Why ClickHouse Beats MySQL for OLAP: Migration, Performance & Pitfalls

ClickHouse is an open‑source column‑store database from Yandex designed for real‑time analytical workloads, offering 100‑1000× speed improvements over traditional row‑based systems.

What is ClickHouse?

ClickHouse is an OLAP (online analytical processing) columnar DBMS.

Key Concepts

OLTP: traditional relational databases focused on transaction consistency (e.g., banking, e‑commerce).

OLAP: warehouse‑type databases focused on reading data for complex analysis and decision support.

In row‑store databases (MySQL, Postgres, MS SQL Server) data is stored row by row:

In column‑store databases (ClickHouse) data is stored column by column:

Comparison of storage methods:

Business Problem

The existing MySQL database contains a 50 million‑row main table and two auxiliary tables; a single join query takes more than 3 minutes, even after indexing, sharding and logical optimizations.

By migrating to ClickHouse, query time was reduced to under 1 second, achieving a 200× speedup.

Data Migration from MySQL to ClickHouse

ClickHouse supports most MySQL syntax, making migration low‑cost. Five migration approaches are available:

create table engine mysql – keep data in MySQL.

insert into … select from – create table then import.

create table as select from – create and import simultaneously.

CSV offline import.

Streamsets.

Example of the third approach (CREATE TABLE AS SELECT):

CREATE TABLE [IF NOT EXISTS] [db.]table_name ENGINE = MergeTree AS SELECT * FROM mysql('host:port','db','database','user','password')

Performance Test

After migration, queries on datasets up to 2 billion rows return 90% of results within 1 second. The following chart shows the performance comparison:

Data Synchronization

A temporary table can be created in ClickHouse, fully sync MySQL data into it, then replace the original table—suitable for moderate data volumes with frequent incremental changes.

Open‑source sync tool Synch uses MySQL binlog to capture SQL statements and processes tasks via a message queue:

Why is ClickHouse Fast?

Only the required columns are read, reducing I/O compared to row‑wise reads.

Same‑type columns achieve up to ten‑fold compression, further lowering I/O.

ClickHouse applies specialized search algorithms tailored to storage scenarios.

Pitfalls Encountered

Data Type Differences

MySQL queries may fail due to type mismatches. Example fix: use LEFT JOIN B b ON toUInt32(h.id) = toUInt32(ec.post_id) to unify unsigned types.

Asynchronous Deletes/Updates

ClickHouse guarantees only eventual consistency; for strict consistency, a full data sync is recommended.

Conclusion

By adopting ClickHouse, the MySQL query bottleneck was eliminated; queries on tables with up to 2 billion rows typically finish within 1 second, and ClickHouse also scales horizontally with clusters for larger workloads.