$bucketAuto (aggregation)¶

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Definition
Behavior
Example

Definition¶

$bucketAuto¶

3.4 新版功能.

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 field, whose value specifies the inclusive lower bound and the exclusive upper bound for the bucket, and a count field that contains the number of documents in the bucket. The count field is included by default when the output is not specified.

The $bucketAuto stage has the following form:

{
  $bucketAuto: {
      groupBy: <expression>,
      buckets: <number>,
      output: {
         <output1>: { <$accumulator expression> },
         ...
      }
      granularity: <string>
  }
}

Field Type Description

groupBy expression An expression to group documents by. To specify a field path, prefix the field name with a dollar sign $ and enclose it in quotes.

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

output

document

Optional. 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

string

Optional. 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"

Behavior¶

The number of buckets in the output may be less than the specified number of buckets, for example:

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 then 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.

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 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" } }

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 ] }
  ]
}

← $bucket (aggregation) $sortByCount (aggregation) →

$bucketAuto (aggregation)¶

Definition¶

Behavior¶

Granularity¶

Renard Series¶

E Series¶

1-2-5 Series¶

Powers of Two Series¶

Comparing Different Granularities¶

Example¶

Single Facet Aggregation¶

Multi-Faceted Aggregation¶