| name | troubleshoot-memcached |
| description | Use when diagnosing issues with Memcached: availability, throughput, hit rate & efficiency, or connections degradation. Queries Netdata via MCP for process liveness, unexpected restart (uptime reset), command rates (cmd_get, cmd_set, cmd_touch), flush all events (cmd_flush), cache hit ratio, applies the diagnostic tree from the Netdata operator playbook, and recommends remediation. |
| version | 0.1.0 |
| author | Netdata |
| license | Apache-2.0 |
| tags | ["netdata","troubleshoot","mcp","memcached"] |
Troubleshoot Memcached
When to use this skill
- Any time the user reports a Memcached service behaving outside its expected envelope (elevated
errors, latency, saturation, resource exhaustion, or unexpected restarts).
- An on-call engineer is paging on a Netdata alert tied to a Memcached instance and wants a
structured triage path.
Key facts
- This skill wraps the Netdata operator playbook for Memcached. It does not replace the playbook; it
routes a coding agent through MCP queries against the same signals the playbook relies on.
- Memcached is a multi-threaded, in-memory key-value cache daemon built on libevent. It does not
persist data to disk; a restart means total data loss. Its design philosophy is "simple and fast":
no replication, no clustering (clients shard), no built-in persistence.
- The playbook decomposes Memcached health into 8 signal domains: Availability, Throughput, Hit Rate
& Efficiency, Connections, Memory & Saturation, Internal State. Each domain maps to one rule file
in this skill.
- Netdata observes the signals listed in the rule files via its native collectors, plus any
OpenTelemetry-shipped metrics that your Memcached instrumentation adds. Both paths end at the same
MCP query surface.
- Netdata's memcached collector emits 14 context(s) under
memcached.*. The rule files enumerate
which contexts surface which domain; the Verification section below names the load-bearing ones
explicitly.
Step-by-step
- Confirm the Memcached service is up. Query Netdata via MCP with
list_nodes and filter by the
host running the target. A missing node means the symptom is at the network or orchestrator
layer, not inside the service.
- Pull the last 15 minutes of signals for the target. Use
query_metrics against the contexts
listed in the domain rule files. Run find_anomalous_metrics in parallel over the same window;
anomalies frame which rule file to read first.
- Correlate with host-level signals (
system.cpu.utilization, system.memory.usage,
system.disk.io_time). Many service-level failures have a host-resource precursor.
- Apply the remediation hinted at in the matching rule file or the operator playbook. Re-run the
MCP queries from the Verification section to confirm the signals returned to expected ranges. A
fix that does not move the signal back is not a fix.
Handy MCP call templates
# Discover metrics from Memcached
list_metrics with q="memcached"
# Pull a specific context over the last window
query_metrics with context="memcached.connections", relative_window=-15m
# Rank anomalies for the service or host
find_anomalous_metrics with node=<host> and context_pattern="memcached.*"
# Correlate a known problem context with others
find_correlated_metrics around the incident window
# Show current alert state
list_raised_alerts scoped to the node
Common mistakes
- Treating Memcached as a generic HTTP or process health check. Memcached has specific failure
archetypes (see Key facts) that generic checks miss.
- Stopping at the first anomalous metric. Several archetypes produce correlated spikes; use
find_correlated_metrics to widen the search before concluding a root cause.
- Quoting percentile latency without the sample count. Low traffic plus a single slow request moves
p99 by seconds.
- Reading dashboards for a window shorter than the failure's fingerprint. Slow-brew failures (queue
growth, bloat, memory fragmentation) need 30+ minutes of data to see the trend.
- Skipping the host-level correlation. A process-level fix for a noisy-neighbour problem does not
hold.
- Assuming alert thresholds are tuned for your workload. Tune against observed Memcached traffic
before escalating an alert configuration issue.
Verification
Run these MCP queries against the Netdata instance that sees the Memcached service. Every context
listed below is a real Netdata chart name; the agent does not need to guess.
1. list_metrics filtered by q="memcached" (returns every memcached.* context Netdata sees)
2. query_metrics with contexts=[memcached.connections, memcached.get, memcached.get_rate, memcached.set_rate, memcached.delete, memcached.cas] and relative_window=-30m
3. find_anomalous_metrics filtered by node=<host> and context_pattern="memcached.*"
Load-bearing contexts for this service:
memcached.connections: Connections (connections/s). Dimensions: current, rejected, total.memcached.get: Get Requests (requests). Dimensions: hints, misses.memcached.get_rate: Get Request Rate (requests/s). Dimensions: rate.memcached.set_rate: Set Request Rate (requests/s). Dimensions: rate.memcached.delete: Delete Requests (requests). Dimensions: hits, misses.memcached.cas: Check and Set Requests (requests). Dimensions: hits, misses, bad value.
A clean result means every context is within its expected band and the find_anomalous_metrics list
is empty or contains only already-acknowledged items. If the fix was real, re-running the same
queries 10 minutes after applying it will show a clean result. If it does not, revert and look
deeper.
When the fix does not hold
If signals drift back into the anomalous range within 30 minutes of a remediation, the cause was
deeper than the applied change. Typical misdiagnoses for Memcached:
- Host-resource pressure masquerading as application bug.
- Dependent service (DB, cache, upstream) causing a secondary symptom in the instrumented service.
- Configuration change that was never reloaded (some subsystems only pick up config on full
restart).
Escalate by widening the query window: 2-6 hours instead of 15 minutes. Slow-moving causes are
invisible at triage window sizes.
References
