perf(table): prune equality deletes by data-file metrics

[fallintoplace]

What

• Port the equality-delete metrics pruning used by Java DeleteFileIndex.
• After partition and sequence filtering, use equality-field null counts and lower/upper bounds from the data file and delete file.
• Keep the match when stats are missing, malformed, nested, or otherwise uncertain.
• Cache typed bounds so the hot loop avoids repeated literal/interface work. ⚡

Java reference: https://github.com/apache/iceberg/blob/main/core/src/main/java/org/apache/iceberg/DeleteFileIndex.java

Why

With many equality deletes in one partition, most delete files can be ruled out from a data file by its metrics. This reduces the delete files attached to each FileScanTask, which also reduces downstream delete-file reads.

Benchmark

Workload: 10,000 data files, 10,000 equality deletes, one partition, 100 disjoint key-range groups. Each data file overlaps 100 delete files (1%).

                                      ns/op       B/op       attached deletes/data file
baseline                              1.41-1.69s  6.66GB     10,000
metrics pruning                       0.93-1.11s  52.5MB     100

The benchmark was run with go test ./table -run ^$ -bench ^BenchmarkEqualityDeleteIndexMetrics$ -benchtime=1x -count=5 on an Apple M1 Pro.

Checks

• go test ./...
• go test -race ./table
• go vet ./... ✅