JSON to Spark

Creates a Spark DataFrame/RDD from given JSON file. See Jackson JSON Parser documentation for more information.

Notice: This feature requires at least Apache Spark 1.5.

This node can access a variety of different file systems. More information about file handling in KNIME can be found in the official File Handling Guide.

Options

Settings

Mode
Select if you like to read a file or folder.
File/Folder
Enter the input path. The required syntax of a path depends on the connected file system. The node description of the respective connector node describes the required path format. You can also choose a previously selected file from the drop-down list, or select a location from the "Browse..." dialog. Note that browsing is disabled in some cases:
  • Browsing is disabled if the connector node hasn't been executed since the workflow has been opened. (Re)execute the connector node to enable browsing.
The location can be exposed as or automatically set via a path flow variable.
Sampling ratio
Infer the type of a collection of JSON records in three stages:
  1. Sample given amount of records and infer the type.
  2. Merge types by choosing the lowest type necessary to cover equal keys.
  3. Replace any remaining null fields with string, the top type.
Primitives
Convert primitives into Strings.
Comments
Allow comments.
Fieldnames
Allow unquoted fieldnames.
Quotes
Feature that determines whether parser will allow use of single quotes (apostrophe, character '\'') for quoting Strings (names and String values).
Numerics
  • Allow numeric leading zeros: Feature that determines whether parser will allow JSON integral numbers to start with additional (ignorable) zeroes (like: 000001).
  • Allow non numeric numbers: Feature that allows parser to recognize set of "Not-a-Number" (NaN) tokens as legal floating number values (similar to how many other data formats and programming language source code allows it). Supported tokens: INF, -INF, Infinity, -Infinity and NaN.

Input Ports

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Spark compatible connection (HDFS, WebHDFS, HttpFS, S3, Blob Storage, ...)
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Spark context

Output Ports

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Spark DataFrame/RDD

Views

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Workflows

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Links

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