qzyu999 opened a new issue, #3715:
URL: https://github.com/apache/iceberg-python/issues/3715

   ## Summary
   
   PyIceberg uses PyArrow as its sole execution engine. PyArrow is a kernel 
library with no memory management, no spill-to-disk, and no join operators. 
Operations that process more data than available memory (CoW deletes, equality 
delete resolution, scan planning for heavily-deleted tables, sorted writes) 
crash with OOM errors.
   
   This issue tracks introducing a pluggable backend interface (`ReadBackend`, 
`WriteBackend`, `ComputeBackend` protocols) and integrating Apache DataFusion 
as the first bounded-memory compute backend.
   
   ## Problem
   
   | Operation | Current Status | OOM Pattern |
   |-----------|---------------|-------------|
   | Equality delete reads | Hard `ValueError` | Anti-join requires all delete 
keys in memory |
   | CoW delete (large files) | OOMs | Materializes entire Parquet file into 
RAM |
   | Scan planning (>100K deletes) | OOMs | All delete entries in Python dict |
   | Sort-on-write | Not implemented | Full sort before write |
   | Positional deletes (millions) | OOMs | Python set of positions |
   
   Tables written by Flink (which uses equality deletes) are completely 
unreadable by PyIceberg today.
   
   ## Solution
   
   1. **Pluggable interface**: `ReadBackend`, `WriteBackend`, `ComputeBackend` 
protocols that decouple PyIceberg from PyArrow
   2. **DataFusion integration**: Bounded-memory sort, join, and filter with 
spill-to-disk via `datafusion-python`
   3. **Migration**: All existing data operations route through the interface 
with zero API changes
   
   ## Deliverables
   
   - [ ] Equality delete resolution (NEW): tables with equality deletes can now 
be read
   - [ ] CoW delete/overwrite streaming (FIX): statistics short-circuit + 
two-pass streaming
   - [ ] Positional delete resolution (IMPROVED): bounded-memory for large 
delete sets
   - [ ] Sort-on-write (NEW): external merge sort when DataFusion installed
   - [ ] Bounded-memory scan planning (NEW): for tables with >100K delete files
   
   ## Related Issues
   
   - #1210 - Support reading equality delete files
   - #3270 - Equality Delete support
   - #3554 - Integrate DataFusion as execution engine
   
   ## Acceptance Criteria
   
   - All existing tests pass without `datafusion` installed (no regression)
   - Tables with equality deletes return correct results
   - CoW delete on 2GB+ files completes without OOM (with DataFusion)
   - Sort-on-write produces sorted files when table has sort order and 
DataFusion installed
   - Property-based tests verify PyArrow and DataFusion backends produce 
identical output
   


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