Data Management System Query Optimization via Fingerprint Cache
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Solution Overview
Problem
Data management systems (DMS) face challenges in efficiently executing queries, as suboptimal queries can lead to increased latency and reduced performance, impacting client satisfaction.
Innovation Solution
The DMS automatically identifies suboptimal queries, stores optimized versions, and uses a fingerprint database to retrieve and execute the optimized queries, reducing execution time and improving reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the DMS executes queries using traditional methods, then the system can handle basic query operations, but query latency increases and performance deteriorates when suboptimal queries are encountered
Solution Approach 1:
The system performs query optimization in advance by analyzing query patterns and pre-computing optimized query plans. When a query is received, the system checks if an optimized version already exists in the cache, avoiding the need to execute suboptimal queries and thereby reducing latency.
Solution Approach 2:
The system creates and stores optimized copies of frequently executed queries in a cache database. Instead of re-executing the same suboptimal query multiple times, the system retrieves and executes the pre-optimized copy, significantly improving execution speed and reducing latency.
2Reliability
If the DMS implements automatic query optimization, then query performance and reliability improve, but system complexity increases due to additional components like fingerprint databases and optimization engines
Solution Approach 1:
The system introduces a fingerprint database as an intermediary layer between the query receiver and the query executor. This mediator stores unique identifiers (fingerprints) of optimized queries and their corresponding execution plans, enabling the system to reliably retrieve and execute optimized queries without significantly complicating the overall architecture.
Solution Approach 2:
The system implements a feedback mechanism where query execution results and performance metrics are continuously monitored. This feedback is used to automatically identify suboptimal queries, generate optimized versions, and update the cache, thereby improving reliability through iterative optimization while keeping the system self-managing rather than requiring complex external control.
3Productivity
If the DMS stores and retrieves optimized queries, then overall system performance improves, but additional storage resources and processing overhead are required
Solution Approach 1:
The system applies optimization selectively rather than uniformly to all queries. It identifies and optimizes only those queries that exhibit suboptimal performance or are frequently executed, storing optimized versions in the cache. This localized approach improves productivity for critical queries while minimizing the additional storage resources required.
Solution Approach 2:
The system changes the state of queries by storing them in different representations (e.g., fingerprint hashes, optimized execution plans) in the cache database. This parameter transformation allows the system to efficiently store and retrieve optimized queries using less space compared to storing complete query definitions, thereby improving throughput while controlling storage consumption.
Data Source
AI summary
Methods, systems, and devices for data management are described. In some systems, a client may request for a data management system (DMS) to execute a first instance of a query to obtain, from a database, first information that is responsive to the query. The DMS may identify, in response to the query being executed in an execution time that is greater than a threshold amount of time, an improved version of the query that is associated with a second execution time that is less than the threshold amount of time. The DMS may store the improved version of the query. The DMS may retrieve the improved version of the query in response to receiving a second instance of the query. The DMS may execute the improved version of the query after retrieving the improved version from storage to obtain, from the database, second information that is responsive to the query.