Search Cluster Snapshot Archiving for Fast Data Retrieval
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Solution Overview
Problem
Existing data archiving and retrieval methods in distributed search and analytics environments face inefficiencies in handling real-time data streams, often leading to complex management practices, data loss, and corruption, with a lack of resilient storage and quick retrieval capabilities.
Innovation Solution
A method involving real-time data ingestion, segmentation across search engine clusters, resiliency snapshot capturing, index snapshot creation via clone API, and partial mount of index snapshots for selective retrieval, integrated with archival search engine clusters and localized caching for quick access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data archiving methods are used, then data storage is achieved, but data retrieval efficiency deteriorates and management complexity increases
Solution Approach 1:
The patent segments data into time-based index snapshots (e.g., daily, weekly, monthly) and stores them in a hierarchical structure. This segmentation enables efficient retrieval by allowing queries to target specific time ranges without scanning entire datasets, thus improving productivity while maintaining reliability through organized data partitioning.
Solution Approach 2:
The system performs preliminary actions by creating index snapshots at predetermined time intervals before data retrieval is needed. These snapshots are pre-processed and stored in an optimized format, enabling fast retrieval operations without requiring real-time processing, thereby resolving the contradiction between reliable archiving and efficient retrieval.
2Speed
If real-time data streaming is implemented, then data processing speed improves, but data loss and corruption risks increase
Solution Approach 1:
The patent implements beforehand cushioning by creating resiliency snapshots at regular intervals during real-time data ingestion. These snapshots serve as backup checkpoints that protect against data loss from failures, corruption, or accidental deletions. The system cushions the real-time processing stream with periodic validation points, maintaining both high speed and reliability.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor data ingestion health and trigger snapshot creation or recovery operations when anomalies are detected. This feedback loop allows the system to maintain real-time processing speed while automatically responding to potential data integrity issues, thus resolving the contradiction between speed and reliability.
3Reliability
If complete data replication is performed, then data availability improves, but storage resource consumption increases
Solution Approach 1:
The patent applies local quality by storing different types of data copies in different locations with different replication factors. Frequently accessed recent data is replicated across multiple nodes for high availability, while older archived snapshots use fewer replicas. This localized differentiation of storage quality maintains data availability where needed while conserving overall storage resources.
Solution Approach 2:
The system uses a nested doll approach by organizing data in hierarchical generations of snapshots (e.g., hourly snapshots nested within daily snapshots, which are nested within weekly snapshots). This nested structure allows the system to maintain multiple levels of data availability without duplicating entire datasets at each level, as inner snapshots are subsets of outer snapshots, thus reducing total storage consumption while maintaining reliability.
Data Source
AI summary
A method and a system for efficient data archiving and retrieval in a distributed search and analytics environment are disclosed. The method includes ingesting a real-time data stream from at least one data source. The method further includes segmenting the ingested data stream into at least one segment and storing each of the at least one segment across a set of nodes in at least one search engine cluster. The method further includes capturing and storing a set of resiliency snapshots of the at least one segment within the at least one search engine cluster after a first predefined time period. Thereafter the method includes creating at least one index snapshot based on the stored set of resiliency snapshots through a clone API after a second predefined time period and mounting the at least one index snapshot onto an archival search engine cluster using a mount option.