Index Sharding with Partitioned Segments for Low-Latency Analytics
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Solution Overview
Problem
Existing database analytic tools are inefficient, costly, and require substantial configuration and training, limiting accessibility and utility of large data volumes stored in complex data storage systems.
Innovation Solution
Implementing index sharding in a low-latency data access and analysis system that partitions and shards data based on characteristics, using hash-partitioning and shard assignment to improve efficiency, robustness, and responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing database analytic tools are used to handle large volumes of data, then data storage capacity is maintained, but system efficiency deteriorates and operational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the index into multiple shards distributed across different nodes. Each shard is independently manageable, allowing parallel processing and reducing the complexity of handling large datasets. The indexing unit partitions constituent data into multiple partitions, then segments and shards them across the distributed system, enabling efficient data access without requiring complex centralized management.
2Quantity of substance
If data volume in the database increases, then data storage capacity is improved, but data access latency increases
Solution Approach 1:
The patent segments large volumes of data into smaller partitions and shards them across multiple nodes in the distributed system. This segmentation allows the system to maintain large data storage capacity while enabling parallel data access operations, thereby reducing access latency even as data volume increases.
Solution Approach 2:
The patent introduces a new dimensional approach by distributing data across multiple nodes and partitions in a distributed architecture. Instead of accessing data in a single sequential dimension, the system enables parallel access across multiple dimensions (nodes, partitions, shards), significantly reducing access latency for large datasets.
3Stability of the object's composition
If traditional indexing methods are used on large datasets, then data organization is maintained, but resource requirements for index building and updating increase
Solution Approach 1:
The patent segments the indexing process by creating independent indexing operations for each partition and shard. Instead of building and updating a single large index that consumes significant resources, the system maintains multiple smaller indexes distributed across nodes, reducing the computational resources required for index building and updating while preserving data organization.
4Reliability
If centralized database analytics are used, then data consistency is maintained, but system responsiveness deteriorates
Solution Approach 1:
The patent segments the centralized database into multiple distributed nodes, each capable of independent data access and processing. This segmentation maintains data consistency through coordinated shard management while enabling parallel operations across nodes, significantly improving system responsiveness without sacrificing reliability.
Data Source
AI summary
Indexing in a low-latency data access and analysis system includes accessing, by an indexing unit of a low-latency data access and analysis system, constituent data from a data source of the low-latency data access and analysis system and indexing the constituent data in an index of the low-latency data access and analysis system by an indexing unit of the low-latency data access and analysis system. Indexing includes partitioning the constituent data based on a characteristic of the constituent data into at least a first partition and a second partition, segmenting the first partition into a first segment of the first partition, sharding the first segment into a first shard of the first segment of the first partition, segmenting, using hash-partitioning, the second partition into one or more segments of the second partition, and for respective segments of the second partition, sharding the respective segment into one or more respective shards.