Dashboard Data Caching for Parallel Query Load Handling
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Solution Overview
Problem
Dashboard systems experience significant slowdowns due to high query loads when multiple users access data simultaneously, exacerbated by redundant database queries and limited processing nodes.
Innovation Solution
Implementing a cache memory to store query results and a request queue to manage database queries, allowing parallel processing and caching of anticipated queries, reducing the load on individual processing nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system executes database queries for each dashboard access, then data retrieval accuracy is maintained, but system performance deteriorates due to high query loads and processing bottlenecks
Solution Approach 1:
The system performs preliminary actions by executing database queries in advance and storing results in a cache memory before they are actually needed. When a dashboard is accessed, the system checks the cache first and returns cached results if available, avoiding redundant database queries. This preliminary caching action maintains data retrieval accuracy while significantly improving system performance by reducing query load.
Solution Approach 2:
The system creates copies of database query results and stores them in cache memory. Instead of repeatedly accessing the original database for the same data, the system retrieves cached copies of the results. This copying mechanism ensures that accurate data is returned while reducing the burden on the database system and improving overall productivity.
2Adaptability or versatility
If multiple users access dashboards simultaneously, then user availability is improved, but system load increases causing slowdowns
Solution Approach 1:
The system prepares and caches dashboard data in advance before multiple users request it. When users access dashboards simultaneously, they can retrieve data from the pre-computed cache rather than triggering separate database queries. This preliminary action maintains high user availability while preserving fast dashboard loading speeds even under concurrent access conditions.
Solution Approach 2:
The cache memory acts as an intermediary between the database and multiple user requests. Instead of multiple users directly querying the database simultaneously (which causes slowdowns), their requests are mediated through the cache layer. The cache satisfies requests from stored data, reducing the load on the database and maintaining both user availability and loading speed.
3Measurement precision
If the system processes each query individually through limited processing nodes, then query processing accuracy is maintained, but processing efficiency deteriorates
Solution Approach 1:
The system creates copies of query results and stores them in cache memory with associated metadata. When the same query is requested again, the system retrieves the copied results from cache rather than re-executing the query. This copying approach maintains query processing accuracy by returning exact match results while dramatically improving processing efficiency by avoiding redundant query execution.
Solution Approach 2:
The cache system provides self-service by automatically checking for matching queries and returning cached results without requiring full query processing. The system serves itself by maintaining and querying the cache, reducing the burden on processing nodes and improving overall processing efficiency while maintaining accuracy for cached queries.
Data Source
AI summary
Systems and methods disclosed herein for improving data retrieval for dashboards using data caching. The system may implement a cache memory to store results for executed queries and/or anticipated queries, allowing for data to be retrieved asynchronously, and may implement a request queue to distribute the queries among processing nodes.