Real-Time Data Infrastructure at Uber
Key Points:
Architecture and Components:
Uber leverages a range of open-source technologies, prominently including Apache Kafka, Apache Flink, and Apache Pinot, to build a robust real-time data infrastructure.
Apache Flink is used extensively for stream processing, supporting both SQL and low-level API interfaces to accommodate different user needs. This enables efficient transformation of SQL queries into Flink jobs, simplifying the deployment and scaling of streaming applications.
Challenges and Solutions:
Resource Estimation and Auto-scaling: Empirical analysis helps in configuring resources for various job types (e.g., CPU-bound vs. memory-bound jobs), and continuous monitoring aids in dynamic scaling based on load.
Job Monitoring and Failure Recovery: Automated job monitoring and failure recovery mechanisms ensure high reliability and operational efficiency.
Unified Platform:
Uber has developed a unified architecture to manage stream processing pipelines, integrating various platforms to handle business logic, workflow management, and SQL compilation. This layered architecture enhances scalability and extensibility.
Lessons Learned:
Open Source Adoption: Uber’s reliance on open-source technologies has facilitated rapid iteration and adaptation to evolving engineering needs, though it has required significant customization to fit Uber's specific requirements.
Rapid System Development: Standardizing interfaces and leveraging a monorepo approach has enabled efficient management of multiple applications, promoting rapid development and deployment.
Future Directions:
Unification of Streaming and Batch Processing: Aiming to simplify the development process by unifying the semantics of stream and batch processing.
Multi-region and Cloud Agnosticism: Enhancing system resilience and flexibility through multi-region deployments and the ability to run infrastructure both on-premises and in the cloud.
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