How to Version Control Clickhouse Queries in PostgreSQL

Galaxy Glossary

How do I version control Clickhouse queries using Git and CI?

Store and track Clickhouse SQL files in Git, apply them reproducibly, and document changes alongside schema migrations.

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Description

Example H2

Example H3

Why version control Clickhouse queries?

Keeping every analytical query in Git guarantees auditability, easy rollback, and teamwork—exactly like code. Developers can peer-review SQL, link it to tickets, and run CI checks before deploying.

What files should live in Git?

Create one .sql file per business object: customers.sql, daily_revenue.sql, etc. Include CREATE VIEW or CREATE TABLE AS statements plus comments describing intent, owner, and last update.

How do I structure directories?

Use a clickhouse/ folder with sub-directories like analytics/, dashboards/, and migrations/.CI pipelines can apply only changed files inside migrations/ to production.

How to apply queries automatically?

Pair Git commits with a CI job—GitHub Actions or GitLab CI—that calls clickhouse-client -n -q "$(cat file.sql)". Tag releases so you can redeploy any historical state.

Can I embed parameters per environment?

Add ERB- or Jinja-style placeholders ({{database}}) in SQL files.During CI, render them from secrets, then pipe to clickhouse-client.

How to version analytical views referencing ecommerce tables?

Place queries like:

CREATE OR REPLACE VIEW v_customer_ltv AS SELECT c.id, sum(oi.quantity * p.price) AS lifetime_value FROM Customers c JOIN Orders o ON o.customer_id = c.id JOIN OrderItems oi ON oi.order_id = o.id JOIN Products p ON p.id = oi.product_id GROUP BY c.id;

Commit and push; reviewers can comment just like on application code.

What Git workflow fits best?

Feature branches for each metric, pull requests for review, squashed merge into main.Tag database releases (v1.4-schema) and deploy via CI.

How do I link schema migrations and queries?

Keep DDL in migrations/ with incremental numbering (e.g., 20240508_01_add_column.sql). CI runs them sequentially; analytical queries in views/ get redeployed afterwards.

Best practice checklist

Use SETTINGS allow_experimental_lightweight_delete = 1 in test env to avoid production accidents.
Lint SQL with sqlfluff or pgFormatter in CI.
Add unit tests with clickhouse-test or snapshot testing tools.

Why How to Version Control Clickhouse Queries in PostgreSQL is important

How to Version Control Clickhouse Queries in PostgreSQL Example Usage


-- File: views/v_top_products.sql
CREATE OR REPLACE VIEW v_top_products AS
SELECT p.id, p.name, sum(oi.quantity) AS units_sold
FROM OrderItems oi
JOIN Products p ON p.id = oi.product_id
GROUP BY p.id, p.name
ORDER BY units_sold DESC
LIMIT 10;

How to Version Control Clickhouse Queries in PostgreSQL Syntax


# Store query as file
e.g. views/v_daily_sales.sql
-------------------------------------------------
CREATE OR REPLACE VIEW v_daily_sales AS
SELECT order_date AS day,
       sum(total_amount) AS revenue
FROM Orders
GROUP BY order_date;
-------------------------------------------------

# Apply via CLI
clickhouse-client --host $CH_HOST --database $DB -n -q "$(cat views/v_daily_sales.sql)" 

# Git commit workflow
 git checkout -b feat/daily-sales-view
 git add views/v_daily_sales.sql
 git commit -m "feat: add daily sales view"
 git push origin feat/daily-sales-view

Common Mistakes

Storing queries only in the Clickhouse server. Without files in Git, rollback is impossible. Fix by extracting all existing views to .sql files and committing them.
Running SQL manually after merging. This drifts environments. Fix by wiring CI/CD to apply every merged migration automatically.