Laboratory: run a CSV through a Session and Workflow

Goal and expected result

This exercise follows Mapping → Session → Workflow in Dova 22.0.5. It uses four synthetic customers and keeps the three whose status is ACTIVE. This is an expected result to verify in your environment, not a new measurement of customer data. For direct browser-upload Mapping Run, see the CSV normalization demonstration.

Download the input · active-customers-input.csvCSV · 147 BDownload, expected output · active-customers-expected.csvCSV · 89 BDownload and file instructions · README.mdMarkdown · 2.0 kBDownload. These contain no credentials and do not automatically import a definition.

Prepare the laboratory

You need authorization to design, publish and execute all three objects, a Repository location and a compatible worker. Put the input on a path authorized for that worker and prepare an independent laboratory output. A path on your laptop is not necessarily visible to a remote worker. If the run dialog requests a file, complete that input for the displayed context; do not assume an earlier attachment remains available.

customer_id,name,status,amount
D001,Demo Alfa,ACTIVE,125.50
D002,Demo Beta,INACTIVE,40.00
D003,Demo Gamma,ACTIVE,80.00
D004,Demo Delta,ACTIVE,0.00

1. Design and publish the Mapping

1. Create m_CustomersActive. Add Flat File input with CSV format, comma delimiter, header and the four sample columns.
2. Connect Row filter: field=status, operator=equals, value=ACTIVE. It filters the record’s value.
3. Add Field selector with customer_id, name and amount. It removes status from both schema and output.
4. Connect Flat File output with CSV format and header. Validate the graph, save and publish the Mapping.

This chain contains four components. It does not convert amount to a number; the exercise retains its text values. Add and configure Convert type before any operation that needs numeric comparisons or ordering.

2. Configure the Session

1. Create s_CustomersActive and select the published Mapping version.
2. Configure laboratory input/output bindings and select an eligible execution profile or worker for your environment.
3. Review parameters and failure/retry policy. For initial acceptance, avoid retries that conceal an unresolved cause.
4. Validate and publish the Session. Check that its reference and schema correspond to the selected Mapping.

3. Arrange and execute the Workflow

1. Create wf_CustomersActive. Add a Session task and select s_CustomersActive under Reusable session.
2. Connect Start → Session → End. Validate, save and publish.
3. Run manually with the test files. Record the attempt ID and open Monitor.
4. Wait for a terminal state and open the Session task to inspect its available output. Do not enable a recurring schedule yet.

4. Compare the output

Compare by customer_id. Without Row sort, do not treat an observed record order as a universal guarantee. CSV quoting or newline differences do not by themselves invalidate the exercise’s values. Do not claim byte identity when only records were compared.

Extend the exercise and diagnose issues

After accepting the minimal Workflow, insert a Timer with Delay (seconds)=5 to observe a wait without changing the data. To explore Decision, use runMode and the expression in Tasks and links. Test both values in separate attempts before scheduling it.

If four records remain, inspect the filter and executed version. For a missing column, inspect Field selector and the target. If execution never starts, check Session publication, paths and worker eligibility. If it fails after writing, review recovery before repeating it.