Index Lifecycle Management for Storage Optimization
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Solution Overview
Problem
Current data management systems face inefficiencies in handling large volumes of time-based data, particularly in search engines like Elasticsearch, where indices grow rapidly and old data loses value, leading to storage issues and inefficient deletion processes.
Innovation Solution
Implementing an index lifecycle management (ILM) policy that transitions indices through phases (hot, warm, cold, delete) based on conditions such as age and size, allowing for resource optimization and efficient data retention and deletion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If indices retain all historical data indefinitely, then data completeness is maintained, but storage costs increase and system performance degrades
Solution Approach 1:
The patent segments the index lifecycle into distinct phases (hot, warm, cold, delete) with different retention policies. Each phase has specific criteria for data retention and deletion, allowing the system to maintain complete data across phases while enabling selective deletion in the delete phase, thus resolving the contradiction between data completeness and storage volume management
Solution Approach 2:
The patent changes the retention parameter dynamically based on the index phase and data age. In hot and warm phases, data is retained with high priority; in cold phase, retention is reduced; in delete phase, deletion is performed. This parameter change approach allows the system to maintain data completeness where needed while reducing storage volume where appropriate
2Quantity of substance
If old data is deleted immediately to free storage space, then storage costs are reduced, but data availability and compliance requirements may be violated
Solution Approach 1:
The patent performs preliminary actions by creating delete phase indices before actually deleting data. The delete phase index is prepared with deletion policies in advance, but actual deletion only occurs after the index is fully rolled over and data is safely stored in the delete phase. This preliminary action ensures data availability is maintained during the transition while enabling storage reduction through controlled deletion
Solution Approach 2:
The delete phase index serves as an intermediary between the warm phase index and actual data deletion. Data is moved to the delete phase index first, where it can be safely managed and reviewed before final deletion. This intermediary mechanism ensures data availability is maintained during the deletion process while enabling storage volume reduction
3Loss of information
If indices are continuously grown without management, then data retention capacity increases, but system scalability and performance deteriorate
Solution Approach 1:
The patent implements dynamic index management where indices automatically transition between phases (hot, warm, cold, delete) based on data age and size criteria. This dynamic approach allows the system to retain data capacity across multiple phases while maintaining performance by automatically moving older data to less frequently accessed phases and enabling deletion of obsolete data
Solution Approach 2:
The patent employs periodic index rollover and phase transition actions. Indices are created periodically with rolling time-based names (e.g., index-2024-01, index-2024-02), and phase transitions occur periodically based on data age criteria. This periodic action maintains data retention capacity while improving system performance through structured data lifecycle management
4Loss of information
If manual data deletion processes are used, then data retention control is precise, but operational efficiency and automation level decrease
Solution Approach 1:
The patent implements self-service automation through the ILM policy framework, which automatically manages index phase transitions and data deletion without manual intervention. The system autonomously monitors index age and size, transitions indices between phases, and performs deletions according to predefined policies, thereby achieving both precise retention control and high automation
Solution Approach 2:
The patent employs feedback mechanisms where the system continuously monitors index characteristics (age, size, data volume) and automatically adjusts phase transitions and deletion actions based on this feedback. The ILM policy framework receives feedback about index state and automatically responds with appropriate actions, achieving precise retention control through automated feedback-driven management
Data Source
AI summary
Methods and systems for index lifecycle management are provided. Exemplary methods include: receiving an ILM policy; determining a first condition and a first action for a first phase using the ILM policy; performing the first action for the first phase when the first condition is met; transition from the first phase to a second phase; determining a second condition and a second action for the second phase using the ILM policy; performing the second action for the second phase when the second condition is met; transition from the second phase to a third phase; determining a third condition and a third action for the third phase using the ILM policy; performing the third action for the third phase when the third condition is met; transition from the third phase to a fourth phase; and deleting the index during the third phase.