Structured Query Processing Over Distributed Clusters
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Solution Overview
Problem
There is a need for a convenient and computationally effective way to process structured queries over remote clusters of non-relational databases, as existing systems lack efficient methods for handling such queries across distributed geographical regions.
Innovation Solution
A search engine system that converts structured queries into search queries executable by local and remote clusters, optimizing query processing by parsing, generating logical and physical plans, and combining results from local and remote clusters, while considering latency and optimizing queries using pattern matching and rule sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If structured queries are processed over remote clusters of non-relational databases, then query capability and versatility are improved, but processing latency and response time increase
Solution Approach 1:
The system performs query optimization and transformation into logical and physical plans before execution, preparing the query structure in advance to reduce processing latency during actual execution
Solution Approach 2:
The query processing is divided into distinct phases: parsing, logical plan generation, optimization, and physical plan generation, allowing each stage to be processed independently and efficiently across distributed clusters
2Adaptability or versatility
If queries are executed on remote clusters located in different geographical regions, then data accessibility and system scalability are improved, but network latency and query execution time worsen
Solution Approach 1:
The system determines which clusters are local versus remote based on the client's location and query characteristics, directing queries to the most appropriate cluster to minimize latency while maintaining data accessibility
Solution Approach 2:
The system introduces an intermediary query processing layer that transforms structured queries into search queries suitable for non-relational databases, enabling efficient execution across distributed clusters by adapting the query format to match the target database structure
3Ease of operation
If structured query language (SQL) is converted to search queries for non-relational databases, then compatibility and ease of operation are improved, but query processing complexity increases
Solution Approach 1:
The system introduces an intermediary transformation layer that automatically converts SQL structured queries into search queries suitable for non-relational databases, hiding the complexity of query adaptation from the user while maintaining compatibility
Solution Approach 2:
The query processing system is designed to handle multiple query types and database structures through a universal transformation framework that can adapt structured queries to different non-relational database formats using standardized logical and physical plan representations
Data Source
AI summary
Systems and methods for processing structured queries over clusters are provided herein. An example system includes a plurality of clusters, wherein a local cluster is configured to receive, from a client, a structured query language (SQL) structured query, determine, based on the SQL structured query, a list of remote clusters of the plurality of clusters, process the SQL structured query to generate a local query executable by a local search engine of the local cluster and remote queries executable by remote search engines of the remote clusters, send the remote queries to the remote clusters to obtain remote results, execute the local query to obtain local results, combine the remote results and the local results to obtain an aggregated result, and return the aggregated result to the client.