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ClickHouse VS Apache Cassandra

Compare ClickHouse VS Apache Cassandra and see what are their differences

ClickHouse logo ClickHouse

ClickHouse is an open-source column-oriented database management system that allows generating analytical data reports in real time.

Apache Cassandra logo Apache Cassandra

The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance.
  • ClickHouse Landing page
    Landing page //
    2019-06-18
  • Apache Cassandra Landing page
    Landing page //
    2022-04-17

ClickHouse features and specs

  • High Performance
    ClickHouse is designed for fast processing of analytical queries, often performing significantly faster than traditional databases due to its columnar storage format and optimized query execution.
  • Scalability
    The system is built to handle extensive datasets by scaling horizontally through distributed cluster configurations, making it suitable for big data applications.
  • Real-time Data Ingestion
    ClickHouse supports real-time data ingestion and can immediately reflect changes in query results, which is valuable for use cases requiring instant data processing and analysis.
  • Cost Efficiency
    The open-source nature of ClickHouse makes it a cost-effective option, especially when compared to other commercial data warehouses.
  • SQL Compatibility
    ClickHouse features strong SQL support, which makes it easier for individuals with SQL expertise to transition and use the platform effectively.
  • Compression
    ClickHouse employs advanced compression algorithms that reduce storage requirements and improve query performance.

Possible disadvantages of ClickHouse

  • Complexity in Setup
    Setting up and managing ClickHouse, particularly in a distributed cluster environment, can be complex and require a higher level of expertise compared to some other database systems.
  • Limited Transaction Support
    ClickHouse is optimized for read-heavy operations and analytics but does not support full ACID transactions, which limits its use for certain transactional use cases.
  • Ecosystem and Tooling
    While the ecosystem is growing, ClickHouse still has fewer tools and third-party integrations compared to more mature databases, which can limit its utility in some environments.
  • Resource Intensive
    Running ClickHouse, especially for large datasets, can be resource-intensive, requiring significant memory and CPU resources.
  • Limited User Management
    The platform has relatively basic user management and security features, which may not meet the needs of enterprises with strict compliance and governance requirements.

Apache Cassandra features and specs

  • Scalability
    Apache Cassandra is designed for linear scalability and can handle large volumes of data across many commodity servers without a single point of failure.
  • High Availability
    Cassandra ensures high availability by replicating data across multiple nodes. Even if some nodes fail, the system remains operational.
  • Performance
    It provides fast writes and reads by using a peer-to-peer architecture, making it highly suitable for applications requiring quick data access.
  • Flexible Data Model
    Cassandra supports a flexible schema, allowing users to add new columns to a table at any time, making it adaptable for various use cases.
  • Geographical Distribution
    Data can be distributed across multiple data centers, ensuring low-latency access for geographically distributed users.
  • No Single Point of Failure
    Its decentralized nature ensures there is no single point of failure, which enhances resilience and fault-tolerance.

Possible disadvantages of Apache Cassandra

  • Complexity
    Managing and configuring Cassandra can be complex, requiring specialized knowledge and skills for optimal performance.
  • Eventual Consistency
    Cassandra follows an eventual consistency model, meaning that there might be a delay before all nodes have the latest data, which may not be suitable for all use cases.
  • Write-heavy Operations
    Although Cassandra handles writes efficiently, write-heavy workloads can lead to compaction issues and increased read latency.
  • Limited Query Capabilities
    Cassandra's query capabilities are relatively limited compared to traditional RDBMS, lacking support for complex joins and aggregations.
  • Maintenance Overhead
    Regular maintenance tasks such as node repair and compaction are necessary to ensure optimal performance, adding to the administrative overhead.
  • Tooling and Ecosystem
    While the ecosystem for Cassandra is growing, it is still not as extensive or mature as those for some other database technologies.

Analysis of ClickHouse

Overall verdict

  • ClickHouse is a powerful and capable columnar DBMS that offers excellent performance for analytical workloads. Its open-source nature allows for flexibility and community-driven improvements, making it a strong option for organizations needing a scalable analytics platform.

Why this product is good

  • ClickHouse is considered a good choice for many use cases due to its high performance in processing large volumes of data and its efficiency in executing complex analytical queries. It is designed to work well with large datasets and provides real-time query capabilities, making it ideal for applications like business intelligence, web analytics, and IoT.

Recommended for

  • Large-scale data analysis
  • Real-time analytics dashboards
  • Businesses needing high-speed query performance
  • Web analytics platforms
  • IoT data processing
  • Financial industry for quick data aggregation

Analysis of Apache Cassandra

Overall verdict

  • Apache Cassandra is an excellent choice if you require a database system that can efficiently manage large-scale data while ensuring high availability and reliability. It is particularly well-suited for use cases that demand a robust, distributed, and scalable database solution.

Why this product is good

  • Apache Cassandra is a highly scalable and distributed NoSQL database management system designed to handle large amounts of data across multiple commodity servers without a single point of failure. It offers robust support for replicating data across multiple data centers, thereby enhancing fault tolerance and availability. Its masterless architecture and linear scalability make it suitable for high throughput online transactional applications.

Recommended for

  • Applications that require high availability and fault tolerance
  • Systems with large volumes of write-heavy workloads
  • Organizations that need multi-data center replication
  • Businesses seeking a scalable solution for distributed databases
  • Use cases needing real-time data processing with low latency

ClickHouse videos

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Apache Cassandra videos

Course Intro | DS101: Introduction to Apache Cassandraโ„ข

More videos:

  • Review - Introduction to Apache Cassandraโ„ข

Category Popularity

0-100% (relative to ClickHouse and Apache Cassandra)
Databases
40 40%
60% 60
Relational Databases
54 54%
46% 46
NoSQL Databases
0 0%
100% 100
Data Warehousing
100 100%
0% 0

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare ClickHouse and Apache Cassandra

ClickHouse Reviews

Rockset, ClickHouse, Apache Druid, or Apache Pinot? Which is the best database for customer-facing analytics?
โ€ClickHouse is an open-source, column-oriented, distributed, and OLAP database thatโ€™s very easy to set up and maintain. โ€œBecause itโ€™s columnar, itโ€™s the best architectural approach for aggregations and for โ€˜sort byโ€™ on more than one column. It also means that group byโ€™s are very fast. Itโ€™s distributed, replication is asynchronous, and itโ€™s OLAPโ€”which means itโ€™s meant for...
Source: embeddable.com
ClickHouse vs TimescaleDB
Recently, TimescaleDB published a blog comparing ClickHouse & TimescaleDB using timescale/tsbs, a timeseries benchmarking framework. I have some experience with PostgreSQL and ClickHouse but never got the chance to play with TimescaleDB. Some of the claims about TimescaleDB made in their post are very bold, that made me even more curious. I thought itโ€™d be a great...
20+ MongoDB Alternatives You Should Know About
ClickHouse may be a great contender for moving analytical workloads from MongoDB. Much faster, and with JSON support and Nested Data Structures, it can be great choice for storing and analyzing document data.
Source: www.percona.com

Apache Cassandra Reviews

16 Top Big Data Analytics Tools You Should Know About
Application Areas: If you want to work with SQL-like data types on a No-SQL database, Cassandra is a good choice. It is a popular pick in the IoT, fraud detection applications, recommendation engines, product catalogs and playlists, and messaging applications, providing fast real-time insights.
9 Best MongoDB alternatives in 2019
The Apache Cassandra is an ideal choice for you if you want scalability and high availability without affecting its performance. This MongoDB alternative tool offers support for replicating across multiple datacenters.
Source: www.guru99.com

Social recommendations and mentions

ClickHouse might be a bit more popular than Apache Cassandra. We know about 60 links to it since March 2021 and only 44 links to Apache Cassandra. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

ClickHouse mentions (60)

  • Setting up ClickHouse on macOS and Testing with Node.js
    ClickHouse is an open-source columnar database built for high-performance analytical queries. This guide shows how I installed ClickHouse on macOS, ran it in the background using a lightweight nohup setup that stores logs and PID in hidden user folders, and tested it with a minimal Node.js + TypeScript example using @clickhouse/client. - Source: dev.to / 15 days ago
  • From Go to Rust: Supercharging Our ClickHouse UDFs with Alloy
    At Agnostic, we build open-source infrastructure for collaborative blockchain data platforms. One of our flagship tools is clickhouse-evm, a suite of high-performance User Defined Functions (UDFs) that brings native Ethereum decoding and querying capabilities directly into ClickHouse. - Source: dev.to / 3 months ago
  • ๐Ÿง  From Hive and Elastic to ClickHouse: What Surprised Me
    Over the past few weeks, Iโ€™ve been diving into ClickHouse โ€” and itโ€™s been full of surprises. - Source: dev.to / 3 months ago
  • Cross-Compiling Haskell under NixOS with Docker
    I attended the AWS Summit 2025 in Singapore. I enjoyed the event. There were booths from various companies which I found interesting, such as GitLab and ClickHouse. More importantly, I got to meet very interesting people. - Source: dev.to / 4 months ago
  • How to Build a Streaming Deduplication Pipeline with Kafka, GlassFlow, and ClickHouse
    ClickHouse: A fast columnar database. It will be our final destination for clean data. And, for simplicity in this tutorial, we'll cleverly use it as our "memory" or state store to remember which events we've already seen recently. - Source: dev.to / 5 months ago
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Apache Cassandra mentions (44)

  • Why You Shouldnโ€™t Invest In Vector Databases?
    In fact, even in the absence of these commercial databases, users can effortlessly install PostgreSQL and leverage its built-in pgvector functionality for vector search. PostgreSQL stands as the benchmark in the realm of open-source databases, offering comprehensive support across various domains of database management. It excels in transaction processing (e.g., CockroachDB), online analytics (e.g., DuckDB),... - Source: dev.to / 5 months ago
  • Data integrity in Ably Pub/Sub
    All messages are persisted durably for two minutes, but Pub/Sub channels can be configured to persist messages for longer periods of time using the persisted messages feature. Persisted messages are additionally written to Cassandra. Multiple copies of the message are stored in a quorum of globally-distributed Cassandra nodes. - Source: dev.to / 11 months ago
  • Which Database is Perfect for You? A Comprehensive Guide to MySQL, PostgreSQL, NoSQL, and More
    Cassandra is a highly scalable, distributed NoSQL database designed to handle large amounts of data across many commodity servers without a single point of failure. - Source: dev.to / over 1 year ago
  • Consistent Hashing: An Overview and Implementation in Golang
    Distributed storage Distributed storage systems like Cassandra, DynamoDB, and Voldemort also use consistent hashing. In these systems, data is partitioned across many servers. Consistent hashing is used to map data to the servers that store the data. When new servers are added or removed, consistent hashing minimizes the amount of data that needs to be remapped to different servers. - Source: dev.to / over 1 year ago
  • Understanding SQL vs. NoSQL Databases: A Beginner's Guide
    On the other hand, NoSQL databases are non-relational databases. They store data in flexible, JSON-like documents, key-value pairs, or wide-column stores. Examples include MongoDB, Couchbase, and Cassandra. - Source: dev.to / over 1 year ago
View more

What are some alternatives?

When comparing ClickHouse and Apache Cassandra, you can also consider the following products

MySQL - The world's most popular open source database

Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.

Apache Doris - Apache Doris is an open-source real-time data warehouse for big data analytics.

MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.