Need advice about which tool to choose?Ask the StackShare community!
CouchDB vs IndexedDB: What are the differences?
Introduction
CouchDB and IndexedDB are both NoSQL databases used for storing and retrieving data, but they have some key differences that set them apart.
-
Data Model:
- CouchDB stores data in a document-based format using JSON documents, providing a flexible schema.
- IndexedDB is an object-oriented database that stores data as key-value pairs, allowing for efficient retrieval based on keys.
-
Query Language:
- CouchDB uses MapReduce functions for querying and processing data, allowing for complex data transformations and aggregations.
- IndexedDB does not have a built-in query language, and developers need to write custom code to retrieve and filter data.
-
Scalability:
- CouchDB is designed to scale horizontally, meaning it can handle large amounts of data by distributing it across multiple servers.
- IndexedDB is usually used in a client-side environment and is not inherently built for scalability across multiple servers.
-
Data Synchronization:
- CouchDB has built-in support for data synchronization, enabling replication between multiple CouchDB instances for offline access and data consistency.
- IndexedDB does not have built-in data synchronization features, requiring developers to implement custom solutions for data replication.
-
Concurrency Control:
- CouchDB uses a conflict resolution mechanism called Multi-Version Concurrency Control (MVCC) to handle concurrent updates to the same document.
- IndexedDB does not provide explicit concurrency control mechanisms, and developers need to implement their own strategies to handle conflicts.
-
Platform Support:
- CouchDB is a standalone database server that can be accessed using various programming languages and platforms, including web browsers.
- IndexedDB is specifically designed for browser environments and can only be accessed within the browser's JavaScript runtime.
In summary, CouchDB offers a document-based data model, MapReduce query language, scalability for large datasets, data synchronization capabilities, concurrency control mechanism, and wide platform support. On the other hand, IndexedDB focuses on key-value data storage, custom querying, client-side use cases, and requires developers to handle data synchronization and concurrency control manually.
I'm currently developing an app that ranks trending stuff ( such as games, memes or movies, etc. ) or events in a particular country or region. Here are the specs: My app does not require registration and requires cookies and localStorage to track users. Users can add new entries to each trending category provided that their country of origin is recorded in cookies. If each category contains more than 100 items then the oldest items get deleted. The question is: what kind of database should I use for managing this app? Thanks in advance
I think your best and cheapest choice is going to be MongoDB, Although Postgres is probably going to be the more scaleable approach, you likely have a good idea of how you want to present your data, and the app seems small enough that you shouldn't need to worry about scaling issues. It also sounds like your app can grow in a linear capacity based on the number of users, and the amount of data, which is the perfect use-case for noSQL databases (linear, predictable scaling).
Correct me if I have any of these assumptions wrong. 1. You're looking to have a relatively high-read with a lower write volume 2. Your app is essentially a list of objects that can belong to a category 3. users can create objects in this list.
I think Mongo is going to be what you're looking for on the following basis: 1. you absolutely need a database that is shared by all users of your app, therefor IndexedDB is out of the question. 2. You have semi-structured data 3. you probably want the cheapest solution.
I think Postgres is wrong for the following reasons: 1. your app is pretty simple in concept, SQL databases will add unnecessary complexity to your system, either through ORMs or SQL queries. (use an ORM if you go with SQL) 2. Hosting SQL databases for production is not cheap! the cheapest solution I know of for Postgres is ElephantSQL. It provides 20MB for free with 5 concurrent connections, you should be okay to manage these limitations if you decide to go Postgres in the end. Whereas mongoDB Atlas has some great free-tier options.
Although your data might be easier to model in Postgres, you can certainly model your data as a single list of items that have a category attached.
I don't want to officially recommend another tool, but you should really checkout prisma, firebase, amplify, or Azure App Services for this app! Just go completely backend-less [Firebase] https://firebase.google.com/ [Amplify] https://aws.amazon.com/amplify/ [Prisma] https://www.prisma.io/ [Azure App Services] https://azure.microsoft.com/en-us/services/app-service/?v=18.51
We implemented our first large scale EPR application from naologic.com using CouchDB .
Very fast, replication works great, doesn't consume much RAM, queries are blazing fast but we found a problem: the queries were very hard to write, it took a long time to figure out the API, we had to go and write our own @nodejs library to make it work properly.
It lost most of its support. Since then, we migrated to Couchbase and the learning curve was steep but all worth it. Memcached indexing out of the box, full text search works great.
Pros of CouchDB
- JSON43
- Open source30
- Highly available18
- Partition tolerant12
- Eventual consistency11
- Sync7
- REST API5
- Attachments mechanism to docs4
- Multi master replication4
- Changes feed3
- REST interface1
- js- and erlang-views1
