Dependencies:

• numpy (for optimizations in FilesCacheClient)

• python-memcached (for MemcachedClient)

• pyzmq (for coherence support)

• dnspython (for dynamic dns-based load-balancing of MemcachedClient)

• cython (for optimizations in InprocCacheClient)

Optional features

When declaring dependencies in your project, you can reference the following optional features:

• mq (everything under chorde.mq, mostly pulls pyzmq)

• shmem (shared memory utils, needed for optimized files cache clients)

• memcache

• elasticache

Basic Usage:

The simplest way to use is to create one of the supported cache clients, and use it directly, like

from chorde.clients.inproc import InprocCacheClient
from chorde import CacheMissError
c = InprocCacheClient(200)
c.put(3, 10, 300) # put value 10 on key 3, TTL 5min
assert 10 == c.get(3)
try:
    c.get(5)
except CacheMissError:
    print "miss"

This creates an In-process LRU cache. The in-process part indicates that it is process-private, and not shared with other processes.

There are two implementations of the LRU, with different performance characteristics. The InprocCacheClient can take alternative store implementations as an argument, see the module for details.

The default LRUCache, accessible as chorde.clients.inproc.Cache, is a regular LRU implemented with a priority queue and a hash table in tandem, so it has O(log n) writes and O(1) reads, but by default all reads entail a write (to update the LRU). That can be disabled by specifying custom options, see the module’s documentation for more details.

There’s an alternative approximate LRU, accessible in chorde.clients.inproc.CuckooCache, that implements a lazy version of a cuckoo hash table, and has O(1) reads and amortized O(1) writes. It’s also quite more space-efficient than the regular LRU, so it’s better suited for very large caches, but its eviction strategy will be approximate, and thus not guaranteed to always evict the actual least-recently-used item.

Shared Caches

The most straightforward way to get a shared cache, is to use a memcache:

from chorde.clients.memcached import MemcachedClient
from chorde import CacheMissError
c = MemcachedClient(["localhost:11211"], checksum_key = "testing")
c.put(3, 10, 300)
assert 10 == c.get(3)
try:
    c.get(5)
except CacheMissError:
    print "miss"

The MemcachedClient is used just like any other client, only it talks to, in this example, a local memcached listening on localhost port 11211. Multiple clients can be given, and a consistent hash on the key will be used to spread the load among them.

If a hostname is given, and the hostname points to multiple IP addresses, the same effect will be obtained, and the distribution will be dynamically updated according to the TTL specified on the DNS entry. This, for example, makes the client work seamlessly with Amazon ElastiCache’s “configuration endpoint”, which is a DNS entry that points to one of the cache nodes. But it only works like that with single-node clusters. For multi-node clusters, use chorde.clients.elasticache.ElastiCacheClient, which goes a step further and queries this configuration endpoint for all the other nodes.

Do beware that the key used against the memcached isn’t the key given, since the client supports any hashable object as key, whereas memcached only supports a subset of string keys. MemcachedClient has none of memcached’s limitations with regards to key format and/or length and value size. It works around memcaches limitations, by constructing a derived key that is suitable for memcached. However, there is overhead in providing huge keys or values, so it’s generally good advise to avoid them anyway.

Values in the MemcachedClient are plickled, compressed and signed with the checksum key, so it is relatively safe from both malicious code injection through pickle, and transmission errors causing cPickle to dump core (which does happen when it is fed unchecked data).

In case the compression becomes a bottleneck, which shouldn’t be a problem unless it is a high-traffic cache with rarely compressible values, one can disable it. Check MemcachedClient’s documentation for more details.

To reduce roundtrips, all clients (in particular MemcachedClients) support getMulti and getTtlMulti, to fetch multiple keys at once:

from chorde.clients.memcached import MemcachedClient
from chorde import CacheMissError
c = MemcachedClient(["localhost:11211"], checksum_key = "testing")
c.put(3, 10, 300)
c.put(4, 20, 300)
assert {3:10, 4:20, 5:None} == dict(c.getMulti([3,4,5], None))

See the documentation on clients.base for more details.

Multilevel caches

A common approach when dealing with remote caches, like the above example using memcached, is to have at least two levels: the memcached itself, and an in-process small cache to avoid having to talk to the memcached all the time.

This can be done straightforwardly with the tiered clients:

from chorde.clients.memcached import MemcachedClient
from chorde.clients.inproc import InprocCacheClient
from chorde.clients.tiered import TieredInclusiveClient
from chorde import CacheMissError
l1 = InprocCacheClient(10)
l2 = MemcachedClient(["localhost:11211"], checksum_key="test")
c = TieredInclusiveClient(l1,l2)
c.put(3, 10, 300)
assert 10 == c.get(3)
try:
    c.get(5)
except CacheMissError:
    print "miss"

Here we build an inclusive tiered client, in which elements on higher levels are promoted into the lower levels by copying, rather than swapping. This means there is duplication among them, but this is usually best in cases like these, where the upper levels are shared among processes.

An exclusive client isn’t provided at this moment, since there is seldom any use for the exclusive pattern on these types of caches.

Decorators

A more natural way to think about caching, is in that it’s a decorator of plain functions.

Rather than explicitly putting and getting from caches, one can simply consider caching as an optimization on an otherwise expensive function.

Decorators in chorde.decorators provide a huge amount of functionality and flexibility, these examples cover only the most basic usage:

Assuming c is the client we want to use for caching,

from chorde.decorators import cached
import random

@cached(c, ttl=300, async_ttl=-60)
def expensive_func(x):
    return x * random.random()

print expensive_func(1)
print expensive_func(1) # Should return the same
print expensive_func.bg()(1) # will refresh asynchronously every minute
print expensive_func.future()(1).result() # same as before, but using the futures interface
print expensive_func.peek(1) # just check the cache
print expensive_func.put(1, _cache_put=5) # write an explicit value
print expensive_func.bg().lazy(1) # don't wait, raise CacheMissError if not available, compute in background
print expensive_func.future().lazy(1).result() # same as before, but using the futures interface

There, the async_ttl means the minimum TTL that triggers an asynchronous recomputation (you can use it to avoid ever having to wait on a recomputation). The negative value makes it relative to the total TTL, so -60 always means recompute every minute (60 seconds). The plain ttl is an absolute limit, no result older than that will ever be returned.

The documentation on chorde.decorators.cached will have more to say about the ways of invoking cached functions.

In general, the terms are:

• lazy: don’t wait for computation, return a cached result or raise CacheMissError. When combined with bg, it will compute in the background.

• peek: don’t compute. Similar to lazy, but it will never trigger a computation

• bg: expensive things (computation) happen on a background threadpool.

• future: return futures rather than results, use the future to get notified of results when they’re available. Actual cache access happens on a threadpool. A non-blocking way of calling.

• refresh: immediately recompute the value.

import tornado.web
import tornado.gen
from chorde.clients.asyncache import makeFutureWrapper

WF = makeFutureWrapper(tornado.web.Future)

...

@tornado.gen.coroutine
def get(self):
    some_result = yield WF(some_func.future()(some_args))

from chorde.external_integration import monkey_patch_tornado
monkey_patch_tornado()

import tornado.web
import tornado.gen

...

@tornado.gen.coroutine
def get(self):
    some_result = yield some_func.future()(some_args)

Additional documentation

• In Depth Look into Caching (Part 1) (English)

• In Depth Look into Caching (Part 2) (English)

• Caching para hordas y estampidas por Claudio Freire - PyCon 2013 (Spanish)