Client-Side GraphQL Schema for Kafka Stream Control
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Solution Overview
Problem
Current data streaming platforms do not allow for client-side definition or specification of streaming requirements without engaging server-side functionality or deploying serverless containers, limiting the ability of web and mobile client applications to control data streaming processes.
Innovation Solution
Implementing a client-side data streaming solution using GraphQL to define functions and rules for mapping topics, transforming them into computable data streams, and utilizing a stream processor registry to execute these definitions, allowing for dynamic creation and filtering of user-specific topics based on offset, filtering, and aggregation rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If server-side functionality or serverless containers are used to define streaming requirements, then data streaming processes can be controlled, but system complexity and deployment requirements increase
Solution Approach 1:
The patent introduces GraphQL as an intermediary layer between the client and the Kafka streaming platform. GraphQL schemas defined on the client side act as intermediaries that translate client requirements into stream processing configurations, eliminating the need for direct server-side control while maintaining full functionality. This mediator approach allows clients to control streaming processes without directly engaging complex server-side infrastructure.
Solution Approach 2:
The patent enables client-side self-service by allowing applications to define their own streaming requirements through GraphQL schemas without requiring server-side intervention or deployment of serverless containers. The client independently configures topic mappings, filtering rules, and aggregation logic, making the system self-sufficient and reducing overall architectural complexity.
2Adaptability or versatility
If fixed server-side streaming pipelines are implemented, then data processing is reliable, but adaptability to different client requirements is limited
Solution Approach 1:
The patent transforms fixed server-side streaming pipelines into dynamic, client-configurable streams through GraphQL. Clients can dynamically define and modify streaming requirements by updating their GraphQL schemas, which automatically translate into new stream processing configurations. This dynamic approach maintains reliability through structured schema validation while providing unlimited adaptability to different client needs.
Solution Approach 2:
The patent enables adaptability by allowing clients to change streaming parameters (filtering criteria, aggregation rules, topic mappings) through GraphQL schema modifications. These parameter changes are translated into corresponding stream processing configurations, allowing the system to adapt to different requirements while maintaining consistent processing reliability through the structured GraphQL interface.
3Adaptability or versatility
If client applications directly control data streaming, then customization is maximized, but the requirement for server-side support increases
Solution Approach 1:
The patent extracts the control functionality from the server side and places it directly on the client side through GraphQL schemas. Clients extract and define their own streaming requirements, filtering logic, and aggregation rules locally, eliminating the need for server-side control infrastructure. This extraction maximizes customization while reducing server-side complexity to merely executing client-defined configurations.
Solution Approach 2:
The patent makes GraphQL schemas universal by designing them to handle multiple streaming functions (topic mapping, filtering, aggregation, transformation) within a single client-side configuration framework. This multi-functional approach allows client applications to control all aspects of data streaming customization without requiring specialized server-side support for each function, reducing overall infrastructure complexity.
Data Source
AI summary
Systems and methods for specifying a stream processing topology via a client-side API without server-side support. A schema may be generated by a client-side application using a query language and transmitted to a stream processor registry, wherein the schema defines a desired data stream. The stream processor registry, acting as a server-side run time corresponding to the query language, may store the schema as metadata. The stream processor registry may generate a stream processing topology based on the metadata to obtain data relevant to the data stream and generate a user-specific topic comprising the data relevant to the data stream. The stream processor registry may filter the data relevant to the data stream based a subscription call by the client to generate a target topic comprising portions of the data relevant to the data stream.