Materialization Service for Low Latency Data Aggregation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data aggregation systems face challenges in providing low-latency, scalable, and resilient data aggregation services due to inconsistent back-end data structures, varying data update rates, and scalability issues, leading to increased resource usage and latency, as well as difficulties in customizing data presentation and providing failover protections.
Innovation Solution
A computer-implemented system and method utilizing a materialization service that generates synchronized data from real-time and stored data sources, employing a data structure like a directed acyclic graph to aggregate and forward data efficiently, optimizing resource usage and reducing latency, while ensuring high resiliency through failover protections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time API calls are made to back-end services for data aggregation, then data freshness is improved, but latency increases and system complexity escalates
Solution Approach 1:
The system performs preliminary data aggregation by materializing data from multiple back-end services into a unified data store in advance. This pre-computed aggregated data is then served to front-end systems, eliminating the need for real-time API calls and reducing latency while maintaining data freshness.
Solution Approach 2:
The patent introduces an intermediary materialization service layer between back-end services and front-end systems. This intermediary aggregates and materializes data from multiple back-end sources, shielding front-end systems from back-end complexity and providing consistent, low-latency responses without direct real-time API calls.
2Loss of time
If data is stored and read from data store upon customer requests, then latency is reduced, but scalability deteriorates and resource usage increases
Solution Approach 1:
The system segments data into different materialization levels (e.g., pre-computed aggregates, real-time data streams) and stores them in appropriately structured data stores. This segmentation allows the system to serve different data types through optimized paths, improving scalability while maintaining low latency for frequently accessed data.
Solution Approach 2:
The materialization service dynamically adjusts data aggregation strategies based on request patterns, data update rates, and system load. It can switch between pre-computed aggregates and real-time data retrieval based on the specific needs of each request, optimizing both latency and scalability under varying conditions.
3Device complexity
If consistently structured data is enforced across back-end services, then data aggregation complexity is reduced, but system adaptability decreases
Solution Approach 1:
The materialization service transforms data from multiple back-end services with different structures into a unified, consistent data format. It changes the parameter representation of data during the materialization process, converting diverse back-end data structures into a standardized internal representation that simplifies aggregation while preserving the adaptability to handle various source formats.
Data Source
AI summary
Systems and methods for low latency materialized information provision are disclosed. For example, a system may include at least one memory storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, via a materialization service, data from multiple sources related to an item associated with a webpage hosted by a server. The data may include first event data sourced from a real-time feed and second event data sourced from stored data. Operations may include generating synchronized data based on the first and second event data. Operations may include receiving a request from the web server for information related to the item, the request being associated with a user segment. Operations may include identifying data to aggregate to fulfill the request, generating a data structure gathering synchronized data using the data structure, and forwarding the gathered synchronized data to the server.