troubleshoot-mongodb

name	troubleshoot-mongodb
description	Use when diagnosing issues with MongoDB: cache pressure cascade, oplog window collapse, connection storm spiral, checkpoint stall write freeze, or silent index regression. Queries Netdata via MCP for MongoDB health signals, 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","mongodb"]

Troubleshoot MongoDB

When to use this skill

• Cache pressure cascade: Cache fills then background eviction fails then application threads evict then latency spikes then connections pile up then ticket exhaustion then everything queues then reconnection storms then self-reinforcing degradation.
• Oplog window collapse: Write volume spikes then oplog fills faster then window shrinks then secondary falls behind then secondary can't catch up before window closes then enters RECOVERING then requires full initial sync then remaining nodes absorb more load then risk of more secondaries falling beh...
• Connection storm spiral: A triggering event (election, deploy, network blip) causes mass reconnection then thread creation overhead then memory spike then ticket contention then existing queries slow then more timeouts then more reconnections then positive feedback loop.
• Checkpoint stall write freeze: Dirty data accumulates then checkpoint falls behind then journal fills then WiredTiger blocks ALL new writes then complete write freeze while reads may still work from cache.
• Silent index regression: Index dropped or planner changes plan then queries silently switch to collection scans then latency increases gradually proportional to collection growth then eventually overwhelms I/O.
• Election storm: Primary becomes unreachable then election then winner unstable then repeated elections then each causes 2-12 second write outage then cluster oscillates.
• Any time the user reports a MongoDB 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 MongoDB instance and wants a structured triage path.

Key facts

• This skill wraps the Netdata operator playbook for MongoDB. It does not replace the playbook; it routes a coding agent through MCP queries against the same signals the playbook relies on.
• MongoDB is a document-oriented database built on interconnected subsystems that an operator must understand to reason about failures effectively.
• Dominant failure archetypes the playbook calls out: Cache pressure cascade; Oplog window collapse; Connection storm spiral; Checkpoint stall write freeze; Silent index regression.
• Netdata observes the signals listed in the rule files via its native collectors, plus any OpenTelemetry-shipped metrics that your MongoDB instrumentation adds. Both paths end at the same MCP query surface.
• Netdata's mongodb collector emits 53 context(s) under mongodb.*. The rule files enumerate which contexts surface which domain; the Verification section below names the load-bearing ones explicitly.

Step-by-step

1. Confirm the MongoDB 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.
2. 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.
3. Check for Cache pressure cascade. Cache fills then background eviction fails then application threads evict then latency spikes then connections pile up then ticket exhaustion then everything queues then reconnection storms then self-reinforcing degradation. Inspect the rule file whose signals move first for this mode.
4. Check for Oplog window collapse. Write volume spikes then oplog fills faster then window shrinks then secondary falls behind then secondary can't catch up before window closes then enters RECOVERING then requires full initial sync then remaining nodes absorb more load then risk of more secondaries falling behind. Inspect the rule file whose signals move first for this mode.
5. Check for Connection storm spiral. A triggering event (election, deploy, network blip) causes mass reconnection then thread creation overhead then memory spike then ticket contention then existing queries slow then more timeouts then more reconnections then positive feedback loop. Inspect the rule file whose signals move first for this mode.
6. Check for Checkpoint stall write freeze. Dirty data accumulates then checkpoint falls behind then journal fills then WiredTiger blocks ALL new writes then complete write freeze while reads may still work from cache. Inspect the rule file whose signals move first for this mode.
7. Check for Silent index regression. Index dropped or planner changes plan then queries silently switch to collection scans then latency increases gradually proportional to collection growth then eventually overwhelms I/O. Inspect the rule file whose signals move first for this mode.
8. Correlate with host-level signals (system.cpu.utilization, system.memory.usage, system.disk.io_time). Many service-level failures have a host-resource precursor.
9. 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 MongoDB
list_metrics with q="mongodb"

# Pull a specific context over the last window
query_metrics with context="mongodb.operations_by_type_rate", relative_window=-15m

# Rank anomalies for the service or host
find_anomalous_metrics with node=<host> and context_pattern="mongodb.*"

# 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 MongoDB as a generic HTTP or process health check. MongoDB 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 MongoDB traffic before escalating an alert configuration issue.

Verification

Run these MCP queries against the Netdata instance that sees the MongoDB service. Every context listed below is a real Netdata chart name; the agent does not need to guess.

1. list_metrics filtered by q="mongodb" (returns every mongodb.* context Netdata sees)
2. query_metrics with contexts=[mongodb.operations_by_type_rate, mongodb.document_operations_rate, mongodb.active_clients_count, mongodb.connections_usage, mongodb.connections_by_state_count, mongodb.connections_rate] and relative_window=-30m
3. find_anomalous_metrics filtered by node=<host> and context_pattern="mongodb.*"

Load-bearing contexts for this service:

• mongodb.operations_by_type_rate: Operations by type (operations/s). Dimensions: insert, query, update, delete, getmore, command.
• mongodb.document_operations_rate: Document operations (operations/s). Dimensions: inserted, deleted, returned, updated.
• mongodb.active_clients_count: Connected clients (clients). Dimensions: readers, writers.
• mongodb.connections_usage: Connections usage (connections). Dimensions: available, used.
• mongodb.connections_by_state_count: Connections By State (connections). Dimensions: active, threaded, exhaust_is_master, exhaust_hello, awaiting_topology_changes.
• mongodb.connections_rate: Connections Rate (connections/s). Dimensions: created.

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 MongoDB:

• 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

• rules/overview.md
• Netdata operator playbook: the authoritative source material this skill summarizes.
• skills/netdata-mcp-integration/ for the transport setup.
• skills/netdata-otel-setup/ if additional application signals are needed beyond what Netdata collects natively.