Apache Cassandra VS ArangoDB

Alternatives to: DuckDB, Apache Cassandra, Amazon RedShift, Google BigQuery, Snowflake, InfluxDB, Prometheus, Amazon Timestream

Cassandra: Cassandra is a highly scalable and fault-tolerant NoSQL database. It is designed to handle large amounts of data across multiple commodity servers, making it suitable for high-availability and data-intensive applications.

Apache Cassandra’s design is centered around a powerful combination of decentralisation and horizontal scalability. Distributed evenly across numerous nodes, its architecture minimizes risks associated with hardware failures while ensuring high availability. Developers benefit from its flexibility in handling read and write operations, making it a robust solution for real-time data processing. For a deeper technical dive and ongoing contributions, developers and stakeholders can visit the Apache Cassandra GitHub repository. The platform’s continuous evolution is fueled by a vibrant community that is as passionate about innovation as it is about maintaining technological excellence. The success of Apache Cassandra also prompts comparisons with emerging funding models, such as those seen in blockchain and NFT projects like the World of Women NFT Collection. Although these token-based funding models are relatively novel, the fundamental principle of community-driven sustainability is a shared hallmark with Apache Cassandra’s approach.

We have something very similar[1] we use in the Apache Cassandra project to test complex cluster behaviours.

We use exactly the same basic technique, using byte weaving to intercept concurrency primitives such as synchronized, LockSupport etc to pause the system thread and run them on some schedule.

We only currently run (deterministic) probabilistic traces though, we can’t search a thread interleaving space.

I have been meaning to publish it for broader consumption for years now, but there’s always something more important to do. It’s great to see some dedicated efforts in this space.

[1] https://github.com/apache/cassandra/tree/trunk/test/simulato...

Cassandra uses Murmur3, a hashing function that generates 64-bit tokens in the range of [−263,263−1][-2^{63},2^{63} - 1] [−263,263−1] . Each vnode in the cluster assigned a token it is responsible for. The token marks the end of a range, and the previous token defines the start. To visualize the token ring, we first need to calculate the token ranges, by:

Apache Cassandra

Apache Cassandra{:target="_blank"} is a highly scalable and fault-tolerant database that can handle large volumes of data across multiple nodes or clusters. It provides fast read and write operations, making it suitable for real-time analytics or applications with high throughput requirements.

Wide Column: Apache Cassandra, ScyllaDB and DynamoDB

Cassandra, a replicated, fault-tolerant, decentralized and scalable database system.

ArangoDB — Native multi-model database supporting graph, document, and key-value data models.

ArangoDB

... or working in an environment or on a problem for which functional patterns apply.

Suppose you are writing a "CRUD" app that writes to a relational database, how do you apply functional programming to that? The whole point of an application like that is that it makes side effects.

In some cases you can break those problems down into functional pieces. Consider Python drivers for a product like

https://www.arangodb.com/

One major problem is that you want drivers that work synchronously and asynchronously, the structure of the average api call is something like

   def query(parameters):

First, you need to choose a specific graph database platform to work with, such as Neo4j, OrientDB, JanusGraph, Arangodb or Amazon Neptune. Once you have selected a platform, you can then start working with graph data using the platform's query language.

Some databases like ArangoDB (https://www.arangodb.com/) allow you to use Javascript instead of SQL.

However, using a type-unsafe, turing-complete language introduces type unsafety and turing-complete problems to the query layer; the usual problems we know and love, such as infinite loops, runtime type errors, exceptions, and the like.

Personally, I'm looking forward to a WASM runtime for databases -- so we can run webassembly on the database. This COULD be carefully designed to be statically checked and, possibly, make it really hard to write runaway loops.

When it comes to graphdb's, my favorite is still ArangoDB, definitely worth checking out if you are looking for alternatives.

https://www.arangodb.com

Personally my favorite db for pet projects is

https://www.arangodb.com/

I think you hear very little about it because ADB users see it as a "secret weapon" to crush their competitors with. I've done large ontology work (MESH and other health ontologies) and IoT work (keep several years of sensor readings for sensors in my house) and workflow systems (select interesting HN articles or jobs I want to apply to) and it has never let me down. I haven't run a real instance serving customers in the cloud though.

For the last few years every eng manager I have worked with has been a fan of

https://www.postgresql.org/

In the early 2000s I thought it overpromised and underdelivered and called it CrashGreSlow but after MySQL got bought by Oracle the pgsql team has worked hard to improve it I think it is great today. It supports all kinds of advanced features such as stored procs, full-text search, JSON equivalent fields, etc.

Hi! I recently came across ArangoDb and used in some POCs, but I really want to know if someone here already used it in a Real World environment or even if chose to not use in a production environment. So... have you ever used ArangoDb? Why? Why not?

For mutual friends, we can build a social graph for every user. Each node in the graph will represent a user and a directional edge will represent followers and followees. After that, we can traverse the followers of a user to find and suggest a mutual friend. This would require a graph database such as Neo4j and ArangoDB.

ArangoDB