InfluxDB vs MongoDB: What are the differences?
InfluxDB: An open-source distributed time series database with no external dependencies. 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.; MongoDB: The database for giant ideas. 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.
InfluxDB and MongoDB can be categorized as "Databases" tools.
"Time-series data analysis" is the top reason why over 35 developers like InfluxDB, while over 788 developers mention "Document-oriented storage" as the leading cause for choosing MongoDB.
InfluxDB and MongoDB are both open source tools. It seems that InfluxDB with 16.6K GitHub stars and 2.37K forks on GitHub has more adoption than MongoDB with 16.2K GitHub stars and 4.08K GitHub forks.
According to the StackShare community, MongoDB has a broader approval, being mentioned in 2175 company stacks & 2143 developers stacks; compared to InfluxDB, which is listed in 116 company stacks and 38 developer stacks.
What is InfluxDB?
What is MongoDB?
Want advice about which of these to choose?Ask the StackShare community!
What tools integrate with InfluxDB?
What tools integrate with MongoDB?
Used MongoDB as primary database. It holds trip data of NYC taxis for the year 2013. It is a huge dataset and it's primary feature is geo coordinates with pickup and drop off locations. Also used MongoDB's map reduce to process this large dataset for aggregation. This aggregated result was then used to show visualizations.
MongoDB fills our more traditional database needs. We knew we wanted Trello to be blisteringly fast. One of the coolest and most performance-obsessed teams we know is our next-door neighbor and sister company StackExchange. Talking to their dev lead David at lunch one day, I learned that even though they use SQL Server for data storage, they actually primarily store a lot of their data in a denormalized format for performance, and normalize only when they need to.
Nearly all of our backend storage is on MongoDB. This has also worked out pretty well. It's enabled us to scale up faster/easier than if we had rolled our own solution on top of PostgreSQL (which we were using previously). There have been a few roadbumps along the way, but the team at 10gen has been a big help with thing.
We use InfluxDB as a store for our data that gets fed into Grafana. It's ideal for this as it's a lightweight storage engine that can be modified on the fly by scripts without having to log into the server itself and manage tables. The HTTP API also makes it ideal for integrating with frontend services.
We are testing out MongoDB at the moment. Currently we are only using a small EC2 setup for a delayed job queue backed by
agenda. If it works out well we might look to see where it could become a primary document storage engine for us.
Used for proofs of concept and personal projects with a document data model, especially with need for strong geographic queries. Often not chosen in long term apps due to chance data model can end up relational as needs develop.
To track time-series of course, utilizing few retention rules and continuous queries to keep time-series data fast and maintanable
InfluxDB ingests information from various sources (mostly Telegraf instances) into one place for monitoring purposes.