Predictive Resource Allocation for Distributed Queries
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Solution Overview
Problem
In cloud database systems, customers face inefficiencies and increased costs due to inaccurate allocation of computing resources for query execution, leading to either idle resources or excessive utilization, as they must specify the amount of resources needed, which can result in unexpected costs based on execution time and resource usage.
Innovation Solution
The system utilizes local and global historical data to predict the required computing resources by analyzing query characteristics, leveraging knowledge from similar queries to make informed decisions on resource allocation, incorporating metadata organization and pruning techniques to efficiently scan relevant micro-partitions and reduce unnecessary resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If customers specify the amount of computing resources needed for query execution, then query execution can be performed, but inaccurate allocation leads to either idle resources or excessive utilization and unexpected costs
Solution Approach 1:
The system automatically determines and allocates computing resources based on query characteristics and historical data, eliminating the need for customers to manually specify resource amounts. The resource allocation predictor analyzes query metadata, table statistics, and execution history to self-determine optimal resource requirements, thereby preventing both resource waste and execution delays.
Solution Approach 2:
The system incorporates feedback mechanisms by analyzing historical query execution data and adjusting future resource allocations accordingly. The resource allocation predictor uses past execution patterns, resource utilization metrics, and query performance data to continuously improve accuracy in predicting and allocating resources, creating a closed-loop optimization system.
2Loss of time
If more computing resources are allocated to ensure adequate query execution, then query response time decreases, but costs increase due to excessive resource utilization
Solution Approach 1:
The system dynamically adjusts computing resource parameters based on query characteristics, data distribution, and execution history. The resource allocation predictor modifies resource allocation decisions by analyzing query complexity, table sizes, join operations, and historical performance metrics, optimizing the balance between execution speed and resource consumption.
Solution Approach 2:
The system applies partial resource allocation by providing just enough computing resources needed for efficient query execution, rather than over-provisioning. The predictor identifies the minimum adequate resource level based on query requirements, avoiding excessive resource consumption while ensuring timely execution through precision rather than excess.
3Ease of operation
If manual resource specification is required from customers, then resource allocation can be controlled, but system complexity increases and ease of operation decreases
Solution Approach 1:
The resource allocation system operates autonomously by automatically analyzing query metadata, table statistics, and execution history to determine optimal resource requirements. This self-service mechanism eliminates the need for customers to manually specify resources, simplifying operation while the system internally manages the complexity of resource prediction and allocation.
Solution Approach 2:
The resource allocation predictor acts as an intermediary between the customer's query and the actual resource allocation. This mediator component translates high-level query intentions into specific resource requirements by analyzing historical data and query characteristics, shielding customers from resource management complexity while enabling precise allocation.
Data Source
AI summary
The subject technology receives a query directed to a set of source tables, each source table organized into a set of micro-partitions. The subject technology determines a set of metadata, the set of metadata comprising table metadata, query metadata, and historical data related to the query. The subject technology predicts, using a machine learning model, an indicator of an amount of computing resources for executing the query based at least in part on the set of metadata. The subject technology generates a query plan for executing the query based at least in part on the predicted indicator of the amount of computing resources. The subject technology executes the query based at least in part on the query plan.