A Visual GUI Client for ClickHouse Analytics

ClickHouse answers analytical queries in fractions of a second — but the schema behind that speed, with its MergeTree engines and materialized views, carries meaning a column list never shows. DbSchema diagrams it, documents it, and queries it from one desktop tool.

See which materialized views aggregate which base tables, as a diagram
Run aggregations, window functions, and array operations in the SQL editor
Let analysts compose GROUP BY queries visually, before they learn the dialect
The ClickHouse JDBC driver downloads itself on the first connection

available for

ISO-27001 certified

Free Community Edition No signup required 15-day Architect trial included

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DbSchema diagramming ClickHouse tables and materialized views

Trusted by teams at

Sub-Second Queries,
Multi-Week Onboarding

The engine is fast; understanding the deployment is not. Which SummingMergeTree feeds that dashboard? Is this table replicated? What refreshes the view the report reads? New engineers spend weeks reconstructing answers the schema itself could give.

DbSchema renders tables, engines, and materialized-view relationships as diagrams — the topology of the cluster, visible instead of remembered.

Query, Explore & Record
Your Analytical Schema

A GUI client that keeps pace with an OLAP engine.

Write analytical SQL against ClickHouse in the DbSchema editor

Analytical SQL, Instant Feedback

Highlighted syntax, ClickHouse-specific functions, results in a tabular grid — iterate on aggregations at the speed the engine answers them.

Build ClickHouse GROUP BY queries in the visual builder

Aggregate Without the Dialect

Analysts pick tables, filters, and grouping; the builder emits ClickHouse-compatible SQL. A gentle on-ramp for people meeting the deployment for the first time.

Document the ClickHouse schema including materialized views

Write the View Topology Down Once

Export HTML documentation covering tables and materialized-view relationships — the reference the analytics team consults instead of interrupting the platform team.

Analytics Tables Deserve
Deliberate Changes Too

Schema work on a production analytics cluster is not the place to improvise. DbSchema keeps the design in a model file, so changes are drafted, versioned, and read as SQL before the cluster runs them.

Sketch ClickHouse schema changes offline

Sketch Changes Offline

Draft new tables and columns in the model without touching the cluster.

Version the ClickHouse schema model in Git

Analytics Schema, Versioned

The model file lives in Git next to your ingestion pipelines.

Review generated ClickHouse DDL before execution

Read the DDL First

Every planned change shows its statements before anything executes.

Promote ClickHouse schema changes from staging to production

Staging First, Then Production

Walk one reviewed change through each cluster deliberately.

ClickHouse Ports and JDBC URL

The ClickHouse JDBC driver downloads automatically and talks to the HTTP interface on port 8123 — native TCP 9000 is not used — so the URL reads jdbc:clickhouse://host:8123/dbname. Supply credentials if the server requires them, and DbSchema reverse-engineers the MergeTree table definitions into the first diagram.

ClickHouse Cloud connects through the HTTPS endpoint on port 8443 with the service username and password. Self-hosted clusters behind TLS also take the HTTPS URL, with certificate trust configured in the connection properties.

Turn Cluster Knowledge Into a Diagram

Free Community Edition; the 15-day Architect trial ships in the same download, no signup.

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Frequently asked questions

Yes, DbSchema connects to ClickHouse over JDBC using the HTTP interface and reverse-engineers tables and materialized views into an interactive ER diagram.

The JDBC driver connects over HTTP on port 8123 by default (native TCP port 9000 is not used). ClickHouse Cloud deployments use HTTPS on port 8443 instead.

Yes, DbSchema renders materialized views alongside their base tables in the ER diagram, making it easier to see which views aggregate which tables.

Teams working with ClickHouse often use these engines too. Explore dedicated guides and JDBC setup for each.