Cassandra vs Neo4j

Overview

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Neo4j

Stacks1.2K

Followers1.4K

Votes351

GitHub Stars15.3K

Forks2.5K

Cassandra vs Neo4j: What are the differences?

Introduction

In this article, we will discuss the key differences between Cassandra and Neo4j databases.

Data Model: Cassandra is a wide-column NoSQL database that uses a tabular structure with rows and columns to store data. It is optimized for write-heavy workloads and can handle a high volume of writes and reads. On the other hand, Neo4j is a graph database that represents data as nodes, relationships, and properties. It excels at handling complex relationships and querying networks of connected data.
Query Language: Cassandra uses the Cassandra Query Language (CQL), which is similar to SQL syntax but with some differences to cater to the distributed nature of the database. CQL allows users to perform CRUD operations and supports limited querying capabilities. In contrast, Neo4j uses the Cypher query language, specifically designed for graph databases. Cypher makes it easy to express complex graph patterns and perform advanced graph traversal queries.
Scalability: Cassandra is built to scale horizontally across nodes in a cluster, allowing for a linear increase in performance as more nodes are added. It uses partitioning and replication to distribute data across the cluster and ensure high availability. On the other hand, Neo4j is designed for smaller-scale deployments and is more suited for scenarios where data fits within the capacity of a single machine.
Data Consistency: Cassandra offers eventual consistency, meaning that changes made to the database will eventually be propagated to all nodes in the cluster. This allows for high availability and fault tolerance but can result in temporary inconsistencies. Neo4j, on the other hand, provides strong consistency guarantees. Every transaction in Neo4j is immediately consistent across the entire graph.
Use Cases: Cassandra is commonly used in applications that require high availability, massive scalability, and fast write performance, such as time series data, logging, and real-time analytics. Neo4j is often chosen for use cases that involve complex data relationships, such as social networks, knowledge graphs, recommendation systems, and fraud detection.
Deployment Complexity: Cassandra requires careful planning and configuration to ensure optimal performance. It relies on manual partitioning and replication strategies, and scaling the cluster requires adding and configuring new nodes. In contrast, Neo4j has a simpler deployment model, as it is typically deployed on a single machine or a small cluster. Scaling in Neo4j involves adding more powerful machines rather than horizontally scaling across multiple nodes.

In summary, Cassandra is a wide-column NoSQL database optimized for scalability and write-heavy workloads, while Neo4j is a graph database designed to handle complex relationships and graph traversal queries. Both databases have different data models, query languages, scalability options, consistency models, and use cases. The choice between Cassandra and Neo4j depends on the specific requirements of the application and the nature of the data being stored.

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Advice on Cassandra, Neo4j

Vinay

Head of Engineering

Sep 19, 2019

Needs advice

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.

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Comments

Detailed Comparison

Cassandra	Neo4j
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.	Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.
-	intuitive, using a graph model for data representation;reliable, with full ACID transactions;durable and fast, using a custom disk-based, native storage engine;massively scalable, up to several billion nodes/relationships/properties;highly-available, when distributed across multiple machines;expressive, with a powerful, human readable graph query language;fast, with a powerful traversal framework for high-speed graph queries;embeddable, with a few small jars;simple, accessible by a convenient REST interface or an object-oriented Java API
Statistics
GitHub Stars 9.5K	GitHub Stars 15.3K
GitHub Forks 3.8K	GitHub Forks 2.5K
Stacks 3.6K	Stacks 1.2K
Followers 3.5K	Followers 1.4K
Votes 507	Votes 351
Pros & Cons
Pros 119 Distributed 98 High performance 81 High availability 74 Easy scalability 53 Replication Cons 3 Reliability of replication 1 Size 1 Updates	Pros 69 Cypher – graph query language 61 Great graphdb 33 Open source 31 Rest api 27 High-Performance Native API Cons 9 Comparably slow 4 Can't store a vertex as JSON 1 Doesn't have a managed cloud service at low cost

What are some alternatives to Cassandra, Neo4j?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

Related Comparisons

Cassandra vs Neo4j: What are the differences?

Introduction

In this article, we will discuss the key differences between Cassandra and Neo4j databases.

Data Model: Cassandra is a wide-column NoSQL database that uses a tabular structure with rows and columns to store data. It is optimized for write-heavy workloads and can handle a high volume of writes and reads. On the other hand, Neo4j is a graph database that represents data as nodes, relationships, and properties. It excels at handling complex relationships and querying networks of connected data.
Query Language: Cassandra uses the Cassandra Query Language (CQL), which is similar to SQL syntax but with some differences to cater to the distributed nature of the database. CQL allows users to perform CRUD operations and supports limited querying capabilities. In contrast, Neo4j uses the Cypher query language, specifically designed for graph databases. Cypher makes it easy to express complex graph patterns and perform advanced graph traversal queries.
Scalability: Cassandra is built to scale horizontally across nodes in a cluster, allowing for a linear increase in performance as more nodes are added. It uses partitioning and replication to distribute data across the cluster and ensure high availability. On the other hand, Neo4j is designed for smaller-scale deployments and is more suited for scenarios where data fits within the capacity of a single machine.
Data Consistency: Cassandra offers eventual consistency, meaning that changes made to the database will eventually be propagated to all nodes in the cluster. This allows for high availability and fault tolerance but can result in temporary inconsistencies. Neo4j, on the other hand, provides strong consistency guarantees. Every transaction in Neo4j is immediately consistent across the entire graph.
Use Cases: Cassandra is commonly used in applications that require high availability, massive scalability, and fast write performance, such as time series data, logging, and real-time analytics. Neo4j is often chosen for use cases that involve complex data relationships, such as social networks, knowledge graphs, recommendation systems, and fraud detection.
Deployment Complexity: Cassandra requires careful planning and configuration to ensure optimal performance. It relies on manual partitioning and replication strategies, and scaling the cluster requires adding and configuring new nodes. In contrast, Neo4j has a simpler deployment model, as it is typically deployed on a single machine or a small cluster. Scaling in Neo4j involves adding more powerful machines rather than horizontally scaling across multiple nodes.

Cassandra vs Neo4j

Overview

Cassandra vs Neo4j: What are the differences?