Generating Mongo query from MySQL query - mysql

Question

I have been using the following MySQL command to construct a heatmap from log data. However, I have a new data set that is stored in a Mongo database and I need to run the same command.
select concat(a.packages '&' b.packages) "Concurrent Packages",
count(*) "Count"
from data a
cross join data b
where a.packages<b.packages and a.jobID=b.jobID
group by a.packages, b.packages
order by a.packages, b.packages;
Keep in mind that the tables a and b do not exist prior to the query. However, they are created from the packages column of the data table, which has jobID as the field which I want to check for matches. In other words if two packages are within the same job I want to add an entry to the concurrent usage count. How can I generate a similar query in Mongo?

Answer 1

This is not a "join" of different documents; it is an operation within one document, and can be done in MongoDB.
You have a SQL TABLE "data" like this:
JobID TEXT,
package TEXT
The best way to store this in MongoDB will be a collection called "data", containing one document per JobID that contains an array of packages:
{
_id: <JobID>,
packages: [
"packageA",
"packageB",
....
]
}
[ Note: you could also implement your data table as only one document in MongoDB, containing an array of jobs which contain each an array of packages. This is not recommended, because you might hit the 16MB document size limit and nested arrays are not (yet) well supported by different queries - if you want to use the data for other purposes as well ]
Now, how to get a result like this ?
{ pair: [ "packageA", "packageB" ], count: 20 },
{ pair: [ "packageA", "packageC" ], count: 11 },
...
As there is no built-in "cross join" of two arrays in MongoDB, you'll have to program it out in the map function of a mapReduce(), emitting each pair of packages as a key:
mapf = function () {
that = this;
this.packages.forEach( function( p1 ) {
that.packages.forEach( function( p2 ) {
if ( p1 < p2 ) {
key = { "pair": [ p1, p2 ] };
emit( key, 1 );
};
});
});
};
[ Note: this could be optimized, if the packages arrays were sorted ]
The reduce function is nothing more than summing up the counters for each key:
reducef = function( key, values ) {
count = 0;
values.forEach( function( value ) { count += value } );
return count;
};
So, for this example collection:
> db.data.find()
{ "_id" : "Job01", "packages" : [ "pA", "pB", "pC" ] }
{ "_id" : "Job02", "packages" : [ "pA", "pC" ] }
{ "_id" : "Job03", "packages" : [ "pA", "pB", "pD", "pE" ] }
we get the following result:
> db.data.mapReduce(
... mapf,
... reducef,
... { out: 'pairs' }
... );
{
"result" : "pairs",
"timeMillis" : 443,
"counts" : {
"input" : 3,
"emit" : 10,
"reduce" : 2,
"output" : 8
},
"ok" : 1,
}
> db.pairs.find()
{ "_id" : { "pair" : [ "pA", "pB" ] }, "value" : 2 }
{ "_id" : { "pair" : [ "pA", "pC" ] }, "value" : 2 }
{ "_id" : { "pair" : [ "pA", "pD" ] }, "value" : 1 }
{ "_id" : { "pair" : [ "pA", "pE" ] }, "value" : 1 }
{ "_id" : { "pair" : [ "pB", "pC" ] }, "value" : 1 }
{ "_id" : { "pair" : [ "pB", "pD" ] }, "value" : 1 }
{ "_id" : { "pair" : [ "pB", "pE" ] }, "value" : 1 }
{ "_id" : { "pair" : [ "pD", "pE" ] }, "value" : 1 }
For more information on mapReduce consult: http://docs.mongodb.org/manual/reference/method/db.collection.mapReduce/ and http://docs.mongodb.org/manual/applications/map-reduce/

Answer 2

You can't. Mongo doesn't do joins. Switching from SQL to Mongo is a lot more involved than migrating your queries.
Typically, you would include all the pertinent information in the same record (rather than normalize the information and select it with a join). Denormalize!