# IJCache
A fast Cache module written in Python.
Usage
=====
```py
import ijcache
# Create a Bit-PLRU Cache object with 32 items
ca = ijcache.BPLRUCache(32)
# Create a 2-Random Choices Cache object with 1024 items
ca = ijcache.TRCCache(1024)
# Add one item
ca.add('k1', 1)
# Lookup an item
ca.lookup('k1')
# Lookup and add in one method
val = ca.ensure('k2', lambda : tuple(range(0, 256)))
# Remove one item
ca.remove('k1')
# With decorator
@ca.cache
def fib(x):
if x == 1: return 1
if x == 0: return 0
return fib(x - 1) + fib(x - 2)
# Custom event handlers
class MyItem(ijcache.Item):
def on_hit(self):
print(f'Hit on {self.value}')
def on_evict(self):
print(f'Evict {self.value}')
ca.add('k2', MyItem(2))
v2 = ca.lookup('k2').value
# Want more? Just see `help(ca)`
```
Benchmarks
==========
View [source](benchmark.py)
bm1.txt
```
1. bplru 1.2433981895446777, 14930352
2. lru 1.26200270652771, 14930352
3. functools 1.2794532775878906, 14930352
4. trc 1.292776107788086, 14930352
5. none 1.3914763927459717, 14930352
```
bm2.txt
```
1. trc 1.2627553939819336, 14930352
2. bplru 1.2631657123565674, 14930352
3. functools 1.2682974338531494, 14930352
4. lru 1.2897653579711914, 14930352
5. none 1.3929316997528076, 14930352
```
bm3.txt
```
1. bplru 1.2370176315307617, 14930352
2. functools 1.2376103401184082, 14930352
3. trc 1.2679831981658936, 14930352
4. lru 1.2752890586853027, 14930352
5. none 1.399376630783081, 14930352
```
I can't direct say which one is best, but in 32 items, bplru is a not bad
choice. And in other cases, functools may better than my implementations,
because it with less wrappings.
Notes
=====
Some codes refer to [karlmcguire/plru](https://github.com/karlmcguire/plru),
thank Karl!
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