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Modern geospatial workflows often involve large datasets and computationally intensive tasks. CubeXpress leverages recursion, concurrency, and parallelism to optimize these operations and handle massive image retrievals from Google Earth Engine (GEE). This section provides a concise overview of each concept and how CubeXpress implements them.

1. Concurrency ⚙️

  • Definition: Concurrency refers to managing multiple tasks such that they all make progress over the same time period—even if they’re not strictly executing at the exact same moment.
  • Usage in CubeXpress:
    • The package uses ThreadPoolExecutor to coordinate concurrent downloads inside getcube().
    • By submitting multiple requests for images simultaneously, CubeXpress maximizes CPU and network usage, drastically reducing the overall download duration.

2. Parallelism 🖥️

  • Definition: Parallelism describes running multiple tasks truly at the same time, often on separate CPU cores.
  • Usage in CubeXpress:
    • On multi-core systems, thread-based parallelism ensures that multiple image downloads or computations happen concurrently—spreading the load across available cores.
    • This can significantly improve performance when dealing with large or numerous GEE images.

Concurrency vs. Parallelism

  • Concurrency: Tasks appear to run simultaneously but may share a single core, each making progress in turn (time-slicing).
  • Parallelism: Tasks literally run at the same time on different CPU cores.

In practice, CubeXpress exploits both—concurrent scheduling of tasks and true parallel execution if multiple CPU cores are available.

3. Recursion 🔄

  • Definition: Recursion is the technique of breaking down a problem into smaller, more manageable subproblems, where a function calls itself until reaching a base case.
  • Usage in CubeXpress:
    • When an image exceeds GEE’s size limits, CubeXpress automatically splits (quad-splits) it into smaller tiles.
    • This splitting continues recursively (max_deep_level) until each tile is sufficiently small to download successfully.
    • This approach ensures you can handle massive requests without manually dividing your region of interest.

CubeXpress enables highly scalable and robust geospatial workflows with Google Earth Engine. These concepts work together to optimize performance, reduce download times, and handle exceptionally large datasets, giving you a seamless experience when building geospatial data cubes.

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