  Key\-value separation is used in LSM\-tree to stored large value in separate
log files to reduce write amplification, but requires garbage collection to
garbage collect invalid values. Existing garbage collection techniques in
LSM\-tree typically adopt static parameter based garbage collection to garbage
collect obsolete values which struggles to achieve low write amplification and
it's challenging to find proper parameter for garbage collection triggering. In
this work we introduce DumpKV, which introduces learning based lifetime aware
garbage collection with dynamic lifetime adjustment to do efficient garbage
collection to achieve lower write amplification. DumpKV manages large values
using trained lightweight model with features suitable for various application
based on past write access information of keys to give lifetime prediction for
each individual key to enable efficient garbage collection. To reduce
interference to write throughput DumpKV conducts feature collection during
L0\-L1 compaction leveraging the fact that LSM\-tree is small under KV
separation. Experimental results show that DumpKV achieves lower write
amplification by 38\%\-73\% compared to existing key\-value separation garbage
collection LSM\-tree stores with small feature storage overhead.
