Time-Series Database Indexing with Periodic Data Aggregation
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Solution Overview
Problem
Existing time-series databases face inefficiencies in indexing and storage due to frequent updates, especially when dealing with high-volume time-series data, leading to high computational expenses for index maintenance.
Innovation Solution
Implementing a timestamp window and window of interest to reduce index updates by aggregating data updates across longer periods, allowing efficient indexing and storage of time-series data with reduced index writes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequent index updates are performed for each data point in high-volume time-series data, then data retrieval accuracy is improved, but computational expense and time consumption for index maintenance increase significantly
Solution Approach 1:
The patent implements periodic index updates at predetermined time intervals instead of updating after every data point insertion. The time-series database performs batch index maintenance operations at scheduled intervals, reducing the frequency of index updates while maintaining acceptable query performance through periodic synchronization of the index structure.
Solution Approach 2:
The patent pre-allocates index structures and prepares indexing templates in advance before data insertion begins. By pre-configuring the index framework and data structures, the system minimizes the computational overhead during actual index maintenance operations, reducing the time penalty associated with frequent updates.
2Measurement precision
If index updates are performed for every data point insertion, then query accuracy is improved, but computational expense increases by an order of magnitude
Solution Approach 1:
The system transitions from continuous index updates to periodic batch updates, performing index maintenance at predetermined time intervals rather than after every insertion. This periodic approach reduces computational expense by consolidating multiple small update operations into fewer batch operations while maintaining query accuracy through timely index synchronization.
Solution Approach 2:
The patent merges multiple individual index update operations into consolidated batch update operations. By combining several data point insertions and performing a single unified index maintenance operation, the system reduces the total computational overhead associated with frequent separate update operations.
3Measurement precision
If fine-grained indexing is maintained for precise data retrieval, then query precision is improved, but index maintenance complexity increases
Solution Approach 1:
The patent implements periodic index maintenance operations that rebuild or update index structures at predetermined intervals. This approach maintains fine-grained indexing precision for accurate queries while reducing complexity by performing maintenance in scheduled batches rather than requiring complex real-time update logic for every insertion operation.
Data Source
AI summary
In the present application, a system and method for storing and retrieving time-series data in a time-series database are disclosed. A data stream collected at a periodic collection time interval is stored. The stored data stream is indexed in an index, including by storing in the index a representative index entry corresponding to a plurality of data elements of the stored data stream collected during a period of time longer than the periodic collection time interval of the data stream.