A Workflow Example

In this chapter, the set up of a workflow is described along a concrete example.

The example consist of 2 service calls whereas the second service processes the result of teh first service call.

The Combined Services

For this example 2 services will be combined in a workflow:

• stock time series service
• covariance generator

Both services have to be available, published and subscribed by an application.

Stock Time Series Service

This service downloads the values of a given stock for a given time window.

It takes as input data:

Input Value	Description
stock_symbols	Array of stock names, e.g. ["IBM","AAPL"]
start_date	The day of the first stock value to be retrieved. Must be in form yyyy-mm-dd, e.g., 2022-01-01
end_date	The day of the last stock value to be retrieved. Must be in form yyyy-mm-dd, e.g., 2022-02-02

It takes as input params:

• no params

It returns as output:

• A Json structure containing for each stock an array of date-value pairs.

Covariance Generator

This service computes the covariance within an array of values.

It takes as input data:

Input Value	Description
time_series_data	A map of objects which contains a map of time/value pairs.

It takes as input params:

Input Value	Description
covariance_estimator	name of algorithm to be taken for computation of covariance
number_of_decimals	number of ...

It returns as output:

• A Json structure containing the shrunk covariance matrix.

The Workflow

Create a new workflow service. Go to the details page and open the workflow model editor.

Create the following flow:

by doing the following steps:

• drag 2 service nodes into the editor
• assign the 2 services to the nodes
  • click on the change type icon, open the list of PlanQK Service Tasks and select the proper service
• connect the nodes (start node -> service node -> service node -> end node)
• drag 4 data object nodes into the editor
• connect the 4 data object nodes to the service nodes as shown in picture

There is nothing to configure for the service nodes, but we need to configure the input/output data of the data object nodes.

Configure the first data object node (in_stock_data):

• Click on the first data object node. On the right side of the editor you find the context menu for this node.
• Enter "in_stock_data" as name of the node.
• Each data node must have a name. The name should not have white spaces within.
• Click on the plus-button of the content param. Enter start_date as name and ${wf_sd} as value. With this configuration you can later pass the start date as parameter wf_sd to the workflow.
• Click again on the plus-button of the content param. Enter end_date as name and ${wf_ed} as value. With this configuration you can later pass the end date as parameter wf_ed to the workflow.
• Click again on the plus-button of the content param. Enter stock_symbols as name and ${wf_stsy} as value. With this configuration you can later pass the array of stock symbols as parameter wf_stsy to the workflow.

Configure the second data object node (out_stock_data):

• Click on the second data object node to open the context menu for this node.
• Enter "out_stock_data" as name of the node.
• Leave the content mapping blank.
• Click on the connection line to the covariance service and change the type of the connection to "transformation association".
• In the context menu of this line, click on the plus-button of the Expressions param. Enter time_series_data as name and ${out_stock_data.jsonPath("$").element()} as value. With this configuration the result of the stock series service, which is a json-object, will be parsed and the content will transformed into the time_series_data parameter which is needed by the covariance service.

Configure the 3rd data object node (params_cov):

• Click on the 3rd data object node to open the context menu for this node.
• Enter "params_cov" as name of the node.
• Click on the plus-button of the content param. Enter params as name and {"covariance_estimator": "LedoitWolf","number_of_decimals": 4} as value. We now have hard coded the configuration for the service how to compute the covariance result.

Configure the 4th data object node (out_covariance):

• Click on the 4th data object node to open the context menu for this node.
• Enter "out_covariance" as name of the node.
• Leave the content mapping blank.

Save, Publish, and Subscribe

At the end, click on the save button to store the edited workflow in your workflow service.

NOTE

There is no automatic save or intermediate save, so be aware to not lose you edit work by just leaving the editor without saving your work.

When the workflow is ready, it has to be deployed to the deployed to the workflow engine. Click on the Deploy button.

Now you can leave the editor and go back to the details page of your service. To trigger an execution of the workflow, the service has to be published (internal or to marketplace). After it is published you can create an application and subscribe to this service.

Start the Service

Now you can trigger an execution of the workflow via the OpenAPI-UI of your application.

As the POST call expects some input params, you have to pass a structure, containing the 3 configured parameter wf_stsy, wf_sd and wf_ed. This is an example of how the request body may look like:

{
  "data": {
    "wf_stsy": {
      "value": "[\"IBM\",\"AAPL\"]",
      "type": "String"
    },
    "wf_sd": {
      "value": "2022-01-02",
      "type": "String"
    },
    "wf_ed": {
      "value": "2022-01-15",
      "type": "String"
    }
  }
}

If the workflow was executed without errors you can request the result via the GET/{id}/result endpoint which you find further below in the OpenAPI-UI.

The result contains an array of variables. Within this array, not only the output variables are listed, but also all input parameters and all intermediate data which are created during the workflow execution.

For this example, the result should contain:

variable name	description	example content
wf_stsy	the stock symbols for the first service passed as input param to the workflow execution	["IBM","AAPL"]
wf_st	the start date for the first service passed as input param to the workflow execution	2022-01-01
wf_ed	the end date for the first service passed as input param to the workflow execution	2022-01-05
out_stock_data	the result of the first service	"AAPL": { "2022-01-01": 179, "2022-01-01": 180 } ...
time_series_data	the transformed out_stock_data which is then passed as input param to the second service	"AAPL": { "2022-01-01": 179, "2022-01-01": 180 } ...
out_covariance	the result of the second service	"shrunk_cov_matrix": { "AAPL": { "AAPL":0.001 }, ... }