Choosing between MySQL and ClickHouse^® for analytics often comes down to a fundamental mismatch: MySQL was built to handle transactions where individual records change frequently, while ClickHouse^® was designed from the ground up to aggregate millions or billions of rows in seconds. Most developers start with MySQL because it's familiar, then hit a wall when analytical queries take minutes instead of milliseconds or start impacting production queries.

This article explains the architectural differences between these databases, compares their performance on analytical workloads, and shows when to use each one—or both together in a hybrid pattern.

OLTP vs OLAP and why it matters for analytics

MySQL is a row-oriented relational database built for online transaction processing (OLTP), where applications frequently update, insert, and delete individual records. ClickHouse^® is a column-oriented database built for online analytical processing (OLAP), where queries aggregate and scan large volumes of data.

The architecture of each database reflects different priorities. OLTP systems like MySQL optimize for data integrity and transactional consistency, making them ideal for e-commerce platforms, user authentication systems, and content management. OLAP systems like ClickHouse^® optimize for query speed on large datasets, making them better for dashboards, log analysis, and time-series data where writes are append-only and reads aggregate millions of rows.

Row storage in OLTP workloads

Row-based storage keeps all columns for a single record together on disk. When you update a user's email address or add a new order, the database quickly locates and modifies that specific row without touching unrelated data.

This layout works well for transactional operations that read or write complete records. Loading a user profile, for example, fetches all columns for that user in one operation.

Columnar storage in OLAP workloads

Column-oriented storage keeps all values for a single column together on disk. When you calculate the average order value across millions of transactions, the database reads only the order_value column instead of scanning entire rows.

This layout works well for analytical queries that aggregate specific columns across many rows. The database skips columns that aren't needed for the query, and it compresses similar values more effectively because they're stored together.

ClickHouse^® architecture at a glance

ClickHouse^® is a columnar database that executes analytical queries on large datasets with sub-second latency. The architecture makes specific tradeoffs that favor read-heavy workloads with append-only writes over transactional workloads requiring frequent updates or deletes.

The database uses a storage engine called MergeTree, which organizes data into sorted parts that merge periodically in the background. This design handles high-throughput data ingestion while maintaining fast query performance, though updates and deletes are more expensive than in row-based databases.

MergeTree engines and column compression

The MergeTree storage engine stores data in sorted, immutable parts that merge asynchronously. When new data arrives, ClickHouse^® writes it to a new part rather than modifying existing data, which lets writes proceed quickly without blocking reads.

Columnar compression typically reduces storage requirements by 10x to 100x compared to row-based storage. Because similar values are stored together, ClickHouse^® applies compression algorithms like LZ4 or ZSTD more effectively, and it uses specialized encodings for specific data types.

Vectorized execution and parallelism

Vectorized processing means ClickHouse^® operates on batches of values at once rather than processing one row at a time. Modern CPUs perform the same operation on multiple values simultaneously using SIMD instructions, which makes columnar operations significantly faster.

ClickHouse^® also parallelizes query execution across multiple CPU cores automatically. A single analytical query might use all available cores to scan different parts of the data simultaneously, which is why ClickHouse^® aggregates billions of rows in seconds on commodity hardware.

Materialized views for pre-aggregations

Materialized views in ClickHouse^® pre-compute and store query results as data arrives. When you create a materialized view that calculates hourly event counts, ClickHouse^® updates those counts incrementally as new events are inserted rather than recalculating from scratch.

This feature helps dashboards that display the same aggregations repeatedly. The materialized view stores pre-computed results, so queries return nearly instantly even when the underlying data contains billions of rows.

MySQL architecture at a glance

MySQL is a row-oriented relational database that prioritizes data integrity and transactional consistency. The architecture supports ACID properties, which guarantee that database operations either complete fully or not at all, even during system failures.

The database uses storage engines like InnoDB, which organize data into B-tree indexes that optimize for point lookups and range scans on individual records. This design makes MySQL efficient for reading or writing specific rows quickly, but it performs poorly when analytical queries scan and aggregate millions of rows.

B-tree indexes and row-based pages

InnoDB stores data in pages that contain complete rows, and it uses B-tree indexes to locate specific records quickly. When you search for a user by ID or retrieve orders within a date range, the B-tree index helps MySQL find the relevant pages without scanning the entire table.

This index structure works well for queries that filter to a small number of rows. It becomes inefficient for analytical queries that read most or all rows in a table, though, because each page contains all columns for the rows it stores.

ACID transactions and replication

MySQL guarantees ACID properties through its transaction system, which ensures that multiple operations either all succeed or all fail together. Transferring money between accounts, for example, requires updating two rows atomically, and MySQL’s transaction isolation prevents other queries from seeing partial results.

MySQL also supports replication, where changes from a primary database copy to one or more replica databases. This lets applications scale read traffic by distributing queries across replicas, though replicas typically lag behind the primary by milliseconds to seconds.

Side-by-side performance and storage benchmarks

ClickHouse^® typically performs 100x to 1000x faster than MySQL for analytical queries due to columnar storage and vectorized execution. The performance difference is most pronounced for queries that aggregate or filter large datasets, while MySQL can be faster for queries that retrieve individual records by primary key.

Storage efficiency also differs significantly. ClickHouse^®’s columnar compression reduces storage requirements by 10x to 100x compared to MySQL for typical analytical datasets, which lowers both storage costs and the amount of data that queries read from disk.

ClickBench aggregation results

ClickBench is an industry benchmark that compares database performance on analytical queries using a real-world web analytics dataset containing 100 million rows. ClickHouse^® consistently ranks among the fastest databases in this benchmark, completing most queries in under one second.

MySQL struggles with ClickBench queries because they require scanning and aggregating large portions of the dataset. Queries that ClickHouse^® completes in milliseconds can take minutes or hours in MySQL.

Compression ratios on real event data

Event data like clickstream logs, application telemetry, and IoT sensor readings compresses particularly well in columnar format. A dataset requiring 1 TB of storage in MySQL might compress to 10-50 GB in ClickHouse^®, depending on data types and column cardinality.

Better compression improves query performance in addition to reducing storage costs. When you scan a billion rows, reading 50 GB from disk is much faster than reading 1 TB, even on the same hardware.

Real-time ingestion and concurrency differences

ClickHouse^® ingests millions of rows per second while simultaneously serving analytical queries with sub-second latency. The MergeTree storage engine writes new data to memory buffers that flush to disk asynchronously, which lets inserts proceed without blocking reads.

Streaming inserts at millions of rows per second

ClickHouse^® handles streaming data ingestion by buffering rows in memory and flushing them to disk in batches. A single ClickHouse^® server typically sustains 500,000 to 1,000,000 rows per second on commodity hardware, and this throughput scales linearly as you add more servers to a cluster.

The database also supports asynchronous inserts, where the client receives an acknowledgment before data is written to disk. This reduces insert latency at the cost of potential data loss if the server crashes before flushing buffered data.

Query scheduling under high concurrency

ClickHouse^® executes hundreds of concurrent analytical queries while maintaining sub-second latency for most queries. The database uses a query scheduler that prioritizes queries based on resource requirements and can cancel queries that exceed configurable time or memory limits.

MySQL handles concurrent queries differently because it’s designed for shorter OLTP queries rather than long-running analytical queries. Under heavy analytical load, MySQL queries can queue up and slow down the entire system, especially when they require full table scans.

Typical analytics use cases and when to choose each database

For most analytical workloads involving time-series data, event logs, or business intelligence, ClickHouse^® delivers significantly better performance and lower infrastructure costs. MySQL remains the better choice for transactional workloads where data integrity and support for complex updates matter more than query speed. For a broader comparison of database options for analytical use cases, see our guide on the best database for real-time analytics.

Event and log analytics

Event data from web applications, mobile apps, and IoT devices typically arrives as append-only streams with high volume and velocity. ClickHouse^® handles this workload well because columnar storage and compression reduce storage costs, and vectorized execution makes aggregation queries fast.

MySQL struggles with event analytics at scale because row-based storage is inefficient for scanning millions or billions of events. Queries that calculate daily active users or track conversion funnels can take minutes or hours in MySQL, while ClickHouse^® completes the same queries in milliseconds to seconds. This performance advantage also enables advanced use cases like real-time anomaly detection on streaming event data.

Time-series dashboards

Dashboards that display metrics over time benefit from ClickHouse^®’s ability to pre-aggregate data using materialized views. The database updates aggregations incrementally as new data arrives, so dashboard queries return results instantly even when the underlying data contains billions of rows.

MySQL can serve time-series dashboards for smaller datasets, but performance degrades as data volume grows. Queries that group by time intervals and calculate aggregates require full table scans in MySQL, which becomes prohibitively slow beyond a few million rows.

Ad hoc BI exploration

Business intelligence tools often generate complex queries with multiple joins, filters, and aggregations that aren’t known in advance. ClickHouse^® handles ad hoc queries well because columnar storage and the query optimizer efficiently scan and aggregate large datasets without requiring pre-built indexes.

MySQL requires careful index planning to support ad hoc queries, and even with appropriate indexes, performance can be poor for queries that aggregate large portions of a table. Analysts may wait minutes or hours for query results, which slows down exploration.

Hybrid patterns: keeping MySQL for writes and ClickHouse^® for reads

Many applications use both MySQL and ClickHouse^® together, with MySQL handling transactional data and ClickHouse^® serving as an analytical extension for reporting and insights. This hybrid approach lets each database focus on what it does best, though it adds complexity in the form of data synchronization pipelines. For teams considering MariaDB as a MySQL alternative with integrated columnar capabilities, our ClickHouse^® vs MariaDB ColumnStore comparison explores that option.

The pattern typically involves writing transactional data to MySQL first, then replicating it to ClickHouse^® asynchronously for analytics. This separation means analytical queries don't impact transactional performance, and you can scale each database independently based on its workload, with some organizations achieving 10x to 20x faster search performance using this hybrid approach.

Asynchronous replication pipelines

Asynchronous replication means data copies from MySQL to ClickHouse^® with some delay, typically ranging from seconds to minutes. Tools like Debezium, Airbyte, or custom scripts capture changes from MySQL's binary log and write them to ClickHouse^® in batches. This pattern works equally well with Aurora MySQL as the transactional database, providing similar benefits with managed infrastructure.

The replication delay is usually acceptable for analytical workloads where real-time accuracy isn't required. Dashboards that display yesterday's metrics or last hour's data work fine with a few minutes of replication lag.

Dual-write with change data capture

Change data capture (CDC) is a pattern where an application writes to both MySQL and ClickHouse^® simultaneously, or where a CDC tool captures changes from MySQL’s transaction log and streams them to ClickHouse^® in real time. This approach reduces replication lag to seconds or less, which matters for real-time dashboards.

CDC tools like Debezium monitor MySQL’s binary log and publish change events to a message queue like Kafka. A separate consumer process reads from Kafka and writes to ClickHouse^®, which decouples the databases and provides resilience if either system experiences downtime.

Developer experience, tooling, and managed options

Developer experience differs significantly between ClickHouse^® and MySQL in terms of setup complexity, operational overhead, and available tooling. MySQL benefits from decades of ecosystem development, including mature ORMs, migration tools, and hosting providers, while ClickHouse^® is newer and has a smaller but growing ecosystem.

Both databases support SQL, but ClickHouse^®’s SQL dialect includes analytical functions—such as powerful ClickHouse^® array functions for working with complex data structures—and syntax that aren’t available in MySQL. Developers familiar with PostgreSQL or MySQL can typically adapt to ClickHouse^® quickly, though concepts like materialized views and table engines require learning.

Schema evolution and migrations

Schema changes in MySQL can be disruptive because operations like adding columns or changing data types may require locking the entire table. Large tables can take hours or days to migrate, during which time the table is unavailable for writes.

ClickHouse^® handles schema changes differently because columnar storage allows adding new columns without rewriting existing data. You can add a column to a table with billions of rows in seconds, though other operations like changing a column’s data type still require rewriting the table.

Local dev and CI workflows

MySQL is straightforward to run locally using Docker or a native installation, and most developers are already familiar with connecting to it from application code. Integration tests can spin up a MySQL container, run migrations, and execute queries without special configuration.

ClickHouse^® also runs in Docker, but developers who are new to it may need to learn about table engines, partitioning, and other concepts that don’t exist in traditional relational databases. Tinybird provides a local development environment that simplifies this learning curve by handling ClickHouse^® configuration and providing a CLI for managing data sources and queries.

API creation with Tinybird pipes

Tinybird makes it easier to expose ClickHouse^® data through parameterized REST APIs by defining SQL queries in pipe files. A pipe file contains SQL with template variables for parameters, and Tinybird automatically generates an API endpoint that accepts those parameters and returns JSON results.

Here’s an example pipe that calculates event counts by type with a configurable date range:

TOKEN events_api_read READ

NODE endpoint
SQL >
  %
  SELECT event_type, count() as event_count
  FROM events
  WHERE date >= {{Date(start_date, '2024-01-01')}}
    AND date <= {{Date(end_date, '2024-12-31')}}
  GROUP BY event_type
  ORDER BY event_count DESC

TYPE endpoint

After deploying this pipe with tb deploy, Tinybird generates an API endpoint that accepts start_date and end_date as query parameters. The API handles query validation, rate limiting, and authorization.

• Sub-second API latency: Query optimizer and caching layer deliver fast responses even under high concurrency
• Streaming ingestion: Data sources ingest millions of events per second from Kafka, webhooks, or direct API calls
• Local development: CLI provides a containerized environment that mirrors production behavior
• Version control: All data sources, pipes, and transformations are defined in text files that can be committed to Git
• Built-in observability: Query performance metrics, error tracking, and resource usage are available without additional setup

Cost and operations comparison

Total cost of ownership for ClickHouse^® versus MySQL depends on data volume, query patterns, and whether you self-host or use a managed service. ClickHouse^® typically requires less hardware due to efficient compression and query performance, but it can be more complex to operate at scale.

MySQL’s operational overhead is well-understood because it’s been widely deployed for decades. Most organizations have experience running MySQL, and there are established patterns for backup, monitoring, and high availability. ClickHouse^® is newer and requires specialized knowledge to tune and scale effectively.

Hardware footprint and cloud spend

ClickHouse^®’s columnar compression means you can store 10x to 100x more data on the same hardware compared to MySQL. A dataset requiring 10 TB of storage in MySQL might fit in 100 GB to 1 TB in ClickHouse^®, which reduces both storage costs and the size of instances needed to query that data.

Query performance also affects cloud costs because faster queries mean you can serve more users with fewer resources. ClickHouse^®’s vectorized execution often completes analytical queries 100x faster than MySQL, which means you can handle the same query volume with smaller instances or fewer replicas.

Maintenance overhead and on-call load

MySQL requires regular maintenance tasks like optimizing tables, managing replication lag, and tuning query performance with indexes. Database administrators typically spend significant time monitoring slow queries and adjusting configuration parameters to maintain performance as data volume grows.

ClickHouse^® requires different maintenance tasks, like monitoring merge performance, managing partition pruning, and tuning compression settings. These tasks require specialized knowledge that may not exist in organizations that have only operated traditional relational databases.

How Tinybird simplifies ClickHouse^® for application analytics

Tinybird is a managed ClickHouse^® platform that handles infrastructure setup, scaling, and optimization so developers can focus on building analytics features rather than operating databases. The platform provides a CLI for local development, a SQL-based pipeline language for defining transformations, and automatic API generation for exposing data to applications.

Developers can define data sources and queries as code in their repository, test them locally against a containerized ClickHouse^® instance, and deploy to production with a single command. This workflow eliminates the need to provision servers, configure clusters, or set up monitoring and backup systems.

Sign up for a free Tinybird plan

Tinybird offers a free tier that includes 10 GB of storage and 100 million query rows per month, which is enough to build and test analytics features for most applications. You can sign up for a free account and start querying data within minutes using the Tinybird CLI.

The free tier includes access to all platform features, including streaming ingestion, materialized views, and API endpoints.

Frequently asked questions about ClickHouse^® vs MySQL

How hard is it to migrate historical data from MySQL to ClickHouse^®?

Migration complexity depends on data volume and schema differences between the two databases. Most migrations involve exporting data from MySQL using mysqldump or SELECT INTO OUTFILE, transforming it to match ClickHouse^®’s schema requirements, and loading it using INSERT statements or the clickhouse-client tool.

For large datasets, you can export data in parallel by partitioning the source table by date or ID ranges. Tools like Airbyte, Fivetran, or custom ETL scripts can automate this process, though you'll handle data type conversions and adjust schemas to take advantage of ClickHouse^®’s columnar storage.

Can ClickHouse^® replace MySQL for OLTP workloads?

ClickHouse^® is not designed for transactional workloads and lacks full ACID transaction support. The database doesn’t support multi-row transactions, foreign key constraints, or efficient updates and deletes on individual rows.

MySQL remains the better choice for OLTP workloads like user authentication, order processing, or content management where data integrity and support for complex updates matter more than analytical query speed.

Does ClickHouse^® support joins like MySQL?

ClickHouse^® supports SQL joins including INNER, LEFT, RIGHT, and FULL OUTER joins, but join performance differs from MySQL due to columnar storage. Joins in ClickHouse^® work best when the right side of the join is small enough to fit in memory, which is why the database provides specialized join algorithms like dictionary joins for dimension tables.

Complex joins across large datasets may require query optimization or denormalization to achieve acceptable performance.

What security features does ClickHouse^® offer out of the box?

ClickHouse^® includes role-based access control (RBAC) that allows you to define users, roles, and permissions at the database, table, and row level. The database also supports SSL/TLS encryption for client connections and can integrate with external authentication systems like LDAP or Kerberos.

Query-level permissions allow you to restrict which users can execute expensive queries or access sensitive data. Security features vary between open-source ClickHouse^® and managed services like ClickHouse^® Cloud or Tinybird, which provide additional features like audit logging and network isolation.

Is there a managed ClickHouse^® service with a free tier?

Several providers offer managed ClickHouse^® including ClickHouse^® Cloud, Altinity, and Tinybird. Tinybird provides a free tier specifically designed for developers building analytics features, with 10 GB of storage and 100 million query rows per month included at no cost.

The free tier includes all platform features like streaming ingestion, materialized views, and API endpoints./