The Tool Desk
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Choose which logs you want to search
“ClickHouse logs” can mean events stored in ClickHouse for observability, or records of queries run by the ClickHouse server. The right setup depends on which you mean.
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| Data you want to inspect | What it contains | Starting point |
|---|---|---|
| Application or infrastructure logs | Events collected from services and systems for observability | Send them through ClickStack’s OpenTelemetry path, or use a supported wide-event format with a timestamp. |
| ClickHouse query history | The server’s recorded query activity and related metadata | Configure a ClickStack log source for system.query_log. |
| A standalone log file | Raw lines in a local file, not necessarily data ingested into a ClickHouse server | Use clickhouse local; parsing and aggregation still require SQL. |
ClickStack is an open-source observability stack that brings together ClickHouse storage, the HyperDX UI, and OpenTelemetry collection. Its interface also supports wide-event data outside the supplied OpenTelemetry schemas, provided events include a timestamp. That does not mean an arbitrary table is automatically ready to search: its source and fields still need to match your data.
See ClickHouse’s ClickStack overview for the current product description.
Search application and infrastructure events in HyperDX
Start with search and filters
In HyperDX, use Lucene-style search and filters to narrow events by the information available in your log source. This is the documented SQL-free path for routine log searches: enter search terms and refine the results in the UI rather than writing a SQL query for each investigation.
Move from events to observability context
When an event search is only the beginning, ClickStack’s UI also supports dashboards, alerts, and correlation across logs, metrics, and traces. SQL is still available for advanced analysis, including work that needs a custom query or aggregation.
ClickHouse says HyperDX needs only a timestamp field to render and visualize events, allowing users to work with their own data formats or custom pipelines. In practice, confirm that your chosen source and field mapping suit the dataset before relying on the UI.
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View ClickHouse’s own query history
To investigate queries executed by the ClickHouse server, configure ClickStack to read the system.query_log table. ClickHouse’s embedded ClickStack walkthrough shows how to enable system log tables and create a log source using the system database, the query_log table, and event_time as its timestamp column.
Configure the source
- Follow the current ClickHouse instructions for embedding ClickStack and enabling system log tables on your server.
- In ClickStack, add a Log Source with database
systemand tablequery_log. - Set the timestamp column to
event_time. - Choose fields for the source. The walkthrough’s default selection includes
query,initial_user,memory_usage,read_rows, andquery_duration_ms. - Open the search view and inspect the available query records.
The walkthrough demonstrates searching query text alongside metadata useful for examining duration, memory use, rows, databases, and tables. Follow the currently published setup steps and verify your server’s configuration; the walkthrough is an example, not a guarantee that every installation has identical settings or records. See ClickHouse’s embedded ClickStack walkthrough.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Know when SQL is still the right tool
HyperDX removes the need to hand-write SQL for common searches, but it does not eliminate SQL from ClickHouse log analysis. Use SQL when an investigation requires custom aggregation or deeper analysis than the UI search provides.
For a local text file, ClickHouse documents clickhouse local with LineAsString to read each raw line without first importing it into a server. That route can help inspect files, but parsing and aggregating their contents use SQL; it is not a SQL-free alternative to HyperDX. The procedure is documented in ClickHouse’s guide to working with log files.
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Self-hosted ClickStack
ClickStack is open source and can be operated as a self-hosted observability stack. That gives you the UI-based search workflow without requiring a managed ClickHouse Cloud deployment, while leaving you responsible for operating the infrastructure.
Managed ClickStack
Managed ClickStack runs on ClickHouse Cloud and is described by ClickHouse as handling cluster management and related operations. It is an optional hosted route, not a requirement for using self-hosted ClickStack. Check ClickHouse’s Managed ClickStack information for current availability and terms.
What to expect from Grafana’s search-first proposal
ClickHouse’s May 2026 Grafana blog describes a search-first interface as an experiment or prototype. Treat that as a proposal rather than an assurance that a generally available SQL-free search mode is part of the Grafana ClickHouse plugin. For the documented UI search workflow described here, ClickStack is the clearest supported starting point. See ClickHouse’s Grafana search-first article for the proposal’s status and context.
Quick Recap
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