Distributed In-Memory Search Architecture with Tiered Network Segments
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Solution Overview
Problem
In-memory databases face challenges in handling large-scale data storage and retrieval due to limited memory size, requiring efficient hardware configurations to manage networking and node operations while minimizing latency and maximizing bandwidth.
Innovation Solution
A distributed computing system with multiple network segments and connection configurations, featuring different bandwidth and latency tiers, connects various server modules such as search managers, analytics agents, and partitioners to optimize data access and processing, allowing for scalable and cost-effective in-memory database operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If all database information is stored in memory to speed up access, then data retrieval speed is improved, but memory size limitations prevent handling large-scale databases
Solution Approach 1:
The database system is divided into multiple nodes, each managing a portion of the data in memory. This segmentation allows the system to handle large-scale databases by distributing data across multiple memory units, overcoming the limitation of individual memory size while maintaining fast in-memory access speeds.
Solution Approach 2:
The patent transitions from a single-memory architecture to a distributed multi-node architecture, adding the dimension of network connectivity between nodes. This allows the system to scale horizontally by adding more nodes to the network, effectively increasing total memory capacity while maintaining fast access through intelligent data placement and retrieval strategies.
2Quantity of substance
If a distributed computing system is implemented to overcome memory limitations, then data storage capacity is improved, but network complexity and configuration difficulty increase
Solution Approach 1:
The distributed database system implements self-configuration capabilities where nodes automatically discover and register themselves with the cluster, allocate data partitions autonomously, and establish network connections without manual intervention. This self-service approach dramatically reduces the complexity of network configuration and system deployment.
Solution Approach 2:
The patent employs universal communication protocols and standardized interfaces that work across all nodes regardless of their specific hardware configurations. This universality simplifies network configuration by providing a consistent method for nodes to interact, reducing the need for node-specific configuration complexity.
3Productivity
If multiple network segments with different bandwidth tiers are used to optimize traffic, then data transmission efficiency is improved, but system complexity increases
Solution Approach 1:
The patent applies different bandwidth tiers to specific network segments based on their functional requirements. Critical paths such as query processing and data retrieval use high-bandwidth low-latency connections, while less time-sensitive operations use lower-bandwidth connections. This local quality approach optimizes transmission efficiency for each segment without requiring the entire network to be complex.
Solution Approach 2:
The network system dynamically routes traffic based on current load conditions and priority levels, adapting bandwidth allocation in real-time. This dynamic behavior allows the system to maximize data transmission efficiency under varying conditions without requiring a static complex network architecture, as the routing logic adapts to needs.
Data Source
AI summary
Disclosed here are distributed computing system connection configurations having multiple connection bandwidth and latency tiers. Also disclosed are connection configurations including a suitable number of network segments, where network segments may be connected to external servers and clusters including search managers, analytics agents, search conductors, dependency managers, supervisors, and partitioners, amongst others. In one or more embodiments, modules may be connected to the network segments using a desired bandwidth and latency tier. Disclosed here are hardware components suitable for running one or more types of modules on one or more suitable nodes. One or more suitable hardware components included in said clusters include CPUs, Memory, and Hard Disk, amongst others.