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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Cassandra vs Mongoose

Cassandra vs Mongoose

OverviewDecisionsComparisonAlternatives

Overview

Cassandra
Cassandra
Stacks3.6K
Followers3.5K
Votes507
GitHub Stars9.5K
Forks3.8K
Mongoose
Mongoose
Stacks2.4K
Followers1.4K
Votes56

Cassandra vs Mongoose: What are the differences?

Introduction:
Cassandra and Mongoose are both popular database technologies used in modern web development. Here are the key differences between the two.

1. **Data Model**:
Cassandra is a NoSQL database that uses a wide-column store data model, allowing flexible schema design and scalability for big data applications. On the other hand, Mongoose is an ODM (Object Data Modeling) library for MongoDB, which follows a document-based data model more suitable for applications with complex, hierarchical data structures.

2. **Query Language**:
Cassandra uses CQL (Cassandra Query Language) for querying data, which is similar to SQL but with some differences due to the distributed nature of Cassandra. Meanwhile, Mongoose uses MongoDB's powerful querying language that supports complex queries and operations like aggregation pipelines, making it easier to manipulate data.

3. **Consistency**:
In Cassandra, users can choose between different consistency levels for reads and writes, providing flexibility in balancing consistency and availability based on application requirements. In contrast, Mongoose enforces strict consistency in a single replica set, ensuring that reads always reflect the latest write operations.

4. **Scalability**:
Cassandra is designed for linear scalability by enabling easy distribution of data across multiple nodes, making it ideal for applications requiring high availability and performance under heavy loads. While MongoDB, which Mongoose interacts with, also offers horizontal scalability, it may require more manual sharding configurations compared to Cassandra's built-in partitioning capabilities.

5. **Transactions**:
Cassandra traditionally lacks support for multi-row transactions, making it challenging to ensure atomicity across multiple data operations. In comparison, MongoDB with Mongoose provides support for multi-document transactions in some scenarios, allowing developers to maintain data integrity in complex transactional workflows.

6. **Community and Ecosystem**:
Cassandra has a robust community and ecosystem, with large-scale deployments in various industries such as social media and financial services. Mongoose, being an ODM for MongoDB, benefits from MongoDB's widespread adoption and support, making it easier to find resources, plugins, and integrations tailored for MongoDB databases.

In Summary, Cassandra and Mongoose differ in their data models, query languages, consistency mechanisms, scalability approaches, transaction support, and community ecosystems, catering to diverse application needs and preferences in database technology.

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Advice on Cassandra, Mongoose

Vinay
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.

174k views174k
Comments

Detailed Comparison

Cassandra
Cassandra
Mongoose
Mongoose

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.

Let's face it, writing MongoDB validation, casting and business logic boilerplate is a drag. That's why we wrote Mongoose. Mongoose provides a straight-forward, schema-based solution to modeling your application data and includes built-in type casting, validation, query building, business logic hooks and more, out of the box.

Statistics
GitHub Stars
9.5K
GitHub Stars
-
GitHub Forks
3.8K
GitHub Forks
-
Stacks
3.6K
Stacks
2.4K
Followers
3.5K
Followers
1.4K
Votes
507
Votes
56
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
  • 17
    Several bad ideas mixed together
  • 17
    Well documented
  • 10
    JSON
  • 8
    Actually terrible documentation
  • 2
    Recommended and used by Valve. See steamworks docs
Cons
  • 3
    Model middleware/hooks are not user friendly
Integrations
No integrations available
Node.js
Node.js
MongoDB
MongoDB

What are some alternatives to Cassandra, Mongoose?

MongoDB

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

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

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

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

SQLite

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

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

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

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

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

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.

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