Distributed Graph Query Parallelization for Lower Latency
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Solution Overview
Problem
Conventional technologies lack the capability to automatically programmatically parallelize distributed graph queries across distributed data sources, leading to increased latency, computational expense, and inefficient resource utilization in searching operations.
Innovation Solution
A graph metaphor is used to model distributed data sources, enabling programmatically parallelized distributed graph queries by determining a parallelized query plan based on query properties, which allows simultaneous execution of queries across multiple data sources, reducing latency and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed graph queries are executed sequentially across multiple data sources, then resource utilization is simplified and easier to manage, but query execution time increases and productivity decreases
Solution Approach 1:
The patent segments the distributed graph query into multiple independent sub-queries that can be executed in parallel across different data sources. The query plan is divided into separate tasks, each handling a specific data source, allowing simultaneous execution without requiring complex coordination between sources.
Solution Approach 2:
The system performs preliminary actions by pre-processing the graph query to identify independent query candidates and their corresponding data sources before execution. This advance analysis enables the creation of a parallelized query plan that specifies execution order and dependencies, eliminating the need for complex runtime coordination.
2Loss of time
If parallelized distributed graph queries are implemented, then query execution speed increases and latency decreases, but the complexity of determining and executing the query plan increases
Solution Approach 1:
The patent introduces an intermediary query planning layer that translates high-level graph queries into parallelized execution plans. This intermediary component handles the complexity of parallelization, dependency resolution, and coordination, shielding the user from complexity while enabling fast parallel execution across distributed data sources.
3Productivity
If sequential querying of distributed data sources is used, then resource consumption is lower and easier to manage, but computational expense and execution time increase
Solution Approach 1:
The system dynamically adjusts the query execution strategy based on available resources and query characteristics. It can switch between sequential and parallel execution modes, optimizing resource utilization while maintaining high productivity. The query plan includes dynamic resource allocation that adapts to system conditions during execution.
Data Source
AI summary
Technology is disclosed for programmatically parallelizing distributed graph queries of a graph metaphor of distributed data sources through various applications or platforms. Query candidates corresponding to distributed data sources are determined by applying a graph query to a graph metaphor of the distributed data sources. A set of query steps representing a set of distributed queries of the query candidates are determined based on corresponding properties of the query candidates from the graph metaphor. The set of query steps are executed in parallel in order to determine a response to the graph query. The data is provided in response to the graph query.