Distributed Sorted Set Indexing for Scalable In-Memory Databases
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Solution Overview
Problem
Conventional in-memory database systems face limitations in scalability and data availability when handling large sorted sets, as they are typically confined to the memory of a single server and lack efficient indexing and horizontal scaling capabilities.
Innovation Solution
A distributed in-memory database system that indexes key-value pairs based on scores, allowing for the distribution of sorted sets across multiple nodes, enabling concurrent processing, horizontal scaling, and data replication across a wide area network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is stored in memory of a single server, then access speed is fast, but scalability is limited
Solution Approach 1:
The patent divides the sorted set data into multiple shards distributed across different server nodes. Each node maintains a portion of the data in its own memory, allowing the system to scale horizontally by adding more nodes while maintaining fast in-memory access speeds for each partition.
Solution Approach 2:
The patent introduces a distributed dimension by organizing data across multiple nodes with hierarchical indexing. The index structure spans multiple nodes, enabling the system to scale from a single-server to multi-node architecture while preserving O(log n) access complexity through the hierarchical index organization.
2Adaptability or versatility
If sorted set is distributed across multiple nodes, then scalability improves, but indexing complexity increases
Solution Approach 1:
The index structure is segmented into hierarchical levels distributed across nodes. Each node maintains local index entries for its sharded data, and parent nodes maintain aggregated indexes. This segmentation reduces the indexing complexity at each individual node while maintaining global scalability.
Solution Approach 2:
The patent employs hierarchical indexing mechanisms that automatically balance and reorganize data across nodes. The indexing system dynamically adjusts to data distribution changes, similar to how vibration systems self-regulate, reducing manual intervention complexity while maintaining scalability.
3Productivity
If data is confined to single server memory, then access efficiency is high, but data availability decreases
Solution Approach 1:
The patent segments data across multiple server nodes, where each node holds a shard of the sorted set in its memory. This segmentation ensures that if one node fails, other nodes continue to serve data, improving data availability while maintaining efficient in-memory access for each partition.
Solution Approach 2:
The system dynamically adjusts data replication parameters and node allocation based on load and availability requirements. By changing operational parameters such as replication factors and shard distribution strategies, the system maintains high access efficiency while improving data availability through distributed redundancy.
4Quantity of substance
If sorted set grows in size, then more data can be stored, but single server memory limit is reached
Solution Approach 1:
The patent segments the growing sorted set into multiple shards distributed across an expanding number of server nodes. As data size increases, new nodes can be added to the cluster, with each node holding a portion of the data in its memory, thus overcoming the single-server memory limit while maintaining in-memory access speeds.
Solution Approach 2:
The system transitions from a single-dimension (single server) to multi-dimensional (distributed cluster) architecture. By organizing data across multiple nodes in a distributed hierarchy, the system can store larger quantities of data in memory collectively across the cluster, enabling horizontal scaling beyond individual server memory constraints.
Data Source
AI summary
Systems, methods, and computer program products of distributing sorted sets to multiple nodes of a distributed in-memory database system are described. The distributed in-memory database system is a database system based on key-value pairs. The system receives data records including user data and scores for sorting. The system maps the user data to keys in the key-value pairs. The system maps the scores to the values in the key-value pairs. The system then distributes the mapped key-value pairs to the nodes. The distributed in-memory database system is indexed on the values.