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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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

Advice on Cassandra and Clickhouse
Vinay Mehta
Needs advice
on
CassandraCassandra
and
ScyllaDBScyllaDB

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.

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Replies (4)
Recommends
on
ScyllaDBScyllaDB

Scylla can handle 1M/s events with a simple data model quite easily. The api to query is CQL, we have REST api but that's for control/monitoring

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Alex Peake
Recommends
on
CassandraCassandra

Cassandra is quite capable of the task, in a highly available way, given appropriate scaling of the system. Remember that updates are only inserts, and that efficient retrieval is only by key (which can be a complex key). Talking of keys, make sure that the keys are well distributed.

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Recommends
on
ScyllaDBScyllaDB

By 55M do you mean 55 million entity changes per 2 minutes? It is relatively high, means almost 460k per second. If I had to choose between Scylla or Cassandra, I would opt for Scylla as it is promising better performance for simple operations. However, maybe it would be worth to consider yet another alternative technology. Take into consideration required consistency, reliability and high availability and you may realize that there are more suitable once. Rest API should not be the main driver, because you can always develop the API yourself, if not supported by given technology.

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Pankaj Soni
Chief Technical Officer at Software Joint · | 2 upvotes · 144.9K views
Recommends
on
CassandraCassandra

i love syclla for pet projects however it's license which is based on server model is an issue. thus i recommend cassandra

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Pros of Cassandra
Pros of Clickhouse
  • 119
    Distributed
  • 98
    High performance
  • 81
    High availability
  • 74
    Easy scalability
  • 53
    Replication
  • 26
    Reliable
  • 26
    Multi datacenter deployments
  • 10
    Schema optional
  • 9
    OLTP
  • 8
    Open source
  • 2
    Workload separation (via MDC)
  • 1
    Fast
  • 19
    Fast, very very fast
  • 11
    Good compression ratio
  • 6
    Horizontally scalable
  • 5
    Great CLI
  • 5
    Utilizes all CPU resources
  • 5
    RESTful
  • 4
    Buggy
  • 4
    Open-source
  • 4
    Great number of SQL functions
  • 3
    Server crashes its normal :(
  • 3
    Has no transactions
  • 2
    Flexible connection options
  • 2
    Highly available
  • 2
    ODBC
  • 2
    Flexible compression options
  • 1
    In IDEA data import via HTTP interface not working

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Cons of Cassandra
Cons of Clickhouse
  • 3
    Reliability of replication
  • 1
    Size
  • 1
    Updates
  • 5
    Slow insert operations

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What is Cassandra?

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.

What is Clickhouse?

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.

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What companies use Cassandra?
What companies use Clickhouse?
See which teams inside your own company are using Cassandra or Clickhouse.
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What are some alternatives to Cassandra and Clickhouse?
HBase
Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
Google Cloud Bigtable
Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
Hadoop
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Redis
Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
Couchbase
Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands.
See all alternatives