Cloudant vs MongoDB

Overview

Cloudant

Stacks86

Followers74

Votes28

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Cloudant vs MongoDB: What are the differences?

Introduction

In this comparison, we will analyze and highlight the key differences between Cloudant and MongoDB, two popular NoSQL databases, based on various factors.

Data Model: Cloudant is a Document-based NoSQL database that stores data in JSON format, allowing for flexible schema and nested documents. On the other hand, MongoDB is also a Document-based NoSQL database, but it uses BSON (Binary JSON) format, which is similar to JSON but includes additional data types like Date and Binary data.
Indexing and Querying: Cloudant uses Apache Lucene for indexing and querying capabilities, providing full-text search, relevance ranking, and geospatial queries. MongoDB, on the other hand, uses a native indexing engine, allowing for efficient indexing and querying based on various criteria like equality, range, and text search.
Scalability and Replication: Cloudant is based on Apache CouchDB, which is known for its peer-to-peer replication and offline availability features. It offers automatic sharding and automatic scaling, making it suitable for large-scale deployments. MongoDB, on the other hand, offers horizontal scalability through sharding, allowing you to distribute data across multiple servers. It also supports replica sets for high availability and data redundancy.
Consistency Model: Cloudant offers an eventually consistent replication model, where updates are asynchronously propagated across nodes, providing high availability and low latency. MongoDB, on the other hand, offers strong consistency by default, ensuring that updates are immediately visible to subsequent reads within a replica set. However, it also provides options for eventual consistency through specific configurations.
Query Language: Cloudant uses a variant of SQL called Cloudant Query, which allows for complex queries using a combination of SQL-like syntax and JSON queries. MongoDB, on the other hand, uses a powerful query language called MongoDB Query Language (MQL), which uses a JSON-like syntax and provides various operators and functions for querying and data manipulation.
Integration and Ecosystem: Cloudant is designed to work seamlessly with IBM Cloud, providing integration with other IBM services like Watson and Analytics tools. It also supports data replication across IBM Cloud regions. MongoDB, on the other hand, has a vast ecosystem and community support, offering various integrations and connectors with popular tools like Apache Kafka, Spark, and Hadoop.

In summary, Cloudant and MongoDB differ in their data model, indexing and querying capabilities, scalability and replication features, consistency model, query language, and integration ecosystems.

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Advice on Cloudant, MongoDB

George

Student

Mar 18, 2020

Needs adviceon

PostgreSQL

Python

Django

Hello everyone,

Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?

*The project is going to use React for the front-end and GraphQL is going to be used for the API.

Thank you all. Any answer or advice would be really helpful!

620k views620k

Comments

Ido

Mar 6, 2020

Decided

My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.

575k views575k

Comments

Mike

Mar 20, 2020

Needs advice

We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.

241k views241k

Comments

Detailed Comparison

Cloudant	MongoDB
Cloudant’s distributed database as a service (DBaaS) allows developers of fast-growing web and mobile apps to focus on building and improving their products, instead of worrying about scaling and managing databases on their own.	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.
Managed- Cloudant's big data experts monitor your data 24/7 to ensure its high availability and safety.;Distributed Multi-Master Database- All read and write transactions can be synced across Cloudant's global data network without global locks, providing true high availability of your data.;Geo-load Balancing- To keep latency low, our geo-load balancing infrastructure routes requests to the copies of the data that are geographically closest to the requestor.;Mobile Sync- Cloudant not only syncs between data centers around the world, but also between data centers and mobile devices.;Incremental MapReduce- Unlike Hadoop, Cloudant’s Incremental MapReduce keeps indexes up-to-date with new transactions and updates without requiring a full reindexing of your data.;Integrated Lucene Search- High-performance full-text indexing and search, without the difficulty and cost of managing text and operational data in separate databases.	Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS
Statistics
GitHub Stars -	GitHub Stars 27.7K
GitHub Forks -	GitHub Forks 5.7K
Stacks 86	Stacks 96.6K
Followers 74	Followers 82.0K
Votes 28	Votes 4.1K
Pros & Cons
Pros 13 JSON 7 REST interface 4 Cheap 3 JavaScript support 1 Great syncing	Pros 829 Document-oriented storage 594 No sql 554 Ease of use 465 Fast 410 High performance Cons 6 Very slowly for connected models that require joins 3 Not acid compliant 2 Proprietary query language
Integrations
AppHarbor Heroku Microsoft Azure Amazon EC2 SoftLayer CloudBees Joyent Cloud Rackspace Cloud Servers cloudControl	No integrations available

What are some alternatives to Cloudant, MongoDB?

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.

Cassandra

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.

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.

Amazon DynamoDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

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.

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Cloudant vs MongoDB: What are the differences?

Introduction

In this comparison, we will analyze and highlight the key differences between Cloudant and MongoDB, two popular NoSQL databases, based on various factors.

Data Model: Cloudant is a Document-based NoSQL database that stores data in JSON format, allowing for flexible schema and nested documents. On the other hand, MongoDB is also a Document-based NoSQL database, but it uses BSON (Binary JSON) format, which is similar to JSON but includes additional data types like Date and Binary data.
Indexing and Querying: Cloudant uses Apache Lucene for indexing and querying capabilities, providing full-text search, relevance ranking, and geospatial queries. MongoDB, on the other hand, uses a native indexing engine, allowing for efficient indexing and querying based on various criteria like equality, range, and text search.
Scalability and Replication: Cloudant is based on Apache CouchDB, which is known for its peer-to-peer replication and offline availability features. It offers automatic sharding and automatic scaling, making it suitable for large-scale deployments. MongoDB, on the other hand, offers horizontal scalability through sharding, allowing you to distribute data across multiple servers. It also supports replica sets for high availability and data redundancy.
Consistency Model: Cloudant offers an eventually consistent replication model, where updates are asynchronously propagated across nodes, providing high availability and low latency. MongoDB, on the other hand, offers strong consistency by default, ensuring that updates are immediately visible to subsequent reads within a replica set. However, it also provides options for eventual consistency through specific configurations.
Query Language: Cloudant uses a variant of SQL called Cloudant Query, which allows for complex queries using a combination of SQL-like syntax and JSON queries. MongoDB, on the other hand, uses a powerful query language called MongoDB Query Language (MQL), which uses a JSON-like syntax and provides various operators and functions for querying and data manipulation.
Integration and Ecosystem: Cloudant is designed to work seamlessly with IBM Cloud, providing integration with other IBM services like Watson and Analytics tools. It also supports data replication across IBM Cloud regions. MongoDB, on the other hand, has a vast ecosystem and community support, offering various integrations and connectors with popular tools like Apache Kafka, Spark, and Hadoop.

In summary, Cloudant and MongoDB differ in their data model, indexing and querying capabilities, scalability and replication features, consistency model, query language, and integration ecosystems.