Data Tree Checkpointing via Share-Latched Leaf Node Copying
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Solution Overview
Problem
Large data management systems face inefficiencies in storing, organizing, and retrieving data due to the complexity of managing large volumes of digital information, particularly in environments where rapid data creation and error recovery are critical, leading to challenges in minimizing data restoration time.
Innovation Solution
A method and system for storing and restoring data tree structures by share-latching nodes, copying leaf nodes into persistent storage, and reconstructing the tree by reading from storage into memory, with the option to copy non-leaf nodes to avoid time-consuming reconstruction and allow concurrent restoration of all leaf nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data tree structures are restored by reconstructing the tree from stored information, then data integrity is maintained, but restoration time increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-copying leaf nodes to persistent storage in a share-latched state before restoration is needed. This allows the restoration process to simply read pre-prepared data from storage rather than reconstructing the entire tree structure, significantly reducing restoration time while maintaining data integrity through the use of checkpoint files that capture the tree state at specific points in time.
Solution Approach 2:
The patent employs copying by creating checkpoint files that contain copies of leaf nodes from the data tree structure. Instead of reconstructing the entire tree during restoration, the system copies relevant leaf nodes from persistent storage into memory, reconstructing only the necessary portions of the tree structure. This selective copying approach maintains data integrity while dramatically reducing restoration time compared to full tree reconstruction.
2Productivity
If all leaf nodes are copied concurrently to persistent storage, then storage efficiency improves, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the leaf node copying process into manageable units. Each leaf node is individually share-latched and copied to persistent storage, allowing concurrent processing of multiple nodes. The system segments the large task of copying the entire tree into smaller, parallelizable operations on individual leaf nodes, improving storage efficiency while managing system complexity through modular processing.
Solution Approach 2:
The patent implements self-service through the use of share-latching mechanisms that allow leaf nodes to be automatically copied to persistent storage without requiring complex coordination. Each leaf node can be independently processed and stored, with the system automatically managing the concurrent operations. This self-service approach improves storage efficiency by enabling parallel processing while keeping system complexity manageable through automatic resource management.
3Adaptability or versatility
If the entire tree structure is restored to memory, then complete data availability is achieved, but memory usage and restoration time increase
Solution Approach 1:
The patent applies the extraction principle by selectively copying only the necessary leaf nodes from persistent storage into memory during restoration, rather than restoring the entire tree structure. The system extracts and restores only the specific data portions that are needed, reducing both memory usage and restoration time while maintaining complete data availability for the required dataset. This selective extraction approach allows the system to avoid the overhead of restoring unnecessary tree structures.
Solution Approach 2:
The patent implements partial action by restoring only the necessary portions of the data tree structure rather than the complete tree. The system performs partial restoration by copying specific leaf nodes from persistent storage into memory based on the restoration requirements, achieving complete data availability for the needed dataset without the time and memory costs of restoring the entire tree structure. This partial action approach optimizes restoration efficiency while maintaining data completeness for the required scope.
Data Source
AI summary
Systems and methods for storing nodes, preferably, leaf nodes, of a data tree structure into storage are disclosed, and in one or more aspects restoring the leaf nodes from storage, preferably to memory. Copying the nodes into storage includes in an embodiment share-latching a first node of a data tree to be copied; copying the first node that is share-latched into storage; determining if there is a sibling second node linked to the first node; following a link between the first copied node and the sibling second node, share-latching the sibling second node, unlatching the first copied node, and copying the sibling second node into storage. Restoring includes copying the leaf nodes from storage, updating the leaf nodes, and creating/recreating the data tree.