ClickHouse Observability

Overview

Set up comprehensive monitoring for ClickHouse using built-in system tables, Prometheus integration, Grafana dashboards, and alerting rules.

Prerequisites

• ClickHouse instance with system.* table access
• Prometheus (or compatible: Grafana Alloy, Victoria Metrics)
• Grafana for dashboards
• AlertManager or PagerDuty for alerts

Instructions

Step 1: Key Metrics from System Tables

-- Real-time server health snapshot
SELECT
    (SELECT count() FROM system.processes) AS running_queries,
    (SELECT value FROM system.metrics WHERE metric = 'MemoryTracking') AS memory_bytes,
    (SELECT value FROM system.metrics WHERE metric = 'Query') AS concurrent_queries,
    (SELECT count() FROM system.merges) AS active_merges,
    (SELECT value FROM system.asynchronous_metrics WHERE metric = 'Uptime') AS uptime_sec;

-- Query throughput (last hour, per minute)
SELECT
    toStartOfMinute(event_time) AS minute,
    count() AS queries,
    countIf(exception_code != 0) AS errors,
    round(avg(query_duration_ms)) AS avg_ms,
    round(quantile(0.95)(query_duration_ms)) AS p95_ms,
    formatReadableSize(sum(read_bytes)) AS total_read
FROM system.query_log
WHERE type IN ('QueryFinish', 'ExceptionWhileProcessing')
  AND event_time >= now() - INTERVAL 1 HOUR
GROUP BY minute ORDER BY minute;

-- Insert throughput (last hour)
SELECT
    toStartOfMinute(event_time) AS minute,
    count() AS inserts,
    sum(written_rows) AS rows_written,
    formatReadableSize(sum(written_bytes)) AS bytes_written
FROM system.query_log
WHERE type = 'QueryFinish' AND query_kind = 'Insert'
  AND event_time >= now() - INTERVAL 1 HOUR
GROUP BY minute ORDER BY minute;

-- Part count per table (merge health indicator)
SELECT database, table, count() AS parts, sum(rows) AS rows,
       formatReadableSize(sum(bytes_on_disk)) AS size
FROM system.parts WHERE active
GROUP BY database, table
HAVING parts > 50
ORDER BY parts DESC;

Step 2: Prometheus Integration

ClickHouse Cloud exposes a managed Prometheus endpoint:

# prometheus.yml
scrape_configs:
  - job_name: clickhouse-cloud
    metrics_path: /v1/organizations/<ORG_ID>/prometheus
    basic_auth:
      username: <API_KEY_ID>
      password: <API_KEY_SECRET>
    static_configs:
      - targets: ['api.clickhouse.cloud']
    params:
      filtered_metrics: ['true']   # 125 critical metrics only

Self-hosted — use clickhouse-exporter or built-in metrics endpoint:

# prometheus.yml
scrape_configs:
  - job_name: clickhouse
    static_configs:
      - targets: ['clickhouse-server:9363']  # Built-in Prometheus endpoint
    metrics_path: /metrics

<!-- Enable Prometheus endpoint in config.xml -->
<prometheus>
    <endpoint>/metrics</endpoint>
    <port>9363</port>
    <metrics>true</metrics>
    <events>true</events>
    <asynchronous_metrics>true</asynchronous_metrics>
</prometheus>

Step 3: Application-Level Metrics

import { Registry, Counter, Histogram, Gauge } from 'prom-client';

const registry = new Registry();

const queryDuration = new Histogram({
  name: 'clickhouse_query_duration_seconds',
  help: 'ClickHouse query duration',
  labelNames: ['query_type', 'status'],
  buckets: [0.01, 0.05, 0.1, 0.25, 0.5, 1, 2.5, 5, 10],
  registers: [registry],
});

const queryErrors = new Counter({
  name: 'clickhouse_query_errors_total',
  help: 'ClickHouse query errors',
  labelNames: ['error_code'],
  registers: [registry],
});

const insertRows = new Counter({
  name: 'clickhouse_insert_rows_total',
  help: 'Total rows inserted into ClickHouse',
  labelNames: ['table'],
  registers: [registry],
});

// Instrumented query wrapper
async function instrumentedQuery<T>(
  queryType: string,
  fn: () => Promise<T>,
): Promise<T> {
  const timer = queryDuration.startTimer({ query_type: queryType });
  try {
    const result = await fn();
    timer({ status: 'success' });
    return result;
  } catch (err: any) {
    timer({ status: 'error' });
    queryErrors.inc({ error_code: err.code ?? 'unknown' });
    throw err;
  }
}

// Expose /metrics endpoint
app.get('/metrics', async (req, res) => {
  res.set('Content-Type', registry.contentType);
  res.send(await registry.metrics());
});

Step 4: Grafana Dashboard Panels

{
  "panels": [
    {
      "title": "Query Rate (QPS)",
      "type": "timeseries",
      "targets": [{ "expr": "rate(clickhouse_query_duration_seconds_count[5m])" }]
    },
    {
      "title": "Query Latency P50 / P95 / P99",
      "type": "timeseries",
      "targets": [
        { "expr": "histogram_quantile(0.5, rate(clickhouse_query_duration_seconds_bucket[5m]))" },
        { "expr": "histogram_quantile(0.95, rate(clickhouse_query_duration_seconds_bucket[5m]))" },
        { "expr": "histogram_quantile(0.99, rate(clickhouse_query_duration_seconds_bucket[5m]))" }
      ]
    },
    {
      "title": "Error Rate",
      "type": "stat",
      "targets": [{
        "expr": "rate(clickhouse_query_errors_total[5m]) / rate(clickhouse_query_duration_seconds_count[5m])"
      }]
    },
    {
      "title": "Insert Throughput (rows/sec)",
      "type": "timeseries",
      "targets": [{ "expr": "rate(clickhouse_insert_rows_total[5m])" }]
    }
  ]
}

Import the official ClickHouse Grafana dashboard: https://grafana.com/grafana/dashboards/23415

Step 5: Alert Rules

# clickhouse-alerts.yml
groups:
  - name: clickhouse
    rules:
      - alert: ClickHouseHighErrorRate
        expr: |
          rate(clickhouse_query_errors_total[5m]) /
          rate(clickhouse_query_duration_seconds_count[5m]) > 0.05
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "ClickHouse error rate > 5%"

      - alert: ClickHouseHighLatency
        expr: |
          histogram_quantile(0.95,
            rate(clickhouse_query_duration_seconds_bucket[5m])) > 5
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "ClickHouse P95 latency > 5 seconds"

      - alert: ClickHouseTooManyParts
        expr: clickhouse_table_parts > 300
        for: 10m
        labels:
          severity: critical
        annotations:
          summary: "Table has > 300 active parts — merges falling behind"

      - alert: ClickHouseMemoryHigh
        expr: clickhouse_server_memory_usage / clickhouse_server_memory_limit > 0.9
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "ClickHouse memory usage > 90%"

      - alert: ClickHouseDiskLow
        expr: clickhouse_disk_free_bytes / clickhouse_disk_total_bytes < 0.15
        for: 10m
        labels:
          severity: critical
        annotations:
          summary: "ClickHouse disk space < 15% free"

Step 6: Structured Logging

import pino from 'pino';

const logger = pino({ name: 'clickhouse' });

// Log query performance for analysis
function logQuery(queryType: string, durationMs: number, rowsRead: number) {
  logger.info({
    service: 'clickhouse',
    query_type: queryType,
    duration_ms: durationMs,
    rows_read: rowsRead,
    status: durationMs > 5000 ? 'slow' : 'ok',
  });
}

Key System Tables for Monitoring

Table	What to Monitor	Frequency
system.processes	Running queries, memory usage	Every 10s
system.query_log	Query performance history	Every 1m
system.parts	Part count, merge health	Every 1m
system.merges	Active merge progress	Every 30s
system.metrics	Server-wide gauges (connections, memory)	Every 10s
system.events	Cumulative counters	Every 1m
system.replicas	Replication lag	Every 30s
system.disks	Disk space	Every 5m

Error Handling

Issue	Cause	Solution
Metrics endpoint empty	Prometheus not configured	Enable /metrics in config
High cardinality alerts	Too many label values	Reduce label cardinality
Missing query_log data	Logging disabled	Set log_queries = 1 in config
Dashboard gaps	Scrape interval too long	Use 10-15s scrape interval

Resources

• Prometheus Integration
• ClickHouse Grafana Dashboard
• System Tables Reference
• Cloud Monitoring

Next Steps

For incident response, see clickhouse-incident-runbook.