Distributed Database Foreign Table Query Processing
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Solution Overview
Problem
Current systems for distributed databases with foreign tables are inefficient as users can only associate one foreign data source with one table, leading to difficulties in query processing, especially with large volumes of data like 24 hourly Apache log files, and require significant resources beyond a single database server.
Innovation Solution
A system with a coordinator node and worker nodes that store data blocks in semi-structured formats with associated foreign table declarations, allowing for distributed query planning and execution, enabling the conversion of semi-structured data into tabular formats for SQL queries, and optimizing query processing by partitioning queries into sub-queries executed on worker nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If one foreign data source is associated with one table in a distributed database, then the data can be queried using standard query language, but the query processing becomes difficult when dealing with large volumes of data from multiple sources
Solution Approach 1:
The patent segments the query processing into two distinct parts: a query planning phase that occurs at the coordinator node, and a query execution phase that occurs at worker nodes. This segmentation allows the system to handle complex queries across multiple foreign data sources by distributing the workload appropriately, reducing the complexity at any single node while maintaining the ability to process diverse semi-structured data formats.
2Productivity
If multiple foreign data sources are processed by a single database server, then data retrieval is possible, but significant resources beyond a single database server are required
Solution Approach 1:
The patent introduces a new dimensional aspect to the database architecture by adding a coordinator node that manages multiple worker nodes. This creates a hierarchical dimension in the system architecture, allowing queries to be planned centrally and executed in parallel across multiple worker nodes, thereby improving data retrieval efficiency without requiring a proportional increase in resources at any single node.
Solution Approach 2:
The patent merges the query planning function at the coordinator node with the query execution function at worker nodes. This combination allows the system to leverage the coordination capabilities of a central node while utilizing the computational resources of distributed worker nodes, achieving efficient data retrieval across multiple foreign data sources without requiring excessive resources at any single location.
3Adaptability or versatility
If 24 hourly Apache log files are stored as separate foreign tables, then each data source can be accessed individually, but query processing becomes difficult and resource-intensive
Solution Approach 1:
The patent segments the handling of multiple foreign data sources (such as 24 hourly Apache log files) by distributing them across multiple worker nodes. Each worker node can process queries against its assigned data sources independently, while the coordinator node manages the overall query plan. This segmentation improves query processing efficiency by enabling parallel execution and reducing the burden on any single node, while maintaining the ability to access all data sources through unified query interfaces.
Data Source
AI summary
A system includes a coordinator node and worker nodes in communication with the coordinator node. Each worker node stores data blocks. Each data block has data in a semi-structured format and each data block has an associated foreign table declaration specifying conversion of the data in the semi-structured format into a tabular format interpretable by a query language. A query processor executed by the coordinator node produces a distributed query plan in response to a query language query. The distributed query plan includes sub-queries. The sub-queries are executed by selected worker nodes of the worker nodes. The selected worker nodes use foreign table declarations to convert data in semi-structured formats into tabular formats of a distributed database to provide tabular data in response to the query language query.