Skip to content

memcached.md

Latest commit

 

History

History
117 lines (84 loc) · 3.48 KB

memcached.md

File metadata and controls

117 lines (84 loc) · 3.48 KB

Home › Backends › Memcached Backend

Memcached Backend

Requires: pip install cachekit[memcached]

Store cache in Memcached with consistent hashing across multiple servers. High-throughput, volatile in-memory caching shared across processes and pods.

Basic Usage

from cachekit.backends.memcached import MemcachedBackend, MemcachedBackendConfig
from cachekit import cache

# Use default configuration (127.0.0.1:11211)
backend = MemcachedBackend()

@cache(backend=backend)
def cached_function():
    return expensive_computation()

Configuration via Environment Variables

# Server list (JSON array format)
export CACHEKIT_MEMCACHED_SERVERS='["mc1:11211", "mc2:11211"]'

# Timeouts
export CACHEKIT_MEMCACHED_CONNECT_TIMEOUT=2.0    # Default: 2.0 seconds
export CACHEKIT_MEMCACHED_TIMEOUT=1.0             # Default: 1.0 seconds

# Connection pool
export CACHEKIT_MEMCACHED_MAX_POOL_SIZE=10        # Default: 10 per server
export CACHEKIT_MEMCACHED_RETRY_ATTEMPTS=2        # Default: 2

# Optional key prefix
export CACHEKIT_MEMCACHED_KEY_PREFIX="myapp:"     # Default: "" (none)

Configuration via Python

Config objects don't require a running Memcached server:

from cachekit.backends.memcached import MemcachedBackendConfig

config = MemcachedBackendConfig(
    servers=["mc1:11211", "mc2:11211", "mc3:11211"],
    connect_timeout=1.0,
    timeout=0.5,
    max_pool_size=20,
    key_prefix="myapp:",
)

To use the config with a live backend:

from cachekit.backends.memcached import MemcachedBackend, MemcachedBackendConfig

config = MemcachedBackendConfig(
    servers=["mc1:11211", "mc2:11211", "mc3:11211"],
    connect_timeout=1.0,
    timeout=0.5,
    max_pool_size=20,
    key_prefix="myapp:",
)

backend = MemcachedBackend(config)

When to Use

Use MemcachedBackend when:

  • Hot in-memory caching with sub-millisecond reads
  • Shared cache across multiple processes/pods (like Redis but simpler)
  • High-throughput read-heavy workloads
  • Applications already using Memcached infrastructure

When NOT to use:

  • Need persistence (Memcached is volatile — data lost on restart)
  • Need distributed locking (use Redis instead)
  • Need TTL inspection/refresh (Memcached doesn't support it)
  • Cache values exceed 1MB (Memcached default slab limit)

Characteristics

  • Latency: 1–5ms per operation (network-dependent)
  • Throughput: Very high (multi-threaded C server)
  • TTL support: Yes (max 30 days)
  • Cross-process: Yes (shared across pods)
  • Persistence: No (volatile memory only)
  • Consistent hashing: Yes (via pymemcache HashClient)

Limitations

  1. No persistence: All data is in-memory. Server restart = data loss.
  2. No locking: No distributed lock support (use Redis for stampede prevention).
  3. 30-day TTL maximum: TTLs exceeding 30 days are automatically clamped.
  4. 1MB value limit: Default Memcached slab size limits values to ~1MB.
  5. No TTL inspection: Cannot query remaining TTL on a key.

See Also