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Cassandra vs TiDB: What are the differences?
Introduction
Cassandra and TiDB are both highly scalable distributed databases designed to handle large amounts of data. However, there are several key differences between these two databases.
Consistency Model: Cassandra uses the eventual consistency model, where updates may take some time to propagate across all replicas, allowing for high availability and low latency. On the other hand, TiDB supports both the strong consistency model (ACID transactions) and the eventual consistency model, providing flexibility based on the use case.
Data Model: Cassandra follows a column-based data model, where data is stored in tables with partitions and rows. It can handle structured, semi-structured, and unstructured data. In contrast, TiDB follows a relational data model with tables, columns, and rows, similar to traditional SQL databases like MySQL.
Data Distribution: Cassandra employs a partition-centric model, distributing data across multiple nodes using a consistent hashing algorithm. Each node is responsible for a range of data partitions. In TiDB, data is distributed through a region-based model, where data is divided into regions that can be dynamically scheduled across multiple nodes. This approach allows automatic load balancing and better performance optimization.
Consistency and Availability Trade-off: Cassandra prioritizes availability over consistency, making it well-suited for use cases where high availability and low latency are crucial. TiDB, however, provides a balance between consistency and availability, making it suitable for applications that require strong consistency guarantees.
Scalability: Both Cassandra and TiDB are horizontally scalable databases that support distributed deployments. However, TiDB offers a more straightforward approach to scaling by enabling horizontal scaling of both compute and storage, while Cassandra requires manual tuning and cluster expansion to scale effectively.
Query Processing: Cassandra provides a query language called CQL (Cassandra Query Language), which is similar to SQL but has some differences in syntax and functionality. TiDB supports standard SQL queries and is fully compatible with the MySQL protocol, making it easy to migrate existing MySQL applications to TiDB without any code changes.
In summary, Cassandra and TiDB diverge in their consistency models, data models, data distribution strategies, consistency and availability trade-offs, scalability approaches, and query processing languages. While Cassandra prioritizes availability and eventual consistency, TiDB offers both strong consistency and eventual consistency, making it more versatile for different use cases.
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 TiDB
- Open source9
- Horizontal scalability7
- Strong ACID5
- HTAP3
- Mysql Compatibility2
- Enterprise Support2
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Cons of Cassandra
- Reliability of replication3
- Size1
- Updates1
Cons of TiDB
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