# Model Monetary Data¶

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## Overview¶

Applications that handle monetary data often require the ability to capture fractional units of currency and need to emulate decimal rounding with exact precision when performing arithmetic. The binary-based floating-point arithmetic used by many modern systems (i.e., float, double) is unable to represent exact decimal fractions and requires some degree of approximation making it unsuitable for monetary arithmetic. This constraint is an important consideration when modeling monetary data.

There are several approaches to modeling monetary data in MongoDB using the numeric and non-numeric models.

### Numeric Model¶

The numeric model may be appropriate if you need to query the
database for exact, mathematically valid matches or need to perform
server-side arithmetic, e.g., `$inc`

, `$mul`

, and
aggregation framework arithmetic.

**The following approaches follow the numeric model:**

- Using the Decimal BSON Type which is a decimal-based floating-point format capable of providing exact precision. Available in MongoDB version 3.4 and later.
- Using a Scale Factor to convert the monetary value
to a 64-bit integer (
`long`

BSON type) by multiplying by a power of 10 scale factor.

### Non-Numeric Model¶

If there is no need to perform server-side arithmetic on monetary data or if server-side approximations are sufficient, modeling monetary data using the non-numeric model may be suitable.

**The following approach follows the non-numeric model:**

- Using two fields for the monetary value:
One field stores the exact monetary value as a
non-numeric
`string`

and another field stores a binary-based floating-point (`double`

BSON type) approximation of the value.

## Numeric Model¶

### Using the Decimal BSON Type¶

*New in version 3.4*.

The `decimal`

BSON type uses the
IEEE 754 decimal128 decimal-based floating-point numbering format.
Unlike binary-based floating-point formats (i.e., the `double`

BSON
type), decimal128 does not approximate decimal values and is able to
provide the exact precision required for working with monetary data.

From the `mongo`

shell `decimal`

values are assigned and
queried using the `NumberDecimal()`

constructor. The following
example adds a document containing gas prices to a `gasprices`

collection:

db.gasprices.insert{ "_id" : 1, "date" : ISODate(), "price" : NumberDecimal("2.099"), "station" : "Quikstop", "grade" : "regular" }

The following query matches the document above:

db.gasprices.find( { price: NumberDecimal("2.099") } )

For more information on the `decimal`

type, see
NumberDecimal.

#### Converting Values to Decimal¶

A collection's values can be transformed to the `decimal`

type by
performing a one-time transformation or by modifying application logic
to perform the transformation as it accesses records.

Alternative to the procedure outlined below, starting in version
4.0, you can use the `$convert`

and its helper
`$toDecimal`

operator to convert values to `NumberDecimal()`

.

##### One-Time Collection Transformation¶

A collection can be transformed by iterating over all documents in the
collection, converting the monetary value to the `decimal`

type, and
writing the document back to the collection.

It is strongly advised to add the `decimal`

value to the
document as a new field and remove the old field later once the
new field's values have been verified.

Be sure to test `decimal`

conversions in an
isolated test environment. Once datafiles are created or modified
with MongoDB version 3.4 they will no longer be compatible with
previous versions and there is no support for downgrading datafiles
containing decimals.

**Scale Factor Transformation:**

Consider the following collection which used the Scale Factor approach and saved the monetary value as a 64-bit integer representing the number of cents:

{ "_id" : 1, "description" : "T-Shirt", "size" : "M", "price" : NumberLong("1999") }, { "_id" : 2, "description" : "Jeans", "size" : "36", "price" : NumberLong("3999") }, { "_id" : 3, "description" : "Shorts", "size" : "32", "price" : NumberLong("2999") }, { "_id" : 4, "description" : "Cool T-Shirt", "size" : "L", "price" : NumberLong("2495") }, { "_id" : 5, "description" : "Designer Jeans", "size" : "30", "price" : NumberLong("8000") }

The `long`

value can be converted to an appropriately formatted
`decimal`

value by multiplying `price`

and
`NumberDecimal("0.01")`

using the `$multiply`

operator.
The following aggregation pipeline assigns the converted value to the
new `priceDec`

field in the `$addFields`

stage:

db.clothes.aggregate( [ { $match: { price: { $type: "long" }, priceDec: { $exists: 0 } } }, { $addFields: { priceDec: { $multiply: [ "$price", NumberDecimal( "0.01" ) ] } } } ] ).forEach( ( function( doc ) { db.clothes.save( doc ); } ) )

The results of the aggregation pipeline can be verified using the
`db.clothes.find()`

query:

{ "_id" : 1, "description" : "T-Shirt", "size" : "M", "price" : NumberLong(1999), "priceDec" : NumberDecimal("19.99") } { "_id" : 2, "description" : "Jeans", "size" : "36", "price" : NumberLong(3999), "priceDec" : NumberDecimal("39.99") } { "_id" : 3, "description" : "Shorts", "size" : "32", "price" : NumberLong(2999), "priceDec" : NumberDecimal("29.99") } { "_id" : 4, "description" : "Cool T-Shirt", "size" : "L", "price" : NumberLong(2495), "priceDec" : NumberDecimal("24.95") } { "_id" : 5, "description" : "Designer Jeans", "size" : "30", "price" : NumberLong(8000), "priceDec" : NumberDecimal("80.00") }

If you do not want to add a new field with the `decimal`

value, the
original field can be overwritten. The following
`update()`

method first checks that `price`

exists and that it is a `long`

, then transforms the `long`

value to
`decimal`

and stores it in the `price`

field:

db.clothes.update( { price: { $type: "long" } }, { $mul: { price: NumberDecimal( "0.01" ) } }, { multi: 1 } )

The results can be verified using the `db.clothes.find()`

query:

{ "_id" : 1, "description" : "T-Shirt", "size" : "M", "price" : NumberDecimal("19.99") } { "_id" : 2, "description" : "Jeans", "size" : "36", "price" : NumberDecimal("39.99") } { "_id" : 3, "description" : "Shorts", "size" : "32", "price" : NumberDecimal("29.99") } { "_id" : 4, "description" : "Cool T-Shirt", "size" : "L", "price" : NumberDecimal("24.95") } { "_id" : 5, "description" : "Designer Jeans", "size" : "30", "price" : NumberDecimal("80.00") }

**Non-Numeric Transformation:**

Consider the following collection which used the
non-numeric
model and saved the monetary value as a `string`

with the exact
representation of the value:

{ "_id" : 1, "description" : "T-Shirt", "size" : "M", "price" : "19.99" } { "_id" : 2, "description" : "Jeans", "size" : "36", "price" : "39.99" } { "_id" : 3, "description" : "Shorts", "size" : "32", "price" : "29.99" } { "_id" : 4, "description" : "Cool T-Shirt", "size" : "L", "price" : "24.95" } { "_id" : 5, "description" : "Designer Jeans", "size" : "30", "price" : "80.00" }

The following function first checks that `price`

exists and that it
is a `string`

, then transforms the `string`

value to a `decimal`

value and stores it in the `priceDec`

field:

db.clothes.find( { $and : [ { price: { $exists: true } }, { price: { $type: "string" } } ] } ).forEach( function( doc ) { doc.priceDec = NumberDecimal( doc.price ); db.clothes.save( doc ); } );

The function does not output anything to the command line. The results
can be verified using the `db.clothes.find()`

query:

{ "_id" : 1, "description" : "T-Shirt", "size" : "M", "price" : "19.99", "priceDec" : NumberDecimal("19.99") } { "_id" : 2, "description" : "Jeans", "size" : "36", "price" : "39.99", "priceDec" : NumberDecimal("39.99") } { "_id" : 3, "description" : "Shorts", "size" : "32", "price" : "29.99", "priceDec" : NumberDecimal("29.99") } { "_id" : 4, "description" : "Cool T-Shirt", "size" : "L", "price" : "24.95", "priceDec" : NumberDecimal("24.95") } { "_id" : 5, "description" : "Designer Jeans", "size" : "30", "price" : "80.00", "priceDec" : NumberDecimal("80.00") }

##### Application Logic Transformation¶

It is possible to perform the transformation to the `decimal`

type from within the application logic. In this scenario the
application modified to perform the transformation as it accesses
records.

The typical application logic is as follows:

- Test that the new field exists and that it is of
`decimal`

type If the new

`decimal`

field does not exist:- Create it by properly converting old field values
- Remove the old field
- Persist the transformed record

### Using a Scale Factor¶

If you are using MongoDB version 3.4 or higher, using the decimal type for modeling monetary data is preferable to the Scale Factor method.

To model monetary data using the scale factor approach:

- Determine the maximum precision needed for the monetary value. For
example, your application may require precision down to the tenth of
one cent for monetary values in
`USD`

currency. - Convert the monetary value into an integer by multiplying the value by a power of 10 that ensures the maximum precision needed becomes the least significant digit of the integer. For example, if the required maximum precision is the tenth of one cent, multiply the monetary value by 1000.
- Store the converted monetary value.

For example, the following scales `9.99 USD`

by 1000 to preserve
precision up to one tenth of a cent.

{ price: 9990, currency: "USD" }

The model assumes that for a given currency value:

- The scale factor is consistent for a currency; i.e. same scaling factor for a given currency.
- The scale factor is a constant and known property of the currency; i.e applications can determine the scale factor from the currency.

When using this model, applications must be consistent in performing the appropriate scaling of the values.

For use cases of this model, see Numeric Model.

## Non-Numeric Model¶

To model monetary data using the non-numeric model, store the value in two fields:

- In one field, encode the exact monetary value as a non-numeric data
type; e.g.,
`BinData`

or a`string`

. - In the second field, store a double-precision floating point approximation of the exact value.

The following example uses the non-numeric model to store
`9.99 USD`

for the price and `0.25 USD`

for the fee:

{ price: { display: "9.99", approx: 9.9900000000000002, currency: "USD" }, fee: { display: "0.25", approx: 0.2499999999999999, currency: "USD" } }

With some care, applications can perform range and sort queries on the field with the numeric approximation. However, the use of the approximation field for the query and sort operations requires that applications perform client-side post-processing to decode the non-numeric representation of the exact value and then filter out the returned documents based on the exact monetary value.

For use cases of this model, see Non-Numeric Model.