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INFO: No database type set, using meta data indicating: HSQL
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INFO: No Micrometer observation registry found, defaulting to ObservationRegistry.NOOP
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INFO: No TaskExecutor has been set, defaulting to synchronous executor.
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INFO: Job: [SimpleJob: [name=job]] launched with the following parameters: [{}]
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INFO: Executing step: [step]
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[main] INFO org.springframework.batch.core.launch.support.TaskExecutorJobLauncher - COMMONS-LOGGING Job: [SimpleJob: [name=job]] launched with the following parameters: [{}]
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[main] INFO org.springframework.batch.core.job.SimpleStepHandler - COMMONS-LOGGING Executing step: [step]
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Hello world!
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INFO: Step: [step] executed in 10ms
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INFO: Job: [SimpleJob: [name=job]] completed with the following parameters: [{}] and the following status: [COMPLETED] in 25ms
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[main] INFO org.springframework.batch.core.step.AbstractStep - COMMONS-LOGGING Step: [step] executed in 3ms
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[main] INFO org.springframework.batch.core.launch.support.TaskExecutorJobLauncher - COMMONS-LOGGING Job: [SimpleJob: [name=job]] completed with the following parameters: [{}] and the following status: [COMPLETED] in 4ms
Copy file name to clipboardExpand all lines: spring-batch-docs/modules/ROOT/pages/job/running.adoc
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@@ -52,11 +52,11 @@ create an `ApplicationContext`. This file
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should contain everything needed to run the complete
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`Job`, including a `JobOperator`, a `JobRepository` and a `JobRegistry` populated with the jobs to operate.
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|`operation`|The name of the operation to execute on the job. Can be one of [`start`, `startNextInstance`, `stop`, `restart`, `abandon`]
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|`jobName` or `jobExecutionId`|Depending on the operation, this can be the name of the job to start or the execution ID of the job to stop, restartor abandon.
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|`jobName` or `jobExecutionId`|Depending on the operation, this can be the name of the job to start or the execution ID of the job to stop, restart, abandon or recover.
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|===============
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When starting a job, all arguments after these are considered to be job parameters, are turned into a `JobParameters` object,
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and must be in the format of `name=value`. In the case of stopping, restartingor abandoning a job, the `jobExecutionId` is
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and must be in the format of `name=value,type,identifying`. In the case of stopping, restarting, abandoning or recovering a job, the `jobExecutionId` is
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expected as the 4th argument, and all remaining arguments are ignored.
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The following example shows a date passed as a job parameter to a job defined in Java:
Copy file name to clipboardExpand all lines: spring-batch-docs/modules/ROOT/pages/retry.adoc
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[NOTE]
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====
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As of version 2.2.0, the retry functionality was pulled out of Spring Batch.
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It is now part of a new library, https://github.com/spring-projects/spring-retry[Spring Retry].
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As of v6.0, Spring Batch does *not* use Spring Retry to automate retry operations within the framework,
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and is now based on the https://docs.spring.io/spring-framework/reference/7.0/core/resilience.html#resilience-annotations-retryable[retry feature] provided by Spring Framework 7.0.
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As of v6.0, Spring Batch does *not* use https://github.com/spring-projects/spring-retry[Spring Retry] to automate retry operations within the framework,
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and is now based on the https://docs.spring.io/spring-framework/reference/7.0/core/resilience.html[core retry feature] provided by Spring Framework 7.0.
Copy file name to clipboardExpand all lines: spring-batch-docs/modules/ROOT/pages/scalability.adoc
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@@ -208,7 +208,7 @@ See the section on xref:step/controlling-flow.adoc#split-flows[Split Flows] for
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[[localChunking]]
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== Local Chunking
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Local chunking is a new feature that allows you to process chunks of items in parallel, locally within the same JVM using multiple threads.
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Local chunking is a new feature in v6.0 that allows you to process chunks of items in parallel, locally within the same JVM using multiple threads.
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This is particularly useful when you have a large number of items to process and want to take advantage of multi-core processors.
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With local chunking, you can configure a chunk-oriented step to use multiple threads to process chunks of items concurrently.
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Each thread will read, process and write its own chunk of items independently, while the step will manage the overall execution and commit the results.
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the grid computing and shared memory product space.
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