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Cassandra vs CockroachDB: What are the differences?
Introduction
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.
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.
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.
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.
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.
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.
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.
Summary
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.
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.
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.
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 CockroachDB
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Cons of Cassandra
- Reliability of replication3
- Size1
- Updates1
Cons of CockroachDB
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