3. SORTING, INDEXING, AND FILTERING ON A REAL TIME EMPLOYEE DATASET USING MONGODB

Employee Dataset using MongoDB
1. CREATE DATABASE :-
use companydb
Output:
switched to db companydb
2. CREATE COLLECTIONS :-
db.createCollection("emp")
Output:
{ "ok" : 1 }
3. INSERT EMPLOYEE DATASET :-
db.emp.insertMany([{eid:101,name:"arun",age:25,gender:'male',de
pt:'it',designation:'software
engineer',salary:55000,experience:2,city:'cbe'},
{eid:102,name:"priya",age:25,gender:"female",dept:'it',designation
:"manager",salary:60000,experience:6,city:'chennai'},
{eid:103,name:"karthick",age:29,gender:'male',dept:'finance',desig
nation:'accountance',salary:61000,experience:5,city:'madurai'},
{eid:104,name:"keerthi",age:38,gender:"female",dept:'sales',desig
nation:"sales manager",salary:88000,experience:8,city:'cbe'},
{eid:105,name:"ravi",age:27,gender:'male',dept:'developer',designa
tion:'it',salary:58000,experience:5,city:'salem'},
{eid:106,name:"divya",age:30,gender:"female",dept:'it',designatio
n:"system analyist",salary:69000,experience:12,city:'chennai'}])
Output:
{ "acknowledged" : true, "insertedIds" : [ ObjectId("..."),
ObjectId("..."), ObjectId("..."), ObjectId("..."), ObjectId("..."),
ObjectId("...") ] }
4. DISPLAY ALL EMPLOYEES :-
db.emp.find()
Output:
{ "_id" : ObjectId("..."), "eid" : 101, "name" : "arun", "age" : 25,
"gender" : "male", "dept" : "it", "designation" : "software
engineer", "salary" : 55000, "experience" : 2, "city" : "cbe" }
{ "_id" : ObjectId("..."), "eid" : 102, "name" : "priya", "age" : 25,
"gender" : "female", "dept" : "it", "designation" : "manager",
"salary" : 60000, "experience" : 6, "city" : "chennai" }
{ "_id" : ObjectId("..."), "eid" : 103, "name" : "karthick", "age" :
29, "gender" : "male", "dept" : "finance", "designation" :
"accountance", "salary" : 61000, "experience" : 5, "city" :
"madurai" }
{ "_id" : ObjectId("..."), "eid" : 104, "name" : "keerthi", "age" : 38,
"gender" : "female", "dept" : "sales", "designation" : "sales
manager", "salary" : 88000, "experience" : 8, "city" : "cbe" }
{ "_id" : ObjectId("..."), "eid" : 105, "name" : "ravi", "age" : 27,
"gender" : "male", "dept" : "developer", "designation" : "it",
"salary" : 58000, "experience" : 5, "city" : "salem" }
{ "_id" : ObjectId("..."), "eid" : 106, "name" : "divya", "age" : 30,
"gender" : "female", "dept" : "it", "designation" : "system
analyist", "salary" : 69000, "experience" : 12, "city" : "chennai" }
5. SORT SALARY IN ASC ORDER :-
db.emp.find().sort({salary:1})
Output:
{ "_id" : ObjectId("..."), "eid" : 101, "name" : "arun", "age" : 25,
"gender" : "male", "dept" : "it", "designation" : "software
engineer", "salary" : 55000, "experience" : 2, "city" : "cbe" }
{ "_id" : ObjectId("..."), "eid" : 105, "name" : "ravi", "age" : 27,
"gender" : "male", "dept" : "developer", "designation" : "it",
"salary" : 58000, "experience" : 5, "city" : "salem" }
{ "_id" : ObjectId("..."), "eid" : 102, "name" : "priya", "age" : 25,
"gender" : "female", "dept" : "it", "designation" : "manager",
"salary" : 60000, "experience" : 6, "city" : "chennai" }
{ "_id" : ObjectId("..."), "eid" : 103, "name" : "karthick", "age" :
29, "gender" : "male", "dept" : "finance", "designation" :
"accountance", "salary" : 61000, "experience" : 5, "city" :
"madurai" }
{ "_id" : ObjectId("..."), "eid" : 106, "name" : "divya", "age" : 30,
"gender" : "female", "dept" : "it", "designation" : "system
analyist", "salary" : 69000, "experience" : 12, "city" : "chennai" }
{ "_id" : ObjectId("..."), "eid" : 104, "name" : "keerthi", "age" : 38,
"gender" : "female", "dept" : "sales", "designation" : "sales
manager", "salary" : 88000, "experience" : 8, "city" : "cbe" }
6. SORT SALARY IN DES ORDER :-
db.emp.find().sort({salary:-1})
Output:
{ "_id" : ObjectId("..."), "eid" : 104, "name" : "keerthi", "age" : 38,
"gender" : "female", "dept" : "sales", "designation" : "sales
manager", "salary" : 88000, "experience" : 8, "city" : "cbe" }
{ "_id" : ObjectId("..."), "eid" : 106, "name" : "divya", "age" : 30,
"gender" : "female", "dept" : "it", "designation" : "system
analyist", "salary" : 69000, "experience" : 12, "city" : "chennai" }
{ "_id" : ObjectId("..."), "eid" : 103, "name" : "karthick", "age" :
29, "gender" : "male", "dept" : "finance", "designation" :
"accountance", "salary" : 61000, "experience" : 5, "city" :
"madurai" }
{ "_id" : ObjectId("..."), "eid" : 102, "name" : "priya", "age" : 25,
"gender" : "female", "dept" : "it", "designation" : "manager",
"salary" : 60000, "experience" : 6, "city" : "chennai" }
{ "_id" : ObjectId("..."), "eid" : 105, "name" : "ravi", "age" : 27,
"gender" : "male", "dept" : "developer", "designation" : "it",
"salary" : 58000, "experience" : 5, "city" : "salem" }
{ "_id" : ObjectId("..."), "eid" : 101, "name" : "arun", "age" : 25,
"gender" : "male", "dept" : "it", "designation" : "software
engineer", "salary" : 55000, "experience" : 2, "city" : "cbe" }
7. SORT BY DEPT AND SALARY :-
db.emp.find().sort({dept:1,salary:-1})
Output:
{ "_id" : ObjectId("..."), "eid" : 105, "name" : "ravi", "age" : 27,
"gender" : "male", "dept" : "developer", "designation" : "it",
"salary" : 58000, "experience" : 5, "city" : "salem" }
{ "_id" : ObjectId("..."), "eid" : 103, "name" : "karthick", "age" :
29, "gender" : "male", "dept" : "finance", "designation" :
"accountance", "salary" : 61000, "experience" : 5, "city" :
"madurai" }
{ "_id" : ObjectId("..."), "eid" : 106, "name" : "divya", "age" : 30,
"gender" : "female", "dept" : "it", "designation" : "system
analyist", "salary" : 69000, "experience" : 12, "city" : "chennai" }
{ "_id" : ObjectId("..."), "eid" : 102, "name" : "priya", "age" : 25,
"gender" : "female", "dept" : "it", "designation" : "manager",
"salary" : 60000, "experience" : 6, "city" : "chennai" }
{ "_id" : ObjectId("..."), "eid" : 101, "name" : "arun", "age" : 25,
"gender" : "male", "dept" : "it", "designation" : "software
engineer", "salary" : 55000, "experience" : 2, "city" : "cbe" }
{ "_id" : ObjectId("..."), "eid" : 104, "name" : "keerthi", "age" : 38,
"gender" : "female", "dept" : "sales", "designation" : "sales
manager", "salary" : 88000, "experience" : 8, "city" : "cbe" }
8. TOP FIVE HIGHEST PAID EMPLOYEES :-
db.emp.find().sort({salary:1}).limit(5)
Output:
{ "_id" : ObjectId("..."), "eid" : 101, "name" : "arun", "age" : 25,
"gender" : "male", "dept" : "it", "designation" : "software
engineer", "salary" : 55000, "experience" : 2, "city" : "cbe" }
{ "_id" : ObjectId("..."), "eid" : 105, "name" : "ravi", "age" : 27,
"gender" : "male", "dept" : "developer", "designation" : "it",
"salary" : 58000, "experience" : 5, "city" : "salem" }
{ "_id" : ObjectId("..."), "eid" : 102, "name" : "priya", "age" : 25,
"gender" : "female", "dept" : "it", "designation" : "manager",
"salary" : 60000, "experience" : 6, "city" : "chennai" }
{ "_id" : ObjectId("..."), "eid" : 103, "name" : "karthick", "age" :
29, "gender" : "male", "dept" : "finance", "designation" :
"accountance", "salary" : 61000, "experience" : 5, "city" :
"madurai" }
{ "_id" : ObjectId("..."), "eid" : 106, "name" : "divya", "age" : 30,
"gender" : "female", "dept" : "it", "designation" : "system
analyist", "salary" : 69000, "experience" : 12, "city" : "chennai" }
9. BOTTOM THREE SALARIES :-
db.emp.find().sort({salary:-1}).limit(3)
Output:
{ "_id" : ObjectId("..."), "eid" : 104, "name" : "keerthi", "age" : 38,
"gender" : "female", "dept" : "sales", "designation" : "sales
manager", "salary" : 88000, "experience" : 8, "city" : "cbe" }
{ "_id" : ObjectId("..."), "eid" : 106, "name" : "divya", "age" : 30,
"gender" : "female", "dept" : "it", "designation" : "system
analyist", "salary" : 69000, "experience" : 12, "city" : "chennai" }
{ "_id" : ObjectId("..."), "eid" : 103, "name" : "karthick", "age" :
29, "gender" : "male", "dept" : "finance", "designation" :
"accountance", "salary" : 61000, "experience" : 5, "city" :
"madurai" }
10. SKIP FIRST THREE RECORDS :-
db.emp.find().sort({salary:-1}).skip(3)
Output:
{ "_id" : ObjectId("..."), "eid" : 102, "name" : "priya", "age" : 25,
"gender" : "female", "dept" : "it", "designation" : "manager",
"salary" : 60000, "experience" : 6, "city" : "chennai" }
{ "_id" : ObjectId("..."), "eid" : 105, "name" : "ravi", "age" : 27,
"gender" : "male", "dept" : "developer", "designation" : "it",
"salary" : 58000, "experience" : 5, "city" : "salem" }
{ "_id" : ObjectId("..."), "eid" : 101, "name" : "arun", "age" : 25,
"gender" : "male", "dept" : "it", "designation" : "software
engineer", "salary" : 55000, "experience" : 2, "city" : "cbe" }
11. EMPLOYEE WORKING IN IT DEPT :-
db.emp.find({dept:'it'})
Output:
{ "_id" : ObjectId("..."), "eid" : 101, "name" : "arun", "age" : 25,
"gender" : "male", "dept" : "it", "designation" : "software
engineer", "salary" : 55000, "experience" : 2, "city" : "cbe" }
{ "_id" : ObjectId("..."), "eid" : 102, "name" : "priya", "age" : 25,
"gender" : "female", "dept" : "it", "designation" : "manager",
"salary" : 60000, "experience" : 6, "city" : "chennai" }
{ "_id" : ObjectId("..."), "eid" : 106, "name" : "divya", "age" : 30,
"gender" : "female", "dept" : "it", "designation" : "system
analyist", "salary" : 69000, "experience" : 12, "city" : "chennai" }
12. SALARY BETWEEN THAT 60000 :-
db.emp.find({salary:{$gt:60000}})
Output:
{ "_id" : ObjectId("..."), "eid" : 103, "name" : "karthick", "age" :
29, "gender" : "male", "dept" : "finance", "designation" :
"accountance", "salary" : 61000, "experience" : 5, "city" :
"madurai" }
{ "_id" : ObjectId("..."), "eid" : 104, "name" : "keerthi", "age" : 38,
"gender" : "female", "dept" : "sales", "designation" : "sales
manager", "salary" : 88000, "experience" : 8, "city" : "cbe" }
{ "_id" : ObjectId("..."), "eid" : 106, "name" : "divya", "age" : 30,
"gender" : "female", "dept" : "it", "designation" : "system
analyist", "salary" : 69000, "experience" : 12, "city" : "chennai" }
13. SALARY BETWEEN 50000 AND 80000 :-
db.emp.find({salary:{$gte:50000,$lte:80000}})
Output:
{ "_id" : ObjectId("..."), "eid" : 101, "name" : "arun", "age" : 25,
"gender" : "male", "dept" : "it", "designation" : "software
engineer", "salary" : 55000, "experience" : 2, "city" : "cbe" }
{ "_id" : ObjectId("..."), "eid" : 102, "name" : "priya", "age" : 25,
"gender" : "female", "dept" : "it", "designation" : "manager",
"salary" : 60000, "experience" : 6, "city" : "chennai" }
{ "_id" : ObjectId("..."), "eid" : 103, "name" : "karthick", "age" :
29, "gender" : "male", "dept" : "finance", "designation" :
"accountance", "salary" : 61000, "experience" : 5, "city" :
"madurai" }
{ "_id" : ObjectId("..."), "eid" : 105, "name" : "ravi", "age" : 27,
"gender" : "male", "dept" : "developer", "designation" : "it",
"salary" : 58000, "experience" : 5, "city" : "salem" }
{ "_id" : ObjectId("..."), "eid" : 106, "name" : "divya", "age" : 30,
"gender" : "female", "dept" : "it", "designation" : "system
analyist", "salary" : 69000, "experience" : 12, "city" : "chennai" }
14. EMPLOYEE AGE LESS THAT 28 :-
db.emp.find({age:{$lte:28}})
Output:
{ "_id" : ObjectId("..."), "eid" : 101, "name" : "arun", "age" : 25,
"gender" : "male", "dept" : "it", "designation" : "software
engineer", "salary" : 55000, "experience" : 2, "city" : "cbe" }
{ "_id" : ObjectId("..."), "eid" : 102, "name" : "priya", "age" : 25,
"gender" : "female", "dept" : "it", "designation" : "manager",
"salary" : 60000, "experience" : 6, "city" : "chennai" }
{ "_id" : ObjectId("..."), "eid" : 105, "name" : "ravi", "age" : 27,
"gender" : "male", "dept" : "developer", "designation" : "it",
"salary" : 58000, "experience" : 5, "city" : "salem" }
15. EMPLOYEE AGE GREATER THAN OR EQUAL TO 30 :-
db.emp.find({age:{$gte:30}})
Output:
{ "_id" : ObjectId("..."), "eid" : 104, "name" : "keerthi", "age" : 38,
"gender" : "female", "dept" : "sales", "designation" : "sales
manager", "salary" : 88000, "experience" : 8, "city" : "cbe" }
{ "_id" : ObjectId("..."), "eid" : 106, "name" : "divya", "age" : 30,
"gender" : "female", "dept" : "it", "designation" : "system
analyist", "salary" : 69000, "experience" : 12, "city" : "chennai" }
16. EMPLOYEE FROM ‘cbe’ :-
db.emp.find({city:'cbe'})
Output:
{ "_id" : ObjectId("..."), "eid" : 101, "name" : "arun", "age" : 25,
"gender" : "male", "dept" : "it", "designation" : "software
engineer", "salary" : 55000, "experience" : 2, "city" : "cbe" }
{ "_id" : ObjectId("..."), "eid" : 104, "name" : "keerthi", "age" : 38,
"gender" : "female", "dept" : "sales", "designation" : "sales
manager", "salary" : 88000, "experience" : 8, "city" : "cbe" }
17. EMPLOYEE FROM CHENNAI AND BANGALORE :-
db.emp.find({city:{$in:["chennai","bangalore"]}})
Output:
{ "_id" : ObjectId("..."), "eid" : 102, "name" : "priya", "age" : 25,
"gender" : "female", "dept" : "it", "designation" : "manager",
"salary" : 60000, "experience" : 6, "city" : "chennai" }
{ "_id" : ObjectId("..."), "eid" : 106, "name" : "divya", "age" : 30,
"gender" : "female", "dept" : "it", "designation" : "system
analyist", "salary" : 69000, "experience" : 12, "city" : "chennai" }
18. EMPLOYEE NOT FROM CBE :-
db.emp.find({city:{$ne:"cbe"}})
Output:
{ "_id" : ObjectId("..."), "eid" : 102, "name" : "priya", "age" : 25,
"gender" : "female", "dept" : "it", "designation" : "manager",
"salary" : 60000, "experience" : 6, "city" : "chennai" }
{ "_id" : ObjectId("..."), "eid" : 103, "name" : "karthick", "age" :
29, "gender" : "male", "dept" : "finance", "designation" :
"accountance", "salary" : 61000, "experience" : 5, "city" :
"madurai" }
{ "_id" : ObjectId("..."), "eid" : 105, "name" : "ravi", "age" : 27,
"gender" : "male", "dept" : "developer", "designation" : "it",
"salary" : 58000, "experience" : 5, "city" : "salem" }
{ "_id" : ObjectId("..."), "eid" : 106, "name" : "divya", "age" : 30,
"gender" : "female", "dept" : "it", "designation" : "system
analyist", "salary" : 69000, "experience" : 12, "city" : "chennai" }
19. IT EMPLOYEE WITH SALARY GREATER THEN 55000 :-
db.emp.find({dept:"it",salary:{$gt:55000}})
Output:
{ "_id" : ObjectId("..."), "eid" : 102, "name" : "priya", "age" : 25,
"gender" : "female", "dept" : "it", "designation" : "manager",
"salary" : 60000, "experience" : 6, "city" : "chennai" }
{ "_id" : ObjectId("..."), "eid" : 106, "name" : "divya", "age" : 30,
"gender" : "female", "dept" : "it", "designation" : "system
analyist", "salary" : 69000, "experience" : 12, "city" : "chennai" }
20. FINANCE OR MANAGER :-
db.emp.find({$or:[{dept:"finance"},{dept:"mange"}]})
Output:
{ "_id" : ObjectId("..."), "eid" : 103, "name" : "karthick", "age" :
29, "gender" : "male", "dept" : "finance", "designation" :
"accountance", "salary" : 61000, "experience" : 5, "city" :
"madurai" }
21. SALARY GREATER THAT 60000 AND EXPERIENCE
GREATER THAT 5 YEAR :-
db.emp.find({$and:[{salary:{$gt:60000}},{experience:{$gt:5}}]})
Output:
{ "_id" : ObjectId("..."), "eid" : 104, "name" : "keerthi", "age" : 38,
"gender" : "female", "dept" : "sales", "designation" : "sales
manager", "salary" : 88000, "experience" : 8, "city" : "cbe" }
{ "_id" : ObjectId("..."), "eid" : 106, "name" : "divya", "age" : 30,
"gender" : "female", "dept" : "it", "designation" : "system
analyist", "salary" : 69000, "experience" : 12, "city" : "chennai" }
22. EMPLOYEE NOT WORKING IN IT :-
db.emp.find({dept:{$ne:'it'}})
Output:
{ "_id" : ObjectId("..."), "eid" : 103, "name" : "karthick", "age" :
29, "gender" : "male", "dept" : "finance", "designation" :
"accountance", "salary" : 61000, "experience" : 5, "city" :
"madurai" }
{ "_id" : ObjectId("..."), "eid" : 104, "name" : "keerthi", "age" : 38,
"gender" : "female", "dept" : "sales", "designation" : "sales
manager", "salary" : 88000, "experience" : 8, "city" : "cbe" }
{ "_id" : ObjectId("..."), "eid" : 105, "name" : "ravi", "age" : 27,
"gender" : "male", "dept" : "developer", "designation" : "it",
"salary" : 58000, "experience" : 5, "city" : "salem" }
23. NAME STRATS WITH “^a” :-
db.emp.find({name:{$regex:'^a'}})
Output:
{ "_id" : ObjectId("..."), "eid" : 101, "name" : "arun", "age" : 25,
"gender" : "male", "dept" : "it", "designation" : "software
engineer", "salary" : 55000, "experience" : 2, "city" : "cbe" }
24. NAME STRATS WITH “a$” :-
db.emp.find({name:{$regex:'a$'}})
Output:
{ "_id" : ObjectId("..."), "eid" : 102, "name" : "priya", "age" : 25,
"gender" : "female", "dept" : "it", "designation" : "manager",
"salary" : 60000, "experience" : 6, "city" : "chennai" }
{ "_id" : ObjectId("..."), "eid" : 106, "name" : "divya", "age" : 30,
"gender" : "female", "dept" : "it", "designation" : "system
analyist", "salary" : 69000, "experience" : 12, "city" : "chennai" }
25. DISPLAY ONLY NAME, DEPT AND SALARY :-
db.emp.find({},{_id:0,name:1,dept:1,salary:1})
Output:
{ "name" : "arun", "dept" : "it", "salary" : 55000 }
{ "name" : "priya", "dept" : "it", "salary" : 60000 }
{ "name" : "karthick", "dept" : "finance", "salary" : 61000 }
{ "name" : "keerthi", "dept" : "sales", "salary" : 88000 }
{ "name" : "ravi", "dept" : "developer", "salary" : 58000 }
{ "name" : "divya", "dept" : "it", "salary" : 69000 }
26. DISTINCT DEPT :-
db.emp.distinct("dept")
Output:
[ "developer", "finance", "it", "sales" ]
27. COUNT EMPLOYEE IN IT DEPT :-
db.emp.countDocuments({dept:'it'})
Output:
3
28. CREATE INDEX ON SALARY :-
db.emp.createIndex({salary:1})
Output:
{ "numIndexesBefore" : 1, "numIndexesAfter" : 2,
"createdCollectionAutomatically" : false, "ok" : 1 }
29. CREATE COMPOUND INDEX :-
db.emp.createIndex({dept:1,salary:-1})
Output:
{ "numIndexesBefore" : 2, "numIndexesAfter" : 3,
"createdCollectionAutomatically" : false, "ok" : 1 }
30. CREATE UNIQUE INDEX ON EID :-
db.emp.createIndex({eid:1},{unique:true})
Output:
{ "numIndexesBefore" : 3, "numIndexesAfter" : 4,
"createdCollectionAutomatically" : false, "ok" : 1 }
31. CREATE TEXT INDEX :-
db.emp.createIndex({destination:"text"})
Output:
{ "numIndexesBefore" : 4, "numIndexesAfter" : 5,
"createdCollectionAutomatically" : false, "ok" : 1 }
32. SEARCH TEXT INDEX :-
db.emp.find({$text:{$search:"manager"}})
Output:
{ "_id" : ObjectId("..."), "eid" : 102, "name" : "priya", "age" : 25,
"gender" : "female", "dept" : "it", "designation" : "manager",
"salary" : 60000, "experience" : 6, "city" : "chennai" }
{ "_id" : ObjectId("..."), "eid" : 104, "name" : "keerthi", "age" : 38,
"gender" : "female", "dept" : "sales", "designation" : "sales
manager", "salary" : 88000, "experience" : 8, "city" : "cbe" }
33. VIEW ALL INDEXES :-
db.emp.getIndexes()
Output:
[ { "v" : 2, "key" : { "_id" : 1 }, "name" : "_id_" },
{ "v" : 2, "key" : { "_fts" : "text", "_ftsx" : 1 }, "name" :
"designation_text", "weights" : { "designation" : 1 },
"default_language" : "english", "language_override" : "language",
"textIndexVersion" : 3 } ]
34. DROP ‘designation_text’ INDEX :-
db.emp.dropIndex("designation_text")
Output:
{ "nIndexesWas" : 2, "ok" : 1 }
35. EXPLAIN QUERY EXECUTION :-
db.emp.find({dept:"it",salary:{$gt:60000}}).explain("executionSta
ts")
Output:
{ "explainVersion" : "1", "queryPlanner" : { "namespace" :
"test.emp",
"parsedQuery" : { "$and" : [
{ "dept" : { "$eq" : "it" } }, { "salary" : { "$gt" : 60000 } } ]
}, "indexFilterSet" : false,
"queryHash" : "DC8924FE", "planCacheShapeHash" :
"DC8924FE", "planCacheKey" : "E215DD3E",
"optimizationTimeMillis" : 0,
"maxIndexedOrSolutionsReached" : false,
"maxIndexedAndSolutionsReached" : false,
"maxScansToExplodeReached" : false, "prunedSimilarIndexes" :
false,
"winningPlan" : { "isCached" : false, "stage" : "COLLSCAN",
"filter" : { "$and" : [ { "dept" : { "$eq" : "it" } }, { "salary" : {
"$gt" : 60000 } } ] }, "direction" : "forward" }, "rejectedPlans" : [
] },
"executionStats" : { "executionSuccess" : true, "nReturned" : 1,
"executionTimeMillis" : 0, "totalKeysExamined" : 0,
"totalDocsExamined" : 6,
"executionStages" : { "isCached" : false, "stage" : "COLLSCAN",
"filter" : { "$and" : [
{ "dept" : { "$eq" : "it" } }, { "salary" : { "$gt" : 60000 } } ] },
"nReturned" : 1, "executionTimeMillisEstimate" : 0, "works" : 7,
"advanced" : 1, "needTime" : 4, "needYield" : 0, "saveState" : 0,
"restoreState" : 0, "isEOF" : 1, "direction" : "forward",
"docsExamined" : 6 } },
"queryShapeHash" :
"E1A563BB2B09C71E2FDC8843C8A4D875A0B743517BF52F
A20C5F3DCF355A379B",
"command" : { "find" : "emp", "filter" : { "dept" : "it", "salary" : {
"$gt" : 60000 } }, "$db" : "test" },
"serverInfo" : { "host" : "Economicslab-11", "port" : 27017,
"version" : "8.0.13", "gitVersion" :
"8dc5cd2a30c4524132e2d44bb314544dc477e611" },
"serverParameters" :
{ "internalQueryFacetBufferSizeBytes" : 104857600,
"internalQueryFacetMaxOutputDocSizeBytes" : 104857600,
"internalLookupStageIntermediateDocumentMaxSizeBytes" :
104857600, "internalDocumentSourceGroupMaxMemoryBytes" :
104857600, "internalQueryMaxBlockingSortMemoryUsageBytes"
: 104857600, "internalQueryProhibitBlockingMergeOnMongoS" :
0, "internalQueryMaxAddToSetBytes" : 104857600,
"internalDocumentSourceSetWindowFieldsMaxMemoryBytes" :
104857600, "internalQueryFrameworkControl" :
"trySbeRestricted",
"internalQueryPlannerIgnoreIndexWithCollationForRegex" : 1 },
"ok" : 1 }
