Memory Graph Query Engine with Document-Format Persistent Storage

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Solution Overview

Problem

Graph databases face high-latency queries due to their data structure, which is difficult to manage and translate to traditional databases, leading to inefficiencies in query processing and storage.

Innovation Solution

An in-memory graph query engine with a persisted storage component that converts data to a document format for efficient storage in persistent storage, maintaining low-latency operations and synchronizing data across multiple engines using a change feed.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If data is stored in a graph database as discrete items, then query flexibility is improved, but query latency increases and data management becomes difficult

Engineering Contradiction:

Improvequery flexibilityVSAvoidquery latency

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system segments the data storage into two distinct parts: an in-memory graph database for flexible queries and a persistent storage component for efficient data retrieval. The graph database stores data as discrete items for query flexibility, while the persistent storage component stores converted data in a document format for fast access, resolving the latency issue without sacrificing query flexibility.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces a persistent storage component as an intermediary between the in-memory graph database and the query processing system. This intermediary component receives data from the graph database, converts it to a document format, and stores it for efficient retrieval, thereby mediating the conflict between query flexibility and query latency.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Loss of time

If data is stored in an in-memory graph query engine, then query latency is reduced, but data persistence and availability are compromised

Engineering Contradiction:

Improvequery latencyVSAvoiddata persistence

Core Design Contradiction:

Loss of timeVSReliability

Solution Approach 1:

The system segments storage functionality into an in-memory component for low-latency queries and a persistent storage component for data durability. The in-memory graph database provides fast query response, while the persistent storage component ensures data persistence and availability, resolving the contradiction between query latency and data reliability.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements a copying mechanism where data is replicated between the in-memory graph database and the persistent storage component. The persistent storage component maintains a copy of the data in a document format, ensuring that data persists even when the in-memory database is restarted or fails, thus improving reliability without sacrificing query performance.

Inventive Principle:

Principle #26Copying

3Ease of operation

If graph database data structure is used, then query processing is simplified, but translation to traditional databases becomes difficult

Engineering Contradiction:

Improvequery processingVSAvoiddatabase compatibility

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The persistent storage component acts as an intermediary that translates data from the graph database format to a document format compatible with traditional databases. This intermediary layer maintains the simplicity of graph query processing while enabling seamless translation to traditional database formats, resolving the compatibility issue.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system changes the data representation parameters by converting graph database data structures into document format in the persistent storage component. This parameter transformation allows the same data to be accessed both as graph structures for simple query processing and as documents for traditional database compatibility, resolving the adaptability contradiction.

Inventive Principle:

Principle #35Parameter changes

4Adaptability or versatility

If data is stored in discrete items in graph database, then data structure flexibility is improved, but data management complexity increases

Engineering Contradiction:

Improvedata structure flexibilityVSAvoiddata management complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments data management into two components: the in-memory graph database that handles flexible data structures and the persistent storage component that manages data in a standardized document format. This segmentation reduces the complexity of data management by separating the flexible query-side operations from the structured storage-side operations.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12386837B2Memory graph query engine with persisted storage

Publication Date: 2025.08.12 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Various examples of improving an in-memory graph query engine using a persisted storage component are provided. The method includes updating data stored in an in-memory graph query engine and, based on updating the data, converting the data to a plain text form that may be more efficiently stored in the persistent storage component. The method further includes updates to additional in-memory graph query engines from the persistent storage component such that in-memory data stored in the graph query engines is synchronized.