The 3.0 release improves event tracking and supports additional audience targeting functionality.
New Features:
- Event tracking:
- The ``track`` method now dispatches its conversion event
*unconditionally*, without first determining whether the user is
targeted by a known experiment that uses the event. This may
increase outbound network traffic.
- In Optimizely results, conversion events sent by 3.0 SDKs don't
explicitly name the experiments and variations that are currently
targeted to the user. Instead, conversions are automatically
attributed to variations that the user has previously seen, as long
as those variations were served via 3.0 SDKs or by other clients
capable of automatic attribution, and as long as our backend
actually received the impression events for those variations.
- In Optimizely results, conversion events sent by 3.0 SDKs are
automatically attributed to variations that the user has
previously seen, as long as our backend has actually received the
impression events for those variations.
- Altogether, this allows you to track conversion events and
attribute them to variations even when you don’t know all of a
user’s attribute values, and even if the user’s attribute values
or the experiment’s configuration have changed such that the user
is no longer affected by the experiment. As a result, **you may
observe an increase in the conversion rate for
previously-instrumented events.** If that is undesirable, you can
reset the results of previously-running experiments after
upgrading to the 3.0 SDK.
- This will also allow you to attribute events to variations from
other Optimizely projects in your account, even though those
experiments don’t appear in the same datafile.
- Note that for results segmentation in Optimizely results, the user
attribute values from one event are automatically applied to all
other events in the same session, as long as the events in
question were actually received by our backend. This behavior was
already in place and is not affected by the 3.0 release.
- Support for all types of attribute values, not just strings.
- All values are passed through to notification listeners.
- Strings, booleans, and valid numbers are passed to the event
dispatcher and can be used for Optimizely results segmentation. A
valid number is a finite float or numbers.Integral in the inclusive range [-2⁵³,
2⁵³].
- Strings, booleans, and valid numbers are relevant for audience
conditions.
- Support for additional matchers in audience conditions:
- An ``exists`` matcher that passes if the user has a non-null value
for the targeted user attribute and fails otherwise.
- A ``substring`` matcher that resolves if the user has a string
value for the targeted attribute.
- ``gt`` (greater than) and ``lt`` (less than) matchers that resolve
if the user has a valid number value for the targeted attribute. A
valid number is a finite float or numbers.Integral in the inclusive range [-2⁵³,
2⁵³].
- The original (``exact``) matcher can now be used to target
booleans and valid numbers, not just strings.
- Support for A/B tests, feature tests, and feature rollouts whose
audiences are combined using ``"and"`` and ``"not"`` operators, not
just the ``"or"`` operator.
- Datafile-version compatibility check: The SDK will remain
uninitialized (i.e., will gracefully fail to activate experiments and
features) if given a datafile version greater than 4.
- Updated Pull Request template and commit message guidelines.
Breaking Changes:
- Conversion events sent by 3.0 SDKs don't explicitly name the experiments
and variations that are currently targeted to the user, so these events
are unattributed in raw events data export. You must use the new *results*
export to determine the variations to which events have been attributed.
- Previously, notification listeners were only given string-valued user
attributes because only strings could be passed into various method
calls. That is no longer the case. You may pass non-string attribute
values, and if you do, you must update your notification listeners to
be able to receive whatever values you pass in.
Bug Fixes:
- Experiments and features can no longer activate when a negatively
targeted attribute has a missing, null, or malformed value.
- Audience conditions (except for the new ``exists`` matcher) no
longer resolve to ``false`` when they fail to find an legitimate
value for the targeted user attribute. The result remains ``null``
(unknown). Therefore, an audience that negates such a condition
(using the ``"not"`` operator) can no longer resolve to ``true``
unless there is an unrelated branch in the condition tree that
itself resolves to ``true``.
- Updated the default event dispatcher to log an error if the request
resolves to HTTP 4xx or 5xx. ([140](https://github.com/optimizely/python-sdk/pull/140))
- All methods now validate that user IDs are strings and that
experiment keys, feature keys, feature variable keys, and event keys
are non-empty strings.