Thread-safe LRU cache with permanency and context-based expiration
| Operation | Best | Average | Worst |
|---|---|---|---|
| Access | Θ(1) | Θ(1) | O(1) |
| Search | Θ(1) | Θ(1) | O(n) |
| Insertion | Θ(1) | Θ(1) | O(n) |
| Deletion | Θ(1) | Θ(1) | O(n) |
| Snapshot | Θ(n) | Θ(n) | Θ(n) |
| Complexity | Value |
|---|---|
| Best | Ω(2n) |
| Average | Ω(2n) |
| Worst | Ω(n + n log(n)) |
This is a simple example LRU cache structure made with API request lookup caching in mind. If you decide to use this, do so at your own peril.
It should be thread-safe on all operations.
Running tool: /usr/local/go/bin/go test -benchmem -run=^$ -bench ^BenchmarkSet100k$ github.com/heucuva/go-wlru
goos: linux
goarch: amd64
pkg: github.com/heucuva/go-wlru
cpu: Intel(R) Core(TM) i7-10710U CPU @ 1.10GHz
BenchmarkSet100k-4 1000000000 0.05225 ns/op 0 B/op 0 allocs/op
PASS
ok github.com/heucuva/go-wlru 0.436s
As long as the data set is relatively small in size (<131072 items), the performance stays fairly good. Once this size is passed, the time to perform access/search/insert/delete operations increase significantly - this is due to the design of the underlying sync.Map implementation, as shown below:
Running tool: /usr/local/go/bin/go test -benchmem -run=^$ -bench ^BenchmarkSet1M$ github.com/heucuva/go-wlru
goos: linux
goarch: amd64
pkg: github.com/heucuva/go-wlru
cpu: Intel(R) Core(TM) i7-10710U CPU @ 1.10GHz
BenchmarkSet1M-4 1000000000 0.7867 ns/op 0 B/op 0 allocs/op
PASS
ok github.com/heucuva/go-wlru 42.616s
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