Golang get array index value in json response - arrays

Question

So I have some query to database (mongodb) which will order results by value field.
all := EValues{}
err := con.Find(bson.M{"name": "somename}).Sort("-value").All(&all)
Json output for this looks like:
"values": [
{
"user_name": "guest7485",
"value": 8911,
"value_date": "2016-03-09T14:40:34.512Z"
},
{
"user_name": "guest7485",
"value": 539,
"value_date": "2016-03-07T14:11:05.217Z"
},
{
"user_name": "guest7485",
"value": 221,
"value_date": "2016-03-07T14:11:08.853Z"
},
{
"user_name": "guest7485",
"value": 77,
"value_date": "2016-03-07T14:11:12.377Z"
}
]
In my json response I need to add parameter "position" which should be basically equal to 1 - first result, 2 - second result and so on, for all results. So my final output should be:
"values": [
{
"position": 1,
"user_name": "guest7485",
"value": 8911,
"value_date": "2016-03-09T14:40:34.512Z"
},
{
"position": 2,
"user_name": "guest7485",
"value": 539,
"value_date": "2016-03-07T14:11:05.217Z"
},
{
"position": 3,
"user_name": "guest7485",
"value": 221,
"value_date": "2016-03-07T14:11:08.853Z"
},
{
"position": 4,
"user_name": "guest7485",
"value": 77,
"value_date": "2016-03-07T14:11:12.377Z"
}
]
I'm wondering how to solve this with mgo and go in general, and I would be really greatfull if someone can give me the most efficient way to solve this.
Update:
Definition of Evalues is bellow:
type EValue struct {
ID bson.ObjectId `json:"-" bson:"_id,omitempty"`
Name string `json:"-" bson:"name"`
UserId bson.ObjectId `json:"-" bson:"userId"`
UserName string `json:"user_name" bson:"userName"`
Value int64 `json:"value" bson:"value"`
AddedTime time.Time `json:"value_date" bson:"addedTime"`
}
type EValues []EValue

Answer 1

Add a position field to EValue:
type EValue struct {
... other fields here
Position int `json:"position" bson:"-"`
}
Loop through db results and set the field:
for i := range all {
all[i].Position = i + 1
}
Marshal the result as JSON.

Answer 2

With MongDB 3.2 this can be done using the $unwind operator where you can pass an object with the field path and the field includeArrayIndex which will hold the array index:
pipeline = [
{ "$match": {"name": "somename"} },
{ "$unwind": { "path": "$values", "includeArrayIndex": "position" } },
{
"$project": {
"name": 1,
"newarray.position": "$position",
"newarray.user_name": "$values.user_name",
"newarray.value_date": "$values.value_date",
"newarray.value": "$values.value",
}
},
{
"$group": {
"_id": "$name",
"values": { "$push": "$newarray" }
}
}
]
db.test.aggregate(pipeline);
Output
> db.test.aggregate(pipeline).pretty();
{
"_id" : "somename",
"values" : [
{
"position" : NumberLong(0),
"user_name" : "guest8911",
"value_date" : "2016-03-09T14:40:34.512Z",
"value" : 8911
},
{
"position" : NumberLong(1),
"user_name" : "guest7485",
"value_date" : "2016-03-07T14:11:05.217Z",
"value" : 539
},
{
"position" : NumberLong(2),
"user_name" : "guest7485",
"value_date" : "2016-03-07T14:11:08.853Z",
"value" : 221
},
{
"position" : NumberLong(3),
"user_name" : "guest7485",
"value_date" : "2016-03-07T14:11:12.377Z",
"value" : 77
}
]
}
>
If this is not supported with the mgo driver, then a not so efficient approach would be to use Map-Reduce for this. The following mongo shell example demonstrates how you can run the operation:
Populate test collection:
db.test.insert({
"name": "somename",
"values": [
{
"user_name": "guest8911",
"value": 8911,
"value_date": "2016-03-09T14:40:34.512Z"
},
{
"user_name": "guest7485",
"value": 539,
"value_date": "2016-03-07T14:11:05.217Z"
},
{
"user_name": "guest7485",
"value": 221,
"value_date": "2016-03-07T14:11:08.853Z"
},
{
"user_name": "guest7485",
"value": 77,
"value_date": "2016-03-07T14:11:12.377Z"
}
]
})
Run the following map-reduce operation:
> mr = db.runCommand({
"mapreduce": "test",
"map": function() {
var arr = []
for(var i=0; i < this.values.length; i++){
var val = this.values[i];
val["position"] = i+1;
arr.push(val);
}
emit(this._id, arr);
},
"reduce" : function() {},
"out": "test_keys"
})
Query resulting collection:
> db[mr.result].find().pretty()
{
"_id" : ObjectId("56e18ab84b9018ec86d2a6bd"),
"value" : [
{
"user_name" : "guest8911",
"value" : 8911,
"value_date" : "2016-03-09T14:40:34.512Z",
"position" : 1
},
{
"user_name" : "guest7485",
"value" : 539,
"value_date" : "2016-03-07T14:11:05.217Z",
"position" : 2
},
{
"user_name" : "guest7485",
"value" : 221,
"value_date" : "2016-03-07T14:11:08.853Z",
"position" : 3
},
{
"user_name" : "guest7485",
"value" : 77,
"value_date" : "2016-03-07T14:11:12.377Z",
"position" : 4
}
]
}
>
Now given the listing above, you can assemble your query in mgo using MapReduce
job := mgo.MapReduce{
Map: "function(){var arr=[];for(var i=0;i<this.values.length; i++){var val=this.values[i];val['position']=i+1;arr.push(val);};emit(this._id,arr);}",
Reduce: "function() { }",
}
var result []struct { Id int "_id"; Value []EValue }
_, err := collection.Find(nil).MapReduce(job, &result)
if err != nil {
panic(err)
}
for _, item := range result {
fmt.Println(item.Value)
}
For more details, check the documentation: https://godoc.org/labix.org/v1/mgo#MapReduce: