> For the complete documentation index, see [llms.txt](https://docs.zingg.ai/latest/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zingg.ai/latest/stepbystep/configuration/field-definitions.md). # Field Definitions Defining which fields should appear in the output and whether and how they need to be used in matching. #### fieldDefinition This is a JSON array representing the fields from the source data to be used for matching, and the kind of matching they need. Each field denotes a **column** from the input. Fields have the following JSON attributes: **fieldName** The **name** of the field from the input data schema. **fields** To be defined later. For now, please keep this as the `fieldName` **dataType** Type of the column - `string, integer, double`, etc. **matchType** * The way to match the given field. Multiple match types can be combined (comma-separated), for example: `FUZZY,NUMERIC`. * Here are the supported match types and descriptions: | Match Type | Description | Applicable To | | --------------------------------: | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------ | | FUZZY | Broad matches tolerant to typos, abbreviations, and other variations. Uses fuzzy string similarity features. | string, integer, long, double, date | | FUZZY\_OPTIMISED | Same semantics as FUZZY but uses an optimized implementation. Provides similar matching quality with significantly lower CPU and memory usage on large datasets. Recommended for production/large-scale matching when FUZZY quality is desired but performance is critical. [Zingg Enterprise Feature](#user-content-fn-1)[^1] | string, integer, long, double, date | | EXACT | Exact match with no tolerance for variation. Preferable for country codes, pin codes, and other categorical variables where you expect no variations. | string, integer, long, date, boolean | | DONT\_USE | Included in the output but no computation is done on this field. Useful for IDs required in the output. DONT\_USE fields are not shown to the labeler when [showConcise](https://github.com/zinggAI/zingg/tree/0.7.0/docs/stepbystep/configuration/label.md) is set to true. | any | | EMAIL | Matches only the local part (before the @) of email addresses. | string | | EMAIL\_OPTIMISED | Same semantics as EMAIL but uses an optimized implementation for much faster evaluation on large datasets while preserving match behavior. Recommended for large-scale runs where many emails are compared. [Zingg Enterprise Feature](#user-content-fn-1)[^1] | string | | PINCODE | Matches postal / pin codes (supports typical local formats such as `xxxxx` or `xxxxx-xxxx` depending on data). | string | | NULL\_OR\_BLANK | By default, Zingg treats nulls as matches. If NULL\_OR\_BLANK is added to a field that also has other match types (e.g., `FUZZY`), Zingg will build an explicit feature for null/blank values so the model can learn their effect. | string, integer, long, date, boolean | | TEXT | Compares overlapping words between two strings. Good for descriptive or long-text fields without many typos. | string | | NUMERIC | Extracts numbers from strings and compares how many are the same across both strings (useful for apartment numbers, building numbers, etc.). | string | | NUMERIC\_WITH\_UNITS | Extracts product codes or numbers with units (e.g., `16gb`) and compares how many are same across both strings | string | | ONLY\_ALPHABETS\_EXACT | Compares only the alphabetic characters and requires an exact match. Useful when numeric parts should be ignored (e.g., building name vs flat number). | string | | ONLY\_ALPHABETS\_FUZZY | Compares only the alphabetic characters using a fuzzy comparison; numeric characters are ignored. Useful for addresses when you want to handle street names fuzzily while numeric parts are handled separately (e.g., via NUMERIC). | string | | ONLY\_ALPHABETS\_FUZZY\_OPTIMISED | Same semantics as ONLY\_ALPHABETS\_FUZZY but uses optimized processing. Use this when you need the fuzzy alphabet-only behavior at production scale. [Zingg Enterprise Feature](#user-content-fn-1)[^1] | string | | MAPPING\_(FILENAME) | Maps input values to canonical values using a mapping file (e.g., nickname maps, company abbreviations, gender codes). Matching is tolerant to common variations defined in the mapping. See [Advanced Match Types](/latest/stepbystep/configuration/adv-matchtypes.md) for mapping file format and examples. | string | [^1]: Zingg Enterprise is the suite of proprietary products licensed by Zingg. Please refer to for individual tier features.