SionoiS
commented
10 months ago A Prolly Tree can be constructed through various methods, including insert-left, insert-right, and insert-random. In scenarios involving insert-right and insert-left, chronological ordering, such as timestamp sequencing of data or messages, can be preserved, leading to the formation of what may be termed an "ordered Prolly Tree."
A bit misleading Prolly trees are always ordered collections (Key only or Key-Value) if not then it's not a Prolly tree. As for the inserting I'm not sure what you mean.
A notable limitation arises with linear or chronological data management in Prolly trees, especially when random edits are involved - such as modifications, insertions, or deletions. Locating specific data for these operations lacks a direct, expedited pathway.
I'm not sure what you mean here either, Prolly trees were designed specifically for this purpose.
Timestamp Boundaries in Leaf Buckets: Implementing timestamp boundaries within the leaf buckets, which hold ordered messageHashes, enhances navigational efficiency. This structure enables more rapid access to the pertinent bucket, leveraging logarithmic complexity.
It would be much simpler to have 2 trees one for key=timestamp value=hashes, one for key=hashes value=none.
Optimization with Flat Binary Search: Further refinement can be achieved using a flat binary search. By utilizing timestamps and other chronological ordering criteria, this search method can expedite the identification and access of specific data points within the tree.
:100: Binary search is always used since keys are ordered.
Employing Bloom Filters for Efficient Filtering: Utilizing Bloom filters presents another expedient method for identifying missing entries within the Prolly Tree. While inherently probabilistic, Bloom filters can significantly streamline the process of locating missing entries. Their probabilistic nature implies a trade-off between accuracy and efficiency, but they can provide substantial benefits in swiftly narrowing down the search space for potential missing or altered data points in the tree structure.
No need IMO, Prolly trees are plenty fast.
Prolly Trees
The Prolly Tree, an amalgamation of Merkle and B-Tree architectures, is devised to streamline data searching and classification. This structure is pivotal in swiftly discerning discrepancies between two data stores, rendering it particularly advantageous for synchronization applications. The Prolly Tree operationalizes by hashing input data and generating a tree-like structure. It ensures that hash contents conforming to a specific pattern are clustered within the same subtree branch. The tree accommodates adjustable branching, controlled through a quotient parameter (Q) applied over the span of possible hash values. A node is designated as the first child in its parental branch if the initial segment of its hash, when interpreted as an unsigned 32-bit integer, falls below the ratio of the hash space to Q.
A Prolly Tree can be constructed through various methods, including insert-left, insert-right, and insert-random. In scenarios involving insert-right and insert-left, chronological ordering, such as timestamp sequencing of data or messages, can be preserved, leading to the formation of what may be termed an "ordered Prolly Tree."
When constructing a Prolly Tree using linearly or chronologically ordered message hashes for synchronization purposes, the process of comparing diffs between two nodes can be depicted as follows:
Issues:
A notable limitation arises with linear or chronological data management in Prolly trees, especially when random edits are involved - such as modifications, insertions, or deletions. Locating specific data for these operations lacks a direct, expedited pathway. Although upto some degree it can be mitigated Using the following ways:
Timestamp Boundaries in Leaf Buckets: Implementing timestamp boundaries within the leaf buckets, which hold ordered messageHashes, enhances navigational efficiency. This structure enables more rapid access to the pertinent bucket, leveraging logarithmic complexity.
Optimization with Flat Binary Search: Further refinement can be achieved using a flat binary search. By utilizing timestamps and other chronological ordering criteria, this search method can expedite the identification and access of specific data points within the tree.
Employing Bloom Filters for Efficient Filtering: Utilizing Bloom filters presents another expedient method for identifying missing entries within the Prolly Tree. While inherently probabilistic, Bloom filters can significantly streamline the process of locating missing entries. Their probabilistic nature implies a trade-off between accuracy and efficiency, but they can provide substantial benefits in swiftly narrowing down the search space for potential missing or altered data points in the tree structure.