Predictive Query Planning for Heterogeneous Data Source Resources
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Solution Overview
Problem
Existing database management systems lack the ability to generate resource-aware query execution plans that account for the predicted availability of computing resources across multiple data sources with different database technologies, leading to inefficient query execution.
Innovation Solution
A database management system (DBMS) generates query execution plans based on predictions from a trained predictive model that estimates the availability of computing resources at various data sources, using model prompting data to optimize query execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a database management system generates query execution plans without considering predicted resource availability, then the query planning process is simple and fast, but query execution efficiency deteriorates due to resource unavailability
Solution Approach 1:
The system performs preliminary actions by training a predictive model to forecast computing resource availability before query execution. The model analyzes historical resource usage patterns and predicts future availability, allowing the query planner to make informed decisions about which data sources to access and how to route queries, thereby avoiding execution failures and improving overall efficiency
Solution Approach 2:
A predictive model is introduced as an intermediary between the query planner and the physical data sources. This intermediary provides resource availability predictions that guide the query planning process, enabling the system to select optimal execution paths without directly monitoring real-time resource states of multiple heterogeneous databases
2Reliability
If the system monitors and predicts resource availability for multiple heterogeneous data sources, then query execution becomes more reliable, but the system complexity increases
Solution Approach 1:
The predictive model serves as a universal interface that handles multiple heterogeneous data sources through a single unified approach. Instead of implementing separate monitoring mechanisms for each database technology, the model learns from aggregated historical data across all sources and provides generalized predictions, simplifying the system architecture while maintaining reliability across diverse data sources
Solution Approach 2:
The system creates a virtual copy of the physical system's resource availability characteristics through the predictive model. Rather than directly interacting with complex heterogeneous data sources to monitor resources, the model generates predicted availability data that mirrors real resource states, allowing the query planner to make reliable decisions without direct exposure to underlying system complexity
3Productivity
If query execution plans are generated based on predicted resource availability, then resource utilization improves, but the time required for query planning increases
Solution Approach 1:
The predictive model is trained in advance on historical resource usage data, performing preliminary analysis of resource availability patterns. Once trained, the model can quickly generate predictions for future time periods without requiring real-time computation during query planning, thus improving resource utilization while minimizing additional planning time
Solution Approach 2:
The system optimizes the balance between prediction accuracy and planning speed by adjusting parameters such as the time window for resource availability predictions and the level of detail in predictions. By changing these parameters, the system can adapt to different query workloads, providing detailed predictions when time permits and simplified predictions when quick planning is needed
Data Source
AI summary
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: receiving, by a database management system, a database query, wherein the database management system is in communication with a plurality of data sources including a first data source and a second data source, wherein the first data source includes a first database of a first database technology hosted on a first computing node, and wherein the second data source includes a second database of a second database technology hosted on a second computing node; generating a query execution plan in response to the receiving the database query, wherein the generating includes performing the generating in dependence on prediction data returned responsively to applying model prompting data to a trained predictive model trained to predict computing resource availability of the first computing node and the second computing node.