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  1. Stackups
  2. Application & Data
  3. Document Databases
  4. Mongodb Hosting
  5. MongoDB vs MongoLab

MongoDB vs MongoLab

OverviewDecisionsComparisonAlternatives

Overview

MongoLab
MongoLab
Stacks438
Followers375
Votes216
MongoDB
MongoDB
Stacks96.6K
Followers82.0K
Votes4.1K
GitHub Stars27.7K
Forks5.7K

MongoDB vs MongoLab: What are the differences?

Introduction:

MongoDB and MongoLab are both popular database technologies used for storing and managing data in web applications. While MongoDB is the actual database software, MongoLab is a Database-as-a-Service (DBaaS) provider that offers MongoDB as a managed service in the cloud.

  1. Deployment Options: MongoDB can be deployed on-premises or in the cloud, giving users more control over their database setup and configurations. On the other hand, MongoLab is specifically designed for cloud deployments, providing a hassle-free setup and management experience for users who prefer a fully managed service without the complexities of infrastructure maintenance.

  2. Scalability: MongoDB allows users to scale their databases horizontally by adding more servers to distribute the workload effectively. MongoLab, being a cloud-native service, offers automatic scalability features that can dynamically adjust resources based on the workload, making it easier for users to handle sudden spikes in traffic or data volume without manual intervention.

  3. Pricing Model: MongoDB is open-source software that can be freely downloaded and used, with different editions available for various use cases. MongoLab, on the other hand, follows a subscription-based pricing model where users pay for the resources and features they require, along with additional support and monitoring services. This can be advantageous for users who prefer a predictable cost structure and don't want to manage the infrastructure themselves.

  4. Security Features: MongoDB provides various security features such as authentication, authorization, encryption, and auditing to secure data and control access to the database. MongoLab enhances these security features by offering additional layers of data protection, compliance certifications, and managed security controls to ensure the confidentiality and integrity of data stored in the cloud environment.

  5. Data Backup and Recovery: MongoDB users are responsible for setting up their own backup and recovery processes to protect against data loss and ensure business continuity. MongoLab simplifies this process by offering automated backups, point-in-time recovery, and disaster recovery solutions as part of their managed service, reducing the operational overhead for users and minimizing the risk of data loss due to unforeseen events.

  6. Support and Maintenance: While MongoDB provides community support through forums, documentation, and online resources, users may need to rely on their internal expertise or hire external consultants for advanced troubleshooting and maintenance tasks. MongoLab offers 24/7 technical support, proactive monitoring, and maintenance services to address any issues, optimize performance, and ensure the smooth operation of MongoDB databases in the cloud.

In Summary, MongoDB offers more deployment flexibility and control, while MongoLab provides a managed cloud service with automated scalability, enhanced security features, predictable pricing, advanced data protection, and comprehensive support services.

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Advice on MongoLab, MongoDB

George
George

Student

Mar 18, 2020

Needs adviceonPostgreSQLPostgreSQLPythonPythonDjangoDjango

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
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
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

MongoLab
MongoLab
MongoDB
MongoDB

mLab is the largest cloud MongoDB service in the world, hosting over a half million deployments on AWS, Azure, and Google.

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.

On-demand provisioning on the major clouds. Seamless, zero-downtime scaling and high availability via auto-failover on production-ready plans; Unlimited backups on Dedicated plans; free daily backup on other plans. Free and easy backup restores; Web GUI for editing documents, running queries (including saved searches), and viewing results in tabular format; Dedicated plans support encryption-at-rest, include SSL for free, and allow for custom firewalls as well as VPC peering
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
438
Stacks
96.6K
Followers
375
Followers
82.0K
Votes
216
Votes
4.1K
Pros & Cons
Pros
  • 61
    Development free tier
  • 46
    Easy setup
  • 38
    Scalable mongo hosting
  • 25
    Heroku plugin
  • 14
    REST API
Cons
  • 1
    Lab bought by MongoDB. Being replaced by Atlas
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
Heroku
Heroku
Microsoft Azure
Microsoft Azure
Amazon EC2
Amazon EC2
Red Hat OpenShift
Red Hat OpenShift
AppFog
AppFog
Rackspace Cloud Servers
Rackspace Cloud Servers
AppHarbor
AppHarbor
Engine Yard Cloud
Engine Yard Cloud
Joyent Cloud
Joyent Cloud
Nodejitsu
Nodejitsu
No integrations available

What are some alternatives to MongoLab, MongoDB?

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.

Cassandra

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

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.

Compose

Compose

Compose makes it easy to spin up multiple open source databases with just one click. Deploy MongoDB for production, take Redis out for a performance test drive, or spin up RethinkDB in development before rolling it out to production.

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.

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