Dynamic Data Tailing Near Real-Time Stream Access
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Solution Overview
Problem
Conventional data management approaches in network environments face challenges in providing near real-time data tailing without incurring excessive cost or latency, particularly when users need to access and process data streams from multiple sources.
Innovation Solution
Implementing a data tailing mechanism where a customer submits a tail request to a front-end endpoint, which functions as a handler for the data stream, allowing for independent transmission of tail data without processing delays, and applying filters to transmit only specific data, thereby reducing latency and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored to persistent storage and then retrieved for user access, then data integrity is ensured, but latency and access time increase significantly
Solution Approach 1:
The system performs preliminary actions by maintaining data in both persistent storage (for integrity) and in-memory caches or buffer regions (for fast access). When data is written to persistent storage, it is simultaneously made available in faster storage layers, so that subsequent read operations can retrieve data without the full persistent storage latency.
Solution Approach 2:
The storage system is segmented into multiple layers with different performance characteristics. Hot data that requires frequent access is separated and placed in faster storage layers, while cold data remains in persistent storage. This segmentation allows the system to provide both data integrity (through persistent storage) and low-latency access (through cached copies).
2Device complexity
If data from multiple sources is consolidated and processed through centralized resources, then data management is simplified, but cost and processing latency increase
Solution Approach 1:
Different parts of the data management system are given different qualities and functions. Edge computing nodes perform local data processing and filtering, while centralized cloud resources handle complex analytics. This allows simple operations to be performed locally without incurring centralized processing latency, while still benefiting from centralized management capabilities when needed.
Solution Approach 2:
The centralized data management system is segmented into distributed edge computing nodes that handle local data consolidation and preprocessing. This segmentation reduces the burden on centralized resources and enables faster local data processing, while centralized resources focus on higher-level management and complex operations.
Data Source
AI summary
A near real time data tailing mechanism can enable data, received on a stream, to be directed to a specified location independent of any processing and persistent storage of that data. A customer can submit a tail request that can be received to a front end of a data management service. When a request is received to store data to the persistent storage, a determination is made that the front end has registered a tail for that data stream and the host receiving the data can forward a copy of the data to the front end, which can cause the data to be transmitted to the specified location. One or more filters can be applied in order to cause only specific data to be transmitted for the tail request. A best effort approach provides an overview of the data in near real time.