FullStory (v1)

FullStory integration summary

Stitch’s FullStory integration relies on the Data Export pack add-on to replicate data through the FullStory Data Export REST API. Refer to the Schema section for a list of objects available for replication.

FullStory feature snapshot

A high-level look at Stitch's FullStory (v1) integration, including release status, useful links, and the features supported in Stitch.

STITCH
Release status	Released on June 27, 2018	Supported by	Singer Community
Stitch plan	Standard	API availability	Available
Singer GitHub repository	singer-io/tap-fullstory
REPLICATION SETTINGS
Anchor Scheduling	Supported	Advanced Scheduling	Supported
Table-level reset	Unsupported	Configurable Replication Methods	Unsupported
DATA SELECTION
Table selection	Unsupported	Column selection	Unsupported
Select all	Unsupported
TRANSPARENCY
Extraction Logs	Supported	Loading Reports	Supported

Connecting FullStory

FullStory setup requirements

To set up FullStory in Stitch, you need:

• A FullStory account with the Data Export Pack add-on. The Data Export Pack is a paid add-on for FullStory accounts that enables you to export raw event data.

  This add-on is required to replicate data using FullStory’s Data Export REST API.

---

Step 1: Retrieve your FullStory API key

1. Sign into your FullStory account.
2. Click the user menu (three dots, upper right corner) > Settings.
3. Click Integrations & API Keys in the menu on the left side of the page.

4. Click API Key:

   

5. Your API key will display on the page. Copy the API key before closing the page.

Step 2: Add FullStory as a Stitch data source

1. Sign into your Stitch account.

2. On the Stitch Dashboard page, click the Add Integration button.

3. Click the FullStory icon.

4. Enter a name for the integration. This is the name that will display on the Stitch Dashboard for the integration; it’ll also be used to create the schema in your destination.

   For example, the name “Stitch FullStory” would create a schema called stitch_fullstory in the destination. Note: Schema names cannot be changed after you save the integration.

5. In the API Key field, paste your FullStory API key.

Step 3: Define the historical replication start date

The Sync Historical Data setting defines the starting date for your FullStory integration. This means that data equal to or newer than this date will be replicated to your data warehouse.

Change this setting if you want to replicate data beyond FullStory’s default setting of 1 year. For a detailed look at historical replication jobs, check out the Syncing Historical SaaS Data guide.

Step 4: Create a replication schedule

In the Replication Frequency section, you’ll create the integration’s replication schedule. An integration’s replication schedule determines how often Stitch runs a replication job, and the time that job begins.

FullStory integrations support the following replication scheduling methods:

• Replication Frequency

• Anchor Scheduling

• Advanced Scheduling using Cron (Advanced or Premium plans only)

To keep your row usage low, consider setting the integration to replicate less frequently. See the Understanding and Reducing Your Row Usage guide for tips on reducing your usage.

Initial and historical replication jobs

After you finish setting up FullStory, its Sync Status may show as Pending on either the Stitch Dashboard or in the Integration Details page.

For a new integration, a Pending status indicates that Stitch is in the process of scheduling the initial replication job for the integration. This may take some time to complete.

Free historical data loads

The first seven days of replication, beginning when data is first replicated, are free. Rows replicated from the new integration during this time won’t count towards your quota. Stitch offers this as a way of testing new integrations, measuring usage, and ensuring historical data volumes don’t quickly consume your quota.

---

FullStory replication

Data updates and FullStory data export bundles

FullStory data bundles event data together based on a time period setting you define. By default, a FullStory data bundle contains data about events that occurred during a 24 hour period.

Note: FullStory makes event bundles available 24 hours the last event in the bundle occurs.

For example: If your bundle period is set to 6 hours, a data export bundle for events that occur on June 1 between 12:00PM - 6:00PM will be available the following day, June 2, at 6:00PM.

Impact on Stitch replication

Because FullStory only makes event data available a full day after events have occurred, records for the current date will only ever be available the next day. Event data that is one day old is considered “up to date” for this integration.

Loading data using Append-Only loading

When Stitch loads the extracted data for FullStory events into your destination, it will do so using Append-Only loading. This is a type of loading behavior where existing rows aren’t updated, but appended to the end of the table. Note: Loading will be append-only even if the destination you’re using supports Upsert loading.

Refer to the Understanding loading behavior guide for more info and examples.

For FullStory, this means that every captured event is equal to a single row in the events table. Using this data, you can view a given user’s event history and construct a timeline of their actions.

Example: Create a user session timeline

The table below contains what sample data might look like for a user who changes their address during a session:

Using this data, we can put together the order of events for this user’s session:

1. The user clicks (EventType: click) the Update Address button on the page at https://example.com/my-account.
2. Next, the user clicks in the Street address text field on the page at https://example.com/update-address.
3. The user changes (EventType: change) the text in the Street address field to 1339 Chestnut Street on the page at https://example.com/update-address.
4. Lastly, the user clicks the Save Changes button on the page at https://example.com/update-address.

---

FullStory table reference

Related	Troubleshooting
Destination & Integration Compatibility Replication Scheduling Syncing Historical SaaS Data Resetting Replication Keys Nested Data Structures & Row Count Impact	Third-Party Downtime Understanding & Reducing Your Usage Re-Authorizing Integrations Replication Issues

---

Questions? Feedback?

Did this article help? If you have questions or feedback, feel free to submit a pull request with your suggestions, open an issue on GitHub, or reach out to us.