Storage Optimization Service for Distributed Data Lakes
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Solution Overview
Problem
Data lakes face challenges in optimizing storage and performance due to varied data types and formats, leading to inefficient data access and processing, as users struggle to manage and maintain massive volumes of heterogeneous data without disrupting user experience or consuming excessive resources.
Innovation Solution
A storage optimization service that monitors data objects in a distributed object store, prioritizes optimizations, and performs automated adjustments to improve performance by consolidating files, changing data formats, and optimizing storage locations, ensuring that optimizations providing the most benefit are executed first and done in a manner that avoids resource disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in a data lake with heterogeneous formats and types, then data versatility and storage capacity are improved, but data access efficiency and processing performance deteriorate
Solution Approach 1:
The patent segments data storage by creating separate storage paths for different data types (structured, semi-structured, unstructured) within the data lake. Each data type is routed to appropriate storage formats and locations, enabling efficient access patterns while maintaining format versatility. This segmentation resolves the contradiction by organizing heterogeneous data into manageable categories that can be accessed efficiently.
Solution Approach 2:
The patent applies local quality by optimizing storage formats and access methods for specific data types. Structured data uses columnar formats for analytical queries, while semi-structured data uses document formats for flexible access. Each data type receives tailored storage optimization, allowing the system to maintain high versatility while achieving efficient access for each specific data category.
2Adaptability or versatility
If multiple different systems are used to manage various data types, then data type specialization is improved, but system complexity and management difficulty worsen
Solution Approach 1:
The patent implements a universal data lake architecture that can handle multiple data types (structured, semi-structured, unstructured) through a single system. The system provides multi-functional capabilities including storage, processing, and analytics for different data types using standardized interfaces, thereby reducing the need for multiple separate systems while maintaining data type specialization through format-specific optimization paths.
3Quantity of substance
If storage optimization is performed on massive volumes of data, then storage efficiency is improved, but resource consumption and processing time worsen
Solution Approach 1:
The patent performs preliminary storage optimization by automatically selecting appropriate storage formats and compression methods when data is first ingested into the data lake. This preliminary action optimizes storage efficiency upfront without requiring resource-intensive reprocessing later, thereby reducing both storage costs and ongoing computational resource consumption.
Solution Approach 2:
The system implements self-service optimization by automatically analyzing incoming data characteristics and selecting optimal storage formats, compression levels, and partitioning strategies without manual intervention. This automation reduces the resource consumption associated with manual optimization processes while maintaining high storage efficiency across massive data volumes.
Data Source
AI summary
Techniques for storage optimization in a distributed object store are described. A storage optimization service of a provider network monitors changes to data objects in a distributed object store that are part of a data lake and are referenced by a table index. The storage optimization service determines whether particular storage optimizations involving the data objects would be beneficial, prioritizes the ordering of these optimizations with a focus on performing impactful optimizations first, while intelligently scheduling the optimizations to avoid overutilization of available resources.