It is difficult for MongoDB to realize the operation of cross summarizing. It is also quite complicated to realize it using high-level languages, like Java, after the desired data is retrieved out. In this case, you can consider using esProc to help MongoDB realize the operation. The following example will teach you how it works in detail.
A collection – student – is given in the following:
db.student.insert ( {school:’school1′, sname : ‘Sean’ , sub1: 4, sub2 :5 })
db.student.insert ( {school:’school1′, sname : ‘chris’ , sub1: 4, sub2 :3 })
db.student.insert ( {school:’school1′, sname : ‘becky’ , sub1: 5, sub2 :4 })
db.student.insert ( {school:’school1′, sname : ‘sam’ , sub1: 5, sub2 :4 })
db.student.insert ( {school:’school2′, sname : ‘dustin’ , sub1: 2, sub2 :2 })
db.student.insert ( {school:’school2′, sname : ‘greg’ , sub1: 3, sub2 :4 })
db.student.insert ( {school:’school2′, sname : ‘peter’ , sub1: 5, sub2 :1 })
db.student.insert ( {school:’school2′, sname : ‘brad’ , sub1: 2, sub2 :2 })
db.student.insert ( {school:’school2′, sname : ‘liz’ , sub1: 3, sub2 :null })
We are expected to produce a cross table as the one in the following, in which each row is a school and the first column holds students whose results of sub1 are a 5 and the second column holds those whose results of sub1 are a 4 and so forth.
esProc script:
| A | |
| 1 | =MongoDB(“mongo://localhost:27017/test?user=test&password=test”) |
| 2 | =A1.find(“student”).fetch() |
| 3 | =A2.group(school) |
| 4 | =A3.new(school:school,~.align@a(5,sub1).(~.len()):sub1,~.align@a(5,sub2).(~.len()):sub2) |
| 5 | =A4.new(school,sub1(5):sub1-5,sub1(4):sub1-4,sub1(3):sub1-3,sub1(2):sub1-2,sub1(1):sub1-1,sub2(5):sub2-5,sub2(4):sub2-4,sub2(3):sub2-3,sub2(2):sub2-2,sub2(1):sub2-1) |
| 6 | >A11.close() |
A1: Connect to MongoDB. Both IP and the port number are localhost:27017. The database name, user name and the password all are test.
A2: Use find function to fetch the collection – student – from MongoDB and create a cursor. Here esProc uses the same parameter format in find function as that in find statement of MongoDB. As esProc’s cursor supports fetching and processing data in batches, the memory overflow caused by importing big data all at once can thus be avoided. In this case, the data can be fetched altogether using fetch function because the size is not big.
A3：Group the data by schools.
A4：Then group each group of data in alignment according to the sequence [1,2,3,4,5] and compute the length of each subgroup.
A5：Put the lengths got in A4 into corresponding positions as required and a record sequence wil be generated as the result.
The result is as follows:
Note：esProc isn’t equipped with a Java driver included in MongoDB. So to access MongoDB using esProc, you must put MongoDB’s Java driver (a version of 2.12.2 or above is required for esProc, e.g. mongo-java-driver-2.12.2.jar) into [esProc installation directory]\common\jdbc beforehand.
The esProc script used to help MongoDB with the computation is easy to be integrated into the Java program. You just need to add another line of code – result A6 to output a result in the form of resultset to Java program. For the detailed code, please refer to esProc Tutorial. In the same way, MongoDB’s Java driver must be put into the classpath of a Java program before the latter accesses MongoDB by calling an esProc program.