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Example of using QTasks in large businesses

QTasks scales for use in high-load systems and is suitable for enterprise-level use thanks to its modular architecture, expandability, and task execution control. This system can be used for task processing in microservice architecture, automation of complex chains, and load distribution.

🏢 Scenario

A large company has dozens of microservices:

  1. They need to be connected via background task processing.
  2. Ensure reliable execution and control.
  3. Maintain scalability, fault tolerance, and integration with external systems (Kafka, Redis, PostgreSQL, REST, gRPC).

⚙️ Distributed task processing

Deploy workers on different servers/containers:

py -m qtasks -A myproject.qtasks_app run --worker-id node1
py -m qtasks -A myproject.qtasks_app run --worker-id node2

Can be run with different configurations using environment variables or command line arguments.

🔀 Integration with Kafka and other brokers

from my_kafka_broker import KafkaBroker

app = QueueTasks(broker=KafkaBroker(...))

Custom Storage, GlobalConfig, and other components are also supported.

🧩 TaskManager for managing task strategies

from qtasks.components.task_manager import TaskManager

class MyManager(TaskManager):
    def mutate(self, task):
        if task.name == "heavy_task":
            task.timeout = 120
        return task

app.config.task_manager = MyManager()

📊 Integration with BI and internal services

Using generate_handler, yield, middlewares, tasks can be used to build complex pipelines:

@app.task(generate_handler=send_to_bi)
def export_metrics():
    for report in generate_big_reports():
        yield report

🧠 Control and monitoring

In conjunction with QTasks WebView, you can:

  • track task status;
  • filter by type/error/time;
  • cancel, restart, view logs;
  • integrate into corporate dashboards.

✅ Result

QTasks in a large company allows you to:

  • flexibly configure components (broker, storage, config);
  • manage distributed task processing;
  • extend task behavior without rewriting the core;
  • integrate with BI, monitoring, logging, Kafka, REST, gRPC, etc.

This makes QTasks the foundation of a framework for enterprise-level automation and event processing.