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Cassandra vs CockroachDB: What are the differences?


Cassandra and CockroachDB are both popular distributed databases that are designed to handle large amounts of data and provide high availability. While they share some similarities, there are several key differences between the two.

  1. Data Model: Cassandra is a NoSQL database that uses a key-value approach, where data is organized into tables with rows and columns. It supports a wide range of data types and allows for flexible schema changes. On the other hand, CockroachDB follows a relational data model, where data is stored in tables with strict schemas and relationships between tables are managed through foreign keys. This allows for more structured and consistent data management.

  2. Consistency Model: Cassandra provides eventual consistency by default, where updates to data can take some time to propagate throughout the system. It supports tunable consistency levels, allowing users to choose between strong consistency and high availability. CockroachDB, on the other hand, provides strong consistency guarantees through its distributed consensus algorithm. It ensures that all replicas of data are consistent at all times, even in the presence of failures.

  3. Transaction Support: Cassandra does not natively support multi-table transactions, and ACID transactions are only supported within a single partition. CockroachDB, on the other hand, provides full support for distributed ACID transactions across multiple tables and partitions. It uses a distributed transactional layer based on the Google Spanner architecture, ensuring data integrity and consistency.

  4. Scaling and Sharding: Cassandra uses a decentralized architecture that allows for linear scalability by adding more nodes to the cluster. It uses consistent hashing to distribute data across nodes based on the partition key. CockroachDB also supports horizontal scalability through automatic sharding of data across nodes. It uses a range partitioning scheme to distribute data, ensuring that data is evenly distributed and can be accessed efficiently.

  5. Fault Tolerance: Cassandra is designed to be highly fault-tolerant, with its decentralized architecture and ability to replicate data across multiple nodes. It uses a gossip protocol for failure detection and automatic replication. CockroachDB also provides high fault tolerance through automatic data replication and distributed consensus. It uses a distributed version of the Raft consensus algorithm to ensure data durability and availability.

  6. Ease of Operations: Cassandra requires manual configuration and management of its cluster, including setting up replication factor, partitioning, and handling node failures. CockroachDB, on the other hand, provides automated operations and self-healing capabilities. It automatically handles tasks such as data rebalancing, node failures, and replication, making it easier to manage and operate.


In summary, Cassandra and CockroachDB differ in their data models, consistency models, transaction support, scaling and sharding mechanisms, fault tolerance approaches, and ease of operations. These differences make each database suitable for different use cases and provide distinct advantages in terms of data management, scalability, and fault tolerance.

Advice on Cassandra and CockroachDB
Umair Iftikhar
Technical Architect at ERP Studio · | 3 upvotes · 441.5K views
Needs advice

Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.

My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.

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Replies (1)

Hi Umair, Did you try MongoDB. We are using MongoDB on a production environment and collecting data from devices like your scenario. We have a MongoDB cluster with three replicas. Data from devices are being written to the master node and real-time dashboard UI is using the secondary nodes for read operations. With this setup write operations are not affected by read operations too.

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Vinay Mehta
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.

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Replies (4)

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

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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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 · 153.8K views

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 CockroachDB
  • 119
  • 98
    High performance
  • 81
    High availability
  • 74
    Easy scalability
  • 53
  • 26
  • 26
    Multi datacenter deployments
  • 10
    Schema optional
  • 9
  • 8
    Open source
  • 2
    Workload separation (via MDC)
  • 1
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    Cons of Cassandra
    Cons of CockroachDB
    • 3
      Reliability of replication
    • 1
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      - No public GitHub repository available -

      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 CockroachDB?

      CockroachDB is distributed SQL database that can be deployed in serverless, dedicated, or on-prem. Elastic scale, multi-active availability for resilience, and low latency performance.

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      What companies use Cassandra?
      What companies use CockroachDB?
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      What are some alternatives to Cassandra and CockroachDB?
      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.
      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 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.
      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