Chapter 2
Serving Graph Architecture: Design Principles
What powers the flexibility, reliability, and extensibility of MLRun Serving Graphs? This chapter lifts the veil on the architectural strategies that make scalable, maintainable, and high-performance model serving possible. Navigate the intricate balance between system modularity, configurability, and operational transparency that forms the cornerstone of modern graph-based serving.
2.1 Component Layering and Architectural Patterns
Serving graphs, as complex abstractions orchestrating data flow and processing logic, benefit substantially from systematic decomposition into layered, reusable components. This decomposition facilitates modularity, maintainability, and extensibility-qualities essential for scalable systems that evolve alongside shifting functional requirements and integration landscapes.
A foundational principle underpinning this decomposition is separation of concerns. By partitioning logic into distinct layers, each responsible for a well-defined aspect of the overall serving process, developers achieve clean interfaces and minimize interdependencies. Typically, serving graph architectures can be logically divided into three core layers: the data ingestion and preprocessing layer, the transformation and orchestration layer, and the output delivery or integration layer.
Data Ingestion and Preprocessing Layer: This foundational layer is tasked with the acquisition and initial conditioning of data. It includes components responsible for interfacing with upstream data sources, performing filtering, normalization, or enrichment tasks that standardize inputs before further processing. Modularizing ingestion promotes encapsulation of data heterogeneity and supports adaptability to changes in source formats, protocols, or partner APIs without propagating disruptions upstream.
Transformation and Orchestration Layer: Constituting the core processing logic, this layer applies business rules, aggregates or transforms data streams, and manages control flow within the serving graph. Architectural patterns at this level emphasize reusability of transformation components through the abstraction of operations into stateless or stateful functions. Orchestration components govern the sequencing and conditional execution of these transformations, often employing declarative specifications or domain-specific languages to define workflows. By isolating orchestration from transformation logic, this layer preserves flexibility for both internal evolution and interaction with external control mechanisms.
Output Delivery and Integration Layer: The final layer handles the packaging, formatting, and routing of processed data to downstream consumers or partner systems. It supports protocols and interfaces that enable seamless integration with external environments, including REST APIs, messaging queues, or specialized partner communication protocols. Components here abstract the complexities of interaction, enabling serving graphs to be extended externally without internal perturbations.
The adoption of plugin-based layering is a pivotal architectural pattern that empowers extensibility within and beyond these layers. Plugins encapsulate distinct functional units-such as a data normalization algorithm, a complex transformation, or a partner-specific integration module-that can be dynamically added, removed, or replaced. This pattern leverages interfaces or abstract base classes that define contracts for plugin behavior, ensuring adherence to expected input/output schemas and lifecycle management.
Consider the following simplified schematic of a plugin interface defining a transformation component:
class TransformationPlugin: def initialize(self, config): """Prepare the plugin with configuration parameters.""" pass def execute(self, data_batch): """Process the input batch and return transformed output.""" raise NotImplementedError def cleanup(self): """Release any held resources.""" pass Incorporating plugin-based layering yields several advantages. Firstly, it confines changes related to new features or bug fixes within discrete components, significantly reducing regression risk. Secondly, it enables parallel development, as teams can work on distinct plugins with minimal overlap. Finally, it facilitates integration with partner systems by allowing custom plugins tailored to specific external requirements to coexist alongside core components.
Architectural patterns that further support sustainable evolution include dependency inversion and event-driven integration. Dependency inversion mandates that high-level modules within the serving graph depend upon abstractions rather than concrete implementations. This inversion is critical for substitutability; different plugins or components can replace one another without affecting the overall system, as long as they adhere to agreed-upon interfaces. For instance, an ingestion adapter for a partner's proprietary data format can be developed without modifying the upstream orchestration logic.
Event-driven integration introduces asynchronous communication and event streams as a decoupling mechanism between internal components and external partners. Instead of tightly coupled synchronous calls, components publish or subscribe to...
