Building a Distributed Rule Engine: A Comprehensive Guide

Reading time ~7 minutes

Introduction

In modern business environments, processing and reacting to events in real-time has become crucial. Whether you're dealing with IoT sensor data, user interactions, or business transactions, the ability to automatically apply business logic to incoming events at scale is essential. This guide explores how to build a distributed rule engine capable of handling hundreds of thousands of events per day while maintaining reliability and performance.

Understanding Rule Engines

A rule engine is a system that evaluates conditions against incoming events and executes predefined actions when those conditions are met. Think of it as an enterprise-scale version of email filtering rules, but with far more sophisticated capabilities and scalability requirements.

Types of Inference

Rule engines typically use one of two main inference strategies:

Forward Inference (Forward Chaining) starts with incoming data/events and applies rules to reach conclusions. This is like a production line: events come in, rules process them, and actions are triggered. For example, when a temperature sensor reports high values, the system triggers cooling system rules.

Backward Inference (Backward Chaining) works in reverse - it starts with a desired goal and works backward to determine what conditions would prove it, like a detective solving a case. While powerful for diagnostic systems and AI applications, backward chaining requires complex state management and isn't typically needed for event processing systems.

This guide focuses on forward inference, as it's sufficient for most event-driven use cases and provides better performance for real-time processing.

Key Components:

  • Event producers: Systems that generate events (sensors, applications, user interactions)
  • Event consumers: Services that process events and apply rules
  • Rule definitions: Conditions and actions specified in a structured format
  • Processing infrastructure: Distributed system for handling events at scale, priority based job processing like Huey

Architecture Design

Infrastructure Overview

The recommended architecture leverages managed Kubernetes (K8s) for scalability and operational efficiency. Here are the main components:

Event Ingestion

  • Load-balanced pods accept events via webhooks (REST API) or WebSocket connections
  • Events are buffered in Redis/RedPanda to handle high-volume scenarios
  • Horizontal pod autoscaling manages processing capacity

Rule Processing

  • Distributed processing nodes evaluate rules against events
  • Redis/RedPanda acts as a distributed cache and message queue
  • Kubernetes manages pod lifecycle and scaling

Storage and Logging

  • Persistent storage for rule definitions
  • Audit logs for compliance and troubleshooting
  • Performance metrics and execution history

Frontend Management Interface

The system requires a management interface for defining and managing rules. While Django is suggested for its rapid development capabilities, any full-stack framework can work. Key considerations include:

  • Integration with Identity and Access Management (IAM)
  • Rule creation and editing interface
  • Monitoring and troubleshooting dashboards
  • Audit log viewer

Rule Definition System

Domain Specific Language (DSL)

To illustrate the core concepts, let's start with a simplified rule format. While real-world implementations often require more complex structures, this basic format helps demonstrate the key elements of rule definitions:

  1. Conditions

    : A basic condition can be expressed as:

    toml

Copy

[condition]
variable = "x"
operator = ">"
value = 2

  2. Actions

    : A simple action definition might look like:

    toml

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[action]
type = "sendPN"
sendTo = "${event.user}"
message = "x value reached ${event.x}"

    Note the use of basic templating for dynamic values.

This simplified format helps understand the fundamental structure, though production systems typically need more sophisticated rule definitions to handle complex business logic.

Change Management

While the rule engine itself uses a database to store and evaluate rules, I strongly recommend managing changes through a Version Control System (VCS):

  • Store configurations as TOML files in Git (better readability than JSON, fewer special characters to escape, and simpler syntax for the shallow hierarchies typical in rule definitions)
  • Use branches to represent different environments (dev, staging, prod)
  • Manage changes through pull requests between these branches
  • Leverage automated testing and deployment pipelines
  • Start with a text-based UI, then build domain-specific interfaces for business teams
  • Use a bot user to automatically commit UI-based changes to Git

This approach aligns with modern GitOps practices - when changes are merged into an environment branch, automated pipelines deploy the updated configurations to that environment's database. Business users can modify rules through the UI while the system maintains proper version control behind the scenes.

Advanced Rule Capabilities

Single-condition rules quickly hit their limits in real applications. Business logic often requires combinations of conditions or time-based triggers. For example, you might need to alert when sensor readings exceed a threshold AND maintenance is due, or when multiple related events occur within a time window.

  1. Composite Conditions
    • Combine multiple conditions using logical operators
    • Support for nested conditions: any(all(A, B), C)
    • Complex event pattern matching
  2. Periodic Rules
    • Cron-based scheduling
    • Kubernetes CronJobs for execution
    • Time-based condition evaluation
  3. Delayed Conditions
    • Evaluate conditions with a time delay (e.g., check if value remains high after 5 minutes)
    • Pull current state at the time of delayed evaluation
    • Useful for confirming persistent conditions rather than reacting to momentary changes

Real-time performance analysis

While tools like Grafana are popular for monitoring, they work best with pre-computed metrics. For rule engine performance analysis, implement a custom endpoint that gives you a graph of processing duration over time for each event. This can be impemented by putting every incoming objectinto Redis with the timestamp in a sorted list (ZSET). Then the same can be done when the computation is completed. This is very useful for troubleshooting of the rule engine as well the producers.

Advanced Topics:

The base rule engine can be extended with several advanced features that help handle real-world complexities.

Handling Downtime

Even with a robust system, you need to account for potential downtime. Implement a periodic reconciliation process that:

  1. Queries event sources for a specific time range
  2. Compares received event IDs against processed event records
  3. Processes any missed events

The event source must provide an API to query events by time range. To optimize this process, maintain identifiers of processed events to quickly detect gaps. Add some time offset to cover overlapping time ranges. Some events are still in some buffer.

I am not sure if this can be called a reconcilation service.

Processing Lanes

Rather than processing all events through the same path, consider implementing hardcoded processing lanes that handle events differently. These lanes provide optimization opportunities and help manage different types of events efficiently. Common examples include:

  • A fast-lane that skips logging for high-throughput, low-risk events
  • A void-lane where certain events can be safely ignored
  • Synching-lane. Propagate changes to another system

Distributed Debouncing

When dealing with event floods, particularly during batch synchronizations, you need a way to detect this behavior across a distributed system. For example, during a data sync, you might transition from "no data syncing" to "data is being synced" states. The challenge is detecting these state changes without triggering on single events.

The novel solution draws inspiration from spiking neural networks. Using Redis as a distributed state store:

  1. Calculate an "activation level" for each incoming event based on timing
  2. Store these activations in a Redis sorted list (ZSET) to maintain ordering and avoid race conditions
  3. When the activation reaches a threshold, the "neuron fires" - triggering the debouncer
  4. For subsequent events, check if enough time has passed to switch back to the original state

This approach effectively manages event floods in a distributed environment while maintaining system consistency.

Medical Applications with FHIR

One use case for such a system is in the medical field.

What is FHIR?

Fast Healthcare Interoperability Resources (FHIR) is a standard for healthcare data exchange. The rule engine can integrate with FHIR systems through

FHIR subscription

The RE can be integrated with FHIR as there is a native Subscription model. The Subscription framework already has some rule engine specified.

Note that this is FHIR server implementation dependent. HAPI FHIR works with the REST API. The Google is not that advanced and the implementation delayed. To save overhead you want with each event the payload and not only the change event to then fetch it from the server.