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This article teaches you how to use pipelines in MongoDB's lookupAggregation Pipeline operator. Before proceeding, you must have enough knowledge about the aggregation pipeline and the Aggregation Pipeline operator to understand the use of pipelines in the Aggregation Pipeline operator $lookupwhen working with MongoDB .$lookup

If you already understand these concepts, you can quickly move on to the last two code examples in this tutorial.

What is an aggregation pipeline

It is the process of collecting data and returning calculation results. This process collects data from different documents, groups them according to specified conditions, and performs various operations on the grouped data.

For example, average, sum, maximum, and minimum. It is just like a SQL aggregate function.

In MongoDB, we can use aggregation in the following three ways.

You can also read here for a deeper understanding of the aggregation pipeline.

$lookupWhat are operators in MongoDB ?

This operator is used to perform a left outer join to merge data from one document to another document in the same database. It filters the documents from the joined collection for further processing.

We can also use this operator to add extra fields to existing documents.

$lookupThe operator adds a new array attribute (field) whose values ​​(elements) match the documents in the joined collection. These transformed documents are then passed to the next stage.

$lookupThe operator has three different syntaxes that we can use based on the project requirement. This tutorial uses $lookupthe syntax to represent it Join conditions & Subqueries on the Joined Collection.

To practice the example code, let's prepare a sample collection with data.

Sample code:

db.createCollection('collection1');
db.createCollection('collection2');

db.collection1.insertMany([
    {"shopId": "001", "shopPosId": "001", "description": "description for 001"},
    {"shopId": "002", "description": "description for 002"},
    {"shopId": "003", "shopPosId": "003", "description": "description for 003"},
    {"shopId": "004", "description": "description for 004"}
]);

db.collection2.insertMany([
    {"shopId": "001", "shopPosId": "0078", "clientUid": "474192"},
    {"shopId": "002", "shopPosId": "0012", "clientUid": "474193"},
    {"shopId": "003", "shopPosId": "0034", "clientUid": "474194"},
    {"shopId": "004", "shopPosId": "0056", "clientUid": "474195"}
]);

Now, we can execute the following command to see the documents inserted in each collection.

db.collection1.find();
db.collection2.find();

Use pipelining in $lookupthe operator to concatenate conditions in MongoDB

To understand how to $lookupuse pipes with the operator, let's join documents from two collections where collection1.shopIdis equal to collection2.shopIdand collection1does not contain shopPosIdthe field.

Only those documents from both collections that satisfy these two conditions will be joined. Refer to the sample code given below.

Sample code:

db.collection2.aggregate([
    {
        "$lookup": {
        "from": "collection1",
        "let": { "shopId": "$shopId" },
        "pipeline": [{
            "$match": {
                "$and": [
                    {"$expr": {"$eq": ['$shopId', '$$shopId'] }},
                    { "shopPosId": { "$exists": false } }
                ]
            }
         }],
         "as": "shopDescription"
        }
    }
]).pretty();

Output:

Did you observe the output given above carefully? Only those documents are joined from both the collections that satisfy both the conditions in the pipeline ( collection1.shopIdequals collection2.shopId, and collection1does not contain shopPosIdfields).

Additionally, those documents that do not meet these conditions have an shopDescriptionempty array called (see the red box in the results above). We can only display those shopDescriptionresult documents that have a non-empty array (see the query below).

Sample code:

db.collection2.aggregate([
    {
        "$lookup": {
        "from": "collection1",
        "let": { "shopId": "$shopId" },
        "pipeline": [{
            "$match": {
                "$and": [
                    {"$expr": {"$eq": ['$shopId', '$$shopId'] }},
                    { "shopPosId": { "$exists": false } }
                ]
            }
         }],
         "as": "shopDescription"
        }
    },
    {
        "$match":{
            "shopDescription": { $exists: true, $not: {$size: 0} }
         }
    }
]).pretty();

Output: