Memcached health checks are less about ensuring the cache is warm and more about verifying the door is unlocked and the cashier is awake.
Let’s see Memcached in action. Imagine you have a web application that uses Memcached to store session data.
# On your application server
# First, set a value
echo "set mykey 0 60 5\r\nvalue\r\n" | nc localhost 11211
# Expected output:
# OK
# Then, get the value
echo "get mykey\r\n" | nc localhost 11211
# Expected output:
# VALUE mykey 0 5
# value
# END
This simple get and set interaction is the core of Memcached’s availability. If these commands fail, your application might fall back to its primary data source, leading to slower response times or even outright errors if the primary is also struggling.
The problem Memcached health checks solve is distinguishing between a slow cache and a dead cache. A slow cache might be due to network latency, a heavily loaded Memcached server, or inefficient key lookups. A dead cache means the Memcached server isn’t responding at all, or is returning errors that indicate it’s fundamentally broken. Health checks aim to catch the latter before it impacts users.
Internally, Memcached is a simple key-value store that runs in user space. It listens on a TCP port (default 11211). When a client sends a command (like get or set), Memcached parses it, performs the operation, and sends back a response. It doesn’t have complex internal states or many moving parts that can fail independently, which is why health checks are often straightforward.
The health check itself is typically a simple get operation for a known key that should be in the cache, or a set and then get of a temporary key. The critical part is how you interpret the response. A successful END after a get means the server is alive and responding. A connection refused, a timeout, or an error response means something is wrong.
The levers you control are primarily the Memcached server’s network configuration, its resource allocation (CPU, RAM), and the client’s timeout settings. For health checks, you’re looking at the client-side perspective: can it reach the Memcached server and get a valid response within an acceptable timeframe?
Most people treat Memcached as a black box and only notice it when their application starts throwing errors. They might see generic "connection refused" messages or timeouts and assume the whole system is down, without pinpointing that it’s specifically the cache layer that’s unavailable. They also often overlook that Memcached doesn’t persist data and a restart means a cold cache, which is a performance issue, not an availability issue.
The stats command is your best friend for deep dives. Executing stats on a Memcached server can reveal a wealth of information, but the truly insightful metric for availability is curr_connections. If this number is unexpectedly zero or has dropped significantly, it suggests clients aren’t even able to establish a connection, pointing to network issues or the Memcached process itself being down. Another key metric is cmd_get and get_misses. A sudden drop in cmd_get with a corresponding rise in get_misses (if you’re checking for specific keys) can indicate that the Memcached server is up but not serving data as expected, possibly due to memory pressure or internal errors.
Understanding the implications of evictions is also crucial. While not a direct health check metric, a high evictions rate means Memcached is constantly having to remove items to make space for new ones. If the cache is under heavy load and evictions are rampant, get operations for frequently accessed but not recently used items will start to miss, making the cache appear less effective, even though the server itself is technically available. This can be mistaken for a deeper problem, but it’s often a sign that your Memcached instance is undersized for your workload.
The next concept to tackle is optimizing Memcached’s memory usage to minimize evictions and improve hit rates.