feat: add memory reservation parameters for Ray minhash deduplication

[xyuzh]

Summary

• Add actor_memory parameter for EdgeBuffer/BTSUnionFind actors (bytes)
• Add task_memory parameter for map_batches tasks (bytes)
• Add actor readiness check before processing
• Refactor map_batches calls to use kwargs dict pattern

Background

For billion-row scale deduplication, memory pressure can cause OOM issues. These parameters allow users to reserve memory for actors and tasks:

ray_bts_minhash_deduplicator:
  actor_memory: 20_000_000_000  # 20GB per actor
  task_memory: 2_000_000_000    # 2GB per task

Test plan

• Test with existing dedup configs (no memory params - backward compatible)
• Test with memory params on large-scale data

🤖 Generated with Claude Code

[gemini-code-assist]

Summary of Changes

Hello @xyuzh, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the robustness and scalability of the Ray-based MinHash deduplication process by introducing explicit memory management parameters. Users can now reserve specific amounts of memory for Ray actors and map_batches tasks, directly addressing potential Out-Of-Memory issues that arise when processing extremely large datasets. These changes aim to provide greater control over resource allocation, leading to more stable and efficient deduplication operations at a billion-row scale.

Highlights

• Memory Reservation for Ray Actors: Introduced an actor_memory parameter that allows users to specify memory reservation in bytes for EdgeBuffer and BTSUnionFind Ray actors, crucial for managing resources in large-scale deduplication.
• Memory Reservation for Ray Tasks: Added a task_memory parameter to reserve memory in bytes for map_batches tasks, preventing Out-Of-Memory (OOM) issues during intensive processing.
• Actor Readiness Check: Implemented a readiness check for BTSUnionFind actors using ray.get([uf.__ray_ready__.remote() for uf in self.union_find_list]) to ensure all actors are fully initialized before processing begins.
• Refactored map_batches Calls: Updated map_batches invocations to use a keyword argument dictionary pattern, making the code cleaner and more flexible for passing dynamic parameters like memory.

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[gemini-code-assist]

Code Review

This pull request introduces memory reservation parameters (actor_memory and task_memory) for Ray-based MinHash deduplication to handle large-scale datasets more effectively and prevent out-of-memory issues. The changes include updating actor and task creation to use these memory settings, adding a readiness check for actors, and refactoring map_batches calls. My review focuses on improving the robustness and maintainability of these changes. I've suggested ensuring all created actors are checked for readiness, not just a subset, and refactoring duplicated code for setting up map_batches arguments into a helper method for better code reuse and clarity. Overall, the changes are a good step towards making the deduplication process more scalable.