Batch Data Distribution to Sharded Readers
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Solution Overview
Problem
Systems that require sharing of state information or synchronization face challenges in scalability and resource management, particularly when balancing timely data updates with resource costs, especially in systems with temporal constraints or design limitations.
Innovation Solution
A scalable system architecture that decouples data ingestion components from application components, using a concentrator to route data based on hash keys and sub-partitions, allowing for horizontal scalability and increased throughput without modifying existing components, and leveraging data storage and streaming services to optimize data distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If frequent updates to data shards are performed, then timely information is provided, but resource cost increases
Solution Approach 1:
The system implements periodic batch updates to data shards instead of continuous frequent updates. Data is collected in batches over time intervals and then updated to shards in consolidated operations, reducing the overhead of frequent individual updates while maintaining acceptable timeliness through configured batch intervals
Solution Approach 2:
The system segments data updates into separate batch operations that are processed independently. By dividing the data update process into discrete batches that can be handled separately, the system reduces the resource cost of frequent updates while maintaining data timeliness through efficient batch processing
2Use of energy by moving object
If larger batch updates are performed, then resource cost is reduced, but information becomes less timely and possibly stale
Solution Approach 1:
The system dynamically adjusts batch update configurations based on system conditions and requirements. By making the batch size and interval configurable rather than fixed, the system can optimize the balance between resource cost and data timeliness dynamically, allowing larger batches when resources are constrained and smaller batches when timeliness is critical
3Reliability
If systems require sharing of state information or synchronization, then coordination between components is achieved, but scalability is limited
Solution Approach 1:
The system extracts the state sharing and synchronization requirements from the core data processing path. By isolating these coordination mechanisms into separate concern, the system allows the main data processing and scaling operations to proceed without being constrained by synchronization overhead, enabling better scalability while maintaining necessary coordination
4Device complexity
If data ingestion components are coupled with application components, then system architecture is simpler, but horizontal scalability is reduced
Solution Approach 1:
The system segments the architecture into distinct data ingestion components and application components that are decoupled from each other. This segmentation allows each component to be independently scaled and managed, enabling horizontal scalability while the modular nature of the segmentation keeps the overall architecture manageable and not excessively complex
Data Source
AI summary
A system may route incoming data to different data shards using a scalable system architecture. The system may include a data ingestion service, such as a naming service that may input data for you by the system. This data may be routed to specific applications, which may be associated with different shards of the data. The system may decouple data ingestion components from application components to enable adding additional capacity at either end of the system, or anywhere between, to accommodate increases in demand and/or to increase throughput. For example, the system may be modified to add additional data ingestion components without modifying the number of applications components to increase throughput of data ingestion. Similarly, the system may be modified to add application components without modifying the number of data ingestion components to increase throughput of data analysis, for example.