06. Sequential quality rules

Correct missing values and negative quantities, replace Excel errors and reject rows containing unacceptable text.

Task and prerequisites

Correct missing values and negative quantities, replace Excel errors and reject rows containing unacceptable text.

This example uses demonstration data and settings. Validate the expected result in your own environment before connecting production destinations. Identifiers demo_* are illustrative: select your own training connections or sources in the editor. The JSON below shows the block and connection structure, not a ready-to-transfer flow archive.

Training input data

quality.csv

ID;Qty;Price
A;;10
B;-2;5
C;3;#N/A
D;4;unknown

Blocks, connections and exact settings

06. Sequential quality rules. Example 06 topology: 3 blocks, 2 connections. Node identifiers and ports from the configured graph are preserved. — **Flow diagram 06**
Example 06 topology: 3 blocks, 2 connections. Node identifiers and ports from the configured graph are preserved.

Add the listed blocks in the designer, open each block's properties and set these values. Names demo_* identify training sources and connections.

Block	Setting
1 File `src`	Role: `source` Source: `demo_quality` Read settings: Sheet: `CSV` Range: `A1:C5` First row is the header: `так` Delimiter: `;`
2 Data cleansing and validation `quality`	Rules: Fields: `Qty` Condition: `empty` Action: `replace` Value: `0` Fields: `Qty` Condition: `negative` Action: `positive` Fields: `Price` Condition: `excel_error` Action: `null` Fields: `Price` Condition: `invalid_number` Action: `skip_row` whitespace_empty: `так` log_limit: `20`
3 Data export `out`	Format: `json` File name: `quality_clean` Purpose: `download` Delimiter: `;` Time zone: `Europe/Kyiv`

Port connections

src out → quality in
quality out → out in

Complete graph configuration as JSON

{
  "version": 3,
  "nodes": [
    {
      "id": "src",
      "type": "file",
      "label": "src",
      "config": {
        "role": "source",
        "source_id": "demo_quality",
        "options": {
          "sheet": "CSV",
          "range": "A1:C5",
          "header": true,
          "delimiter": ";"
        }
      }
    },
    {
      "id": "quality",
      "type": "quality",
      "label": "quality",
      "config": {
        "rules": [
          {
            "fields": [
              "Qty"
            ],
            "condition": "empty",
            "action": "replace",
            "value": 0
          },
          {
            "fields": [
              "Qty"
            ],
            "condition": "negative",
            "action": "positive"
          },
          {
            "fields": [
              "Price"
            ],
            "condition": "excel_error",
            "action": "null"
          },
          {
            "fields": [
              "Price"
            ],
            "condition": "invalid_number",
            "action": "skip_row"
          }
        ],
        "whitespace_empty": true,
        "log_limit": 20
      }
    },
    {
      "id": "out",
      "type": "export",
      "label": "out",
      "config": {
        "format": "json",
        "filename": "quality_clean",
        "destination": "download",
        "delimiter": ";",
        "timezone": "Europe/Kyiv"
      }
    }
  ],
  "edges": [
    {
      "source": "src",
      "target": "quality",
      "source_port": "out",
      "target_port": "in"
    },
    {
      "source": "quality",
      "target": "out",
      "source_port": "out",
      "target_port": "in"
    }
  ]
}

Expected result

ID	Qty	Price
A	0	10
B	2	5
C	3	NULL

Row D is rejected. Rules run sequentially: #N/A first becomes NULL and is no longer considered an invalid number by the next rule.

Run and verify

Upload quality.csv and save the rules in the illustrated order.
Run the flow and open quality-block details and the replacement log.
Check the 3 result rows, Qty(A)=0, Qty(B)=2 and Price(C)=NULL; the log is limited to 20 change examples.

Common errors

Swapping the last two rules changes the result: #N/A may be rejected before being replaced with NULL.
condition=invalid_number rejects nonnumeric values. condition=text rejects any nonempty text, including numeric CSV strings; it is unsuitable for this example.
log_limit limits examples in the log, not the number of processed rows.