- #Spark url extractor driver
- #Spark url extractor code
Getting result time is the time that the driver spends fetching task results from workers. Result serialization time is the time spent serializing the task result on an executor before sending it back to the driver. GC time is the total JVM garbage collection time. Summary metrics for all task are represented in a table and in a timeline. For stages belonging to Spark DataFrame or SQL execution, this allows to cross-reference Stage execution details to the relevant details in the Web-UI SQL Tab page where SQL plan graphs and execution plans are reported. Notably, Whole Stage Code Generation operations are also annotated with the code generation id. Nodes are grouped by operation scope in the DAG visualization and labelled with the operation scope name (BatchScan, WholeStageCodegen, Exchange, etc).
There is also a visual representation of the directed acyclic graph (DAG) of this stage, where vertices represent the RDDs or DataFrames and the edges represent an operation to be applied. The stage detail page begins with information like total time across all tasks, Locality level summary, Shuffle Read Size / Records and Associated Job IDs. Task detail can be accessed by clicking on the description. Only in failed stages, failure reason is shown. In active stages, it’s possible to kill the stage with the kill link. In Fair scheduling mode there is a table that displays pools propertiesĪfter that are the details of stages per status (active, pending, completed, skipped, failed). The Stages tab displays a summary page that shows the current state of all stages of all jobs inĪt the beginning of the page is the summary with the count of all stages by status (active, pending, completed, skipped, and failed)
Shuffle write: Bytes and records written to disk in order to be read by a shuffle in a future stage. Shuffle read: Total shuffle bytes and records read, includes both data read locally and data read from remote executors. Output: Bytes written in storage in this stage. Input: Bytes read from storage in this stage. List of stages (grouped by state active, pending, completed, skipped, and failed). An example of DAG visualization for sc.parallelize(1 to 100).unt(). DAG visualization: Visual representation of the directed acyclic graph of this job where vertices represent the RDDs or DataFrames and the edges represent an operation to be applied on RDD. Event timeline: Displays in chronological order the events related to the executors (added, removed) and the stages of the job. Associated SQL Query: Link to the sql tab for this job. Number of stages per status (active, pending, completed, skipped, failed). Job Status: (running, succeeded, failed). This page displays the details of a specific job identified by its job ID. When you click on a specific job, you can see the detailed information of this job. Details of jobs grouped by status: Displays detailed information of the jobs including Job ID, description (with a link to detailed job page), submitted time, duration, stages summary and tasks progress bar. Event timeline: Displays in chronological order the events related to the executors (added, removed) and the jobs. Number of jobs per status: Active, Completed, Failed. Total uptime: Time since Spark application started. The information that is displayed in this section is The details page further shows the event timeline,ĭAG visualization, and all stages of the job. Page, you see the details page for that job. Progress of all jobs and the overall event timeline. The summary page shows high-level information, such as the status, duration, and The Jobs tab displays a summary page of all jobs in the Spark application and a details pageįor each job. 
To monitor the status and resource consumption of your Spark cluster. Apache Spark provides a suite of web user interfaces (UIs) that you can use
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