Bε-tree leaf sorting and non-leaf bloom filters
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Solution Overview
Problem
Append-only Bε-trees experience reduced query efficiency and increased overhead due to unsorted data in nodes, and the large size of bloom filters required for optimization, which can exceed available memory space.
Innovation Solution
The new Bε-tree stores data in leaves in a sorted manner and only creates bloom filters for non-leaf nodes, eliminating filters for leaves and reducing memory usage while maintaining sorted data in leaves for efficient querying.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bloom filters are created for all nodes including leaves in append-only Bε-trees, then query efficiency is improved, but memory space consumption increases significantly
Solution Approach 1:
The patent segments the Bε-tree into two distinct types of nodes: leaf nodes and non-leaf nodes. Bloom filters are applied selectively only to non-leaf nodes, while leaf nodes store data in sorted order without filters. This segmentation allows the system to optimize query operations at different levels of the tree with appropriate data structures, reducing overall memory consumption while maintaining query efficiency.
Solution Approach 2:
The patent applies different optimization strategies to different parts of the Bε-tree based on their specific needs. Non-leaf nodes use bloom filters for efficient key existence checking before descent, while leaf nodes use sorted storage for efficient final key searches. This local quality approach ensures each node type has the optimal structure for its function, balancing memory usage and query performance.
2Loss of energy
If data in leaf nodes is stored in unsorted manner to simplify writes, then write amplification is reduced, but query operations become less efficient
Solution Approach 1:
The patent segments the write and query operations into distinct phases. During writes, data is appended to leaf nodes in unsorted order to minimize write amplification. During queries, the sorted structure of leaf nodes enables efficient key searches without requiring full tree traversal or re-sorting operations.
Solution Approach 2:
The patent performs preliminary sorting of data in leaf nodes during the initial build phase or during periodic maintenance operations. This preliminary action ensures that leaf nodes maintain sorted order for efficient queries, while allowing flexible append-only writes during normal operation without requiring continuous re-sorting.
Data Source
AI summary
Certain aspects provide systems and methods for performing an operation on a Bε-tree. A method comprises writing a message associated with the operation to a first slot in a first buffer of a first non-leaf node of the Bε-tree in an append-only manner, wherein a first filter associated with the first slot is used for query operations associated with the first slot. The method further comprises determining that the first buffer is full and, upon determining to flush the message to a non-leaf child node, flushing the message in an append-only manner to a second slot in a second buffer of the non-leaf child node, wherein a second filter associated with the second slot is used for query operations associated with the second slot. The method further comprises, upon determining to flush the message to a leaf node, flushing the message to the leaf node in a sorted manner.