zulip/zephyr/lib/cache.py

from __future__ import absolute_import

from functools import wraps

from django.core.cache import cache as djcache
from django.core.cache import get_cache
from django.conf import settings

from zephyr.lib.utils import statsd, statsd_key, make_safe_digest
import time
import base64
import random
import sys
import os
import os.path
import hashlib

memcached_time_start = 0
memcached_total_time = 0
memcached_total_requests = 0

def get_memcached_time():
    return memcached_total_time

def get_memcached_requests():
    return memcached_total_requests

def memcached_stats_start():
    global memcached_time_start
    memcached_time_start = time.time()

def memcached_stats_finish():
    global memcached_total_time
    global memcached_total_requests
    global memcached_time_start
    memcached_total_requests += 1
    memcached_total_time += (time.time() - memcached_time_start)

def get_or_create_key_prefix():
    filename = os.path.join(settings.DEPLOY_ROOT, "memcached_prefix")
    try:
        fd = os.open(filename, os.O_CREAT | os.O_EXCL | os.O_RDWR, 0444)
        prefix = base64.b16encode(hashlib.sha256(str(random.getrandbits(256))).digest())[:32].lower() + ':'
        # This does close the underlying file
        with os.fdopen(fd, 'w') as f:
            f.write(prefix + "\n")
    except OSError:
        # The file already exists
        tries = 1
        while tries < 10:
            with file(filename, 'r') as f:
                prefix = f.readline()[:-1]
            if len(prefix) == 33:
                break
            tries += 1
            prefix = ''
            time.sleep(0.5)

    if not prefix:
        sys.exit("Could not read memcache key prefix file")

    return prefix

KEY_PREFIX = get_or_create_key_prefix()

def get_cache_backend(cache_name):
    if cache_name is None:
        return djcache
    return get_cache(cache_name)

def cache_with_key(keyfunc, cache_name=None, timeout=None, with_statsd_key=None):
    """Decorator which applies Django caching to a function.

       Decorator argument is a function which computes a cache key
       from the original function's arguments.  You are responsible
       for avoiding collisions with other uses of this decorator or
       other uses of caching."""

    def decorator(func):
        @wraps(func)
        def func_with_caching(*args, **kwargs):
            key = keyfunc(*args, **kwargs)

            val = cache_get(key, cache_name=cache_name)

            extra = ""
            if cache_name == 'database':
                extra = ".dbcache"

            if with_statsd_key is not None:
                metric_key = with_statsd_key
            else:
                metric_key = statsd_key(key)

            status = "hit" if val is not None else "miss"
            statsd.incr("cache%s.%s.%s" % (extra, metric_key, status))

            # Values are singleton tuples so that we can distinguish
            # a result of None from a missing key.
            if val is not None:
                return val[0]

            val = func(*args, **kwargs)

            cache_set(key, val, cache_name=cache_name, timeout=timeout)

            return val

        return func_with_caching

    return decorator

def cache_set(key, val, cache_name=None, timeout=None):
    memcached_stats_start()
    cache_backend = get_cache_backend(cache_name)
    ret = cache_backend.set(KEY_PREFIX + key, (val,), timeout=timeout)
    memcached_stats_finish()
    return ret

def cache_get(key, cache_name=None):
    memcached_stats_start()
    cache_backend = get_cache_backend(cache_name)
    ret = cache_backend.get(KEY_PREFIX + key)
    memcached_stats_finish()
    return ret

def cache_get_many(keys, cache_name=None):
    keys = [KEY_PREFIX + key for key in keys]
    memcached_stats_start()
    ret = get_cache_backend(cache_name).get_many(keys)
    memcached_stats_finish()
    return dict([(key[len(KEY_PREFIX):], value) for key, value in ret.items()])

def cache_set_many(items, cache_name=None, timeout=None):
    new_items = {}
    for key in items:
        new_items[KEY_PREFIX + key] = items[key]
    items = new_items
    memcached_stats_start()
    ret = get_cache_backend(cache_name).set_many(items, timeout=timeout)
    memcached_stats_finish()
    return ret

# Required Arguments are as follows:
# * object_ids: The list of object ids to look up
# * cache_key_function: object_id => cache key
# * query_function: [object_ids] => [objects from database]
# Optional keyword arguments:
# * setter: Function to call before storing items to cache (e.g. compression)
# * extractor: Function to call on items returned from cache
#   (e.g. decompression).  Should be the inverse of the setter
#   function.
# * id_fetcher: Function mapping an object from database => object_id
#   (in case we're using a key more complex than obj.id)
# * cache_transformer: Function mapping an object from database =>
#   value for cache (in case the values that we're caching are some
#   function of the objects, not the objects themselves)
def generic_bulk_cached_fetch(cache_key_function, query_function, object_ids,
                              extractor=lambda obj: obj,
                              setter=lambda obj: obj,
                              id_fetcher=lambda obj: obj.id,
                              cache_transformer=lambda obj: obj):
    cache_keys = {}
    for object_id in object_ids:
        cache_keys[object_id] = cache_key_function(object_id)
    cached_objects = cache_get_many([cache_keys[object_id]
                                     for object_id in object_ids])
    for (key, val) in cached_objects.items():
        cached_objects[key] = extractor(cached_objects[key][0])
    needed_ids = [object_id for object_id in object_ids if
                  cache_keys[object_id] not in cached_objects]
    db_objects = query_function(needed_ids)

    items_for_memcached = {}
    for obj in db_objects:
        key = cache_keys[id_fetcher(obj)]
        item = cache_transformer(obj)
        items_for_memcached[key] = (setter(item),)
        cached_objects[key] = item
    if len(items_for_memcached) > 0:
        cache_set_many(items_for_memcached)
    return dict((object_id, cached_objects[cache_keys[object_id]]) for object_id in object_ids
                if cache_keys[object_id] in cached_objects)

def cache(func):
    """Decorator which applies Django caching to a function.

       Uses a key based on the function's name, filename, and
       the repr() of its arguments."""

    func_uniqifier = '%s-%s' % (func.func_code.co_filename, func.func_name)

    @wraps(func)
    def keyfunc(*args, **kwargs):
        # Django complains about spaces because memcached rejects them
        key = func_uniqifier + repr((args, kwargs))
        return key.replace('-','--').replace(' ','-s')

    return cache_with_key(keyfunc)(func)

def message_cache_key(message_id):
    return "message:%d" % (message_id,)

def user_profile_by_email_cache_key(email):
    # See the comment in zephyr/lib/avatar.py:gravatar_hash for why we
    # are proactively encoding email addresses even though they will
    # with high likelihood be ASCII-only for the foreseeable future.
    return 'user_profile_by_email:%s' % (make_safe_digest(email),)

def user_profile_by_id_cache_key(user_profile_id):
    return "user_profile_by_id:%s" % (user_profile_id,)

# Called by models.py to flush the user_profile cache whenever we save
# a user_profile object
def update_user_profile_cache(sender, **kwargs):
    user_profile = kwargs['instance']
    items_for_memcached = {}
    items_for_memcached[user_profile_by_email_cache_key(user_profile.email)] = (user_profile,)
    items_for_memcached[user_profile_by_id_cache_key(user_profile.id)] = (user_profile,)
    cache_set_many(items_for_memcached)

def status_dict_cache_key(user_profile):
    return "status_dict:%d" % (user_profile.realm_id,)

def update_user_presence_cache(sender, **kwargs):
    user_profile = kwargs['instance'].user_profile
    if kwargs['update_fields'] is None or "status" in kwargs['update_fields']:
        # If the status of the user changed, flush the user's realm's
        # entry in the UserPresence cache to avoid giving out stale state
        djcache.delete(KEY_PREFIX + status_dict_cache_key(user_profile))