Cassandra vs Clickhouse

Overview

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Clickhouse

Stacks431

Followers543

Votes85

Cassandra vs Clickhouse: What are the differences?

Cassandra and ClickHouse are two popular database management systems designed for handling large volumes of data. Let's explore the key differences between them.

Data Model: Cassandra is a NoSQL database that follows a distributed hash table model. It provides a flexible schema design, allowing for dynamic addition and modification of columns. On the other hand, ClickHouse is an analytical database that follows a column-oriented model. It is optimized for OLAP workloads and provides high performance on analytical queries.
Scalability: Cassandra is designed to scale horizontally by distributing data across multiple nodes in a cluster. It offers automatic data partitioning and replication for fault tolerance. ClickHouse also supports horizontal scaling, but it achieves high performance by utilizing efficient compression algorithms, vectorized query execution, and extensive use of disk storage.
Data Consistency: Cassandra provides tunable consistency levels, allowing users to trade off between data consistency and performance. It offers eventual consistency by default but can also provide strong consistency when required. ClickHouse, on the other hand, sacrifices strong consistency in favor of high performance and low latency. It focuses on providing eventual consistency for analytical workloads.
Query Language: Cassandra uses CQL (Cassandra Query Language), which is similar to SQL but with some differences. It supports a wide range of query operations, including filtering, aggregations, and conditional updates. ClickHouse, on the other hand, uses its own query language called ClickHouse SQL. It is specifically designed for analytical queries and supports advanced features like window functions, materialized views, and sampling.
Data Storage: Cassandra stores data on disk using an LSM (Log-Structured Merge) tree. It is optimized for high write throughput and can handle write-heavy workloads efficiently. ClickHouse, on the other hand, stores data in columnar format, which allows for better compression and faster analytical queries. It is optimized for read-heavy workloads and can efficiently handle large amounts of data.
Use Cases: Due to its flexible data model and ability to handle high write throughput, Cassandra is often used for applications that require high availability and fault tolerance, such as real-time streaming, IoT, and e-commerce. ClickHouse, on the other hand, is well-suited for analytical workloads that involve large volumes of data and require fast query performance, such as data warehousing, business intelligence, and ad hoc analytics.

In summary, Cassandra is a distributed NoSQL database with a flexible schema design and tunable consistency levels, suitable for high availability scenarios. ClickHouse is a column-oriented analytical database optimized for OLAP workloads, offering high performance and efficient storage for large volumes of data.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Cassandra, Clickhouse

Vinay

Head of Engineering

Sep 19, 2019

Needs advice

The problem I have is - we need to process & change(update/insert) 55M Data every 2 min and this updated data to be available for Rest API for Filtering / Selection. Response time for Rest API should be less than 1 sec.

The most important factors for me are processing and storing time of 2 min. There need to be 2 views of Data One is for Selection & 2. Changed data.

174k views174k

Comments

Detailed Comparison

Cassandra	Clickhouse
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.	It allows analysis of data that is updated in real time. It offers instant results in most cases: the data is processed faster than it takes to create a query.
Statistics
GitHub Stars 9.5K	GitHub Stars -
GitHub Forks 3.8K	GitHub Forks -
Stacks 3.6K	Stacks 431
Followers 3.5K	Followers 543
Votes 507	Votes 85
Pros & Cons
Pros 119 Distributed 98 High performance 81 High availability 74 Easy scalability 53 Replication Cons 3 Reliability of replication 1 Size 1 Updates	Pros 21 Fast, very very fast 11 Good compression ratio 7 Horizontally scalable 6 Utilizes all CPU resources 5 RESTful Cons 5 Slow insert operations

What are some alternatives to Cassandra, Clickhouse?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

Related Comparisons

Cassandra vs Clickhouse: What are the differences?

Cassandra and ClickHouse are two popular database management systems designed for handling large volumes of data. Let's explore the key differences between them.

Data Model: Cassandra is a NoSQL database that follows a distributed hash table model. It provides a flexible schema design, allowing for dynamic addition and modification of columns. On the other hand, ClickHouse is an analytical database that follows a column-oriented model. It is optimized for OLAP workloads and provides high performance on analytical queries.
Scalability: Cassandra is designed to scale horizontally by distributing data across multiple nodes in a cluster. It offers automatic data partitioning and replication for fault tolerance. ClickHouse also supports horizontal scaling, but it achieves high performance by utilizing efficient compression algorithms, vectorized query execution, and extensive use of disk storage.
Data Consistency: Cassandra provides tunable consistency levels, allowing users to trade off between data consistency and performance. It offers eventual consistency by default but can also provide strong consistency when required. ClickHouse, on the other hand, sacrifices strong consistency in favor of high performance and low latency. It focuses on providing eventual consistency for analytical workloads.
Query Language: Cassandra uses CQL (Cassandra Query Language), which is similar to SQL but with some differences. It supports a wide range of query operations, including filtering, aggregations, and conditional updates. ClickHouse, on the other hand, uses its own query language called ClickHouse SQL. It is specifically designed for analytical queries and supports advanced features like window functions, materialized views, and sampling.
Data Storage: Cassandra stores data on disk using an LSM (Log-Structured Merge) tree. It is optimized for high write throughput and can handle write-heavy workloads efficiently. ClickHouse, on the other hand, stores data in columnar format, which allows for better compression and faster analytical queries. It is optimized for read-heavy workloads and can efficiently handle large amounts of data.
Use Cases: Due to its flexible data model and ability to handle high write throughput, Cassandra is often used for applications that require high availability and fault tolerance, such as real-time streaming, IoT, and e-commerce. ClickHouse, on the other hand, is well-suited for analytical workloads that involve large volumes of data and require fast query performance, such as data warehousing, business intelligence, and ad hoc analytics.

Cassandra vs Clickhouse

Overview