How to Unit Test ClickHouse Queries in PostgreSQL

Galaxy Glossary

How do I unit-test ClickHouse SQL queries?

Unit-testing ClickHouse SQL verifies that query logic returns the expected result set, performance plan, and error handling before code reaches production.

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Description

Why unit-test ClickHouse SQL?

Unit tests catch logic errors early, protect dashboards from silent breakage, and document business rules. Fast, deterministic ClickHouse reads make test suites run in seconds.

What tools can I use?

Most teams rely on clickhouse-client + shell scripts, Python’s pytest, or CI services. The pattern is identical: seed minimal data, run the query, and assert equality with an expected data set.

How do I structure a test case?

Create an ephemeral test database, load fixture rows, run the target query into a temporary table, then compare it to an expected table via EXCEPT/UNION ALL. Return zero rows on success.

Can I keep tests idempotent?

Wrap every test in -- begin fixture and -- end fixture blocks. Use DROP TABLE IF EXISTS and CREATE TEMPORARY TABLE so reruns start clean.

Example: validating total sales per customer

We test a view that returns each customer’s lifetime spend. The suite seeds Customers, Orders, and OrderItems, runs the aggregation, and fails if the result diverges from the expected two-row fixture.

Python/pytest snippet

def test_lifetime_spend(ch): ch.execute("DROP TABLE IF EXISTS expected_spend") ch.execute("CREATE TEMPORARY TABLE expected_spend AS SELECT * FROM VALUES (1,'Alice',150.00), (2,'Bob',0) FORMAT TabSeparated") ch.execute(open("../migrations/create_view_lifetime_spend.sql").read()) diff = ch.execute("SELECT * FROM (SELECT * FROM test.lifetime_spend EXCEPT SELECT * FROM expected_spend) LIMIT 1") assert diff == []

Best practices

Keep fixtures tiny, cover edge cases (zero orders, negative stock), run tests on every commit, and fail fast by returning the first mismatching row.

Common mistakes

Not isolating state. Shared databases lead to flaky tests—always use dedicated test DBs or CREATE TEMPORARY TABLE.

Over-seeding data. Large fixtures hide bugs and slow pipelines. Seed the absolute minimum rows per test.

Related questions

How do I test materialized views?

Insert fixture rows, call OPTIMIZE TABLE ... FINAL to flush async merges, then assert against the view.

Can I test query plans?

Yes. Run EXPLAIN AST and compare to a stored plan snapshot. Fail the test if joins swap order or indexes disappear.

Why How to Unit Test ClickHouse Queries in PostgreSQL is important

How to Unit Test ClickHouse Queries in PostgreSQL Example Usage


-- Full self-contained test block
DROP DATABASE IF EXISTS test;
CREATE DATABASE test;
USE test;

CREATE TEMPORARY TABLE Customers (id UInt8, name String, email String, created_at DateTime);
CREATE TEMPORARY TABLE Orders (id UInt8, customer_id UInt8, order_date Date, total_amount Decimal(10,2));
INSERT INTO Customers VALUES (1,'Alice','a@e.com','2024-01-01 00:00:00');
INSERT INTO Orders VALUES (1,1,'2024-01-05',150.00);

CREATE TEMPORARY TABLE actual AS
SELECT customer_id AS id, SUM(total_amount) AS lifetime_spend
FROM Orders GROUP BY customer_id;

CREATE TEMPORARY TABLE expected AS SELECT * FROM VALUES(1,150.00) FORMAT TabSeparated;

-- Assertion query (returns nothing if test passes)
SELECT * FROM (
    SELECT * FROM actual EXCEPT SELECT * FROM expected
    UNION ALL
    SELECT * FROM expected EXCEPT SELECT * FROM actual
) LIMIT 1;

How to Unit Test ClickHouse Queries in PostgreSQL Syntax


-- 1️⃣ Create a dedicated test database
CREATE DATABASE IF NOT EXISTS test;

-- 2️⃣ Switch to it
USE test;

-- 3️⃣ Fixture tables (ecommerce example)
CREATE TEMPORARY TABLE Customers (id UInt8, name String, email String, created_at DateTime);
CREATE TEMPORARY TABLE Orders (id UInt8, customer_id UInt8, order_date Date, total_amount Decimal(10,2));
CREATE TEMPORARY TABLE Products (id UInt8, name String, price Decimal(10,2), stock UInt16);
CREATE TEMPORARY TABLE OrderItems (id UInt8, order_id UInt8, product_id UInt8, quantity UInt8);

-- 4️⃣ Seed minimal rows
INSERT INTO Customers VALUES (1,'Alice','a@e.com','2024-01-01 00:00:00');
INSERT INTO Orders VALUES (1,1,'2024-01-05',150.00);
INSERT INTO Products VALUES (7,'Keyboard',75.00,10);
INSERT INTO OrderItems VALUES (1,1,7,2);

-- 5️⃣ Run query under test
CREATE TEMPORARY TABLE actual AS
SELECT c.id, c.name, SUM(o.total_amount) AS lifetime_spend
FROM Customers c LEFT JOIN Orders o ON c.id = o.customer_id
GROUP BY c.id, c.name;

-- 6️⃣ Define expected result
CREATE TEMPORARY TABLE expected AS SELECT * FROM VALUES(1,'Alice',150.00) FORMAT TabSeparated;

-- 7️⃣ Assertion — should return 0 rows
SELECT * FROM (
    SELECT * FROM actual EXCEPT SELECT * FROM expected
    UNION ALL
    SELECT * FROM expected EXCEPT SELECT * FROM actual
) LIMIT 1;

Common Mistakes

Running tests against shared staging clusters causes unpredictable failures. Fix by spinning up a containerised ClickHouse instance per CI job or using <code>CREATE DATABASE test_{{CI_JOB_ID}}</code>.
Comparing floating-point aggregates with equality checks (<code>=</code>) leads to false negatives. Instead, use <code>abs(actual - expected) < 1e-9</code>.

Frequently Asked Questions (FAQs)

What is the fastest way to seed data?

Use VALUES syntax or insert TSV fixtures through clickhouse-client --query and input redirection. Both load thousands of rows in milliseconds.

Can I unit-test INSERT mutations?

Yes. Wrap the INSERT in a CREATE TABLE ... AS statement, run it, then compare the table contents against an expected snapshot.

How do I integrate with GitHub Actions?

Start ClickHouse with the official Docker image, run clickhouse-client commands inside the job, and fail the step when any assertion query returns rows.

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