Working with JSON v2

Version 2: This section describes how you can import JSON files into Dataprep by Trifacta, convert them to tabular format, wrangle them, and then export them back in the same JSON format.

The basic task is described by way of example. In the example task, the JSON file must be imported into Dataprep by Trifacta, a new column must be inserted into the JSON, and the resulting JSON must be exported in the same structure.

Enable

This method of working with JSON is enabled by default.

Note

When this feature is enabled, all JSON imported datasets created under the legacy method must be recreated to behave like v2 datasets with respect to conversion and schema management. Features developed in the future may not retroactively be supported in the v1 legacy mode. You should convert to using the v2 method.

For more information on disabling, see Dataprep Project Settings Page.

You can choose to continue using the legacy method of working with JSON.

Note

The legacy version of JSON import is required if you are working with compressed JSON files or only Newline JSON files.

You should migrate your flows to using the new version.

Note

The legacy version of working with JSON is likely to be deprecated in a future release.

For more information on migrating to the new version, see Working with JSON v1.

Requirements

JSON input

Recommended limit of 1 GB in source file size. Since conversion happens within the Trifacta node, this limit may vary depending on the memory of the Trifacta node.
Each JSON record must be less than 20 MB in size.
Filename extensions must be .json or .JSON.
Conversion of compressed JSON files is not supported. Compressed JSON files can be imported using the previous method.
For best results, all keys and values should be quoted and imported as strings.
Note
Escape characters that make JSON invalid can cause your JSON file to fail to import.
- You can escape quote values to treat them as literals in your strings using the backslash character. For example: \"
- When the values are imported into the Transformer page, the Trifacta Application re-infers the data type for each column.

JSON structure	Description	Supported?
Newline	The newline character (`\n`) denotes the end of a record. Each record can contain the keys (object or array) and values for the JSON object. Tip This version is supported through through both versions of JSON import, but it performs better in v1. If you are using the Newline form of JSON exclusively, you should use v1.	Supported
Top-level object	Top-level row contains keys for mapping JSON objects	Supported
Top-level array	Top-level row contains array of objects	Supported

JSON output

Note

JSON-formatted files that are generated by Dataprep by Trifacta are rendered in JSON Lines format, which is a single line per-record variant of JSON. For more information, see http://jsonlines.org.

Dataprep by Trifacta 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:

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

Notes:

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

Task

Import the JSON file.
Any nested data must be unnested within columns. Each level in the JSON hierarchy must be un-nested in a separate step.
When all of the JSON data is in tabular form, perform any Wrangle transformations.
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.
Run the job, generating a JSON output.

Step - Import the file

Through the Import Data page, navigate and select your JSON file for import.
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.
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.
Complete the rest of the import process.
In Flow View, add the JSON-based imported dataset to your flow and create a recipe for it.
1. Select the recipe, and click Edit Recipe....

In the Transformer page, the example above should look like the following:

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

Note

For JSON records that have multiple levels in the hierarchy, you should unnest the top level of the hierarchy first, followed by each successive level.

Tip

The easiest way to unnest is to select the column header for the column containing your nested data. Unnest should be one of the suggested options, and the suggestion should include the specification for the paths to the key values. If not, you can use the following process.

In the Recipe panel, click New Step.
In the Search panel, enter unnest values into new columns.
Specify the following transformation. Substitute the Paths to elements values below with the top-level keys in your JSON records:
Transformation Name
Unnest values into new columns
Parameter: Column
metrics
Parameter: Path to elements1
[0]
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.
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.
Click the column head again, or specify the following transformation to unnest the Object column:
Transformation Name
Unnest Objects into columns
Parameter: Column
0
Parameter: Path to elements1
rank
Parameter: Path to elements2
score
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.
Repeat the above process for the next level in the hierarchy.
You can now delete the source columns. In the example, these source columns are named metrics and 0.
Tip
SHIFT + click these columns and then select Delete columns from the right panel. Click Add.
Repeat the above steps for each nested JSON object.
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
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.

Transformation Name	`Unnest values into new columns`
Parameter: Column	metrics
Parameter: Path to elements1	[0]

Transformation Name	`Unnest Objects into columns`
Parameter: Column	0
Parameter: Path to elements1	rank
Parameter: Path to elements2	score

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

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.

If you ran a job on the example dataset, the output would look like the following:

{"rank":1043,"score":9679,"caption":"Such a good boy!","id":"9kt8ex","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"}
{"rank":1041,"score":9683,"caption":"We sure love our fur babies.","id":"a8guou","url":"https://www.example.com/mljnmq521.jpg","filename":"mljnmq521.jpg"}

Suppose you want to nest the url and filename columns into a nested array called, resources.

Re-nest the lower hierarchies until have you have a single flat record, containing some Object type columns that hold the underlying hierarchies. When the re-nested JSON records are exported, secondary hierarchies appear as escaped string values. More details later.

Tip

The following steps reshape your data. You may wish to create a new recipe as an output of the previous recipe where you can add the following steps.

Steps:

SHIFT + click the url and filename columns. Then, select Nest columns in the right-hand panel. This transformation should look like the following:
Transformation Name
Nest columns into Objects
Parameter: column1
url
Parameter: column2
filename
Parameter: Nest columns to
Object
Parameter: New column name
column1
column1 now contains an Object mapping of the two columns. You can now nest this column again into an Array:
Transformation Name
Nest columns into Objects
Parameter: Columns
column1
Parameter: Nest columns to
Array
Parameter: New column name
resources
Delete column1.
Continue nesting other columns in a similar fashion. Repeat the above steps for the next level of the hierarchy in your dataset.
You must re-nested from the bottom of the target hierarchy to the top.
Note
Do not nest the columns at the top level of the hierarchy.
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.

Transformation Name	`Nest columns into Objects`
Parameter: column1	url
Parameter: column2	filename
Parameter: Nest columns to	Object
Parameter: New column name	column1

Transformation Name	`Nest columns into Objects`
Parameter: Columns	column1
Parameter: Nest columns to	Array
Parameter: New column name	resources

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.

When the job completes, you can click the JSON link in the Output Destinations tab of the Job Details page to download your JSON file. See Job Details Page.

Output file for the above example should look like the following:

{"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:

{
  "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.

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

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.

In the Recipe panel, clickNew Step.
In the Search panel, enter unnest values into new columns.

Specify the following transformation. Substitute the Paths to elements values below with the top-level keys in your JSON records:

Transformation Name	`Unnest values into new columns`
Parameter: Column	object
Parameter: Path to elements	id
Parameter: Path to elements	score
Parameter: Path to elements	short
Parameter: Path to elements	title
Parameter: Paths to elements	ups
Parameter: Path to elements	url

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

Transformation Name	`New formula`
Parameter: Formula type	Single row formula
Parameter: Formula	AVERAGE(score)
Parameter: New column name	Average_score

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

In this section:

Working with JSON v2

Enable

Requirements

JSON input

JSON output

Example

Example 1 - Rows of JSON records

Task

Step - Import the file

Step - Unnest JSON records

Step - Wrangle your dataset

Step - Nest the JSON records

Step - Generate JSON output

Example 2 - Top-level array of JSON records

Search results