRepository().getLastJobExecution(getFirstJob().getName(), builder.toJobParameters()); logger.debug(jobExecution.toString()); } catch (JobExecutionAlreadyRunningException | JobRestartException | JobInstanceAlreadyCompleteException | JobParametersInvalidException e) { logger.error(e); } } public Job getFirstJob() { return firstJob; } public void setFirstJob(Job firstJob) { this.firstJob = firstJob; } public Job getSecondJob() { return secondJob; } public void setSecondJob(Job secondJob) { this.secondJob = secondJob; } public Job getThirdJob() { return thirdJob; } public void setThirdJob(Job thirdJob) { this.thirdJob = thirdJob; } public JobLauncher getJobLauncher() { return jobLauncher; } public void setJobLauncher(JobLauncher jobLauncher) { this.jobLauncher = jobLauncher; } public JobRepository getJobRepository() { return jobRepository; } public void setJobRepository(JobRepository jobRepository) { this.jobRepository = jobRepository; } } STEP 6 : CREATE applicationContext.xml Spring Configuration file, applicationContext.xml, is created. It covers Tasklets and BatchProcessStarter definitions. STEP 7 : CREATE jobContext.xml Spring Configuration file, jobContext.xml, is created. Jobs’ flows are the following : FirstJob’ s flow : 1) FirstStep is started. 2) After FirstStep is completed with COMPLETED status, SecondStep is started. 3) After SecondStep is completed with COMPLETED status, ThirdStep is started. 4) After ThirdStep is completed with COMPLETED status, FirstJob execution is completed with COMPLETED status. SecondJob’ s flow : 1) FourthStep is started. 2) After FourthStep is completed with COMPLETED status, FifthStep is started. 3) After FifthStep is completed with COMPLETED status, SecondJob execution is completed with COMPLETED status. ThirdJob’ s flow : 1) SixthStep is started. 2) After SixthStep is completed with COMPLETED status, SeventhStep is started. 3) After SeventhStep is completed with FAILED status, ThirdJob execution is completed FAILED status. FirstJob’ s flow is same with the first execution. STEP 8 : CREATE Application CLASS Application Class is created to run the application. package com.onlinetechvision.exe; import org.springframework.context.ApplicationContext; import org.springframework.context.support.ClassPathXmlApplicationContext; import com.onlinetechvision.spring.batch.BatchProcessStarter; /** * Application Class starts the application. * * @author onlinetechvision.com * @since 27 Nov 2012 * @version 1.0.0 * */ public class Application { /** * Starts the application * * @param String[] args * */ public static void main(String[] args) { ApplicationContext appContext = new ClassPathXmlApplicationContext("jobContext.xml"); BatchProcessStarter batchProcessStarter = (BatchProcessStarter)appContext.getBean("batchProcessStarter"); batchProcessStarter.start(); } } STEP 9 : BUILD PROJECT After OTV_SpringBatch_TaskletStep_Oriented_Processing Project is built, OTV_SpringBatch_TaskletStep-0.0.1-SNAPSHOT.jar will be created. STEP 10 : RUN PROJECT After created OTV_SpringBatch_TaskletStep-0.0.1-SNAPSHOT.jar file is run, the following console output logs will be shown : First Job’ s console output : 25.11.2012 21:29:19 INFO (SimpleJobLauncher.java:118) - Job: [FlowJob: [name=firstJob]] launched with the following parameters: [{currentTime=1353878959462}] 25.11.2012 21:29:19 DEBUG (AbstractJob.java:278) - Job execution starting: JobExecution: id=0, version=0, startTime=null, endTime=null, lastUpdated=Sun Nov 25 21:29:19 GMT 2012, status=STARTING, exitStatus=exitCode=UNKNOWN; exitDescription=, job=[JobInstance: id=0, version=0, JobParameters=[{currentTime=1353878959462}], Job=[firstJob]] 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:135) - Resuming state=firstJob.firstStep with status=UNKNOWN 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:143) - Handling state=firstJob.firstStep 25.11.2012 21:29:20 INFO (SimpleStepHandler.java:133) - Executing step: [firstStep] 25.11.2012 21:29:20 DEBUG (AbstractStep.java:180) - Executing: id=1 25.11.2012 21:29:20 DEBUG (SuccessfulStepTasklet.java:33) - Task Result : First Task is executed... 25.11.2012 21:29:20 DEBUG (AbstractStep.java:209) - Step execution success: id=1 25.11.2012 21:29:20 DEBUG (AbstractStep.java:273) - Step execution complete: StepExecution: id=1, version=3, name=firstStep, status=COMPLETED, exitStatus=COMPLETED, readCount=0, filterCount=0, writeCount=0 readSkipCount=0, writeSkipCount=0, processSkipCount=0, commitCount=1, rollbackCount=0 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:156) - Completed state=firstJob.firstStep with status=COMPLETED 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:143) - Handling state=firstJob.secondStep 25.11.2012 21:29:20 INFO (SimpleStepHandler.java:133) - Executing step: [secondStep] 25.11.2012 21:29:20 DEBUG (AbstractStep.java:180) - Executing: id=2 25.11.2012 21:29:20 DEBUG (SuccessfulStepTasklet.java:33) - Task Result : Second Task is executed... 25.11.2012 21:29:20 DEBUG (AbstractStep.java:209) - Step execution success: id=2 25.11.2012 21:29:20 DEBUG (AbstractStep.java:273) - Step execution complete: StepExecution: id=2, version=3, name=secondStep, status=COMPLETED, exitStatus=COMPLETED, readCount=0, filterCount=0, writeCount=0 readSkipCount=0, writeSkipCount=0, processSkipCount=0, commitCount=1, rollbackCount=0 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:156) - Completed state=firstJob.secondStep with status=COMPLETED 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:143) - Handling state=firstJob.thirdStep 25.11.2012 21:29:20 INFO (SimpleStepHandler.java:133) - Executing step: [thirdStep] 25.11.2012 21:29:20 DEBUG (AbstractStep.java:180) - Executing: id=3 25.11.2012 21:29:20 DEBUG (SuccessfulStepTasklet.java:33) - Task Result : Third Task is executed... 25.11.2012 21:29:20 DEBUG (AbstractStep.java:273) - Step execution complete: StepExecution: id=3, version=3, name=thirdStep, status=COMPLETED, exitStatus=COMPLETED, readCount=0, filterCount=0, writeCount=0 readSkipCount=0, writeSkipCount=0, processSkipCount=0, commitCount=1, rollbackCount=0 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:156) - Completed state=firstJob.thirdStep with status=COMPLETED 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:143) - Handling state=firstJob.end3 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:156) - Completed state=firstJob.end3 with status=COMPLETED 25.11.2012 21:29:20 DEBUG (AbstractJob.java:294) - Job execution complete: JobExecution: id=0, version=1, startTime=Sun Nov 25 21:29:19 GMT 2012, endTime=null, lastUpdated=Sun Nov 25 21:29:19 GMT 2012, status=COMPLETED, exitStatus=exitCode=COMPLETED;exitDescription=, job=[JobInstance: id=0, version=0, JobParameters=[{currentTime=1353878959462}], Job=[firstJob]] 25.11.2012 21:29:20 INFO (SimpleJobLauncher.java:121) - Job: [FlowJob: [name=firstJob]] completed with the following parameters: [{currentTime=1353878959462}] and the following status: [COMPLETED] 25.11.2012 21:29:20 DEBUG (BatchProcessStarter.java:44) - JobExecution: id=0, version=2, startTime=Sun Nov 25 21:29:19 GMT 2012, endTime=Sun Nov 25 21:29:20 GMT 2012, lastUpdated=Sun Nov 25 21:29:20 GMT 2012, status=COMPLETED, exitStatus=exitCode=COMPLETED;exitDescription=, job=[JobInstance: id=0, version=0, JobParameters=[{currentTime=1353878959462}], Job=[firstJob]] Second Job’ s console output : 25.11.2012 21:29:20 INFO (SimpleJobLauncher.java:118) - Job: [FlowJob: [name=secondJob]] launched with the following parameters: [{currentTime=1353878959462}] 25.11.2012 21:29:20 DEBUG (AbstractJob.java:278) - Job execution starting: JobExecution: id=1, version=0, startTime=null, endTime=null, lastUpdated=Sun Nov 25 21:29:20 GMT 2012, status=STARTING, exitStatus=exitCode=UNKNOWN;exitDescription=, job=[JobInstance: id=1, version=0, JobParameters=[{currentTime=1353878959462}], Job=[secondJob]] 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:135) - Resuming state=secondJob.fourthStep with status=UNKNOWN 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:143) - Handling state=secondJob.fourthStep 25.11.2012 21:29:20 INFO (SimpleStepHandler.java:133) - Executing step: [fourthStep] 25.11.2012 21:29:20 DEBUG (AbstractStep.java:180) - Executing: id=4 25.11.2012 21:29:20 DEBUG (SuccessfulStepTasklet.java:33) - Task Result : Fourth Task is executed... 25.11.2012 21:29:20 DEBUG (AbstractStep.java:273) - Step execution complete: StepExecution: id=4, version=3, name=fourthStep, status=COMPLETED, exitStatus=COMPLETED, readCount=0, filterCount=0, writeCount=0 readSkipCount=0, writeSkipCount=0, processSkipCount=0, commitCount=1, rollbackCount=0 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:156) - Completed state=secondJob.fourthStep with status=COMPLETED 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:143) - Handling state=secondJob.fifthStep 25.11.2012 21:29:20 INFO (SimpleStepHandler.java:133) - Executing step: [fifthStep] 25.11.2012 21:29:20 DEBUG (AbstractStep.java:180) - Executing: id=5 25.11.2012 21:29:20 DEBUG (SuccessfulStepTasklet.java:33) - Task Result : Fifth Task is executed... 25.11.2012 21:29:20 DEBUG (AbstractStep.java:273) - Step execution complete: StepExecution: id=5, version=3, name=fifthStep, status=COMPLETED, exitStatus=COMPLETED, readCount=0, filterCount=0, writeCount=0 readSkipCount=0, writeSkipCount=0, processSkipCount=0, commitCount=1, rollbackCount=0 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:156) - Completed state=secondJob.fifthStep with status=COMPLETED 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:143) - Handling state=secondJob.end5 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:156) - Completed state=secondJob.end5 with status=COMPLETED 25.11.2012 21:29:20 DEBUG (AbstractJob.java:294) - Job execution complete: JobExecution: id=1, version=1, startTime=Sun Nov 25 21:29:20 GMT 2012, endTime=null, lastUpdated=Sun Nov 25 21:29:20 GMT 2012, status=COMPLETED, exitStatus=exitCode=COMPLETED;exitDescription=, job=[JobInstance: id=1, version=0, JobParameters=[{currentTime=1353878959462}], Job=[secondJob]] 25.11.2012 21:29:20 INFO (SimpleJobLauncher.java:121) - Job: [FlowJob: [name=secondJob]] completed with the following parameters: [{currentTime=1353878959462}] and the following status: [COMPLETED] 25.11.2012 21:29:20 DEBUG (BatchProcessStarter.java:48) - JobExecution: id=1, version=2, startTime=Sun Nov 25 21:29:20 GMT 2012, endTime=Sun Nov 25 21:29:20 GMT 2012, lastUpdated=Sun Nov 25 21:29:20 GMT 2012, status=COMPLETED, exitStatus=exitCode=COMPLETED;exitDescription=, job=[JobInstance: id=1, version=0, JobParameters=[{currentTime=1353878959462}], Job=[secondJob]] Third Job’ s console output : 25.11.2012 21:29:20 INFO (SimpleJobLauncher.java:118) - Job: [FlowJob: [name=thirdJob]] launched with the following parameters: [{currentTime=1353878959462}] 25.11.2012 21:29:20 DEBUG (AbstractJob.java:278) - Job execution starting: JobExecution: id=2, version=0, startTime=null, endTime=null, lastUpdated=Sun Nov 25 21:29:20 GMT 2012, status=STARTING, exitStatus=exitCode=UNKNOWN;exitDescription=, job=[JobInstance: id=2, version=0, JobParameters=[{currentTime=1353878959462}], Job=[thirdJob]] 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:135) - Resuming state=thirdJob.sixthStep with status=UNKNOWN 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:143) - Handling state=thirdJob.sixthStep 25.11.2012 21:29:20 INFO (SimpleStepHandler.java:133) - Executing step: [sixthStep] 25.11.2012 21:29:20 DEBUG (AbstractStep.java:180) - Executing: id=6 25.11.2012 21:29:20 DEBUG (SuccessfulStepTasklet.java:33) - Task Result : Sixth Task is executed... 25.11.2012 21:29:20 DEBUG (AbstractStep.java:273) - Step execution complete: StepExecution: id=6, version=3, name=sixthStep, status=COMPLETED, exitStatus=COMPLETED, readCount=0, filterCount=0, writeCount=0 readSkipCount=0, writeSkipCount=0, processSkipCount=0, commitCount=1, rollbackCount=0 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:156) - Completed state=thirdJob.sixthStep with status=COMPLETED 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:143) - Handling state=thirdJob.seventhStep 25.11.2012 21:29:20 INFO (SimpleStepHandler.java:133) - Executing step: [seventhStep] 25.11.2012 21:29:20 DEBUG (AbstractStep.java:180) - Executing: id=7 25.11.2012 21:29:20 DEBUG (FailedStepTasklet.java:33) - Task Result : Error occurred! 25.11.2012 21:29:20 DEBUG (TaskletStep.java:456) - Rollback for Exception: java.lang.Exception: Error occurred! 25.11.2012 21:29:20 DEBUG (TransactionTemplate.java:152) - Initiating transaction rollback on application exception ... 25.11.2012 21:29:20 DEBUG (AbstractPlatformTransactionManager.java:821) - Initiating transaction rollback 25.11.2012 21:29:20 DEBUG (ResourcelessTransactionManager.java:54) - Rolling back resourceless transaction on [org.springframework.batch.support.transaction.ResourcelessTransactionManager$ResourcelessTransaction@40874c04] 25.11.2012 21:29:20 DEBUG (RepeatTemplate.java:291) - Handling exception: java.lang.Exception, caused by: java.lang.Exception: Error occurred! 25.11.2012 21:29:20 DEBUG (RepeatTemplate.java:251) - Handling fatal exception explicitly (rethrowing first of 1): java.lang.Exception: Error occurred! 25.11.2012 21:29:20 ERROR (AbstractStep.java:222) - Encountered an error executing the step ... 25.11.2012 21:29:20 DEBUG (ResourcelessTransactionManager.java:34) - Committing resourceless transaction on [org.springframework.batch.support.transaction.ResourcelessTransactionManager$ResourcelessTransaction@66a7d863] 25.11.2012 21:29:20 DEBUG (AbstractStep.java:273) - Step execution complete: StepExecution: id=7, version=2, name=seventhStep, status=FAILED, exitStatus=FAILED, readCount=0, filterCount=0, writeCount=0 readSkipCount=0, writeSkipCount=0, processSkipCount=0, commitCount=0, rollbackCount=1 25.11.2012 21:29:20 DEBUG (ResourcelessTransactionManager.java:34) - Committing resourceless transaction on [org.springframework.batch.support.transaction.ResourcelessTransactionManager$ResourcelessTransaction@156f803c] 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:156) - Completed state=thirdJob.seventhStep with status=FAILED 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:143) - Handling state=thirdJob.fail8 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:156) - Completed state=thirdJob.fail8 with status=FAILED 25.11.2012 21:29:20 DEBUG (AbstractJob.java:294) - Job execution complete: JobExecution: id=2, version=1, startTime=Sun Nov 25 21:29:20 GMT 2012, endTime=null, lastUpdated=Sun Nov 25 21:29:20 GMT 2012, status=FAILED, exitStatus=exitCode=FAILED;exitDescription=, job=[JobInstance: id=2, version=0, JobParameters=[{currentTime=1353878959462}], Job=[thirdJob]] 25.11.2012 21:29:20 INFO (SimpleJobLauncher.java:121) - Job: [FlowJob: [name=thirdJob]] completed with the following parameters: [{currentTime=1353878959462}] and the following status: [FAILED] 25.11.2012 21:29:20 DEBUG (BatchProcessStarter.java:52) - JobExecution: id=2, version=2, startTime=Sun Nov 25 21:29:20 GMT 2012, endTime=Sun Nov 25 21:29:20 GMT 2012, lastUpdated=Sun Nov 25 21:29:20 GMT 2012, status=FAILED, exitStatus=exitCode=FAILED; exitDescription=, job=[JobInstance: id=2, version=0, JobParameters=[{currentTime=1353878959462}], Job=[thirdJob]] First Job’ s console output after restarting : 25.11.2012 21:29:20 INFO (SimpleJobLauncher.java:118) - Job: [FlowJob: [name=firstJob]] launched with the following parameters: [{currentTime=1353878960660}] 25.11.2012 21:29:20 DEBUG (AbstractJob.java:278) - Job execution starting: JobExecution: id=3, version=0, startTime=null, endTime=null, lastUpdated=Sun Nov 25 21:29:20 GMT 2012, status=STARTING, exitStatus=exitCode=UNKNOWN;exitDescription=, job=[JobInstance: id=3, version=0, JobParameters=[{currentTime=1353878960660}], Job=[firstJob]] 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:135) - Resuming state=firstJob.firstStep with status=UNKNOWN 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:143) - Handling state=firstJob.firstStep 25.11.2012 21:29:20 INFO (SimpleStepHandler.java:133) - Executing step: [firstStep] 25.11.2012 21:29:20 DEBUG (AbstractStep.java:180) - Executing: id=8 25.11.2012 21:29:20 DEBUG (SuccessfulStepTasklet.java:33) - Task Result : First Task is executed... 25.11.2012 21:29:20 DEBUG (AbstractStep.java:209) - Step execution success: id=8 25.11.2012 21:29:20 DEBUG (AbstractStep.java:273) - Step execution complete: StepExecution: id=8, version=3, name=firstStep, status=COMPLETED, exitStatus=COMPLETED, readCount=0, filterCount=0, writeCount=0 readSkipCount=0, writeSkipCount=0, processSkipCount=0, commitCount=1, rollbackCount=0 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:156) - Completed state=firstJob.firstStep with status=COMPLETED 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:143) - Handling state=firstJob.secondStep 25.11.2012 21:29:20 INFO (SimpleStepHandler.java:133) - Executing step: [secondStep] 25.11.2012 21:29:20 DEBUG (AbstractStep.java:180) - Executing: id=9 25.11.2012 21:29:20 DEBUG (SuccessfulStepTasklet.java:33) - Task Result : Second Task is executed... 25.11.2012 21:29:20 DEBUG (TaskletStep.java:417) - Applying contribution: [StepContribution: read=0, written=0, filtered=0, readSkips=0, writeSkips=0, processSkips=0, exitStatus=EXECUTING] 25.11.2012 21:29:20 DEBUG (AbstractStep.java:209) - Step execution success: id=9 25.11.2012 21:29:20 DEBUG (AbstractStep.java:273) - Step execution complete: StepExecution: id=9, version=3, name=secondStep, status=COMPLETED, exitStatus=COMPLETED, readCount=0, filterCount=0, writeCount=0 readSkipCount=0, writeSkipCount=0, processSkipCount=0, commitCount=1, rollbackCount=0 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:156) - Completed state=firstJob.secondStep with status=COMPLETED 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:143) - Handling state=firstJob.thirdStep 25.11.2012 21:29:20 INFO (SimpleStepHandler.java:133) - Executing step: [thirdStep] 25.11.2012 21:29:20 DEBUG (AbstractStep.java:180) - Executing: id=10 25.11.2012 21:29:20 DEBUG (SuccessfulStepTasklet.java:33) - Task Result : Third Task is executed... 25.11.2012 21:29:20 DEBUG (TaskletStep.java:417) - Applying contribution: [StepContribution: read=0, written=0, filtered=0, readSkips=0, writeSkips=0, processSkips=0, exitStatus=EXECUTING] 25.11.2012 21:29:20 DEBUG (AbstractStep.java:209) - Step execution success: id=10 25.11.2012 21:29:20 DEBUG (AbstractStep.java:273) - Step execution complete: StepExecution: id=10, version=3, name=thirdStep, status=COMPLETED, exitStatus=COMPLETED, readCount=0, filterCount=0, writeCount=0 readSkipCount=0, writeSkipCount=0, processSkipCount=0, commitCount=1, rollbackCount=0 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:156) - Completed state=firstJob.thirdStep with status=COMPLETED 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:143) - Handling state=firstJob.end3 25.11.2012 21:29:20 DEBUG (SimpleFlow.java:156) - Completed state=firstJob.end3 with status=COMPLETED 25.11.2012 21:29:20 DEBUG (AbstractJob.java:294) - Job execution complete: JobExecution: id=3, version=1, startTime=Sun Nov 25 21:29:20 GMT 2012, endTime=null, lastUpdated=Sun Nov 25 21:29:20 GMT 2012, status=COMPLETED, exitStatus=exitCode=COMPLETED;exitDescription=, job=[JobInstance: id=3, version=0, JobParameters=[{currentTime=1353878960660}], Job=[firstJob]] 25.11.2012 21:29:20 INFO (SimpleJobLauncher.java:121) - Job: [FlowJob: [name=firstJob]] completed with the following parameters: [{currentTime=1353878960660}] and the following status: [COMPLETED] 25.11.2012 21:29:20 DEBUG (BatchProcessStarter.java:57) - JobExecution: id=3, version=2, startTime=Sun Nov 25 21:29:20 GMT 2012, endTime=Sun Nov 25 21:29:20 GMT 2012, lastUpdated=Sun Nov 25 21:29:20 GMT 2012, status=COMPLETED, exitStatus=exitCode=COMPLETED;exitDescription=, job=[JobInstance: id=3, version=0, JobParameters=[{currentTime=1353878960660}], Job=[firstJob]] STEP 11 : DOWNLOAD https://github.com/erenavsarogullari/OTV_SpringBatch_TaskletStep REFERENCES : Spring Batch – Reference Documentation Spring Batch – API Documentation

While lying in bed recovering from an injury a few years ago, I was stumbling around through the myriad of video podcasts I subscribe to and decided to take a look at some of the videos in The George Lucas Educational Foundation Integrated Studies series. That's where I came across this gem featuring Pixar's Randy Nelson who is the Dean of Pixar University. It has had an extremely profound impact on how I think and collaborate. He's giving a short talk entitled Learning and Working in the Collaborative Age at the Apple Education Leadership Summit in April of 2008. Take a look: In his very casual and easy style, Nelson starts off by talking about how PIxar uses improv as a method of collaboration. In that method, two principles have surfaced that have guided Pixar: Accept every offer. You don't know where that offer is going to go. But one thing is for sure: If you don't accept that offer, it's going nowhere! So you have a sure thing on one hand: a dead end. And you have possibility on the other. Make you partner look good. That means that everybody on your team is going to try to make you look good and vice versa. And it's not about judgement or saying "This is pretty good. How can I make it better?". It's about saying "Here's where I'm starting. What can I do with this?". Nelson calls this "plus-ing". I passed this video along to my friend Bert Decker, CEO of Decker Communications, to get his take on this as it is right up his alley. Here's what Bert had to say: "Randy talks about ‘plus-ing’. Sue [Walden ofImprovWorks] calls it “yes, and...” What we mention in our advanced course is two essential rules of improv that you can apply to all communications, (and life for that matter) is: Always positive (yes, and...) Support your partner And of course there’s ‘forward lean’ but that comes even before improv...." Based on those two principles, Pixar looks to find people who are really good at something. And Pixar is really good at being innovative. So, how do you find people who are really good at being innovative? If something has never been done before and it's truly innovative, how do you find the people to do it. According to Nelson "You look for people who have seen failure and figured out how to make something from it. The core skill of innovators is error recovery not failure avoidance. We're looking for resiliency and adaptability." Wow, how many places think like this? I mean really think this way and not just pay the lip service. Not many trust me. It's so great to see a hugely successful organization express this attitude out loud and really mean it. What Pixar has realized is that a great predictor of innovation is mastery of something. It could be mastery of anything. The important thing is the personality that goes along with mastery. It's that sense of "I'm going to get to the top of that mountain" that you can use in your enterprise. It's called depth. Nelson goes on to say that given the fast pace of business these days, there's very little chance that people are going to achieve mastery on the job. You want them to be masters coming in the door. Another predictor of success is breadth. No one-trick ponies. We want to find people with lots of experiences (not necessarily "experience"). People with a breadth of experiences are deeply interested in many things. My favorite quote from Nelson: "We're looking for people who are interested...not interesting." Interested is tough, interesting is easy. Interested is a real skill. If you say "I've got a problem", interested people lean in. They amplify you. They want to know what YOU want to know. The notion of breadth leads to Nelson's third predictor, communication. Another awesome quote, especially for all of you developers and techies out there: "Communication involves translation." If you just emit tech, nobody really hears you. The translation gets pushed to the receiving end of the conversation and gets garbled. Do the translation at the SENDING end so that it doesn't have to be done at the receiving end and the listener can say, "I understand". So, no non-communicative techies! Nelson says that "Communication is not something the emitter can measure." You can't declare yourself as articulate or a good communicator...only your listener can. People who are interested are more likely to view communication as a destination rather than as a source. Nelson postulates that breadth and a broad range of experiences is the thing that fuels that. To me, this notion of communication as a destination not a source is extremely crucial to the success of teams comprised of so many different skillsets and levels of technical expertise. According to Nelson though, the most important predictor of success and innovation is collaboration. But what is collaboration? Real collaboration? It's not cooperation. We've been conditioned to jump to this answer very quickly. We all think "We have to cooperate to get our jobs done. That's collaboration." But, all this really means is we're not getting in each other's way. Nelson says that the things that get done in a cooperative enterprise could, in effect, all be done by one person if we had enough time and resources. He says that there is nothing in a cooperative workplace that job one does that can make job two better. Job one can prevent job two from getting done, but there's nothing job one can do to make job two better. Collaboration is not a synonym for cooperation. So what does collaboration mean if it's not about cooperation? Nelson says that collaboration for Pixar means AMPLIFICATION. It means connecting a group of individuals that are INTERESTED in each other, that bring separate DEPTH to the problem and that bring a BREADTH that gives them interest in the entire solution. And most importantly, it allows them to COMMUNICATE on multiple different levels: verbally, in writing, feeling, acting, pictures. In all of these ways, Nelson says: "They find the most articulate way to get a high fidelity notion across to a broad range of people so they can each pull on the right lever." I absolutely love this definition of collaboration and it's all rooted in a collective vision that everyone understands and can relate to. After listening to Nelson walk through these four points with passion and enthusiasm, it's no wonder why Pixar has been immensely successful in their endeavors. After a little digging and emailing, I found that indeed, Pixar's HR department uses all four of these predictors for the basis of their hires. They don't just look at a candidate's experience or resume. In a 2006 New York Times interview, Nelson said: "The problem with the Hollywood model is that it’s generally the day you wrap production that you realize you’ve finally figured out how to work together," Mr. Nelson said. "We’ve made the leap from an idea-centered business to a people-centered business. Instead of developing ideas, we develop people. Instead of investing in ideas, we invest in people. We’re trying to create a culture of learning, filled with lifelong learners. It’s no trick for talented people to be interesting, but it’s a gift to be interested. We want an organization filled with interested people." The things Nelson describes are intangible, you can't write them down. But when you talk with and work with people who possess these traits, you know who they are right away. And they're the kind of people you want on your team. Give me 10 people like this over 100 people with years of experience and you can do incredible things.

At my new job we’re reigniting an effort to move to continuous delivery for our software releases. We figured that we could learn a thing or two from Facebook, so we reached out to Chuck Rossi, Facebook’s first release engineer and the head of their release engineering team. He generously gave us an hour of his time, offering insights into how Facebook releases software, as well as specific improvements we could make to our existing practice. This post describes several highlights of that conversation. What’s so good about Facebook release engineering? The core capability my company wants to reproduce is Facebook’s ability to release its frontend web UI on demand, invisibly and with high levels of control and quality. In fact Facebook does a traditional-style large weekly release each Tuesday, as well as not-so-traditional two daily pushes on all other weekdays. They are also able to release on demand as needed. This capability is impressive in any context; it’s all the more impressive when you consider Facebook’s incredible scale: Over 1B users worldwide About 700 developers committing against their frontend source code repo Single frontend code binary about 1.5GB in size Pushed out to many thousands of servers (the number is not public) Changes can go from check-in to end users in as quickly as 40 minutes Release process almost entirely invisible to the users Holy cow. While the release engineering problem for my company is considerably smaller than the one confronting Facebook, it’s not by any means small. (Facebook is so massive that user bases orders of magnitude smaller than Facebook can still have nontrivial scale.) We don’t have to contend with the 1B users, 700 developers, 1.5GB binary or many thousands of servers. But we do want to be able to release on demand, quickly, reliably and invisibly to our users. How Facebook pushes twice daily to over 1B users The common thread running through the practices below is that they reject the supposed tradeoff between speed and quality. Releases are going to happen twice a day, and this needs to occur without sacrificing quality. Indeed, the quality requirements are very high. So any approach to quality incompatible with the always-be-pushing requirement is a non-starter. Here are some of the key themes and techniques. Empower your release engineers Chuck mentioned early on that the whole thing rides on having an empowered release engineering team. Ultimately release engineers have to strike a balance between development’s desire to ship software and operations’ desire to keep everything running smoothly. Release engineers therefore need access to the information that tells them whether a given change is a good risk for some upcoming push, as well as the authority to reject changes that aren’t in fact good risks. At the same time, we want release engineers that “get it” when it comes to software development. We don’t want them blocking changes just because they don’t understand them, or just because they can. Facebook’s release engineers are all programmers, so they understand the importance of shipping software, and they know how to look at test plans, stack traces and the code itself should the need arise. Empowerment is part cultural, part process and part tool-related. On the cultural side, Chuck introduces new hires to the release process, and makes it clear that the release engineering team makes the decision. As part of that presentation, he explains how the development, test and review processes generate data about the risk associated with a change. The highly integrated toolset, based largely around Facebook’s open source Phabricator suite, provides visibility into that change risk data. Just to give you an idea of the expectation on the developers, there are a number of factors that determine whether a change will go through: The size of the diff. Bigger = more risky. The quality of the test plan. The amount of back-and-forth that occurred in the code review (see below). The more back-and-forth, the more rejections, the more requests for change—the more risk. The developer’s “push karma”. Developers with a history of pushing garbage through get more scrutiny. They track this, though any given developer’s push karma isn’t public. The day of the week. Mondays are for small, not-risky changes because they don’t want to wreck Tuesday’s bigger weekly release. Wednesdays allow the bigger changes that were blocked for Monday. Thursdays allow normal changes. Changes for Friday can’t be too risky, partly because weekend traffic tends to be heavier than Friday traffic (so they don’t want any nasty weekend surprises), and partly because developers can be harder to reach on weekends. The release engineers evaluate every change against these criteria, and then decide accordingly. They process 30-300 changes per day. Test suite should take no longer than the slowest test When you’re releasing code twice a day, you have to take testing very seriously. Part of this is making sure that developers write tests, and part of this is running the full test suite—including integration and acceptance tests—against every change before pushing it. In some development organizations, one major challenge with doing this is that integration tests are slow, and so running a full regression against every change becomes impractical. Such organizations—especially those that practice a lot of manual regression testing—often handle this by postponing full regression testing until late in the release cycle. This makes regression testing more cost-feasible because it happens only once per release. But if we’re trying to push twice daily, the run-regression-at-the-end-of-the-release-cycle approach doesn’t work. And neither does truncating the test suite. We can’t give up the quality. Facebook’s alternative is simple: apply extreme parallelization such that it’s the slowest integration test that limits the performance of the overall suite. Buy as many machines as are required to make this real. Now we can run the full battery of tests quickly against every single change. No more speed/quality tradeoff. Code review EVERYTHING Chuck was at Google before he joined Facebook, and apparently at both Google and Facebook they review every code change, no matter how small. Whereas some development shops either practice code review only in limited contexts or else not at all, pre-push code reviews are fundamental to Facebook’s development and release process. The process flat out doesn’t work without them. As the session progressed, I came to understand some reasons why. One key reason is that it promotes the right-sizing of changes so they can be developed, tested, understood and cherry-picked appropriately. Since Facebook releases are based on sets of cherry picks, commits need to be smallish and coherent in a way that reviews promote. And (as noted above) the release engineers depend upon the review process to generate data as to any given change’s riskiness so they can decide whether to perform the cherry pick. Another important benefit is that pre-push code reviews can make it feasible to pursue a single monolithic code repo strategy (often favored for frontend applications involving multiple components that must be tested together), because breaking changes are much less likely to make it into the central, upstream repo. Facebook has about 700 developers committing against a single source repository, so they can’t afford to have broken builds. Facebook uses Phabricator (specifically, Differential and Arcanist) for code reviews. Practice canary releases Testing and pre-push reviews are critical, but they aren’t the entire quality strategy. The problem is that testing and reviews don’t (and can’t) catch everything. So there has to be a way to detect and limit the impact of problems that make their way into the production environment. Facebook handles this using “canary releases”. The name comes from the practice of using canaries to test coal mines for the presence of poisonous gases. Facebook starts by pushing to six internal servers that their employees see. If no problems surface, they push to 2% of their overall server fleet and once again watch closely to see how it goes. If that passes, they release to 100% of the fleet. There’s a bunch of instrumentation in place to make sure that no fatal errors, performance issues and other such undesirables occur during the phased releases. Decouple stuff Chuck made a number of suggestions that I consider to fall under the general category “decouple stuff”. Whereas many of the previous suggestions were more about process, the ones below are more architectural in nature. Decouple the user from the web server. Sessions are stateless, so there’s no server affinity. This makes it much easier to push without impacting users (e.g., downtime, forcing them to reauthenticate, etc.). It also spreads the pain of a canary-test-gone-wrong across the entire user population, thus thinning it out. Users who run into a glitch can generally refresh their browser to get another server. Decouple the UI from the service. Facebook’s operational environment is extremely large and dynamic. Because of this, the environment is never homogeneous with respect to which versions of services and UI are running on the servers. Even though pushes are fast, they’re not instantaneous, so there has to be an accommodation for that reality. It becomes very important for engineers to design with backward and forward compatibility in mind. Contracts can evolve over time, but the evolution has to occur in a way that avoids strong assumptions about which exact software versions are operating across the contract. Decouple pushes from feature activation. Facebook uses dark launches and feature flags to decouple binary pushes from the activation of features. The general concept is for the features to exist in latent form in the production environment, with a means to activate and deactivate them at will. Dark launches and feature flags further erode the speed/quality tradeoff. You can release code without activating it, giving you a way to get it out the door without impacting users. And when you do activate it, you have a way to turn it off immediately should a problem arise. These techniques also simplify source code management because you can just manage everything on trunk instead of having a bunch of branches sitting around waiting to be merged. Facebook uses an internally-developed tool called Gatekeeper to manage feature flags. Gatekeeper allows Facebook to turn feature flags on and off, and to do that in a flexibly segmented fashion. Recap and concluding thoughts I mentioned earlier that Facebook rejects the apparent tradeoff between speed and quality. At their core, the practices above amount to ways to maintain quality in the face of rapid fire releases. As the overall release practice and infrastructure matures, opportunities for further speedups and quality enhancements emerge. As you can see, our one hour conversation was packed with a lot of outstanding information. I hope that others might benefit from this material in the way that I know my company will. Thanks Chuck! Additional resources for Facebook release engineering Facebook publishes a great deal of useful information about their release engineering processes. Here are some good resources to learn more, mostly directly from Chuck himself. Push: Tech Talk – May 26, 2011 (video): This is a class that Chuck gives to new developers when they join Facebook. It’s just slightly out of date as Facebook now does two daily pushes instead of one. Outstanding information about release schedule, branching strategy, cultural norms, tools and more. Just under an hour but well worth the watch. Release engineering and push karma: Chuck Rossi: Interview covering some highlights of the Facebook release process and its supporting culture. Ship early and ship twice as often: Chuck explains how Facebook moved from a once-per-day push schedule to a twice-per-day schedule. Release Engineering at Facebook: Secondary source with highlights on the Facebook release process. Hammering Usernames: Facebook explains how they use dark launches to mitigate risk. Girish Patangay keynote Velocity Europe 2012 “Move Fast and Ship Things” (video) – Keynote by Facebook’s Girish Patangay describing some additional elements of the Facebook release process, including its use of a BitTorrent-based system to push a large binary very quickly out to many thousands of servers.

Everyone is thinking why in the world would anyone pick static, when you can be dynamic? Usually the thought process is, "what language am I most proficient in, that can do the job." Totally not a bad way to go about it. Now does this choice affect anything else? Testing? Speed of development? Robustness? Dynamic vs. Static Dynamic languages are languages that don’t necessarily need variables to be declared before they are used. Examples of dynamic languages are Python, Ruby, and PHP. So in dynamic languages the following is possible: num = 10 We have successfully assigned a value to variable without declaring it before hand. Simple enough, try doing this in Java (you can’t). This can *increase* development speed, without having to write boilerplate code. This can somewhat be a double edge sword, since dynamic languages types are checked during runtime, there is no way to tell if there is a bug in code until it is run. I know you can test, but you can’t test for everything. You can’t test for everything. Here is an example albeit trivial. def get_first_problem(problems): for problem in problems: problam = problem + 1 return problam Now if you are raging to some serious dubstep, its easy enough to miss that small typo, you go screw it and do it live, and deploy to production. Python will simply create the new variable and not a single thing will be said. Only you can stop bugs in production! Static languages are languages that variables need to be declared before use and type checking is done at compile time. Examples of static languages include Java, C, and C++. So in static languages the following is enforced static int awesomeNumber; awesomeNumber = 10; Many argue this increases robustness as well as decrease chances of Runtime Errors. Since the compiler will catch those horrible horrible mistakes you made throughout your code. Your methods contracts are tighter, downside to this is crap ton of boilerplate code. Weak and Strong Typing can be often be confused with dynamic and static languages. Weak typed languages can lead to philosophical questions like what does the number 2 added to the word ‘two’ give you? Things like this are possible with a weak typed language. a = 2 b = "2" concatenate(a, b) // Returns "22" add(a, b) // Returns 4 Traditionally languages may place restriction on what transaction may occur for example in a strong typed language adding a string and integer will result in a type error as shown below. >>> a = 10 >>> b = 'ten' >>> a + b Traceback (most recent call last): File "", line 1, in TypeError: unsupported operand type(s) for +: 'int' and 'str' >>> Conclusion Regardless of where you land on this discussion, claiming one is better than the other would lead to flame war, but there are places where each is strong. Dynamic languages are good for fast quick development cycles and prototyping, while static languages are better suited to longer development cycles where trivial bugs could be extremely costly (telecommunication systems, air traffic control). For example if some giant company called Moo Corp. spent millions of dollars on QA and Testing and a bug somehow gets into the field, to fix it would mean another round of testing. When sitting in that chair the choice is clear static languages FTW, its a hard job but someone has to milk the cows. Test, test, and test. Just a little food for thought, for when you are starting your next project. You never know what limitations you maybe placing on yourself and your team. What do you do consider when selecting a programming language for a project?

Hadoop MapReduce is a YARN-based system for parallel processing of large data sets. In this article, learn to quickly start writing the simplest MapReduce job.

With the advent of Continuous Integration and Continuous Delivery, our builds are split into different steps creating the deployment pipeline. Some of these steps can be compiled and run fast tests, run slow tests, run automated acceptance tests, or releasing the application, to cite a few. Most of us are using Jenkins/Hudson to implement Continuous Integration/Delivery, and we manage job orchestration combining some Jenkins plugins like build pipeline, parameterized-build, join or downstream-ext. We have to configure all of them which implies polluting the job configuration through multiple jobs, which , makes the system configuration very complex to maintain. Build Flow enables us to define an upper level flow item to manage job orchestration and link up rules, using a dedicated DSL. Let's see a very simple example: First step is installing the plugin. Go to Jenkins -> Manage Jenkins -> Plugin Manager -> Available and find for CloudBees Build Flowplugin. Then you can go to Jenkins -> New Job and you will see a new kind of job called Build Flow. In this example we are going to name it build-all-yy. And now you only have to program using flow DSL how this job should orchestrate the other jobs. In "Define build flow using flow DSL" input text you can specify the sequence of commands to execute. In current example I have already created two jobs, one executing clean compile goal (yy-compile job name) and the other one executing javadoc goal (yy-javadoc job name). I know that this deployment pipeline is not real in a true environment but for now it is enough. Then we want javadoc job running after project is compiled. To configure this we don't have to create any upstream or downstream actions, simply add next lines at DSL text area: build("yy-compile"); build("yy-javadoc"); Save and execute build-all-yy job and both projects will be built in a sequential way. Now suppose that we add a third job called yy-sonar which runs sonar goal that generates code quality sonar report. In this case it seems obvious that after project is compiled, generation of javadocs and code quality jobs can be run in parallel. So script is changed to: build("yy-compile") parallel ( {build("yy-javadoc")}, {build("yy-sonar")} ) This plugin also supports more operations like retry (similar behaviour of retry-failed-job plugin) or guard-rescue, that it works mostly like a try+finally block. Also you can create parameterized builds, accessing to build execution or printing to Jenkins console. Next example will print build number of yy-compile job execution: b = build("yy-compile") out.println b.build.number And finally you can also have a quick graphical overview of the execution in Status section. It is true that could be improved more, but for now it is acceptable, and can be used without any problem. Build Flow plugin is in its early stages, in fact it is only at version 0.4. But will be a plugin to be considered in future, and I think it is good to know that it exists. Moreover is being developed by CloudBees folks so it is a guarantee of being fully supported by Jenkins. We Keep Learning. Alex. Warning: In order to run parallel tasks with the plugin Anonymous users must have Read Job access (Jenkins -> Manage Jenkins -> Configure System). There is an issue already inserted into Jira to fix this problem.

Once you’ve been working with Jenkins and uberSVN for a while, you may find yourself in a situation where you have several jobs that need to run in a specific order, for example: Job 1 and Job 3 can run simultaneously. BUT Job 2 should only start when Job 1 and Job 3 have finished running. AND Job 4 should only start when Job 2 has finished. How can you implement this complicated setup? This is where Jenkins’ ‘Advanced Project Options’ and build triggers come in handy. In this tutorial, we’ll walk through the different options for scheduling jobs using Jenkins and uberSVN, the free ALM platform for Apache Subversion. Note, this tutorial assumes you have already created a job and configured it to automatically poll your Subversion repository. 1) Open the Jenkins tab of your uberSVN installation and select a job. 2) Click the ‘Configure’ option from the left-hand menu. 3) In the ‘Advanced Project Options’ tab, select the ‘Advanced…’ button 4) This contains two options that are useful for ordering your jobs: Block build when upstream project is building – blocks builds when a dependency is in the queue, or building. Note, these dependencies include both direct and transitive dependencies. Block build when downstream project is building – blocks builds when a child of the project is in the queue, or building. This applies to both direct and transitive children. If this option doesn’t meet your needs, you can explicitly name a project (or projects) that must be built before your job is allowed to run. To set this: 1) Scroll down to the ‘Build triggers’ tab on the configure page. 2) Select the ‘Build after other projects are built’ checkbox. This will bring up a text box where you can list any number of projects. Utilized properly, the build triggers and advanced project options should allow you to organize your jobs into a schedule. Tip, if you need even more control over your build schedule, there are plenty of scheduling plugins available. To add plugins to Jenkins, simply: 1) Open the ‘Manage Jenkins’ screen. 2) Click the ‘Manage Plugins’ link. 3) Open the ‘Available’ tab and select the appropriate plugins from the list.

we wanted to set up a nightly integration build with our projects so that we could run unit and integration tests on the latest version of our applications and their underlying libraries. we have a number of libraries that are shared across multiple projects and we wanted this build to run every night and use the latest versions of those libraries even if our applications had a specific release version defined in their maven pom file. in this way we would be alerted early if someone added a change to one of the dependency libraries that could potentially break an application when the developer upgraded the dependent library in a future version of the application. the chart below illustrates our dependencies between our libraries and our applications. updating versions nightly both the crossdock-shared and messaging-shared libraries depend on the siesta framework library. the crossdock web service and crossdockmessaging applications both depend on the crossdock-shared and messaging-shared libraries. because of the dependency structure, we wanted the siestaframework library built first. the crossdock-shared and messaging-shared libraries could be built in parallel, but we didn’t want the builds for the crossdock web service and crossdockmessaging applications to begin until all the libraries had finished building. we also wanted the nightly build to tag a subversion with the build date as well as upload the artifact to our nexus “nightly build” repository. the resulting artifact would look something like siestaframework-20120720.jar also as i had mentioned, even though the crossdockmessaging app may specify in its pom file it depends on version 5.0.4 of the siestaframework library. for the purposes of the nightly build, we wanted it to use the freshly built siestaframework-nightly-20120720.jar version of the library. the first problem to tackle was getting the current date into the project’s version number. for this i started with the jenkins zentimestamp plugin . with this plugin the format of jenkin’s build_id timestamp can be changed. i used this to specify using the format of yyyymmdd for the timestamp. the next step was to get the timestamp into the version number of the project. i was able to accomplish this by using the maven versions plugin. one thing the versions plugin can do is allow you to dynamically override the version number in the pom file at build time. the code snippet from the siestaframework’s pom file is below. org.codehaus.mojo versions-maven-plugin 1.3.1 at this point the jenkins job can be configured to invoke the “versions;set” goal, passing in the new version string to use. the ${build_id} jenkins variable will have the newly formatted date string. this will produce an artifact with the name siestaframework-nightly-20120720.jar uploading artifacts to a nightly repository since this job needed to upload the artifact to a different repository from our release repository that's defined in our project pom files, the “altdeploymentrepository” property was used to pass in the location of the nightly repository. the deployment portion of the siestaframework job specifies the location of the nightly repository where ${lynden_nightly_repo} is a jenkins variable containing the nightly repo url. tagging subversion finally, the jenkins subversion tagging plugin was used to tag svn if the project was successfully built. the plugin provides a post-build action for the job with the configuration section shown below. dynamically updating dependencies so now that the main project is set up, the dependent projects are set up in a similar way, but need to be configured to use the siestaframework-nightly-20120720 of the dependency rather than whatever version they currently have specified in their pom file. this can be accomplished by changing the pom to use a property for the version number of the dependency. for example, if the snippet below was the original pom file— com.lynden siestaframework 5.0.1 —changing it to the following would allow the siestaframework version to be set dynamically: 5.0.1 com.lynden siestaframework ${siesta.version} this version can then be overriden by the jenkins job. the example below shows the jenkins configuration for the crossdock-shared build. enforcing build order the final step in this process is setting up a structure to enforce the build order of the projects. the dependencies are set up in such a way that siestaframework needs to be built first, and the crossdock-shared and messaging-shared libraries can be run concurrently once siestaframework finishes. the crossdock web service and crossdockmessaging application jobs can be run concurrently, too, but not until after both shared libraries have finished. setting up the crossdock-shared and messaging-shared jobs to be built after the siestaframework finishes is pretty straightforward. in the jenkins job configuration for both the shared libraries, the following build trigger is added: to satisfy the requirement that the apps build only after all libraries have built, i enlisted the help of the join plugin . the join plugin can be used to execute a job once all “downstream” jobs have completed. what does this mean exactly? looking at the diagram below, the crossdock-shared and the messaging-shared jobs are “downstream” from the siestaframework job. once both of these jobs complete, a join trigger can be used to start other jobs. in this case, rather than having the join trigger kick off other app jobs directly, i created a dummy join job. in this way, as we add more application builds, we don’t need to keep modifying the siestaframework job with the new application job we just added. to illustrate the configuration, siestaframework has a new post-build action (below): join-build is a jenkins job i configured that does not do anything when executed. then our crossdock web service and crossdockmessaging applications define their builds to trigger as soon as join-build has completed. in this way we are able to run builds each night that will update to the latest version of our dependencies as well as tag svn and archive the binaries to nexus. i’d love to hear feedback from anyone who is handling nightly builds via jenkins, and how they have handled the configuration and build issues.

Here are the most important subjects for software engineering, with brief explanations: 1.Object oriented analysis & design: For better maintainability, reusability and faster development, the most well accepted approach, shortly OOAD and its SOLID principals are very important for software engineering. 2.Software quality factors: Software engineering depends on some very important quality factors. Understanding and applying them is crucial. 3.Data structures & algorithms: Basic data structures like array, list, stack, tree, map, set etc. and useful algorithms are vital for software development. Their logical structure should be known. 4. Big-O notation: Big-O notation indicates the performance of an algorithm/code section. Understanding it is very important for comparing performances. 5.UML notation: UML is the universal and complete language for software design & analysis. If there is lack of UML in a development process, it feels there is no engineering. 6.Software processes and metrics: Software enginnering is not a random process. It requires a high level of systematic and some numbers to monitor those techniques. So, processes and metrics are essential. 7.Design patterns: Design patterns are standard and most effective solutions for specific problems. If you don't want to reinvent the wheel, you should learn them. 8.Operating systems basics: Learning OS basics is very important because all applications runs on it. By learning it, we can have better vision, viewpoints and performance for our applications. 9.Computer organization basics: All applications including OS requires a hardware for physical interaction. So, learning computer organization basics is vital again for better vision, viewpoints and performance. 10.Network basics: Network is related with computer organization, OS and the whole information transfer process. In any case we will face it while software development. So, it is important to learn network basics. 11.Requirement analysis: Requirement analysis is the starting point and one of the most important parts of software engineering. Performing it correctly and practically needs experience but it is very essential. 12.Software testing: Testing is another important part of software engineering. Unit testing, its best practices and techniques like black box, white box, mocking, TDD, integration testing etc. are subjects which must be known. 13.Dependency management: Library (JAR, DLL etc.) management, and widely known tools (Maven, Ant, Ivy etc.) are essential for large projects. Otherwise, antipatterns like Jar Hell are inevitable. 14.Continuous integration: Continuous integration brings easiness and automaticity for testing large modules, components and also performs auto-versioning. Its aim and tools (like Hudson etc.) should be known. 15.ORM (Object relational mapping): ORM and its widely known implementation Hibernate framework is an important technique for mapping objects into database tables. It reduces code length and maintenance time. 16.DI (Dependency Injection): DI or IoC (Inversion of Control) and its widely known implementation Spring framework makes life easy for object creation and lifetime management on big enterprise applications. 17.Version controlling systems: VCS tools (SVN, TFS, CVS etc.) are very important by saving so much time for collaborative works and versioning. Their logical viewpoint and standard cammands should be known. 18.Internationalization (i18n): i18n by extracting strings into external files is the best way of supporting multiple languages in our applications. Its practices on different IDEs and technologies must be known. 19.Architectural patterns: Understanding architectural design patterns (like MVC, MVP, MVVM etc.) is essential for producing a maintainable, clean, extendable and testable source code. 20.Writing clean code: Working code is not enough, it must be readable and maintainable also. So, code formatting and readable code development techniques are needed to be known and applied.

Recently I needed the ability to use Reportlab’s flowables, but place them in fixed locations. Some of you are probably wondering why I would want to do that. The nice thing about flowables, like the Paragraph, is that they’re easily styled. If I could bold something or center something AND put it in a fixed location, then that would rock! It took a lot of Googling and trial and error, but I finally got a decent template put together that I could use for mailings. In this article, I’m going to show you how to do this too. Getting Started You’ll need to make sure you have Reportlab or you’ll end up with a whole lot of nothing. You can go here to grab it. While you wait for it to download you can continue reading this article or go do something else productive. Are you ready now? Then let’s get this show on the road! Now we just need to come up with an example. Fortunately I was working on something at my job that I’ve been able to dummy up into the following silly and incomplete form letter. Study the code closely because you never know when there will be a test from reportlab.lib.pagesizes import letter from reportlab.lib.styles import getSampleStyleSheet from reportlab.lib.units import mm, inch from reportlab.pdfgen import canvas from reportlab.platypus import Image, Paragraph, Table ######################################################################## class LetterMaker(object): """""" #---------------------------------------------------------------------- def __init__(self, pdf_file, org, seconds): self.c = canvas.Canvas(pdf_file, pagesize=letter) self.styles = getSampleStyleSheet() self.width, self.height = letter self.organization = org self.seconds = seconds #---------------------------------------------------------------------- def createDocument(self): """""" voffset = 65 # create return address address = """ Jack Spratt 222 Ioway Blvd, Suite 100 Galls, TX 75081-4016 """ p = Paragraph(address, self.styles["Normal"]) # add a logo and size it logo = Image("snakehead.jpg") logo.drawHeight = 2*inch logo.drawWidth = 2*inch ## logo.wrapOn(self.c, self.width, self.height) ## logo.drawOn(self.c, *self.coord(140, 60, mm)) ## data = [[p, logo]] table = Table(data, colWidths=4*inch) table.setStyle([("VALIGN", (0,0), (0,0), "TOP")]) table.wrapOn(self.c, self.width, self.height) table.drawOn(self.c, *self.coord(18, 60, mm)) # insert body of letter ptext = "Dear Sir or Madam:" self.createParagraph(ptext, 20, voffset+35) ptext = """ The document you are holding is a set of requirements for your next mission, should you choose to accept it. In any event, this document will self-destruct %s seconds after you read it. Yes, %s can tell when you're done...usually. """ % (self.seconds, self.organization) p = Paragraph(ptext, self.styles["Normal"]) p.wrapOn(self.c, self.width-70, self.height) p.drawOn(self.c, *self.coord(20, voffset+48, mm)) #---------------------------------------------------------------------- def coord(self, x, y, unit=1): """ # http://stackoverflow.com/questions/4726011/wrap-text-in-a-table-reportlab Helper class to help position flowables in Canvas objects """ x, y = x * unit, self.height - y * unit return x, y #---------------------------------------------------------------------- def createParagraph(self, ptext, x, y, style=None): """""" if not style: style = self.styles["Normal"] p = Paragraph(ptext, style=style) p.wrapOn(self.c, self.width, self.height) p.drawOn(self.c, *self.coord(x, y, mm)) #---------------------------------------------------------------------- def savePDF(self): """""" self.c.save() #---------------------------------------------------------------------- if __name__ == "__main__": doc = LetterMaker("example.pdf", "The MVP", 10) doc.createDocument() doc.savePDF() Now you’ve seen the code, so we’ll spend a little time going over how it works. First off we create a Canvas object that we can use without our LetterMaker class. We also create a styles dict and set up a few other class variables. In the createDocument method, we create a Paragraph (an address) using some HTML-like tags to control the font and line breaking behavior. Then we create a logo and size it before putting both items into a Reportlab Table object. You’ll note that I’ve left in a couple commented out lines that show how to place the logo without the table. We use the coord method to help position the flowable. I found it on StackOverflow and thought it was pretty handy. The body of the letter uses a little string substitution and puts the result into another Paragraph. We also use a stored offset to help us position things. I find that storing a couple of offsets for certain portions of the code is very helpful. If you use them carefully then you can just change a couple of offsets to move the content around on the document rather than having to edit the position of each element. If you need to draw lines or shapes, you can do them in the usual way with your canvas object. Wrapping Up I hope this code will help you in your PDF creation endeavors. I have to admit that I’m posting it on here as much for my own future benefit as for your own. I’m a little sad I had to strip out so much from it, but my organization wouldn’t like it very much if I posted the original. Regardless, you now have the tools to create some pretty fancy PDF documents with Python. Now you just have to get out there and do it!

See original post at https://muhammadkhojaye.blogspot.com/2012/04/how-to-run-amazon-elastic-mapreduce-job.html Introduction Amazon EMR is a web service which can be used to easily and efficiently process enormous amounts of data. It uses a hosted Hadoop framework running on the web-scale infrastructure of Amazon EC2 and Amazon S3. Amazon EMR removes most of the cumbersome details of Hadoop while taking care of provisioning of Hadoop, running the job flow, terminating the job flow, moving the data between Amazon EC2 and Amazon S3, and optimizing Hadoop. In this tutorial, we will use a developed WordCount Java example using Hadoop and thereafter, we execute our program on Amazon Elastic MapReduce. Prerequisites You must have valid AWS account credentials. You should also have a general familiarity with using the Eclipse IDE before you begin. The reader can also use any other IDE of their choice. Step 1 – Develop MapReduce WordCount Java Program In this section, we are first going to develop a WordCount application. A WordCount program will determine how many times different words appear in a set of files. In Eclipse (or whatever the IDE you are using), Create simple Java Project with the name "WordCount". Create a java class name Map and override the map method as follow, public class Map extends Mapper { private final static IntWritable one = new IntWritable(1); private Text word = new Text(); @Override public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); StringTokenizer tokenizer = new StringTokenizer(line); while (tokenizer.hasMoreTokens()) { word.set(tokenizer.nextToken()); context.write(word, one); } } } Create a java class named Reduce and override the reduce method as shown below, public class Reduce extends Reducer { @Override protected void reduce(Text key, java.lang.Iterable values, org.apache.hadoop.mapreduce.Reducer.Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable value : values) { sum += value.get(); } context.write(key, new IntWritable(sum)); } } Create a java class named WordCount and defined the main method as below, public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); Job job = new Job(conf, "wordcount"); job.setJarByClass(WordCount.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); job.setMapperClass(Map.class); job.setReducerClass(Reduce.class); job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(TextOutputFormat.class); FileInputFormat.addInputPath(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); job.waitForCompletion(true); } Export the WordCount program in a jar using eclipse and save it to some location on disk. Make sure that you have provided the Main Class (WordCount.jar) during extraction ofu8u the jar file as shown below. Our jar is ready!!! Step 2 – Upload the WordCount JAR and Input Files to Amazon S3 Now we are going to upload the WordCount jar to Amazon S3. First, go to the following URL: https://console.aws.amazon.com/s3/home Next, click “Create Bucket”, give your bucket a name, and click the “Create” button. Select your new S3 bucket in the left-hand pane. Upload the WordCount JAR and sample input file for counting the words. Step 3 – Running an Elastic MapReduce job Now that the JAR is uploaded into S3, all we need to do is to create a new Job flow. let's execute the steps below. (I encourage readers to check out the following link for details regarding each step, How to Create a Job Flow Using a Custom JAR ) Sign in to the AWS Management Console and open the Amazon Elastic MapReduce console at https://console.aws.amazon.com/elasticmapreduce/ Click Create New Job Flow. In the DEFINE JOB FLOW page, enter the following details, a) Job Flow Name = WordCountJob b) Select Run your own applications) Select Custom JAR in the drop-down list) Click Continue In the SPECIFY PARAMETERS page, enter values in the boxes using the following table as a guide, and then click Continue.JAR Location = bucketName/jarFileLocationJAR Arguments =s3n://bucketName/inputFileLocations3n://bucketName/outputpath Please note that the output path must be unique each time we execute the job. The Hadoop always create a folder with the same name specified here. After executing the job, just wait and monitor your job that runs through the Hadoop flow. You can also look for errors by using the Debug button. The job should be complete within 10 to 15 minutes (can also depend on the size of the input). After completing the job, You can view results in the S3 Browser panel. You can also download the files from S3 and can analyze the outcome of the job. Amazon Elastic MapReduce Resources Amazon Elastic MapReduce Documentation,http://aws.amazon.com/documentation/elasticmapreduce/ Amazon Elastic MapReduce Getting Started Guide,http://docs.amazonwebservices.com/ElasticMapReduce/latest/GettingStartedGuide/ Amazon Elastic MapReduce Developer Guide,http://docs.amazonwebservices.com/ElasticMapReduce/latest/DeveloperGuide/ Apache Hadoop,http://hadoop.apache.org/ See more at https://muhammadkhojaye.blogspot.com/2012/04/how-to-run-amazon-elastic-mapreduce-job.html

How to create a real-time face detection system using HTML5, JavaScript, and OpenCV, leveraging WebRTC for webcam access and WebSockets for client-server communication.

Introduction NCron is a .Net scheduling framework, it is a .Net version of Cron - the time based job scheduler found on unix like operating systems or Cron4j - scheduling library for Java. Ncron is light weight and easy to use, with little learning curve. It comes with some cool advantages, being that you can use it in C#, Vb.net or any other .Net programming language. It takes your mind off the details of scheduling and you can focus on how to implement the business logic of your application or the job to be scheduled. Details such as threading and timers have been taken care of. Ncron Library You can point your browser to http://code.google.com/p/ncron/downloads/detail?name=ncron-2.1.zip to download the ncron library. You need to add reference to the Ncron library in your project so as to be able to access the classes and functionalities of the Ncron scheduling framework. Scheduling a Job When creating a job to be scheduled using NCron, the job is wrapped up in a class which must extend the class NCron.CronJob and override a void method Execute public class MyJob : NCron.CronJob { public override void Execute() { System.IO.File.Copy(@"c:\\output.out", @"f:\\output.out"); } } The job to be scheduled will be placed in the Execute method. The next thing to do is to give NCron control over the job execution, by calling the static method Bootstrap.Init() at the entry point of your application, for example this can be put in the Main method. You should have a static setup method, which I called JobSetup method that will be passed into the Bootstrap.Init() method. using System; using System.Collections.Generic; using System.Linq; using System.Text; using NCron.Fluent.Crontab; using NCron.Fluent.Generics; using NCron.Service; namespace NcronExample { public class Program { private static void Main(string[] args) { Bootstrap.Init(args, JobSetup); } private static void JobSetup(SchedulingService schedulingService) { schedulingService.At("* * * * *").Run(); } } } The line of code inside the JobSetup method is to specify how the Job is going to be run, and the parameter in the schedulingService.At() method is known as crontab expression which I will discuss shortly. The SchedulingService class has a number of methods of interest. service.Daily().Run(); //runs the scheduled job once every day service.Hourly().Run(); //runs the scheduled job once every hour service.Weekly().Run(); //runs the scheduled job once every week Crontab Expression A crontab expression is a string comprising of 5 characters, which are seperated by space. This crontab expression when parsed produces occurrences of time based on a given schedule expressed in the crontab format. NCron parses crontab expression through the use of NCrontab(Crontab for .Net) an open source library for parsing crontab expressions. A regular crontab expression is of the form * * * * * where the first * is for minute which can be from 0-59. The second * is for hour which can also be from 0-23. The third * is for day of the month from 1-31. The fourth * is for month from 1-12. The last * is for day of week from 0-6 where 0 represents Sunday. The asterisk or wildcard character if left in the expression indicates all valid or legal values for that column. If yIf you want the scheduled job to run every minute, the expresion will be in the form below. * * * * * The The expression below causes the scheduler to run the job at the fifth minute of every ninth hour everyday. 5 9 * * * To run a job every tenth minute of every hour from Monday to Friday only, the expression will be in the form below. 10 * * * 1,2,3,4,5 You can read more on crontab expressions at http://code.google.com/p/ncrontab/wiki/CrontabExamples Deploying the Scheduled Job After the application has been built and compiled, you can deploy the scheduled job as a service by opening command prompt and change directory to where the executable of the application is and then run the command. ncronexample install To install the scheduled job as a service, and that is it !!!

Sometimes Quartz is not capable of running your job at the time when you desired. There are three reasons for that: all worker threads were busy running other jobs (probably with higher priority) the scheduler itself was down the job was scheduled with start time in the past (probably a coding error) You can increase the number of worker threads by simply customizing the org.quartz.threadPool.threadCount in quartz.properties (default is 10). But you cannot really do anything when the whole application/server/scheduler was down. The situation when Quartz was incapable of firing given trigger is called misfire. Do you know what Quartz is doing when it happens? Turns out there are various strategies (called misfire instructions) Quartz can take and also there are some defaults if you haven't thought about it. But in order to make your application robust and predictable (especially under heavy load or maintenance) you should really make sure your triggers and jobs are configured conciously. There are different configuration options (available misfire instructions) depending on the trigger chosen. Also Quartz behaves differently depending on trigger setup (so called smart policy). Although the misfire instructions are described in the documentation, I found it hard to understand what do they really mean. So I created this small summary article. Before I dive into the details, there is yet another configuration option that should be described. It is org.quartz.jobStore.misfireThreshold (in milliseconds), defaulting to 60000 (a minute). It defines how late the trigger should be to be considered misfired. With default setup if trigger was suppose to be fired 30 seconds ago, Quartz will happily just run it. Such delay is not considered misfiring. However if the trigger is discovered 61 seconds after the scheduled time - the special misfire handler thread takes care of it, obeying the misfire instruction. For test purposes we will set this parameter to 1000 (1 second) so that we can test misfiring quickly. Simple trigger without repeating In our first example we will see how misfiring is handled by simple triggers scheduled to run only once: val trigger = newTrigger(). startAt(DateUtils.addSeconds(new Date(), -10)). build() The same trigger but with explicitly set misfire instruction handler: val trigger = newTrigger(). startAt(DateUtils.addSeconds(new Date(), -10)). withSchedule( simpleSchedule(). withMisfireHandlingInstructionFireNow() //MISFIRE_INSTRUCTION_FIRE_NOW ). build() For the purpose of testing I am simply scheduling the trigger to run 10 seconds ago (so it is 10 seconds late by the time it is created!) In real world you would normally never schedule triggers like that. Instead imagine the trigger was set correctly but by the time it was scheduled the scheduler was down or didn't have any free worker threads. Nevertheless, how will Quartz handle this extraordinary situation? In the first code snippet above no misfire handling instruction is set (so called smart policy is used in that case). The second code snippet explicitly defines what kind of behaviour do we expect when misfiring occurs. See the table: Instruction Meaning smart policy - default See: withMisfireHandlingInstructionFireNow withMisfireHandlingInstructionFireNow MISFIRE_INSTRUCTION_FIRE_NOW The job is executed immediately after the scheduler discovers misfire situation. This is the smart policy. Example scenario: you have scheduled some system clean up at 2 AM. Unfortunately the application was down due to maintenance by that time and brought back on 3 AM. So the trigger misfired and the scheduler tries to save the situation by running it as soon as it can - at 3 AM. withMisfireHandlingInstructionIgnoreMisfires MISFIRE_INSTRUCTION_IGNORE_MISFIRE_POLICY QTZ-283 See: withMisfireHandlingInstructionFireNow withMisfireHandlingInstructionNextWithExistingCount MISFIRE_INSTRUCTION_RESCHEDULE_NEXT_WITH_EXISTING_COUNT See: withMisfireHandlingInstructionNextWithRemainingCount withMisfireHandlingInstructionNextWithRemainingCount MISFIRE_INSTRUCTION_RESCHEDULE_NEXT_WITH_REMAINING_COUNT Does nothing, misfired execution is ignored and there is no next execution. Use this instruction when you want to completely discard the misfired execution. Example scenario: the trigger was suppose to start recording of a program in TV. There is no point of starting recording when the trigger misfired and is already 2 hours late. withMisfireHandlingInstructionNowWithExistingCount MISFIRE_INSTRUCTION_RESCHEDULE_NOW_WITH_EXISTING_REPEAT_COUNT See: withMisfireHandlingInstructionFireNow withMisfireHandlingInstructionNowWithRemainingCount MISFIRE_INSTRUCTION_RESCHEDULE_NOW_WITH_REMAINING_REPEAT_COUNT See: withMisfireHandlingInstructionFireNow Simple trigger repeating fixed number of times This scenario is much more complicated. Imagine we have scheduled some job to repeat fixed number of times: val trigger = newTrigger(). startAt(dateOf(9, 0, 0)). withSchedule( simpleSchedule(). withRepeatCount(7). withIntervalInHours(1). WithMisfireHandlingInstructionFireNow() //or other ). build() In this example the trigger is suppose to fire 8 times (first execution + 7 repetitions) every hour, beginning at 9 AM today (startAt(dateOf(9, 0, 0)). Thus the last execution should occur at 4 PM. However assume that due to some reason the scheduler was not capable of running jobs at 9 and 10 AM and it discovered that fact at 10:15 AM, i.e. 2 firings misfired. How will the scheduler behave in this situation? Instruction Meaning smart policy - default See: withMisfireHandlingInstructionNowWithExistingCount withMisfireHandlingInstructionFireNow MISFIRE_INSTRUCTION_FIRE_NOW See: withMisfireHandlingInstructionNowWithRemainingCount withMisfireHandlingInstructionIgnoreMisfires MISFIRE_INSTRUCTION_IGNORE_MISFIRE_POLICYQTZ-283 Fires all triggers that were missed as soon as possible and then goes back to ordinary schedule. Example scenario: With this strategy in our example the scheduler will fire jobs scheduled at 9 and 10 AM immediately. Then it will wait to 11 AM and go back to ordinary schedule. Note: When handling misfires it is equally important to realize that the actual job execution time might be way after the scheduled time. This means you cannot simply rely on current system date, but you need to use JobExecutionContext .getScheduledFireTime(): def execute(context: JobExecutionContext) { val date = context.getScheduledFireTime //... } withMisfireHandlingInstructionNextWithExistingCount MISFIRE_INSTRUCTION_RESCHEDULE_NEXT_WITH_EXISTING_COUNT The scheduler won't do anything immediately. Instead it will wait for next scheduled time and run all triggers with scheduled intervals. See also: withMisfireHandlingInstructionNextWithRemainingCount Example scenario: at 10:15 the scheduler discovers 2 misfired executions. It waits until next scheduled time (11 AM) and fires all 8 scheduled executions every hour, stopping at 6 PM (the trigger should have stopped at 4 PM). withMisfireHandlingInstructionNextWithRemainingCount MISFIRE_INSTRUCTION_RESCHEDULE_NEXT_WITH_REMAINING_COUNT The scheduler discards misfired executions and waits for the next scheduled time. The total number of trigger executions will be less then configured. Example scenario: at 10:15 two misfired executions are discarded. The scheduler waits for next scheduled time (11 AM) and fires remaining triggers up to 4 PM. Effectively it behaves as if misfire never occurred. withMisfireHandlingInstructionNowWithExistingCount MISFIRE_INSTRUCTION_RESCHEDULE_NOW_WITH_EXISTING_REPEAT_COUNT First misfired trigger is executed immediately. Then the scheduler waits desired interval and executes all remaining triggers. Effectively the first fire time of the misfired trigger is moved to current time with no other changes. Example scenario: at 10:15 the scheduler runs the first misfired execution. Then it waits 1 hour and fires the second one at 11:15 AM. All 8 executions are performed, the last one at 5:15 PM withMisfireHandlingInstructionNowWithRemainingCount MISFIRE_INSTRUCTION_RESCHEDULE_NOW_WITH_REMAINING_REPEAT_COUNT First misfired execution runs immediately. Remaining misfired executions are discarded. Triggers that were not misfired are executed with desired interval. Example scenario: at 10:15 the scheduler runs the first misfired execution (from 9 AM). It discards remaining misfired executions (the one from 10 AM) and waits 1 hour to execute six more triggers: 11:15, 12:15, … 4:15 PM Simple trigger repeating infinitely In this scenario trigger repeats infinite number of times at a given interval: val trigger = newTrigger(). startAt(dateOf(9, 0, 0)). withSchedule( simpleSchedule(). withRepeatCount(SimpleTrigger.REPEAT_INDEFINITELY). withIntervalInHours(1). WithMisfireHandlingInstructionFireNow() //or other ). build() Once again trigger should fire on every hour, beginning at 9 AM today (startAt(dateOf(9, 0, 0)). However the scheduler was not capable of running jobs at 9 and 10 AM and it discovered that fact at 10:15 AM, i.e. 2 firings misfired. This is a more general situation compared to simple trigger running fixed number of times. Instruction Meaning smart policy - default See: withMisfireHandlingInstructionNextWithRemainingCount withMisfireHandlingInstructionFireNow MISFIRE_INSTRUCTION_FIRE_NOW See: withMisfireHandlingInstructionNowWithRemainingCount withMisfireHandlingInstructionIgnoreMisfires MISFIRE_INSTRUCTION_IGNORE_MISFIRE_POLICYQTZ-283 The scheduler will immediately run all misfired triggers, then continue on schedule. Example scenario: the triggers scheduled at 9 and 10 AM are executed immediately. Future invocations (next scheduled at 11 AM) are executed according to the plan. withMisfireHandlingInstructionNextWithExistingCount MISFIRE_INSTRUCTION_RESCHEDULE_NEXT_WITH_EXISTING_COUNT See: withMisfireHandlingInstructionNextWithRemainingCount withMisfireHandlingInstructionNextWithRemainingCount MISFIRE_INSTRUCTION_RESCHEDULE_NEXT_WITH_REMAINING_COUNT Does nothing, misfired executions are discarded. Then the scheduler waits for next scheduled interval and goes back to schedule. Example scenario: Misfired execution at 9 and 10 AM are discarded. The first execution occurs at 11 AM. withMisfireHandlingInstructionNowWithExistingCount MISFIRE_INSTRUCTION_RESCHEDULE_NOW_WITH_EXISTING_REPEAT_COUNT See: withMisfireHandlingInstructionNowWithRemainingCount withMisfireHandlingInstructionNowWithRemainingCount MISFIRE_INSTRUCTION_RESCHEDULE_NOW_WITH_REMAINING_REPEAT_COUNT The first misfired execution is run immediately, remaining are discarded. Next execution happens after desired interval. Effectively the first execution time is moved to current time. Example scenario: the scheduler fires misfired trigger immediately at 10:15 AM. Then waits an hour and runs the second one at 11:15 AM and continues with 1 hour interval. CRON triggers CRON triggers are the most popular ones amongst Quartz users. However there are also two other available triggers: DailyTimeIntervalTrigger (e.g. fire every 25 minutes) and CalendarIntervalTrigger (e.g. fire every 5 months). They support triggering policies not possible in both CRON and simple triggers. However they understand the same misfire handling instructions as CRON trigger. val trigger = newTrigger(). withSchedule( cronSchedule("0 0 9-17 ? * MON-FRI"). withMisfireHandlingInstructionFireAndProceed() //or other ). build() In this example the trigger should fire every hour between 9 AM and 5 PM, from Monday to Friday. But once again first two invocations were missed (so the trigger misfired) and this situation was discovered at 10:15 AM. Note that available misfire instructions are different compared to simple triggers: Instruction Meaning smart policy - default See: withMisfireHandlingInstructionFireAndProceed withMisfireHandlingInstructionIgnoreMisfires MISFIRE_INSTRUCTION_IGNORE_MISFIRE_POLICYQTZ-283 All misfired executions are immediately executed, then the trigger runs back on schedule. Example scenario: the executions scheduled at 9 and 10 AM are executed immediately. The next scheduled execution (at 11 AM) runs on time. withMisfireHandlingInstructionFireAndProceed MISFIRE_INSTRUCTION_FIRE_ONCE_NOW Immediately executes first misfired execution and discards other (i.e. all misfired executions are merged together). Then back to schedule. No matter how many trigger executions were missed, only single immediate execution is performed. Example scenario: the executions scheduled at 9 and 10 AM are merged and executed only once (in other words: the execution scheduled at 10 AM is discarded). The next scheduled execution (at 11 AM) runs on time. withMisfireHandlingInstructionDoNothing MISFIRE_INSTRUCTION_DO_NOTHING All misfired executions are discarded, the scheduler simply waits for next scheduled time. Example scenario: the executions scheduled at 9 and 10 AM are discarded, so basically nothing happens. The next scheduled execution (at 11 AM) runs on time. QTZ-283Note: QTZ-283: MISFIRE_INSTRUCTION_IGNORE_MISFIRE_POLICY not working with JDBCJobStore - apparently there is a bug when JDBCJobStore is used, keep an eye on that issue. As you can see various triggers behave differently based on the actual setup. Moreover, even though the so called smart policy is provided, often the decision is based on business requirements. Essentially there are three major strategies: ignore, run immediately and continue and discard and wait for next. They all have different use-cases: Use ignore policies when you want to make sure all scheduled executions were triggered, even if it means multiple misfired triggers will fire. Think about a job that generates report every hour based on orders placed during that last hour. If the server was down for 8 hours, you still want to have that reports generated, as soon as you can. In this case the ignore policies will simply run all triggers scheduled during that 8 hour as fast as scheduler can. They will be several hours late, but will eventually be executed. Use now* policies when there are jobs executing periodically and upon misfire situation they should run as soon as possible, but only once. Think of a job that cleans /tmp directory every minute. If the scheduler was busy for 20 minutes and finally can run this job, you don't want to run in 20 times! One is enough, but make sure it runs as fast it can. Then back to your normal one-minute intervals. Finally next* policies are good when you want to make sure your job runs at particular points in time. For example you need to fetch stock prices quarter past every hour. They change rapidly so if your job misfired and it is already 20 minutes past full hour, don't bother. You missed the correct time by 5 minutes and now you don't really care. It is better to have a gap rather than an inaccurate value. In this case Quartz will skip all misfired executions and simply wait for the next one.

I am starting a little series about Quartz scheduler internals, tips and tricks, this is chapter 0 - how to configure persistent job store.

We’ve been trying to grow our team for a few months now and the title we’re hiring for is Devops Engineer. One of the candidates our recruiters reached out to, let’s call him John, came back to us with a bunch of questions including: How do you feel about hiring someone with a devops title? It’s a very legittimate question, Devops is a cultural and professional movement, so how could it be a job title? What I argued in my reply to this fella is that Devops isn’t the job title, Devops Engineer is, and in this sense Devops is just a qualifier and I strongly believe a very useful one. I really sympathise with those that are fighting hard to keep Devops real and avoid the same faith that some refer to as the sad commercialisation of Agile. My campaign to make of devops a job title isn’t a campaign to come up with a set of bullet points that define Devops as a job so that I can put it on a resume or build it into a product. My argument here is that the guy I’m trying to hire, John, I want him to be a certain kind of guy and the best way I have to describe what I want is Devops Engineer. I’m looking for an operations guy , but I want him to be open to developers, consider engineering and the company as a whole, be focused on delivering value and not rathole into fights about technology or claim root access only on principle. I want that guy to have great communication skills and the interest to explore what’s besides his infrastructure, to be wanting to borrow as much good he can find in other disciplines across the organisation. And then of course there is the practical part, the desire to automate and escape a boring manual routine, the familiarity with cloud that willing or not has powered the movement, and even more specific things like configuration management. You may argue that this is just a good engineer or what systems engineers are becoming, in other words nothing new under the sun. And you may be right, but job titles are in many ways just another way to communicate, to broadcast an intent and a need. So you know what I told John about hiring Devops Engineers? That I felt pretty damn proud about it. The true ones, not the ones slapping it on their CV to get a job, are fantastic engineers and I can’t but encourage them to start to respond to that qualifier. Likewise the companies and individuals seeking them out are likely the ones building great groups those people will want to be members of. Yes, the moment it becomes a keyword recruiters start to match against we’re likely to see a spur of fakes trying to land a job, but that’s nothing new under the sun. Signed, a Devops manager Source: http://www.spikelab.org/devops-job-title/

If you need to schedule jobs in Java, it is fairly common in the industry to use Quartz directly or via Spring integration, but you might want to think twice.