NoSQL [mongodb] SQL to Aggregation Mapping Chart 몽고디비 SQL 쿼리 매핑

2020.09.23 17:33

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[mongodb] SQL to Aggregation Mapping Chart 몽고디비 SQL 쿼리 매핑

 

SQL to Aggregation Mapping Chart

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The aggregation pipeline allows MongoDB to provide native aggregation capabilities that corresponds to many common data aggregation operations in SQL.

The following table provides an overview of common SQL aggregation terms, functions, and concepts and the corresponding MongoDB aggregation operators:

SQL Terms, Functions, and Concepts MongoDB Aggregation Operators
WHERE $match
GROUP BY $group
HAVING $match
SELECT $project
ORDER BY $sort
LIMIT $limit
SUM() $sum
COUNT()
$sum
$sortByCount
join $lookup
SELECT INTO NEW_TABLE $out
MERGE INTO TABLE $merge (Available starting in MongoDB 4.2)
UNION ALL $unionWith (Available starting in MongoDB 4.4)

For a list of all aggregation pipeline and expression operators, see Aggregation Pipeline Quick Reference.

SEE ALSO

SQL to MongoDB Mapping Chart

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Examples

The following table presents a quick reference of SQL aggregation statements and the corresponding MongoDB statements. The examples in the table assume the following conditions:

  • The SQL examples assume two tables, orders and order_lineitem that join by the order_lineitem.order_id and the orders.id columns.

  • The MongoDB examples assume one collection orders that contain documents of the following prototype:

    copy
    copied
    {
  cust_id: "abc123",
  ord_date: ISODate("2012-11-02T17:04:11.102Z"),
  status: 'A',
  price: 50,
  items: [ { sku: "xxx", qty: 25, price: 1 },
           { sku: "yyy", qty: 25, price: 1 } ]
}
SQL Example MongoDB Example Description
 
SELECT COUNT(*) AS count
FROM orders
 
db.orders.aggregate( [
   {
     $group: {
        _id: null,
        count: { $sum: 1 }
     }
   }
] )
Count all records from orders
 
SELECT SUM(price) AS total
FROM orders
 
db.orders.aggregate( [
   {
     $group: {
        _id: null,
        total: { $sum: "$price" }
     }
   }
] )
Sum the price field from orders
 
SELECT cust_id,
       SUM(price) AS total
FROM orders
GROUP BY cust_id
 
db.orders.aggregate( [
   {
     $group: {
        _id: "$cust_id",
        total: { $sum: "$price" }
     }
   }
] )
For each unique cust_id, sum the price field.
 
SELECT cust_id,
       SUM(price) AS total
FROM orders
GROUP BY cust_id
ORDER BY total
 
db.orders.aggregate( [
   {
     $group: {
        _id: "$cust_id",
        total: { $sum: "$price" }
     }
   },
   { $sort: { total: 1 } }
] )
For each unique cust_id, sum the price field, results sorted by sum.
 
SELECT cust_id,
       ord_date,
       SUM(price) AS total
FROM orders
GROUP BY cust_id,
         ord_date
 
db.orders.aggregate( [
   {
     $group: {
        _id: {
           cust_id: "$cust_id",
           ord_date: { $dateToString: {
              format: "%Y-%m-%d",
              date: "$ord_date"
           }}
        },
        total: { $sum: "$price" }
     }
   }
] )
For each unique cust_id, ord_date grouping, sum the price field. Excludes the time portion of the date.
 
SELECT cust_id,
       count(*)
FROM orders
GROUP BY cust_id
HAVING count(*) > 1
 
db.orders.aggregate( [
   {
     $group: {
        _id: "$cust_id",
        count: { $sum: 1 }
     }
   },
   { $match: { count: { $gt: 1 } } }
] )
For cust_id with multiple records, return the cust_id and the corresponding record count.
 
SELECT cust_id,
       ord_date,
       SUM(price) AS total
FROM orders
GROUP BY cust_id,
         ord_date
HAVING total > 250
 
db.orders.aggregate( [
   {
     $group: {
        _id: {
           cust_id: "$cust_id",
           ord_date: { $dateToString: {
              format: "%Y-%m-%d",
              date: "$ord_date"
           }}
        },
        total: { $sum: "$price" }
     }
   },
   { $match: { total: { $gt: 250 } } }
] )
For each unique cust_id, ord_date grouping, sum the price field and return only where the sum is greater than 250. Excludes the time portion of the date.
 
SELECT cust_id,
       SUM(price) as total
FROM orders
WHERE status = 'A'
GROUP BY cust_id
 
db.orders.aggregate( [
   { $match: { status: 'A' } },
   {
     $group: {
        _id: "$cust_id",
        total: { $sum: "$price" }
     }
   }
] )
For each unique cust_id with status A, sum the price field.
 
SELECT cust_id,
       SUM(price) as total
FROM orders
WHERE status = 'A'
GROUP BY cust_id
HAVING total > 250
 
db.orders.aggregate( [
   { $match: { status: 'A' } },
   {
     $group: {
        _id: "$cust_id",
        total: { $sum: "$price" }
     }
   },
   { $match: { total: { $gt: 250 } } }
] )
For each unique cust_id with status A, sum the price field and return only where the sum is greater than 250.
 
SELECT cust_id,
       SUM(li.qty) as qty
FROM orders o,
     order_lineitem li
WHERE li.order_id = o.id
GROUP BY cust_id
 
db.orders.aggregate( [
   { $unwind: "$items" },
   {
     $group: {
        _id: "$cust_id",
        qty: { $sum: "$items.qty" }
     }
   }
] )
For each unique cust_id, sum the corresponding line item qty fields associated with the orders.
 
SELECT COUNT(*)
FROM (SELECT cust_id,
             ord_date
      FROM orders
      GROUP BY cust_id,
               ord_date)
      as DerivedTable
 
db.orders.aggregate( [
   {
     $group: {
        _id: {
           cust_id: "$cust_id",
           ord_date: { $dateToString: {
              format: "%Y-%m-%d",
              date: "$ord_date"
           }}
        }
     }
   },
   {
     $group: {
        _id: null,
        count: { $sum: 1 }
     }
   }
] )
Count the number of distinct cust_id, ord_date groupings. Excludes the time portion of the date.
 

[출처] https://docs.mongodb.com/manual/reference/sql-aggregation-comparison/

 

 

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