How to Choose ClickHouse over MySQL for High-Speed Analytics

Why use ClickHouse over MySQL for analytical workloads?

ClickHouse is a column-oriented OLAP database that delivers sub-second analytics on large datasets, often outperforming MySQL for read-heavy workloads.

Welcome to the Galaxy, Guardian!

Oops! Something went wrong while submitting the form.

Description

Why pick ClickHouse instead of MySQL for analytics?

ClickHouse stores data by column, compresses aggressively, and processes queries using vectorized execution. This design slashes I/O and CPU costs for aggregates, enabling millisecond-level dashboards even on billions of rows. MySQL’s row storage is optimized for OLTP, so wide scans and heavy GROUP BYs strain its buffer pool and disks.

When does ClickHouse shine the most?

Use ClickHouse for time-series metrics, log analytics, customer behavior funnels, or any workload where 90% of queries are reads, append-only writes are acceptable, and sub-second latency matters. Retain MySQL for high-concurrency transactions, referential integrity, and complex JOINs across many small tables.

How does ClickHouse store data differently?

Columns are written to separate files, allowing ClickHouse to read only required fields. Data parts are merged asynchronously, keeping writes fast. MySQL’s InnoDB stores entire rows together, forcing full-row reads even when you need one column.

What is the basic ClickHouse table syntax?

The core pattern is CREATE TABLE with an ENGINE such as MergeTree, plus ORDER BY and partitioning. Sorting keys accelerate range scans and aggregations.

ClickHouse vs MySQL: Sample schema

Below shows identical ecommerce tables in both systems, highlighting engine choices and primary keys.

How to migrate analytics queries?

Rewrite COUNT(*), SUM(), AVG(), and GROUP BY queries verbatim—ClickHouse speaks standard SQL. Remove MySQL-specific functions like GROUP_CONCAT() or convert them (e.g., use arrayStringConcat).

Best practices for ClickHouse performance

• Use ORDER BY (customer_id, order_date) to cluster data.
• Partition large fact tables by day or month.
• Compress with CODEC(ZSTD) for disk savings.
• Avoid DELETE; instead, use TTLs or version tables.

What common mistakes slow ClickHouse?

Ignoring proper ORDER BY keys leads to scattered reads. Over-partitioning creates thousands of small parts, hurting merge performance.

Why How to Choose ClickHouse over MySQL for High-Speed Analytics is important

How to Choose ClickHouse over MySQL for High-Speed Analytics Example Usage


-- Real-time revenue last 30 days in ClickHouse
SELECT toDate(order_date) AS day,
       sum(total_amount) AS revenue
FROM Orders
WHERE order_date >= now() - INTERVAL 30 DAY
GROUP BY day
ORDER BY day;

-- Same query in MySQL (likely slower)
SELECT DATE(order_date) AS day,
       SUM(total_amount) AS revenue
FROM Orders
WHERE order_date >= NOW() - INTERVAL 30 DAY
GROUP BY day
ORDER BY day;

How to Choose ClickHouse over MySQL for High-Speed Analytics Syntax


-- ClickHouse table
CREATE TABLE Orders
(
    id UInt64,
    customer_id UInt64,
    order_date DateTime,
    total_amount Decimal(10,2)
)
ENGINE = MergeTree
PARTITION BY toYYYYMM(order_date)
ORDER BY (customer_id, order_date)
SETTINGS index_granularity = 8192;

-- MySQL equivalent
CREATE TABLE Orders (
    id BIGINT PRIMARY KEY,
    customer_id BIGINT,
    order_date DATETIME,
    total_amount DECIMAL(10,2),
    KEY idx_customer_date (customer_id, order_date)
) ENGINE=InnoDB;

Common Mistakes

Mistake: Keeping default ORDER BY (). Without a sort key, ClickHouse scans entire parts, negating columnar benefits. Fix: choose an ORDER BY that matches your common filter or GROUP BY columns.
Mistake: Running frequent UPDATE/DELETE on large tables. MergeTree rewrites whole parts, creating disk churn. Fix: design append-only tables and set TTLs or use CollapsingMergeTree for soft deletes.

Frequently Asked Questions (FAQs)

Is ClickHouse a drop-in replacement for MySQL?

No. ClickHouse lacks full ACID transactions, foreign keys, and row-level locks. Use it side-by-side with MySQL, not instead of, for transactional workloads.

Can ClickHouse handle JOINs?

Yes, but it excels at star-schema joins where dimension tables fit in memory. Avoid massive multi-table joins common in OLTP designs.

How do I load MySQL data into ClickHouse?

Dump to CSV/Parquet, or use the mysql() table function / Kafka. Schedule incremental loads via binlog replication tools like Altinity’s clickhouse-mysql-data-reader.