This repository has been migrated to https://github.com/Eppo-exp/eppo-multiplatform.
Eppo is a modular flagging and experimentation analysis tool. Eppo's Python SDK is built to make assignments in multi-user server side contexts. Before proceeding you'll need an Eppo account.
- Feature gates
- Kill switches
- Progressive rollouts
- A/B/n experiments
- Mutually exclusive experiments (Layers)
- Dynamic configuration
pip install eppo-server-sdk
Begin by initializing a singleton instance of Eppo's client. Once initialized, the client can be used to make assignments anywhere in your app.
import eppo_client
from eppo_client.config import Config, AssignmentLogger
client_config = Config(api_key="<SDK-KEY-FROM-DASHBOARD>")
eppo_client.init(client_config)
import eppo_client
client = eppo_client.get_instance()
user = get_current_user()
variation = eppoClient.get_boolean_assignment(
'show-new-feature',
user.id,
{ 'country': user.country },
False
)
Every Eppo flag has a return type that is set once on creation in the dashboard. Once a flag is created, assignments in code should be made using the corresponding typed function:
get_boolean_assignment(...)
get_numeric_assignment(...)
get_integer_assignment(...)
get_string_assignment(...)
get_json_assignment(...)
Each function has the same signature, but returns the type in the function name. For booleans use get_boolean_assignment
, which has the following signature:
get_boolean_assignment(
flag_key: str,
subject_key: str,
subject_attributes: Dict[str, Any],
default_value: bool
) -> bool:
The init
function accepts the following optional configuration arguments.
Option | Type | Description | Default |
---|---|---|---|
assignment_logger |
AssignmentLogger | A callback that sends each assignment to your data warehouse. Required only for experiment analysis. See example below. | None |
is_graceful_mode |
bool | When true, gracefully handles all exceptions within the assignment function and returns the default value. | True |
poll_interval_seconds |
Optional[int] | The interval in seconds at which the SDK polls for configuration updates. If set to None , polling is disabled. |
300 |
poll_jitter_seconds |
int | The jitter in seconds to add to the poll interval. | 30 |
initial_configuration |
Optional[Configuration] | If set, the client will use this configuration until it fetches a fresh one. | None |
To use the Eppo SDK for experiments that require analysis, pass in a callback logging function to the init
function on SDK initialization. The SDK invokes the callback to capture assignment data whenever a variation is assigned. The assignment data is needed in the warehouse to perform analysis.
The code below illustrates an example implementation of a logging callback using Segment, but you can use any system you'd like. The only requirement is that the SDK receives a log_assignment
callback function. Here we define an implementation of the Eppo SegmentAssignmentLogger
interface containing a single function named log_assignment
:
from eppo_client.assignment_logger import AssignmentLogger
import analytics
# Connect to Segment.
analytics.write_key = "<SEGMENT_WRITE_KEY>"
class SegmentAssignmentLogger(AssignmentLogger):
def log_assignment(self, assignment):
analytics.track(assignment["subject"], "Eppo Randomization Assignment", assignment)
client_config = Config(api_key="<SDK-KEY-FROM-DASHBOARD>", assignment_logger=SegmentAssignmentLogger())
The SDK may see many duplicate assignments in a short period of time, and if you have configured a logging function, they will be transmitted to your downstream event store. This increases the cost of storage as well as warehouse costs during experiment analysis.
To mitigate this, a caching assignment logger is optionally available with configurable cache behavior.
The caching can be configured individually for assignment logs and bandit action logs using AssignmentCacheLogger
.
AssignmentCacheLogger
optionally accepts two caches. We recommend using cachetools
but any subclass of MutableMapping
works.
import cachetools
from eppo_client.assignment_logger import AssignmentLogger, AssignmentCacheLogger
class MyLogger(AssignmentLogger):
# implement your logger
pass
client_config = Config(
api_key="<SDK-KEY-FROM-DASHBOARD>",
assignment_logger=AssignmentCacheLogger(
MyLogger(),
# cache 1024 least recently used assignments
assignment_cache=cachetools.LRUCache(maxsize=1024),
# cache bandit assignment for no longer than 10 minutes
bandit_cache=cachetools.TTLCache(maxsize=2048, ttl=600),
),
)
To support the use-case of needing to bootstrap a front-end client, the Eppo SDK provides a function to export flag configurations to a JSON string.
Use the get_flag_configurations
function to export flag configurations to a JSON string and then send it to the front-end client.
from fastapi import JSONResponse
import eppo_client
import json
client = eppo_client.get_instance()
flag_configurations = client.get_flag_configurations()
# Convert flag configurations to a JSON string
flag_config_json = json.dumps(flag_configurations)
# Create a JSONResponse object with the stringified JSON
response = JSONResponse(content={"flagConfigurations": flag_config_json})
Eppo's SDKs are built for simplicity, speed and reliability. Flag configurations are compressed and distributed over a global CDN (Fastly), typically reaching your servers in under 15ms. Server SDKs continue polling Eppo’s API at 30-second intervals. Configurations are then cached locally, ensuring that each assignment is made instantly. Evaluation logic within each SDK consists of a few lines of simple numeric and string comparisons. The typed functions listed above are all developers need to understand, abstracting away the complexity of the Eppo's underlying (and expanding) feature set.
To publish a new version of the SDK, set the version as desired in eppo_client/version.py
, then create a new Github release. The CI/CD configuration will handle the build and publish to PyPi.