Data Pipeline
  • Data Pipeline
    • About Data Pipeline
    • Design Philosophy
    • Low Code Visual Authoring
    • Real-time and Batch Orchestration
    • Event based Process Orchestration
    • ML and Data Ops
    • Distributed Compute
    • Fault Tolerant and Auto-recovery
    • Extensibility via Custom Scripting
  • Getting Started
    • Homepage
      • List Pipelines
      • Create
        • Creating a New Pipeline
          • Adding Components to Canvas
          • Connecting Components
            • Events [Kafka and Data Sync]
          • Memory and CPU Allocations
        • Creating a New Job
          • Job Editor Page
          • Task Components
            • Readers
              • HDFS Reader
              • MongoDB Reader
              • DB Reader
              • S3 Reader
              • Azure Blob Reader
              • ES Reader
              • Sandbox Reader
            • Writers
              • HDFS Writer
              • Azure Writer
              • DB Writer
              • ES Writer
              • S3 Writer
              • Sandbox Writer
              • Mongodb Writer
              • Kafka Producer
            • Transformations
          • PySpark Job
          • Python Job
      • List Jobs
      • List Components
      • Delete Orphan Pods
      • Scheduler
      • Data Channel
      • Cluster Event
      • Trash
      • Settings
    • Pipeline Workflow Editor
      • Pipeline Toolbar
        • Pipeline Overview
        • Pipeline Testing
        • Search Component in Pipelines
        • Push Pipeline (to VCS/GIT)
        • Pull Pipeline
        • Full Screen
        • Log Panel
        • Event Panel
        • Activate/Deactivate Pipeline
        • Update Pipeline
        • Failure Analysis
        • Pipeline Monitoring
        • Delete Pipeline
        • Pipeline Component Configuration
        • Pipeline Failure Alert History
      • Component Panel
      • Right-side Panel
    • Testing Suite
    • Activating Pipeline
    • Monitoring Pipeline
    • Job Monitoring
  • Components
    • Adding Components to Workflow
    • Component Architecture
    • Component Base Configuration
    • Resource Configuration
    • Intelligent Scaling
    • Connection Validation
    • Readers
      • S3 Reader
      • HDFS Reader
      • DB Reader
      • ES Reader
      • SFTP Stream Reader
      • SFTP Reader
      • Mongo DB Reader
        • MongoDB Reader Lite (PyMongo Reader)
        • MongoDB Reader
      • Azure Blob Reader
      • Azure Metadata Reader
      • ClickHouse Reader (Docker)
      • Sandbox Reader
      • Azure Blob Reader
    • Writers
      • S3 Writer
      • DB Writer
      • HDFS Writer
      • ES Writer
      • Video Writer
      • Azure Writer
      • ClickHouse Writer (Docker)
      • Sandbox Writer
      • MongoDB Writers
        • MongoDB Writer
        • MongoDB Writer Lite (PyMongo Writer)
    • Machine Learning
      • DSLab Runner
      • AutoML Runner
    • Consumers
      • SFTP Monitor
      • MQTT Consumer
      • Video Stream Consumer
      • Eventhub Subscriber
      • Twitter Scrapper
      • Mongo ChangeStream
      • Rabbit MQ Consumer
      • AWS SNS Monitor
      • Kafka Consumer
      • API Ingestion and Webhook Listener
    • Producers
      • WebSocket Producer
      • Eventhub Publisher
      • EventGrid Producer
      • RabbitMQ Producer
      • Kafka Producer
      • Synthetic Data Generator
    • Transformations
      • SQL Component
      • Dateprep Script Runner
      • File Splitter
      • Rule Splitter
      • Stored Producer Runner
      • Flatten JSON
      • Email Component
      • Pandas Query Component
      • Enrichment Component
      • Mongo Aggregation
      • Data Loss Protection
      • Data Preparation (Docker)
      • Rest Api Component
      • Schema Validator
    • Scripting
      • Script Runner
      • Python Script
        • Keeping Different Versions of the Python Script in VCS
    • Scheduler
  • Custom Components
  • Advance Configuration & Monitoring
    • Configuration
      • Default Component Configuration
      • Logger
    • Data Channel
    • Cluster Events
    • System Component Status
  • Version Control
  • Use Cases
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  1. Getting Started

Monitoring Pipeline

This Page explains How we can monitor the Pipelines.

PreviousActivating PipelineNextJob Monitoring

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The user can monitor a pipeline together with all the components associated with the same by using the Pipeline Monitoring icon. The user gets information about Pipeline components, Status, Types, Last Activated (Date and Time), Last Deactivated (Date and Time), Total Allocated and Consumed CPU%, Total allocated and consumed memory, Number of Records, and Component logs all displayed on the same page.

Go through the below-given video to get a basic idea on the pipeline monitoring functionality.

  • Navigate to the Pipeline List page.

  • Click the Monitor icon.

Or

  • Navigate to the Pipeline Workflow Editor page.

  • Click the Pipeline Monitoring icon on the Header panel.

  • The Pipeline Monitoring page opens displaying the details of the selected pipeline.

  • The Monitor tab opens by default.

  • If there are multiple instances for a single component, click on the drop-down.

  • Details for each instance will be displayed.

Monitoring page for Docker component in Real-Time

Monitoring page for Docker component in Batch:

Monitoring page for Spark Component:

Monitoring page for Spark Component - Driver:

Monitoring page for Spark Component- Executer:

  • If memory allocated to the component is less than required, then it will be displayed in red color.

  • Open the Data Metrics tab.

  • Specify the time period by providing the from and to dates.

  • Choose an interval option or select the custom interval.

  • The component specific data metrics get displayed. The green color nodes indicate that the data has been loaded.​ Click on green color icon to get all the details of processed data as shown in the below images.

  • Clear: It will clear all the monitoring and data metrics logs for all the components in the pipeline.

Displaying Pipeline Monitoring and Data Metrics
Accessing the Pipeline Monitoring from the Pipeline List page.
Docker component- Real time
Docker component- Real time
Docker component- Batch
Monitoring details for Spark component
Spark component- Driver
Spark component- Executer
Data metrics page
Clear option in monitoring page