Orchestration Framework Documentation
This guide covers core concepts, API integration, architectural patterns, and implementation best practices for the EAQ Orchestration Framework.
Getting Started
Prerequisites
-
Python 3.8+
Required for SDK installation and execution
-
EAQ Credentials
API key and project ID for authentication
-
Project Setup
Basic understanding of EAQ framework architecture
Installation
pip install eaq-orchestration==2.1.5
Core Architecture
Workflow Engine
Distributed task scheduler with automatic dependency resolution and dynamic priority management
- ✓ Sub-millisecond task scheduling
- ✓ Automatic SLA enforcement
- ✓ Real-time execution monitoring
Execution Layer
Adaptive execution environment that auto-scales based on system load and job complexity
Optimizes resource allocation using machine learning models for execution pattern prediction
CPU Workers
12+ instances
GPU Nodes
Auto-scaling
Memory Usage
Real-time tracking
Queue Length
Dynamic management
Orchestration API
Core Methods
| Endpoint | Description | Parameters |
|---|---|---|
| POST /v2/workflows | Create a new workflow |
name, config, dependencies
|
| PUT /v2/workflows/{id} | Update workflow configuration |
workflow_id, new_config
|
| GET /v2/workflows | List active workflows | - |
| DELETE /v2/workflows/{id} | Delete workflow |
workflow_id
|
Configuration Management
YAML Configuration Example
framework:
environment:
type: production
max_workers: 48
execution:
timeout: 3600
autoscaling:
min_replicas: 5
max_replicas: 100
dependencies:
- query-framework: 2.3
- security: latest
Implementation Best Practices
Resource Management
- 1. Monitor memory usage during execution spikes
- 2. Use dedicated workspaces for large workflows
- 3. Leverage auto-scheduler for optimal distribution
Error Handling
Retry Policy
Automatic retries with jittered exponential backoff (up to 3 attempts)
Error Monitoring
Integration with enterprise-level alerting systems for SLA violations