How to Choose ClickHouse over MariaDB in PostgreSQL Workloads

Galaxy Glossary

Why choose ClickHouse over MariaDB?

Learn when and how ClickHouse outperforms MariaDB for real-time analytics workloads.

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Description

Example H2

Example H3

Why pick ClickHouse instead of MariaDB?

Column-oriented storage lets ClickHouse scan only the needed columns, executing aggregates on billions of rows in milliseconds. MariaDB’s row store reads whole rows, slowing wide-table analytics. Choose ClickHouse when dashboards need sub-second response and data volume is terabytes.

When does ClickHouse shine?

Queries that use GROUP BY, ORDER BY, JOIN, or window functions on large fact tables benefit most. ClickHouse’s vectorized engine and compression reduce CPU and IO, whereas MariaDB hits disk sooner.

Can ClickHouse handle high-speed inserts?

Batch INSERTs reach ≥5M rows/s. Wrap single-row inserts in a Buffer or Kafka engine to avoid write-amplification. MariaDB stays better for OLTP row-by-row writes.

How do schemas differ?

ClickHouse favors denormalized tables and immutable columns; UPDATE/DELETE are costly. MariaDB supports fully normalized schemas with ACID writes. Use ETL to load finished transactions into ClickHouse.

What about clustering & scaling?

Add shards or replicas to scale ClickHouse almost linearly; Zookeeper/Keeper handles metadata. MariaDB Galera clusters scale reads easily but write scaling plateaus after ~10 nodes.

Best practices for ecommerce analytics migration

• Keep live Orders in MariaDB.
• Hourly ETL to ClickHouse using INSERT SELECT.
• Create materialized views (MV) for KPIs.
• Use SummingMergeTree for daily revenue totals.

ClickHouse vs MariaDB performance example

-- ClickHouse: avg order value per customer SELECT customer_id, avg(total_amount) AS avg_order_value FROM Orders GROUP BY customer_id; -- MariaDB equivalent (takes 50× longer on 1B rows): SELECT customer_id, AVG(total_amount) AS avg_order_value FROM Orders GROUP BY customer_id;

Why How to Choose ClickHouse over MariaDB in PostgreSQL Workloads is important

How to Choose ClickHouse over MariaDB in PostgreSQL Workloads Example Usage


-- ClickHouse materialized view to pre-aggregate daily revenue
CREATE MATERIALIZED VIEW daily_revenue
ENGINE = SummingMergeTree
PARTITION BY toYYYYMM(order_date)
ORDER BY order_date AS
SELECT toDate(order_date) AS order_date,
       sum(total_amount) AS daily_total
FROM Orders
GROUP BY order_date;

How to Choose ClickHouse over MariaDB in PostgreSQL Workloads Syntax


-- ClickHouse table for analytics on Orders
CREATE TABLE Orders
(
    id UInt64,
    customer_id UInt64,
    order_date DateTime,
    total_amount Decimal(12,2)
)
ENGINE = MergeTree
PARTITION BY toYYYYMM(order_date)
ORDER BY (customer_id, order_date);

-- MariaDB transactional table
CREATE TABLE Orders (
    id BIGINT PRIMARY KEY,
    customer_id BIGINT,
    order_date DATETIME,
    total_amount DECIMAL(12,2),
    INDEX idx_customer_date(customer_id, order_date)
) ENGINE = InnoDB;

-- ETL example: load last hour of data into ClickHouse
INSERT INTO clickhouse.Orders
SELECT *
FROM mariadb.Orders
WHERE order_date >= NOW() - INTERVAL 1 HOUR;

Common Mistakes

Relying on ClickHouse for high-frequency OLTP writes. Fix: keep transactional inserts in MariaDB and batch-load into ClickHouse.
Normalizing schemas in ClickHouse. Fix: denormalize wide tables or use materialized views; JOINs on billions of rows stay expensive.