Scouting Queries for Distributed Graph Query Planning
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Solution Overview
Problem
Efficient query planning for distributed graph queries is challenging due to the complexity of modeling partitioning and networking costs, leading to suboptimal performance in distributed graph query engines.
Innovation Solution
The approach involves using depth-first traversals for scouting queries to gather information about different query plans, limiting their execution to minimize overhead, and reusing results to select the optimal query plan based on throughput and confidence values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If scouting queries are executed to gather information about different query plans, then query plan selection accuracy is improved, but execution overhead increases
Solution Approach 1:
The patent applies partial action by limiting scouting query execution through time limits and result thresholds. Instead of executing scouting queries exhaustively to gather complete information, the system stops execution when sufficient information is obtained or when time/resource limits are reached, thereby reducing overhead while maintaining adequate selection accuracy.
Solution Approach 2:
The patent uses preliminary action by executing scouting queries before final query plan selection to gather advance information about data distribution, partitioning characteristics, and networking costs. This preliminary information gathering enables more accurate query plan selection without requiring exhaustive exploration during actual query execution.
2Productivity
If multiple scouting queries are executed to evaluate different query plans, then query plan optimization is improved, but work duplication increases
Solution Approach 1:
The patent applies discarding and recovering by storing results from scouting queries and reusing them across multiple query plan evaluations. When scouting queries execute and gather information about data characteristics, partitioning, and networking costs, these results are cached and reused for subsequent query plans that share similar characteristics, avoiding redundant execution and reducing work duplication.
Solution Approach 2:
The patent implements universality by designing scouting queries that can serve multiple purposes: evaluating different query plans, gathering data distribution statistics, measuring networking costs, and characterizing partitioning patterns. A single scouting query execution can provide information useful for optimizing multiple different query plans, reducing the need for separate evaluations.
3Loss of time
If scouting queries are limited in execution to minimize overhead, then execution time is reduced, but information completeness deteriorates
Solution Approach 1:
The patent applies feedback by continuously monitoring scouting query execution progress and results, comparing them against thresholds and time limits. The system uses this feedback to dynamically decide whether to continue or stop execution: if sufficient information is gathered within the time limit, execution stops; if not, the system may adjust parameters or accept partial information. This feedback mechanism balances execution time against information completeness.
Solution Approach 2:
The patent substitutes mechanical exhaustive exploration with a more efficient information gathering approach. Instead of systematically evaluating all possible query plan variations through complete scouting query execution, the system uses statistical sampling, heuristic estimations, and partial execution results to infer query plan performance, replacing exhaustive mechanical evaluation with smarter approximation methods.
Data Source
AI summary
A graph processing system is provided for executing scouting queries for improving query planning. A query planner creates a plurality of scouting queries, each scouting query in the plurality of scouting queries corresponding to a query plan for a graph query and having an associated confidence value. A graph processing system performs limited execution of the plurality of scouting queries and determines a metric value for each scouting query in the plurality of scouting queries based on execution of the scouting query. The system determines a score for each scouting query in the plurality of scouting queries based on its metric value and the confidence value of the corresponding query plan and selects a query plan based on the scores of the plurality of scouting queries. The system executes the graph query based on the selected query plan.