On this page

Overview

Use this Snap to remove duplicate records from input documents. When you use multiple matching criteria to deduplicate your data, it is evaluated using each criterion separately, and then aggregated to give the final result.

Prerequisites

None.

Limitations

None.

Troubleshooting

None.

Modes

Ultra pipelines: Does not support Ultra pipelines.
Spark mode: Not supported in Spark mode.

Snap Input and Output

Input/Output	Type of View	Number of Views	Compatible Upstream and Downstream Snaps	Description
Input	Document	Min: 1 Max: 1	Mapper Copy Numeric to Categorical	A document with data containing duplicate records.
Output	Document	Min: 1 Max: 2	Filter Shuffle Principal Component Analysis	First output view: Required. A document containing deduplicated records. Second output view: Displays a document containing the duplicate records.

Snap Settings

Parameter Name	Data Type	Description	Default Value	Example
Label	String	Specify a unique name for the Snap. You can modify this to be more specific, especially if you have more than one of the same Snap in your Pipeline.	N/A	Deduplicate Office Names
Threshold	Decimal	Required. The minimum confidence required for documents to be considered matched as duplicates using the matching criteria. Minimum Value: 0 Maximum Value: 1	0.8	0.95
Confidence	Check box	Select this check box to include each match's confidence levels in the output.	Deselected	N/A
Matching Criteria	Fieldset	Enables you to specify the settings that you want to use to match input documents with the matching criteria.	N/A	N/A
Field	JSONPath	The field in the input dataset that you want to use for matching and identifying duplicates.	N/A	$name
Cleaner	String	Select the cleaner that you want to use on the selected fields. A cleaner makes comparison easier by removing variations from data, which are not likely to indicate genuine differences. For example, a cleaner might strip everything except digits from a ZIP code. Or, it might normalize and lowercase text. Depending on the nature of the data in the identified input fields, you can select the kind of cleaner you want to use from the options available: None Text Number Date Time	None	Text
Comparator	String	A comparator compares two values and produces a similarity indicator, which is represented by a number that can range from 0 (completely different) to 1 (exactly equal). Choose the comparator that you want to use on the selected fields, from the drop-down list: Levenshtein: Calculates the least number of edit operations (additions, deletions, and substitutions) required to change one string into another. Weighted Levenshtein: Calculates the least number of edit operations (additions, deletions, and substitutions) required to change one string into another. Each type of symbol has a different weight: number has the highest weight, while punctuation has the lowest weight. This makes "Main Street 12" very different from "Main Street 14", while "Main Street 12" is quite similar to "MainStreet12". Longest Common Substring: Identifies the longest string that is a substring of both strings. Q-Grams: Breaks a string into a set of consecutive symbols; for example, 'abc' is broken into a set containing 'ab' and 'bc'. Then, the ratio of the overlapping part is calculated. Exact: Identifies and classifies a match as either an exact match or not a match at all. An exact match assigns a score that equals the value in High. Else, it assigns a score that equals the value in Low. Soundex: Compares strings by converting them into Soundex codes. These codes begin with the first letter of the name, followed by a three-digit code that represents the first three remaining consonants. The letters A, E, I, O, U, Y, H, and W are not coded. Thus, the names 'Mathew' and 'Matthew' would generate the same Soundex code: M-300. This enables you to quickly identify strings that refer to the same person or place, but have variations in their spelling. Metaphone: Metaphone is similar to Soundex; only it improves on the Soundex algorithm by using information about variations and inconsistencies in English spelling and pronunciation to produce a more accurate encoding. Numeric: Calculates the ratio of the smaller number to the greater. Date Time: Computes the difference between two date-time data and produces a similarity measure ranging from 0.0 (meaning completely different) and 1.0 (meaning exactly equal). This property requires data in epoch format. If the date-time data in your dataset is not in epoch format, you must select Date Time in the Cleaner property to convert the date-time data into the epoch format.	Levenshtein	Numeric
Low	Decimal	A decimal value representing the level of probability of the input documents to be matched if the specified fields are completely unlike. If this value is left empty, a value of 0.3 is applied automatically.	N/A	0.1
High	Decimal	A decimal value representing the level of probability of the input documents to be matched if the specified fields are a complete match. If this value is left empty, a value of 0.95 is applied automatically.	NA	0.8
Snap Execution	String	Specifies the execution type: Validate & Execute: Performs limited execution of the Snap (up to 50 records) during Pipeline validation; performs full execution of the Snap (unlimited records) during Pipeline execution. Execute only: Performs full execution of the Snap during Pipeline execution; does not execute the Snap during Pipeline validation. Disabled: Disables the Snap and, by extension, its downstream Snaps.	Validate & Execute	N/A

Examples

Deduplicating the List of Childhood Centers in Chicago

In this example, you deduplicate the data in a CSV file containing a list of childhood centers in Chicago.

You add a File Reader Snap to the Pipeline and configure it to read the source CSV file stored online:

The File Reader Snap displays the contents of the file, which contains many duplicate entries:
You add a CSV Parser Snap to the Pipeline to interpret the input data as a CSV document.
You add a Deduplicate Snap to the Pipeline and configure it to use the name, address, ZIP, and phone details in the input document as fields for deduplication:
You also add an additional output view to the Snap, where the Snap can display the duplicate data. Now, the Snap has two output views, one for the cleaned (deduplicated) data, and another for the duplicated records that the Snap filtered out.

The Snap, when executed, offers the following two output documents (Output0 and Output1). Output0 contains the deduplicated data, while Output1 contains the duplicate data:
You attach a CSV Formatter Snap to each output view of the Deduplicate Snap to structure the outputs as CSV documents. You then connect a File Writer Snap to each CSV Formatter Snap to write the input data as files.
The Pipeline, when run, generates two output documents: one containing deduplicated data, and the other containing the duplicate data: