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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. MongoDB vs Pouchdb

MongoDB vs Pouchdb

OverviewDecisionsComparisonAlternatives

Overview

MongoDB
MongoDB
Stacks96.6K
Followers82.0K
Votes4.1K
GitHub Stars27.7K
Forks5.7K
Pouchdb
Pouchdb
Stacks148
Followers242
Votes6
GitHub Stars17.5K
Forks1.5K

MongoDB vs Pouchdb: What are the differences?

Introduction

MongoDB and PouchDB are both NoSQL databases that offer different functionalities and features.

  1. Data Storage and Synchronization:

    • MongoDB is a document-oriented database that stores data in a flexible, JSON-like format called BSON. It is designed for large-scale, high-performance applications and supports horizontal scaling through sharding.
    • PouchDB is a client-side database that stores data locally in the browser or on mobile devices using technologies like IndexedDB or WebSQL. It can also synchronize data with a remote server when an internet connection is available.
  2. Platform and Environment:

    • MongoDB can be used as a standalone server, or it can be deployed in a cluster configuration on multiple servers to handle large amounts of data and high traffic. It supports various operating systems and can be integrated with programming languages like JavaScript, Python, and Java.
    • PouchDB is primarily designed to run in the browser environment and is commonly used with JavaScript frameworks like React or Angular. It can also be utilized in hybrid mobile app development using platforms like Cordova or PhoneGap.
  3. Scalability:

    • MongoDB is horizontally scalable and can handle large amounts of data by distributing it across multiple servers through sharding. This allows for increased storage capacity, improved performance, and fault tolerance.
    • PouchDB is more suitable for smaller applications or situations where data syncing and offline capabilities are required. It is not designed to handle large-scale data storage or high levels of concurrent user traffic.
  4. Querying and Indexing:

    • MongoDB provides a powerful query language that supports complex queries, including aggregation pipelines and map-reduce functions. It also supports indexing to optimize query performance.
    • PouchDB supports basic querying capabilities but does not have as extensive query language as MongoDB. It relies on MapReduce functions for querying, which may not be as efficient for complex queries.
  5. Replication and Conflict Resolution:

    • MongoDB has built-in replication features that allow data to be replicated across multiple servers for fault tolerance and data redundancy. Conflict resolution mechanisms are also available to handle conflicts that may arise during replication.
    • PouchDB utilizes a synchronization mechanism called "replication" to keep data consistent between the client-side and server-side databases. It uses a conflict resolution algorithm based on timestamp comparisons to resolve conflicts in case of discrepancies.
  6. Community and Ecosystem:

    • MongoDB has a large and active community with extensive documentation, tutorials, and resources available online. It also has a rich ecosystem of third-party tools, libraries, and frameworks that integrate seamlessly with MongoDB.
    • PouchDB has a smaller community compared to MongoDB but is highly focused on the client-side development environment. It has good documentation and support within the JavaScript community.

Summary

In summary, MongoDB is a powerful document-oriented database designed for high-performance, scalable applications, while PouchDB is a client-side database that focuses on offline capabilities and synchronization with a remote server.

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Advice on MongoDB, Pouchdb

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

MongoDB
MongoDB
Pouchdb
Pouchdb

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.

PouchDB enables applications to store data locally while offline, then synchronize it with CouchDB and compatible servers when the application is back online, keeping the user's data in sync no matter where they next login.

Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS
Cross browser compatibility; Lightweight; Easy to learn; Open source
Statistics
GitHub Stars
27.7K
GitHub Stars
17.5K
GitHub Forks
5.7K
GitHub Forks
1.5K
Stacks
96.6K
Stacks
148
Followers
82.0K
Followers
242
Votes
4.1K
Votes
6
Pros & Cons
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
Pros
  • 2
    Offline cache
  • 1
    Repication
  • 1
    Free
  • 1
    JSON
  • 1
    Very fast

What are some alternatives to MongoDB, Pouchdb?

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.

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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