This page provides you with instructions on how to extract data from Recurly and analyze it in Grafana. (If the mechanics of extracting data from Recurly 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 Recurly?
Recurly, a software-as-a-service (SaaS) billing management platform, enables businesses to process payments across several payment channels.
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 Recurly
Recurly uses a REST API to allow developers to get data out of the service. The API supports endpoints for billing information, coupons, plans, invoices, and more.
To get a list of Recurly accounts for a given subdomain, you could call
GET /v2/accounts, with any of seven optional parameters for selecting and sorting the output.
Sample Recurly data
Results of Recurly API calls are returned as XML files. An XML file returned from a "list accounts" call to the Recurly API might look like this:
<account href="https://your-subdomain.recurly.com/v2/accounts/1"> <adjustments href="https://your-subdomain.recurly.com/v2/accounts/1/adjustments"/> <billing_info href="https://your-subdomain.recurly.com/v2/accounts/1/billing_info"/> <invoices href="https://your-subdomain.recurly.com/v2/accounts/1/invoices"/> <redemptions href="https://your-subdomain.recurly.com/v2/accounts/1/redemptions"/> <subscriptions href="https://your-subdomain.recurly.com/v2/accounts/1/subscriptions"/> <transactions href="https://your-subdomain.recurly.com/v2/accounts/1/transactions"/> <account_code>1</account_code> <state>active</state> <username>verena1234</username> <email>email@example.com</email> <cc_emails>firstname.lastname@example.org,email@example.com</cc_emails> <first_name>Verena</first_name> <last_name>Example</last_name> <company_name>New Company Name</company_name> <vat_number nil="nil"/> <tax_exempt type="boolean">false</tax_exempt> <address> <address1>123 Main St.</address1> <address2 nil="nil"/> <city>Philadelphia</city> <state>PA</state> <zip>19107</zip> <country>US</country> <phone nil="nil"/> </address> <accept_language nil="nil"/> <has_live_subscription type="boolean">true</has_live_subscription> <has_active_subscription type="boolean">true</has_active_subscription> <has_future_subscription type="boolean">false</has_future_subscription> <has_canceled_subscription type="boolean">false</has_canceled_subscription> <has_past_due_invoice type="boolean">false</has_past_due_invoice> <hosted_login_token>96e74bd5e14d18e6da463a0d638a2621</hosted_login_token> <created_at type="datetime">2017-12-08T20:59:43Z</created_at> <updated_at type="datetime">2017-12-11T17:56:24Z</updated_at> <closed_at nil="nil"/> </account>
Preparing Recurly data
If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. Recurly's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.
Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. This means you'll likely have to create additional tables to capture the unpredictable cardinality in each record.
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 Recurly 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 Recurly.
And remember, as with any code, once you write it, you have to maintain it. If Recurly 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 Recurly to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing Recurly 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 Recurly to Redshift, Recurly to BigQuery, Recurly to Azure SQL Data Warehouse, Recurly to PostgreSQL, Recurly to Panoply, and Recurly 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 Recurly with Grafana. With just a few clicks, Stitch starts extracting your Recurly 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.