How to Migrate from ClickHouse to MariaDB in PostgreSQL

Galaxy Glossary

How do I migrate data and schema from ClickHouse to MariaDB safely?

The process moves data, schema, and workloads from ClickHouse’s columnar engine to MariaDB’s row-oriented storage using exports, transforms, and imports.

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Description

Table of Contents

Why migrate from ClickHouse to MariaDB?

Teams move OLAP data into MariaDB when they need transactional consistency, wider ORM support, or lower operational overhead. MariaDB also suits mixed workloads that ClickHouse’s append-only design may hinder.

What prerequisites do I need?

Install clickhouse-client, MariaDB ≥10.5, and enough disk for an intermediate CSV or Parquet dump. Confirm both servers use the same UTC timezone to avoid timestamp drift.

How do I export data from ClickHouse?

Use clickhouse-client --query with FORMAT CSV or FORMAT Parquet to stream tables into files or directly over STDOUT. Split large tables per day to speed retries.

Using clickhouse-client to dump tables

clickhouse-client --query="SELECT * FROM ecommerce.Customers" --format=CSV > customers.csv

How do I transform data for MariaDB?

Convert unsigned integers to signed, map DateTime64 to DATETIME(6), and flatten nested data. A lightweight Python or awk script can adjust CSV headers before import.

How do I import data into MariaDB?

Load with LOAD DATA INFILE for fastest bulk speed. Disable foreign-key checks and autocommit during import, then re-enable to enforce integrity.

What SQL changes should I know?

ClickHouse’s UInt64 becomes BIGINT; LowCardinality(String) becomes VARCHAR with an index. Replace ENGINE = MergeTree with InnoDB.

Data types

Match numeric precision exactly to avoid silent truncation of totals or prices.

Engine differences

ClickHouse partitions by month; replicate this with MariaDB PARTITION BY RANGE (TO_DAYS(order_date)) for large fact tables.

How do I validate the migrated data?

Run row counts and checksums in both systems. For example, compare SUM(total_amount) per day in Orders. Mismatches signal type or timezone errors.

Best practices for zero-downtime migration?

Replicate new ClickHouse inserts to MariaDB via Kafka or an ETL tool while bulk import runs. Cut traffic once lag is near zero, then promote MariaDB to primary.

Can I roll back if something fails?

Keep ClickHouse in read-only mode for a grace period and swap DNS only after validation passes. This lets you revert instantly without data loss.

Why How to Migrate from ClickHouse to MariaDB in PostgreSQL is important

How to Migrate from ClickHouse to MariaDB in PostgreSQL Example Usage


-- Compare order totals per day after migration
SELECT  DATE(order_date)       AS day,
        SUM(total_amount)      AS clickhouse_total
FROM    clickhouse.ecommerce.Orders
GROUP BY day
UNION ALL
SELECT  DATE(order_date)       AS day,
        SUM(total_amount)      AS mariadb_total
FROM    mariadb.ecommerce.Orders
GROUP BY day;

How to Migrate from ClickHouse to MariaDB in PostgreSQL Syntax


-- 1. Dump ClickHouse tables to CSV
clickhouse-client --query="SELECT * FROM ecommerce.Customers" --format=CSV > customers.csv

-- 2. Create equivalent MariaDB schema
CREATE TABLE Customers (
  id          BIGINT PRIMARY KEY,
  name        VARCHAR(255),
  email       VARCHAR(255) UNIQUE,
  created_at  DATETIME(6)
) ENGINE=InnoDB;

CREATE TABLE Orders (
  id            BIGINT PRIMARY KEY,
  customer_id   BIGINT,
  order_date    DATETIME(6),
  total_amount  DECIMAL(10,2),
  INDEX idx_order_date (order_date),
  FOREIGN KEY (customer_id) REFERENCES Customers(id)
) ENGINE=InnoDB;

-- 3. Bulk-load data
LOAD DATA INFILE '/path/customers.csv'
INTO TABLE Customers
FIELDS TERMINATED BY ','
ENCLOSED BY '"'
LINES TERMINATED BY '\n'
(id, name, email, created_at);

-- 4. Validate counts
SELECT COUNT(*) FROM Customers;

Common Mistakes

Frequently Asked Questions (FAQs)

Is there a direct replication tool?

No official tool exists, but Debezium or custom Kafka pipelines can stream inserts from ClickHouse into MariaDB during cutover.

How do I handle ClickHouse arrays?

Flatten arrays into link tables (e.g., OrderItems) or serialize as JSON; choose based on query patterns.

Can I migrate materialized views?

Recreate views manually in MariaDB, translating ClickHouse functions to MySQL equivalents such as GROUP_CONCAT or window functions.

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