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Documentation Index

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Querying ClickHouse’s system database

All ClickHouse instances come with a set of system tables contained in the system database that contain information about:
  • Server states, processes, and environment.
  • Server’s internal processes.
  • Options used when the ClickHouse binary was built.
Directly querying these tables is useful for monitoring ClickHouse deployments, especially for deep introspection and debugging.

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Using the ClickHouse Cloud Console

The ClickHouse Cloud console comes with a SQL console and dashboarding tools that can be used for querying system tables. For example, the query below reviews how many (and how often) new parts are created during the last two hours: 
SELECT
    count() AS new_parts,
    toStartOfMinute(event_time) AS modification_time_m,
    table,
    sum(rows) AS total_written_rows,
    formatReadableSize(sum(size_in_bytes)) AS total_bytes_on_disk
FROM clusterAllReplicas(default, system.part_log)
WHERE (event_type = 'NewPart') AND (event_time > (now() - toIntervalHour(2)))
GROUP BY
    modification_time_m,
    table
ORDER BY
    modification_time_m ASC,
    table DESC
More example queriesFor additional monitoring queries, see the following resources:You can also use these queries to create a custom dashboard in the Cloud Console.

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Built-in advanced observability dashboard

ClickHouse comes with a built-in advanced observability dashboard feature which can be accessed by $HOST:$PORT/dashboard (requires user and password) that shows Cloud Overview metrics contained in system.dashboards.
This dashboard requires direct authentication to the ClickHouse instance and is separate from the Cloud Console Advanced Dashboard, which is accessible through the Cloud Console UI without additional authentication.
For more information on the available visualizations and how to use them for troubleshooting, see the advanced dashboard documentation.

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Querying across nodes and versions

To comprehensively view the entire cluster, users can leverage the clusterAllReplicas function in combination with the merge function. The clusterAllReplicas function allows querying system tables across all replicas within the “default” cluster, consolidating node-specific data into a unified result. When combined with the merge function, this can be used to target all system data for a specific table in a cluster. For example, to find the top 5 longest-running queries across all replicas in the last hour: 
SELECT
    type,
    event_time,
    query_duration_ms,
    query,
    read_rows,
    tables
FROM clusterAllReplicas(default, system.query_log)
WHERE event_time >= (now() - toIntervalMinute(60)) AND type = 'QueryFinish'
ORDER BY query_duration_ms DESC
LIMIT 5
FORMAT VERTICAL
This approach is particularly valuable for monitoring and debugging cluster-wide operations, ensuring users can effectively analyze the health and performance of their ClickHouse Cloud deployment. For more details, see querying across nodes.

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System considerations

Querying system tables directly adds query load to your production service, prevents ClickHouse Cloud instances from idling (which can impact costs), and couples monitoring availability to production system health. If the production system fails, monitoring may also be affected.
For real-time production monitoring with operational separation, consider using the Prometheus-compatible metrics endpoint or the Cloud Console dashboards, both of which use pre-scraped metrics and do not issue queries to the underlying service.