MongoDB is a typical NoSQL database. Its document-oriented structure makes both storage and access convenient and efficient. But the database has rather weak computing ability. Computations on MongoDB data, particularly complex ones, are hard to handle. A data computing engine having powerful computing capability is needed to work with MongoDB to achieve relevant computing tasks.

The open-source esProc SPL is a specialized structured data computation engine. It supplies rich class libraries and all-around, database-independent computational capabilities. SPL has an independent procedural syntax that is particularly good at handling complex computations. It can help MongoDB increase its ability to compute, accomplish grouping & aggregation, joins, subqueries, and all the other computing tasks effortlessly.

Regular queries

It is easy to achieve JOINs MongoDB finds it difficult to handle in SPL:

	A	B
1	`=mongo_open("mongodb://127.0.0.1:27017/raqdb")`	Connect to MongoDB
2	`=mongo_shell(A1,"c1.find()").fetch()`	Fetch data from MongoDB
3	`=mongo_shell(A1,"c2.find()").fetch()`
4	`=A2.join(user1:user2,A3:user1:user2,output)`	Perform join
5	`>A1.close()`	Close connection to MongoDB

SPL can reuse the result of handling data of a table that is repeatedly involved in computations:

	A	B
1	`=mongo_open("mongodb://127.0.0.1:27017/raqdb")`
2	`=mongo_shell(A1,"course.find(,{_id:0})").fetch()`	Fetch data from MongoDB
3	`=A2.group(Sno).((avg = ~.avg(Grade), ~.select(Grade>avg))).conj()`	Get documents where grading level is above average
4	`>A1.close()`

Perform IN conditional query in SPL:

	A	B
1	`=mongo_open("mongodb://localhost:27017/test")`
2	`=mongo_shell(A1,"orders.find(,{_id:0})")`	Fetch data from MongoDB
3	`=mongo_shell(A1,"employee.find({STATE:'California'},{_id:0})").fetch()`	Select certain employee documents
4	`=A3.(EID).sort()`	Get EID field and sort it
5	`=A2.select(A4.pos@b(SELLERID)).fetch()`	Perform binary search
6	`>A1.close()`

SPL’s technique to turn foreign key values to objects – the object-referencing foreign key – creates efficient foreign key pointers:

	A	B
1	`=mongo_open("mongodb://localhost:27017/local")`
2	`=mongo_shell(A1,"Progress.find({}, {_id:0})").fetch()`	Fetch Progress data
3	`=A2.groups(courseid; count(userId):popularityCount)`	Group and count by course
4	`=mongo_shell(A1,"Course.find(,{title:1})").fetch()`	Get Course data
5	`=A3.switch(courseid,A4:_id)`	Foreign-key-based join
6	`=A5.new(popularityCount,courseid.title)`	Create result set
7	`=A1.close()`

SPL achieves APPLY algorithm in a simple way:

	A	B
1	`=mongo_open("mongodb://127.0.0.1:27017/raqdb")`
2	`=mongo_shell(A1,"users.find()").fetch()`	Fetch users data
3	`=mongo_shell(A1,"workouts.find()").fetch()`	Fetch workouts data
4	`=A2.conj(A3.select(A2.workouts.pos(_id)).derive(A2.name))`	Get matching _id values from the sequence of workouts documents
5	`>A1.close()`

SPL’s way of performing set-oriented calculations – intersection, union, difference, and concatenation:

	A	B
1	`=mongo_open("mongodb://127.0.0.1:27017/raqdb")`
2	`=mongo_shell(A1,"emp1.find()").fetch()`
3	`=mongo_shell(A1,"emp2.find()").fetch()`
4	`=[A2,A3].conj()`	Concatenation of sequences
5	`=[A2,A3].merge@ou()`	Union by whole row comparison
6	`=[A2,A3].merge@ou(_id, NAME)`	Union by key value comparison
7	`=[A2,A3].merge@oi()`	Intersection by whole row comparison
8	`=[A2,A3].merge@oi(_id, NAME)`	Intersection by key value comparison
9	`=[A2,A3].merge@od()`	Difference by whole row comparison
10	`=[A2,A3].merge@od(_id, NAME)`	Difference by key value comparison
11	`>A1.close()`

Get sequence number of a member in a sequence in SPL:

	A	B
1	`=mongo_open("mongodb://localhost:27017/local")`
2	`=mongo_shell(A1,"users.find({name:'jim'},{name:1,friends:1,_id:0})").fetch()`
3	`=A2.friends.pos("luke")`	Get sequence numbers of members in sequence friends
4	`=A1.close()`

Perform intersection of multi-member collections in SPL:

	A	B
1	[Chemical, Biology, Math]	Courses
2	`=mongo_open("mongodb://127.0.0.1:27017/raqdb")`
3	`=mongo_shell(A2,"student.find()").fetch()`	Fetch student data
4	`=A3.select(Lesson^A1!=[])`	Get documents where at least one course is selected
5	`=A4.new(_id, Name, ~.Lesson^A1:Lession)`	Get the final result
6	`>A2.close()`

Complex queries Getting TopN in SPL:

	A	B
1	`=mongo_open("mongodb://127.0.0.1:27017/test")`
2	`=mongo_shell(A1,"last3.find(,{_id:0};{variable:1})").fetch()`	Get last3 data and sort it by variable
3	`for A2;variable =A3.top(3;-timestamp)`	Get the three documents having the latest timestamps
4	`=@	B3`
5	`=B4.minp(~.timestamp)`	Get documents with the earliest timestamp
6	`>mongo_close(A1)`

Summarize a nested-structure collection in SPL:

	A
1	`=mongo_open("mongodb://127.0.0.1:27017/raqdb")`
2	`=mongo_shell(A1,"computer.find()").fetch()`
3	`=A2.new(_id:ID,income.array().sum():INCOME,output.array().sum():OUTPUT)`
4	`>A1.close()`

Combine subdocuments made up of multiple attributes in SPL:

	A	B	C
1	`=mongo_open("mongodb://localhost:27017/local")`
2	`=mongo_shell(A1,"c1.find(,{_id:0};{name:1})").fetch()`
3	`=create(_id, readUsers)`		Create result table sequence
4	`for A2;name =A4.conj(acls.read.users	acls.append.users	acls.edit.users
5	{% raw %}`=@	A3.insert(0, A4.name, B4)`
6	`=A1.close()`

Query nested List subdocument in SPL:

	A	B
1	`=mongo_open("mongodb://localhost:27017/local")`
2	`=mongo_shell(A1,"Cbettwen.find(,{_id:0})").fetch()`
3	`=A2.conj((t=~.objList.data.dataList,t.select((s=float(~.split@c1()(1)), s>6154 && s<=6155))))`	Get eligible strings
4	`=A1.close()`

SPL Cross-sector aggregation:

	A
1	`=mongo_open("mongodb://localhost:27017/local")`
2	`=mongo_shell(A1,"student.find()").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	`=A1.close()`

SPL segment-based grouping:

	A	B
1	[3000,5000,7500,10000,15000]	Intervals of Sales for segmentation
2	`=mongo_open("mongodb://127.0.0.1:27017/raqdb")`
3	`=mongo_shell(A2,"sales.find()").fetch()`
4	`=A3.groups(A1.pseg(~.SALES):Segment;count(1): number)`	Group data and count employees by SALES intervals
5	`>A2.close()`

SPL class-based grouping:

	A	B
1	`=mongo_open("mongodb://127.0.0.1:27017/raqdb")`
2	`=mongo_shell(A1,"books.find()")`
3	`=A2.groups(addr,book;count(book):Count)`	Grouping & count
4	`=A3.groups(addr;sum(Count):Total)`	Grouping & sum
5	`=A3.join(addr,A4:addr,Total)`	Join operation
6	`>A1.close()`

Data writing Export data as CSV in SPL:

	A	B
1	`=mongo_open("mongodb://localhost:27017/raqdb")`
2	`=mongo_shell(A1,"carInfo.find(,{_id:0})")`
3	`=A2.conj((t=~,cars.car.new(t.id:id,t.cars.name, ~:car)))`	Split each car field value into multiple rows
4	`=file("D:\\data.csv").export@tc(A3)`	Export as CSV
5	`>A1.close()`

SPL database update (from MongoDB to MySQL):

	A	B
1	`=mongo_open("mongodb://localhost:27017/raqdb")`	Connect to MongoDB
2	`=mongo_shell(A1,"course.find(,{_id:0})").fetch()`
3	`=connect("myDB1")`	Connect to MySQL
4	`=A3.query@x("select * from course2").keys(Sno, Cno)`
5	`>A3.update(A2:A4,course2,Sno,Cno, Grade; Sno,Cno)`	Update data into MySQL
6	`>A1.close()`

SPL database update (from MySQL to MongoDB):

	A	B
1	`=connect("mysql")`	Connect to MySQL
2	`=A1.query@x("select * from course2")`	Get data of course2 table
3	`=mongo_open("mongodb://localhost:27017/raqdb")`	Connect to MongoDB
4	`=mongo_insert(A3, "course",A2)`	Insert records of MySQL table course2 into the MongoDB collection
5	`>A3.close()`

Mixed computations

SPL enables convenient mixed computation between MongoDB and another data source:

	A	B
1	`=mongo_open("mongodb://localhost:27017/test")`	Connect to MongoDB
2	`=mongo_shell(A1,"emp.find({'$and':[{'Birthday':{'$gte':'"+string(begin)+"'}},{'Birthday':{'$lte':'"+string(end)+"'}}]},{_id:0})").fetch()`	Get records within a specified time interval
3	`=A1.close()`	Close MongoDB connection
4	`=myDB1.query("select * from cities")`	Get data of cities table in MySQL
5	`=A2.switch(CityID,A4:CityID)`	Foreign-key-based join
6	`=A5.new(EID,Dept,CityID.CityName:CityName,Name,Gender)`	Create result set
7	`return A6`	Return the result set

SQL support

Besides the native syntax, SPL offers support of SQL92 standard. You can use SQL to query MongoDB. To achieve the above join operation, for instance:

	A
1	`=mongo_open("mongodb://127.0.0.1:27017/test")`
2	`=mongo_shell(A1,"c1.find()").fetch()`
3	`=mongo_shell@x(A1,"c2.find()").fetch()`
4	`$select s.* from {A2} as s left join {A3} as r on s.user1=r.user1 and s.user2=r.user2 where r.income>0.3`

Integration into application

SPL provides standard JDBC/ODBC drivers through which SPL can be conveniently integrated into an application or invoked by it. To invoke SPL code through JDBC, for instance:

…
Class.forName("com.esproc.jdbc.InternalDriver");
Connection conn = DriverManager.getConnection("jdbc:esproc:local://");
PrepareStatement st=con.prepareStatement("call splScript(?)"); // splScript is the name of SPL script file
st.setObject(1,"California");
st.execute();
ResultSet rs = st.getResultSet();
…

With all those functionalities, you’ll sure to be impressed by MongoDB’s strikingly boosted computing ability. Try your hand now.

Download SPL here

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The Open-source SPL Boosts MongoDB Computing Ability

Regular queries

Mixed computations

SQL support

Integration into application