analyzing-mongodb

name	analyzing-mongodb
description	Use when working with Mongodb — mongoDB database analysis, performance tuning, query optimization, and health monitoring. Covers collection analysis, index recommendations, aggregation pipelines, replica set health, Atlas cluster management, and slow query investigation.
connection_type	mongodb
preload	false

MongoDB Analysis Skill

Analyze and optimize MongoDB databases with safe, read-only operations.

MANDATORY: Two-Phase Execution

You MUST follow this two-phase pattern. Skipping Phase 1 causes hallucinated collection names and schema errors.

Phase 1: Discovery (ALWAYS run first)

// Run this discovery script first — NEVER skip
// Connect: mongosh "$MONGODB_URI" --quiet --eval "..."

// 1. List databases and sizes
db.adminCommand({ listDatabases: 1, nameOnly: false })
   .databases
   .forEach(d => print(d.name, d.sizeOnDisk));

// 2. List collections in target database
use targetDb;
db.getCollectionNames().forEach(c => {
    const stats = db[c].stats();
    print(c, stats.count, stats.storageSize, stats.totalIndexSize);
});

// 3. Sample schema (never assume field names)
db.collectionName.findOne();
db.collectionName.aggregate([{ $sample: { size: 5 } }]);

Phase 1 outputs:

• List of databases and sizes
• List of collections with document counts
• Sample documents to understand actual field names

Phase 2: Analysis (only after Phase 1)

Only reference collections, databases, and field names confirmed in Phase 1.

Shell Script Patterns

Helper Function

#!/bin/bash

# Core MongoDB query runner — always use this
mongo_eval() {
    local db="$1"
    local script="$2"
    mongosh "$MONGODB_URI/$db" --quiet --eval "$script"
}

# Atlas CLI helper (if Atlas connection available)
atlas_cmd() {
    atlas "$@" --projectId "$MONGODB_ATLAS_PROJECT_ID" --output json
}

Anti-Hallucination Rules

• NEVER reference a collection by name without confirming it exists via db.getCollectionNames()
• NEVER reference a field name in queries without seeing it in a sample document
• NEVER assume index names — always list indexes via db.collection.getIndexes()
• NEVER assume Atlas cluster names — always run atlas clusters list first
• ALWAYS use --quiet flag with mongosh to suppress connection banners

Safety Rules

• READ-ONLY ONLY: Use only find, aggregate, explain, stats, currentOp, serverStatus
• FORBIDDEN: insertMany, updateMany, deleteMany, drop, dropDatabase, createIndex without explicit user request
• ALWAYS add .limit() to find queries — default cap at 100 documents
• NEVER run db.collection.find() without a limit on large collections
• Use explain() before running expensive aggregations on large collections

Common Operations

Database Health Overview

#!/bin/bash
echo "=== MongoDB Server Status ==="
mongo_eval admin "
    const s = db.serverStatus();
    print('Version:', s.version);
    print('Uptime (hours):', Math.round(s.uptime/3600));
    print('Connections active:', s.connections.current);
    print('Connections available:', s.connections.available);
    print('Ops/sec inserts:', s.opcounters.insert);
    print('Ops/sec queries:', s.opcounters.query);
    print('Ops/sec updates:', s.opcounters.update);
    print('Mem resident MB:', s.mem.resident);
    print('Mem virtual MB:', s.mem.virtual);
"

echo ""
echo "=== Replica Set Status ==="
mongo_eval admin "
    try {
        const rs = rs.status();
        rs.members.forEach(m => print(m.name, m.stateStr, m.health, m.optimeDate));
    } catch(e) { print('Not a replica set'); }
"

echo ""
echo "=== Databases ==="
mongo_eval admin "
    db.adminCommand({listDatabases:1}).databases
      .sort((a,b) => b.sizeOnDisk - a.sizeOnDisk)
      .forEach(d => print(d.name, Math.round(d.sizeOnDisk/1024/1024) + 'MB'));
"

Collection Analysis

#!/bin/bash
DB_NAME="${1:-myDatabase}"

echo "=== Collections in $DB_NAME ==="
mongo_eval "$DB_NAME" "
    db.getCollectionNames().forEach(c => {
        const stats = db[c].stats({scale: 1024*1024});
        print(c, stats.count + ' docs', Math.round(stats.storageSize) + 'MB data', Math.round(stats.totalIndexSize) + 'MB indexes');
    });
" | sort -t' ' -k3 -rn | head -20

echo ""
echo "=== Indexes per Collection ==="
mongo_eval "$DB_NAME" "
    db.getCollectionNames().forEach(c => {
        const idxs = db[c].getIndexes();
        print(c + ': ' + idxs.length + ' indexes');
        idxs.forEach(i => print('  -', JSON.stringify(i.key), i.unique ? '[UNIQUE]' : ''));
    });
"

Slow Query Analysis

#!/bin/bash
DB_NAME="${1:-myDatabase}"
SLOW_MS="${2:-100}"

echo "=== Slow Query Log (>=${SLOW_MS}ms) ==="
mongo_eval "$DB_NAME" "
    db.system.profile.find(
        { millis: { \$gte: ${SLOW_MS} } },
        { ns: 1, op: 1, millis: 1, command: 1, ts: 1 }
    )
    .sort({ millis: -1 })
    .limit(20)
    .forEach(q => {
        print(q.ts.toISOString().substr(0,19),
              q.op,
              q.ns,
              q.millis + 'ms',
              JSON.stringify(q.command || {}).substr(0,100));
    });
" 2>/dev/null || echo "Profiler not enabled. Enable with: db.setProfilingLevel(1, {slowms: ${SLOW_MS}})"

echo ""
echo "=== Currently Running Operations ==="
mongo_eval admin "
    db.currentOp({ active: true, secs_running: { \$gte: 1 } })
      .inprog
      .forEach(op => print(op.opid, op.secs_running + 's', op.op, op.ns, JSON.stringify(op.command || {}).substr(0,80)));
"

Index Effectiveness Analysis

#!/bin/bash
DB_NAME="$1"
COLLECTION="$2"

if [ -z "$DB_NAME" ] || [ -z "$COLLECTION" ]; then
    echo "Usage: $0 <database> <collection>"
    exit 1
fi

echo "=== Index Usage Stats for $DB_NAME.$COLLECTION ==="
mongo_eval "$DB_NAME" "
    // Index access stats (requires MongoDB 3.2+)
    db['$COLLECTION'].aggregate([
        { \$indexStats: {} }
    ]).forEach(i => {
        print(i.name, 'accesses:', i.accesses.ops, 'since:', i.accesses.since.toISOString().substr(0,10));
    });
"

echo ""
echo "=== Unused Indexes (0 accesses) ==="
mongo_eval "$DB_NAME" "
    db['$COLLECTION'].aggregate([{ \$indexStats: {} }])
        .toArray()
        .filter(i => i.accesses.ops == 0 && i.name != '_id_')
        .forEach(i => print('UNUSED:', i.name, JSON.stringify(i.key)));
"

echo ""
echo "=== Collection Size vs Index Size ==="
mongo_eval "$DB_NAME" "
    const s = db['$COLLECTION'].stats({scale: 1024*1024});
    print('Documents:', s.count);
    print('Data size:', Math.round(s.size) + 'MB');
    print('Storage size:', Math.round(s.storageSize) + 'MB');
    print('Index size:', Math.round(s.totalIndexSize) + 'MB');
    print('Index ratio:', Math.round(s.totalIndexSize/s.size*100) + '%');
"

Query Performance with explain()

#!/bin/bash
DB_NAME="$1"
COLLECTION="$2"

echo "=== Query Plan Analysis ==="
mongo_eval "$DB_NAME" "
    // ALWAYS use explain() before running expensive queries on large collections
    db['$COLLECTION'].find({ /* your filter here */ })
        .explain('executionStats')
        .executionStats
        |> ({
            nReturned: @.nReturned,
            executionTimeMs: @.executionTimeMillis,
            docsExamined: @.totalDocsExamined,
            keysExamined: @.totalKeysExamined,
            indexUsed: @.executionStages?.inputStage?.indexName || 'COLLSCAN'
        })
        |> print(JSON.stringify(@, null, 2));
" 2>/dev/null

# Simpler version for older mongosh
mongo_eval "$DB_NAME" "
    const plan = db['$COLLECTION'].find({}).explain('executionStats');
    const stats = plan.executionStats;
    print('Docs examined:', stats.totalDocsExamined);
    print('Docs returned:', stats.nReturned);
    print('Exec time ms:', stats.executionTimeMillis);
    print('Stage:', plan.queryPlanner.winningPlan.stage);
    if (plan.queryPlanner.winningPlan.inputStage) {
        print('Index used:', plan.queryPlanner.winningPlan.inputStage.indexName || 'NONE (COLLSCAN)');
    }
"

Atlas Cluster Management (if Atlas connection)

#!/bin/bash
echo "=== Atlas Clusters ==="
atlas clusters list 2>/dev/null | jq -r '.[] | "\(.name)\t\(.stateName)\t\(.mongoDBVersion)\t\(.providerSettings.instanceSizeName)"' || echo "Atlas CLI not configured"

echo ""
echo "=== Atlas Metrics (last 1h) ==="
CLUSTER_NAME=$(atlas clusters list 2>/dev/null | jq -r '.[0].name' || echo "")
if [ -n "$CLUSTER_NAME" ]; then
    atlas metrics process "$CLUSTER_NAME" \
        --granularity PT1M --period P1H \
        --type CONNECTIONS --type OPCOUNTER_CMD \
        2>/dev/null | jq -r '.measurements[] | "\(.name): \(.dataPoints[-1].value // 0)"'
fi

Aggregation Pipeline Examples

#!/bin/bash
DB_NAME="$1"
COLLECTION="$2"

echo "=== Document Count by Date (last 30 days) ==="
mongo_eval "$DB_NAME" "
    // Adapt date field name based on Phase 1 discovery
    db['$COLLECTION'].aggregate([
        {
            \$match: {
                createdAt: { \$gte: new Date(Date.now() - 30*24*60*60*1000) }
            }
        },
        {
            \$group: {
                _id: { \$dateToString: { format: '%Y-%m-%d', date: '\$createdAt' } },
                count: { \$sum: 1 }
            }
        },
        { \$sort: { _id: -1 } },
        { \$limit: 30 }
    ]).forEach(r => print(r._id, r.count));
" 2>/dev/null || echo "Adjust date field name after Phase 1 discovery"

Output Format

Present results as a structured report:

Analyzing Mongodb Report
════════════════════════
Resources discovered: [count]

Resource       Status    Key Metric    Issues
──────────────────────────────────────────────
[name]         [ok/warn] [value]       [findings]

Summary: [total] resources | [ok] healthy | [warn] warnings | [crit] critical
Action Items: [list of prioritized findings]

Target ≤50 lines of output. Use tables for multi-resource comparisons.

Counter-Rationalizations

Shortcut	Counter	Why
"I'll skip discovery and check known resources"	Always run Phase 1 discovery first	Resource names change, new resources appear — assumed names cause errors
"The user only asked for a quick check"	Follow the full discovery → analysis flow	Quick checks miss critical issues; structured analysis catches silent failures
"Default configuration is probably fine"	Audit configuration explicitly	Defaults often leave logging, security, and optimization features disabled
"Metrics aren't needed for this"	Always check relevant metrics when available	API/CLI responses show current state; metrics reveal trends and intermittent issues
"I don't have access to that"	Try the command and report the actual error	Assumed permission failures prevent useful investigation; actual errors are informative

Common Pitfalls

• Schema assumptions: MongoDB is schema-flexible — never assume field names without sampling documents
• Missing --quiet: Without --quiet, mongosh outputs connection banners that corrupt script output
• Large .find() without limit: Always add .limit(N) — collections can have millions of documents
• explain() on production: Use explain('queryPlanner') for plan-only (no execution); explain('executionStats') runs the query
• Replica set reads: Prefer readPreference: 'secondaryPreferred' for analytics to avoid impacting primary
• Aggregation memory: Default 100MB memory limit per stage — add { allowDiskUse: true } for large aggregations
• Timezone handling: ISODate stores in UTC — always convert user-specified times to UTC before querying
• Atlas vs self-hosted: Atlas has different metric APIs than db.serverStatus() — check connection type in Phase 1