$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. Thecount
field is included by default when theoutput
document is not specified.
The
$bucketAuto
stage has the following form:{ $bucketAuto: { groupBy: <expression>, buckets: <number>, output: { <output1>: { <$accumulator expression> }, ... } granularity: <string> } } FieldTypeDescriptiongroupBy
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 whenoutput
is specified. Explicitly specify thecount
expression as part of theoutput
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 areNaN
.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.
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 ofbuckets
.The
granularity
has fewer intervals than the number ofbuckets
.The
granularity
is not fine enough to evenly distribute documents into the specified number ofbuckets
.
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 = 1
10 1/5 = 1.584 ~ 1.6
10 2/5 = 2.511 ~ 2.5
10 3/5 = 3.981 ~ 4.0
10 4/5 = 6.309 ~ 6.3
10 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 = 1
2 1 = 2
2 2 = 4
2 3 = 8
2 4 = 16
2 5 = 32
and 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
0 to 99:
{ _id: 0 } { _id: 1 } ... { _id: 99 }
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 ] } ] }