Alternatives to Mongoose logo

Alternatives to Mongoose

MongoDB, Anaconda, Python, and Mongoid are the most popular alternatives and competitors to Mongoose.
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What is Mongoose and what are its top alternatives?

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.
Mongoose is a tool in the Object Document Mapper (ODM) category of a tech stack.
Mongoose is an open source tool with 23.5K GitHub stars and 3.3K GitHub forks. Here’s a link to Mongoose's open source repository on GitHub

Top Alternatives to 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. ...

  • Anaconda

    Anaconda

    A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda. ...

  • Python

    Python

    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...

  • Mongoid

    Mongoid

    The philosophy of Mongoid is to provide a familiar API to Ruby developers who have been using Active Record or Data Mapper, while leveraging the power of MongoDB's schemaless and performant document-based design, dynamic queries, and atomic modifier operations. ...

Mongoose alternatives & related posts

MongoDB logo

MongoDB

64K
53.5K
4.1K
The database for giant ideas
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PROS OF MONGODB
  • 824
    Document-oriented storage
  • 591
    No sql
  • 546
    Ease of use
  • 465
    Fast
  • 406
    High performance
  • 256
    Free
  • 215
    Open source
  • 179
    Flexible
  • 142
    Replication & high availability
  • 109
    Easy to maintain
  • 41
    Querying
  • 37
    Easy scalability
  • 36
    Auto-sharding
  • 35
    High availability
  • 31
    Map/reduce
  • 26
    Document database
  • 24
    Easy setup
  • 24
    Full index support
  • 15
    Reliable
  • 14
    Fast in-place updates
  • 13
    Agile programming, flexible, fast
  • 11
    No database migrations
  • 7
    Easy integration with Node.Js
  • 7
    Enterprise
  • 5
    Enterprise Support
  • 4
    Great NoSQL DB
  • 3
    Aggregation Framework
  • 3
    Support for many languages through different drivers
  • 3
    Drivers support is good
  • 2
    Schemaless
  • 2
    Easy to Scale
  • 2
    Fast
  • 2
    Awesome
  • 2
    Managed service
  • 1
    Consistent
CONS OF MONGODB
  • 5
    Very slowly for connected models that require joins
  • 3
    Not acid compliant
  • 1
    Proprietary query language

related MongoDB posts

Jeyabalaji Subramanian

Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

Based on the above criteria, we selected the following tools to perform the end to end data replication:

We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

See more
Robert Zuber

We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

See more
Anaconda logo

Anaconda

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The Enterprise Data Science Platform for Data Scientists, IT Professionals and Business Leaders
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PROS OF ANACONDA
    Be the first to leave a pro
    CONS OF ANACONDA
      Be the first to leave a con

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      Shared insights
      on
      JavaJavaAnacondaAnacondaPythonPython

      I am going to learn machine learning and self host an online IDE, the tool that i may use is Python, Anaconda, various python library and etc. which tools should i go for? this may include Java development, web development. Now i have 1 more candidate which are visual studio code online (code server). i will host on google cloud

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      Guillaume Simler

      Jupyter Anaconda Pandas IPython

      A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.

      The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead

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      Python logo

      Python

      149.4K
      123.4K
      6.5K
      A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
      149.4K
      123.4K
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      PROS OF PYTHON
      • 1.1K
        Great libraries
      • 933
        Readable code
      • 824
        Beautiful code
      • 771
        Rapid development
      • 674
        Large community
      • 420
        Open source
      • 380
        Elegant
      • 270
        Great community
      • 262
        Object oriented
      • 209
        Dynamic typing
      • 71
        Great standard library
      • 53
        Very fast
      • 50
        Functional programming
      • 37
        Scientific computing
      • 36
        Easy to learn
      • 31
        Great documentation
      • 25
        Matlab alternative
      • 23
        Productivity
      • 23
        Easy to read
      • 20
        Simple is better than complex
      • 18
        It's the way I think
      • 17
        Imperative
      • 15
        Very programmer and non-programmer friendly
      • 15
        Free
      • 14
        Powerfull language
      • 14
        Powerful
      • 13
        Fast and simple
      • 12
        Scripting
      • 11
        Machine learning support
      • 9
        Explicit is better than implicit
      • 8
        Ease of development
      • 8
        Unlimited power
      • 8
        Clear and easy and powerfull
      • 7
        Import antigravity
      • 6
        Print "life is short, use python"
      • 6
        It's lean and fun to code
      • 5
        Fast coding and good for competitions
      • 5
        Flat is better than nested
      • 5
        There should be one-- and preferably only one --obvious
      • 5
        Python has great libraries for data processing
      • 5
        High Documented language
      • 5
        I love snakes
      • 5
        Although practicality beats purity
      • 5
        Great for tooling
      • 4
        Readability counts
      • 3
        Plotting
      • 3
        CG industry needs
      • 3
        Beautiful is better than ugly
      • 3
        Complex is better than complicated
      • 3
        Great for analytics
      • 3
        Multiple Inheritence
      • 3
        Now is better than never
      • 3
        Lists, tuples, dictionaries
      • 3
        Rapid Prototyping
      • 3
        Socially engaged community
      • 2
        List comprehensions
      • 2
        Web scraping
      • 2
        Many types of collections
      • 2
        Ys
      • 2
        Easy to setup and run smooth
      • 2
        Generators
      • 2
        Special cases aren't special enough to break the rules
      • 2
        If the implementation is hard to explain, it's a bad id
      • 2
        If the implementation is easy to explain, it may be a g
      • 2
        Simple and easy to learn
      • 2
        Import this
      • 2
        No cruft
      • 2
        Easy to learn and use
      • 1
        Better outcome
      • 1
        It is Very easy , simple and will you be love programmi
      • 1
        Powerful language for AI
      • 1
        Should START with this but not STICK with This
      • 1
        Flexible and easy
      • 1
        Batteries included
      • 1
        Good
      • 1
        A-to-Z
      • 1
        Only one way to do it
      • 1
        Because of Netflix
      • 1
        Pip install everything
      • 0
        Powerful
      • 0
        Pro
      CONS OF PYTHON
      • 51
        Still divided between python 2 and python 3
      • 29
        Performance impact
      • 26
        Poor syntax for anonymous functions
      • 21
        GIL
      • 19
        Package management is a mess
      • 14
        Too imperative-oriented
      • 12
        Dynamic typing
      • 12
        Hard to understand
      • 10
        Very slow
      • 8
        Not everything is expression
      • 7
        Indentations matter a lot
      • 7
        Explicit self parameter in methods
      • 6
        No anonymous functions
      • 6
        Poor DSL capabilities
      • 6
        Incredibly slow
      • 6
        Requires C functions for dynamic modules
      • 5
        The "lisp style" whitespaces
      • 5
        Fake object-oriented programming
      • 5
        Hard to obfuscate
      • 5
        Threading
      • 4
        Circular import
      • 4
        The benevolent-dictator-for-life quit
      • 4
        Official documentation is unclear.
      • 4
        Lack of Syntax Sugar leads to "the pyramid of doom"
      • 4
        Not suitable for autocomplete
      • 2
        Meta classes
      • 1
        Training wheels (forced indentation)

      related Python posts

      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 39 upvotes · 4.2M views

      How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

      Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

      Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

      https://eng.uber.com/distributed-tracing/

      (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

      Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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      Nick Parsons
      Director of Developer Marketing at Stream · | 35 upvotes · 1.4M views

      Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.

      We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)

      We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.

      Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.

      #FrameworksFullStack #Languages

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      Mongoid logo

      Mongoid

      82
      68
      4
      Ruby ODM framework for MongoDB
      82
      68
      + 1
      4
      PROS OF MONGOID
      • 1
        Can be used without Rails
      • 1
        Supports Referenced and Embedded Associations
      • 1
        Easy to add 'created_at' and 'updated_at'' timestamps
      • 1
        Drop-in-and-forget replacement for activerecord
      CONS OF MONGOID
        Be the first to leave a con

        related Mongoid posts