MongoDB to Grafana

This page provides you with instructions on how to extract data from MongoDB and analyze it in Grafana. (If the mechanics of extracting data from MongoDB seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is MongoDB?

MongoDB, or just Mongo, is an open source NoSQL database that stores data in JSON format. It uses a document-oriented data model, and data fields can vary by document. MongoDB isn't tied to any specified data structure, meaning that there's no particular format or schema for data in a Mongo database.

What is Grafana?

Grafana is an open source platform for time series analytics. It can run on-premises on all major operating systems or be hosted by Grafana Labs via GrafanaCloud. Grafana allows users to create, explore, and share dashboards to query, visualize, and alert on data.

Getting data out of MongoDB

The process of pulling data out of MongoDB depends on how you've loaded data into MongoDB. In some cases, it may be impossible to extract all of your data, because NoSQL databases don't require structure (i.e. specific columns). Relational databases, such as those used for data warehouses, use a more traditional, rigid structure. You'll need to defined a structure in the relational database into which you can insert MongoDB data.

Don't stress about the confusing data structure. Lots of the data that's loaded into MongoDB is created by a computer, so it probably has a pretty predictable structure. If you can find specific fields that exist for every record, you're well on your way. Make sure these fields appear in the records of each collection you'd like to replicate from MongoDB. There are many ways to do this. The most popular method to get data from MongoDB is to use the find() command.

Sample MongoDB data

MongoDB stores and returns JSON-formatted data. Here's an example of what a response might look like to a query against the products collection.

db.products.find( { qty: { $gt: 25 } }, { _id: 0, qty: 0 } )

{ "item" : "pencil", "type" : "no.2" }
{ "item" : "bottle", "type" : "blue" }
{ "item" : "paper" }

Loading data into Grafana

Analyzing data in Grafana requires putting it into a format that Grafana can read. Grafana natively supports nine data sources, and offers plugins that provide access to more than 50 more. Generally, it's a good idea to move all your data into a data warehouse for analysis. MySQL, Microsoft SQL Server, and PostgreSQL are among the supported data sources, and because Amazon Redshift is built on PostgreSQL and Panoply is built on Redshift, those popular data warehouses are also supported. However, Snowflake and Google BigQuery are not currently supported.

Analyzing data in Grafana

Grafana provides a getting started guide that walks new users through the process of creating panels and dashboards. Panel data is powered by queries you build in Grafana's Query Editor. You can create graphs with as many metrics and series as you want. You can use variable strings within panel configuration to create template dashboards. Time ranges generally apply to an entire dashboard, but you can override them for individual panels.

Keeping MongoDB data up to date

Fine job! You are the proud developer of a script that moves data from MongoDB to your data warehouse. This works as a one-shot deal. It's good to think about what will happen when there is new and updated data in MongoDB.

One option that works would be to load the entire MongoDB dataset all over again. That would certainly update the data, but it's not very efficient and can also cause terribly latency.

The smartest way to get data updated from MongoDB would be to identify keys that can be used as bookmarks to store where you script left off on the last run. Fields like updated_at, modified_at, or other auto-incrementing data are useful here. With that done, you can set up your script as a cron job or continuous loop to identify new data as it appears.

From MongoDB to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing MongoDB data in Grafana is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites MongoDB to Redshift, MongoDB to BigQuery, MongoDB to Azure SQL Data Warehouse, MongoDB to PostgreSQL, MongoDB to Panoply, and MongoDB to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate MongoDB with Grafana. With just a few clicks, Stitch starts extracting your MongoDB data via the API, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Grafana.