Timestamp-Based Indexing for Time-Series Data Retrieval
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Solution Overview
Problem
Conventional indexing techniques in relational database management systems are inefficient for handling large sets of time series data, leading to slow data retrieval and sorting times, especially in real-time high-capacity networks where users need to interactively search and display time series query results.
Innovation Solution
A method for timestamp-based indexing in a high-capacity network storage system that processes time-ordered data streams, accumulates items with timestamps, and stores virtual offset information to enable efficient sorting and retrieval, using a distributed data management platform with session managers and catalogs to manage storage and retrieval operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional indexing techniques are used in relational database management systems, then data can be stored and retrieved, but retrieval time and sorting time increase significantly when dealing with large sets of time series data
Solution Approach 1:
The patent segments time series data into discrete time-windowed buckets, where each bucket contains data items within a specific time range. This segmentation allows the system to quickly locate and retrieve only the relevant time window data without scanning the entire dataset, thereby reducing retrieval time and improving efficiency when handling large volumes of time series data
Solution Approach 2:
The patent performs preliminary indexing actions by pre-computing and storing time-windowed bucket identifiers and metadata before actual data retrieval occurs. The system pre-organizes data into time-based buckets with associated index structures, so when a retrieval request comes in, the system can immediately access the pre-prepared time window data without performing complex sorting or filtering operations at query time
2Loss of time
If conventional indexing techniques are used, then data storage is possible, but sorting time for displaying time series query results to users increases significantly
Solution Approach 1:
The patent segments data into time-windowed buckets that are inherently sorted by time range. Each bucket represents a contiguous time interval, eliminating the need for post-retrieval sorting operations. This segmentation approach maintains data in a naturally sorted state, allowing the system to display time series results to users without performing time-consuming sorting operations
Solution Approach 2:
The patent changes the indexing parameter from traditional database row-based indexing to time-windowed bucket-based indexing. By organizing data according to time parameters rather than sequential storage positions, the system enables direct access to time-sorted data, eliminating the need for separate sorting operations and significantly reducing the time required to display time series results
3Productivity
If conventional indexing is used, then data can be stored, but the system efficiency decreases in real-time high-capacity networks where interactive search is required
Solution Approach 1:
The patent segments the indexing structure into hierarchical time-windowed buckets, where data is organized by time ranges rather than stored sequentially. This segmentation creates a simplified index structure that maps time queries directly to specific bucket ranges, reducing the complexity of search operations in real-time high-capacity networks while maintaining high system efficiency for interactive queries
Solution Approach 2:
The patent inverts the traditional indexing approach by organizing data from the query perspective (time-based access patterns) rather than the storage perspective (sequential storage). Instead of storing data and then creating indexes for retrieval, the system pre-organizes data into time-windowed buckets that directly match typical time-series query patterns, simplifying the indexing complexity while improving real-time search efficiency
Data Source
AI summary
A method for storing time-based data streams in a high-capacity network is provided. A time-based data storage request is received from an application. The data storage request is associated with one or more data streams. Each of the data streams includes a plurality of time-ordered items having a header. The header includes two or more timestamps representing a time interval associated with each of the plurality of time-ordered items. The received data storage request is processed to accumulate time-ordered data items in a plurality of data files and to identify time-based information and corresponding virtual offset information related to the accumulated data items. The identified time-based information and the virtual offset information related to the accumulated data items are stored in a data repository. A determination is made whether adjustment is necessary for sorting the stored data in the repository. The sorting adjustment is selectively performed responsive to the determination.