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Each application may consist of multiple *application attempts*. If an application fails, it may be retried as a new attempt. Each attempt runs in a container. In a sense, a container provides the context for basic unit of work performed by a YARN application. All work that is done within the context of a container is performed on the single worker node on which the container was allocated. See [Apache Hadoop YARN Concepts](https://hadoop.apache.org/docs/r2.7.4/hadoop-yarn/hadoop-yarn-site/WritingYarnApplications.html) for further reference.
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Application logs (and the associated container logs) are critical in debugging problematic Hadoop applications. YARN provides a nice framework for collecting, aggregating, and storing application logs with the [Log Aggregation](https://hortonworks.com/blog/simplifying-user-logs-management-and-access-in-yarn/) feature. The Log Aggregation feature makes accessing application logs more deterministic. It aggregates logs across all containers on a worker node and stores them as one aggregated log file per worker node. The log is stored on the default file system after an application finishes. Your application may use hundreds or thousands of containers, but logs for all containers run on a single worker node are always aggregated to a single file. So there is only 1 log per worker node used by your application. Log Aggregation is enabled by default on HDInsight clusters version 3.0 and above. Aggregated logs are located in default storage for the cluster. The following path is the HDFS path to the logs:
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Application logs (and the associated container logs) are critical in debugging problematic Hadoop applications. YARN provides a nice framework for collecting, aggregating, and storing application logs with the [Log Aggregation](https://hortonworks.com/blog/simplifying-user-logs-management-and-access-in-yarn/) feature. The Log Aggregation feature makes accessing application logs more deterministic. It aggregates logs across all containers on a worker node and stores them as one aggregated log file per worker node. The log is stored on the default file system after an application finishes. Your application may use hundreds or thousands of containers, but logs for all containers run on a single worker node are always aggregated to a single file. So there's only 1 log per worker node used by your application. Log Aggregation is enabled by default on HDInsight clusters version 3.0 and above. Aggregated logs are located in default storage for the cluster. The following path is the HDFS path to the logs:
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/app-logs/<user>/logs/<applicationId>
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In the path, `user` is the name of the user who started the application. The `applicationId` is the unique identifier assigned to an application by the YARN RM.
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The aggregated logs are not directly readable, as they are written in a [TFile][T-file], [binary format][binary-format] indexed by container. Use the YARN ResourceManager logs or CLI tools to view these logs as plain text for applications or containers of interest.
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The aggregated logs aren't directly readable, as they're written in a [TFile](https://issues.apache.org/jira/secure/attachment/12396286/TFile%20Specification%2020081217.pdf), [binary format](https://issues.apache.org/jira/browse/HADOOP-3315) indexed by container. Use the YARN ResourceManager logs or CLI tools to view these logs as plain text for applications or containers of interest.
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## YARN CLI tools
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@@ -52,7 +52,7 @@ Specify the <applicationId>, <user-who-started-the-application>, <conta
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## YARN ResourceManager UI
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The YARN ResourceManager UI runs on the cluster headnode. It is accessed through the Ambari web UI. Use the following steps to view the YARN logs:
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The YARN ResourceManager UI runs on the cluster headnode. It's accessed through the Ambari web UI. Use the following steps to view the YARN logs:
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1. In your web browser, navigate to https://CLUSTERNAME.azurehdinsight.net. Replace CLUSTERNAME with the name of your HDInsight cluster.
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2. From the list of services on the left, select **YARN**.
@@ -63,8 +63,4 @@ The YARN ResourceManager UI runs on the cluster headnode. It is accessed through
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