Dynamic Data Partitioning Using Query Predicate Analysis
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Solution Overview
Problem
Generating formatted data for efficient querying in distributed storage systems is challenging, leading to inefficient use of compute resources and query bottlenecks due to poorly formatted data and suboptimal partitioning based on unknown query predicates.
Innovation Solution
A computer-implemented service dynamically selects and adjusts partition keys based on historical query analysis to optimize data partitioning, using natural language processing to identify common query predicates and adaptively repartition data to match query behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If data is partitioned using a fixed partition key, then data organization is simple and stable, but query efficiency deteriorates when query patterns change
Solution Approach 1:
The patent implements dynamic partitioning by monitoring query patterns and automatically adjusting partition keys based on observed query predicates. The system transitions from static to dynamic partitioning, where the partition configuration adapts to changing query workloads, thereby maintaining query efficiency without sacrificing data organization stability.
Solution Approach 2:
The system incorporates feedback mechanisms by analyzing historical query patterns and using this information to optimize partition key selection. Query performance metrics are fed back into the partitioning system, which then adjusts partition configurations to align with actual query behavior, resolving the contradiction between stability and efficiency.
2Productivity
If data is repartitioned frequently to match query patterns, then query efficiency improves, but computational overhead and system complexity increase
Solution Approach 1:
The patent implements periodic repartitioning based on time windows and query pattern stability thresholds. Instead of continuous adjustment, the system evaluates query patterns at intervals and only triggers repartitioning when significant changes are detected, reducing computational overhead while maintaining query efficiency.
Solution Approach 2:
The system changes partitioning parameters (partition keys, bucket counts) dynamically based on observed query predicate distributions. By adjusting these parameters in response to measured query patterns rather than using fixed configurations, the system achieves high query efficiency without requiring complex real-time partitioning logic.
3Reliability
If all data is scanned to ensure complete query results, then query accuracy is maintained, but query performance and resource utilization deteriorate
Solution Approach 1:
The patent segments data into partitions based on query predicate patterns, allowing the query system to scan only relevant partitions rather than all data. By dividing the data storage into logically separated segments that correspond to common query filters, the system maintains complete result accuracy while dramatically reducing the scanned data volume.
Solution Approach 2:
The system applies different partitioning strategies to different data segments based on local query patterns. High-cardinality predicates that frequently appear in queries receive dedicated partitioning treatment, while less important dimensions use simpler partitioning, optimizing query performance for each data region while ensuring complete results are returned.
Data Source
AI summary
Techniques and systems can partition data with a partition key to provide data partitioned based on the partition key. Queries performed against the data partitioned based on the partition key can be queried to identify a query predicate shared by at least a plurality of the queries. That query predicate can be used to reparation the data to generate other partitioned data or to replace the data partitioned based on the partition key.