How to Migrate from ClickHouse to BigQuery

What does “migrating from ClickHouse to BigQuery” mean?

Moving table structures and data files from a self-managed ClickHouse cluster into Google BigQuery so you can run serverless analytics without managing infrastructure.

Which high-level steps should I follow?

1️⃣ Export data from ClickHouse
2️⃣ Upload the export to Google Cloud Storage (GCS)
3️⃣ Create matching tables in BigQuery
4️⃣ Load the files from GCS into BigQuery
5️⃣ Validate row counts & query results

How do I export ClickHouse tables quickly?

Use clickhouse-client --query with OUTFILE to write each table to a compressed Parquet or CSV file.Parquet keeps column types intact, reducing BigQuery schema hassles.

Example export command

clickhouse-client \ --query="SELECT * FROM Customers" \ --format=Parquet \ > customers.parquet

How do I move files to Google Cloud Storage?

Install the gcloud CLI and run gsutil cp. Keep paths consistent so your load jobs can use wildcards.

Example upload

gsutil cp *.parquet gs://ecom-migration/2024-05/

How do I create compatible tables in BigQuery?

Use the BigQuery UI or bq mk.Match ClickHouse types (e.g., String → STRING, Decimal(10,2) → NUMERIC).

Sample DDL for Orders

CREATE TABLE `ecom.Orders` ( id INT64, customer_id INT64, order_date DATE, total_amount NUMERIC);

How do I load Parquet files into BigQuery?

Run bq load --source_format=PARQUET --replace for each table. Parquet automatically reads column names and types.

Load command

bq load --source_format=PARQUET \ ecom.Customers \ gs://ecom-migration/2024-05/customers.parquet

How do I validate the migration?

Run COUNT(*), MIN/MAX, or checksums in both systems.Differences usually indicate timezone or null-handling issues.

Quick row-count check

SELECT COUNT(*) FROM `ecom.Customers`;

What is an end-to-end example query in BigQuery?

After loading, verify joins behave as expected.

Cross-table join

SELECT c.name, o.order_date, o.total_amountFROM `ecom.Customers` cJOIN `ecom.Orders` o USING (id)WHERE o.order_date > DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY);

Best practices for zero downtime

• Export during low-traffic windows
• Replicate new ClickHouse inserts into a staging bucket
• Perform a final incremental load before cutover

Common mistakes and how to avoid them

Mistake 1 – Ignoring nested data types: ClickHouse Array or Nested columns need BigQuery ARRAY or STRUCT.Cast or flatten before export.

Mistake 2 – Loading CSV without a schema file: BigQuery may auto-detect wrong types. Prefer Parquet or provide an explicit JSON schema.

Need quick answers?

Use Galaxy’s AI copilot to generate consistent DDL and load scripts automatically, reducing manual errors.

FAQ

Can I stream data instead of bulk loading?

Yes. Use Dataflow or Pub/Sub to stream ClickHouse inserts into BigQuery for near-real-time sync.

Does BigQuery support ClickHouse materialized views?

No direct equivalent.Recreate them as scheduled queries or materialized views within BigQuery.

How do I migrate permissions?

Map ClickHouse users to Google IAM roles; BigQuery controls access at the dataset/table level.

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