Data Transform Component for Dependent Data Pre-loading
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Data storage systems face challenges in efficiently managing large data sets and supporting multiple concurrent users, particularly in identifying and optimizing the storage and retrieval of dependent data across distributed and tiered storage systems while maintaining service level objectives.
Innovation Solution
A data storage system with a data transform component that identifies data structures, locates dependent data, and copies it into memory or cache, transforming and preparing it for analytics while adhering to user-configurable policies and service level objectives, and dynamically managing storage tiers to ensure efficient access and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If dependent data is copied into memory to improve access performance, then data retrieval speed is improved, but memory capacity is consumed and service level objectives may be compromised
Solution Approach 1:
The system performs preliminary actions by proactively copying dependent data into memory before it is actually needed for analytics operations. The data transform component identifies data structures and their dependent data, then pre-loads this data into memory or cache, preparing it in advance for upcoming IO operations. This prevents performance degradation when data is actually accessed.
Solution Approach 2:
The system dynamically adjusts memory allocation and data copying behavior based on real-time conditions. The data transform component monitors memory availability, service level objectives, and data access patterns, then dynamically decides which data to copy, when to copy it, and how to manage cache eviction. This dynamic adaptation allows the system to maintain performance while respecting memory constraints.
2Productivity
If data is transformed and prepared for analytics in advance, then analytics performance is improved, but processing time and computational resources are consumed
Solution Approach 1:
The system performs data transformation and preparation in advance as a preliminary action. The data transform component identifies data structures, locates dependent data, transforms it according to analytics requirements, and prepares it for access before actual analytics operations occur. This pre-processing eliminates or reduces the processing time needed during actual analytics execution.
Solution Approach 2:
The system maintains continuous data transformation and preparation operations that run alongside data storage and retrieval operations. The data transform component operates continuously to transform data as it is accessed or pre-loaded, ensuring that data is always ready for analytics without interrupting the overall workflow. This continuous processing maximizes productivity while managing processing time efficiently.
3Adaptability or versatility
If multiple data structures are managed simultaneously in memory, then data access flexibility is improved, but system complexity increases
Solution Approach 1:
The system segments data management by organizing data into distinct data structures with clear relationships. The data transform component identifies and separates dependent data from parent data structures, managing them as organized units rather than a monolithic complex. This segmentation simplifies the management of multiple data structures while maintaining access flexibility.
Solution Approach 2:
The data transform component acts as an intermediary between the storage layer and the analytics layer. It mediates by identifying data structures, locating dependent data, transforming data formats, and preparing data for access. This intermediary function abstracts the complexity of managing multiple data structures from the analytics operations, providing flexible access while containing system complexity in a dedicated component.
4Reliability
If service level objectives are strictly enforced, then system reliability is improved, but data copying operations are restricted and performance optimization is limited
Solution Approach 1:
The system implements feedback mechanisms where the data transform component continuously monitors service level objective compliance and adjusts its operations accordingly. When SLOs are approaching being violated, the system prioritizes copying critical dependent data. When SLOs are comfortably met, the system can perform more aggressive copying optimizations. This feedback loop ensures reliability while maximizing copying efficiency within acceptable parameters.
Solution Approach 2:
The system dynamically adjusts data copying operations based on real-time service level status. The data transform component monitors SLO compliance and dynamically modifies copying priorities, thresholds, and strategies. When reliability requirements are tight, copying operations are restricted to critical data. When reliability is comfortably met, the system optimizes copying for maximum performance. This dynamic adaptation resolves the contradiction between reliability and productivity.
Data Source
AI summary
A computing node of a data storage system includes a data transform component. The data transform component identifies data that is maintained by the storage system and has a particular data structure. The data transform component may also perform analytics on the data based on the data structure. For example, different data sets that are dependent, e.g. related via association with the same application or type of application, or which are typically accessed in temporal proximity, can be discovered based on data structure and analytics. Data relationships may be used to prepare dependent data for access in advance of an IO associated with that dependent data.