Difference between revisions of "MongoDB QueryData"
Line 94: | Line 94: | ||
Find all employees whose commission is less than 1000: | Find all employees whose commission is less than 1000: | ||
db.emp.find({comm: {$lt:1000}}) | db.emp.find({comm: {$lt:1000}}) | ||
+ | |||
+ | Working with the date field (hiredate) is more complex, since you have to create a new date for the comparison. | ||
+ | |||
+ | For example, find all employees who start after the 1st January 2000: | ||
+ | db.emp.find({hiredate: {$lt: new Date("2000-01-01")}}) | ||
+ | |||
+ | Find employees who started on the 16th October 2015: | ||
+ | db.emp.find({hiredate: new Date("2015-10-16")}) | ||
=== Find One document === | === Find One document === |
Latest revision as of 14:13, 5 March 2018
Main Page >> MongoDB >>MongoDB Workbook >> Querying Collections
Querying a collection
The find() function can be used to query the documents.
The format is:
db.collectionName.find(optional_query_criteria)
Where the query_criteria follows a pattern:
db.collectionName.find({keyField: "value"})
Note:
- the criteria is enclosed in curly brackets: {}
- the value needs quotes if it is a string or date value
- all names and values are case sensitive
- quotes are optional for the fieldName, so long as they do not contain spaces
Find all documents
For example, show all the data so far in the dept collection:
db.dept.find()
The results should look like:
{ "_id" : 10, "deptno" : 10, "dname" : "ACCOUNTING", "loc" : "NEW YORK" } { "_id" : 20, "deptno" : 20, "dname" : "RESEARCH", "loc" : "DALLAS" } { "_id" : 40, "deptno" : 40, "dname" : "OPERATIONS", "loc" : "BOSTON" } { "_id" : 50, "deptno" : 40, "dname" : "OPERATIONS V2", "loc" : "BOSTON" } { "_id" : 30, "deptno" : 30, "dname" : "SALES", "loc" : "CHICAGO" }
To show the documents in the emp collection:
db.emp.find()
The data comes back messy. The pretty() function can be used to improve the layout:
db.emp.find().pretty()
A subset of the emp collection is shown below:
> db.emp.find().pretty() { "_id" : ObjectId("5a09e79ac536e890d5a7a666"), "empno" : 7782, "ename" : "CLARK", "job" : "MANAGER", "mgr" : 7839, "hiredate" : ISODate("1989-06-09T00:00:00Z"), "sal" : 2450, "deptno" : 10 } { "_id" : ObjectId("5a09e79ac536e890d5a7a667"), "empno" : 7839, "ename" : "KING", "job" : "PRESIDENT", "hiredate" : ISODate("1980-11-17T00:00:00Z"), "sal" : 5000, "deptno" : 10 } ....
Note the object ids are now system generated (and will be different values in your own data).
Find with query criteria
If working with a large collection, you will not want all the documents returned.
Find all the employees are are clerks:
db.emp.find({job:"CLERK"})
For numerical data, the greater than (>) and less than (<) operators are represented by $gt and $lt respectively. Note, for these operators, the search criteria must be enclosed in {} brackets.
Find all employees who earn more than 2400:
db.emp.find({sal: {$gt:2400}})
Find all employees whose commission is less than 1000:
db.emp.find({comm: {$lt:1000}})
Working with the date field (hiredate) is more complex, since you have to create a new date for the comparison.
For example, find all employees who start after the 1st January 2000:
db.emp.find({hiredate: {$lt: new Date("2000-01-01")}})
Find employees who started on the 16th October 2015:
db.emp.find({hiredate: new Date("2015-10-16")})
Find One document
To find just one document requires the use of the equivalent of a primary key field. This can be a field that the user takes responsibility to keep unique, such as the deptno:
db.dept.find({deptno:10})
Or the object id can be used, which will be unique:
db.dept.find({_id:10})
In the emp collection, the _ids are system generated and generally along the lines of: '5a0727e99ba81dee9b1cc6a3', so less easy to use!
List all the records in emp:
db.emp.find().pretty()
and pick an _id from the collection and then try and find one record.
For example (note, your object id will be different):
db.emp.find( {_id : ObjectId("5a0727e99ba81dee9b1cc6a3")}).pretty()
The function ObjectId() must be used to convert the value into an object id.
Next Step
MongoDB Aggregation Pipeline