Cloud Database Query Layer for Platform Independence
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Solution Overview
Problem
Current technologies lack a generic, platform-independent interface for interacting with cloud databases, requiring re-writing code for different libraries and making data migration between clouds time-consuming, costly, and prone to introducing bugs.
Innovation Solution
A modified version of Structured Query Language (SQL) is used to provide a query layer for cloud databases, incorporating a cost-based optimizer that converts row-store queries into column-store queries, enabling efficient data retrieval and reducing selection and join operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud databases use different proprietary APIs and syntax (e.g., Hadoop syntax, Amazon EC2 syntax), then each cloud database can be optimized for its specific architecture, but code must be re-written for each library and platform independence is lost
Solution Approach 1:
The patent implements a universal SQL query layer that can interact with multiple different cloud database systems (Hadoop, Amazon EC2, and other column-store databases) through a single standardized interface. This allows the same SQL code to work across different platforms without rewriting, achieving platform independence while maintaining the ability to query various cloud database architectures.
Solution Approach 2:
The patent introduces an intermediary SQL query layer that sits between the user and the underlying cloud database systems. This intermediary layer translates standardized SQL queries into the specific syntax and APIs required by different cloud databases, eliminating the need for users to directly interact with proprietary APIs and reducing code complexity.
2Productivity
If traditional row-store databases are used, then existing applications can be hosted without modification, but query performance on cloud column-store databases is suboptimal
Solution Approach 1:
The patent changes the fundamental parameter of data storage organization by converting row-store database structures into column-store database structures. This transformation optimizes query performance for cloud databases by storing data in columns rather than rows, enabling efficient data retrieval while the SQL layer maintains compatibility with existing applications.
3Adaptability or versatility
If data is migrated from one cloud database to another using proprietary APIs, then data can be moved between platforms, but the process is time-consuming, costly, and prone to introducing bugs
Solution Approach 1:
The patent enables universal data portability between different cloud databases through a standardized SQL interface. Data can be queried and migrated between Hadoop, Amazon EC2, and other column-store databases using the same SQL syntax, eliminating the need for proprietary API knowledge and reducing migration time and errors.
Data Source
AI summary
A method, non-transitory computer readable medium, and apparatus for receiving data from a cloud database. One or more queries requesting data from the cloud database are received. The one or more queries are converted from a row-store database query into a column-store database query. An optimal join plan is identified for the one or more queries using a cost based optimizer based on metadata for one or more relations in the cloud database. The optimal join plan is executed using a cloud application programming interface.