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Data Connector for Stripe

You can import Stripe objects, such as invoices, charges, and customer events, into Treasure Data.

Table of Contents


  • Basic knowledge of Treasure Data
  • Basic knowledge of Stripe
  • (Optional) Stripe Webhooks

Option 1: Use Web Console

Create a new connection

Go to Treasure Data Connections. Locate and select Stripe. The dialog opens.

Authentication Methods:

Method 1: OAuth —recommended

Select an existing OAuth connection for Stripe, or click the link under OAuth connection to create a new one.

Create a new OAuth connection

Login to your Stripe account in popup window:

And grant access to Treasure Data app:

You will be redirected back to Treasure Data Connections. Repeat the first step (Create a new connection) and choose your new OAuth connection.

Method 2: Secret Key for Internal Testing Purposes: —deprecated, replaced by OAuth

Enter the Live Secret Key from Stripe —> Your Account —> Account Settings —> API Keys.

Create a new transfer

After creating the connection, you are automatically taken to the My Connections tab. Look for the connection you created and click New Transfer. Note: the incremental option is only supported for event object.

The following dialog will open. Provide information details and click Next.

Next, you will see a Preview of your data similar to what is shown in the following dialog. To make any changes, click Advanced Settings otherwise, click Next.

Third step is to select the database and table where you want to transfer the data, as shown in the following dialog:

Finally, specify the schedule of the data transfer by completing the dialog as shown and click Start Transfer:

You will see the new data transfer in progress listed under the My Input Transfers tab and a corresponding job is listed in the Jobs section.

Now, you are ready to start analyzing your data!

Parallel data import

You can improve data import performance by using parallel import.

The Start datetime and End datetime for the data connector can be used to specify the timeframe in which parallel HTTP requests are sent to the Stripe API. If you do not specify the End datetime, the connector attempts to get a timestamp of latest record stored at Stripe and use that timestamp as the End datetime.

In following example, four time ranges are created and will process in parallel.

Start datetime: 2016-01-25T00:00:00
End datetime: 2017-05-25T00.00.00
Time splitting
  Period for each parallel input: 6
  unit: month

Time range 1
  gte: 2016-01-25T00:00:00
  lt: 2016-01-31T00:00:00 # start_datetime to first end of the month
Time range 2
  gte: 2016-01-31T00:00:00 # has 6 month period
  lt: 2016-07-31T00.00.00
Time range 3
  gte: 2016-07-31T00.00.00.000Z # has 6 month period
  lt: 2017-02-28T00.00.00
Time range 4
  gte: 2017-02-28T00.00.00
  lt: 2017-05-25T00.00.00 # to end_datetime

Option 2: Use Command Line

Step 0: Install ‘td’ command v0.11.9 or later

Install the latest Treasure Data Toolbelt.

$ td --version

Step 1: Create Configuration File

Prepare configuration file (for eg: load.yml), similiar to the following example, with your Stripe account access information.

  type: stripe
  client_id: xxxxxxxxxxxxx
  client_secret: xxxxxxxxxxxxx
  refresh_token: xxxxxxxxxxxxx
  target: event
  incremental: true
  start_datetime: 2017-01-01T07:36:22.000Z
  formula: gt
  mode: replace

This example dumps Stripe Event object:

  • client_id and client_secret: your Stripe app credentials
  • refresh_token: Stripe OAuth2 refresh_token, to grant access to your Stripe app, using an Stripe user account
  • target: the Stripe object to import. Supported values: account, application_fee, balance_history, charge, coupon, customer, dispute, file_upload, event, invoice_item, invoice, order, plan, product, refund, subscription, transfer
  • start_datetime: the date that import of data starts, format is: yyyy-MM-ddThh:mm:ss.000Z
  • formula: used to formulate start_datetime. Supported values: gt(greater than), gte(greater than or equal), lt(less than), lte(less than or equal).
  • incremental: specify if data import is continuous or once, default as true. Incremental is only supported when target is event.

For more details on available out modes, see Appendix A

Step 2 (optional): Preview data to import

You can preview data to be imported using the command td connector:preview.

$ td connector:preview load.yml
| id:string                      | object:string | api_version:string | ...
| "evt_19cs52EC2hHOQuTd4SejXK0W" | "event"       | "2016-07-06"       |
| "evt_19cs52EC2hHOQuTdG7Qm4fS3" | "event"       | "2016-07-06"       |
| "evt_19cs4REC2hHOQuTdhZZD58uw" | "event"       | "2016-07-06"       |
| "evt_19cs4QEC2hHOQuTdjADeyQcC" | "event"       | "2016-07-06"       |
| "evt_19cs3oEC2hHOQuTdjZm6u0pB" | "event"       | "2016-07-06"       |

Step 3: Execute Load Job

Finally, submit the load job. It may take a couple of hours depending on the data size. Users need to specify the database and table where their data are stored.

It is recommended to specify --time-column option, because Treasure Data’s storage is partitioned by time (see also data partitioning) If the option is available, the Data Connector will choose the first long or timestamp column as the partitioning time. The type of the column specified by --time-column must be either of long and timestamp type.

If your data doesn’t have a time column you can add a time column using the add_time filter option. More details at add_time filter plugin

$ td connector:issue load.yml --database td_sample_db --table td_sample_table --time-column updated_date

The td connector:issue command assumes that you have already created database(td_sample_db) and table(td_sample_table). If the database or the table do not exist in TD, this command will not succeed. You must create the database and table manually or use the --auto-create-table option with the td connector:issue command to auto create the database and table:

$ td connector:issue load.yml --database td_sample_db --table td_sample_table --time-column updated_date --auto-create-table
You can assign Time Format column to the "Partitioning Key" by "--time-column" option.

Scheduled execution

You can schedule periodic Data Connector execution for periodic Stripe import. We take great care in distributing and operating our scheduler in order to achieve high availability. By using this feature, you no longer need a cron daemon on your local datacenter.

Create the schedule

A new schedule can be created using the td connector:create command. The name of the schedule, cron-style schedule, the database and table where their data will be stored, and the Data Connector configuration file are required.

$ td connector:create \
    daily_stripe_import \
    "10 0 * * *" \
    td_sample_db \
    td_sample_table \
The `cron` parameter also accepts these three options: `@hourly`, `@daily` and `@monthly`.
By default, schedule is setup in UTC timezone. You can set the schedule in a timezone using -t or --timezone option. The `--timezone` option only supports extended timezone formats like 'Asia/Tokyo', 'America/Los_Angeles' etc. Timezone abbreviations like PST, CST are *not* supported and may lead to unexpected schedules.

List the Schedules

You can see the list of currently scheduled entries by td connector:list.

$ td connector:list
| Name                  | Cron         | Timezone | Delay | Database     | Table           | Config                     |
| daily_stripe_import   | 10 0 * * *   | UTC      | 0     | td_sample_db | td_sample_table | {"type"=>"stripe", ... }  |

Show the Setting and History of Schedules

td connector:show shows the execution setting of a schedule entry.

% td connector:show daily_Stripe_import
Name     : daily_stripe_import
Cron     : 10 0 * * *
Timezone : UTC
Delay    : 0
Database : td_sample_db
Table    : td_sample_table

td connector:history shows the execution history of a schedule entry. To investigate the results of each individual execution, please use td job <jobid>.

% td connector:history daily_stripe_import
| JobID  | Status  | Records | Database     | Table           | Priority | Started                   | Duration |
| 578066 | success | 10000   | td_sample_db | td_sample_table | 0        | 2015-04-18 00:10:05 +0000 | 160      |
| 577968 | success | 10000   | td_sample_db | td_sample_table | 0        | 2015-04-17 00:10:07 +0000 | 161      |
| 577914 | success | 10000   | td_sample_db | td_sample_table | 0        | 2015-04-16 00:10:03 +0000 | 152      |
| 577872 | success | 10000   | td_sample_db | td_sample_table | 0        | 2015-04-15 00:10:04 +0000 | 163      |
| 577810 | success | 10000   | td_sample_db | td_sample_table | 0        | 2015-04-14 00:10:04 +0000 | 164      |
| 577766 | success | 10000   | td_sample_db | td_sample_table | 0        | 2015-04-13 00:10:04 +0000 | 155      |
| 577710 | success | 10000   | td_sample_db | td_sample_table | 0        | 2015-04-12 00:10:05 +0000 | 156      |
| 577610 | success | 10000   | td_sample_db | td_sample_table | 0        | 2015-04-11 00:10:04 +0000 | 157      |
8 rows in set

Delete the Schedule

td connector:delete removes the schedule.

$ td connector:delete daily_stripe_import


A) Modes for out plugin

You can specify file import mode in out section of load.yml.

append (default)

This is the default mode and records are appended to the target table.

  mode: append

replace (In td 0.11.10 and later)

This mode replaces data in the target table. Any manual schema changes made to the target table remains intact with this mode.

  mode: replace

B) Setup Stripe Webhooks

Purpose: To use webhooks to capture events that happen in Stripe account, instead of through direct API requests.

Login to Stripe account, click on Webhooks —> Settings —> Add endpoint

In the URL text box, enter{td_database}/{td_table}?td_write_key={td_account_write_api_key}

Next, select events that you want to ingest into the {td_database}.{td_table} specified in the URL.

You can setup additional endpoints to ingest different types of event into different {td_database}.{td_table}.

Last modified: May 24 2018 16:38:16 UTC

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