QPR ProcessAnalyzer API: Difference between revisions
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QPR ProcessAnalyzer API | QPR ProcessAnalyzer API can be used to build integrations with other applications and automate operations in the process mining system. | ||
QPR ProcessAnalyzer API is a JSON based API following the REST design principles. All methods (except the [[Web_API:_Token|token]] and [[Web_API:_Serverinfo|serverinfo]]) require a prior login to establish a session. The session is initialized with the [[Web_API:_Token|token]] call with username and password, and the access token is returned as a response for a successful login. The methods requiring prior authenticated session, need to have a HTTP request header ''Authorization'' with value ''Bearer <access token>'' to identify the session. | |||
Url for calling the API has the following form (replace the server hostname with a correct one): | |||
Url for calling the API has the following form (replace the | |||
<pre> | <pre> | ||
https://customer.onqpr.com/qprpa/api/< | https://customer.onqpr.com/qprpa/api/<methodName> | ||
</pre> | </pre> | ||
Following | Following methods are available: | ||
{| class="wikitable" | |||
!'''Method''' | |||
! '''Description''' | |||
|- | |||
||[[Web_API:_Token|token]] | |||
||Login user using username and password and get a session token as a response. | |||
|- | |||
||[[Web_API:_Signout|api/signout]] | |||
||Logs out a user session. | |||
|- | |||
||[[Web_API:_Expression|api/expression]] | |||
||Runs an expression. | |||
|- | |||
||[[Web_API:_Expression/query|api/expression/query]] | |||
||Runs query written using the expression language and returns result data as response. | |||
|- | |||
||[[Web_API:_Filters|api/filters]] | |||
||Get filters for all models or filters for a single model. | |||
|- | |||
||[[Web_API:_Serverinfo|api/serverinfo]] | |||
||Returns common system information needed by UI, such as the default UI language and in whether SSO has been configured. | |||
|- | |||
||[[Web_API:_Importfile|api/importfile]] | |||
||Import data into datatable from .csv, .xes or .pacm file. | |||
|- | |||
||[[Web_API:_Usersettings|api/usersettings]] | |||
||Save user specific settings to the server. | |||
|- | |||
||[[Web_API:_Operations/terminate|api/operations/terminate]] | |||
||Stops the defined tasks (by the task id) to save computing resources. | |||
|- | |||
||[[Web_API:_Cancel|api/analysis/cancel]] | |||
||Stops currently running tasks (by the task identifier) to save computing resources. | |||
|- | |||
||[[Web_API:_saml2/acs|api/saml2/acs]] | |||
||Identity provider (IdP) will send the SAML 2.0 assertion to this endpoint, which responses with 302 to redirect to QPR ProcessAnalyzer UI. | |||
|- | |||
||[[Web_API:_saml2|api/saml2]] | |||
||Returns the SAML 2.0 service provider (SP) metadata, if SAML 2.0 authentication has been configured. | |||
|} | |||
In addition, there are | In addition, there are methods for | ||
*[[Web API for Workspace Elements|workspace elements]] | * [[Web API for Workspace Elements|moving and deleting workspace elements]] | ||
* [[Web_API_for_Projects|projects]] | |||
* [[Web_API_for_Dashboards|dashboards]] | |||
* [[Web_API_for_Models|models]] | |||
* [[Web_API_for_Datatables|datatables]] | * [[Web_API_for_Datatables|datatables]] | ||
* [[ | * [[Web_API_for_Scripts|scripts]] | ||
* [[Web_API_for_User_Management|user management | * [[Web_API_for_User_Management|users, groups and roles]] | ||
== Examples == | |||
=== Trigger script run === | |||
Following function written in Python starts a script in QPR ProcessAnalyzer by calling the REST API. The function does following: (1) login to QPR ProcessAnalyzer, (2) start the script, and (3) log out. The call just starts the script without waiting for it to complete (asynchronous behavior). | |||
<syntaxhighlight lang="python" line> | |||
def startQprProcessAnalyzerScript(serverUrl: str, username: str, password: str, scriptId: int): | |||
loginData = { | |||
"grant_type": "password", | |||
"username": username, | |||
"password": password | |||
} | |||
loginResponse = requests.post( | |||
url = serverUrl + "/token", | |||
data = loginData | |||
) | |||
loginResponse.raise_for_status() | |||
sessionToken = loginResponse.json().get("access_token") | |||
startScriptResponse = requests.post( | |||
url = serverUrl + "/api/scripts/run/" + str(scriptId), | |||
headers = { | |||
"Authorization": "Bearer " + sessionToken, | |||
"Content-type": "application/json" | |||
} | |||
) | |||
startScriptResponse.raise_for_status() | |||
logOutResponse = requests.post( | |||
url = serverUrl + "/api/signout", | |||
headers = { | |||
"Authorization": "Bearer " + sessionToken, | |||
"Content-type": "application/json" | |||
} | |||
) | |||
logOutResponse.raise_for_status() | |||
</syntaxhighlight> | |||
The function can be called as follows: | |||
<syntaxhighlight lang="python" line> | |||
startQprProcessAnalyzerScript( | |||
serverUrl = "https://server.onqpr.com/qprpa", | |||
username = "qpr", | |||
password = "demo", | |||
scriptId = 1 | |||
) | |||
</syntaxhighlight> | |||
The script id can be found in the scripts list in the Workspace. | |||
=== Synchronize users and groups === | |||
Following script written in Python updates QPR ProcessAnalyzer users and groups based on the provided dataset. This script can be extended to fetch the user data from an external source (e.g., Azure AD) to implement a complete user management integration between the systems. | |||
This script performs following steps: | |||
# Read the provided dataset and store it to in-memory structures. | |||
# Read all users from QPR ProcessAnalyzer (including which groups the users belong to). (POST /api/expression/query) | |||
# Read all groups from QPR ProcessAnalyzer. (POST /api/expression/query) | |||
# Determine the gap between the current state in the user management and the provided dataset. | |||
# Create new users appearing in the dataset to QPR ProcessAnalyzer. (POST /api/users) | |||
# Inactivate non-existing users in the dataset from QPR ProcessAnalyzer. (PUT /api/users) | |||
# Activate existing inactive users in QPR ProcessAnalyzer that exist in the dataset. (PUT /api/users) | |||
# Add users to groups and remove from groups based on the determined gap in the state. (PUT/DELETE /api/users/memberships) | |||
Users are queried as follows: | |||
<pre> | |||
{ | |||
"Dimensions": null, | |||
"Values": [ | |||
{ | |||
"Name": "Id", | |||
"Expression": "Id" | |||
}, | |||
{ | |||
"Name": "Name", | |||
"Expression": "Name" | |||
}, | |||
{ | |||
"Name": "IsActive", | |||
"Expression": "IsActive" | |||
}, | |||
{ | |||
"Name": "Groups", | |||
"Expression": "ToJson(OrderByValue(Groups.Id))" | |||
} | |||
], | |||
"Root": "Users", | |||
"ContextType": "generic" | |||
} | |||
</pre> | |||
Groups are queried as follows: | |||
<pre> | <pre> | ||
{ | |||
"Dimensions": null, | |||
"Values": [ | |||
{ | |||
"Name": "Id", | |||
"Expression": "Id" | |||
}, | |||
{ | |||
"Name": "Name", | |||
"Expression": "Name" | |||
} | } | ||
], | |||
"Root": "UserGroups", | |||
"ContextType": "generic" | |||
} | |||
</pre> | </pre> | ||
Request /api/users/memberships body: | |||
<pre> | <pre> | ||
{ | |||
"GroupId": 1, | |||
" | "MemberId": 2, | ||
" | "RoleName": "Member" | ||
} | |||
" | |||
} | |||
</pre> | </pre> | ||
= | <syntaxhighlight lang="python" line> | ||
... | |||
</syntaxhighlight> | |||
__NOTOC__ | __NOTOC__ | ||
[[Category: QPR ProcessAnalyzer]] | [[Category: QPR ProcessAnalyzer]] |
Latest revision as of 19:04, 13 October 2024
QPR ProcessAnalyzer API can be used to build integrations with other applications and automate operations in the process mining system.
QPR ProcessAnalyzer API is a JSON based API following the REST design principles. All methods (except the token and serverinfo) require a prior login to establish a session. The session is initialized with the token call with username and password, and the access token is returned as a response for a successful login. The methods requiring prior authenticated session, need to have a HTTP request header Authorization with value Bearer <access token> to identify the session.
Url for calling the API has the following form (replace the server hostname with a correct one):
https://customer.onqpr.com/qprpa/api/<methodName>
Following methods are available:
Method | Description |
---|---|
token | Login user using username and password and get a session token as a response. |
api/signout | Logs out a user session. |
api/expression | Runs an expression. |
api/expression/query | Runs query written using the expression language and returns result data as response. |
api/filters | Get filters for all models or filters for a single model. |
api/serverinfo | Returns common system information needed by UI, such as the default UI language and in whether SSO has been configured. |
api/importfile | Import data into datatable from .csv, .xes or .pacm file. |
api/usersettings | Save user specific settings to the server. |
api/operations/terminate | Stops the defined tasks (by the task id) to save computing resources. |
api/analysis/cancel | Stops currently running tasks (by the task identifier) to save computing resources. |
api/saml2/acs | Identity provider (IdP) will send the SAML 2.0 assertion to this endpoint, which responses with 302 to redirect to QPR ProcessAnalyzer UI. |
api/saml2 | Returns the SAML 2.0 service provider (SP) metadata, if SAML 2.0 authentication has been configured. |
In addition, there are methods for
- moving and deleting workspace elements
- projects
- dashboards
- models
- datatables
- scripts
- users, groups and roles
Examples
Trigger script run
Following function written in Python starts a script in QPR ProcessAnalyzer by calling the REST API. The function does following: (1) login to QPR ProcessAnalyzer, (2) start the script, and (3) log out. The call just starts the script without waiting for it to complete (asynchronous behavior).
def startQprProcessAnalyzerScript(serverUrl: str, username: str, password: str, scriptId: int):
loginData = {
"grant_type": "password",
"username": username,
"password": password
}
loginResponse = requests.post(
url = serverUrl + "/token",
data = loginData
)
loginResponse.raise_for_status()
sessionToken = loginResponse.json().get("access_token")
startScriptResponse = requests.post(
url = serverUrl + "/api/scripts/run/" + str(scriptId),
headers = {
"Authorization": "Bearer " + sessionToken,
"Content-type": "application/json"
}
)
startScriptResponse.raise_for_status()
logOutResponse = requests.post(
url = serverUrl + "/api/signout",
headers = {
"Authorization": "Bearer " + sessionToken,
"Content-type": "application/json"
}
)
logOutResponse.raise_for_status()
The function can be called as follows:
startQprProcessAnalyzerScript(
serverUrl = "https://server.onqpr.com/qprpa",
username = "qpr",
password = "demo",
scriptId = 1
)
The script id can be found in the scripts list in the Workspace.
Synchronize users and groups
Following script written in Python updates QPR ProcessAnalyzer users and groups based on the provided dataset. This script can be extended to fetch the user data from an external source (e.g., Azure AD) to implement a complete user management integration between the systems.
This script performs following steps:
- Read the provided dataset and store it to in-memory structures.
- Read all users from QPR ProcessAnalyzer (including which groups the users belong to). (POST /api/expression/query)
- Read all groups from QPR ProcessAnalyzer. (POST /api/expression/query)
- Determine the gap between the current state in the user management and the provided dataset.
- Create new users appearing in the dataset to QPR ProcessAnalyzer. (POST /api/users)
- Inactivate non-existing users in the dataset from QPR ProcessAnalyzer. (PUT /api/users)
- Activate existing inactive users in QPR ProcessAnalyzer that exist in the dataset. (PUT /api/users)
- Add users to groups and remove from groups based on the determined gap in the state. (PUT/DELETE /api/users/memberships)
Users are queried as follows:
{ "Dimensions": null, "Values": [ { "Name": "Id", "Expression": "Id" }, { "Name": "Name", "Expression": "Name" }, { "Name": "IsActive", "Expression": "IsActive" }, { "Name": "Groups", "Expression": "ToJson(OrderByValue(Groups.Id))" } ], "Root": "Users", "ContextType": "generic" }
Groups are queried as follows:
{ "Dimensions": null, "Values": [ { "Name": "Id", "Expression": "Id" }, { "Name": "Name", "Expression": "Name" } ], "Root": "UserGroups", "ContextType": "generic" }
Request /api/users/memberships body:
{ "GroupId": 1, "MemberId": 2, "RoleName": "Member" }
...