This page provides you with instructions on how to extract data from Heroku and load it into Panoply. (If this manual process sounds onerous, 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 Panoply?
Panoply can spin up a new Amazon Redshift instance in just a few clicks. Panoply's managed data warehouse service uses machine learning and natural language processing (NLP) to learn, model, and automate data management activities from source to analysis. It can import data with no schema, no modeling, and no configuration, and lets you use analysis, SQL, and visualization tools just as you would if you were creating a Redshift data warehouse on your own.
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 Panoply
Once you've identified all the columns you want to insert, you can use the CREATE TABLE statement in Reshift to set up a table to receive your data.
With the table built, you might think that the easiest way to migrate your data (especially if there isn't much of it) would be to build INSERT statements to add data to your Redshift table row by row. Think again! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, we suggest moving the data into Amazon S3 and then using the COPY command to load it into Redshift.
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.
Other data warehouse options
Panoply is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, Snowflake, or Microsoft Azure SQL Data Warehouse, which are RDBMSes that use similar SQL syntax. Others choose a data lake, like Amazon S3. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Snowflake, To Azure SQL Data Warehouse, and To S3.
Easier and faster alternatives
If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.
Thankfully, products like Stitch were built to move data from Heroku to Panoply automatically. 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 your Panoply data warehouse.