Tracking specific events
As a Snowplow user, you have the access to a wide selection of event types and associated methods for tracking as well as the ability to define your own event types:
Function | Description |
---|---|
track_page_view() | Track views of web pages |
track_page_ping() | Track engagement on web pages over time |
track_link_click() | Track link clicks |
track_form_change() | Track form changes |
track_form_submit() | Track that a form was submitted |
track_site_search() | Track when a user searches your site |
track_screen_view() | Track screen views (non-web e.g. in-app) |
track_mobile_screen_view() | Track mobile screen views |
track_ecommerce_transaction() | Track ecommerce transaction |
track_ecommerce_transaction_item() | Track an item of an ecommerce transaction |
track_add_to_cart() | Track an add to cart event |
track_remove_from_cart() | Track a remove from cart event |
track_struct_event() | Track a Snowplow custom structured event |
track_self_describing_event() | Track an event that you have defined yourself |
Common tracking parameters
All events are tracked with specific methods on the tracker instance, of the form track_XXX()
, where XXX
is the name of the event to track. The parameters that are common for all track methods are:
context
tstamp
event_subject
(**New to v0.9.0)
1. Custom context
Custom context can be used to augment any standard Snowplow event type, including self describing events, with additional data.
Custom context can be added as an extra argument to any of Snowplow’s track..()
methods.
When you track an event, some of the data that you track will be specific to that event. A lot of the data you want to record, however, will describe entities that are tracked across multiple events. For example, a media company might want to track the following events:
- User views video listing
- User plays video
- User pauses video
- User shares video
- User favorites video
- User reviews video
Whilst each of those events is a different type, all of them involve capturing data about the user and the video. Both the 'user' and 'video' are entities that are tracked across multiple event types. Both are candidates to be "custom context". You as a Snowplow user can define your own custom contexts (including associated schemas) and then send data for as many custom contexts as you wish with any Snowplow event. So if you want, you can define your own "user context", and then send additional user data in that object with any event. Other examples of context include:
- articles
- videos
- products
- categories
- pages / page_types
- environments
Each tracking method accepts an additional optional context parameter after all the parameters specific to that method:
The context
argument should consist of an array of one or more instances of SelfDescribingJson
class. This class isomorphic to self-describing JSON, to be more precisely - it has Iglu URI attribute and data itself.
For example, if a server-side Python application can determine visitor's geoposition, this can be attached to the event, using the geolocation_context
that is predefined on Iglu Central:
from snowplow_tracker import SelfDescribingJson
geo_context = SelfDescribingJson(
"iglu:com.snowplowanalytics.snowplow/geolocation_context/jsonschema/1-0-0",
{
"latitude": -23.2,
"longitude": 43.0
}
)
As another example, if a visitor arrives on a page advertising a movie, the context object might look like this (movie_poster
is custom context, not predefined):
poster_context = SelfDescribingJson(
"iglu:com.acme_company/movie_poster/jsonschema/2-1-1",
{
"movie_name": "Solaris",
"poster_country": "JP",
"poster_year": "1978-01-01"
}
)
This is how to fire a page view event with both above contexts:
tracker.track_page_view("http://www.films.com", "Homepage", context=[poster_context, geo_context])
Important: Even if only one custom context is being attached to an event, it still needs to be wrapped in an array.
Note also that you should not pass in an empty array of contexts as this will fail validation. Instead of an empty array, you can pass in None
.
2. Timestamp argument
Each track...()
method supports an optional timestamp as an argument. The timestamp should be in milliseconds since the Unix epoch, the same format as generated by time.time() * 1000
.
Generally, according to the Snowplow Tracker Protocol, every event tracked will be recorded with two timestamps:
the
dvce_created_tstamp
, which is the timestamp when the event was createdthe
dvce_sent_tstamp
, which is the timestamp when the event was sent
These are going to be used downstream, to calculate the derived_tstamp
for the event, which takes also into account the collector timestamp, in order to best approximate the exact time the event occurred.
The optional timestamp argument is for the cases where you might want to set the event timestamp yourself. If this argument is not provided or set to None, then the Python Tracker will use the current time to be the dvce_created_tstamp
for the event.
Here is an example tracking a structured event and supplying the optional timestamp argument. We can explicitly supply None
for the intervening arguments which are empty:
tracker.track_struct_event("some cat", "save action", None, None, None, 1368725287000)
Alternatively, we can use the argument name:
tracker.track_struct_event("some cat", "save action", tstamp=1368725287000)
**Prior to v0.9.0
Before version 0.9.0 of the Python Tracker, providing a snowplow_tracker.timestamp.TrueTimestamp
object as the timestamp argument will attach a true timestamp to the event, replacing the device timestamp. For example:
from snowplow_tracker.tracker import TrueTimestamp
tracker.track_struct_event("some cat", "save action", tstamp=TrueTimestamp(1368725287000))
Above will attach ttm
(true_tstamp
) parameter instead of default dtm
. You can also use, plain integer, DeviceTimestamp
or None
to send device_sent_timestamp
.
**New to v0.9.0
Since version 0.9.0, providing the optional timestamp argument will only set the true timestamp (true_tstamp) of the event. The type of this argument can only be the unix time in milliseconds. If you migrate from previous version, make sure to replace any references to Timestamp objects, since the Timestamp class (along with the TrueTimestamp and DeviceTimestamp subclasses) do not exist.
tracker.track_struct_event("some cat", "save action", tstamp=1368725287000)
3. Event subject (since v0.9.0)
Since version 0.9.0, it is possible to set the Subject per-event, in order to augment the event with extra information without having to change the Subject at the Tracker level. This provides a thread safe way to track multiple subjects.
This is supported as an optional keyword argument by all track methods. For example:
evSubject = Subject().set_user_id("1234")
tracker.track_page_view("www.example.com", event_subject=evSubject)
Tracker method return values
All tracker methods will return the tracker instance, allowing tracker methods to be chained:
e = AsyncEmitter("d3rkrsqld9gmqf.cloudfront.net")
t = Tracker(e)
t.track_page_view("http://www.example.com").track_screen_view("title screen")
Since v0.13.0 we recommend using track_mobile_screen_view()
instead of the deprecated track_screen_view()
method.
Track self-describing event
Use track_self_describing_event()
to track an event types that you have defined yourself.
This method's arguments are:
Argument | Description | Required? | Type |
---|---|---|---|
event_json | The properties of the event | Yes | SelfDescribingJson |
context | Custom context for the event | No | List(SelfDescribingJson) |
tstamp | When the unstructured event occurred | No | Positive integer |
event_subject (since v0.9.0) | The subject for the event | No | Subject |
Example:
from snowplow_tracker import SelfDescribingJson
tracker.track_self_describing_event(SelfDescribingJson(
"iglu:com.example_company/save-game/jsonschema/1-0-2",
{
"save_id": "4321",
"level": 23,
"difficultyLevel": "HARD",
"dl_content": True
}
))
The event_json
is represented using the SelfDescribingJson class. It has two fields: schema
and data
. data
is a dictionary containing the properties of the unstructured event. schema
identifies the JSON schema against which data
should be validated. This schema should be available in your Iglu schema registry and your Snowplow pipeline configured so that that that registry is included in your Iglu resolver.
For more on JSON schema, see the blog post.
Many Snowplow users use the above method to track all their events i.e. only record event types that they have defined. However, there are a number of "out of the box" events that have dedicated tracking methods. These are detailed below:
Track page view
Use track_page_view()
to track a user viewing a page within your app or website. The arguments are:
Argument | Description | Required? | **Type** |
---|---|---|---|
page_url | The URL of the page | Yes | Non-empty string |
page_title | The title of the page | No | String |
referrer | The address which linked to the page | No | String |
context | Custom context for the event | No | List(SelfDescribingJson) |
tstamp | When the pageview occurred | No | Positive integer |
event_subject (since v0.9.0) | The subject for the event | No | Subject |
Example:
tracker.track_page_view("www.example.com", "example", "www.referrer.com")
Track page pings
Use track_page_ping()
to track engagement with a web page over time, via a heartbeat event. (Each ping represents a single heartbeat.)
Arguments are:
Argument | Description | Required? | **Type** |
---|---|---|---|
page_url | The URL of the page | Yes | Non-empty string |
page_title | The title of the page | No | String |
referrer | The address which linked to the page | No | String |
min_x | Minimum page X offset seen in the last ping period | No | Positive integer |
max_x | Maximum page X offset seen in the last ping period | No | Positive integer |
min_y | Minimum page Y offset seen in the last ping period | No | Positive integer |
max_y | Maximum page Y offset seen in the last ping period | No | Positive integer |
context | Custom context for the event | No | List(SelfDescribingJson) |
tstamp | When the pageview occurred | No | Positive integer |
event_subject (since v0.9.0) | The subject for the event | No | Subject |
Example:
tracker.track_page_ping("http://mytesturl/test2", "Page title 2", "http://myreferrer.com", 0, 100, 0, 500, None)
Track mobile screen view
Use track_mobile_screen_view()
to track a user viewing a screen (or equivalent) within your app.
Argument | Description | Required? | **Type** |
---|---|---|---|
id_ | Unique identifier for this screen (UUID) | No | string |
name | Human-readable name for this screen | No | Non-empty string |
type | The type of screen that was viewed e.g feed / carousel. | No | string |
previous_name | The name of the previous screenview. | No | string |
previous_id | The id of the previous screenview. | No | string |
previous_type | The type of the previous screenview. | No | string |
transition_type | The type of transition that led to the screen being viewed. | No | string |
context | Custom context for the event | No | List(SelfDescribingJson) |
tstamp | When the screen was viewed | No | Positive integer |
event_subject (since v0.9.0) | The subject for the event | No | Subject |
Example:
tracker.track_mobile_screen_view(id_="1368725287001", name="Profile Page", type="feed", previous_name="Home Page", previous_id="1368725287000", previous_type="feed")
Since v0.13.0 we recommend using track_mobile_screen_view()
instead of the deprecated track_screen_view()
method.
Track ecommerce transactions
Use track_ecommerce_transaction()
to track an ecommerce transaction. Arguments:
Argument | Description | Required? | **Type** |
---|---|---|---|
order_id | ID of the eCommerce transaction | Yes | Non-empty string |
total_value | Total transaction value | Yes | Int or Float |
affiliation | Transaction affiliation | No | String |
tax_value | Transaction tax value | No | Int or Float |
shipping | Delivery cost charged | No | Int or Float |
city | Delivery address city | No | String |
state | Delivery address state | No | String |
country | Delivery address country | No | String |
currency | Transaction currency | No | String |
items | Items in the transaction | Yes | List |
context | Custom context for the event | No | List(SelfDescribingJson) |
tstamp | When the transaction event occurred | No | Positive integer |
event_subject (since v0.9.0) | The subject for the event | No | Subject |
The items
argument is an array of Python dictionaries representing the items in the transaction. track_ecommerce_transaction
fires multiple events: one "transaction" event for the transaction as a whole, and one "transaction item" event for each element of the items
array. Each transaction item event will have the same timestamp, order_id, currency (and event_subject, since v0.9.0) as the main transaction event.
These are the fields that can appear in a transaction item dictionary:
Field | Description | Required? | **Type** |
---|---|---|---|
"sku" | Item SKU | Yes | Non-empty string |
"price" | Item price | Yes | Int or Float |
"quantity" | Item quantity | Yes | Int |
"name" | Item name | No | String |
"category" | Item category | No | String |
"context" | Custom context for the event | No | List |
Example of tracking a transaction containing two items:
tracker.track_ecommerce_transaction("6a8078be", 35, city="London", currency="GBP", items=
[{
"sku": "pbz0026",
"price": 20,
"quantity": 1
},
{
"sku": "pbz0038",
"price": 15,
"quantity": 1
}])
Track ecommerce transaction items
Use track_ecommerce_transaction_item()
to track an individual line item.
Arguments:
Argument | Description | Required? | **Type** |
---|---|---|---|
id | Order ID | Yes | Non-empty string |
sku | Item SKU | Yes | Non-empty string |
price | Item price | Yes | Int or Float |
quantity | Item quantity | Yes | Int |
name | Item name | No | String |
category | Item category | No | String |
context | Custom context for the event | No | List(SelfDescribingJson) |
tstamp | When the transaction event occurred | No | Positive integer |
event_subject (since v0.9.0) | The subject for the event | No | Subject |
Example:
tracker.track_ecommerce_transaction_item("order-789", "2001", 49.99, 1, "Green shoes", "clothing")
Track structured events
Use track_struct_event()
to track a custom event happening in your app which fits the Google Analytics-style structure of having up to five fields (with only the first two required):
Argument | Description | Required? | **Type** |
---|---|---|---|
category | The grouping of structured events which this action belongs to | Yes | Non-empty string |
action | Defines the type of user interaction which this event involves | Yes | Non-empty string |
label | A string to provide additional dimensions to the event data | No | String |
property | A string describing the object or the action performed on it | No | String |
value | A value to provide numerical data about the event | No | Int or Float |
context | Custom context for the event | No | List(SelfDescribingJson) |
tstamp | When the structured event occurred | No | Positive integer |
event_subject (since v0.9.0) | The subject for the event | No | Subject |
Example:
tracker.track_struct_event("shop", "add-to-basket", None, "pcs", 2)
Track link clicks
Use track_link_click()
to track individual link click events. Arguments are:
Argument | Description | Required? | **Type** |
---|---|---|---|
target_url | The URL of the page | Yes | Non-empty string |
element_id | ID attribute of the HTML element | No | String |
element_classes | Classes of the HTML element | No | List(string) |
element_target | Target element | No | String |
element_content | The content of the HTML element | No | String |
context | Custom context for the event | No | List(SelfDescribingJson) |
tstamp | When the pageview occurred | No | Positive integer |
event_subject (since v0.9.0) | The subject for the event | No | Subject |
Basic example:
tracker.track_link_click("http://my-target-url2/path")
Advanced example:
tracker.track_link_click("http://my-target-url2/path", "element id 2", None, "element target", "element content")
Track add-to-cart events
Use track_add_to_cart()
to track adding items to a cart on an ecommerce site. Arguments are:
Argument | Description | Required? | **Type** |
---|---|---|---|
sku | Item SKU or ID | Yes | Non-empty string |
quantity | Number of items added to cart | Yes | Integer |
name | Item's name | No | String |
category | Item's category | No | String |
unit_price | Item's price | No | Int or Float |
currency | Currency | No | String |
context | Custom context for the event | No | List(SelfDescribingJson) |
tstamp | When the pageview occurred | No | Positive integer |
event_subject (since v0.9.0) | The subject for the event | No | Subject |
Example:
tracker.track_add_to_cart("123", 2, "The Devil's Dance", "Books", 23.99, "USD", None )
Track remove-from-cart events
Use track_remove_from_cart()
to track removing items from a cart on an ecommerce site. Arguments are:
Argument | Description | Required? | **Type** |
---|---|---|---|
sku | Item SKU or ID | Yes | Non-empty string |
quantity | Number of items added to cart | Yes | Integer |
name | Item's name | No | String |
category | Item's category | No | String |
unit_price | Item's price | No | Int or Float |
currency | Currency | No | String |
context | Custom context for the event | No | List(SelfDescribingJson) |
tstamp | When the pageview occurred | No | Positive integer |
event_subject (since v0.9.0) | The subject for the event | No | Subject |
Basic example:
tracker.track_remove_from_cart("123", 1)
Advanced example:
tracker.track_remove_from_cart("123", 2, "The Devil's Dance", "Books", 23.99, "USD")
Track form change
Use track_from_change()
to track changes in website form inputs over session. Arguments are:
Argument | Description | Required? | **Type** |
---|---|---|---|
form_id | ID attribute of the HTML form | Yes | Non-empty string |
element_id | ID attribute of the HTML element | Yes | String |
node_name | Type of input element | Yes | Valid node_name |
value | Value of input element | Yes | String |
type_ | Type of data the element represents | No | Non-empty string |
element_classes | Classes of the HTML element | No | List(string) |
context | Custom context for the event | No | List(SelfDescribingJson) |
tstamp | When the pageview occurred | No | Positive integer |
event_subject (since v0.9.0) | The subject for the event | No | Subject |
Basic example:
tracker.track_form_change("signupForm", "ageInput", "age", "24")
Advanced example:
tracker.track_form_change("signupForm", "ageInput", "age", "24", "number", ["signup__number", "form__red"])
Track submitted forms
Use track_form_submit()
to track sumbitted forms. Arguments are:
Argument | Description | Required? | **Type** |
---|---|---|---|
form_id | ID attribute of the HTML form | Yes | Non-empty string |
form_classes | Classes of the HTML form | No | List(str) |
elements | Value of input element | No | List(dict) |
context | Custom context for the event | No | List(SelfDescribingJson) |
tstamp | When the pageview occurred | No | Positive integer |
event_subject (since v0.9.0) | The subject for the event | No | Subject |
Basic example:
tracker.track_form_submit("registrationForm")
Advanced example:
tracker.track_form_submit("signupForm", ["signup__warning"], {"name": "email", "value": "tracker@example.com", "nodeName": "INPUT", "type": "email"})
Track site searches
Use track_site_search()
to track a what user searches on your website. Arguments are:
Argument | Description | Required? | Type |
---|---|---|---|
terms | Search terms | Yes | List(str) |
filters | Filters applied to search | No | List(Dict[str,str]) |
total_results | Total number of results | No | Integer |
page_results | Number of pages of results | No | Integer |
context | Custom context for the event | No | List(SelfDescribingJson) |
tstamp | When the pageview occurred | No | Positive integer |
event_subject (since v0.9.0) | The subject for the event | No | Subject |
Basic example:
tracker.track_site_search(["analytics", "snowplow", "tracker"])
Advanced example:
tracker.track_site_search(["pulp fiction", "reviews"], {"nswf": true}, 215, 22)
truck_unstruct_event
This functionally is equivalent to track_self_describing_event
. We believe that the method name is misleading: this method is used to track events that are structured in nature (they have an associated schema), which is why we believe referring to them as self-describing
events makes more sense than referring to them as unstructured events
.
The method is provided for reasons of backwards compatibility.