Learned static functions combine per-key ML-predicted prefix codes with classic static function storage to compress static key-value mappings beyond zero-order entropy limits.
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TurtleKV uses a balanced TurtleTree on-disk structure and flexible memory tuning knobs to deliver strong performance across inserts, mixed workloads, point queries, and scans in YCSB tests, matching or beating SplinterDB, RocksDB, and WiredTiger.
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Learned Static Function Data Structures
Learned static functions combine per-key ML-predicted prefix codes with classic static function storage to compress static key-value mappings beyond zero-order entropy limits.
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Dynamic read & write optimization with TurtleKV
TurtleKV uses a balanced TurtleTree on-disk structure and flexible memory tuning knobs to deliver strong performance across inserts, mixed workloads, point queries, and scans in YCSB tests, matching or beating SplinterDB, RocksDB, and WiredTiger.