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client
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"""A module for Split.io SDK API clients."""
import
logging
import
json
from
collections
import
namedtuple
import
copy
from
splitio
.
client
import
input_validator
from
splitio
.
engine
.
evaluator
import
Evaluator
,
CONTROL
,
EvaluationDataFactory
,
AsyncEvaluationDataFactory
from
splitio
.
engine
.
splitters
import
Splitter
from
splitio
.
models
.
impressions
import
Impression
,
Label
,
ImpressionDecorated
from
splitio
.
models
.
events
import
Event
,
EventWrapper
,
SdkEvent
from
splitio
.
models
.
telemetry
import
get_latency_bucket_index
,
MethodExceptionsAndLatencies
from
splitio
.
optional
.
loaders
import
asyncio
from
splitio
.
util
.
time
import
get_current_epoch_time_ms
,
utctime_ms
_LOGGER
=
logging
.
getLogger
(
__name__
)
EvaluationOptions
=
namedtuple
(
'EvaluationOptions'
, [
'properties'
])
class
ClientBase
(
object
):
# pylint: disable=too-many-instance-attributes
"""Entry point for the split sdk."""
_FAILED_EVAL_RESULT
=
{
'treatment'
:
CONTROL
,
'configurations'
:
None
,
'impression'
: {
'label'
:
Label
.
EXCEPTION
,
'change_number'
:
None
,
},
'impressions_disabled'
:
False
}
_NON_READY_EVAL_RESULT
=
{
'treatment'
:
CONTROL
,
'configurations'
:
None
,
'impression'
: {
'label'
:
Label
.
NOT_READY
,
'change_number'
:
None
},
'impressions_disabled'
:
False
}
def
__init__
(
self
,
factory
,
recorder
,
events_manager
,
labels_enabled
=
True
,
fallback_treatment_calculator
=
None
):
"""
Construct a Client instance.
:param factory: Split factory (client & manager container)
:type factory: splitio.client.factory.SplitFactory
:param labels_enabled: Whether to store labels on impressions
:type labels_enabled: bool
:param recorder: recorder instance
:type recorder: splitio.recorder.StatsRecorder
:rtype: Client
"""
self
.
_factory
=
factory
self
.
_labels_enabled
=
labels_enabled
self
.
_recorder
=
recorder
self
.
_splitter
=
Splitter
()
self
.
_feature_flag_storage
=
factory
.
_get_storage
(
'splits'
)
# pylint: disable=protected-access
self
.
_segment_storage
=
factory
.
_get_storage
(
'segments'
)
# pylint: disable=protected-access
self
.
_events_storage
=
factory
.
_get_storage
(
'events'
)
# pylint: disable=protected-access
self
.
_evaluator
=
Evaluator
(
self
.
_splitter
,
fallback_treatment_calculator
)
self
.
_telemetry_evaluation_producer
=
self
.
_factory
.
_telemetry_evaluation_producer
self
.
_telemetry_init_producer
=
self
.
_factory
.
_telemetry_init_producer
self
.
_fallback_treatment_calculator
=
fallback_treatment_calculator
self
.
_events_manager
=
events_manager
@
property
def
ready
(
self
):
"""Return whether the SDK initialization has finished."""
return
self
.
_factory
.
ready
@
property
def
destroyed
(
self
):
"""Return whether the factory holding this client has been destroyed."""
return
self
.
_factory
.
destroyed
def
_client_is_usable
(
self
):
if
self
.
destroyed
:
_LOGGER
.
error
(
"Client has already been destroyed - no calls possible"
)
return
False
if
self
.
_factory
.
_waiting_fork
():
_LOGGER
.
error
(
"Client is not ready - no calls possible"
)
return
False
return
True
@
staticmethod
def
_validate_treatment_input
(
key
,
feature
,
attributes
,
method
,
evaluation_options
=
None
):
"""Perform all static validations on user supplied input."""
matching_key
,
bucketing_key
=
input_validator
.
validate_key
(
key
,
'get_'
+
method
.
value
)
if
not
matching_key
:
raise
_InvalidInputError
()
feature
=
input_validator
.
validate_feature_flag_name
(
feature
,
'get_'
+
method
.
value
)
if
not
feature
:
raise
_InvalidInputError
()
if
not
input_validator
.
validate_attributes
(
attributes
,
'get_'
+
method
.
value
):
raise
_InvalidInputError
()
evaluation_options
=
ClientBase
.
_validate_treatment_options
(
'get_'
+
method
.
value
,
evaluation_options
)
return
matching_key
,
bucketing_key
,
feature
,
attributes
,
evaluation_options
@
staticmethod
def
_validate_treatments_input
(
key
,
features
,
attributes
,
method
,
evaluation_options
=
None
):
"""Perform all static validations on user supplied input."""
matching_key
,
bucketing_key
=
input_validator
.
validate_key
(
key
,
'get_'
+
method
.
value
)
if
not
matching_key
:
raise
_InvalidInputError
()
features
=
input_validator
.
validate_feature_flags_get_treatments
(
'get_'
+
method
.
value
,
features
)
if
not
features
:
raise
_InvalidInputError
()
if
not
input_validator
.
validate_attributes
(
attributes
,
'get_'
+
method
.
value
):
raise
_InvalidInputError
()
evaluation_options
=
ClientBase
.
_validate_treatment_options
(
'get_'
+
method
.
value
,
evaluation_options
)
return
matching_key
,
bucketing_key
,
features
,
attributes
,
evaluation_options
@
staticmethod
def
_validate_treatment_options
(
method_name
,
evaluation_options
=
None
):
evaluation_options
=
input_validator
.
validate_evaluation_options
(
evaluation_options
,
method_name
)
if
evaluation_options
==
None
:
return
None
if
evaluation_options
.
properties
is
not
None
:
valid
,
properties
,
size
=
input_validator
.
valid_properties
(
evaluation_options
.
properties
,
method_name
)
evaluation_options
=
EvaluationOptions
(
properties
)
if
not
valid
:
evaluation_options
=
EvaluationOptions
(
None
)
return
evaluation_options
def
_build_impression
(
self
,
key
,
bucketing
,
feature
,
result
,
properties
=
None
):
"""Build an impression based on evaluation data & it's result."""
return
ImpressionDecorated
(
Impression
(
matching_key
=
key
,
feature_name
=
feature
,
treatment
=
result
[
'treatment'
],
label
=
result
[
'impression'
][
'label'
]
if
self
.
_labels_enabled
else
None
,
change_number
=
result
[
'impression'
][
'change_number'
],
bucketing_key
=
bucketing
,
time
=
utctime_ms
(),
previous_time
=
None
,
properties
=
json
.
dumps
(
properties
)
if
properties
is
not
None
else
None
),
disabled
=
result
[
'impressions_disabled'
])
def
_build_impressions
(
self
,
key
,
bucketing
,
results
,
properties
=
None
):
"""Build an impression based on evaluation data & it's result."""
return
[
self
.
_build_impression
(
key
,
bucketing
,
feature
,
result
,
properties
)
for
feature
,
result
in
results
.
items
()
]
def
_validate_track
(
self
,
key
,
traffic_type
,
event_type
,
value
=
None
,
properties
=
None
):
"""
Validate track call parameters
:param key: user key associated to the event
:type key: str
:param traffic_type: traffic type name
:type traffic_type: str
:param event_type: event type name
:type event_type: str
:param value: (Optional) value associated to the event
:type value: Number
:param properties: (Optional) properties associated to the event
:type properties: dict
:return: validation, event created and its properties size.
:rtype: tuple(bool, splitio.models.events.Event, int)
"""
if
self
.
destroyed
:
_LOGGER
.
error
(
"Client has already been destroyed - no calls possible"
)
return
False
,
None
,
None
if
self
.
_factory
.
_waiting_fork
():
_LOGGER
.
error
(
"Client is not ready - no calls possible"
)
return
False
,
None
,
None
key
=
input_validator
.
validate_track_key
(
key
)
event_type
=
input_validator
.
validate_event_type
(
event_type
)
value
=
input_validator
.
validate_value
(
value
)
valid
,
properties
,
size
=
input_validator
.
valid_properties
(
properties
,
'track'
)
if
key
is
None
or
event_type
is
None
or
traffic_type
is
None
or
value
is
False
\
or
valid
is
False
:
return
False
,
None
,
None
event
=
Event
(
key
=
key
,
traffic_type_name
=
traffic_type
,
event_type_id
=
event_type
,
value
=
value
,
timestamp
=
utctime_ms
(),
properties
=
properties
,
)
return
True
,
event
,
size
def
_get_properties
(
self
,
evaluation_options
):
return
evaluation_options
.
properties
if
evaluation_options
!=
None
else
None
def
_get_fallback_treatment_with_config
(
self
,
feature
):
fallback_treatment
=
self
.
_fallback_treatment_calculator
.
resolve
(
feature
,
""
)
return
fallback_treatment
.
treatment
,
fallback_treatment
.
config
def
_get_fallback_eval_results
(
self
,
eval_result
,
feature
):
result
=
copy
.
deepcopy
(
eval_result
)
fallback_treatment
=
self
.
_fallback_treatment_calculator
.
resolve
(
feature
,
result
[
"impression"
][
"label"
])
result
[
"impression"
][
"label"
]
=
fallback_treatment
.
label
result
[
"treatment"
]
=
fallback_treatment
.
treatment
result
[
"configurations"
]
=
fallback_treatment
.
config
return
result
def
_check_impression_label
(
self
,
result
):
return
result
[
'impression'
][
'label'
]
==
None
or
(
result
[
'impression'
][
'label'
]
!=
None
and
result
[
'impression'
][
'label'
].
find
(
Label
.
SPLIT_NOT_FOUND
)
==
-
1
)
def
_validate_sdk_event_info
(
self
,
sdk_event
,
callback_handle
):
if
not
self
.
_check_sdk_event
(
sdk_event
):
return
False
if
not
hasattr
(
callback_handle
,
'__call__'
):
_LOGGER
.
warning
(
"Client Event Subscription: The callback handle passed must be of type function, ignoring event subscribing action."
)
return
False
return
True
def
_check_sdk_event
(
self
,
sdk_event
):
if
not
isinstance
(
sdk_event
,
SdkEvent
):
_LOGGER
.
warning
(
"Client Event Subscription: The event passed must be of type SdkEvent, ignoring event subscribing action."
)
return
False
return
True
class
Client
(
ClientBase
):
# pylint: disable=too-many-instance-attributes
"""Entry point for the split sdk."""
def
__init__
(
self
,
factory
,
recorder
,
events_manager
,
labels_enabled
=
True
,
fallback_treatment_calculator
=
None
):
"""
Construct a Client instance.
:param factory: Split factory (client & manager container)
:type factory: splitio.client.factory.SplitFactory
:param labels_enabled: Whether to store labels on impressions
:type labels_enabled: bool
:param recorder: recorder instance
:type recorder: splitio.recorder.StatsRecorder
:rtype: Client
"""
ClientBase
.
__init__
(
self
,
factory
,
recorder
,
events_manager
,
labels_enabled
,
fallback_treatment_calculator
)
self
.
_context_factory
=
EvaluationDataFactory
(
factory
.
_get_storage
(
'splits'
),
factory
.
_get_storage
(
'segments'
),
factory
.
_get_storage
(
'rule_based_segments'
))
def
destroy
(
self
):
"""
Destroy the underlying factory.
Only applicable when using in-memory operation mode.
"""
self
.
_factory
.
destroy
()
def
on
(
self
,
sdk_event
,
callback_handle
):
if
not
self
.
_validate_sdk_event_info
(
sdk_event
,
callback_handle
):
return
self
.
_events_manager
.
register
(
sdk_event
,
callback_handle
)
def
get_treatment
(
self
,
key
,
feature_flag_name
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Get the treatment for a feature flag and key, with an optional dictionary of attributes.
This method never raises an exception. If there's a problem, the appropriate log message
will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param feature_flag_name: The name of the feature flag for which to get the treatment
:type feature_flag_name: str
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: The treatment for the key and feature flag
:rtype: str
"""
try
:
treatment
,
_
=
self
.
_get_treatment
(
MethodExceptionsAndLatencies
.
TREATMENT
,
key
,
feature_flag_name
,
attributes
,
evaluation_options
)
return
treatment
except
:
_LOGGER
.
error
(
'get_treatment failed'
)
treatment
,
_
=
self
.
_get_fallback_treatment_with_config
(
feature_flag_name
)
return
treatment
def
get_treatment_with_config
(
self
,
key
,
feature_flag_name
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Get the treatment and config for a feature flag and key, with optional dictionary of attributes.
This method never raises an exception. If there's a problem, the appropriate log message
will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param feature: The name of the feature flag for which to get the treatment
:type feature: str
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: The treatment for the key and feature flag
:rtype: tuple(str, str)
"""
try
:
return
self
.
_get_treatment
(
MethodExceptionsAndLatencies
.
TREATMENT_WITH_CONFIG
,
key
,
feature_flag_name
,
attributes
,
evaluation_options
)
except
Exception
:
_LOGGER
.
error
(
'get_treatment_with_config failed'
)
return
self
.
_get_fallback_treatment_with_config
(
feature_flag_name
)
def
_get_treatment
(
self
,
method
,
key
,
feature
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Validate key, feature flag name and object, and get the treatment and config with an optional dictionary of attributes.
:param key: The key for which to get the treatment
:type key: str
:param feature_flag_name: The name of the feature flag for which to get the treatment
:type feature_flag_name: str
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param method: The method calling this function
:type method: splitio.models.telemetry.MethodExceptionsAndLatencies
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: The treatment and config for the key and feature flag
:rtype: dict
"""
if
not
self
.
_client_is_usable
():
# not destroyed & not waiting for a fork
return
self
.
_get_fallback_treatment_with_config
(
feature
)
start
=
get_current_epoch_time_ms
()
if
not
self
.
ready
:
_LOGGER
.
error
(
"Client is not ready - no calls possible"
)
self
.
_telemetry_init_producer
.
record_not_ready_usage
()
try
:
key
,
bucketing
,
feature
,
attributes
,
evaluation_options
=
self
.
_validate_treatment_input
(
key
,
feature
,
attributes
,
method
,
evaluation_options
)
except
_InvalidInputError
:
return
self
.
_get_fallback_treatment_with_config
(
feature
)
result
=
self
.
_get_fallback_eval_results
(
self
.
_NON_READY_EVAL_RESULT
,
feature
)
if
self
.
ready
:
try
:
ctx
=
self
.
_context_factory
.
context_for
(
key
, [
feature
])
input_validator
.
validate_feature_flag_names
({
feature
:
ctx
.
flags
.
get
(
feature
)},
'get_'
+
method
.
value
)
result
=
self
.
_evaluator
.
eval_with_context
(
key
,
bucketing
,
feature
,
attributes
,
ctx
)
except
RuntimeError
as
e
:
_LOGGER
.
error
(
'Error getting treatment for feature flag'
)
_LOGGER
.
debug
(
'Error: '
,
exc_info
=
True
)
self
.
_telemetry_evaluation_producer
.
record_exception
(
method
)
result
=
self
.
_get_fallback_eval_results
(
self
.
_FAILED_EVAL_RESULT
,
feature
)
properties
=
self
.
_get_properties
(
evaluation_options
)
if
self
.
_check_impression_label
(
result
):
impression_decorated
=
self
.
_build_impression
(
key
,
bucketing
,
feature
,
result
,
properties
)
self
.
_record_stats
([(
impression_decorated
,
attributes
)],
start
,
method
)
return
result
[
'treatment'
],
result
[
'configurations'
]
def
get_treatments
(
self
,
key
,
feature_flag_names
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Evaluate multiple feature flags and return a dictionary with all the feature flag/treatments.
Get the treatments for a list of feature flags considering a key, with an optional dictionary of
attributes. This method never raises an exception. If there's a problem, the appropriate
log message will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param features: Array of the names of the feature flags for which to get the treatment
:type feature: list
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: Dictionary with the result of all the feature flags provided
:rtype: dict
"""
try
:
with_config
=
self
.
_get_treatments
(
key
,
feature_flag_names
,
MethodExceptionsAndLatencies
.
TREATMENTS
,
attributes
,
evaluation_options
)
return
{
feature_flag
:
result
[
0
]
for
(
feature_flag
,
result
)
in
with_config
.
items
()}
except
Exception
:
return
{
feature
:
self
.
_get_fallback_treatment_with_config
(
feature
)[
0
]
for
feature
in
feature_flag_names
}
def
get_treatments_with_config
(
self
,
key
,
feature_flag_names
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Evaluate multiple feature flags and return a dict with feature flag -> (treatment, config).
Get the treatments for a list of feature flags considering a key, with an optional dictionary of
attributes. This method never raises an exception. If there's a problem, the appropriate
log message will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param features: Array of the names of the feature flags for which to get the treatment
:type feature: list
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: Dictionary with the result of all the feature flags provided
:rtype: dict
"""
try
:
return
self
.
_get_treatments
(
key
,
feature_flag_names
,
MethodExceptionsAndLatencies
.
TREATMENTS_WITH_CONFIG
,
attributes
,
evaluation_options
)
except
Exception
:
return
{
feature
: (
self
.
_get_fallback_treatment_with_config
(
feature
))
for
feature
in
feature_flag_names
}
def
get_treatments_by_flag_set
(
self
,
key
,
flag_set
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Get treatments for feature flags that contain given flag set.
This method never raises an exception. If there's a problem, the appropriate log message
will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param flag_set: flag set
:type flag_sets: str
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: Dictionary with the result of all the feature flags provided
:rtype: dict
"""
return
self
.
_get_treatments_by_flag_sets
(
key
, [
flag_set
],
MethodExceptionsAndLatencies
.
TREATMENTS_BY_FLAG_SET
,
attributes
,
evaluation_options
)
def
get_treatments_by_flag_sets
(
self
,
key
,
flag_sets
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Get treatments for feature flags that contain given flag sets.
This method never raises an exception. If there's a problem, the appropriate log message
will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param flag_sets: list of flag sets
:type flag_sets: list
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: Dictionary with the result of all the feature flags provided
:rtype: dict
"""
return
self
.
_get_treatments_by_flag_sets
(
key
,
flag_sets
,
MethodExceptionsAndLatencies
.
TREATMENTS_BY_FLAG_SETS
,
attributes
,
evaluation_options
)
def
get_treatments_with_config_by_flag_set
(
self
,
key
,
flag_set
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Get treatments for feature flags that contain given flag set.
This method never raises an exception. If there's a problem, the appropriate log message
will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param flag_set: flag set
:type flag_sets: str
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: Dictionary with the result of all the feature flags provided
:rtype: dict
"""
return
self
.
_get_treatments_by_flag_sets
(
key
, [
flag_set
],
MethodExceptionsAndLatencies
.
TREATMENTS_WITH_CONFIG_BY_FLAG_SET
,
attributes
,
evaluation_options
)
def
get_treatments_with_config_by_flag_sets
(
self
,
key
,
flag_sets
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Get treatments for feature flags that contain given flag set.
This method never raises an exception. If there's a problem, the appropriate log message
will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param flag_set: flag set
:type flag_sets: str
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: Dictionary with the result of all the feature flags provided
:rtype: dict
"""
return
self
.
_get_treatments_by_flag_sets
(
key
,
flag_sets
,
MethodExceptionsAndLatencies
.
TREATMENTS_WITH_CONFIG_BY_FLAG_SETS
,
attributes
,
evaluation_options
)
def
_get_treatments_by_flag_sets
(
self
,
key
,
flag_sets
,
method
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Get treatments for feature flags that contain given flag sets.
This method never raises an exception. If there's a problem, the appropriate log message
will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param flag_sets: list of flag sets
:type flag_sets: list
:param method: Treatment by flag set method flavor
:type method: splitio.models.telemetry.MethodExceptionsAndLatencies
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: Dictionary with the result of all the feature flags provided
:rtype: dict
"""
feature_flags_names
=
self
.
_get_feature_flag_names_by_flag_sets
(
flag_sets
,
'get_'
+
method
.
value
)
if
feature_flags_names
==
[]:
_LOGGER
.
warning
(
"%s: No valid Flag set or no feature flags found for evaluating treatments"
,
'get_'
+
method
.
value
)
return
{}
if
'config'
in
method
.
value
:
return
self
.
_get_treatments
(
key
,
feature_flags_names
,
method
,
attributes
,
evaluation_options
)
with_config
=
self
.
_get_treatments
(
key
,
feature_flags_names
,
method
,
attributes
,
evaluation_options
)
return
{
feature_flag
:
result
[
0
]
for
(
feature_flag
,
result
)
in
with_config
.
items
()}
def
get_treatments_by_flag_set
(
self
,
key
,
flag_set
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Get treatments for feature flags that contain given flag set.
This method never raises an exception. If there's a problem, the appropriate log message
will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param flag_set: flag set
:type flag_sets: str
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: Dictionary with the result of all the feature flags provided
:rtype: dict
"""
return
self
.
_get_treatments_by_flag_sets
(
key
, [
flag_set
],
MethodExceptionsAndLatencies
.
TREATMENTS_BY_FLAG_SET
,
attributes
,
evaluation_options
)
def
get_treatments_by_flag_sets
(
self
,
key
,
flag_sets
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Get treatments for feature flags that contain given flag sets.
This method never raises an exception. If there's a problem, the appropriate log message
will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param flag_sets: list of flag sets
:type flag_sets: list
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: Dictionary with the result of all the feature flags provided
:rtype: dict
"""
return
self
.
_get_treatments_by_flag_sets
(
key
,
flag_sets
,
MethodExceptionsAndLatencies
.
TREATMENTS_BY_FLAG_SETS
,
attributes
,
evaluation_options
)
def
get_treatments_with_config_by_flag_set
(
self
,
key
,
flag_set
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Get treatments for feature flags that contain given flag set.
This method never raises an exception. If there's a problem, the appropriate log message
will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param flag_set: flag set
:type flag_sets: str
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: Dictionary with the result of all the feature flags provided
:rtype: dict
"""
return
self
.
_get_treatments_by_flag_sets
(
key
, [
flag_set
],
MethodExceptionsAndLatencies
.
TREATMENTS_WITH_CONFIG_BY_FLAG_SET
,
attributes
,
evaluation_options
)
def
get_treatments_with_config_by_flag_sets
(
self
,
key
,
flag_sets
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Get treatments for feature flags that contain given flag set.
This method never raises an exception. If there's a problem, the appropriate log message
will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param flag_set: flag set
:type flag_sets: str
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: Dictionary with the result of all the feature flags provided
:rtype: dict
"""
return
self
.
_get_treatments_by_flag_sets
(
key
,
flag_sets
,
MethodExceptionsAndLatencies
.
TREATMENTS_WITH_CONFIG_BY_FLAG_SETS
,
attributes
,
evaluation_options
)
def
_get_feature_flag_names_by_flag_sets
(
self
,
flag_sets
,
method_name
):
"""
Sanitize given flag sets and return list of feature flag names associated with them
:param flag_sets: list of flag sets
:type flag_sets: list
:return: list of feature flag names
:rtype: list
"""
sanitized_flag_sets
=
input_validator
.
validate_flag_sets
(
flag_sets
,
method_name
)
feature_flags_by_set
=
self
.
_feature_flag_storage
.
get_feature_flags_by_sets
(
sanitized_flag_sets
)
if
feature_flags_by_set
is
None
:
_LOGGER
.
warning
(
"Fetching feature flags for flag set %s encountered an error, skipping this flag set."
%
(
flag_sets
))
return
[]
return
feature_flags_by_set
def
_get_treatments
(
self
,
key
,
features
,
method
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Validate key, feature flag names and objects, and get the treatments and configs with an optional dictionary of attributes.
:param key: The key for which to get the treatment
:type key: str
:param feature_flag_names: Array of feature flag names for which to get the treatments
:type feature_flag_names: list(str)
:param method: The method calling this function
:type method: splitio.models.telemetry.MethodExceptionsAndLatencies
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: The treatments and configs for the key and feature flags
:rtype: dict
"""
start
=
get_current_epoch_time_ms
()
if
not
self
.
_client_is_usable
():
return
input_validator
.
generate_control_treatments
(
features
,
self
.
_fallback_treatment_calculator
)
if
not
self
.
ready
:
_LOGGER
.
error
(
"Client is not ready - no calls possible"
)
self
.
_telemetry_init_producer
.
record_not_ready_usage
()
try
:
key
,
bucketing
,
features
,
attributes
,
evaluation_options
=
self
.
_validate_treatments_input
(
key
,
features
,
attributes
,
method
,
evaluation_options
)
except
_InvalidInputError
:
return
input_validator
.
generate_control_treatments
(
features
,
self
.
_fallback_treatment_calculator
)
results
=
{
n
:
self
.
_get_fallback_eval_results
(
self
.
_NON_READY_EVAL_RESULT
,
n
)
for
n
in
features
}
if
self
.
ready
:
try
:
ctx
=
self
.
_context_factory
.
context_for
(
key
,
features
)
input_validator
.
validate_feature_flag_names
({
feature
:
ctx
.
flags
.
get
(
feature
)
for
feature
in
features
},
'get_'
+
method
.
value
)
results
=
self
.
_evaluator
.
eval_many_with_context
(
key
,
bucketing
,
features
,
attributes
,
ctx
)
except
RuntimeError
as
e
:
_LOGGER
.
error
(
'Error getting treatment for feature flag'
)
_LOGGER
.
debug
(
'Error: '
,
exc_info
=
True
)
self
.
_telemetry_evaluation_producer
.
record_exception
(
method
)
results
=
{
n
:
self
.
_get_fallback_eval_results
(
self
.
_FAILED_EVAL_RESULT
,
n
)
for
n
in
features
}
properties
=
self
.
_get_properties
(
evaluation_options
)
imp_decorated_attrs
=
[
(
i
,
attributes
)
for
i
in
self
.
_build_impressions
(
key
,
bucketing
,
results
,
properties
)
if
i
.
Impression
.
label
==
None
or
(
i
.
Impression
.
label
!=
None
and
i
.
Impression
.
label
.
find
(
Label
.
SPLIT_NOT_FOUND
))
==
-
1
]
self
.
_record_stats
(
imp_decorated_attrs
,
start
,
method
)
return
{
feature
: (
results
[
feature
][
'treatment'
],
results
[
feature
][
'configurations'
])
for
feature
in
results
}
def
_record_stats
(
self
,
impressions_decorated
,
start
,
operation
):
"""
Record impressions.
:param impressions_decorated: Generated impressions
:type impressions_decorated: list[tuple[splitio.models.impression.ImpressionDecorated, dict]]
:param start: timestamp when get_treatment or get_treatments was called
:type start: int
:param operation: operation performed.
:type operation: str
"""
end
=
get_current_epoch_time_ms
()
self
.
_recorder
.
record_treatment_stats
(
impressions_decorated
,
get_latency_bucket_index
(
end
-
start
),
operation
,
'get_'
+
operation
.
value
)
def
track
(
self
,
key
,
traffic_type
,
event_type
,
value
=
None
,
properties
=
None
):
"""
Track an event.
:param key: user key associated to the event
:type key: str
:param traffic_type: traffic type name
:type traffic_type: str
:param event_type: event type name
:type event_type: str
:param value: (Optional) value associated to the event
:type value: Number
:param properties: (Optional) properties associated to the event
:type properties: dict
:return: Whether the event was created or not.
:rtype: bool
"""
if
not
self
.
ready
:
_LOGGER
.
warning
(
"track: the SDK is not ready, results may be incorrect. Make sure to wait for SDK readiness before using this method"
)
self
.
_telemetry_init_producer
.
record_not_ready_usage
()
start
=
get_current_epoch_time_ms
()
should_validate_existance
=
self
.
ready
and
self
.
_factory
.
_sdk_key
!=
'localhost'
# pylint: disable=protected-access
traffic_type
=
input_validator
.
validate_traffic_type
(
traffic_type
,
should_validate_existance
,
self
.
_factory
.
_get_storage
(
'splits'
),
# pylint: disable=protected-access
)
is_valid
,
event
,
size
=
self
.
_validate_track
(
key
,
traffic_type
,
event_type
,
value
,
properties
)
if
not
is_valid
:
return
False
try
:
return_flag
=
self
.
_recorder
.
record_track_stats
([
EventWrapper
(
event
=
event
,
size
=
size
,
)],
get_latency_bucket_index
(
get_current_epoch_time_ms
()
-
start
))
return
return_flag
except
Exception
:
# pylint: disable=broad-except
self
.
_telemetry_evaluation_producer
.
record_exception
(
MethodExceptionsAndLatencies
.
TRACK
)
_LOGGER
.
error
(
'Error processing track event'
)
_LOGGER
.
debug
(
'Error: '
,
exc_info
=
True
)
return
False
class
ClientAsync
(
ClientBase
):
# pylint: disable=too-many-instance-attributes
"""Entry point for the split sdk."""
def
__init__
(
self
,
factory
,
recorder
,
events_manager
,
labels_enabled
=
True
,
fallback_treatment_calculator
=
None
):
"""
Construct a Client instance.
:param factory: Split factory (client & manager container)
:type factory: splitio.client.factory.SplitFactory
:param labels_enabled: Whether to store labels on impressions
:type labels_enabled: bool
:param recorder: recorder instance
:type recorder: splitio.recorder.StatsRecorder
:rtype: Client
"""
ClientBase
.
__init__
(
self
,
factory
,
recorder
,
events_manager
,
labels_enabled
,
fallback_treatment_calculator
)
self
.
_context_factory
=
AsyncEvaluationDataFactory
(
factory
.
_get_storage
(
'splits'
),
factory
.
_get_storage
(
'segments'
),
factory
.
_get_storage
(
'rule_based_segments'
))
async
def
destroy
(
self
):
"""
Destroy the underlying factory.
Only applicable when using in-memory operation mode.
"""
await
self
.
_factory
.
destroy
()
async
def
on
(
self
,
sdk_event
,
callback_handle
):
if
not
self
.
_validate_sdk_event_info
(
sdk_event
,
callback_handle
):
return
await
self
.
_events_manager
.
register
(
sdk_event
,
callback_handle
)
async
def
get_treatment
(
self
,
key
,
feature_flag_name
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Get the treatment for a feature and key, with an optional dictionary of attributes, for async calls
This method never raises an exception. If there's a problem, the appropriate log message
will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param feature: The name of the feature for which to get the treatment
:type feature: str
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: The treatment for the key and feature
:rtype: str
"""
try
:
treatment
,
_
=
await
self
.
_get_treatment
(
MethodExceptionsAndLatencies
.
TREATMENT
,
key
,
feature_flag_name
,
attributes
,
evaluation_options
)
return
treatment
except
:
_LOGGER
.
error
(
'get_treatment failed'
)
treatment
,
_
=
self
.
_get_fallback_treatment_with_config
(
feature_flag_name
)
return
treatment
async
def
get_treatment_with_config
(
self
,
key
,
feature_flag_name
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Get the treatment for a feature and key, with an optional dictionary of attributes, for async calls
This method never raises an exception. If there's a problem, the appropriate log message
will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param feature: The name of the feature for which to get the treatment
:type feature: str
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: The treatment for the key and feature
:rtype: str
"""
try
:
return
await
self
.
_get_treatment
(
MethodExceptionsAndLatencies
.
TREATMENT_WITH_CONFIG
,
key
,
feature_flag_name
,
attributes
,
evaluation_options
)
except
Exception
:
_LOGGER
.
error
(
'get_treatment_with_config failed'
)
return
self
.
_get_fallback_treatment_with_config
(
feature_flag_name
)
async
def
_get_treatment
(
self
,
method
,
key
,
feature
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Validate key, feature flag name and object, and get the treatment and config with an optional dictionary of attributes, for async calls
:param key: The key for which to get the treatment
:type key: str
:param feature_flag_name: The name of the feature flag for which to get the treatment
:type feature_flag_name: str
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param method: The method calling this function
:type method: splitio.models.telemetry.MethodExceptionsAndLatencies
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: The treatment and config for the key and feature flag
:rtype: dict
"""
if
not
self
.
_client_is_usable
():
# not destroyed & not waiting for a fork
return
self
.
_get_fallback_treatment_with_config
(
feature
)
start
=
get_current_epoch_time_ms
()
if
not
self
.
ready
:
_LOGGER
.
error
(
"Client is not ready - no calls possible"
)
await
self
.
_telemetry_init_producer
.
record_not_ready_usage
()
try
:
key
,
bucketing
,
feature
,
attributes
,
evaluation_options
=
self
.
_validate_treatment_input
(
key
,
feature
,
attributes
,
method
,
evaluation_options
)
except
_InvalidInputError
:
return
self
.
_get_fallback_treatment_with_config
(
feature
)
result
=
self
.
_get_fallback_eval_results
(
self
.
_NON_READY_EVAL_RESULT
,
feature
)
if
self
.
ready
:
try
:
ctx
=
await
self
.
_context_factory
.
context_for
(
key
, [
feature
])
input_validator
.
validate_feature_flag_names
({
feature
:
ctx
.
flags
.
get
(
feature
)},
'get_'
+
method
.
value
)
result
=
self
.
_evaluator
.
eval_with_context
(
key
,
bucketing
,
feature
,
attributes
,
ctx
)
except
Exception
as
e
:
# toto narrow this
_LOGGER
.
error
(
'Error getting treatment for feature flag'
)
_LOGGER
.
debug
(
'Error: '
,
exc_info
=
True
)
await
self
.
_telemetry_evaluation_producer
.
record_exception
(
method
)
result
=
self
.
_get_fallback_eval_results
(
self
.
_FAILED_EVAL_RESULT
,
feature
)
properties
=
self
.
_get_properties
(
evaluation_options
)
if
self
.
_check_impression_label
(
result
):
impression_decorated
=
self
.
_build_impression
(
key
,
bucketing
,
feature
,
result
,
properties
)
await
self
.
_record_stats
([(
impression_decorated
,
attributes
)],
start
,
method
)
return
result
[
'treatment'
],
result
[
'configurations'
]
async
def
get_treatments
(
self
,
key
,
feature_flag_names
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Evaluate multiple feature flags and return a dictionary with all the feature flag/treatments, for async calls
Get the treatments for a list of feature flags considering a key, with an optional dictionary of
attributes. This method never raises an exception. If there's a problem, the appropriate
log message will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param features: Array of the names of the feature flags for which to get the treatment
:type feature: list
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: Dictionary with the result of all the feature flags provided
:rtype: dict
"""
try
:
with_config
=
await
self
.
_get_treatments
(
key
,
feature_flag_names
,
MethodExceptionsAndLatencies
.
TREATMENTS
,
attributes
,
evaluation_options
)
return
{
feature_flag
:
result
[
0
]
for
(
feature_flag
,
result
)
in
with_config
.
items
()}
except
Exception
:
return
{
feature
:
self
.
_get_fallback_treatment_with_config
(
feature
)[
0
]
for
feature
in
feature_flag_names
}
async
def
get_treatments_with_config
(
self
,
key
,
feature_flag_names
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Evaluate multiple feature flags and return a dict with feature flag -> (treatment, config), for async calls
Get the treatments for a list of feature flags considering a key, with an optional dictionary of
attributes. This method never raises an exception. If there's a problem, the appropriate
log message will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param features: Array of the names of the feature flags for which to get the treatment
:type feature: list
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: Dictionary with the result of all the feature flags provided
:rtype: dict
"""
try
:
return
await
self
.
_get_treatments
(
key
,
feature_flag_names
,
MethodExceptionsAndLatencies
.
TREATMENTS_WITH_CONFIG
,
attributes
,
evaluation_options
)
except
Exception
:
return
{
feature
: (
self
.
_get_fallback_treatment_with_config
(
feature
))
for
feature
in
feature_flag_names
}
async
def
get_treatments_by_flag_set
(
self
,
key
,
flag_set
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Get treatments for feature flags that contain given flag set.
This method never raises an exception. If there's a problem, the appropriate log message
will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param flag_set: flag set
:type flag_sets: str
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: Dictionary with the result of all the feature flags provided
:rtype: dict
"""
return
await
self
.
_get_treatments_by_flag_sets
(
key
, [
flag_set
],
MethodExceptionsAndLatencies
.
TREATMENTS_BY_FLAG_SET
,
attributes
,
evaluation_options
)
async
def
get_treatments_by_flag_sets
(
self
,
key
,
flag_sets
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Get treatments for feature flags that contain given flag sets.
This method never raises an exception. If there's a problem, the appropriate log message
will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param flag_sets: list of flag sets
:type flag_sets: list
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: Dictionary with the result of all the feature flags provided
:rtype: dict
"""
return
await
self
.
_get_treatments_by_flag_sets
(
key
,
flag_sets
,
MethodExceptionsAndLatencies
.
TREATMENTS_BY_FLAG_SETS
,
attributes
,
evaluation_options
)
async
def
get_treatments_with_config_by_flag_set
(
self
,
key
,
flag_set
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Get treatments for feature flags that contain given flag set.
This method never raises an exception. If there's a problem, the appropriate log message
will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param flag_set: flag set
:type flag_sets: str
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
:return: Dictionary with the result of all the feature flags provided
:rtype: dict
"""
return
await
self
.
_get_treatments_by_flag_sets
(
key
, [
flag_set
],
MethodExceptionsAndLatencies
.
TREATMENTS_WITH_CONFIG_BY_FLAG_SET
,
attributes
,
evaluation_options
)
async
def
get_treatments_with_config_by_flag_sets
(
self
,
key
,
flag_sets
,
attributes
=
None
,
evaluation_options
=
None
):
"""
Get treatments for feature flags that contain given flag set.
This method never raises an exception. If there's a problem, the appropriate log message
will be generated and the method will return the CONTROL treatment.
:param key: The key for which to get the treatment
:type key: str
:param flag_set: flag set
:type flag_sets: str
:param attributes: An optional dictionary of attributes
:type attributes: dict
:param evaluation_options: An optional dictionary of options
:type evaluation_options: dict
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