Pipeline flow
Pipeline Flow in Octopipe
The pipeline flow in Octopipe represents the journey of data from its initial extraction through transformation and finally to its destination. This document provides a comprehensive explanation of the entire flow, detailing each stage, the interactions between components, and the mechanisms that ensure reliable execution.Overview
Octopipe’s pipeline flow is designed to:- Streamline data movement.
- Ensure data integrity.
- Provide real-time monitoring and logging.
- Offer flexibility for both local and cloud-based development.
Detailed Pipeline Flow
-
Initialization
- User Action:
The process begins when the developer initializes a new pipeline using the
octopipe init
command. - Configuration: The CLI collects essential information such as pipeline name, description, and hosting preferences (local or cloud).
- User Action:
The process begins when the developer initializes a new pipeline using the
-
Source Setup
- Connector Configuration: Users add data sources by configuring connectors. This includes setting API endpoints, database credentials, and other necessary parameters.
- Type Safe API Generation: As soon as a source is added, Octopipe generates a type safe API using an LLM to derive types, ensuring the data structure is well defined from the start.
-
Destination Configuration
- Destination Setup: Users specify where the data should be loaded. Common destinations include relational databases, data warehouses, or file storage systems.
- Schema Labeling: The destination schema is pulled and then labeled by the user, ensuring that the subsequent mapping is accurate.
-
Transformation Creation
- Mapping Process: The transform layer is created by mapping the type safe API schema to the labeled destination schema.
- User Approval: Before execution, the generated transformation is presented for user review, allowing for corrections or enhancements.
- Deployment to Spark: Once approved, the transformation logic is deployed to Spark, which will perform the data processing.
-
Pipeline Orchestration
- Scheduling: Airflow is employed to schedule the pipeline execution based on user-defined cron expressions.
- Task Dependencies: Each pipeline step (extraction, transformation, load) is defined as a task with dependencies, ensuring correct execution order.
- Real-Time Adjustments: Kafka plays a critical role in handling any real-time data streaming and messaging between the steps.
-
Execution and Monitoring
- Pipeline Start:
The pipeline is initiated using the
octopipe start
command. Airflow triggers tasks based on the defined schedule. - Log Streaming:
Developers can monitor the progress via
octopipe logs
, which streams real-time logs to the console. - Status Checks:
The
octopipe status
command provides an overview of running pipelines, including details on task completion and any errors encountered.
- Pipeline Start:
The pipeline is initiated using the
Error Handling and Recovery
- Automated Retries: If any task fails, Airflow’s built-in retry mechanisms kick in to re-execute the failed step.
- Detailed Logs: Comprehensive logging allows developers to pinpoint issues quickly.
- User Intervention: In the event of persistent errors, developers can stop the pipeline, adjust configurations, and restart the process seamlessly.
Real-World Example
Imagine a scenario where a marketing pipeline is built to pull campaign data from an API, transform it, and load it into a data warehouse:- Step 1: The pipeline is initialized, and the marketing API is added as a data source.
- Step 2: The destination, a data warehouse, is configured with its schema labeled by the user.
- Step 3: The transform layer maps the API data to the warehouse schema.
- Step 4: Airflow schedules the pipeline to run daily, while Kafka ensures any real-time updates are captured.
- Step 5: Spark processes the transformations, and the pipeline executes end-to-end with continuous monitoring.
Monitoring and Optimization
- Visual Dashboards: Future enhancements will include detailed dashboards for visualizing pipeline performance and health.
- Performance Tuning: Users can fine-tune each component’s parameters, such as Spark’s memory allocation or Airflow’s scheduling intervals.
- Iterative Improvement: The system supports iterative improvements where developers can refine transformation logic based on pipeline performance data.