Graph Data Key Generation for Query Optimization
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Solution Overview
Problem
Current database systems face challenges in efficiently storing and querying graph data, particularly in NoSQL key-value stores, due to the complexity of graph relationships and the lack of effective mechanisms for handling queries across distributed nodes.
Innovation Solution
The approach involves generating multiple keys for each graph statement based on its components, using techniques like concatenation or hashing, and employing key patterns to facilitate efficient querying, along with the use of persistent data samples for optimizing query execution plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If graph data is stored in a NoSQL key-value store with distributed nodes, then scalability and availability are improved, but query efficiency and relationship handling deteriorate
Solution Approach 1:
The patent segments graph data into multiple key-value pairs by generating different keys from graph statements (e.g., subject-predicate-object combinations). Each key-value pair represents a specific relationship aspect, allowing distributed storage while enabling efficient querying through key-based access patterns that preserve relationship semantics.
Solution Approach 2:
The patent introduces key generation functions and key patterns as intermediaries between the graph data model and the key-value store. These intermediaries transform complex graph relationships into searchable key formats, bridging the gap between NoSQL simplicity and graph relationship complexity without requiring full relational database functionality.
2Ease of operation
If multiple keys are generated for each graph statement, then query flexibility and efficiency are improved, but storage overhead and key management complexity increase
Solution Approach 1:
The patent creates a universal key generation mechanism that produces multiple keys from a single graph statement using consistent rules and patterns. These keys serve multiple purposes: direct lookup, relationship traversal, and statistical analysis. The same key generation logic handles different query types uniformly, reducing the need for separate management systems for different key types.
Solution Approach 2:
The patent varies key parameters (such as which graph components are included, ordering, and formatting) to generate different keys from the same graph statement. This parameter variation enables diverse query patterns without creating entirely separate key management systems, as all keys follow the same generation methodology with different parameter settings.
3Loss of time
If persistent data samples are used for query optimization, then query execution time is reduced, but data processing overhead and memory usage increase
Solution Approach 1:
The patent uses partial data samples rather than complete graph data for query optimization. By analyzing representative subsets of the data, the system generates statistics and execution plans without processing the entire dataset. This partial action provides sufficient optimization information while consuming manageable memory resources.
Solution Approach 2:
The patent performs preliminary analysis on data samples to pre-compute statistics, cardinality estimates, and optimal execution plans before actual query execution. This preliminary action on sampled data prepares optimization information in advance, reducing the processing time required during actual query execution without requiring full data to be loaded into memory simultaneously.
Data Source
AI summary
Techniques for storing and querying graph data in a key-value store are provided. A graph statement (e.g., an RDF graph statement) includes a plurality of values, at least two of which correspond to nodes in a graph. A key is generated based on the graph statement. The key may be generated based on concatenating hash values that are generated based on the plurality of values. The key-value store stores the key. The value that corresponds to the key may be a null or empty value. In response to a graph query (e.g., in SPARQL) that includes one or more query patterns, each of which includes one or more values, a key is generated based on the one or more values and sent to the key-value store, which returns one or more other keys, each of which is a superset of the generated key.