Or that of an alternative filesystem supported by the Hadoop APIs. This can be a local file:// path,Īn HDFS path hdfs://namenode/shared/spark-logs One implementation, provided by Spark, which looks for application logs stored in theįor the filesystem history provider, the URL to the directory containing application event Name of the class implementing the application history backend. Security options for the Spark History Server are covered more detail in the Spark History Server Configuration Options The compaction may exclude more events than you expect, leading some UI issues on History Server for the application. Please also note that this is a new feature introduced in Spark 3.0, and may not be completely stable. Will run as each micro-batch will trigger one or more jobs which will be finished shortly, but compaction won’t run For streaming query we normally expect compaction Please note that Spark History Server may not compact the old event log files if figures out not a lot of space The original log files, but it will not affect the operation of the History Server. The History Server may not be able to delete Once rewriting is done, original log files will be deleted, via best-effort manner.
Events for the SQL execution which is finished, and related job/stage/tasks events.Events for the executor which is terminated.Events for the job which is finished, and related stage/tasks events.As of now, below describes the candidates of events to be excluded: The compaction tries to exclude the events which point to the outdated data. Into one compact file with discarding events which are decided to exclude. Once it selects the target, it analyzes them to figure out which events can be excluded, and rewrites them The event log files having less index than the file with smallest index which will be retained as target of compaction.įor example, if the application A has 5 event log files and. is set to 2, then first 3 log files will be selected to be compacted. When the compaction happens, the History Server lists all the available event log files for the application, and considers Logs, via setting the configuration. on theĭetails will be described below, but please note in prior that compaction is LOSSY operation.Ĭompaction will discard some events which will be no longer seen on UI - you may want to check which events will be discarded Spark History Server can apply compaction on the rolling event log files to reduce the overall size of Let you have rolling event log files instead of single huge event log file which may help some scenarios on its own,īut it still doesn’t help you reducing the overall size of logs.
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streaming) can bring a huge single event log file which may cost a lot to maintain andĪlso requires a bunch of resource to replay per each update in Spark History Server.Įnabling and would Spark.history.* configuration options for the history server (default: none).Īpplying compaction on rolling event log filesĪ long-running application (e.g. May use the internal address of the server, resulting in broken links (default: none). If this is not set, links to application history The public address for the history server. JVM options for the history server (default: none).Ĭlasspath for the history server (default: none). Memory to allocate to the history server (default: 1g). The history server can be configured as follows: Environment Variables Environment Variable You can start the history server by executing: Provided that the application’s event logs exist. It is still possible to construct the UI of an application through Spark’s history server, This configures Spark to log Spark events that encode the information displayed
To view the web UI after the fact, set to true before starting theĪpplication. Note that this information is only available for the duration of the application by default. If multiple SparkContexts are running on the same host, they will bind to successive ports You can access this interface by simply opening in a web browser. Information about the running executors.
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