Incremental Out-of-Place Dataset Updates in Data Stores
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Solution Overview
Problem
The increasing complexity and operational costs of managing large datasets in data stores are exacerbated by the need to maintain data sorting during updates, leading to inefficiencies in query processing and storage management.
Innovation Solution
Implementing incremental out-of-place updates, where updates are stored separately and applied later, allowing data to be compacted and resorted without blocking query processing, and enabling efficient query servicing by generating updated data chunks in new storage locations while reclaiming space for other data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data updates are performed in-place while maintaining sorting order, then data consistency is preserved, but query processing is blocked and operational complexity increases
Solution Approach 1:
The patent segments the data storage structure into immutable data chunks and separate update logs. Updates are recorded in append-only log files rather than modifying existing data chunks in-place. This segmentation allows query processing to continue accessing the original sorted data chunks while updates are applied asynchronously, eliminating query blocking while maintaining data consistency.
Solution Approach 2:
The patent introduces an intermediary update log structure that mediates between incoming updates and the main data storage. The log file serves as a buffer where updates are first recorded, then periodically applied to data chunks. This intermediary layer allows the system to maintain sorted data chunks for efficient querying while capturing all updates without blocking access.
2Quantity of substance
If data is compacted and resorted during updates, then storage efficiency is improved, but update operations become more complex and time-consuming
Solution Approach 1:
The patent applies preliminary action by pre-allocating sorted data chunks and maintaining them in sorted order. Instead of sorting data during update operations, the system pre-organizes data into sorted chunks that can be efficiently queried. Update logs are also maintained in a structured format that facilitates efficient application without requiring complex sorting operations during updates.
Solution Approach 2:
The patent uses copying by creating new data chunks with updated data rather than modifying existing chunks in-place. When updates are applied, the system copies relevant data from the update log and applies it to create new versions of data chunks. This copying approach simplifies update operations by avoiding complex in-place modifications and sorting, while still achieving storage efficiency through incremental updates.
3Measurement precision
If updates are applied immediately to maintain current data state, then data accuracy is maintained, but storage space is wasted and operational costs increase
Solution Approach 1:
The patent implements periodic action by applying updates in batches rather than immediately for each update operation. The system periodically processes update logs and applies accumulated updates to data chunks at optimized intervals. This periodic application reduces the frequency of expensive write operations, lowers operational costs, and reduces energy consumption while maintaining data accuracy through the append-only log structure that preserves the complete update history.
Data Source
AI summary
A data store may implement incremental out-of-place updates to a dataset. A dataset may maintain data across different storage locations linked together according to an ordering schema for servicing queries. As updates to the dataset are received, the updates may be persisted but not maintained in-place. In order to update the data store and maintain the ordering schema, incremental updates to the dataset may be performed without blocking queries directed toward the dataset. The dataset may be divided into multiple data chunks that correspond to different storage locations and an updated version of the data chunk may be generated in new storage locations. The new storage locations may then replace the storage locations of the prior version of the data chunk in order to link the new storage locations to the other linked storage locations in the dataset for servicing queries.