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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.
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.
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.
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
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.
i love syclla for pet projects however it's license which is based on server model is an issue. thus i recommend cassandra
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.
Pros of Cassandra
- Distributed119
- High performance98
- High availability81
- Easy scalability74
- Replication53
- Reliable26
- Multi datacenter deployments26
- Schema optional10
- OLTP9
- Open source8
- Workload separation (via MDC)2
- Fast1
Pros of Clickhouse
- Fast, very very fast21
- Good compression ratio11
- Horizontally scalable7
- Utilizes all CPU resources6
- RESTful5
- Open-source5
- Great CLI5
- Great number of SQL functions4
- Buggy4
- Server crashes its normal :(3
- Highly available3
- Flexible connection options3
- Has no transactions3
- ODBC2
- Flexible compression options2
- In IDEA data import via HTTP interface not working1
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Cons of Cassandra
- Reliability of replication3
- Size1
- Updates1
Cons of Clickhouse
- Slow insert operations5
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