Cache misses in Memcached can silently destroy the performance advantages you expect from an in-memory cache. On a Hong Kong VPS hosting latency-sensitive applications, recurring misses will translate directly to slower responses, higher database load, and unhappy users. This article explains why Memcached misses occur, how to diagnose them with real-world tools and metrics, and which configuration and infrastructure choices — including decisions about Hong Kong Server placement versus alternatives like a US VPS or US Server — reduce misses and keep your cache reliable.
How Memcached works (core principles)
Memcached is a distributed memory key-value store that relies on RAM to serve objects extremely fast. Two core mechanisms determine whether a get() call is a hit or miss:
- Key lookup and hashing: Clients hash keys and map them to a server (or to an in-process instance). If the key is not present in the server’s hash table, the request is a miss.
- Slab allocation and LRU eviction: Memcached divides memory into slab classes (object size buckets). When memory fills, the LRU (Least Recently Used) or an eviction policy frees slots. If an object was evicted or never stored, subsequent gets are misses.
Understanding both the hashing and the memory allocator is essential to diagnosing misses: some misses are caused by client-side mapping errors, others by runtime memory pressure and fragmentation.
Common causes of cache misses
Here are the most frequent technical reasons for misses that you’ll encounter on a VPS:
- Evictions due to insufficient memory — If Memcached’s -m memory limit is too low relative to your working set, objects are evicted to make room. Check stats get_misses vs evictions to confirm.
- High fragmentation from slab sizing — Objects that cross slab boundaries or many variable-sized objects can cause wasted memory and early evictions.
- TTL expiry — Short expiry times (set by the application) will naturally cause more misses.
- Inconsistent hashing or server list changes — Adding/removing servers without consistent hashing causes many keys to map to new servers until warmed up.
- Network packet loss / UDP issues — Memcached can use UDP; dropped packets or fragmented UDP packets cause false misses at high throughput.
- Connection limits and drops — Too many concurrent connections (-c) or OS limits (somaxconn, ephemeral port exhaustion) can cause failed gets that appear as misses.
- Virtualization and noisy neighbors on VPS — I/O or CPU contention on a shared hypervisor can delay processing and increase packet drops or timeouts, contributing to miss-like behavior.
Practical diagnostics and metrics
Use the built-in stats interface and several tools to inspect what’s happening.
Memcached stats to monitor
- stats: get_hits, get_misses — basic hit ratio. Compute hit ratio as get_hits / (get_hits + get_misses).
- stats: evictions — number of objects evicted to free memory.
- stats slabs and stats items — slab usage and per-class item counts to find fragmentation hotspots.
- stats: curr_connections, conn_yields — connection pressure signs.
- stats: bytes, limit_maxbytes — current memory usage vs configured limit (-m).
- stats: allowed_fails, auth_cmds (if using SASL) — security-related rejections which can masquerade as failures.
Query Memcached easily with telnet (telnet host port then “stats”) or tools like memcached-tool and memcached-top. For benchmark-driven diagnosis, memtier_benchmark or mcperf simulate realistic workloads and show how misses scale under load.
Network and OS counters
- netstat -s and ss to detect UDP packet drops, retransmits, or socket errors.
- Linux /proc/net/udp and iptables conntrack limits which may affect high UDP/TCP throughput.
- sysctl net.core.rmem_max, net.core.wmem_max, net.core.somaxconn — increase these if you observe receive buffer overflows or SYN drops at high QPS.
- conntrack and ephemeral port exhaustion on busy application servers connecting to remote Memcached instances (common when using remote Hong Kong Server vs local in-process caches).
Tuning Memcached on a Hong Kong VPS
For VPS environments (including Hong Kong VPS hosting), you must tune both Memcached and the host OS to avoid cache misses under realistic load.
Memory and slab tuning
- Set -m to a value that comfortably fits your working set plus headroom. Track evictions; any eviction count > 0 is a red flag.
- Use slab_reassign (available in many Linux distributions) to move memory between classes if you see one slab class starved while others are underused. The memcached-tool and telnet “stats slabs” output guide this manual adjustment.
- Consider using a single object size strategy if your application allows (fixed-size values reduce fragmentation).
Connection and thread tuning
- Increase -c (max connections) appropriately; monitor curr_connections and conn_yields.
- Use -t threads equal to available vCPUs to prevent single-thread bottlenecks on multi-core VPS instances.
- On the OS, set somaxconn and increase file descriptor limits (ulimit -n) to match expected concurrency.
Network protocol and OS tuning
- Prefer TCP for reliability at very high loads; UDP is faster but vulnerable to packet loss. If you must use UDP, increase net.core.rmem_max and tune the application’s UDP packet size to avoid IP fragmentation.
- Tweak TCP parameters: tcp_tw_reuse, tcp_fin_timeout, and keepalive settings if you have many short-lived connections.
- If using a remote Memcached (e.g., a Hong Kong Server accessed from remote app servers or the opposite with a US VPS), minimize latency and packet loss by colocating caches with application servers when possible.
Application-level strategies
- Use consistent hashing (Ketama) to minimize mass-migration of keys when scaling servers up/down; this reduces cold-start misses after topology changes.
- Warm caches on deploys using scripts that pre-populate frequently used keys and avoid bursts of misses post-deploy.
- Implement negative cache patterns carefully: short TTLs for negative results, but not so short they cause frequent re-querying of backends.
- Consider caching the pointer to the object in smaller items, and store large objects in object stores if memory pressure is a recurring issue.
Comparative considerations: Hong Kong Server vs US VPS / US Server
Where you host Memcached matters. A Hong Kong Server colocated with your Asia-Pacific user base reduces RTT and packet loss for regional traffic. If your users are global, a multi-region caching strategy can help — edge caches in Hong Kong for APAC and US VPS/US Server instances for North American traffic.
- Latency: Local Hong Kong VPS reduces the chances of transient network-induced misses and timeouts. Cross-region connections (Hong Kong ↔ US) increase RTT and make the system more sensitive to transient packet loss.
- Throughput limits: Some VPS providers throttle network bursts or have noisy neighbors; choose plans with guaranteed resources if you rely on Memcached at scale.
- Failover: Using a combination of Hong Kong Server and US Server deployments with application-aware fallbacks reduces the risk of catastrophic cache miss storms when one region experiences instability.
Monitoring, alerting and capacity planning
Continuous monitoring is critical. Alert on:
- a sustained drop in hit ratio (e.g., hit ratio falls below 90% depending on your baseline),
- any non-zero evictions spike,
- connection errors, packet drops, or rapidly increasing curr_connections.
Use time-series monitoring (Prometheus + Grafana, or the provider’s metrics) to correlate get_misses with CPU, memory, and network metrics. Regularly run memtier_benchmark to exercise your cache under load and validate OS tuning changes.
Summary and best practices
Fixing cache misses on Memcached running on a Hong Kong VPS requires a holistic approach: properly size memory and tune slabs, configure threads and connections to match available vCPU and OS limits, pick TCP vs UDP based on reliability needs, and keep network stacks tuned to avoid buffer overflows. Where possible, colocate caches with application servers — for APAC users that often means a Hong Kong Server — and use consistent hashing and warm-up procedures to avoid miss storms after topology changes. Monitor get_hits/get_misses and evictions constantly and benchmark with tools like memtier_benchmark to validate changes.
If you need hosting optimized for low-latency, predictable performance for Memcached and application stacks, consider an appropriately sized VPS in the region your users live in. For regional APAC traffic, a Hong Kong VPS is often preferable to a remote US VPS, while in multi-region deployments a mix of Hong Kong Server and US Server instances can deliver the best global experience.
To explore VPS options suitable for Memcached deployments, see Server.HK’s Hong Kong VPS plans and cloud offerings: https://server.hk/cloud.php.
