2024-10-17 19:49:03 +05:30
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## Queries-B2
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2024-10-16 11:27:45 +05:30
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2024-10-17 19:49:03 +05:30
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### Group A
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> [!NOTE]
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> Use Employee database created in Assignment B-01 and perform following aggregation operation
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> Refer [Queries-B1](https://git.kska.io/sppu-te-comp-content/DatabaseManagementSystems/src/branch/main/Practical/Assignment-B1/Queries-B1.md)
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1. Return Designation with Total Salary Above 200000:
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```mongodb
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db.Employee.aggregate([
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{
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$group: {
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_id: "$Designation",
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TotalSalary: { $sum: "$Salary" }
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}
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},
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{
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$match: {
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TotalSalary: { $gt: 200000 }
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}
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}
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])
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2024-10-16 11:27:45 +05:30
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```
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2. Find Employee with Total Salary for Each City with Designation "DBA":
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```mongodb
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db.Employee.aggregate([
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{
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$match: { Designation: "DBA" }
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},
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{
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$group: {
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_id: { $arrayElemAt: ["$Address.PAddr", 0] },
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TotalSalary: { $sum: "$Salary" }
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}
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}
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])
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2024-10-16 11:27:45 +05:30
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```
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3. Find Total Salary of Employee with Designation "DBA" for Each Company:
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```mongodb
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db.Employee.aggregate([
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{
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$match: { Designation: "DBA" }
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},
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{
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$group: {
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_id: "$Company_name",
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TotalSalary: { $sum: "$Salary" }
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}
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}
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])
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2024-10-17 19:49:03 +05:30
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2024-10-16 11:27:45 +05:30
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```
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4. Returns Names and _id in Upper Case and in Alphabetical Order:
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```mongodb
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db.Employee.aggregate([
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{
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$project: {
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_id: 1,
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Name: { $toUpper: { $concat: ["$Name.FName", " ", "$Name.LName"] } }
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}
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},
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{
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$sort: { Name: 1 }
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}
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])
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2024-10-17 19:49:03 +05:30
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2024-10-16 11:27:45 +05:30
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```
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5. Count All Records from Collection:
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```mongodb
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db.Employee.countDocuments()
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```
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6. For Each Unique Designation, Find Avg Salary and Output Sorted by AvgSal:
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```mongodb
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db.Employee.aggregate([
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{
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$group: {
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_id: "$Designation",
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AvgSalary: { $avg: "$Salary" }
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}
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},
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{
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$sort: { AvgSalary: 1 }
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}
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])
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2024-10-17 19:49:03 +05:30
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2024-10-16 11:27:45 +05:30
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```
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7. Return Separate Value in the Expertise Array Where Name of Employee is "Swapnil":
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```mongodb
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db.Employee.aggregate([
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{
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$match: { "Name.FName": "Swapnil" }
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},
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{
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$unwind: "$Expertise"
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},
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{
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$project: { Expertise: 1 }
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}
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])
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2024-10-17 19:49:03 +05:30
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2024-10-16 11:27:45 +05:30
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```
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8. Return Separate Value in the Expertise Array and Return Sum of Each Element of Array:
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```mongodb
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db.Employee.aggregate([
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{
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$unwind: "$Expertise"
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},
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{
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$group: {
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_id: "$Expertise",
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TotalCount: { $sum: 1 }
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}
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}
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])
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2024-10-17 19:49:03 +05:30
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2024-10-16 11:27:45 +05:30
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```
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9. Return Array for Designation Whose Address is "Pune":
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```mongodb
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db.Employee.aggregate([
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{
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$match: { "Address.PAddr": { $regex: "Pune" } }
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},
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{
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$project: { Designation: 1 }
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}
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])
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2024-10-17 19:49:03 +05:30
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2024-10-16 11:27:45 +05:30
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```
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10. Return Max and Min Salary for Each Company:
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```mongodb
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db.Employee.aggregate([
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{
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$group: {
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_id: "$Company_name",
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MaxSalary: { $max: "$Salary" },
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MinSalary: { $min: "$Salary" }
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}
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}
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])
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2024-10-17 19:49:03 +05:30
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2024-10-16 11:27:45 +05:30
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```
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2024-10-17 19:49:03 +05:30
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### Group B
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> [!NOTE]
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> Use Employee database created in Assignment B-01 and perform following aggregation operation
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> Refer [Queries-B1](https://git.kska.io/sppu-te-comp-content/DatabaseManagementSystems/src/branch/main/Practical/Assignment-B1/Queries-B1.md)
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2024-10-16 11:27:45 +05:30
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1. Create Single Field Indexes on Designation:
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```mongodb
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db.Employee.createIndex({ Designation: 1 })
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2024-10-16 11:27:45 +05:30
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```
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2. Create Compound Indexes on Name and Age:
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```mongodb
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db.Employee.createIndex({ "Name.FName": 1, Age: -1 })
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2024-10-16 11:27:45 +05:30
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```
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3. Create Multikey Indexes on Expertise Array:
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```mongodb
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db.Employee.createIndex({ Expertise: 1 })
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2024-10-16 11:27:45 +05:30
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```
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4. Return a List of All Indexes on Collection:
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```mongodb
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db.Employee.getIndexes()
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```
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5. Rebuild Indexes:
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```mongodb
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db.Employee.reIndex()
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2024-10-16 11:27:45 +05:30
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```
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6. Drop Index on Remove Specific Index:
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```mongodb
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db.Employee.dropIndex("empIndex")
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```
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7. Remove All Indexes Except for the _id Index from a Collection:
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```mongodb
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db.Employee.dropIndexes()
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2024-10-16 11:27:45 +05:30
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```
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