Graph Database Ingestion Configuration Automation
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Solution Overview
Problem
Existing methods for configuring and managing data ingestion in distributed graph databases are error-prone and labor-intensive, particularly due to the need for manual editing of complex deployment schedules, which can lead to costly errors and inefficiencies in propagating changes across large networks of machines.
Innovation Solution
The introduction of graph-based ingestion configuration metadata that is self-describing and encoded directly into the graph database, allowing for independent configuration of ingestion pipelines across machines and clusters, using a declarative query language to simplify the process and ensure traceability and early detection of errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual editing of deployment schedules is used to configure data ingestion, then ingestion pipelines can be set up, but the process becomes error-prone and labor-intensive
Solution Approach 1:
The system automatically generates deployment schedules by reading the declarative ingestion configuration and autonomously propagating changes across the distributed graph database system, eliminating the need for manual editing while maintaining reliability
Solution Approach 2:
The patent replaces manual mechanical editing of deployment schedules with an automated computational system that interprets declarative configuration and executes propagation logic, substituting human error with programmatic precision
2Adaptability or versatility
If complex deployment schedules are manually edited, then ingestion configuration can be customized, but the complexity increases and errors become more likely
Solution Approach 1:
The patent separates the declarative ingestion configuration from the execution logic, allowing users to define only the desired outcome in a simple configuration file while the system handles the complex propagation scheduling automatically
Solution Approach 2:
The system introduces an intermediary layer (deployment schedule generator) that translates simple declarative configuration into complex propagation schedules, shielding users from complexity while enabling customization
3Adaptability or versatility
If manual propagation of changes across network is performed, then configuration can be customized, but time consumption increases
Solution Approach 1:
The system continuously monitors for configuration changes and automatically propagates them across the distributed system without interruption or manual intervention, ensuring continuous operation and eliminating propagation delays
Solution Approach 2:
The patent pre-defines propagation strategies and timing mechanisms in the declarative configuration, allowing the system to execute changes efficiently without manual coordination or delay
Data Source
AI summary
The disclosed technologies are capable of reading ingestion configuration data for a client of a plurality of clients of a graph database, transforming the ingestion configuration data from a declarative representation into a graph representation of the ingestion configuration data, storing the graph representation of the ingestion configuration data in the graph database, providing the graph representation of the ingestion configuration data to a data service, where the data service comprises one or more of (i) a physical grouping of at least one computing device configured to store at least a portion of the graph database, (ii) a logical grouping of at least one computing device configured to store at least a portion of the graph database, or (iii) a combination of (i) and (ii).