Heroku to Snowflake

This page provides you with instructions on how to extract data from Heroku and load it into Snowflake. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

About Snowflake

Snowflake is a data warehouse solution that is entirely cloud based. It's a managed service. If you don't want to deal with hardware, software, or upkeep for a data warehouse you're going to love Snowflake. It runs on the wicked fast Amazon Web Services architecture using EC2 and S3 instances. Snowflake is designed to be flexible and easy to work with where other relational databases are not. One example of this is the query execution. Snowflake creates virtual warehouses where query processing takes place. These virtual warehouses run on separate compute clusters, so querying one of these virtual warehouses doesn't slow down the others. If you have ever had to wait for a query to complete, you know the value of speed and efficiency for query processing.

Getting data out of Heroku

Let's start off by extracting the data you want from Heroku’s servers. You can do this using the Heroku API. Full API documentation can be found at this site.

A common use case for extracting Heroku data is retrieving server logs or other event logs. There are some API endpoints related to logs but also command-line tools like the logs command that allow you to retrieve this data.

Sample Heroku data

Here is 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="204.204.204.204" 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="204.204.204.204" 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 4.1.81.209 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 data for Snowflake

Depending on the structure that you data is in, you may need to prepare it for loading. Take a look at the supported data types for Snowflake and make sure that the data you've got will map neatly to them. If you have a lot of data, you should compress it. Gzip, bzip2, Brotli, Zstandard v0.8 and deflate/raw deflate compression types are all supported.

One important thing to note here is that you don't need to define a schema in advance when loading JSON data into Snowflake. Onward to loading!

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 Snowflake

There is a good reference for this step in the Data Loading Overview section of the Snowflake documentation. If there isn’t much data that you’re trying to load, then you might be able to use the data loading wizard in the Snowflake web UI. Chances are, the limitations on that tool will make it a non-starter as a reliable ETL solution. There two main steps to getting data into Snowflake:

  • Use the PUT command to stage files
  • Use the COPY INTO table command to load prepared data into the awaiting table from the prior step.

For the COPY step, you’ll have the option of copying from your local drive, or from Amazon S3. One of Snowflakes’ slick features lets you to make a virtual warehouse that will power the insertion process.

Keeping Heroku data up to date

Hooray! You've written a script to move Heroku data into your data warehouse. Wouldn't it be great if that was all there was to it? Consider what's going to happen in the event that new data is created in Heroku, and it needs to make its way into your data warehouse?

One scenario, depending on the design of your script, is to simply load the entire dataset all over again. This is almost guaranteed to be slow and painful. Delays can be costly if you've got deadlines to meet.

The best thing you can do is build your script to identify new an updated information and incrementally update in the destination. This can be accomplished by using primary keys in your logic. Some good examples would be modified_at, updated_at, or some other auto-incrementing field. When you've built in this functionality, you can set up your script as a cron job or continuous loop to grab new data as it appears.

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 solve this problem automatically. With just a few clicks, Stitch starts extracting your Heroku data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Snowflake data warehouse.