![]() ![]() To get access to the internal Kubernetes resources, kubectl provides a tool ("Port Forwarding") that allows access from your localhost. However the pod runs within the internal Kubernetes network. Spark Driver Pod hosts Spark-UI on port 4040. The Driver pod's name usually is in spark-driver format. Once the job is submitted successfully, run kubectl get pods -n -w command to watch all the pods, until you observe the driver pod is in the "Running" state. ![]() The driver’s thread dump is shown."sparkSubmitParameters": "-conf =4 -conf =20G -conf =20G -conf =4"Īws emr-containers start-job-run -cli-input-json file:///spark-python.json In the Executors table, in the driver row, click the link in the Thread Dump column. To view the driver’s thread dump in the Spark UI: The Spark UI is accessible only when the Spark cluster is up and the Spark driver is ready to. Thread dumps are also useful for debugging issues where the driver appears to be hanging (for example, no Spark progress bars are showing) or making no progress on queries (for example, Spark progress bars are stuck at 100%). This button will create a new Browser tab of the Spark UI. (If the task has finished running, you will not find a matching thread). In the Thread dump for executor table, click the row where the Thread Name column contains (TID followed by the Task ID value that you noted earlier. In that row, click the link in the Thread Dump column. In the Executors table, find the row that contains the Executor ID value that corresponds to the Executor ID value that you noted earlier. In the stage’s Tasks list, find the target task that corresponds to the thread dump you want to see, and note its Task ID and Executor ID values. In the job’s Stages table, find the target stage that corresponds to the thread dump you want to see, and click the link in the Description column. In the Jobs table, find the target job that corresponds to the thread dump you want to see, and click the link in the Description column. To view a specific task’s thread dump in the Spark UI: Thread dumps are useful in debugging a specific hanging or slow-running task. You can click the links in the description to drill further into the task level execution.Ī thread dump shows a snapshot of a JVM’s thread states. Since Spark Structured Streaming internally checkpoints the stream and it reads from the checkpoint instead of depending on the previous batches, they are shown as grayed stages.)Īt the bottom of the page, you will also find the list of jobs that were executed for this batch. ![]() In this case, those stages correspond to the dependency on previous batches because of updateStateBykey. If the data is checkpointed or cached, then Spark would skip recomputing those stages. Spark is smart enough to skip some stages if they don’t need to be recomputed. (The grayed boxes represents skipped stages. Remove old inactive dags from airflow dag list UI (Not tracked) It shows up in this list because the. The resulting stream was then used to update a global state using updateStateByKey. (Note: This is not the devtools of your browser. In this case, you can see that the batch read input from Kafka direct stream followed by a flat map operation and then a map operation. This is a very useful to understand the order of operations and dependencies for every batch. The job details page shows a DAG visualization.
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