Split Python API

This API wrapper is designed to work with Split, the platform for controlled rollouts, serving features to your users via the Split feature flag to manage your complete customer experience.

For specific instructions on how to use Split Admin REST API refer to our official API documentation.

Full documentation on this Python wrapper is available in this link.

Using in Harness Mode

Starting with version 3.5.0, the Split API client supports operating in "harness mode" to interact with both Split and Harness APIs. This is required for usage in environments that have been migrated to Harness and want to use the new features. Existing API keys will continue to work with the non-deprecated endpoints after migration, but new Harness Tokens will be required for Harness mode.

For detailed information about Harness API endpoints, please refer to the official Harness API documentation.

Authentication in Harness Mode

The client supports multiple authentication scenarios:

1. Harness-specific endpoints always use the 'x-api-key' header format
2. Split endpoints will use the 'x-api-key' header when using the harness_token
3. Split endpoints will use the normal 'Authorization' header when using the apikey
4. If both harness_token and apikey are provided, the client will use the harness_token for Harness endpoints and the apikey for Split endpoints

Base URLs and Endpoints

• Existing, non-deprecated Split endpoints continue to use the Split base URLs
• New Harness-specific endpoints use the Harness base URL (https://app.harness.io/)

Deprecated Endpoints

The following Split endpoints are deprecated and cannot be used in harness mode:

• /workspaces: POST, PATCH, DELETE, PUT verbs are deprecated
• /apiKeys: POST verb for apiKeyType == 'admin' is deprecated
• /users: all verbs are deprecated
• /groups: all verbs are deprecated
• /restrictions: all verbs are deprecated

Non-deprecated endpoints will continue to function as they did before the migration.

Basic Usage

To use the client in harness mode:

from splitapiclient.main import get_client

# Option 1: Use harness_token for Harness endpoints and apikey for Split endpoints
client = get_client({
    'harness_mode': True,
    'harness_token': 'YOUR_HARNESS_TOKEN',  # Used for Harness-specific endpoints
    'apikey': 'YOUR_SPLIT_API_KEY',         # Used for existing Split endpoints
    'account_identifier': 'YOUR_HARNESS_ACCOUNT_ID'  # Required for Harness operations
})

# Option 2: Use harness_token for all operations (if apikey is not provided)
client = get_client({
    'harness_mode': True,
    'harness_token': 'YOUR_HARNESS_TOKEN',  # Used for both Harness and Split endpoints
    'account_identifier': 'YOUR_HARNESS_ACCOUNT_ID'
})

Working with Split Resources in Harness Mode

You can still access standard Split resources with some restrictions:

# List workspaces (read-only in harness mode)
for ws in client.workspaces.list():
    print(f"Workspace: {ws.name}, Id: {ws.id}")

# Find a specific workspace
ws = client.workspaces.find("Default")

# List environments in a workspace
for env in client.environments.list(ws.id):
    print(f"Environment: {env.name}, Id: {env.id}")

Working with Harness-specific Resources

Harness mode provides access to several Harness-specific resources through dedicated microclients:

• token
• harness_apikey
• service_account
• harness_user
• harness_group
• role
• resource_group
• role_assignment
• harness_project

Basic example:

# Account identifier is required for all Harness operations
account_id = 'YOUR_ACCOUNT_IDENTIFIER'

# List all tokens
tokens = client.token.list(account_id)
for token in tokens:
    print(f"Token: {token.name}, ID: {token.id}")

# List service accounts
service_accounts = client.service_account.list(account_id)
for sa in service_accounts:
    print(f"Service Account: {sa.name}, ID: {sa.id}")

For most creation, update, and delete endpoints for harness specific resources, you will need to pass through the JSON body directly.

Example:

# Create a new service account
sa_data = {
    'name': sa_name,
    'identifier': sa_identifier,
    'email': "[email protected]",
    'accountIdentifier': account_id,
    'description': 'Service account for test',
    'tags': {'test': 'test tag'}
}
        
new_sa = client.service_account.create(sa_data, account_id)

# Add a user to a group
client.harness_user.add_user_to_groups(user.id, [group.id], account_id)

For detailed examples and API specifications for each resource, please refer to the Harness API documentation.

Setting Default Account Identifier

To avoid specifying the account identifier with every request:

# Set default account identifier when creating the client
client = get_client({
    'harness_mode': True,
    'harness_token': 'YOUR_HARNESS_TOKEN',
    'account_identifier': 'YOUR_ACCOUNT_IDENTIFIER'
})

# Now you can make calls without specifying account_identifier in each request
tokens = client.token.list()  # account_identifier is automatically included
projects = client.harness_project.list()  # account_identifier is automatically included

Quick Setup

Install the splitapiclient:

pip install splitapiclient

Import the client object and initialize connection using an Admin API Key:

from splitapiclient.main import get_client
client = get_client({'apikey': 'ADMIN API KEY'})

Enable optional logging:

import logging
logging.basicConfig(level=logging.DEBUG)

Standard Split API Usage

Workspaces

Fetch all workspaces:

for ws in client.workspaces.list():
    print("\nWorkspace:" + ws.name + ", Id: " + ws.id)

Find a specific workspace:

ws = client.workspaces.find("Defaults")
print("\nWorkspace:" + ws.name + ", Id: " + ws.id)

Environments

Fetch all Environments:

ws = client.workspaces.find("Defaults")
for env in client.environments.list(ws.id):
    print("\nEnvironment: " + env.name + ", Id: " + env.id)

Add new environment:

ws = client.workspaces.find("Defaults")
env = ws.add_environment({'name': 'new_environment', 'production': False})

Splits

Fetch all Splits:

ws = client.workspaces.find("Defaults")
for split in client.splits.list(ws.id):
    print("\nSplit: " + split.name + ", " + split._workspace_id)

Add new Split:

split = ws.add_split({'name': 'split_name', 'description': 'new UI feature'}, "user")
print(split.name)

Add tags:

split.associate_tags(['my_new_tag', 'another_new_tag'])

Add Split to environment:

ws = client.workspaces.find("Defaults")
split = client.splits.find("new_feature", ws.id) 
env = client.environments.find("Production", ws.id)
tr1 = treatment.Treatment({"name":"on","configurations":""})
tr2 = treatment.Treatment({"name":"off","configurations":""})
bk1 = bucket.Bucket({"treatment":"on","size":50})
bk2 = bucket.Bucket({"treatment":"off","size":50})
match = matcher.Matcher({"attribute":"group","type":"IN_LIST_STRING","strings":["employees"]})
cond = condition.Condition({'matchers':[match.export_dict()]})
rl = rule.Rule({'condition':cond.export_dict(), 'buckets':[bk1.export_dict(), bk2.export_dict()]})
defrl = default_rule.DefaultRule({"treatment":"off","size":100}) 
data={"treatments":[tr1.export_dict() ,tr2.export_dict()],"defaultTreatment":"off", "baselineTreatment": "off","rules":[rl.export_dict()],"defaultRule":[defrl.export_dict()], "comment": "adding split to production"}
splitdef = split.add_to_environment(env.id, data)

Kill Split:

ws = client.workspaces.find("Defaults")
env = client.environments.find("Production", ws.id)
splitDef = client.split_definitions.find("new_feature", env.id, ws.id)
splitDef.kill()

Restore Split:

splitDef.restore()

Fetch all Splits in an Environment:

ws = client.workspaces.find("Defaults")
env = client.environments.find("Production", ws.id)
for spDef in client.split_definitions.list(env.id, ws.id):
    print(spDef.name + str(spDef._default_rule))

Submit a Change request to update a Split definition:

splitDef = client.split_definitions.find("new_feature", env.id, ws.id)
definition= {"treatments":[ {"name":"on"},{"name":"off"}],
    "defaultTreatment":"off", "baselineTreatment": "off",
    "rules": [],
    "defaultRule":[{"treatment":"off","size":100}],"comment": "updating default rule"
}
splitDef.submit_change_request(definition, 'UPDATE', 'updating default rule', 'comment', ['[email protected]'], '')

Rule-Based Segments

Rule-based segments allow you to define audience segments using complex rule structures and exclusion logic. Added in version 3.5.1, they offer enhanced functionality for targeting users.

Fetch all Rule-Based Segments:

ws = client.workspaces.find("Defaults")
for segment in client.rule_based_segments.list(ws.id):
    print("\nRule-Based Segment: " + segment.name + ", " + segment.description)

Add new Rule-Based Segment:

segment_data = {
    'name': 'advanced_users',
    'description': 'Users who match advanced criteria'
}
rule_segment = ws.add_rule_based_segment(segment_data, "user")
print(rule_segment.name)

Add Rule-Based Segment to environment:

ws = client.workspaces.find("Defaults")
segment = client.rule_based_segments.find("advanced_users", ws.id)
env = client.environments.find("Production", ws.id)
segdef = segment.add_to_environment(env.id)

Remove Rule-Based Segment from environment:

ws = client.workspaces.find("Defaults")
segment = client.rule_based_segments.find("advanced_users", ws.id)
env = client.environments.find("Production", ws.id)
success = segment.remove_from_environment(env.id)

Remove Rule based segment from workspace

ws = client.workspaces.find("Defaults")
success = ws.delete_rule_based_segment("advanced_users")

Rule-Based Segment Structure

Rule-based segment definitions support multiple rule types and matching conditions:

# Examples of different matcher types
matchers = [
    # String matching
    {
        'type': 'IN_LIST_STRING',
        'attribute': 'device',
        'strings': ['mobile', 'tablet']
    },
    # Numeric comparisons
    {
        'type': 'GREATER_THAN_OR_EQUAL_NUMBER',
        'attribute': 'age',
        'number': 21
    },
    {
        'type': 'LESS_THAN_OR_EQUAL_NUMBER',
        'attribute': 'account_age_days',
        'number': 30
    },
    {
        'type': 'BETWEEN_NUMBER',
        'attribute': 'purchases',
        'between': {'from': 5, 'to': 20}
    },
    # Boolean conditions
    {
        'type': 'BOOLEAN',
        'attribute': 'subscribed',
        'bool': True
    },
    # Date/time matching
    {
        'type': 'ON_DATE',
        'attribute': 'sign_up_date',
        'date': 1623456789000  # timestamp in milliseconds
    },
    # Dependency on another split
    {
        'type': 'IN_SPLIT',
        'attribute': '',
        'depends': {'splitName': 'another_split', 'treatment': 'on'}
    }
]

# Multiple conditions using combiners
condition = {
    'combiner': 'AND',  # Can only be 'AND'
    'matchers': matchers
}

Update Rule-Based Segment definition with rules:

ws = client.workspaces.find("Defaults")
env = client.environments.find("Production", ws.id)
segdef = client.rule_based_segment_definitions.find("advanced_users", env.id, ws.id)

# Define rules that match users in a certain list
rules_data = {
    'rules': [
        {
            'condition': {
                'combiner': 'AND',
                'matchers': [
                    {
                        'type': 'GREATER_THAN_OR_EQUAL_NUMBER',
                        'attribute': 'age',
                        'number': 30
                    },
                    {
                        'type': 'BOOLEAN',
                        'attribute': 'completed_tutorials',
                        'bool': True
                    }
                ]
            }
        }
    ]
}

# Update the segment definition with the rules
updated_segdef = segdef.update(rules_data) # the workspace id is passed in from the find operation - so it's not needed here

Update Rule-Based Segment definition with excluded keys and excluded segments:

ws = client.workspaces.find("Defaults")
env = client.environments.find("Production", ws.id)
segdef = client.rule_based_segment_definitions.find("advanced_users", env.id, ws.id)

# Define rules and exclusion data
update_data = {
    'rules': [
        {
            'condition': {
                'combiner': 'AND',
                'matchers': [
                    {
                        'type': 'GREATER_THAN_OR_EQUAL_NUMBER',
                        'attribute': 'age',
                        'number': 30
                    }
                ]
            }
        }
    ],
    'excludedKeys': ['user1', 'user2', 'user3'],
    'excludedSegments': [
        {
            'name': 'beta_testers',
            'type': 'standard_segment'
        }
    ]
}

# Update the segment definition with rules and exclusions
updated_segdef = segdef.update(update_data)

Submit a Change request to update a Rule-Based Segment definition:

ws = client.workspaces.find("Defaults")
env = client.environments.find("Production", ws.id)
segdef = client.rule_based_segment_definitions.find("advanced_users", env.id, ws.id)

# New rules for the change request
rules = [
    {
        'condition': {
            'combiner': 'AND',
            'matchers': [
                {
                    'type': 'GREATER_THAN_OR_EQUAL_NUMBER',
                    'attribute': 'age',
                    'number': 25
                },
                {
                    'type': 'BOOLEAN',
                    'attribute': 'completed_tutorials',
                    'bool': True
                }
            ]
        }
    }
]

# Define excluded keys and segments for the change request
excluded_keys = ['user1', 'user2']
excluded_segments = [
    {
        'name': 'test_users',
        'type': 'rule_based_segment'
    }
]

# Submit change request with all parameters
segdef.submit_change_request(
    rules=rules,
    excluded_keys=excluded_keys,
    excluded_segments=excluded_segments,
    operation_type='UPDATE',
    title='Lower age threshold to 25',
    comment='Including more users in advanced segment',
    approvers=['[email protected]'],
    workspace_id=ws.id
)

List all change requests:

for cr in client.change_requests.list():
    if cr._split is not None:
        print(cr._id + ", " + cr._split['name'] + ", " + cr._title + ", " + str(cr._split['environment']['id'])) 
    if cr._segment is not None:
        print(cr._id + ", " + cr._segment['name'] + ", " + cr._title)

Approve specific change request:

for cr in client.change_requests.list():
    if cr._split['name'] == 'new_feature':
        cr.update_status("APPROVED", "done")

Users and Groups

Fetch all Active users:

for user in client.users.list('ACTIVE'):
    print(user.email + ", " + user._id) 

Invite new user:

group = client.groups.find('Administrators')
userData = {'email': '[email protected]', 'groups': [{'id': '', 'type': 'group'}]}
userData['groups'][0]['id'] = group._id
client.users.invite_user(userData)

Delete a pending invite:

for user in client.users.list('PENDING'):
    print(user.email + ", " + user._id + ", " + user._status)
    if user.email == '[email protected]': 
        client.users.delete(user._id)

Update user info:

data = {'name': 'new_name', 'email': '[email protected]', '2fa': False, 'status': 'ACTIVE'}
user = client.users.find('[email protected]')
user.update_user(user._id, data)

Fetch all Groups:

for group in client.groups.list():
    print(group._id + ", " + group._name)

Create Group:

client.groups.create_group({'name': 'new_group', 'description': ''})

Delete Group:

group = client.groups.find('new_group')
client.groups.delete_group(group._id)

Replace existing user group:

user = client.users.find('[email protected]')
group = client.groups.find('Administrators')
data = [{'op': 'replace', 'path': '/groups/0', 'value': {'id': '<groupId>', 'type': 'group'}}]
data[0]['value']['id'] = group._id
user.update_user_group(data)

Add user to new group

user = client.users.find('[email protected]')
group = client.groups.find('Administrators')
data = [{'op': 'add', 'path': '/groups/-', 'value': {'id': '<groupId>', 'type': 'group'}}]
data[0]['value']['id'] = group._id
user.update_user_group(data)

About Split

Commitment to Quality:

Split's APIs are in active development and are constantly tested for quality. Unit tests are developed for each wrapper based on the unique needs of that language, and integration tests, load and performance tests, and behavior consistency tests are running 24/7 via automated bots. In addition, monitoring instrumentation ensures that these wrappers behave under the expected parameters of memory, CPU, and I/O.

About Split:

Split is the leading platform for intelligent software delivery, helping businesses of all sizes deliver exceptional user experiences, and mitigate risk, by providing an easy, secure way to target features to customers. Companies like WePay, LendingTree and thredUP rely on Split to safely launch and test new features and derive insights on their use. Founded in 2015, Split's team comes from some of the most innovative enterprises in Silicon Valley, including Google, LinkedIn, Salesforce and Splunk. Split is based in Redwood City, California and backed by Accel Partners and Lightspeed Venture Partners. To learn more about Split, contact [email protected], or start a 14-day free trial at www.split.io/trial.

Try Split for Free:

Split is available as a 14-day free trial. To create an account, visit split.io/trial.

Learn more about Split:

Visit split.io/product for an overview of Split, or visit our documentation at docs.split.io for more detailed information.

System Status:

We use a status page to monitor the availability of Split's various services. You can check the current status at status.split.io.