Distributed Database Segment Visibility Management
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Solution Overview
Problem
Current database architectures face challenges in managing and optimizing performance for large volumes of time-stamped records, particularly in distributed databases handling Big Data, where maintaining proper visibility and load distribution across hardware resources is complex due to varying data storage needs and access requirements.
Innovation Solution
The system employs a distributed database architecture with broker nodes, real-time and historical nodes, and deep storage, utilizing a timeline view structure and segment merging techniques to ensure efficient data access and load balancing, allowing for flexible time intervals and data distribution across nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in a distributed database with multiple nodes, then data capacity and availability are improved, but system complexity and difficulty of managing visibility increase
Solution Approach 1:
The distributed database is divided into multiple independent nodes, each capable of storing and processing data segments. This segmentation allows the system to scale horizontally while maintaining manageable complexity at each node level, as each node operates semi-autonomously with standardized interfaces.
Solution Approach 2:
A coordinator node acts as an intermediary between client applications and the distributed data nodes. This mediator manages the complexity of data distribution, visibility, and coordination across nodes, shielding users from the underlying system complexity while enabling large-scale data storage.
2Ease of operation
If segments overlap in time intervals to ensure data visibility, then data accessibility is improved, but storage redundancy and management complexity increase
Solution Approach 1:
Segment time intervals are designed to be dynamic and adjustable rather than fixed. The overlap between segments can be configured based on specific visibility requirements, allowing the system to adapt the degree of redundancy to match actual data access patterns and requirements.
Solution Approach 2:
The system allows modification of segment parameters including time interval duration, overlap magnitude, and distribution patterns. By changing these parameters, the system can optimize the balance between data accessibility and storage efficiency for different workloads and requirements.
3Speed
If segment size is reduced to improve query performance, then query response time is improved, but number of segments and management overhead increase
Solution Approach 1:
Data is divided into smaller segments that can be independently queried and managed. This segmentation enables faster query response times by reducing the amount of data that needs to be scanned in each operation, while the standardized segment structure keeps management overhead manageable.
Solution Approach 2:
The distributed database system implements automatic segment management capabilities including self-organization, load balancing, and metadata tracking. This self-service approach reduces manual management overhead by enabling the system to automatically handle segment creation, distribution, and maintenance tasks.
4Productivity
If load distribution is optimized based on hardware rules, then system performance is improved, but configuration complexity and setup time increase
Solution Approach 1:
The system accepts hardware specifications as input parameters and automatically adjusts data distribution and segment placement based on these parameters. By changing the configuration parameters to reflect actual hardware capabilities, the system optimizes performance without requiring complex manual tuning of distribution algorithms.
Solution Approach 2:
The distributed database implements automatic load distribution algorithms that monitor hardware resources and dynamically adjust data placement to optimize performance. This self-service capability eliminates the need for manual configuration of load balancing rules, reducing setup complexity while maintaining optimal performance.
Data Source
AI summary
A distributed database of time stamped records can be used to store time series data such as events occurring on the Internet. A distributed database of time stamped records can store segments of data that contain events for different time intervals. The volume of events occurring on the Internet introduces a “Big Data” variable that makes collections of data sets so large and complex they are difficult to manage. Disclosed are systems and methods to manage segments of a distributed database of time stamped records for optimal size (for storage and performance reasons etc.) and for proper visibility to data when different segments contain data for overlapping time periods.