meta-memcache 0.2.0

Modern, pure python, memcache client with support for new meta commands.

meta-memcache-py

Modern, pure python, memcache client with support for new meta commands.

Usage:

Install:

pip install meta-memcache

Configure a pool:

from meta_memcache import (
    Key,
    ServerAddress,
    ShardedCachePool,
    connection_pool_factory_builder,
)

pool = ShardedCachePool.from_server_addresses(
    servers=[
        ServerAddress(host="1.1.1.1", port=11211),
        ServerAddress(host="2.2.2.2", port=11211),
        ServerAddress(host="3.3.3.3", port=11211),
    ],
    connection_pool_factory_fn=connection_pool_factory_builder(),
)

The design is very pluggable. Rather than supporting a lot of features, it relies on dependency injection to configure behavior.

The CachePools expect a connection_pool_factory_fn callback to build the internal connection pool. There is a default builder provided to tune the most frequent things:

def connection_pool_factory_builder(
    initial_pool_size: int = 1,
    max_pool_size: int = 3,
    mark_down_period_s: float = DEFAULT_MARK_DOWN_PERIOD_S,
    connection_timeout: float = 1,
    recv_timeout: float = 1,
    no_delay: bool = True,
    read_buffer_size: int = 4096,
) -> Callable[[ServerAddress], ConnectionPool]:

• initial_pool_size: How many connections open for each host in the pool
• max_pool_size: Maximim number of connections to keep open for each host in the pool. Note that if there are no connections available in the pool, the client will open a new connection always instead of of just blocking waiting for a free connection.
• mark_down_period_s: When a network failure is detected, the destination host is marked down, and requests will fail fast, instead of trying to reconnect causing clients to block. A single client request will be checking if the host is still down every mark_down_period_s, while the others fail fast.
• connection_timeout: Timeout to stablish initial connection, ideally should be < 1 s for memcache servers in local network.
• recv_timeout: Timeout of requests. Ideally should be < 1 s for memcache servers in local network.
• no_delay: Wether to configure socket with NO_DELAY. This library tries to send requests as a single write(), so enabling no_delay is a good idea.
• read_buffer_size: This client tries to minimize memory allocation by reading bytes from the socket into a reusable read buffer. If the memcache response size is < read_buffer_size no memory allocations happen for the network read. Note: Each connection will have its own read buffer, so you must find a good balance between memory usage and reducing memory allocations. Note: While reading from the socket has zero allocations, the values will be deserialized and those will have the expected memory allocations.

If you need to customize how the sockets are created (IPv6, add auth, unix sockets) you will need to implement your own connection_pool_factory_builder and override the socket_factory_fn.

Use the pool:

cache_pool.set(key=Key("bar"), value=1, ttl=1000)

Keys:

Custom routing:

You can control the routing of the keys setting a custom routing_key:

Key("key:1:2", routing_key="key:1")

This is useful if you have several keys related with each other. You can use the same routing key, and they will be placed in the same server. This is handy for speed of retrieval (single request instead of fan-out) and/or consistency (all will be gone or present, since they are stored in the same server). Note this is also risky, if you place all keys of a user in the same server, and the server goes down, the user life will be misserable.

Unicode keys:

Unicode keys are supported, the keys will be hashed according to Meta commands binary encoded keys specification.

To use this, mark the key as unicode:

Key("🍺", unicode=True)

Large keys:

Large keys are also automatically supported and binary encoded as above. But don't use large keys :)

Design:

The code relies on dependency injection to allow to configure a lot of the aspects of the cache client. For example, instead of supporting a lot of features on how to connect, authenticate, etc, a socket_factory_fn is required and you can customize the socket creation to your needs. We provide some basic sane defaults, but you should not have a lot of issues to customize it for your needs.

Regarding cache client features, relies in inheritance to abstract different layers of responsibility, augment the capabilities while abstracting details out:

Low level meta commands:

The low-level class is BaseCachePool. Implements the connection pool handling as well as the memcache protocol, focusing on the new Memcache MetaCommands: meta get, meta set, meta delete and meta arithmetic. They implement the full set of flags, and features, but are very low level. You won't use this unless you are implementing some custom high-level command. See below for the usual memcache api.

High level commands:

The CachePool augments the low-level class and implements the more high-level memcache operations (get, set, touch, cas...), plus the new meta memcache goodies for high qps caching needs: Atomic Stampeding control, Herd Handling, Early Recache, Serve Stale, No Reply, Probabilistic Hot Cache, Hot Key Cache Invalidation...

    def set(
        self,
        key: Key,
        value: Any,
        ttl: int,
        no_reply: bool = False,
        cas_token: Optional[int] = None,
        stale_policy: Optional[StalePolicy] = None,
    ) -> bool:

    def delete(
        self,
        key: Key,
        cas_token: Optional[int] = None,
        no_reply: bool = False,
        stale_policy: Optional[StalePolicy] = None,
    ) -> bool:

    def touch(
        self,
        key: Key,
        ttl: int,
        no_reply: bool = False,
    ) -> bool:

    def get_or_lease(
        self,
        key: Key,
        lease_policy: LeasePolicy,
        touch_ttl: Optional[int] = None,
        recache_policy: Optional[RecachePolicy] = None,
    ) -> Optional[Any]:

    def get_or_lease_cas(
        self,
        key: Key,
        lease_policy: LeasePolicy,
        touch_ttl: Optional[int] = None,
        recache_policy: Optional[RecachePolicy] = None,
    ) -> Tuple[Optional[Any], Optional[int]]:

    def get(
        self,
        key: Key,
        touch_ttl: Optional[int] = None,
        recache_policy: Optional[RecachePolicy] = None,
    ) -> Optional[Any]:

    def multi_get(
        self,
        keys: List[Key],
        touch_ttl: Optional[int] = None,
        recache_policy: Optional[RecachePolicy] = None,
    ) -> Dict[Key, Optional[Any]]:

    def get_cas(
        self,
        key: Key,
        touch_ttl: Optional[int] = None,
        recache_policy: Optional[RecachePolicy] = None,
    ) -> Tuple[Optional[Any], Optional[int]]:

    def get_typed(
        self,
        key: Key,
        cls: Type[T],
        touch_ttl: Optional[int] = None,
        recache_policy: Optional[RecachePolicy] = None,
        error_on_type_mismatch: bool = False,
    ) -> Optional[T]:

    def get_cas_typed(
        self,
        key: Key,
        cls: Type[T],
        touch_ttl: Optional[int] = None,
        recache_policy: Optional[RecachePolicy] = None,
        error_on_type_mismatch: bool = False,
    ) -> Tuple[Optional[T], Optional[int]]:

    def _get_delta_flags(
        self,
        delta: int,
        refresh_ttl: Optional[int] = None,
        no_reply: bool = False,
        cas_token: Optional[int] = None,
        return_value: bool = False,
    ) -> Tuple[Set[Flag], Dict[IntFlag, int], Dict[TokenFlag, bytes]]:

    def delta(
        self,
        key: Key,
        delta: int,
        refresh_ttl: Optional[int] = None,
        no_reply: bool = False,
        cas_token: Optional[int] = None,
    ) -> bool:

    def delta_initialize(
        self,
        key: Key,
        delta: int,
        initial_value: int,
        initial_ttl: int,
        refresh_ttl: Optional[int] = None,
        no_reply: bool = False,
        cas_token: Optional[int] = None,
    ) -> bool:

    def delta_and_get(
        self,
        key: Key,
        delta: int,
        refresh_ttl: Optional[int] = None,
        cas_token: Optional[int] = None,
    ) -> Optional[int]:

    def delta_initialize_and_get(
        self,
        key: Key,
        delta: int,
        initial_value: int,
        initial_ttl: int,
        refresh_ttl: Optional[int] = None,
        cas_token: Optional[int] = None,
    ) -> Optional[int]:

Pool level features:

Finally in cache_pools.py a few classes implement the pool-level semantics:

• ShardedCachePool: implements a consistent hashing cache pool using uhashring's HashRing.
• ShardedWithGutterCachePool: implements a sharded cache pool like above, but with a 'gutter pool' (See Scaling Memcache at Facebook), so when a server of the primary pool is down, requests are sent to the 'gutter' pool, with TTLs overriden and lowered on the fly, so they provide some level of caching instead of hitting the backend for each request.

These pools also provide a write failure tracker feature, usefull if you are serious about cache consistency. If you have transient network issues, some writes might fail, and if the server comes back without being restarted or the cache flushed, the data will be stale. This allows for failed writes to be collected and logged. Affected keys can then be invalidated later and eventual cache consistency guaranteed.

It should be trivial to implement your own cache pool if you need custom sharding, shadowing, pools that support live migrations, etc. Feel free to contribute!