Learn when and how ClickHouse outperforms MariaDB for real-time analytics workloads.
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.
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.
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.
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.
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.
• 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: 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;
No. It offers eventual consistency; use MariaDB for transactions.
Yes, but benefit appears with ≥16 GB RAM and NVMe SSDs.
Most ANSI SQL plus array, bitmap, and time-series functions are available.