This page provides you with instructions on how to extract data from Heroku and analyze it in Grafana. (If the mechanics of extracting data from Heroku 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 Heroku?
Heroku is a cloud platform that lets companies build, deploy, monitor, and scale apps.
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 Heroku
You can extract the data you want from Heroku's servers using the Heroku API. A common use case for extracting Heroku data is retrieving server logs or other event logs. There are some API endpoints related to logs, as well as command-line tools like the logs command that let you retrieve this data.
Sample Heroku data
Here's an example set of commands and responses you might see when interacting with the
logs command-line tool.
$ heroku logs --ps router 2012-02-07T09:43:06.123456+00:00 heroku[router]: at=info method=GET path="/stylesheets/dev-center/library.css" host=devcenter.heroku.com fwd="184.108.40.206" dyno=web.5 connect=1ms service=18ms status=200 bytes=13 2012-02-07T09:43:06.123456+00:00 heroku[router]: at=info method=GET path="/articles/bundler" host=devcenter.heroku.com fwd="220.127.116.11" dyno=web.6 connect=1ms service=18ms status=200 bytes=20375 $ heroku logs --source app 2012-02-07T09:45:47.123456+00:00 app[web.1]: Rendered shared/_search.html.erb (1.0ms) 2012-02-07T09:45:47.123456+00:00 app[web.1]: Completed 200 OK in 83ms (Views: 48.7ms | ActiveRecord: 32.2ms) 2012-02-07T09:45:47.123456+00:00 app[worker.1]: [Worker(host:465cf64e-61c8-46d3-b480-362bfd4ecff9 pid:1)] 1 jobs processed at 23.0330 j/s, 0 failed ... 2012-02-07T09:46:01.123456+00:00 app[web.6]: Started GET "/articles/buildpacks" for 18.104.22.168 at 2012-02-07 09:46:01 +0000 $ heroku logs --source app --ps worker 2012-02-07T09:47:59.123456+00:00 app[worker.1]: [Worker(host:260cf64e-61c8-46d3-b480-362bfd4ecff9 pid:1)] Article#record_view_without_delay completed after 0.0221 2012-02-07T09:47:59.123456+00:00 app[worker.1]: [Worker(host:260cf64e-61c8-46d3-b480-362bfd4ecff9 pid:1)] 5 jobs processed at 31.6842 j/s, 0 failed ...
Preparing Heroku data
This part could be the trickiest: you need to map the data that comes out of each Heroku API endpoint or log extraction into a schema that can be inserted into your destination database. This means that, for each value in the response, you need to identify a predefined datatype (i.e. INTEGER, DATETIME, etc.) and build a table that can receive them. Depending on your log files, you may also opt to break those up into raw logs and more meaningful metadata or log portions.
The Heroku API documentation can give you a good sense of what fields will be provided by each endpoint, along with their corresponding datatypes.
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 Heroku data up to date
At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.
Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Heroku.
And remember, as with any code, once you write it, you have to maintain it. If Heroku modifies its API, or the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.
From Heroku to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing Heroku 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 Heroku to Redshift, Heroku to BigQuery, Heroku to Azure Synapse Analytics, Heroku to PostgreSQL, Heroku to Panoply, and Heroku 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 Heroku with Grafana. With just a few clicks, Stitch starts extracting your Heroku data, 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.