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  • SQL to Aggregation Framework Mapping Chart

     

    SQL to Aggregation Framework Mapping Chart

    The aggregation framework allows MongoDB to provide native aggregation capabilities that corresponds to many common data aggregation operations in SQL. If you’re new to MongoDB you might want to consider the Frequently Asked Questions section for a selection of common questions.

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

    SQL Terms, Functions, and ConceptsMongoDB Aggregation Operators
    WHERE $match
    GROUP BY $group
    HAVING $match
    SELECT $project
    ORDER BY $sort
    LIMIT $limit
    SUM() $sum
    COUNT() $sum
    join No direct corresponding operator;however, the $unwind operator allows for somewhat similar functionality, but with fields embedded within the document.

    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 andorder_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:

      {
        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 } ]
      }
      
    • The MongoDB statements prefix the names of the fields from thedocuments in the collection orders with a$ character when they appear as operands to the aggregation operations.

    SQL ExampleMongoDB ExampleDescription
    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: "$ord_date" },
                   total: { $sum: "$price" } } }
    ] )
    
    For each uniquecust_id, ord_date grouping, sum the price field.
    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: "$ord_date" },
                   total: { $sum: "$price" } } },
       { $match: { total: { $gt: 250 } } }
    ] )
    
    For each unique cust_id, ord_dategrouping, sum the price field and return only where the sum is greater than 250.
    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_idwith 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_idwith 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: "$ord_date" } } },
       { $group: { _id: null, count: { $sum: 1 } } }
    ] )
    
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  • 原文地址:https://www.cnblogs.com/fx2008/p/2986592.html
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