ClickHouse Incident Runbook

Overview

Step-by-step procedures for triaging and resolving ClickHouse incidents using built-in system tables and SQL commands.

Severity Levels

Level	Definition	Response	Examples
P1	ClickHouse unreachable / all queries failing	< 15 min	Server down, OOM, disk full
P2	Degraded performance / partial failures	< 1 hour	Slow queries, merge backlog
P3	Minor impact / non-critical errors	< 4 hours	Single table issue, warnings
P4	No user impact	Next business day	Monitoring gaps, optimization

Quick Triage (Run First)

# 1. Is ClickHouse alive?
curl -sf 'http://localhost:8123/ping' && echo "UP" || echo "DOWN"

# 2. Can it answer a query?
curl -sf 'http://localhost:8123/?query=SELECT+1' && echo "OK" || echo "QUERY FAILED"

# 3. Check ClickHouse Cloud status
curl -sf 'https://status.clickhouse.cloud' | head -5

-- 4. Server health snapshot (run if server responds)
SELECT
    version()                         AS version,
    formatReadableTimeDelta(uptime())  AS uptime,
    (SELECT count() FROM system.processes) AS running_queries,
    (SELECT value FROM system.metrics WHERE metric = 'MemoryTracking')
        AS memory_bytes,
    (SELECT count() FROM system.merges) AS active_merges;

-- 5. Recent errors
SELECT event_time, exception_code, exception, substring(query, 1, 200) AS q
FROM system.query_log
WHERE type = 'ExceptionWhileProcessing'
  AND event_time >= now() - INTERVAL 10 MINUTE
ORDER BY event_time DESC
LIMIT 10;

Decision Tree

Server responds to ping?
├─ NO → Check process/container status, disk space, OOM killer logs
│       └─ Container/process dead → Restart, check logs
│       └─ Disk full → Emergency: drop old partitions, expand disk
│       └─ OOM killed → Reduce max_memory_usage, add RAM
└─ YES → Queries succeeding?
    ├─ NO → Check error codes below
    │   └─ Auth errors (516) → Verify credentials, check user exists
    │   └─ Too many queries (202) → Kill stuck queries, reduce concurrency
    │   └─ Memory exceeded (241) → Kill large queries, reduce max_threads
    └─ YES but slow → Performance triage below

Remediation Procedures

P1: Server Down / OOM

# Check if process was OOM-killed
dmesg | grep -i "out of memory" | tail -5
journalctl -u clickhouse-server --since "10 minutes ago" | tail -20

# Restart
sudo systemctl restart clickhouse-server
# or for Docker:
docker restart clickhouse

# Verify recovery
curl 'http://localhost:8123/?query=SELECT+version()'

P1: Disk Full

-- Find largest tables
SELECT database, table,
       formatReadableSize(sum(bytes_on_disk)) AS size,
       sum(rows) AS rows
FROM system.parts WHERE active
GROUP BY database, table
ORDER BY sum(bytes_on_disk) DESC
LIMIT 10;

-- Emergency: drop old partitions
ALTER TABLE analytics.events DROP PARTITION '202301';
ALTER TABLE analytics.events DROP PARTITION '202302';

-- Check free space
SELECT name, formatReadableSize(free_space) AS free,
       formatReadableSize(total_space) AS total
FROM system.disks;

P2: Stuck / Long-Running Queries

-- Find stuck queries
SELECT
    query_id,
    user,
    elapsed,
    formatReadableSize(memory_usage) AS memory,
    substring(query, 1, 200) AS query_preview
FROM system.processes
ORDER BY elapsed DESC;

-- Kill a specific query
KILL QUERY WHERE query_id = 'abc-123-def';

-- Kill all queries from a user
KILL QUERY WHERE user = 'runaway_user';

-- Kill all queries running longer than 5 minutes
KILL QUERY WHERE elapsed > 300;

P2: Too Many Parts (Merge Backlog)

-- Check part counts
SELECT database, table, count() AS parts
FROM system.parts WHERE active
GROUP BY database, table
HAVING parts > 200
ORDER BY parts DESC;

-- Check active merges
SELECT database, table, progress, elapsed,
       formatReadableSize(total_size_bytes_compressed) AS size
FROM system.merges;

-- Temporary: raise the limit to prevent INSERT failures
ALTER TABLE analytics.events MODIFY SETTING parts_to_throw_insert = 1000;

-- Wait for merges to catch up, then lower back
-- Root cause: too many small inserts — batch them

P2: Memory Pressure

-- Who's using the most memory?
SELECT user, query_id, elapsed,
       formatReadableSize(memory_usage) AS memory,
       substring(query, 1, 200) AS q
FROM system.processes
ORDER BY memory_usage DESC;

-- Kill the largest query
KILL QUERY WHERE query_id = '<largest_query_id>';

-- Reduce per-query memory for all users
ALTER USER app_writer SETTINGS max_memory_usage = 5000000000;  -- 5GB

P3: Replication Lag (Clustered/Cloud)

-- Check replica status
SELECT
    database, table,
    is_leader,
    total_replicas,
    active_replicas,
    queue_size,
    inserts_in_queue,
    merges_in_queue,
    log_pointer,
    last_queue_update
FROM system.replicas
WHERE active_replicas < total_replicas OR queue_size > 0;

Post-Incident Evidence Collection

-- Export error window from query log
SELECT *
FROM system.query_log
WHERE event_time BETWEEN '2025-01-15 14:00:00' AND '2025-01-15 15:00:00'
  AND (type = 'ExceptionWhileProcessing' OR query_duration_ms > 10000)
FORMAT JSONEachRow
INTO OUTFILE '/tmp/incident-queries.json';

-- Metrics snapshot during incident window
SELECT metric, value
FROM system.metrics
FORMAT TabSeparatedWithNames
INTO OUTFILE '/tmp/incident-metrics.tsv';

Communication Templates

Internal (Slack):

[P1] INCIDENT: ClickHouse [Issue Type]
Status: INVESTIGATING / MITIGATING / RESOLVED
Impact: [What users see]
Root cause: [If known]
Actions taken: [What you did]
Next update: [Time]
Commander: @[name]

Postmortem Template:

## ClickHouse Incident: [Title]
- Date: YYYY-MM-DD
- Duration: X hours Y minutes
- Severity: P[1-4]

### Timeline
- HH:MM — [Event/action]

### Root Cause
[Technical explanation]

### Resolution
[What fixed it]

### Action Items
- [ ] [Preventive measure] — Owner — Due date

Error Handling

Symptom	Likely Cause	First Action
All queries fail	Server down	Check process, restart
Inserts fail	Too many parts	KILL QUERY long merges, raise limit
Selects slow	Memory pressure	Kill large queries, add filters
Disk alerts	No TTL / no cleanup	Drop old partitions
Replication lag	Network / merge backlog	Check system.replicas

Resources

• ClickHouse Cloud Status
• System Tables Reference
• KILL QUERY

Next Steps

For data compliance, see clickhouse-data-handling.