Cassandra vs OrientDB

Overview

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

OrientDB

Stacks77

Followers107

Votes14

Cassandra vs OrientDB: What are the differences?

## Introduction
In this markdown, we will discuss the key differences between Cassandra and OrientDB to provide insights into their unique features and functionalities.

1. **Data Model**: Cassandra follows a wide-column model, known for its schema-less design and horizontal scaling capabilities, making it suitable for high write and read workloads, especially in distributed environments. On the other hand, OrientDB is a multi-model database, supporting document, graph, object, and key/value models, allowing users to choose the most appropriate data model for their use case.
   
2. **Query Language**: Cassandra uses CQL (Cassandra Query Language), a SQL-like language for querying and data manipulation, which simplifies database interactions for users familiar with SQL. In contrast, OrientDB utilizes SQL-like language but with extensions to support graph operations, making it efficient for traversing and querying graph data.

3. **Consistency Mechanism**: Cassandra offers tunable consistency levels, providing users with the flexibility to choose between strong or eventual consistency based on their application requirements. On the contrary, OrientDB employs automatic sharding and distributed ACID transactions to ensure data consistency across distributed nodes.

4. **Scalability**: Cassandra is known for its linear horizontal scalability, allowing it to handle massive amounts of data and requests by adding more nodes to the cluster seamlessly. OrientDB also supports horizontal scalability but requires more manual intervention in data distribution across nodes compared to Cassandra.

5. **Primary Use Cases**: Cassandra is commonly used for high-velocity time-series data, messaging apps, recommendation engines, and logging due to its ability to handle large volumes of data with high write throughput. In comparison, OrientDB is preferred for use cases requiring complex relationships and graph traversal like social networks, fraud detection, and network analysis.

6. **Indexing Mechanism**: Cassandra utilizes a built-in secondary indexing mechanism but lacks full-text search capabilities, making it less suitable for search-intensive applications. In contrast, OrientDB provides versatile indexing options, including full-text search, spatial indexes, and automatic graph indexes, making it suitable for applications requiring extensive search capabilities.

In Summary, the key differences between Cassandra and OrientDB lie in their data models, query languages, consistency mechanisms, scalability approaches, primary use cases, and indexing mechanisms, each catering to distinct application needs and user preferences.

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Advice on Cassandra, OrientDB

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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Detailed Comparison

Cassandra	OrientDB
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.	It is an open source NoSQL database management system written in Java. It is a Multi-model database, supporting graph, document, key/value, and object models, but the relationships are managed as in graph databases with direct connections between records.
Statistics
GitHub Stars 9.5K	GitHub Stars -
GitHub Forks 3.8K	GitHub Forks -
Stacks 3.6K	Stacks 77
Followers 3.5K	Followers 107
Votes 507	Votes 14
Pros & Cons
Pros 119 Distributed 98 High performance 81 High availability 74 Easy scalability 53 Replication Cons 3 Reliability of replication 1 Updates 1 Size	Pros 4 Great graphdb 2 Great support 2 Open source 1 Rest api 1 Highly-available Cons 4 Unstable

What are some alternatives to Cassandra, OrientDB?

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 OrientDB: What are the differences?

## Introduction
In this markdown, we will discuss the key differences between Cassandra and OrientDB to provide insights into their unique features and functionalities.

1. **Data Model**: Cassandra follows a wide-column model, known for its schema-less design and horizontal scaling capabilities, making it suitable for high write and read workloads, especially in distributed environments. On the other hand, OrientDB is a multi-model database, supporting document, graph, object, and key/value models, allowing users to choose the most appropriate data model for their use case.
   
2. **Query Language**: Cassandra uses CQL (Cassandra Query Language), a SQL-like language for querying and data manipulation, which simplifies database interactions for users familiar with SQL. In contrast, OrientDB utilizes SQL-like language but with extensions to support graph operations, making it efficient for traversing and querying graph data.

3. **Consistency Mechanism**: Cassandra offers tunable consistency levels, providing users with the flexibility to choose between strong or eventual consistency based on their application requirements. On the contrary, OrientDB employs automatic sharding and distributed ACID transactions to ensure data consistency across distributed nodes.

4. **Scalability**: Cassandra is known for its linear horizontal scalability, allowing it to handle massive amounts of data and requests by adding more nodes to the cluster seamlessly. OrientDB also supports horizontal scalability but requires more manual intervention in data distribution across nodes compared to Cassandra.

5. **Primary Use Cases**: Cassandra is commonly used for high-velocity time-series data, messaging apps, recommendation engines, and logging due to its ability to handle large volumes of data with high write throughput. In comparison, OrientDB is preferred for use cases requiring complex relationships and graph traversal like social networks, fraud detection, and network analysis.

6. **Indexing Mechanism**: Cassandra utilizes a built-in secondary indexing mechanism but lacks full-text search capabilities, making it less suitable for search-intensive applications. In contrast, OrientDB provides versatile indexing options, including full-text search, spatial indexes, and automatic graph indexes, making it suitable for applications requiring extensive search capabilities.

Cassandra vs OrientDB

Overview