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Scalability limits of OMERO (large datasets ~500TB) and channel handling limitations for high-dimensional images (100+ channels) #542

Description

@ZYsAccount

We are currently deploying OMERO in a large-scale spatial proteomics / multiplex imaging environment and evaluating its suitability as a long-term data management and visualization platform.

During our testing with OMERO (Docker-based deployment), we encountered two important questions that are not clearly addressed in the current documentation.

We would greatly appreciate clarification from the development team.

  1. Data scale / system limits (~500TB)

We are planning to manage very large imaging datasets, potentially reaching hundreds of terabytes (~500TB total storage).

We would like to understand:

Is there any practical or architectural limit for OMERO in total managed dataset size?
Would OMERO remain stable and performant at ~500TB scale?
Are there known bottlenecks in:
OMERO.server performance under large metadata load
PostgreSQL scalability (number of images / datasets)
ManagedRepository / Pixels storage layer
Web viewer or API performance at large scale
Are there recommended best practices for such scale, e.g.:
multiple OMERO servers
distributed storage backend
database tuning strategies

Any guidance or real-world deployment experience at this scale would be very helpful.

  1. Channel number limitation (high-dimensional imaging)

We are working with highly multiplexed imaging data.

We successfully imported a pyramidal OME-TIFF containing 184 channels.

Observations:
Import completes successfully without errors
Image is accessible in OMERO
However, OMERO.web / iviewer does not allow practical visualization of all channels
Channel rendering becomes unusable / incomplete in UI

  1. Environment
    openmicroscopy/omero-server:5
    openmicroscopy/omero-web-standalone:5

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