Matcher

The matcher takes two datasets and finds records that correspond to each other. It outputs three arrays: matched pairs, unmatched records from the left side, and unmatched records from the right side.

Basic Usage

{
  "type": "matcher",
  "properties": {
    "left": "@input.invoices",
    "right": "@input.payments",
    "matchOn": ["invoice_id"],
    "outputMatched": "matched",
    "outputUnmatchedLeft": "unmatched_invoices",
    "outputUnmatchedRight": "unmatched_payments"
  }
}

This matches invoices to payments by exact invoice_id. Records with matching IDs land in @matched. Invoices with no payment go to @unmatched_invoices. Payments with no invoice go to @unmatched_payments.

Properties Reference

Matching Criteria

Exact Key Matching (matchOn)

Fields listed in matchOn must match exactly. This is the primary matching criteria — records are only compared if their matchOn fields align.

{
  "matchOn": ["invoice_id"]
}

Multiple keys create a composite match — all must match:

{
  "matchOn": ["vendor_id", "invoice_number"]
}

Numeric Tolerance (tolerance)

Allow the amount field to differ by an absolute value. A tolerance of 50 means amounts within $50 of each other are still considered a match.

{
  "matchOn": ["invoice_id"],
  "tolerance": 50
}

With this configuration, an invoice for $1,000.00 would match a payment of $950.00–$1,050.00. Smaller tolerances such as 0.02 are still valid when you truly mean two cents.

Date Window (dateWindowDays)

Allow date fields to differ by up to N days:

{
  "matchOn": ["invoice_id"],
  "dateWindowDays": 3
}

An invoice dated January 10 would match a payment dated January 7–13.

Fuzzy Text Matching (fuzzyThreshold + descriptionKey)

Compare text fields using fuzzy string similarity. The threshold is 0–100 where 100 is an exact match:

{
  "matchOn": ["vendor_id"],
  "fuzzyThreshold": 85,
  "descriptionKey": "description"
}

This matches records where vendor_id is identical and the description fields are at least 85% similar. Useful for matching line-item descriptions that may be worded differently across systems.

Custom Rules (rules)

Define additional matching rules evaluated by the condition engine:

{
  "matchOn": ["invoice_id"],
  "rules": [
    {
      "condition": {
        "lessOrEqual": [
          { "abs": { "subtract": ["@left.amount", "@right.amount"] } },
          50
        ]
      }
    }
  ]
}

Custom rules use the same condition operators as workflow conditions, with @left and @right referencing the current pair being compared.

Output Format

Matched Pairs

Each matched record contains both the left and right record:

[
  {
    "a": { "invoice_id": "INV-001", "amount": 1000, "vendor": "Acme" },
    "b": { "invoice_id": "INV-001", "amount": 1000, "vendor": "Acme Corp" },
    "match_score": 0.95,
    "amount_difference": 0
  }
]

The a field is the left record, b is the right record. match_score reflects overall match quality. amount_difference shows numeric deviation when tolerance matching is used.

Unmatched Records

Unmatched arrays contain the original records with no modifications:

[
  { "invoice_id": "INV-099", "amount": 5000, "vendor": "NewVendor" }
]

Worked Example

Input:

{
  "invoices": [
    { "invoice_id": "INV-001", "amount": 1000.00, "date": "2025-01-10", "description": "Monthly service fee" },
    { "invoice_id": "INV-002", "amount": 2500.00, "date": "2025-01-15", "description": "Equipment rental" },
    { "invoice_id": "INV-003", "amount": 750.00, "date": "2025-01-20", "description": "Consulting hours" }
  ],
  "payments": [
    { "invoice_id": "INV-001", "amount": 1000.00, "date": "2025-01-12", "description": "Monthly service" },
    { "invoice_id": "INV-002", "amount": 2475.00, "date": "2025-01-15", "description": "Equip rental Jan" }
  ]
}

Matcher configuration:

{
  "type": "matcher",
  "properties": {
    "left": "@input.invoices",
    "right": "@input.payments",
    "matchOn": ["invoice_id"],
    "tolerance": 50,
    "dateWindowDays": 3,
    "fuzzyThreshold": 80,
    "descriptionKey": "description",
    "outputMatched": "reconciled",
    "outputUnmatchedLeft": "exceptions"
  }
}

Results:

• @reconciled: INV-001 (exact match), INV-002 (amount difference $25 within tolerance, descriptions 80%+ similar)
• @exceptions: INV-003 (no matching payment found)

Using Uploaded Documents

Instead of embedding datasets in the execution payload, upload files to Document Storage and reference them with the doc: prefix:

{
  "type": "matcher",
  "properties": {
    "left": "doc:doc_a1b2c3d4e5f6",
    "right": "doc:doc_x7y8z9w0v1u2",
    "matchOn": ["invoice_id"],
    "tolerance": 50,
    "outputMatched": "matched",
    "outputUnmatchedLeft": "exceptions"
  }
}

CSV files resolve to Array<Object> with header rows as keys. JSON files resolve as-is. Pin a specific version with doc:doc_xxx@2 for audit reproducibility.

Large Dataset Optimization

For large datasets (10,000+ records per side), the matcher automatically switches to an indexed matching strategy when the infrastructure supports it. This provides significant performance improvements by pre-indexing records by their matchOn keys rather than performing pairwise comparison.

No configuration change is needed — the matcher detects the optimal strategy based on dataset size automatically.