Aggregation with the Zip Code Data Set

Data Model

Each document in the zipcodes collection has the following form:

{
  "_id": "10280",
  "city": "NEW YORK",
  "state": "NY",
  "pop": 5574,
  "loc": [
    -74.016323,
    40.710537
  ]
}

• The _id field holds the zip code as a string.

• The city field holds the city name. A city can have more than one zip code associated with it as different sections of the city can each have a different zip code.

• The state field holds the two letter state abbreviation.

• The pop field holds the population.

• The loc field holds the location as a longitude latitude pair.

aggregate() Method

All of the following examples use the aggregate() helper in mongosh.

The aggregate() method uses the aggregation pipeline to process documents into aggregated results. An aggregation pipeline consists of stages with each stage processing the documents as they pass along the pipeline. Documents pass through the stages in sequence.

The aggregate() method in mongosh provides a wrapper around the aggregate database command. See the documentation for your driver for a more idiomatic interface for data aggregation operations.

Return States with Populations above 10 Million

The following aggregation operation returns all states with total population greater than 10 million:

db.zipcodes.aggregate( [
   { $group: { _id: "$state", totalPop: { $sum: "$pop" } } },
   { $match: { totalPop: { $gte: 10*1000*1000 } } }
] )

In this example, the aggregation pipeline consists of the $group stage followed by the $match stage:

• The $group stage groups the documents of the zipcode collection by the state field, calculates the totalPop field for each state, and outputs a document for each unique state.

  The new per-state documents have two fields: the _id field and the totalPop field. The _id field contains the value of the state; i.e. the group by field. The totalPop field is a calculated field that contains the total population of each state. To calculate the value, $group uses the $sum operator to add the population field (pop) for each state.

  After the $group stage, the documents in the pipeline resemble the following:

  {
    "_id" : "AK",
    "totalPop" : 550043
  }

• The $match stage filters these grouped documents to output only those documents whose totalPop value is greater than or equal to 10 million. The $match stage does not alter the matching documents but outputs the matching documents unmodified.

The equivalent SQL for this aggregation operation is:

SELECT state, SUM(pop) AS totalPop
FROM zipcodes
GROUP BY state
HAVING totalPop >= (10*1000*1000)

Return Average City Population by State

The following aggregation operation returns the average populations for cities in each state:

db.zipcodes.aggregate( [
   { $group: { _id: { state: "$state", city: "$city" }, pop: { $sum: "$pop" } } },
   { $group: { _id: "$_id.state", avgCityPop: { $avg: "$pop" } } }
] )

In this example, the aggregation pipeline consists of the $group stage followed by another $group stage:

• The first $group stage groups the documents by the combination of city and state, uses the $sum expression to calculate the population for each combination, and outputs a document for each city and state combination. [1]

  After this stage in the pipeline, the documents resemble the following:

  {
    "_id" : {
      "state" : "CO",
      "city" : "EDGEWATER"
    },
    "pop" : 13154
  }

• A second $group stage groups the documents in the pipeline by the _id.state field (i.e. the state field inside the _id document), uses the $avg expression to calculate the average city population (avgCityPop) for each state, and outputs a document for each state.

The documents that result from this aggregation operation resembles the following:

{
  "_id" : "MN",
  "avgCityPop" : 5335
}

Return Largest and Smallest Cities by State

The following aggregation operation returns the smallest and largest cities by population for each state:

db.zipcodes.aggregate( [
   { $group:
      {
        _id: { state: "$state", city: "$city" },
        pop: { $sum: "$pop" }
      }
   },
   { $sort: { pop: 1 } },
   { $group:
      {
        _id : "$_id.state",
        biggestCity:  { $last: "$_id.city" },
        biggestPop:   { $last: "$pop" },
        smallestCity: { $first: "$_id.city" },
        smallestPop:  { $first: "$pop" }
      }
   },
  // the following $project is optional, and
  // modifies the output format.
  { $project:
    { _id: 0,
      state: "$_id",
      biggestCity:  { name: "$biggestCity",  pop: "$biggestPop" },
      smallestCity: { name: "$smallestCity", pop: "$smallestPop" }
    }
  }
] )

In this example, the aggregation pipeline consists of a $group stage, a $sort stage, another $group stage, and a $project stage:

• The first $group stage groups the documents by the combination of the city and state, calculates the sum of the pop values for each combination, and outputs a document for each city and state combination.

  At this stage in the pipeline, the documents resemble the following:

  {
    "_id" : {
      "state" : "CO",
      "city" : "EDGEWATER"
    },
    "pop" : 13154
  }

• The $sort stage orders the documents in the pipeline by the pop field value, from smallest to largest; i.e. by increasing order. This operation does not alter the documents.

• The next $group stage groups the now-sorted documents by the _id.state field (i.e. the state field inside the _id document) and outputs a document for each state.

  The stage also calculates the following four fields for each state. Using the $last expression, the $group operator creates the biggestCity and biggestPop fields that store the city with the largest population and that population. Using the $first expression, the $group operator creates the smallestCity and smallestPop fields that store the city with the smallest population and that population.

  The documents, at this stage in the pipeline, resemble the following:

  {
    "_id" : "WA",
    "biggestCity" : "SEATTLE",
    "biggestPop" : 520096,
    "smallestCity" : "BENGE",
    "smallestPop" : 2
  }

• The final $project stage renames the _id field to state and moves the biggestCity, biggestPop, smallestCity, and smallestPop into biggestCity and smallestCity embedded documents.

The output documents of this aggregation operation resemble the following:

{
  "state" : "RI",
  "biggestCity" : {
    "name" : "CRANSTON",
    "pop" : 176404
  },
  "smallestCity" : {
    "name" : "CLAYVILLE",
    "pop" : 45
  }
}