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To find students whose test one score is less than the total average of all tests, use MongoDB's aggregation framework with $project, $group, $filter, and $map operators to calculate averages and filter results. Syntax db.collection.aggregate([ { $project: { Name: 1, Value1: 1, average: { $avg: ["$Value1", "$Value2", "$Value3"] } } }, { $group: { _id: null, students: { $push: {Name: "$Name", Value1: "$Value1"} }, totalAverage: { $avg: "$average" } } }, { $project: { filteredNames: { $map: { input: { $filter: { input: "$students", cond: ... Read More
The fastest way to update the whole document (all fields) in MongoDB is to use replaceOne() method. This method replaces the entire document while preserving the _id field. Syntax db.collection.replaceOne( { filter }, { newDocument } ); Sample Data db.demo431.insertMany([ {"Name": "Chris", "Age": 32}, {"Name": "David", "Age": 31}, {"Name": "John", "Age": 24}, {"Name": "Bob", "Age": 22} ]); { "acknowledged": true, ... Read More
To group records in MongoDB and display specific values using dot notation, use the $group aggregation stage with field references like "$details.Name" to extract values from nested documents or arrays. Syntax db.collection.aggregate([ { $group: { "_id": { "fieldName": "$arrayField.nestedField" } } } ]); Sample Data db.demo430.insertOne({ "details": [ ... Read More
To create a new field and count elements from another field in MongoDB, use $addFields with $map and $size operators. This allows you to transform array elements and count their sizes in a single aggregation. Syntax db.collection.aggregate([ { "$addFields": { "arrayField": { "$map": { ... Read More
To perform conditional upserts and multiple inserts when updating documents in MongoDB, use the bulkWrite() method. This allows you to combine update operations and insert operations in a single transaction-like operation. Syntax db.collection.bulkWrite([ { "updateOne": { "filter": { field: value }, "update": { $set: { newField: newValue } } }}, { "insertOne": { "document": { field1: value1, field2: value2 } ... Read More
To update a MongoDB document with Student Id and Name, use the update() method with $set operator. The $set operator modifies specific fields without affecting other document properties. Syntax db.collection.update( { "field": "matchValue" }, { $set: { "field": "newValue" } } ); Sample Data Let us create a collection with student documents : db.demo427.insertMany([ { "StudentId": 101, "StudentName": "Chris Brown" }, { "StudentId": 102, "StudentName": "David Miller" }, { "StudentId": 103, "StudentName": ... Read More
Use the $eq operator along with find() to match ID values within arrays and fetch documents. The $eq specifies an equality condition and matches documents where any array element equals the specified value. Syntax db.collection.find({"arrayField": {$eq: "value"}}); // Alternative (equivalent) syntax db.collection.find({"arrayField": "value"}); Sample Data Let us create a collection with documents ? db.demo426.insertMany([ {"Ids": ["110", "120", "101"]}, {"Ids": ["100", "201", "401"]}, {"Ids": ["501", "600", "700"]} ]); { "acknowledged": true, ... Read More
To upsert many documents in MongoDB, use bulk operations with the upsert() option. This allows you to update multiple documents if they exist, or insert new ones if they don't match the query criteria. Syntax var bulk = db.collection.initializeUnorderedBulkOp(); bulk.find({queryCondition}).upsert().update({ $setOnInsert: {fieldsForNewDocuments}, $set: {fieldsToUpdate} }); bulk.execute(); Sample Data Let us create a collection with duplicate documents − db.demo425.insertMany([ {"Name": "Chris", "Age": 21}, {"Name": "David", "Age": 23}, {"Name": "Chris", "Age": 21}, ... Read More
To extract a MongoDB document with a specific string, use the $match operator in an aggregation pipeline to filter documents containing the target string value. Syntax db.collection.aggregate([ { $match: { "field": "specific_string" } } ]); Create Sample Data db.demo424.insertMany([ { "Information": [ { id: 10, ... Read More
To speed up the $group phase in MongoDB aggregation, optimize your pipeline by reducing the dataset size before grouping and using indexes effectively. The key is to filter, project, and limit data early in the pipeline. Syntax db.collection.aggregate([ { $match: { /* filter conditions */ } }, { $project: { /* only needed fields */ } }, { $unwind: "$arrayField" }, { $group: { _id: "$field", count: { $sum: 1 } } }, { $sort: { count: ... Read More