Data_Engineering_Part4

Page 1: Spark Job Definition Inline Monitoring

  • The Spark job definition Inline Monitoring feature allows users to:

    • View Spark job submission and run status in real-time.

    • Inspect past runs and configurations of Spark job definitions.

  • Users can navigate to the Spark application detail page for additional specifics.

Pipeline Spark Activity Inline Monitoring

  • Deep links have been integrated into the Notebook and Spark job activities within the Pipeline.

  • Users can:

    • View execution details of Spark applications.

    • Access snapshots from the respective Notebook and Spark job definitions.

    • Retrieve Spark logs for troubleshooting.

  • If Spark activities fail, inline error messages will be displayed.

Next Steps for Users

  • Use the Apache Spark advisor for real-time advice on notebooks.

  • Browse recent Spark application runs in the Fabric monitoring hub.

  • Monitor Spark jobs through notebooks and monitor capacity consumption.

  • Utilize the extended Apache Spark history server for debugging and diagnosing applications.

Page 2: Apache Spark Run Series Analysis

Overview

  • The Apache Spark run series feature is available for Spark version 3.4 and above.

  • It categorizes Spark applications based on:

    • Recurring pipeline activities.

    • Manual notebook runs.

    • Spark job runs from the same notebook or job definition.

Key Features of Run Series Analysis

  1. Autotune Analysis:

    • Compare autotune outcomes and performance metrics across runs.

  2. Run Series Comparison:

    • Evaluate run durations with past performances and data input/output.

  3. Outlier Detection:

    • Identify and analyze outliers in performance data.

  4. Detailed Run Instance View:

    • Provide granular details for individual runs to notice performance bottlenecks.

Usage Recommendations

  • Employ this feature for performance tuning, particularly if:

    • You’re analyzing production job health.

    • Optimizing long-running jobs.

Page 3: Examples of Run Series Analysis

Visual Representation

  • Each run instance is depicted with a vertical bar in graphs indicating duration.

  • Red bars signal anomalies detected in specific run instances.

Detailed Run Instance Information

  • Users can:

    • Zoom in/out for specific time windows.

    • Access metrics such as:

      • Duration trends.

      • Average durations and expected performance.

Page 4: Related Content

  • Utilize Apache Spark advisor for advisory within notebooks.

  • Navigate to monitoring hub and view recent Spark applications.

Page 5: Apache Spark Advisor

Functionality

  • This advisor analyzes commands and provides real-time advice for Notebook runs.

  • Offers built-in patterns to help avoid common mistakes, focusing on:

    • Code optimization.

    • Error analysis to find the root causes of failures.

Built-in Advice Examples

  • Caching advice before using randomSplit to avoid inconsistent results.

  • Warnings about naming conflicts between views and tables.

Page 6: Error Messages and Recommendations

Key Error Messages

  • Handling issues with unexpected queries or hints.

  • Suggestions on enabling configurations to enhance performance and reduce errors.

Page 7: The User Experience with Spark Advisor

Real-time Feedback

  • The Spark advisor displays advice as users execute commands, allowing immediate insights into potential issues.

  • Categories of assistance include Info, Warning, and Error messages, which can be viewed directly in notebook cells.

Page 8: Error Handling

  • Spark Advisor Settings:

    • Users can opt to show/hide specific diagnostics.

    • Management of diagnostic messages between user sessions is customizable.

Page 9: Feedback and Community Interaction

  • User feedback options present for improving the guidance offered in Spark environments.

Page 10: Monitoring Hub Overview

Functionality of the Monitoring Hub

  • Centralized portal to view ongoing Apache Spark application activities triggered from various sources.

  • Search and filter applications based on various criteria, including submitter, status, and item type.

Actions available

  • Cancel in-progress applications.

  • View detailed execution metrics for Spark applications.

Page 11: Usability Improvements

Customization Options

  • Users can sort and filter applications in the Monitoring Hub based on multiple parameters.

Page 12: Overview of Spark Job Definitions

  • Users should navigate through the Monitoring Hub to access recent runs of their Spark job definitions and applications.

Page 13-14: Upstream View for Pipelines

  • If scheduled jobs run in pipelines, both pipeline activities and upstream activities can be viewed for better understanding of workflows.

Page 15: Monitoring Spark Applications

  • Overview of submission statuses and Spark application management techniques are provided for users managing workload scheduling.

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