Multi-Stage Data Attribute Prefetching for Lower API Latency
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Solution Overview
Problem
Data attribute retrieval in multi-stage processes is inefficient, consuming processing and network resources and introducing latency due to frequent communications with remote data sources via API calls.
Innovation Solution
A data management device prefetches eligible data attributes based on prefetch eligibility criteria, including data dependency and stage parameters, before the start of the multi-stage process, and stores them in a data cache for subsequent stages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data attributes are retrieved during each stage of the multi-stage process via API calls to remote data sources, then the data retrieval process can accommodate dynamic data needs, but processing time and network resource consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by identifying and retrieving data attributes that are likely to be needed in future stages before they are actually requested. The query processor analyzes the multi-stage query plan and proactively fetches attributes during idle periods or between stages, storing them in memory for rapid access. This eliminates the need for time-consuming API calls during critical processing stages, thereby reducing latency while maintaining the ability to handle dynamic data requirements.
2Productivity
If data attributes are cached for reuse across stages, then processing speed improves, but memory resource consumption increases
Solution Approach 1:
The system applies local quality by selectively caching data attributes based on their specific characteristics and reuse probability. Rather than uniformly caching all attributes, the query processor identifies which attributes are most likely to be reused across stages and caches only those. This targeted approach optimizes memory utilization by allocating storage resources to the most beneficial attributes, thereby improving processing speed without proportionally increasing overall memory consumption.
Solution Approach 2:
The system changes parameters by dynamically adjusting cache size allocations and eviction policies based on workload characteristics. The query processor monitors memory usage patterns and adapts caching strategies in real-time, expanding cache capacity for high-value attributes and applying intelligent eviction for less critical data. This parameter adaptation allows the system to maximize processing speed benefits while keeping memory consumption within acceptable bounds.
3Loss of time
If the system prefetches data attributes before the multi-stage process, then latency decreases, but unnecessary data retrieval and processing overhead increase
Solution Approach 1:
The system implements feedback mechanisms where the query processor continuously monitors actual data access patterns during the multi-stage process and uses this information to refine prefetching decisions. By comparing predicted versus actual data needs, the system learns to prefetch only the attributes that are genuinely required, eliminating wasteful prefetching operations. This feedback-driven approach reduces processing overhead by avoiding the retrieval and processing of unnecessary data while maintaining low latency for actually needed attributes.
Data Source
AI summary
In some implementations, a device may obtain configuration information for a multi-stage process, the configuration information indicating a set of data attributes to be used during the multi-stage process. The device may identify, based on one or more parameters of respective data attributes included in the set of data attributes, one or more data attributes to be obtained prior to a start of the multi-stage process, wherein the one or more parameters are indicated by the configuration information. The device may obtain, via one or more data sources and prior to the start of the multi-stage process, the one or more data attributes. The device may perform, using at least one of the one or more data attributes, one or more stages of the multi-stage process.