How to Choose MariaDB over ClickHouse for Mixed Workloads

Why should I use MariaDB instead of ClickHouse?

MariaDB excels over ClickHouse when you need ACID-compliant transactions, frequent single-row writes, broad SQL compatibility, and simpler administration for OLTP or mixed OLTP/OLAP workloads.

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Description

Example H2

Example H3

Why pick MariaDB instead of ClickHouse?

Choose MariaDB when your application executes many small writes, requires strict ACID guarantees, or depends on full ANSI SQL, foreign keys, and rich indexing. MariaDB’s InnoDB engine optimizes point lookups and transactional integrity, which ClickHouse’s columnar storage sacrifices for aggregation speed.

How do their storage engines differ?

MariaDB uses row-oriented engines like InnoDB that store complete rows together, ideal for random reads and writes.ClickHouse stores each column separately, enabling blazing-fast scans but slowing single-row updates. Understanding this trade-off helps teams align engine choice with workload patterns.

When is MariaDB faster than ClickHouse?

For queries that retrieve or modify a handful of rows—such as updating a customer’s email or inserting one order—MariaDB’s clustered indexes avoid full table scans.ClickHouse incurs overhead converting row updates into columnar parts, making it slower for high-velocity OLTP traffic.

Example: single-row update latency

-- MariaDB UPDATE Customers SET email = 'new@mail.com' WHERE id = 42;. -- ClickHouse (not recommended; requires ALTER UPDATE) ALTER TABLE Customers UPDATE email = 'new@mail.com' WHERE id = 42;

MariaDB executes instantly, while ClickHouse rewrites data parts asynchronously, delaying consistency.

Which features does MariaDB offer that ClickHouse lacks?

MariaDB supports transactions, foreign keys, stored procedures, views, and triggers—essential for complex business logic. ClickHouse omits or limits these to prioritize analytics speed. If your codebase relies on such constructs, migration effort rises sharply.

Can MariaDB still handle analytics?

Yes.ColumnStore and InnoDB’s parallel query features let MariaDB process moderate analytics without separate infrastructure. While not as fast as ClickHouse on terabyte-scale aggregates, it avoids ETL overhead and schema duplication.

Best practices when choosing MariaDB

Use InnoDB, enable proper primary keys, and partition large tables to sustain analytics reads. Add covering indexes for frequent dimensions like order_date and customer_id.Offload heavy summarizations to materialized views or periodic batch tables.

Common mistakes to avoid

First, expecting ClickHouse-style scan speed in MariaDB without indexes results in full-table scans. Second, assuming ClickHouse can replace MariaDB for transactional workloads leads to painful workarounds and data loss risk.

Why How to Choose MariaDB over ClickHouse for Mixed Workloads is important

How to Choose MariaDB over ClickHouse for Mixed Workloads Example Usage


-- Retrieve last 5 orders for a customer in MariaDB
SELECT o.id, o.order_date, o.total_amount
FROM Orders o
JOIN Customers c ON c.id = o.customer_id
WHERE c.email = 'alice@shop.com'
ORDER BY o.order_date DESC
LIMIT 5;

How to Choose MariaDB over ClickHouse for Mixed Workloads Syntax


-- MariaDB table definitions
CREATE TABLE Customers (
  id INT PRIMARY KEY AUTO_INCREMENT,
  name VARCHAR(100),
  email VARCHAR(150) UNIQUE,
  created_at DATETIME DEFAULT NOW()
) ENGINE=InnoDB;

CREATE TABLE Orders (
  id INT PRIMARY KEY AUTO_INCREMENT,
  customer_id INT,
  order_date DATE,
  total_amount DECIMAL(10,2),
  FOREIGN KEY (customer_id) REFERENCES Customers(id)
) ENGINE=InnoDB;

-- Typical OLTP query
START TRANSACTION;
INSERT INTO Orders (customer_id, order_date, total_amount)
VALUES (42, CURRENT_DATE, 99.99);
COMMIT;

-- ClickHouse equivalent (no FK, no transaction)
CREATE TABLE Orders (
  id UInt32,
  customer_id UInt32,
  order_date Date,
  total_amount Decimal(10,2)
) ENGINE = MergeTree() ORDER BY id;

INSERT INTO Orders VALUES (1, 42, today(), 99.99);

Common Mistakes

Ignoring the need for ACID: Teams move OLTP data to ClickHouse for faster analytics, then discover UPDATEs and DELETEs are slow or unsupported. Fix by keeping MariaDB for transactional writes and replicating to ClickHouse for reporting.
Assuming foreign keys exist: ClickHouse lacks referential integrity, so JOIN logic must enforce consistency manually. Keep relational constraints in MariaDB or implement upstream validation.

Frequently Asked Questions (FAQs)

Is MariaDB slower than ClickHouse for analytics?

For large scans and aggregates, yes. ClickHouse’s columnar engine and vectorized execution outperform MariaDB. However, MariaDB can meet small-to-medium analytics needs with indexes and partitioning.

Can I run both databases together?

Absolutely. Use MariaDB as the source of truth and replicate data into ClickHouse via tools like MaterializedMySQL or custom pipelines for heavy reporting queries.

Does MariaDB support columnar storage like ClickHouse?

MariaDB ColumnStore offers columnar tables but lacks ClickHouse’s compression and parallelism maturity. It suits moderate datasets where unified SQL is preferred over maximum speed.