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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2018/04/05 10:34:00 UTC
[jira] [Assigned] (SPARK-23859) Initial PR for Instrumentation
improvements: UUID and logging levels
[ https://issues.apache.org/jira/browse/SPARK-23859?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-23859:
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Assignee: Apache Spark (was: Weichen Xu)
> Initial PR for Instrumentation improvements: UUID and logging levels
> --------------------------------------------------------------------
>
> Key: SPARK-23859
> URL: https://issues.apache.org/jira/browse/SPARK-23859
> Project: Spark
> Issue Type: Sub-task
> Components: ML
> Affects Versions: 2.3.0
> Reporter: Joseph K. Bradley
> Assignee: Apache Spark
> Priority: Major
>
> This is a subtask for an initial PR to improve MLlib's Instrumentation class for logging. It will address a couple of issues and use the changes in LogisticRegression as an example class.
> Issues:
> * The UUID is currently generated from an atomic integer. This is a problem since the integer is reset whenever a persisted Estimator is loaded on a new cluster. We should just use a random UUID to get a new UUID each time with high probability.
> * We use both Instrumentation and Logging to log stuff. Let's standardize around Instrumentation in MLlib since it can associate logs with the Estimator or Transformer which produced the logs (via a prefix with the algorithm's name or UUID).
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