`tap-google-analytics`

Singer tap for extracting data from the Google Analytics Data API (GA4)

Capabilities

catalog
state
discover
about
stream-maps
schema-flattening

Settings

Setting	Required	Default	Description
start_date	True	None	The earliest record date to sync
property_id	True	None	Google Analytics Property ID
client_secrets	False	None	Google Analytics Client Secrets Dictionary
key_file_location	False	None	File Path to Google Analytics Client Secrets
oauth_credentials	False	None	Google Analytics OAuth Credentials
reports	False	None	Google Analytics Reports Definition
end_date	False	None	The last record date to sync
stream_maps	False	None	Config object for stream maps capability. For more information check out Stream Maps.
stream_map_config	False	None	User-defined config values to be used within map expressions.
flattening_enabled	False	None	'True' to enable schema flattening and automatically expand nested properties.
flattening_max_depth	False	None	The max depth to flatten schemas.

A full list of supported settings and capabilities is available by running: tap-google-analytics --about

Installation

Source Authentication and Authorization

Authorization Methods

tap-google-analytics supports two different ways of authorization:

Service account based authorization, where an administrator manually creates a service account with the appropriate permissions to view the account, property, and view you wish to fetch data from
OAuth access_token based authorization, where this tap gets called with a valid access_token and refresh_token produced by an OAuth flow conducted in a different system.

If you're setting up tap-google-analytics for your own organization and only plan to extract from a handful of different views in the same limited set of properties, Service Account based authorization is the simplest. When you create a service account Google gives you a json file with that service account's credentials called the client_secrets.json, and that's all you need to pass to this tap, and you only have to do it once, so this is the recommended way of configuring tap-google-analytics.

If you're building something where a wide variety of users need to be able to give access to their Google Analytics, tap-google-analytics can use an access_token granted by those users to authorize it's requests to Google. This access_token is produced by a normal Google OAuth flow, but this flow is outside the scope of tap-google-analytics. This is useful if you're integrating tap-google-analytics with another system, like Stitch Data might do to allow users to configure their extracts themselves without manual config setup. This tap expects an access_token, refresh_token, client_id and client_secret to be passed to it in order to authenticate as the user who granted the token and then access their data.

Required Analytics Reporting APIs & OAuth Scopes

In order for tap-google-analytics to access your Google Analytics Account, it needs the Analytics Reporting API and the Analytics API (which are two different things) enabled. If using a service account to authorize, these need to be enabled for a project inside the same organization as your Google Analytics account (see below), or if using an OAuth credential set, they need to be enabled for the project the OAuth client ID and secret come from.

If using the OAuth authorization method, the OAuth flow conducted elsewhere must request at minimum the analytics.readonly OAuth scope to get an access_token authorized to hit these APIs

Creating service account credentials

If you have already have a valid client_secrets.json for a service account, or if you are using OAuth based authorization, you can skip the rest of this section.

As a first step, you need to create or use an existing project in the Google Developers Console:

Sign in to the Google Account you are using for managing Google Analytics (you must have Manage Users permission at the account, property, or view level).
Open the Service accounts page. If prompted, select a project or create a new one to use for accessing Google Analytics.
Click Create service account.

In the Create service account window, type a name for the service account, and select Furnish a new private key. Then click Save and store it locally as client_secrets.json.

If you already have a service account, you can generate a key by selecting 'Edit' for the account and then selecting the option to generate a key.

Your new public/private key pair is generated and downloaded to your machine; it serves as the only copy of this key. You are responsible for storing it securely.

Add service account to the Google Analytics account

The newly created service account will have an email address that looks similar to:

quickstart@PROJECT-ID.iam.gserviceaccount.com

Use this email address to add a user to the Google analytics view you want to access via the API. For using tap-google-analytics only Read & Analyze permissions are needed.

Enable the APIs

Visit the Google Analytics Reporting API dashboard and make sure that the project you used in the Create credentials step is selected.

From this dashboard, you can enable/disable the API for your account, set Quotas and check usage stats for the service account you are using with tap-google-analytics.

Visit the Google Analytics API dashboard, make sure that the project you used in the Create credentials step is selected and enable the API for your account.

Configuration Settings

A sample config for tap-google-analytics might look like this:

One of the credentials keys must exist:

key_file_location
oauth_credentials
client_secrets

sample_config.json

{
  "property_id": "123456789",
  "reports": "reports.json",
  "start_date": "2019-05-01T00:00:00Z",
  "end_date": "2019-06-01T00:00:00Z",
  "key_file_location": "client_secrets.json",
  or
  "client_secrets": {
    "foo": "bar"
  },
  or
  "oauth_credentials": {
    "foo": "bar"
  }
}

Reports

You can easily run tap-google-analytics by itself or in a pipeline using Meltano.

As the Google Analytics Reports are defined dynamically and there are practically infinite combinations of dimensions and metrics a user can ask for, the entities and their schema (i.e. the Catalog for this tap) are not static. So, this tap behaves more or less similarly to a tap extracting data from a Data Source (e.g. a Postgres Database).

The difference of tap-google-analytics to a database tap is that the Catalog (available entities/streams and their schema) is dynamic but not available to be discovered at run time by connecting to the Data Source. It must be dynamically generated based on the reports the user wants to generate by connecting to the Google Analytics Data API.

To that end, this tap uses an additional JSON file for the definition of the reports that the user wants to be generated. You can check, as an example, the JSON file used as a default in tap_google_analytics/defaults/default_report_definition.json. Those report definitions could be part of the config.json, but we prefer to keep config.json small and clean and provide the definitions by using an additional file.

Based on the report(s) definition, it generates a valid Catalog that follows the Singer spec.

It then behaves as any Singer compatible tap and uses that Catalog (or any Catalog generated by a tap-google-analytics) to generate the requested reports. The additional JSON file for defining the reports is only required for generating an initial Catalog.

When no report definitions are provided by the user, tap-google-analytics generates a default Catalog with some common reports provided:

website_overview: Most common metrics (users, new users, sessions, avg session duration, page views, bounce rate, etc) per day
traffic_sources: Most common metrics per day, source, medium and social network
locations: Most common metrics per day and various location dimensions (continent, country, region, city, etc)
four_weekly_active_users: Monthly active users (past 30 days) per day
weekly_active_users: Weekly active users (past 7 days) per day
daily_active_users: Daily active users (past 1 day) per day
devices: Most common metrics per day, device category, operating system and browser
transactions: Most common metrics per day, transactions, revenue per user, purchase revenue, and total revenue.

This tap only allows incremental syncs using STATE records if the date dimension is included in the report. Otherwise it does not save state and does a full load each time. Without the date dimension this can be mitigated by allowing for chunked runs using [start_date, end_date).

If not provided and the tap runs without a --catalog also provided, use tap_google_analytics/defaults/default_report_definition.json as the default definition.

The reports.json file structure expected by the reports config key is really simple:

reports.json

[
  { "name" : "name of stream to be used",
    "dimensions" :
    [
      "Google Analytics Dimension",
      "Another Google Analytics Dimension",
      ... up to 7 dimensions per stream ...
    ],
    "metrics" :
    [
      "Google Analytics Metric",
      "Another Google Analytics Metric",
      ... up to 10 metrics per stream ...
    ]
  },
  {

  	... another stream definition ...
  },
  ... as many streams / reports as the user wants ...
]

For example, if you want to extract user stats per day in a users_per_day stream and session stats per day and country in a sessions_per_country_day stream:

[
  { "name" : "users_per_day",
    "dimensions" :
    [
      "date"
    ],
    "metrics" :
    [
      "users",
      "newUsers"
    ]
  },
  { "name" : "sessions_per_country_day",
    "dimensions" :
    [
      "date",
      "country"
    ],
    "metrics" :
    [
      "sessions",
      "sessionsPerUser",
      "avgSessionDuration"
    ]
  }
]

You can check tap-google-analytics/defaults/default_report_definition.json for a more lengthy, detailed example.

Segments

If you want to use the segment dimension, you must specify the segment IDs in your reports.json stream / report config:

[
  {
    "name": "acquisition",
    "dimensions": [
      "date",
      "segment",
      "channelGrouping"
    ],
    "metrics": [
      "users",
      "newUsers",
      "sessions"
    ],
    "segments": [
      "gaid::-1",
      "gaid::U7LSsrWRTq6JIIS8G8brrQ"
    ]
  }
]

Segment IDs can be found with the GA Query explorer. The account configured for authentication must either own the segment, or have "Collaborate" access to the GA view as well as the segment itself having its Segment Availability set to "Collaborators and I can apply/edit Segment in this View".

Usage

You can easily run tap-google-analytics by itself or in a pipeline using Meltano.

Executing the Tap Directly

tap-google-analytics --version
tap-google-analytics --help
tap-google-analytics --config CONFIG --discover > ./catalog.json

Developer Resources

Implementation details

This tap makes some explicit decisions:

All dimensions are part of the stream's key definition (table-key-properties).
The {start_date, end_date} parameters for the report query are also added to the schema as {report_start_date, report_end_date}.

This is important for defining the date range the records are for, especially when 'date' is not part of the requested Dimensions.
If 'date' has not been added as one of the Dimensions, then the {report_start_date, report_end_date} attributes are also added as keys.

For example, if a user requests to see user stats by device or by source, the {start_date, end_date} can be used as part of the key uniquely identifying the generated stats.

That way we can properly identify rows with no date dimension included and also update those rows over overlapping runs of the tap.
All streams and attributes are set to "inclusion": "automatic"
There is a custom metadata keyword defined for all schema attributes:

ga_type: dimension | metric

This keyword is required for processing the catalog afterwards and generating the query to be send to GA API, as dimensions and metrics are not treated equally (they are sent as separate lists of attributes)
The tap dynamically fetches the available dimensions and metrics and their data type. We have to use the Google Analytics API V3 for fetching those lists, as they are not provided in the Analytics Reporting API V4.

We use those lists to check for invalid dimension or metric names requested by the user and inform her about those errors before the tap runs and starts making requests to the Google Analytics API.

We also use the dynamically fetched dimensions and metrics lists to set the data type for those attributes and cast the values accordingly (in case of integer or numeric values)

Tap shortcomings (contributions are more than welcome):

This tap only allows incremental syncs using STATE records if the date dimension is included in the report. Incremental syncs should be supported using other combinations of Time Dimensions if provided.
We may be checking for valid Google Analytics Metrics and Dimensions and report back to the user with errors, but we are not checking for valid combinations. Not all dimensions and metrics can be queried together and we should add a black list of metrics and dimensions that can not be used with specific dimensions (e.g. impressions with browser) or with other metrics (e.g. XXdayUsers with other XXdayUsers or users).

The Available Metrics/Dimensions and combos page provides a way to discover those restrictions.

Initialize your Development Environment

pipx install poetry
poetry install

Create and Run Tests

Create tests within the tap_google_analytics/tests subfolder and then run:

poetry run tox
poetry run tox -e pytest
poetry run tox -e format
poetry run tox -e lint

The tests require an environment variable called CLIENT_SECRETS which is either the escaped client secret json file content as a string (e.g. "{"type": "service_account","project_id":...}") or the base64 encoded version of that same escaped string. Base64 is an option primarily for CI to pass secrets in the recommended fashion.

You can also test the tap-google-analytics CLI interface directly using poetry run:

poetry run tap-google-analytics --help

Testing with Meltano

Note: This tap will work in any Singer environment and does not require Meltano. Examples here are for convenience and to streamline end-to-end orchestration scenarios.

Your project comes with a custom meltano.yml project file already created. Open the meltano.yml and follow any "TODO" items listed in the file.

Next, install Meltano (if you haven't already) and any needed plugins:

# Install meltano
pipx install meltano
# Initialize meltano within this directory
cd tap-google-analytics
meltano install

Now you can test and orchestrate using Meltano:

# Test invocation:
meltano invoke tap-google-analytics --version
# OR run a test `elt` pipeline:
meltano elt tap-google-analytics target-jsonl

SDK Dev Guide

See the dev guide for more instructions on how to use the SDK to develop your own taps and targets.

Repository History and Contributors

https://gitlab.com/meltano/tap-google-analytics
https://github.com/MeltanoLabs/tap-google-analytics - Migrated to MeltanoLabs and ported to the Meltano SDK
https://gitlab.com/hotglue/tap-google-analytics - Hard forked to hotglue's GitLab repo to convert it from Universal Analytics to GA4
https://github.com/connorflyn/tap-google-analytics-v4 - Hard forked back to GitHub by @connorflyn to add Google Service Account authorization
https://github.com/z3z1ma/tap-google-analytics-v4 and https://github.com/radbrt/tap-google-analytics-v4 - Forks to address bugs
This repo forks off of https://github.com/radbrt/tap-google-analytics-v4 which was the tip of the fork tree with all the most recent changes and bug fixes

Name		Name	Last commit message	Last commit date
Latest commit History 113 Commits
.github		.github
.secrets		.secrets
tap_google_analytics		tap_google_analytics
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
meltano.yml		meltano.yml
mypy.ini		mypy.ini
poetry.lock		poetry.lock
pyproject.toml		pyproject.toml
tap-google-analytics.sh		tap-google-analytics.sh
tox.ini		tox.ini

License

dadosfera/tap-google-analytics

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