Data Stream Processing with Predictive Caching for Lagging Inputs
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Solution Overview
Problem
Existing data stream processing systems face delays due to constant lag in input streams, which can accumulate and hinder timely production of time-sensitive outputs, especially when the delay exceeds processing granularity.
Innovation Solution
The system optimizes processing by storing data points from different streams to be processed later, utilizing predictable patterns to pre-compute and cache outputs for lagging data, and parallelizing processing to reduce resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system waits for all data points from lagging streams to arrive before processing, then data completeness is improved, but processing time and output delay increase
Solution Approach 1:
The system performs preliminary processing on non-lagging data streams before all data points are available. It computes intermediate results and stores them temporarily, then completes the final output once lagging data arrives, avoiding complete reprocessing and reducing overall delay
Solution Approach 2:
The processing is divided into segments: data that is available is processed immediately while data that is lagging is handled separately. This segmentation allows the system to work on available data without being blocked by lagging streams, improving throughput while maintaining data completeness
2Productivity
If the system processes all data streams in parallel without caching, then processing speed is improved, but resource requirements increase
Solution Approach 1:
The system pre-computes and caches results for data points that are likely to be requested, based on predictable patterns in the data streams. When actual data points arrive, the system checks the cache first and retrieves pre-computed results when possible, reducing the need for full parallel processing and lowering resource requirements
Solution Approach 2:
Instead of maintaining multiple full copies of processing state in parallel, the system creates cached copies of computed results that can be reused. This copying approach reduces memory requirements compared to full parallel processing while maintaining the ability to serve multiple data points efficiently
3Loss of time
If the system caches predicted output values for lagging data points, then processing time is reduced, but memory usage increases
Solution Approach 1:
The caching strategy is applied selectively rather than uniformly. The system identifies which data streams have predictable patterns and caches results for those specific streams, while leaving unpredictable streams to be processed in real-time. This local application of caching reduces overall memory usage compared to caching all possible outputs
Solution Approach 2:
The system dynamically adjusts caching parameters such as cache size and prediction horizon based on the actual behavior of data streams. When streams exhibit strong predictable patterns, the system increases caching aggressiveness; when patterns are weak, it reduces caching to minimize memory usage while still providing time benefits for predictable portions
Data Source
AI summary
Systems and methods include receiving an input including a plurality of data points; processing the input at times where data points are received; preprocessing the input at times where some data points are lagging, the preprocessing includes a computation based on one or more predicted input values for lagging data points and storing one or more predicted output values in a cache based on the one or more predicted input values; and responsive to receiving the lagging data points, obtaining an associated output value from the cache.