Notebook for
The Definitive Guide to Spring Batch
Michael T. Minella
Citation (APA): Minella, M. T. (2019). The Definitive Guide to Spring Batch [Kindle Android version]. Retrieved from Amazon.com
1. Batch and Spring
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why do we need batch processing at all?
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You don’t always have all the required information immediately. Batch processing allows you to collect information required for a given process before starting the required processing.
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The most common use case for Spring Batch is probably ETL processing or extract, transform, load.
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Another use case is data migration.
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A third common use case for Spring Batch is any process that requires parallel processing.
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Spring Batch include data validation, formatting of output, the ability to implement complex business rules in a reusable way, and the ability to handle large data sets.
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Note A common misconception is that Spring Batch is or has a scheduler. It doesn’t. There is no way within the framework to schedule a job to run at a given time or based on a given event. There are a number of ways to launch a job, from a simple cron script to Quartz or even an enterprise scheduler like Control-M, but none within the framework itself.
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A job is a process that executes from start to finish without interruption or interaction. A job can consist of a number of steps.
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Spring Cloud Task is a project under the Spring Cloud umbrella that provides facilities for the execution of finite tasks in a cloud environment.
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Spring Cloud Data Flow project is a tool for orchestrating microservices on a cloud platform (CloudFoundry, Kubernetes, or Local). Developing your batch applications as microservices will allow you to deploy them in a dynamic way using Spring Cloud Data Flow.
2. Spring Batch 101
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a batch job is a collection of states and transitions from one to the next. In essence, a Spring Batch job is nothing more than a state machine.
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Using the use case of the nightly processing of a user’s bank account as an example,
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When you look deeper at an individual step, you see a self-contained unit of work that is the main building block of a job.
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There are two main types of steps: a tasklet step and a chunk based step .
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(each call to the execute method gets its own transaction).
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It’s commonly used for things like initialization, running a stored procedure, sending notifications, and so on.
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Note that I stated a step has up to three parts. A step isn’t required to have an ItemProcessor. It is okay to have a step that consists of just an ItemReader and an ItemWriter (common in data-migration jobs, for example).
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One of the advantages of the way Spring has structured a job is that it decouples each step into its own independent processor. Each step is responsible for obtaining its own data, applying the required business logic to it, and then writing the data to the appropriate location.
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JobRepository
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maintaining the state of a job as well as various processing metrics (start time, end time, status, number of reads/writes, etc.). Typically backed by a relational database,
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As each Step is executed, the JobRepository is updated with the current state.
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A JobInstance is a logical execution of a Spring Batch job.
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Figure 2-3. Relationship of JobInstance, JobExecution, and StepExecution
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A JobExecution is a physical execution of a Spring Batch job. Every time you launch a Job, you’ll get a new JobExecution. You may not get a new JobInstance. The obvious example for this is restarting a failed job.
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When a job is run the first time, you get a new JobInstance and a JobExecution. If that execution fails, on the restart, you won’t get a new JobInstance since it’s still the same logical run (executed with the same identifying parameters). However, you will get a new JobExecution to track the second physical run. So a JobInstance can have multiple JobExecutions within it.
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StepExecution is the physical execution of a Step. There is no concept of a StepInstance in Spring Batch.
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A JobExecution can (and usually does) have multiple StepExecution instances associated with it.
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There are five different ways to parallelize your work: dividing work via multithreaded steps, parallel execution of full steps, asynchronous ItemProcessor/ItemWriter configurations, remote chunking, and partitioning.
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each chunk being wrapped within its own transaction.
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If you have 10,000 records, and the commit count is set at 50 records, your job will process records 1 to 50 and then commit, process 51 to 100 and commit, and so on, until all 10,000 records have been processed.
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the input is then sent via a form of durable communication (a message broker like RabbitMQ or ActiveMQ, for example) to a remote worker ItemProcessor
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This approach is good for scenarios where the cost of I/O is small compared to the actual processing.
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@EnableBatchPocessing annotation.
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It provides Spring bean definitions for most of the batch infrastructure so you don’t have to, including:
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Returning RepeatStatus.FINISHED indicates to Spring Batch that you are done with this Tasklet.
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RepeatStatus.CONTINUABLE. In this case, Spring Batch will call your Tasklet again.
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Spring Boot has a component called the JobLauncherCommandLineRunner . This component is loaded at startup when Spring Batch is found on the classpath and it uses our JobLauncher to run any Job definitions found in the ApplicationContext.
3. Sample Job
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Once we can read the data, we’ll validate it using an ItemProcessor so that we can minimize errors in the write stage. From there, we’ll use the appropriate ItemWriter implementation to do the correct update based on the record type.
4. Understanding Jobs and Steps
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You can execute a job as many times as you need to with the same configuration. Because of this there is no reason to define the same job multiple times.
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If a file isn’t there, it handles the error gracefully. It doesn’t wait for a file to be delivered (that’s the responsibility of a scheduler, and so on).
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The execution of a job begins with a job runner. The job runner is intended to execute the job requested by name with the parameters passed. Spring Batch provides two job runners:
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Spring Boot provides yet another way to launch your jobs via the JobLauncherCommandLineRunner . This CommandLineRunner implementation looks for all beans of type Job defined in your ApplicaitonContext and executes them on startup (unless configured otherwise).
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uses the TaskExecutor interface from Core Spring to execute the requested job.
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If an org.springframework.core.task.SyncTaskExecutor is used, the job is executed in the same thread as the JobLauncher. Any other option executes the job in its own thread.
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When a batch job is run, an org.springframework.batch.core.JobInstance is created. A JobInstance represents a logical run of the job and is identified by the job name and the identifying parameters passed to the job for this run.
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Note A JobInstance can only be executed once to a successful completion. Because a JobInstance is identified by the job name and identifying parameters passed in, this means you can only run a job once with the same identifying parameters.
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BATCH_JOB_INSTANCE.JOB_KEY is actually a hash of the name and identifying parameters).
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A JobExecution is an actual attempt to run the job. If a job runs from start to finish the first time, there is only one JobExecution related to a given JobInstance. If a job ends in an error state after the first run, a new JobExecution is created each time an attempt is made to run the JobInstance (by passing in the same identifying parameters to the same job). For each JobExecution that Spring Batch creates for your job, a record in the BATCH_JOB_EXECUTION table is created. As the JobExecution executes, its state is maintained in the BATCH_JOB_EXECUTION_CONTEXT as well. This allows Spring Batch to restart a job at the correct point if an error occurs .
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spring.batch.initialize-schema=always
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We’ve also configured the application to automatically create the batch schema if it isn’t there.2
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So how do you pass parameters to your jobs? Spring Batch allows you not only to pass parameters to your jobs but also to automatically increment them3 or validate them before your job runs.
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name=Michael
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Note Spring Batch’s JobParameters are different than configuring properties via Spring Boot’s command line capabilities. As such, job parameters are not passed in with a -- prefix. Spring Batch’s JobParameters are different than system properties and should not be passed to the batch application via -D arguments on the command line.
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org.springframework.batch.core.StepContribution, which contains information about the current transaction that has not been committed yet (write count, read count, and so on). The second parameter is an instance of ChunkContext. It provides the state of the job at the point of execution. If you’re in a Tasklet, it contains any information about the chunk you’re processing. Information about that chunk includes information about the step and job. As you might guess, ChunkContext has a reference to org.springframework.batch.core.scope.context.StepContext, which contains your JobParameters.
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.getJobParameters() .get("name");
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@StepScope
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validator.setRequiredKeys(new
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validator.setOptionalKeys(new
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Up to now, you’ve been running under the limitation that a job can only be run once with a given set of identifying parameters.
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org.springframework.batch.core.JobParametersIncrementer is an interface that Spring Batch provides to allow you to uniquely generate parameters for a given job. You can add a timestamp to each run.
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you may want to have a parameter be a timestamp with each run of the job. This is common in jobs that run once a day. To do so, you need to create your own implementation of JobParametersIncrementer.
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JobExecutionListener. This interface provides two methods, beforeJob(JobExecution jobExecution) and afterJob(JobExecution jobExecution).
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afterJob method is that it’s called regardless of the status the job finishes in.
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.listener(new JobLoggerListener())
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Spring Batch provides the @BeforeJob and @AfterJob annotations just for that use.
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Spring Batch needs to wrap the listener for us to inject it into our job.
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.listener(JobListenerFactoryBean.getListener( new JobLoggerListener()))
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ExecutionContext Batch processes are stateful by their nature. They need to know what step they’re on. They need to know how many records they have processed within that step. These and other stateful elements are vital to not only the ongoing processing for any batch process but also restarting it if the process failed before. For example, suppose a batch process that processes a million transactions a night goes down after processing 900,000 of those records. Even with periodic commits along the way, how do you know where to pick back up when you restart? The idea of reestablishing that execution state can be daunting, which is why Spring Batch handles it for you.
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state for a job is maintained in the job execution’s ExecutionContext.
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ExecutionContext is essentially the session for your batch job.
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ExecutionContext is that you actually have multiple ExecutionContexts over the course of your job.
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Figure 4-3. The relationship between ExecutionContexts
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ExecutionContext provides a “safe” way to store data. The storage is safe because everything that goes into an ExecutionContext is persisted in the job repository.
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ExecutionContext is part of the JobExecution or StepExecution
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jobContext.put(“user.name", name);
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job defines the entire process, a step is the building block of a job. It is an independent, sequential batch processor. I call it a batch processor for a reason. A step contains all of the pieces a unit of work requires. It handles its own input. It can have its own processor. It handles its own output. Transactions are self-contained within a step. It’s by design that steps are as disjointed. This allows you as the developer to structure your job as freely as needed.
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Tasklet interface that we have used up to this point allows a developer to create a block of code that is executed within the scope of a transaction repeatedly until the Tasklet.execute method returns RepeatStatus.FINISHED
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A chunk-based step consists of at least two and up to three main components: an ItemReader, an optional ItemProcessor, and an ItemWriter. Using these components, Spring Batch processes records in chunks, or groups of records. Each chunk is executed within its own transaction allowing Spring Batch to restart after the last successful transaction after a failure .
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Using these three components, the framework performs three loops. The first loop is with the ItemReader. It reads all the records to be processed within this chunk into memory. The second loop is with the optional ItemProcessor. If an ItemProcessor is configured, the items that were read into memory will be looped over, each one being passed through the ItemProcessor. Finally all of the items are passed in a single call to the ItemWriter where they can be written out at once. This single call to the ItemWriter allows for IO optimizations by batching the physical write. Figure 4-4 shows a sequence diagram of how chunk-based processing works .
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keep in mind the chunk-based processing that Spring Batch is based on.
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The other way to create a tasklet step is to implement the Tasklet interface
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There, you implement the execute method required in the interface and return a RepeatStatus object to tell Spring Batch what to do after you completed processing.
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There are two options with this: RepeatStatus.CONTINUABLE and RepeatStatus.FINISHED. These two values can be confusing at first glance. If you return RepeatStatus.CONTINUABLE, you aren’t saying that the job can continue. You’re telling Spring Batch to run the Tasklet again. Say, for example, that you wanted to execute a particular Tasklet in a loop until a given condition was met, yet you still wanted to use Spring Batch to keep track of how many times the Tasklet was executed, transactions, and so on. Your Tasklet could return RepeatStatus.CONTINUABLE until the condition was met.
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custom implementations of the Tasklet interface are not the only way to use the Tasklet step. Spring Batch provides three other implementations of Tasklet: CallableTaskletAdapter, MethodInvokingTaskletAdapter, and SystemCommandTasklet.
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Obviously you would use this if you wanted to execute the logic of your step in another thread than the thread in which the step is being executed.
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configure the CallableTaskletAdapter as a normal Spring Bean, then register it as the Tasklet in your step.
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.tasklet(tasklet())
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tasklet() {
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One thing to note with CallableTaskletAdapter is that although the tasklet is executed in a different thread than the step itself, this doesn’t parallelize your step execution. The execution of this step won’t be considered complete until the Callable object returns a valid RepeatStatus object. Until this step is considered complete, no other steps in the flow in which this step is configured will execute.
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It allows you to execute a preexisting method on another class as a tasklet in your job. Say for example you already have a service that does a piece of logic that you want to run once in your batch job. Instead of writing an implementation of the Tasklet interface that really just wraps that method call, you can use MethodInvokingTaskletAdapter to call the method.
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methodInvokingTaskletAdapter.setTargetObject(service()); methodInvokingTaskletAdapter.setTargetMethod("serviceMethod");
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service()
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org.springframework.batch.core.step.tasklet.SystemCommandTasklet. This Tasklet is used to—you guessed it—execute a system command! The system command specified is executed asynchronously.
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interruptOnCancel attribute in the listing is optional but indicates to Spring Batch whether to kill the thread the system process is associated with if the job exits abnormally
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systemCommandTasklet.setCommand("rm -rf /tmp.txt");
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You’re highly discouraged from configuring a synchronous task executor due to the potential of locking up your job if the system command causes problems.
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chunks are defined by their commit intervals. If the commit interval is set to 50 items, then your job reads in 50 items, processes 50 items, and then writes out 50 items at once.
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.chunk(10)
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We then identify it as a chunk-based step via the chunk method. The 10 we pass to it is the configuration for the commit interval so in this example, the job will commit after processing 10 records.
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It’s important to note the commit interval. It’s set at 10 in the example. This means no records will be written until 10 records are read and processed. If an error occurs after processing nine items, Spring Batch will roll back the current chunk (transaction) and mark the job as failed. If you were to set the commit interval value to 1, your job would read in a single item, process that item, and then write that item. Essentially, you would be going back to item-based processing.
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you needed to know now that it’s important to set commit interval as high as reasonably possible to get the best performance on the write side
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Say that you have a job that needs to process chunks that aren’t all the same size (processing all transactions for an account in a single transaction, for example). Spring Batch provides the ability to programmatically define when a chunk is complete via an implementation of the org.springframework.batch.repeat.CompletionPolicy interface . The CompletionPolicy interface allows the implementation of decision logic to decide if a given chunk is complete. Spring Batch comes with a number of implementations of this interface.
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org.springframework.batch.repeat.policy.SimpleCompletionPolicy, which counts the number of items processed and flags a chunk complete when the configured threshold is reached.
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org.springframework.batch.repeat.policy.TimeoutTerminationPolicy. This allows you to configure a timeout on a chunk so that it may exit gracefully after a given amount of time. What does “exit gracefully” mean in this context? It means that the chunk is considered complete and all transaction processing continues normally
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TimeoutTerminationPolicy is more likely to be used as part of org.springframework.batch.repeat.policy.CompositeCompletionPolicy. This policy lets you configure multiple policies that determine whether a chunk has completed.
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When you use CompositeCompletionPolicy, if any of the policies consider a chunk complete, then the chunk is flagged as complete.
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Using the implementations of the CompletionPolicy interface isn’t your only option to determine how large a chunk is. You can also implement it yourself.
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It’s important to note that an implementation of the CompletionPolicy interface is intended to be stateful and should be able to determine if a chunk has been completed by its own internal state . The start method resets this internal state to whatever is required by the implementation at the beginning of the chunk.
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org.springframework.batch.core.StepExecutionListener and org.springframework.batch.core.ChunkListener interfaces , both of which allow the processing of logic at the beginning and end of a step and chunk respectively. Notice that the Step’s listener is named the StepExecutionListener and not just StepListener.
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An example would be doing some basic integrity checks after importing a file (whether the correct number of records were written to the database, and so on).
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Within a job in Spring Batch , steps are executed in the order you specify using the next method on the StepBuilder. If you want to execute steps in a different order, it’s quite easy: you configure transitions.
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.on("FAILED").to(failureStep())
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.from(firstStep()).on("*").to(successStep())
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BatchStatus is an attribute of the JobExecution or StepExecution that identifies the current state of the job or step. ExitStatus is the value returned to Spring Batch at the end of a job or step. Spring Batch evaluates the ExitStatus for its transitions.
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Because the values of the ExitStatus are really just Strings, the ability to use wildcards can make things interesting. Spring Batch allows for two wildcards in on criteria : * matches zero or more characters. For example, C* matches C, COMPLETE, and CORRECT. ? matches a single character. In this case, ?AT matches CAT or KAT but not THAT.
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For example, what if you didn’t want to execute a step if you skipped any records in the current step? You wouldn’t know that from the ExitStatus alone .
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Spring Batch has provided a programmatic way to determine what to do next. You do this by creating an implementation of the org.springframework.batch.core.job.flow.JobExecutionDecider interface.
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The interesting part about a job that is stopped is that the job can be restarted from where it left off, although no error has occurred. This state is very useful in scenarios when human intervention or some other check or handling is required between steps .
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It’s important to note that these states are identified by Spring Batch evaluating the ExitStatus of the step to determine what BatchStatus to persist in the JobRepository. ExitStatus can be returned from a step, chunk, or job. BatchStatus is maintained in StepExecution or JobExecution and persisted in the JobRepository.
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.on("FAILED").end()
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.on("FAILED").fail()
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.on("FAILED").stopAndRestart(successStep())
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a step can be defined as a bean. This lets you extract the definition of your steps from a given job into reusable components.
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In Spring Batch, there are three options for how to externalize the order of steps. The first is to create a flow, which is an independent sequence of steps. The second is to use the flow step; although the configuration is very similar, the state persistence in the JobRepository is slightly different. The last way is to actually call another job from within your job.
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Flow preProcessingFlow()
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.start(preProcessingFlow())
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What is the difference between passing the Flow to the JobBuilder and the FlowStep? It comes down to what happens in the JobRepository. Using the flow method on the JobBuilder yields the same results as if you configured the steps in your job. Using a FlowStep adds an additional entry. When you use a FlowStep, Spring Batch records the step that includes the flow as a separate step. Why is this a good thing? The main benefit is for monitoring and reporting purposes. Using a FlowStep allows you to see the impact of the flow as a whole instead of having to aggregate the individual steps.
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spring.batch.job.names=conditionalStepLogicJob must be configured in your application.properties to prevent Spring Boot from executing preProcessingJob automatically on startup.
6. Running a Job
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You wouldn’t want a job that is expected to take hours processing millions of records to have to start back at the beginning if a failure occurs. Spring Batch provides restart functionality that allows for it to pick up where it left off.
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When using Spring Boot with Spring Batch, a special CommandLineRunner is used, the JobLauncherCommandLineRunner. The JobLauncherCommandLineRunner uses Spring Batch’s JobLauncher to execute your job.
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When Spring Boot executes all the CommandLineRunners configured in the ApplicationContext, if you have the spring-boot-starter-batch on your classpath, the JobLauncherCommandLineRunner will run any Job definitions it finds in the context.
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you probably don’t want that job to execute when the application is started up. To set this behavior, Spring Boot exposes the property spring.batch.job.enabled equal to false in your application.yml (this property is set to true by default).
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Properties properties = new Properties(); properties.put("spring.batch.job.enabled", false); application.setDefaultProperties(properties);
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Spring Boot supports this use case by allowing you to configure what jobs are run at startup by using the spring.batch.job.names to identify the names of the jobs to be executed on startup. Spring Boot will take this comma-delimited list and execute them in order.
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It handles the determination of whether the run is part of an existing JobInstance or a new one and acts accordingly.
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By default, it uses a synchronous TaskExecutor so the jobs are executed synchronously by the SimpleJobLauncher
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We’ll use the SimpleJobLauncher when we create our REST API to launch our job. Conveniently, when using the @EnableBatchProcessing annotation , Spring Batch provides a SimpleJobLauncher for you out of the box so we don’t need to do anything to get one.
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By default, the JobLauncher will execute the job synchronously so we can return the ExitStatus to the user. It’s important to note that most batch jobs don’t run this fast due to the amount of processing involved. Because of this, running them asynchronously in this case would be a better fit (in which case, we’d just return the JobExecution’s id).
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When using a JobParametersIncrementer , it is the caller of the JobLauncher’s responsibility to apply those changes to the parameters. Once parameters have made it to the Job, they are immutable.
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.getNextJobParameters(job)
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Quartz has three main components: a scheduler, a trigger, and a job.
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A scheduler, which is obtained from a SchedulerFactory , serves as a registry of JobDetails (a reference to a Quartz job) and triggers and is responsible for executing a job when its associated trigger fires.
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A job is a unit of work that can be executed.
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A trigger defines when a job is to be run. When a trigger fires, telling Quartz to execute a job, a JobDetails object is created to define the individual execution of the job.
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The model of defining a Job and a JobDetails object is very similar to the way Spring Batch defines a Job and a JobInstance.
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Write your own Quartz Job to launch your job using Spring’s QuartzJobBean.
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Configure a JobDetailBean provided by Spring to create a Quartz JobDetail.
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Quartz does have the option to store metadata in a database,
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It is important to note that we are using an incrementer here. That is basically a requirement since we’ll be running a job multiple times without the ability to provide unique job parameters in another way.
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... public class BatchScheduledJob extends QuartzJobBean
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executeInternal(JobExecutionContext
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Using that builder, we provide the Job class we created (BatchScheduledJob in our case), we tell Quartz to not delete the job definition if it is not associated with a trigger (since we’ll use Spring to configure the trigger independently), and tell it to build our JobDetail.
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.storeDurably()
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.forJob(quartzJobDetail())
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If we don’t read in the same number of records as the footer record specifies, we should not continue.
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Once it gets to the footer record, if the number of expected records match, processing will continue. However, if they do not, our custom ItemReader will also provide an AfterStep method that will set the ExitStatus to STOPPED.
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StepExecutionListener.afterStep(StepExecution stepExecution) . This method will be called once our step is complete giving us the opportunity to return a specific ExitStatus. In our case, this method will look at the number of records read and compare that with the value saved in the footer of the file. If they match, it will return the ExitStatus set by the framework. Otherwise, it will return ExitStatus.STOPPED. This will allow us to stop the Job from continuing if the file is invalid.
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The final Step, the generateAccountSummaryStep, actually reuses the ItemReader from the applyTransactionsStep since we’re reading the same data (just doing something different with it). This is why the accountSummaryReader is step scoped, so we get a new instance for each step.
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.on("STOPPED").stopAndRestart(importTransactionFileStep())
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it says if the ExitStatus is STOPPED, stop the job and restart it back at the same step (in essence, start the job over again if it is programmatically stopped).
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The call to end() is required because we were building a flow using the transitions APIs.
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There is a slightly cleaner approach. We replace the afterStep with a beforeStep to get a handle on the StepExecution. Once you have access to that, when we read the footer record, you can call the StepExecution#setTerminateOnly() method . This method sets a flag that tells Spring Batch to end after the step is complete
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this.stepExecution.setTerminateOnly();
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Job Failure It should come as little surprise that the default behavior of Spring Batch is probably the safest: stopping the job and rolling back the current commit. This is one of the driving concepts of chunk-based processing. It allows you to commit the work you’ve successfully completed and pick up where you left off when you restart.
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By default, Spring Batch considers a step and job failed when any exception is thrown.
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There is a big difference between the examples of stopping via StepExecution and stopping the job with an exception. That difference is the state in which the job is left. In the StepExecution example, the job was stopped after a step is complete in the STOPPED ExitStatus . In the exception case, the step didn’t finish. In fact, it was part way through the step when the exception was thrown. Because of this, the step and job are labeled with the ExitStatus FAILED.
Highlight (yellow) - Location 4380
When a step is identified as FAILED, Spring Batch doesn’t start the step over from the beginning. Instead, Spring Batch is smart enough to remember what chunk you were on when the exception was thrown. When you restart the job, Spring Batch picks up at the chunk it left off on.
Highlight (yellow) - Location 4387
it’s up to you to determine what can and can’t be restarted. If you have a batch process that imports a file in the first step, and that job fails in the second step, you probably do not want to reimport the file.
Highlight (yellow) - Location 4394
if you choose to call the preventRestart() method, as shown in Listing 6-28, then when the job fails or is stopped for any reason, you won’t be able to re-execute it.
Highlight (yellow) - Location 4398
.preventRestart()
Highlight (yellow) - Location 4467
Note If the job has the BatchStatus of COMPLETE, the JobInstance can’t be rerun regardless of whether you configure all the steps to allowStartIfComplete(true);


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Chapter 5 source:https://www.abs.gov.au/websitedbs/a3121120.nsf/home/statistical+language+-+measures+of+central+tendency
<IP_ADDRESS> Skewed distributions:
When a distribution is skewed, the mode remains the most commonly occurring value, the median remains the middle value in the distribution, but the mean is generally ‘pulled’ in the direction of the tails. In a skewed distribution, the median is often a preferred measure of central tendency, as the mean is not usually in the middle of the distribution.
A distribution is said to be positively or right skewed when the tail on the right side of the distribution is longer than the left side. In a positively skewed distribution it is common for the mean to be ‘pulled’ toward the right tail of the distribution. Although there are exceptions to this rule, generally, most of the values, including the median value, tend to be less than the mean value.
The following graph shows a larger retirement age data set with a distribution which is right skewed. The data has been grouped into classes, as the variable being measured (retirement age) is continuous. The mode is 54 years, the modal class is 54-56 years, the median is 56 years and the mean is 57.2 years.A distribution is said to be negatively or left skewed when the tail on the left side of the distribution is longer than the right side. In a negatively skewed distribution, it is common for the mean to be ‘pulled’ toward the left tail of the distribution. Although there are exceptions to this rule, generally, most of the values, including the median value, tend to be greater than the mean value.
The following graph shows a larger retirement age dataset with a distribution which left skewed. The mode is 65 years, the modal class is 63-65 years, the median is 63 years and the mean is 61.8 years.
<IP_ADDRESS> QQ Plots
QQ Plots (Quantile-Quantile plots) are plots of two quantiles against each other. A quantile is a fraction where certain values fall below that quantile. For example, the median is a quantile where 50% of the data fall below that point and 50% lie above it. The purpose of QQ plots is to find out if two sets of data come from the same distribution.
A 45 degree angle is plotted on the Q Q plot; if the two data sets come from a common distribution, the points will fall on that reference line.(from Wikipedia)
# Solution 1
qplot(sample = carat, data = diamonds)
# solution 2
ggplot(diamonds)+
geom_qq(aes(sample = carat))+
geom_qq_line(aes(sample = carat))
5.0.1 Confidence interval
(Taken from Summary and Analysis of Extension Program Evaluation in R by Salvatore S. Mangiafico)
A confidence interval is a range in which it is estimated the true population value lies. A confidence interval is used to indicate how accurate a calculated statistic is likely to be. Confidence intervals can be calculated for a variety of statistics, such as the mean, median, or slope of a linear regression.
Most of the statistics we use assume we are analyzing a sample which we are using to represent a larger population.
<IP_ADDRESS> Statistics and parameters
When we calculate the sample mean, the result is a statistic. It’s an estimate of the population mean, but our calculated sample mean would vary depending on our sample.
In theory, there is a mean for the population of interest, and we consider this population mean a parameter. Our goal in calculating the sample mean is estimating the population parameter.
<IP_ADDRESS> Point estimates and confidence intervals
Our sample mean is a point estimate for the population parameter. A point estimate is a useful approximation for the parameter, but considering the confidence interval for the estimate gives us more information.
As a definition of confidence intervals, if we were to sample the same population many times and calculated a sample mean and a 95% confidence interval each time, then 95% of those intervals would contain the actual population mean.
if we want to compare the means of two groups to see if they are statistically different, we will use a t-test, or similar test, calculate a p-value, and draw a conclusion. An alternative approach would be to construct 95% or 99% confidence intervals about the mean for each group. If the confidence intervals of the two means don’t overlap, we are justified in calling them statistically different.
<IP_ADDRESS> Methods
The traditional method is appropriate for normally distributed data or with large sample sizes. It produces an interval that is symmetric about the mean.
For skewed data, confidence intervals by bootstrapping may be more reliable.For routine use, I recommend using bootstrapped confidence intervals, particularly the BCa or percentile methods.
The groupwiseMean function in the rcompanion package can produce confidence intervals both by traditional and bootstrap methods, for grouped and ungrouped data.
<IP_ADDRESS>.1 The traditional method
The data must be housed in a data frame. By default, the function reports confidence intervals by the traditional method.
In the groupwiseMean function, the measurement and grouping variables can be indicated with formula notation, with the measurement variable on the left side of the tilde (~), and grouping variables on the right.
The confidence level is indicated by, e.g., the conf = 0.95 argument. The digits option indicates the number of significant digits to which the output is rounded. Note that in the output, the means and other statistics are rounded to 3 significant figures.
#install.packages("rcompanion")
library(rcompanion)
dataset3 = data.frame(dataset1)
groupwiseMean(dataset1 ~ 1,
data = dataset3,
conf = 0.95,
digits = 3)
## .id n Mean Conf.level Trad.lower Trad.upper
## 1 <NA> 12 6 0.95 5.28 6.72
groupwiseMean(data ~ type,
data = dataset,
conf = 0.95,
digits = 3)
## type n Mean Conf.level Trad.lower Trad.upper
## 1 dataset1 12 6 0.95 5.28 6.72
## 2 dataset2 12 6 0.95 3.99 8.01
<IP_ADDRESS>.2 The Bootstrapped method
In the groupwiseMean function, the type of confidence interval is requested by setting certain options to TRUE. These options are traditional, normal, basic, percentile and bca. The boot option reports an optional statistic, the mean by bootstrap. The R option indicates the number of iterations to calculate each bootstrap statistic.
groupwiseMean(data ~ type,
data = dataset,
conf = 0.95,
digits = 3,
R = 10000,
boot = TRUE,
traditional = FALSE,
normal = FALSE,
basic = FALSE,
percentile = FALSE,
bca = TRUE)
## type n Mean Boot.mean Conf.level Bca.lower Bca.upper
## 1 dataset1 12 6 6.00 0.95 5.33 6.50
## 2 dataset2 12 6 5.99 0.95 4.25 7.59
5.0.2 Unusual values
For unusual values in the dataset, you can do two things:
drop the entire row with the strange values
replace the unusual values with missing values
unusual = diamonds %>%
filter(y < 3 | y > 20)%>%
arrange(y)
unusual
## # A tibble: 9 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 1 Very Good H VS2 63.3 53 5139 0 0 0
## 2 1.14 Fair G VS1 57.5 67 6381 0 0 0
## 3 1.56 Ideal G VS2 62.2 54 12800 0 0 0
## 4 1.2 Premium D VVS1 62.1 59 15686 0 0 0
## 5 2.25 Premium H SI2 62.8 59 18034 0 0 0
## 6 0.71 Good F SI2 64.1 60 2130 0 0 0
## 7 0.71 Good F SI2 64.1 60 2130 0 0 0
## 8 0.51 Ideal E VS1 61.8 55 2075 5.15 31.8 5.12
## 9 2 Premium H SI2 58.9 57 12210 8.09 58.9 8.06
# drop the entire row with the strange values
diamonds_new = diamonds%>%
filter(between(y,3,20))
head(arrange(diamonds_new,y))
## # A tibble: 6 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.2 Premium D VS2 62.3 60 367 3.73 3.68 2.31
## 2 0.2 Premium F VS2 62.6 59 367 3.73 3.71 2.33
## 3 0.2 Very Good E VS2 63.4 59 367 3.74 3.71 2.36
## 4 0.2 Premium D VS2 61.7 60 367 3.77 3.72 2.31
## 5 0.2 Ideal E VS2 62.2 57 367 3.76 3.73 2.33
## 6 0.2 Premium E SI2 60.2 62 345 3.79 3.75 2.27
# replace the unusual values with missing values
diamonds_replace = diamonds %>%
mutate(y2 = ifelse(y<3 | y> 20, NA, y))
head(arrange(diamonds_replace, y ))
## # A tibble: 6 x 11
## carat cut color clarity depth table price x y z y2
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
## 1 1 Very Good H VS2 63.3 53 5139 0 0 0 NA
## 2 1.14 Fair G VS1 57.5 67 6381 0 0 0 NA
## 3 1.56 Ideal G VS2 62.2 54 12800 0 0 0 NA
## 4 1.2 Premium D VVS1 62.1 59 15686 0 0 0 NA
## 5 2.25 Premium H SI2 62.8 59 18034 0 0 0 NA
## 6 0.71 Good F SI2 64.1 60 2130 0 0 0 NA


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Name
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Impressions
Shure SE110 in-ear headphones
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Our favorite iPhone Solution
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OHL Roundup: Wright scores winner, Frontenacs edge Steelheads 3-2
KINGSTON, Ont. — Shane Wright broke a tie in the third period with his second goal of the game, leading the Kingston Frontenacs to a 3-2 victory over the Mississauga Steelheads on Sunday in the Ontario Hockey League.
by Canadian PressKINGSTON, Ont. — Shane Wright broke a tie in the third period with his second goal of the game, leading the Kingston Frontenacs to a 3-2 victory over the Mississauga Steelheads on Sunday in the Ontario Hockey League.
Jake Murray also scored and added an assist for Kingston (5-14-4).
James Hardie tied the game on a penalty shot 4:35 into the third period for the Steelheads (7-15-1). Nicholas Canade also scored.
Frontenacs goaltender Christian Propp made 35 saves. Mississauga's Joe Ranger stopped 28 shots.
---
BATTALION 4 ICEDOGS 3
NORTH BAY, Ont. — Matthew Struthers opened the game with a short-handed goal 1:35 in, and Luke Moncada scored the winner midway through the third as the Battalion (5-19-0) edged Mississauga (9-12-4).
---
KNIGHTS 4 OTTERS 0
ERIE, Pa. — Dylan Myskiw stopped all 26 shots he faced and Liam Foudy had a goal and an assist as London (14-5-2) extended its win streak to four games with a victory over the Otters (11-7-7).
---
SPITFIRES 4 ATTACK 3 (OT)
WINDSOR, Ont. — Jean-Luc Foudy scored 3:56 into overtime and Connor Corcoran had a goal and an assist to lift the Spitfires (15-3-3) over Owen Sound (10-9-4).
---
GENERALS 7 WOLVES 0
OSHAWA, Ont. — Zachary Paputsakis stopped all 27 shots he faced and Brett Neumann scored on a power play for his 18th goal of the season as the Generals (16-6-1) downed Sudbury (14-11-1).
---
GREYHOUNDS 5 STING 2
SARNIA, Ont. — Cole MacKay tallied three points (2G, 1A), while Tanner Dickinson scored the winner at 8:38 of the first period in the Sault Ste Marie's (10-14-1) victory over Sarnia (11-12-0).
---
This report by The Canadian Press was first published Nov. 24, 2019.
The Canadian Press


Microsoft’s Power Virtual Agents allows anyone to build a bot without writing a single line of code
by PradeepMicrosoft Bot Framework allows developers to easily create powerful bots by taking advantage of Azure and AI services. At Ignite 2019, Microsoft announced Power Virtual Agents, a new service that will allow anyone to create a bot without writing a single line of code. This will help subject matter experts in organizations to create their bots using a guided, no-code graphical interface.
Also, Power Virtual Agents is already integrated with Microsoft’s Power Platform, allowing them to use hundreds of pre-built connectors available via Power Automate. If the bot needs advanced features, you can take advantage of Microsoft’s Cognitive Services and Bot Framework which are fully integrated within this new service.
Microsoft Power Virtual Agents is available today in public preview.


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DARK MATTER
- DARK MATTER IS SPACE MATTER WE CANNOT SEE AS IT DOES NOT EMIT LIGHT.
- THERE IS MORE DARK MATTER IN THE UNIVERSE THAN BRIGHT.
- SCIENTISTS BELIEVE THAT ALMOST 90% OF MATTER IS DARK.
- DARK MATTER IS OF TWO KINDS-GALACTIC AND INTERGALACTIC.
- THE MATTER IN GALAXIES IS CALLED GALACTIC WHILE THE MATTER BETWEEN THEM IS CALLED INTERGALACTIC.
- INTERGALACTIC MATTER IS MADE UP OF WIMPs(WEAKLY INTERACTING MASSIVE PARTICLES).
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Going to School at Franklin College. Studying Quantitative Anaylsis. Anticpated Graduation 2020. I am a swimmer here at Franklin Colllege and I love math. I also love computers and computer programing. It adds value to me by making me slow down and have to rethhink what I am doing or makes me go back over my work which in turn makes me learn it even greater. I have great time management because of swimming in college. I also am a great team memeber becasue of the swim team. With these two values I find myself a very valued person when I go to work.
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Keeva Keenan thriving under Vera Pauw after Colin Bell snub
With the backing of Celtic colleague Damien Duff, Keeva Keenan has taken the Ireland women’s team by storm under the Vera Pauw regime.
The full-back arrived on the international stage as a 16-year-old matchwinner against England at the U19 Euro finals in 2015, only to be cast aside by Colin Bell.
Keenan’s exclusion from the senior ranks during the Bell era was puzzling given she was operating to at least semi-professional level in the Scottish league.
The Dubliner departed champions Glasgow City to join Celtic in early 2018 and is their reigning Player of the Year.
The biggest club in Scottish football broke new ground earlier this year by upgrading the status of their squad to full-time professionals.
Proof that the investment is yielding success came on Sunday when they inflicted a first defeat in three years on Glasgow City.
Duff, currently assistant to Neil Lennon, has been tracking the progress of his compatriot and Keena herself feels a changing of the guard is imminent.
READ MORE
Denise O’Sullivan 'one of the best players in the world', says Ireland boss
“Glasgow City have won every title for the past 13 seasons,” explained the defender yesterday at an Irish camp building up to Tuesday’s Euro qualifier in Greece.
“The day is coming that another team will take over. Celtic are doing great things with the women’s section.
“We train mostly at Lennoxstown, the same facility used by the men’s squad. I’m really enjoying my football there."
I met Damien Duff and Jonathan Hayes at the Player of the Year ceremony at the end of last season and they were giving their congratulations. They couldn’t have been nicer.
Success at international level, by earning a first-ever major qualification, would also enhance Keenan’s reputation.
Pauw’s appointment in September triggered an overhaul but the comeback of Keenan was instigated by assistant boss Eileen Gleeson.
“I turned Eileen down as a kid when I signed for Shelbourne rather than her Peamount side,” she explained. “We become enemies in the national league but always respected each other.
“My mother Rachel and Eileen were good friends through work and I was delighted to get the call-up.
“Being put straight into the starting team for the qualification win over Ukraine was a major shock. There were nerves but the win puts us in a great position to challenge to reach the 2021 Euros.”
Her euphoria is a contrast to her one and only previous experience in camp.
“My first day involved no training and the 14-hour meeting in a small room of a hotel,” she says, referring to the infamous strike by the players before that Slovakia friendly. “That was an interesting time but I didn’t get a shout from Colin after that.
“I thought my chance of playing for Ireland again had gone but I’m glad to be part of the new set-up.”
READ MORE
Changing of the guard in Ireland squad with Parrott in and Long out for Denmark clash


Micronaut® Foundation is a not-for-profit organization that exists to support and collectively lead the open source Micronaut project.
Micronaut Foundation serves to:
- Ensure technical innovation and advancement of the Micronaut framework as a free and open public use software development framework for a growing global community
- Evangelize and promote the Micronaut framework as a leading technology in the JVM space
- Build and support an ecosystem of complementary documentation, functionality, and services.
Contributing Members
The bylaws of the Micronaut Foundation provide that Object Computing will maintain, in perpetuity, a majority interest in the Foundation to serve as a steward of the Micronaut framework and its growing global open source community. Object Computing has committed an initial contribution of $2M to fund open source product development and evangelism for the Micronaut framework. “Our focus continues to be directed at building and helping our community create better technology,” said Jeff Scott Brown. “This is what we do and will continue to do. The community is an important part of this journey for us — the most important part of the journey. Our commitment to honor and serve this community continues to grow.” The Foundation relies on the financial support of our Contributing Members. If you are interested in becoming a Contributing Member, please contact us at<EMAIL_ADDRESS>The Micronaut Foundation Technology Advisory Board
The Micronaut Foundation is supported by a Technology Advisory Board that ensures the Framework continues to reflect and serve its diverse and growing user community. The Board will meet quarterly to discuss and make recommendations regarding the Micronaut framework's roadmap and technical direction.
“The formation of the Foundation reflects the growing need to balance the diverse range of interests from the Micronaut framework's community and contributing organizations,” said Graeme Rocher, co-founder of the Micronaut framework. “As the Micronaut framework's adoption continues to accelerate, the additional governance provided by the Foundation will ensure all interested parties have a voice, resulting in a much more inclusive approach to the direction of the project.”
Please click here to find minutes from our past board meeting. For more information and for organizations interested in nominating a representative to the Micronaut Foundation Technical Advisory Board, please contact<EMAIL_ADDRESS>Sponsorship
As a not-for-profit organization, the Micronaut Foundation relies on the financial support of our Contributing Members to support and grow the Micronaut framework. Please consider donating and becoming a sponsor today!
The generous support of our Corporate Sponsors allows the Micronaut Foundation to continue to ensure technical innovation and advancement of the Micronaut framework as a free and open public use software development framework for a growing global community. See more details here on how to support the foundation as an organization through our Corporate Sponsorship Program.
The Micronaut Foundation is also supported by our Community Member Sponsorship Program. If you are interested in supporting the Micronaut Foundation as an individual, please see our Community Member Sponsorship page for details.
See all the current supporters on our Sponsors List.
Contact
The Foundation's Board Of Directors may be reached at foundation@micronaut.io


From small teams to huge organizations, Redash helps tens of thousands of users to query databases and visualise data in a new and meaningful way.
by {{customer.person}}, {{customer.role}} at {{customer.customer}}
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Studio71 uses Redash to build a data driven culture, save time and support sales and talent management
Read Case Study →BubbleIQ uses Redash to support sales, save engineering time and report towards investors
Read Case Study →"Alooma and Redash share the same mission: to liberate data and unleash its full potential. Knowing that analytics and Business Intelligence (BI) were going to be paramount for running a data company, we wanted something that gave us full transparency down to the schemas without any abstraction layers. We needed to write our own SQL and collaborate over queries and dashboards in a simple way..."
Continue Reading on Medium →


A seed node.js restful server with continuous integration and code quality metrics through https://travis-ci.org/, https://coveralls.io/ and https://codeclimate.com.


This project gives an in depth look into techniques that will take anyone's directive writing skills to the next level. Want to become an AngularJS ninja? Then explore and practice the infinite possibilities directives create in the JS world.
Learn how to incorporate animations into custom directives.
Easy to digest examples that shed light on difficult AngularJS concepts.
Get all the details of these examples in the Black Belt repo and "Mastering AngularJS Directives" the book.


Apple warns of risks from German law to open up mobile payments
Could hurt data protection and the security of financial information.
by Holger HansenApple said on Friday moves in Germany to force it to open up its Apple Pay mobile payments system to rivals could hurt data protection and the security of financial information.
A German parliamentary committee unexpectedly voted in a late-night session on Wednesday to force the tech giant to open up Apple Pay to rival providers in Germany.
This came in the form of an amendment to an anti-money laundering law that was adopted late on Thursday by the full parliament and is set to come into effect early next year.
The legislation, which did not name Apple specifically, will force operators of electronic money infrastructure to offer access to rivals for a reasonable fee.
The law highlights the growing desire in Germany for tighter regulation of US technology companies.
Apple Pay, which lets people pay with their iPhones, is a fast growing area of the company's business, one which threatens to undermine traditional banks' long-standing dominance of retail payment systems.
"We are surprised at how suddenly this legislation was introduced," Apple said on Friday. "We fear that the draft law could be harmful to user friendliness, data protection and the security of financial information."
A person close to the government coalition said Chancellor Angela Merkel's office had pushed for the committee to withdraw the amendment
That charge was denied by a senior official in the office who said there had been complete consensus within the government over the move. The only question mark had been over whether the Finance Ministry had checked the legislation was legally watertight. With that confirmed, the office had no further reservations, the official said.