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Cassandra vs Citus: What are the differences?
Introduction
In this article, we will discuss the key differences between Cassandra and Citus.
Scalability: One of the key differences between Cassandra and Citus is their approach to scalability. Cassandra is a distributed database that is designed to scale horizontally across multiple nodes. It achieves this through its partitioning strategy and peer-to-peer architecture, allowing it to handle large amounts of data and high write throughput. On the other hand, Citus is an extension to PostgreSQL that scales vertically by distributing the data across multiple machines using sharding. It leverages PostgreSQL's shared-nothing architecture to provide high scalability.
Data Model: Another significant difference between Cassandra and Citus is their data model. Cassandra is a NoSQL database that follows a column-oriented data model. It stores data in tables that are organized into column families, with each row consisting of multiple columns. This flexible schema allows for dynamic and fast data access. Citus, on the other hand, is an extension to PostgreSQL, which follows a relational data model. It uses tables with rows and columns to store data, and supports SQL queries for data retrieval and manipulation.
Consistency Model: Cassandra and Citus differ in their consistency models. Cassandra offers tunable consistency, allowing users to choose between strong consistency or eventual consistency. It uses the quorum-based replication strategy to provide high availability and fault tolerance. Citus, on the other hand, follows the strong consistency model by default. It ensures that every database transaction is atomic, consistent, isolated, and durable (ACID), which is important for applications that require strict consistency guarantees.
Replication: Replication is handled differently in Cassandra and Citus. Cassandra uses a masterless architecture with peer-to-peer replication, where each node in the cluster can accept write requests and handle read requests. It replicates data across multiple nodes using a replication factor defined for each keyspace. Citus, on the other hand, uses a distributed database model with a master node that coordinates writes and distributes data across multiple worker nodes. It provides automatic sharding and replicates data based on the primary key.
Query Language: Both Cassandra and Citus have different query languages. Cassandra uses CQL (Cassandra Query Language), which is similar to SQL but has some differences due to its NoSQL nature. CQL supports a wide range of queries and provides features like secondary indexes and materialized views. On the other hand, Citus uses SQL as its query language, as it is an extension to PostgreSQL. It supports the full range of SQL queries and provides advanced features like window functions, JSON functions, and common table expressions.
Use Cases: Cassandra and Citus are suited for different types of use cases. Cassandra is well-suited for applications that require high scalability, high availability, and fault tolerance, such as big data analytics, time series data, and IoT applications. It is designed to handle large volumes of writes and reads across multiple nodes. Citus, on the other hand, is suitable for applications that require horizontal scalability with strong consistency, such as transactional workloads, multi-tenant applications, and real-time analytics. It provides a familiar SQL interface and can leverage PostgreSQL's rich ecosystem of extensions and tools.
In summary, Cassandra and Citus differ in their scalability approaches, data models, consistency models, replication strategies, query languages, and use cases. While Cassandra excels in distributed scalability and offers eventual consistency, Citus provides vertical scalability with strong consistency using a relational data model and SQL capabilities.
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.
Fauna is a serverless database where you store data as JSON. Also, you have build in a HTTP GraphQL interface with a full authentication & authorization layer. That means you can skip your Backend and call it directly from the Frontend. With the power, that you can write data transformation function within Fauna with her own language called FQL, we're getting a blazing fast application.
Also, Fauna takes care about scaling and backups (All data are sharded on three different locations on the globe). That means we can fully focus on writing business logic and don't have to worry anymore about infrastructure.
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 Citus
- Multi-core Parallel Processing6
- Drop-in PostgreSQL replacement3
- Distributed with Auto-Sharding2
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Cons of Cassandra
- Reliability of replication3
- Size1
- Updates1
Cons of Citus
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