What is Mongoose and what are its top alternatives?
Top Alternatives to Mongoose
- 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
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 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
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
- Document-oriented storage828
- No sql593
- Ease of use549
- Fast465
- High performance408
- Free256
- Open source215
- Flexible180
- Replication & high availability143
- Easy to maintain110
- Querying42
- Easy scalability38
- Auto-sharding37
- High availability36
- Map/reduce31
- Document database27
- Full index support25
- Easy setup25
- Reliable16
- Fast in-place updates15
- Agile programming, flexible, fast14
- No database migrations12
- Easy integration with Node.Js8
- Enterprise8
- Enterprise Support6
- Great NoSQL DB5
- Drivers support is good3
- Aggregation Framework3
- Support for many languages through different drivers3
- Awesome2
- Schemaless2
- Managed service2
- Fast2
- Easy to Scale2
- Consistent1
- Acid Compliant1
- Very slowly for connected models that require joins6
- Not acid compliant3
- Proprietary query language1
related MongoDB posts









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!
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.
Anaconda
related Anaconda posts
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
Which one of these should I install? I am a beginner and starting to learn to code. I have Anaconda, Visual Studio Code ( vscode recommended me to install Git) and I am learning Python, JavaScript, and MySQL for educational purposes. Also if you have any other pro-tips or advice for me please share.
Yours thankfully, Darkhiem
Python
- Great libraries1.1K
- Readable code937
- Beautiful code830
- Rapid development774
- Large community677
- Open source422
- Elegant381
- Great community273
- Object oriented266
- Dynamic typing211
- Great standard library73
- Very fast54
- Functional programming51
- Easy to learn39
- Scientific computing39
- Great documentation32
- Productivity25
- Matlab alternative25
- Easy to read24
- Simple is better than complex20
- It's the way I think18
- Imperative17
- Free15
- Very programmer and non-programmer friendly15
- Powerfull language14
- Powerful14
- Fast and simple13
- Scripting12
- Machine learning support12
- Explicit is better than implicit9
- Ease of development8
- Unlimited power8
- Clear and easy and powerfull8
- Import antigravity7
- It's lean and fun to code6
- Print "life is short, use python"6
- Great for tooling5
- There should be one-- and preferably only one --obvious5
- Python has great libraries for data processing5
- High Documented language5
- I love snakes5
- Although practicality beats purity5
- Flat is better than nested5
- Fast coding and good for competitions5
- Readability counts4
- Lists, tuples, dictionaries3
- CG industry needs3
- Now is better than never3
- Multiple Inheritence3
- Great for analytics3
- Complex is better than complicated3
- Plotting3
- Beautiful is better than ugly3
- Rapid Prototyping3
- Socially engaged community3
- List comprehensions2
- Web scraping2
- Many types of collections2
- Ys2
- Easy to setup and run smooth2
- Generators2
- Special cases aren't special enough to break the rules2
- If the implementation is hard to explain, it's a bad id2
- If the implementation is easy to explain, it may be a g2
- Simple and easy to learn2
- Import this2
- No cruft2
- Easy to learn and use2
- Flexible and easy1
- Batteries included1
- Powerful language for AI1
- Should START with this but not STICK with This1
- Good1
- It is Very easy , simple and will you be love programmi1
- Better outcome1
- إسلام هشام1
- Because of Netflix1
- A-to-Z1
- Only one way to do it1
- Pip install everything1
- Powerful0
- Pro0
- Still divided between python 2 and python 351
- Performance impact29
- Poor syntax for anonymous functions26
- GIL21
- Package management is a mess19
- Too imperative-oriented14
- Dynamic typing12
- Hard to understand12
- Very slow10
- Not everything is expression8
- Indentations matter a lot7
- Explicit self parameter in methods7
- No anonymous functions6
- Poor DSL capabilities6
- Incredibly slow6
- Requires C functions for dynamic modules6
- The "lisp style" whitespaces5
- Fake object-oriented programming5
- Hard to obfuscate5
- Threading5
- Circular import4
- The benevolent-dictator-for-life quit4
- Official documentation is unclear.4
- Lack of Syntax Sugar leads to "the pyramid of doom"4
- Not suitable for autocomplete4
- Meta classes2
- Training wheels (forced indentation)1
related Python posts











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
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
- Can be used without Rails1
- Supports Referenced and Embedded Associations1
- Easy to add 'created_at' and 'updated_at'' timestamps1
- Drop-in-and-forget replacement for activerecord1