Amazon DocumentDB Aggregation Query

Amazon DocumentDB is a hosted document database service that supports the MongoDB protocol and API, and is compatible with MongoDB 3.6. Aggregated queries are a powerful feature of MongoDB, and Amazon DocumentDB fully supports them. Aggregated queries can process document collections through multiple steps to generate aggregated results. Here are some common aggregation query operations supported by Amazon DocumentDB: 1. '$match': Used to filter documents that meet the specified criteria. For example, suppose we have a student collection, where each document contains the student's name and grades. The following aggregate query will match student documents with scores greater than 80. script db.students.aggregate([ { $match: { score: { $gt: 80 } } } ]) 2. '$group': Used to group documents based on specified fields or expressions. The following aggregation query will group student documents based on grade fields and calculate the number of students in each grade. script db.students.aggregate([ { $group: { _id: "$grade", count: { $sum: 1 } } } ]) 3. '$project': Used to select specific fields in the document or add calculated fields. The following aggregation query will select the name field and score field of the student document. script db.students.aggregate([ { $project: { name: 1, score: 1 } } ]) 4. '$sort': Used to sort documents. The following aggregate query will sort student documents in descending order based on the score field. script db.students.aggregate([ { $sort: { score: -1 } } ]) 5. '$limit' and '$skip': Used to limit the size of the aggregated result set and skip a specified number of documents. The following aggregate query will return the top 5 student documents with the highest scores. script db.students.aggregate([ { $sort: { score: -1 } }, { $limit: 5 } ]) These are just some basic examples of aggregation query operations. Amazon DocumentDB also supports more aggregation pipeline operators and functions, such as' $windind ',' $lookup ',' $avg ',' $max ',' $min ', and so on. You can use these operators and functions according to your own needs and data model.