Scalable Metadata Index for Time-Series Database Query Routing
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Solution Overview
Problem
As the scale and complexity of distributed time-series databases increase, managing and querying large datasets become increasingly costly and complicated, with existing systems struggling to maintain high availability and low latency for ingestion and query operations.
Innovation Solution
A scalable metadata index is implemented using a directed acyclic graph (DAG) or tree-like data structure, which supports fast lookups, rebalances nodes, and efficiently routes incoming data and queries across storage tiers, allowing for efficient storage and retrieval of time-series data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the scale and scope of database systems increase to handle larger datasets, then the capacity to store and process data improves, but the costs to search, analyze, and manage data increase
Solution Approach 1:
The patent divides the database system into multiple distributed nodes that independently store and process portions of the dataset. Each node maintains local metadata and can independently handle queries, eliminating the need for centralized management and reducing the computational overhead associated with managing large-scale data collections.
Solution Approach 2:
The patent introduces a hierarchical metadata structure with multiple levels of abstraction. Instead of flat metadata storage, the system organizes metadata in tiers (global metadata, regional metadata, local metadata), adding a dimensional layer to the data organization that enables efficient navigation and reduces the complexity of searching through large datasets.
2Quantity of substance
If traditional database systems are used to manage large datasets, then data storage capacity increases, but the latency for ingestion and query operations increases
Solution Approach 1:
The patent pre-computes and stores metadata information (such as data locations, partitions, and statistical summaries) before queries are executed. This preliminary organization of data and metadata enables rapid query routing and data retrieval, eliminating the need for full dataset scans and reducing query latency even as dataset size increases.
Solution Approach 2:
The patent introduces a metadata service layer that acts as an intermediary between query processors and data storage nodes. This metadata service maintains indexes and routing information, enabling fast lookup of data locations without requiring direct access to the entire dataset, thereby reducing both ingestion and query latency in distributed systems.
3Device complexity
If centralized metadata management is used in distributed databases, then system simplicity is maintained, but scalability and availability deteriorate
Solution Approach 1:
The patent segments the centralized metadata management function into distributed metadata nodes that operate autonomously. Each node manages a portion of the metadata space and can independently respond to queries, eliminating the single point of failure inherent in centralized metadata management while maintaining manageable complexity through standardized node interfaces.
Solution Approach 2:
The patent combines multiple distributed metadata nodes into a cohesive system where each node contributes to the overall metadata service. Through consistent hashing and replication strategies, the system merges the capabilities of individual nodes to provide unified metadata access, achieving both scalability and high availability while keeping individual node complexity low.
Data Source
AI summary
Methods, systems, and computer-readable media for a scalable metadata index for a time-series database are disclosed. A time-series database receives a query comprising spatial and temporal boundaries of requested time-series data in a table. Using traversal of a metadata index, the time-series database determines one or more storage locations in the one or more data sources corresponding to the spatial and temporal boundaries. The metadata index includes a root node representing spatial and temporal boundaries of the table. The metadata index includes one or more leaf nodes representing the spatial and temporal boundaries of the requested time-series data. The one or more leaf nodes indicate the one or more storage locations in the one or more data sources. The time-series database obtains one or more elements of the requested time-series data from the one or more storage locations in the one or more data sources.