.. | ||
arena_test.go | ||
arena.go | ||
flush_iterator.go | ||
iterator.go | ||
LICENSE | ||
node.go | ||
race_test.go | ||
README.md | ||
skl_test.go | ||
skl.go |
arenaskl
Fast, lock-free, arena-based Skiplist implementation in Go that supports iteration in both directions.
Advantages
Arenaskl offers several advantages over other skiplist implementations:
- High performance that linearly scales with the number of cores. This is achieved by allocating from a fixed-size arena and by avoiding locks.
- Iterators that can be allocated on the stack and easily cloned by value.
- Simple-to-use and low overhead model for detecting and handling race conditions with other threads.
- Support for iterating in reverse (i.e. previous links).
Limitations
The advantages come at a cost that prevents arenaskl from being a general-purpose skiplist implementation:
- The size of the arena sets a hard upper bound on the combined size of skiplist nodes, keys, and values. This limit includes even the size of deleted nodes, keys, and values.
- Deletion is not supported. Instead, higher-level code is expected to add deletion tombstones and needs to process those tombstones appropriately.
Pedigree
This code is based on Andy Kimball's arenaskl code:
https://github.com/andy-kimball/arenaskl
The arenaskl code is based on the skiplist found in Badger, a Go-based KV store:
https://github.com/dgraph-io/badger/tree/master/skl
The skiplist in Badger is itself based on a C++ skiplist built for Facebook's RocksDB:
https://github.com/facebook/rocksdb/tree/master/memtable
Benchmarks
The benchmarks consist of a mix of reads and writes executed in parallel. The fraction of reads is indicated in the run name: "frac_X" indicates a run where X percent of the operations are reads.
The results are much better than skiplist
and slist
.
name time/op
ReadWrite/frac_0-8 470ns ±11%
ReadWrite/frac_10-8 462ns ± 3%
ReadWrite/frac_20-8 436ns ± 2%
ReadWrite/frac_30-8 410ns ± 2%
ReadWrite/frac_40-8 385ns ± 2%
ReadWrite/frac_50-8 360ns ± 4%
ReadWrite/frac_60-8 386ns ± 1%
ReadWrite/frac_70-8 352ns ± 2%
ReadWrite/frac_80-8 306ns ± 3%
ReadWrite/frac_90-8 253ns ± 4%
ReadWrite/frac_100-8 28.1ns ± 2%
Note that the above numbers are for concurrent operations using 8x parallelism. The same benchmarks without concurrency (use these numbers when comparing vs batchskl):
name time/op
ReadWrite/frac_0 1.53µs ± 1%
ReadWrite/frac_10 1.46µs ± 2%
ReadWrite/frac_20 1.39µs ± 3%
ReadWrite/frac_30 1.28µs ± 3%
ReadWrite/frac_40 1.21µs ± 2%
ReadWrite/frac_50 1.11µs ± 3%
ReadWrite/frac_60 1.23µs ±17%
ReadWrite/frac_70 1.16µs ± 4%
ReadWrite/frac_80 959ns ± 3%
ReadWrite/frac_90 738ns ± 5%
ReadWrite/frac_100 81.9ns ± 2%
Forward and backward iteration are also fast:
name time/op
IterNext 3.97ns ± 5%
IterPrev 3.88ns ± 3%