Multi-Dimensional Data Storage for Time Series Scalability
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Solution Overview
Problem
Current systems managing large volumes of time series data, such as IoT networks, face scalability limitations due to hardware constraints and high costs, often requiring distributed software architectures that complicate data processing and storage.
Innovation Solution
A scalable data processing and management architecture using a multi-dimensional data storage structure with time-ordered data partitions and sequence numbers for parallel processing and efficient data operations, allowing for streamlined data ingestion, storage, and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If distributed software architectures are implemented to manage large volumes of time series data, then scalability is improved, but system complexity increases
Solution Approach 1:
The patent divides time series data into discrete time intervals (buckets) and further segments them into manageable partitions. Each partition contains data for a specific time range and can be independently processed, stored, and queried. This segmentation enables horizontal scalability by allowing data to be distributed across multiple nodes while maintaining simple, consistent access patterns within each partition.
Solution Approach 2:
The patent introduces multiple dimensions for organizing time series data: time intervals (buckets), device identifiers, and metric types. By adding these dimensional layers, the system achieves scalability without increasing operational complexity, as each dimension provides an independent axis for data organization and query optimization.
2Productivity
If data is stored in time-ordered partitions, then data retrieval efficiency is improved, but storage structure complexity increases
Solution Approach 1:
Data is pre-organized into time-ordered partitions and buckets during the ingestion phase. This preliminary organization enables efficient range queries and time-based filtering without requiring complex sorting or scanning operations at query time. The partitioning structure is established in advance, allowing rapid data retrieval while maintaining a systematic storage layout.
3Productivity
If parallel processing is implemented during data ingestion and retrieval, then processing speed is improved, but coordination complexity increases
Solution Approach 1:
The patent segments data into independent partitions that can be processed in parallel across multiple compute nodes. Each partition is self-contained with its own time range and data set, eliminating the need for complex inter-node coordination during processing. This segmentation enables straightforward parallelization while maintaining data consistency through independent processing of discrete units.
Data Source
AI summary
This disclosure relates to, among other things, scalable data processing, storage, and/or management systems and methods. Certain embodiments disclosed herein provide for a multi-dimensional data storage structure that may facilitate parallel processing of data during both data ingestion and data retrieval and/or access processes involving one or more storage layers In various embodiments, sequence number schemes associated with ingested data records may allow for improved management of data stored in one or more layers (e.g., hot and/or cold data storage layers).