# $bucketAuto (aggregation)

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## Definition

`$bucketAuto`

Categorizes incoming documents into a specific number of groups, called buckets, based on a specified expression. Bucket boundaries are automatically determined in an attempt to evenly distribute the documents into the specified number of buckets.

Each bucket is represented as a document in the output. The document for each bucket contains:

An

`_id`

object that specifies the bounds of the bucket.The

`_id.min`

field specifies the inclusive lower bound for the bucket.The

`_id.max`

field specifies the upper bound for the bucket. This bound is exclusive for all buckets except the final bucket in the series, where it is inclusive.

A

`count`

field that contains the number of documents in the bucket. The`count`

field is included by default when the`output`

document is not specified.

The

`$bucketAuto`

stage has the following form:{ $bucketAuto: { groupBy: <expression>, buckets: <number>, output: { <output1>: { <$accumulator expression> }, ... } granularity: <string> } } FieldTypeDescription`groupBy`

expressionAn expression to group documents by. To specify a field path, prefix the field name with a dollar sign`$`

and enclose it in quotes.`buckets`

integerA positive 32-bit integer that specifies the number of buckets into which input documents are grouped.`output`

documentOptional. A document that specifies the fields to include in the output documents in addition to the

`_id`

field. To specify the field to include, you must use accumulator expressions:<outputfield1>: { <accumulator>: <expression1> }, ... The default

`count`

field is not included in the output document when`output`

is specified. Explicitly specify the`count`

expression as part of the`output`

document to include it:output: { <outputfield1>: { <accumulator>: <expression1> }, ... count: { $sum: 1 } } `granularity`

stringOptional. A string that specifies the preferred number series to use to ensure that the calculated boundary edges end on preferred round numbers or their powers of 10.

Available only if the all

`groupBy`

values are numeric and none of them are`NaN`

.The suppported values of

`granularity`

are:`"R5"`

`"R10"`

`"R20"`

`"R40"`

`"R80"`

`"1-2-5"`

`"E6"`

`"E12"`

`"E24"`

`"E48"`

`"E96"`

`"E192"`

`"POWERSOF2"`

## Considerations

`$bucketAuto`

and Memory Restrictions

The `$bucketAuto`

stage has a limit of 100 megabytes of RAM.
By default, if the stage exceeds this limit, `$bucketAuto`

returns an error. To allow more space for stage processing, use the
allowDiskUse option to enable aggregation
pipeline stages to write data to temporary files.

## Tip

### See also:

## Behavior

There may be less than the specified number of buckets if:

The number of input documents is less than the specified number of buckets.

The number of unique values of the

`groupBy`

expression is less than the specified number of`buckets`

.The

`granularity`

has fewer intervals than the number of`buckets`

.The

`granularity`

is not fine enough to evenly distribute documents into the specified number of`buckets`

.

If the `groupBy`

expression refers to an array or document, the
values are arranged using the same ordering as in `$sort`

before determining the bucket boundaries.

The even distribution of documents across buckets depends on the
cardinality, or the number of unique values, of the `groupBy`

field. If
the cardinality is not high enough, the $bucketAuto stage may not evenly
distribute the results across buckets.

### Granularity

The `$bucketAuto`

accepts an optional `granularity`

parameter which
ensures that the boundaries of all buckets adhere to a specified
preferred number series.
Using a preferred number series provides more control on where the
bucket boundaries are set among the range of values in the `groupBy`

expression. They may also be used to help logarithmically and evenly
set bucket boundaries when the range of the `groupBy`

expression
scales exponentially.

#### Renard Series

The Renard number series are sets of numbers derived by taking either
the 5 ^{th}, 10 ^{th}, 20 ^{th},
40 ^{th}, or 80 ^{th} root of 10, then including
various powers of the root that equate to values between 1.0 to 10.0
(10.3 in the case of `R80`

).

Set `granularity`

to `R5`

, `R10`

, `R20`

, `R40`

, or `R80`

to
restrict bucket boundaries to values in the series. The values of the
series are multiplied by a power of 10 when the `groupBy`

values are
outside of the 1.0 to 10.0 (10.3 for `R80`

) range.

## Example

The `R5`

series is based off of the fifth root of 10, which is
1.58, and includes various powers of this root (rounded) until 10 is
reached. The `R5`

series is derived as follows:

10

^{0/5}= 110

^{1/5}= 1.584 ~ 1.610

^{2/5}= 2.511 ~ 2.510

^{3/5}= 3.981 ~ 4.010

^{4/5}= 6.309 ~ 6.310

^{5/5}= 10

The same approach is applied to the other Renard series to offer finer
granularity, i.e., more intervals between 1.0 and 10.0 (10.3 for
`R80`

).

#### E Series

The E number series are similar to the
Renard series in that they subdivide the
interval from 1.0 to 10.0 by the 6 ^{th},
12 ^{th}, 24 ^{th}, 48 ^{th},
96 ^{th}, or 192 ^{nd} root of ten with a
particular relative error.

Set `granularity`

to `E6`

, `E12`

, `E24`

, `E48`

, `E96`

, or
`E192`

to restrict bucket boundaries to values in the series. The
values of the series are multiplied by a power of 10 when the
`groupBy`

values are outside of the 1.0 to 10.0 range. To learn more
about the E-series and their respective relative errors, see
preferred number series.

#### 1-2-5 Series

The `1-2-5`

series behaves like a three-value
Renard series, if such a series existed.

Set `granularity`

to `1-2-5`

to restrict bucket boundaries to
various powers of the third root of 10, rounded to one significant
digit.

## Example

The following values are part of the `1-2-5`

series:
0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, and so
on...

#### Powers of Two Series

Set `granularity`

to `POWERSOF2`

to restrict bucket boundaries to
numbers that are a power of two.

## Example

The following numbers adhere to the power of two Series:

2

^{0}= 12

^{1}= 22

^{2}= 42

^{3}= 82

^{4}= 162

^{5}= 32and so on...

A common implementation is how various computer components, like
memory, often adhere to the `POWERSOF2`

set of preferred numbers:

1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, and so on....

#### Comparing Different Granularities

The following operation demonstrates how specifying different values
for `granularity`

affects how `$bucketAuto`

determines bucket
boundaries. A collection of `things`

have an `_id`

numbered from
1 to 100:

{ _id: 1 } { _id: 2 } ... { _id: 100 }

Different values for `granularity`

are substituted into the following
operation:

db.things.aggregate( [ { $bucketAuto: { groupBy: "$_id", buckets: 5, granularity: <granularity> } } ] )

The results in the following table demonstrate how different values for
`granularity`

yield different bucket boundaries:

Granularity | Results | Notes |
---|---|---|

No granularity | { "_id" : { "min" : 0, "max" : 20 }, "count" : 20 }{ "_id" : { "min" : 20, "max" : 40 }, "count" : 20 }{ "_id" : { "min" : 40, "max" : 60 }, "count" : 20 }{ "_id" : { "min" : 60, "max" : 80 }, "count" : 20 }{ "_id" : { "min" : 80, "max" : 99 }, "count" : 20 } | |

R20 | { "_id" : { "min" : 0, "max" : 20 }, "count" : 20 }{ "_id" : { "min" : 20, "max" : 40 }, "count" : 20 }{ "_id" : { "min" : 40, "max" : 63 }, "count" : 23 }{ "_id" : { "min" : 63, "max" : 90 }, "count" : 27 }{ "_id" : { "min" : 90, "max" : 100 }, "count" : 10 } | |

E24 | { "_id" : { "min" : 0, "max" : 20 }, "count" : 20 }{ "_id" : { "min" : 20, "max" : 43 }, "count" : 23 }{ "_id" : { "min" : 43, "max" : 68 }, "count" : 25 }{ "_id" : { "min" : 68, "max" : 91 }, "count" : 23 }{ "_id" : { "min" : 91, "max" : 100 }, "count" : 9 } | |

1-2-5 | { "_id" : { "min" : 0, "max" : 20 }, "count" : 20 }{ "_id" : { "min" : 20, "max" : 50 }, "count" : 30 }{ "_id" : { "min" : 50, "max" : 100 }, "count" : 50 } | The specified number of buckets exceeds the number of intervals
in the series. |

POWERSOF2 | { "_id" : { "min" : 0, "max" : 32 }, "count" : 32 }{ "_id" : { "min" : 32, "max" : 64 }, "count" : 32 }{ "_id" : { "min" : 64, "max" : 128 }, "count" : 36 } | The specified number of buckets exceeds the number of intervals
in the series. |

## Example

Consider a collection `artwork`

with the following documents:

{ "_id" : 1, "title" : "The Pillars of Society", "artist" : "Grosz", "year" : 1926, "price" : NumberDecimal("199.99"), "dimensions" : { "height" : 39, "width" : 21, "units" : "in" } } { "_id" : 2, "title" : "Melancholy III", "artist" : "Munch", "year" : 1902, "price" : NumberDecimal("280.00"), "dimensions" : { "height" : 49, "width" : 32, "units" : "in" } } { "_id" : 3, "title" : "Dancer", "artist" : "Miro", "year" : 1925, "price" : NumberDecimal("76.04"), "dimensions" : { "height" : 25, "width" : 20, "units" : "in" } } { "_id" : 4, "title" : "The Great Wave off Kanagawa", "artist" : "Hokusai", "price" : NumberDecimal("167.30"), "dimensions" : { "height" : 24, "width" : 36, "units" : "in" } } { "_id" : 5, "title" : "The Persistence of Memory", "artist" : "Dali", "year" : 1931, "price" : NumberDecimal("483.00"), "dimensions" : { "height" : 20, "width" : 24, "units" : "in" } } { "_id" : 6, "title" : "Composition VII", "artist" : "Kandinsky", "year" : 1913, "price" : NumberDecimal("385.00"), "dimensions" : { "height" : 30, "width" : 46, "units" : "in" } } { "_id" : 7, "title" : "The Scream", "artist" : "Munch", "price" : NumberDecimal("159.00"), "dimensions" : { "height" : 24, "width" : 18, "units" : "in" } } { "_id" : 8, "title" : "Blue Flower", "artist" : "O'Keefe", "year" : 1918, "price" : NumberDecimal("118.42"), "dimensions" : { "height" : 24, "width" : 20, "units" : "in" } }

### Single Facet Aggregation

In the following operation, input documents are grouped into four
buckets according to the values in the `price`

field:

db.artwork.aggregate( [ { $bucketAuto: { groupBy: "$price", buckets: 4 } } ] )

The operation returns the following documents:

{ "_id" : { "min" : NumberDecimal("76.04"), "max" : NumberDecimal("159.00") }, "count" : 2 } { "_id" : { "min" : NumberDecimal("159.00"), "max" : NumberDecimal("199.99") }, "count" : 2 } { "_id" : { "min" : NumberDecimal("199.99"), "max" : NumberDecimal("385.00") }, "count" : 2 } { "_id" : { "min" : NumberDecimal("385.00"), "max" : NumberDecimal("483.00") }, "count" : 2 }

### Multi-Faceted Aggregation

The `$bucketAuto`

stage can be used within the
`$facet`

stage to process multiple aggregation pipelines on
the same set of input documents from `artwork`

.

The following aggregation pipeline groups the documents from the
`artwork`

collection into buckets based on `price`

, `year`

, and
the calculated `area`

:

db.artwork.aggregate( [ { $facet: { "price": [ { $bucketAuto: { groupBy: "$price", buckets: 4 } } ], "year": [ { $bucketAuto: { groupBy: "$year", buckets: 3, output: { "count": { $sum: 1 }, "years": { $push: "$year" } } } } ], "area": [ { $bucketAuto: { groupBy: { $multiply: [ "$dimensions.height", "$dimensions.width" ] }, buckets: 4, output: { "count": { $sum: 1 }, "titles": { $push: "$title" } } } } ] } } ] )

The operation returns the following document:

{ "area" : [ { "_id" : { "min" : 432, "max" : 500 }, "count" : 3, "titles" : [ "The Scream", "The Persistence of Memory", "Blue Flower" ] }, { "_id" : { "min" : 500, "max" : 864 }, "count" : 2, "titles" : [ "Dancer", "The Pillars of Society" ] }, { "_id" : { "min" : 864, "max" : 1568 }, "count" : 2, "titles" : [ "The Great Wave off Kanagawa", "Composition VII" ] }, { "_id" : { "min" : 1568, "max" : 1568 }, "count" : 1, "titles" : [ "Melancholy III" ] } ], "price" : [ { "_id" : { "min" : NumberDecimal("76.04"), "max" : NumberDecimal("159.00") }, "count" : 2 }, { "_id" : { "min" : NumberDecimal("159.00"), "max" : NumberDecimal("199.99") }, "count" : 2 }, { "_id" : { "min" : NumberDecimal("199.99"), "max" : NumberDecimal("385.00") }, "count" : 2 }, { "_id" : { "min" : NumberDecimal("385.00"), "max" : NumberDecimal("483.00") }, "count" : 2 } ], "year" : [ { "_id" : { "min" : null, "max" : 1913 }, "count" : 3, "years" : [ 1902 ] }, { "_id" : { "min" : 1913, "max" : 1926 }, "count" : 3, "years" : [ 1913, 1918, 1925 ] }, { "_id" : { "min" : 1926, "max" : 1931 }, "count" : 2, "years" : [ 1926, 1931 ] } ] }