Distributed Query Engine Using Shard Sampling for Rapid Data Analysis
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Solution Overview
Problem
Existing data analytics systems face challenges in performing rapid data analysis due to query performance issues, especially for large datasets, as pre-calculating data aggregates across all dimensions is infeasible, leading to slow data analysis processes.
Innovation Solution
A system utilizing distributed computing with an event database, string lookup database, and query engine that translates event data strings into integers, samples data shards, and employs a multi-pass query method to achieve quick analysis of large data volumes, minimizing pre-processing overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If pre-calculating data aggregates and indexes is performed, then query performance is improved, but it is infeasible to do this across all dimensions in a large dataset
Solution Approach 1:
The patent segments the large dataset into multiple data shards distributed across different storage nodes. Each shard can be independently processed and queried, allowing the system to handle large datasets without requiring complete pre-processing of all data. This segmentation enables parallel processing and reduces the complexity burden on any single processing unit.
Solution Approach 2:
The system performs preliminary actions by pre-calculating and storing aggregates only for frequently queried dimensions or subsets of data, rather than all dimensions. This selective pre-processing improves query performance for common operations while avoiding the infeasibility of pre-processing entire large datasets across all dimensions.
2Quantity of substance
If data is stored in a distributed file system, then data capacity and accessibility are improved, but query performance suffers due to lack of data locality
Solution Approach 1:
The patent implements local quality by organizing data in the distributed file system with data locality considerations. Related data and frequently accessed aggregates are co-located on the same storage nodes, improving query performance by reducing data movement while maintaining the distributed architecture's capacity and accessibility benefits.
Solution Approach 2:
The system uses a nested structure where data shards are organized hierarchically within the distributed file system, with aggregates nested within or near their source data. This nesting optimizes query performance by allowing the system to access pre-computed aggregates close to the source data without requiring complete data movement across the distributed system.
3Loss of information
If complex queries are executed on large datasets, then comprehensive analysis is achieved, but query time becomes unacceptably long
Solution Approach 1:
For complex queries on large datasets, the system applies partial action by executing queries on sampled subsets of data shards rather than scanning all data. This approach provides sufficiently comprehensive analysis for many use cases while dramatically reducing query time. The system can adjust the sampling rate to balance analysis completeness against query time requirements.
Solution Approach 2:
The patent introduces intermediary aggregate structures that mediate between raw data and final query results. These aggregates serve as intermediate computational results that can be combined to answer complex queries without requiring complete scans of all source data, thus achieving comprehensive analysis with reduced query time.
Data Source
AI summary
A method for rapid data analysis includes receiving and interpreting a first query operating on a first dataset partitioned into shards by a first field; collecting a first data sample from a first set of data shards; calculating a first result to the first query based on analysis of the first data sample; and partitioning a second dataset into shards by a second field based on the first result.