Requirements and estimate the number of Docker Swarm node for OCR conversion

requirements and estimate the number of Docker Swarm nodes you'll need for your Flask API with image-to-OCR using Tesseract

Understanding the Resource Requirements:

• CPU: Tesseract is CPU-intensive, especially when processing large images or PDFs.  

• RAM: Handling 15MB images and potentially PDFs requires sufficient RAM, especially during processing.

• Network: Transferring 15MB of data per request can put a strain on your network bandwidth.

• Concurrent Requests: You anticipate 20 concurrent requests.

Estimating Resource Usage:

1. CPU Usage:

   • OCR processing is a CPU-bound task. Each request will likely consume significant CPU resources.  

   • With 4 cores per machine, you need to distribute the load effectively.

2. RAM Usage:

   • Loading and processing 15MB images and PDFs will consume a substantial amount of RAM.

   • You'll need to consider the memory footprint of the Flask application, Tesseract, and any intermediate data structures.

3. Network Usage:

   • 20 requests * 15MB = 300MB of data transfer per second (if they all arrive simultaneously). This requires a robust network connection.

Docker Swarm Setup:

1. Resource Allocation:

   • With 8GB of RAM, you might be able to allocate 1-2GB per container, depending on the efficiency of your Flask application and Tesseract.

   • With 4 CPU cores, you can allocate 1-2 cores per container.

2. Container Replication:

   • To handle 20 concurrent requests, you'll need multiple container replicas.

   • A conservative approach would be to aim for 2-3 replicas per node, to maintain some redundancy.

3. Node Utilization:

   • You want to avoid overloading any single node.

   • Distribute the load evenly across your 5 machines.

Calculations and Recommendations:

1. Replicas per Node:

   • If each replica can handle 5-10 concurrent requests, you'll need 2-4 replicas to handle 20 requests.

2. Total Replicas:

   • To be safe, I would recommend to start with 10 replicas. This will allow for some head room, and if the processing of the images takes a long time, allow for new requests to be processed.

3. Distribution:

   • Docker Swarm will distribute these replicas across your 5 nodes.  

   • This equates to 2 replicas per machine.

4. Testing:

   • Load testing is crucial. Use tools like locust or jmeter to simulate the expected load and monitor your system's performance.

   • Monitor CPU, RAM, and network usage during testing.

   • Adjust the number of replicas and resource allocations as needed.

Docker Swarm Considerations:

• Load Balancing: Docker Swarm's built-in load balancing will distribute requests across your replicas.  

• Resource Limits: Set resource limits (CPU and RAM) for your Docker containers to prevent them from consuming excessive resources.  

• Monitoring: Implement monitoring tools to track the performance of your Docker Swarm and containers.

• Network: Ensure a high-bandwidth, low-latency network connection between your Docker Swarm nodes.

• Storage: If your images and PDFs are stored on a shared volume, ensure that the storage system can handle the concurrent access.

Conclusion:

• With 5 machines, you should be able to handle the load, but thorough testing is essential.

• Start with 2 replicas per machine.

• Monitor resource usage and adjust the number of replicas as needed.

• Load testing is crucial.

Remember that these are estimations. The actual resource usage will depend on the complexity of your OCR tasks and the efficiency of your code.