Distributed Database Query Translation for Scalability

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Solution Overview

Problem

Distributed database systems face challenges in scalability and reliability, particularly in supporting multitenancy and efficient data retrieval, due to limitations in query processing and storage operations.

Innovation Solution

A distributed database system that translates table-oriented queries into GET, RANGE QUERY, or SCAN operations based on the query's key information, utilizing a key-item storage paradigm to efficiently locate and retrieve data across a cluster of servers, allowing for scalable and reliable data access.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If a distributed database system uses independent servers with no master node, then scalability and reliability are improved, but query processing complexity increases

Engineering Contradiction:

Improvesystem reliabilityVSAvoidquery processing complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent introduces a query processing subsystem as an intermediary layer between the client and the distributed storage subsystem. This mediator translates high-level table-oriented queries into low-level key-item operations (GET, RANGE QUERY, SCAN), thereby hiding the complexity of the distributed architecture from users while maintaining scalability and reliability benefits

Inventive Principle:

Principle #24Intermediary (Mediator)

2Ease of operation

If table-oriented queries are used for data access, then ease of operation is improved, but translation to key-item operations increases processing overhead

Engineering Contradiction:

Improvequery ease of operationVSAvoidquery processing time

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The query processing subsystem performs preliminary translation of table-oriented queries into optimized key-item operations before execution. By pre-processing the query translation and planning phase, the system prepares efficient execution paths that minimize runtime overhead while maintaining user-friendly query interfaces

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system segments the query processing into distinct phases: query translation, operation generation (GET/RANGE/SCAN), and execution. This segmentation allows each phase to be optimized independently, reducing overall processing time while maintaining ease of operation for users

Inventive Principle:

Principle #1Segmentation

3Reliability

If data is replicated across multiple servers, then reliability is improved, but data retrieval complexity increases

Engineering Contradiction:

Improvedata availabilityVSAvoiddata retrieval complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system implements data replication across multiple storage nodes, creating copies of data items for fault tolerance and availability. The query processing subsystem automatically manages these copies by translating queries into operations that can efficiently access replicated data without requiring users to understand the replication complexity

Inventive Principle:

Principle #26Copying

Data Source

PatentUS11481391B1Query language operations using a scalable key-item data store

Publication Date: 2022.10.25 AMAZON TECH INC

AI summary

A distributed database system maintains data for a logical table by storing, on a plurality of storage nodes, a collection of key-item pairs. The distributed database system receives a query of the logical table, and identifies one or more portions of a key specified by the query. Based on the one or more portions of the key, the distributed database causes at least one of a get, range query, or scan operation to be performed by one or more of the storage nodes. Results for the query are generated based on one or more items obtained by performance of the operation.