* Implement an UDP interface for low-latency operations.
* What about a special coding that is about storing the "rdb" serialized format instead of the actual value? This can be used when we have LRU in order to super-compress data into memory, for data not accessed frequetly. It's a VM-alike strategy but fully in memory, may reduce the space to hold some dataset in an impressive way. Trivial to implement.
VERSION 2.x TODO
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* BRPOPLPUSH
* BRPOPLPUSH
* Save dataset / fsync() on SIGTERM
* Save dataset / fsync() on SIGTERM
* Change the implementation of ZCOUNT to use the augmented skiplist in order to be much faster.
* Change the implementation of ZCOUNT to use the augmented skiplist in order to be much faster.
* Write doc for ZCOUNT, and for open / closed intervals of sorted sets range operations.
Virtual Memory sub-TODO:
Virtual Memory optimizations:
* Use multiple open FDs against the VM file, one for thread.
* Use multiple open FDs against the VM file, one for thread.
* vm-min-age <seconds> option
* Check what happens performance-wise if instead of creating threads again and again the same threads are reused forever. Note: this requires a way to disable this clients in the child, but waiting for empty new jobs queue can be enough.
* Check what happens performance-wise if instead to create threads again and again the same threads are reused forever. Note: this requires a way to disable this clients in the child, but waiting for empty new jobs queue can be enough.
* Implement LEN, PEEK, POKE, SETBIT, GETBIT
* Implement LEN, PEEK, POKE, SETBIT, GETBIT
VERSION 2.2 TODO (Fault tolerant sharding)
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* Redis-cluster, a fast intermediate layer (proxy) that implements consistent hashing and fault tollerant nodes handling.