Worker Use Cases in SleakOps
Explore the most common use cases for SleakOps Worker Workloads — from asynchronous message processing to scheduled tasks, autoscaling, and pipeline orchestration.
What is a Worker Workload?
In SleakOps, a Worker is a Workload type designed for background processing. Unlike a Web Service, a Worker doesn't expose an HTTP endpoint — it runs continuously, consuming messages from a queue or executing tasks triggered by events or schedules.
Use Cases
1. Background Message and Task Processing
A Worker can consume messages from a queue or task backend such as RabbitMQ, AWS SQS, Redis, PostgreSQL (listen/notify), Kafka, etc.
Common examples:
- Asynchronous event processing (email sending, push notifications, database updates)
- Data ingestion into real-time analytics systems
- Microservice orchestration via event queues
2. Scheduled and Event-Driven Tasks
When an application needs to execute tasks based on a cron schedule, system events, or user-defined triggers, Workers handle execution without blocking other processes.
Common examples:
- Recurring tasks: log cleanup, cache regeneration, periodic data updates
- Event-driven tasks: when a user action requires background processing (report generation, image/video processing)
Two execution strategies:
- Run the task directly inside the Worker (simpler, less recommended for Kubernetes clusters)
- Send a message to a queue so a specialized Worker processes it
3. Autoscaling Workers Based on Load
Workers can be scaled dynamically based on the number of pending messages in the queue or resource consumption in the cluster.
Common examples:
- Launch additional Worker instances during traffic spikes to process tasks in parallel
- Use KEDA (Kubernetes Event-Driven Autoscaling) to scale Workers based on queue depth metrics
4. File Processing and ETL (Extract, Transform, Load)
Workers are well-suited for handling heavy files or processing large data volumes asynchronously.
Common examples:
- Processing CSV/JSON files and loading them into a database
- Format conversion (images, videos, PDFs)
- Log ingestion and real-time data analysis
5. Workflow Orchestration and Pipelines
Some tasks require multiple chained steps that can be managed by Workers.
Common examples:
- Machine Learning pipeline: data download → preprocessing → model training → evaluation
- Approval workflows in enterprise applications (purchase requests moving through multiple stages)
6. Security and Compliance Workers
Workers can run security and compliance checks in the background.
Common examples:
- Log monitoring for anomalies or unauthorized access attempts
- Vulnerability scanning on containers or infrastructure
- Permission management and audit in authentication systems
7. Third-Party API Integrations
Workers handle third-party API integrations without blocking the main application.
Common examples:
- Data synchronization with CRM, ERP, or other external services
- Sending data to third-party services (Stripe, Twilio, Google Sheets)
- Monitoring external API changes and updating the application
Choosing the Right Worker Pattern
| Pattern | When to use |
|---|---|
| Single Worker consuming a queue | Simple async tasks with one consumer |
| Multiple Workers on the same queue | High-throughput processing requiring parallelism |
| Worker + KEDA autoscaling | Variable load; scale to zero when idle |
| Chained Workers (pipeline) | Complex multi-step workflows |
| Cron-triggered Worker | Recurring scheduled tasks |