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  • D s product
     can generate a JSON file as an output for your job. Characteristics of generated JSON files:
    • Newline-delimited: The end of each record is the \n character. If your downstream system is expecting comma-delineated records except for the last one, additional work is required outside of the application.
    • Non-nested: Each record in the generated file is flat. 
      • For multi-level JSON hierarchies, you can nest columns together and leave the top level as a set of columns in the data grid. However, on output, the second and lower hierarchies appear as quoted string values in the output. Additional cleanup is required outside of the application. 

Example

Example 1 - Rows of JSON records

The following example contains records of images from a website:

...

  • Each row is a complete JSON record containing keys and values.

    Tip

    Tip: Nested JSON, such as metrics above, can be inserted as part of a record. It can then be unnested within the application.

  • Each key's value must have a comma after it, except for the final key value in any row. 

    Info

    NOTE: The end of a JSON record is the right curly bracket (}). Commas are not added to the end of each line in this format.

...

Workflow

  1. Import the JSON file.

  2. Any nested data must be unnested within columns. Each level in the JSON hierarchy must be un-nested in a separate step.
  3. When all of the JSON data is in tabular form, perform any 
    D s lang
     transformations. 
  4. If you need to rebuild the loose JSON hierarchy, you must nest the lower levels of the JSON hierarchy back into their original form.
    1. If it is ok to write out flat JSON records, you can export without nesting the data again.
  5. Run the job, generating a JSON output. 

Step - Import the file 

  1. Through the Import Data page, navigate and select your JSON file for import. 

    Info

    NOTE: File formats are detected based on the file extension. Please verify that your file extension is .json or .JSON, which ensures that it is passed through the conversion service.

    1. The file is passed through the conversion process, which reviews the JSON file and stores it on the base storage layer in a format that can be easily ingested as in row-per-record format. This process happens within the Import Data page. You can track progress on the right side of the screen.

  2. After the file has been converted, click the Preview icon on the right side of the screen. In the Preview, you can review the first few rows of the imported file.

    1. If some rows are missing from the preview, then you may have a syntax error in the first row after the last well-structured row. You should try to fix this in source and re-import.

    2. If all of the rows are problematic, your data is likely malformed.

  3. Complete the rest of the import process. For more information, see  Import Data Page

  4. Add the JSON-based imported dataset to a flow and create a recipe for it. For more information, see Flow View Page.
    1. Select the recipe, and click Edit Recipe...

...

metricscaptionidurlfilename
[{"rank":"1043","score":"9679"}]Such a good boy!9kt8exhttps://www.example.com/w285fpp11.jpgw285fpp11.jpg
[{"rank":"1042","score":"9681"}]This sweet puppy has transformed our life!9x2774https://www.example.com/fmll0cy11.jpgfmll0cy11.jpg
[{"rank":"1041","score":"9683"}]We sure love our fur babies.a8guouhttps://www.example.com/mljnmq521.jpgmljnmq521.jpg


Step - Unnest JSON records

Your JSON records are in tabular format. If you have nested JSON objects within your JSON records, the next step is to unnest your JSON records. 

...

  1. In the Recipe panel, click New Step
  2. In the Search panel, enter unnest values into new columns
  3. Specify the following transformation. Substitute the Paths to elements values below with the top-level keys in your JSON records:

    D trans
    Typeref
    p01NameColumn
    p01Valuemetrics
    p02NamePath to elements1
    p02Value[0]
    SearchTermUnnest values into new columns

    Tip

    Tip: You can choose to remove the original from the source or not. In deeper or wider JSON files, removing can help to identify what remains to be unnested.

  4. In the above transformation, the bracketing array around the set of values has been broken down into raw JSON. This value may now be interpreted as a String data type. From the column drop-down, you can select Object data type.
  5. Click the column head again, or specify the following transformation to unnest the Object column:
    D trans
    p03Valuescore
    Typeref
    p01NameColumn
    p01Value0
    p02NamePath to elements1
    p02Valuerank
    p03NamePath to elements2
    SearchTermUnnest Objects into columns

    1. In the above, each Paths to elements entry specifies a key in the JSON record. The key's associated value becomes the value in the new column, which is given the same name as the key. 
    2. So, this step breaks out the key-value pairs for the specified keys into separate columns in the dataset. 

  6. Repeat the above process for the next level in the hierarchy. 
  7. You can now delete the source columns. In the example, these source columns are named metrics and 0

    Tip

    Tip: SHIFT + click these columns and then select Delete columns from the right panel. Click Add.

  8. Repeat the above steps for each nested JSON object.

    Tip

    Tip: If the above set of steps needs to be applied to multiple files, you might consider stopping your work and returning to Flow View. Select this recipe and click Add New Recipe. If you add successive steps in another recipe, the first one can be used for doing initial processing of your JSON files, separate from any wrangling that you may do for individual files.

    Tip

    Tip: The unnesting process may have moved some columns into positions that are different from their order in the original JSON. Use the Move command from the column menu to reposition your columns.

Step - Wrangle your dataset

Your JSON data is ready for wrangling. Continue adding steps until you have transformed your data as needed and are ready to run a job on it.

Step - Nest the JSON records

Info

NOTE: If your desired JSON output does not include multiple hierarchies, you can skip this section. The generated JSON files are a single JSON record per row.

...

  1. SHIFT + click the url and filename columns. Then, select Nest columns in the right-hand panel. This transformation should look like the following:

    D trans
    p03ValueObject
    Typeref
    p01Namecolumn1
    p01Valueurl
    p02Namecolumn2
    p02Valuefilename
    p03NameNest columns to
    p04Valuecolumn1
    p04NameNew column name
    SearchTermNest columns into Objects
  2. column1 now contains an Object mapping of the two columns. You can now nest this column again into an Array:
    D trans
    p03Valueresources
    Typeref
    p01NameColumns
    p01Valuecolumn1
    p02NameNest columns to
    p02ValueArray
    p03NameNew column name
    SearchTermNest columns into Objects
  3. Delete column1.
  4. Continue nesting other columns in a similar fashion. Repeat the above steps for the next level of the hierarchy in your dataset.
  5. You must re-nested from the bottom of the target hierarchy to the top. 

    Info

    NOTE: Do not nest the columns at the top level of the hierarchy.

  6. When the column names contain all of the keys that you wish to generate in the top-level JSON output, you can run the job.

Step - Generate JSON output

When you are ready, you can run the job. Create or modify a publishing action to generate a JSON file for output. See Run Job Page.

...

Code Block
{"rank":1043,"score":9679,"caption":"Such a good boy!","id":"9kt8ex","url":"https://www.example.com/w285fpp11.jpg","filename":"w285fpp11.jpg","resources":[{"url":"https://www.example.com/w285fpp11.jpg","filename":"w285fpp11.jpg"}]}
{"rank":1042,"score":9681,"caption":"This sweet puppy has transformed our life!","id":"9x2774","url":"https://www.example.com/fmll0cy11.jpg","filename":"fmll0cy11.jpg","resources":[{"url":"https://www.example.com/fmll0cy11.jpg","filename":"fmll0cy11.jpg"}]}
{"rank":1041,"score":9683,"caption":"We sure love our fur babies.","id":"a8guou","url":"https://www.example.com/mljnmq521.jpg","filename":"mljnmq521.jpg","resources":[{"url":"https://www.example.com/mljnmq521.jpg","filename":"mljnmq521.jpg"}]}

Example 2 - Top-level array of JSON records

Your JSON may be formatted as a single top-level object containing an array of JSON records. The following example contains records of messages about individual diet and exercise achievements: 

Code Block
{
  "object": [
    {
      "score": 19669,
      "title": "M/07/1'3\" [23lbs > 13lbs = 10lbs] Still a bit to go, but my owner no longer refers to me as his chunky boy!",
      "ups": 19669,
      "id": "9kt8ex",
      "url": "https://i.redd.it/bzygw285fpp11.jpg",
      "short": "bzygw285fpp11.jpg"
    },
    {
      "score": 19171,
      "title": "M/29/5'11\" [605 pounds > 375 pounds = 230 pounds lost] (14 months) Still considered super morbidly obese but I've made some good progress.",
      "ups": 19171,
      "id": "9x2774",
      "url": "https://i.redd.it/wbbufmll0cy11.jpg",
      "short": "wbbufmll0cy11.jpg"
    },
    {
      "score": 16778,
      "title": "F/28/5\u20197\u201d [233lbs to 130lbs] Got tired of being obese and took control of my life!",
      "ups": 16778,
      "id": "a8guou",
      "url": "https://i.redd.it/3t0kmljnmq521.jpg",
      "short": "3t0kmljnmq521.jpg"
    },
    {
      "score": 16743,
      "title": "M/22/5'11\" [99lbs > 150lbs = 51lbs] Anorexia my recovery",
      "ups": 16743,
      "id": "atla3n",
      "url": "https://i.redd.it/9t6tvsjs16i21.jpg",
      "short": "9t6tvsjs16i21.jpg"
    }
  ]
}

The outer JSON is a single key-value pair:

  • key: object
  • value: array of JSON records

When source JSON records structured in this manner are imported, each JSON record in the object is imported into a separate row.  You can unnest this data by applying an Unnest values transformation. 

Info

NOTE: The object can contain only one nested array of JSON data. If the object contains multiple nested arrays, it is not not broken into separate rows. All unnesting must be performed in your recipe steps


Suppose you want to compute the average of all workout scores. First, you must unnest the JSON records and then apply the AVERAGE function.

Steps:

Tip

Tip: The easiest way to unnest is to select the column header for the column containing your data. After you select the column header, you are provided with suggestions to Unnest Values into new columns. You can use the Unnest suggestion and click Add. The following steps illustrate how to create this transformation manually.

  1. In the Recipe panel, click  New Step
  2. In the Search panel, enter unnest values into new columns
  3. Specify the following transformation. Substitute the Paths to elements values below with the top-level keys in your JSON records:

    D trans
    p03Valuescore
    p06NamePaths to elements
    p01NameColumn
    p06Valueups
    p03NamePath to elements
    p07Valueurl
    p04Valueshort
    SearchTermUnnest values into new columns
    p07NamePath to elements
    Typeref
    p05NamePath to elements
    p01Valueobject
    p02NamePath to elements
    p02Valueid
    p05Valuetitle
    p04NamePath to elements

  4. The above step breaks out the key-value pairs for the specified keys into separate columns in the dataset. Each Paths to elements entry specifies a key in the JSON record, which is used to create a new column of the same name. The key's associated value becomes a cell value in the new column. 

  5. You can now delete the source column. In the example, the source column is object

    Tip

    Tip: You can choose to remove the original from the source or not. In deeper or wider JSON files, removing can help to identify what remains to be unnested. When you're done unnesting a column and have removed data from the original, you should have an empty column.

Results:

id

score

short

title

ups

url

9kt8ex

19669

bzygw285fpp11.jpg

M/07/1'3" [23lbs > 13lbs = 10lbs] Still a bit to go, but my owner no longer refers to me as his chunky boy!

19669

https://i.redd.it/bzygw285fpp11.jpg

9x2774

19171

wbbufmll0cy11.jpg

M/29/5'11" [605 pounds > 375 pounds = 230 pounds lost] (14 months) Still considered super morbidly obese but I've made some good progress.

19171

https://i.redd.it/wbbufmll0cy11.jpg

a8guou

16778

3t0kmljnmq521.jpg

F/28/5’7” [233lbs to 130lbs] Got tired of being obese and took control of my life!

16778

https://i.redd.it/3t0kmljnmq521.jpg

atla3n

16743

9t6tvsjs16i21.jpg

M/22/5'11" [99lbs > 150lbs = 51lbs] Anorexia my recovery

16743

https://i.redd.it/9t6tvsjs16i21.jpg

Now you can find the average score by applying average function. 

D trans
p03ValueAverage_score
Typeref
p01NameFormula type
p01ValueSingle row formula
p02NameFormula
p02ValueAVERAGE(score)
p03NameNew column name
SearchTermNew formula

Results:

id

score

short

title

ups

url

Average_score

9kt8ex

19669

bzygw285fpp11.jpg

M/07/1'3" [23lbs > 13lbs = 10lbs] Still a bit to go, but my owner no longer refers to me as his chunky boy!

19669

https://i.redd.it/bzygw285fpp11.jpg

18090.25

9x2774

19171

wbbufmll0cy11.jpg

M/29/5'11" [605 pounds > 375 pounds = 230 pounds lost] (14 months) Still considered super morbidly obese but I've made some good progress.

19171

https://i.redd.it/wbbufmll0cy11.jpg

18090.25

a8guou

16778

3t0kmljnmq521.jpg

F/28/5’7” [233lbs to 130lbs] Got tired of being obese and took control of my life!

16778

https://i.redd.it/3t0kmljnmq521.jpg

18090.25

atla3n

16743

9t6tvsjs16i21.jpg

M/22/5'11" [99lbs > 150lbs = 51lbs] Anorexia my recovery

16743

https://i.redd.it/9t6tvsjs16i21.jpg

18090.25