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ArangoDB vs Cassandra: What are the differences?
Introduction
When considering database management systems for a project, ArangoDB and Cassandra are two popular options. While both are NoSQL databases, they have key differences that should be considered before making a decision.
Data Model: ArangoDB is a multi-model database that supports key-value pairs, documents, and graphs within a single query interface, making it versatile for different data structures. On the other hand, Cassandra follows a column-family data model, which is ideal for handling large amounts of data with high availability and partition tolerance but may be more limited in terms of data structure flexibility.
Query Language: ArangoDB uses its query language, AQL (ArangoDB Query Language), which allows for complex queries across different data models. In contrast, Cassandra uses CQL (Cassandra Query Language), a SQL-like language that is optimized for querying high-volume, low-latency data.
Consistency Model: ArangoDB supports both strong and eventual consistency levels, giving users the flexibility to choose the level of consistency needed for their application. On the other hand, Cassandra offers tunable consistency, allowing users to choose between strong, eventual, and other consistency levels based on their requirements.
Scalability: ArangoDB is horizontally scalable, meaning it can distribute data across multiple nodes to handle growing amounts of data and traffic. Cassandra, on the other hand, is known for its linear scalability, making it a popular choice for large-scale distributed systems that require seamless scaling.
Fault Tolerance: ArangoDB provides automatic sharding and replication for data redundancy and fault tolerance, ensuring data availability even in the case of node failures. In comparison, Cassandra is designed with fault tolerance in mind, using a masterless architecture and peer-to-peer communication to prevent any single point of failure.
Use Cases: ArangoDB is suitable for applications that require flexibility in data modeling and complex queries across different data types, such as social networks and content management systems. In contrast, Cassandra is best suited for use cases that prioritize high availability, partition tolerance, and linear scalability, such as real-time analytics and messaging platforms.
In Summary, ArangoDB and Cassandra differ in aspects such as data model flexibility, query language, consistency levels, scalability options, fault tolerance mechanisms, and ideal use cases.
Hello All, I'm building an app that will enable users to create documents using ckeditor or TinyMCE editor. The data is then stored in a database and retrieved to display to the user, these docs can contain image data also. The number of pages generated for a single document can go up to 1000. Therefore by design, each page is stored in a separate JSON. I'm wondering which database is the right one to choose between ArangoDB and PostgreSQL. Your thoughts, advice please. Thanks, Kashyap
Which Graph DB features are you planning to use?
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 ArangoDB
- Grahps and documents in one DB37
- Intuitive and rich query language26
- Good documentation25
- Open source25
- Joins for collections21
- Foxx is great platform15
- Great out of the box web interface with API playground14
- Good driver support6
- Low maintenance efforts6
- Clustering6
- Easy microservice creation with foxx5
- You can write true backendless apps4
- Managed solution available2
- Performance0
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
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Cons of ArangoDB
- Web ui has still room for improvement3
- No support for blueprints standard, using custom AQL2
Cons of Cassandra
- Reliability of replication3
- Size1
- Updates1
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