Managed Query Execution Platform Indexed Views
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Solution Overview
Problem
Data lakes face challenges in scaling computing resources efficiently, leading to increased costs and performance degradation, and require lengthy data transformation processes to make data accessible for analytics and AI applications.
Innovation Solution
A managed query execution platform with a distributed architecture of computing machines and storage devices, utilizing indexed views and elastic query engines to accelerate SQL queries and commands, allowing for fast data processing without additional resource allocation and data transformation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If more computing resources are allocated to improve application performance, then query execution speed is improved, but overall cost increases
Solution Approach 1:
The system segments computing resources by deploying query execution containers that can be independently allocated and managed. Each container handles specific query workloads, allowing granular resource allocation that improves query execution speed without requiring proportional increases in overall computing resource allocation across the entire system.
Solution Approach 2:
The system dynamically allocates and scales query execution containers based on actual workload demands. The containerized architecture enables flexible resource orchestration where computing resources are activated only when needed for query execution, rather than maintaining static over-provisioned resources, thus improving speed while controlling resource consumption and costs.
2Adaptability or versatility
If data transformation processes are performed to make data accessible for analytics, then data usability is improved, but processing time increases
Solution Approach 1:
The system performs data transformation and preparation in advance by executing queries against the data lake and materializing results in columnar storage format. This preliminary action transforms raw data into analytics-ready formats before actual analytical queries are executed, making data immediately usable without requiring lengthy transformation processes during query execution.
Solution Approach 2:
The system introduces an intermediary layer (columnar storage with materialized views) between the raw data lake and analytical query engines. This intermediary pre-processes and structures data in optimized formats, serving as a buffer that eliminates the need for real-time data transformation during query execution, thus improving data usability while minimizing processing time.
Data Source
AI summary
A managed query execution platform, and methods thereof are provided. The, managed query execution platform includes a plurality of computing machines; a plurality of storage devices; and a communication fabric configured to allow communication of the plurality of computing machines with the plurality of storage devices over a storage communication protocol, wherein the plurality of storage devices are configured to maintain at least one indexed view, and wherein the plurality of computing machines are configured to execute queries on the at least one indexed view.