


How can I Retrieve the Count of All Linux Users by Selecting Specific Venue IDs in MongoDB using Go?
Retrieve Item List by Checking Multiple Attribute Values in MongoDB in Go
Problem:
Retrieve selected items by identifying multiple conditions in MongoDB, analogous to the IN condition in MySQL:
<code class="sql">SELECT * FROM venuelist WHERE venueid IN (venueid1, venueid2)</code>
Consider the JSON structure:
<code class="json">{ "_id" : ObjectId("57f940c4932a00aba387b0b0"), "tenantID" : 1, "date" : "2016-10-09 00:23:56", "venueList" : [ { "id" : "VID1212", "sum" : [ { "name" : "linux", "value" : 12 }, { "name" : "ubuntu", "value" : 4 } ], "ssidList" : [ { "id" : "SSID1212", "sum" : [ { "name" : "linux", "value" : 8 }, { "name" : "ubuntu", "value" : 6 } ], "macList" : [ { "id" : "12:12:12:12:12:12", "sum" : [ { "name" : "linux", "value" : 12 }, { "name" : "ubuntu", "value" : 1 } ] } ] } ] }, { "id" : "VID4343", "sum" : [ { "name" : "linux", "value" : 2 } ], "ssidList" : [ { "id" : "SSID4343", "sum" : [ { "name" : "linux", "value" : 2 } ], "macList" : [ { "id" : "43:43:43:43:43:34", "sum" : [ { "name" : "linux", "value" : 2 } ] } ] } ] } ] }</code>
Task: Retrieve the count of all Linux users by selecting venue IDs 'VID1212' and 'VID4343'.
Solution:
- Use the aggregation framework to filter documents based on venueList IDs using $match.
- Unwind venueList and sum subdocument arrays using $unwind.
- Filter again using $match for desired IDs.
- Use $group** to aggregate filtered documents, using **$sum accumulator for desired values.
- Condition using a ternary operator ($cond) to create independent count fields.
<code class="go">// Import the necessary packages. import ( "context" "fmt" "go.mongodb.org/mongo-driver/bson" "go.mongodb.org/mongo-driver/bson/primitive" "go.mongodb.org/mongo-driver/mongo" "go.mongodb.org/mongo-driver/mongo/options" ) // retrieveItemListByAttributeValues retrieves items based on multiple attribute values in MongoDB. func retrieveItemListByAttributeValues(client *mongo.Client) { // Create a new context. ctx := context.Background() // Define the pipeline. pipeline := mongo.Pipeline{ {{"$match", bson.D{{"venueList.id", bson.D{{"$in", bson.A{"VID1212", "VID4343"}}}}}}}, {{"$unwind", "$venueList"}}, {{"$match", bson.D{{"venueList.id", bson.D{{"$in", bson.A{"VID1212", "VID4343"}}}}}}}, {{"$unwind", "$venueList.sum"}}, { {"$group", bson.D{ {"_id", nil}, {"linux", bson.D{{"$sum", bson.M{"$cond", bson.A{bson.VC.Bool(true), "$venueList.sum.value", 0}}}}}}, {"ubuntu", bson.D{{"$sum", bson.M{"$cond", bson.A{bson.VC.Bool(true), "$venueList.sum.value", 0}}}}}}, }}, }, } // Execute the aggregation pipeline. aggRes, err := client.Database("test").Collection("venuelist").Aggregate(ctx, pipeline) if err != nil { panic(err) } // Iterate over the results. for aggRes.Next(ctx) { var result bson.M if err := aggRes.Decode(&result); err != nil { panic(err) } // Print the result. fmt.Println(result) } // Close the cursor. if err := aggRes.Close(ctx); err != nil { panic(err) } }</code>
Alternative Solution:
For improved performance and flexibility, consider the following alternative pipeline:
<code class="go">pipeline := mongo.Pipeline{ {{"$match", bson.D{{"venueList.id", bson.D{{"$in", bson.A{"VID1212", "VID4343"}}}}}}}, {{"$unwind", "$venueList"}}, {{"$match", bson.D{{"venueList.id", bson.D{{"$in", bson.A{"VID1212", "VID4343"}}}}}}}, {{"$unwind", "$venueList.sum"}}, { {"$group", bson.D{ {"_id", "$venueList.sum.name"}, {"count", bson.D{{"$sum", "$venueList.sum.value"}}}, }}, }, { {"$group", bson.D{ {"_id", nil}, {"counts", bson.D{{"$push", bson.D{ {"name", "$_id"}, {"count", "$count"}, }}}}, }}, }, }</code>
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