Better Alternatives To A Project Stage

The quintessential tool used in MongoDB's Query Language (MQL) to define or restrict fields to return is a projection. In the MongoDB Aggregation Framework, the analogous facility for specifying fields to include or exclude is the $project stage. For many earlier versions of MongoDB, this was the only tool to define which fields to keep or omit. However, $project comes with a few usability challenges:

  1. $project is confusing and non-intuitive. You can only choose to include fields or exclude fields in a single stage, but not both. There is one exception, though, where you can exclude the _id field yet still define other fields to include (this only applies to the _id field). It's as if $project has an identity crisis.

  2. $project is verbose and inflexible. If you want to define one new field or revise one field, you will have to name all other fields in the projection to include. If each input record has 100 fields and the pipeline needs to employ a $project stage for the first time, things become tiresome. To include a new 101st field, you now also have to name all the original 100 fields in this new $project stage too. You will find this irritating if you have an evolving data model, where additional new fields appear in some records over time. Because you use a $project for inclusion, then each time a new field appears in the data set, you must go back to the old aggregation pipeline to modify it to name the new field explicitly for inclusion in the results. This is the antithesis of flexibility and agility.

In MongoDB version 4.2, the $set and $unset stages were introduced, which, in most cases, are preferable to using $project for declaring field inclusion and exclusion. They make the code's intent much clearer, lead to less verbose pipelines, and, critically, they reduce the need to refactor a pipeline whenever the data model evolves. How this works and guidance on when to use $set & $unset stages is described in the section When To Use Set & Unset, further below.

Despite the challenges, though, there are some specific situations where using $project is advantageous over $set/$unset. These situations are described in the section When To Use Project further below.

MongoDB version 3.4 addressed some of the disadvantages of $project by introducing a new $addFields stage, which has the same behaviour as $set. $set came later than $addFieldsand $set is actually just an alias for $addFields. However, back then, the Aggregation Framework provided no direct equivalent to $unset. Both $set and $unset stages are available in modern versions of MongoDB, and their counter purposes are obvious to deduce by their names ($set Vs $unset). The name $addFields doesn't fully reflect that you can modify existing fields rather than just adding new fields. This book prefers $set over $addFields to help promote consistency and avoid any confusion of intent. However, if you are wedded to $addFields, use that instead, as there is no behavioural difference.

When To Use $set & $unset

You should use $set & $unset stages when you need to retain most of the fields in the input records, and you want to add, modify or remove a minority subset of fields. This is the case for most uses of aggregation pipelines.

For example, imagine there is a collection of credit card payment documents similar to the following:

// INPUT  (a record from the source collection to be operated on by an aggregation)
{
  _id: ObjectId("6044faa70b2c21f8705d8954"),
  card_name: "Mrs. Jane A. Doe",
  card_num: "1234567890123456",
  card_expiry: "2023-08-31T23:59:59.736Z",
  card_sec_code: "123",
  card_provider_name: "Credit MasterCard Gold",
  transaction_id: "eb1bd77836e8713656d9bf2debba8900",
  transaction_date: ISODate("2021-01-13T09:32:07.000Z"),
  transaction_curncy_code: "GBP",
  transaction_amount: NumberDecimal("501.98"),
  reported: true
}

Then imagine an aggregation pipeline is required to produce modified versions of the documents, as shown below:

// OUTPUT  (a record in the results of the executed aggregation)
{
  card_name: "Mrs. Jane A. Doe",
  card_num: "1234567890123456",
  card_expiry: ISODate("2023-08-31T23:59:59.736Z"), // Field type converted from text
  card_sec_code: "123",
  card_provider_name: "Credit MasterCard Gold",
  transaction_id: "eb1bd77836e8713656d9bf2debba8900",
  transaction_date: ISODate("2021-01-13T09:32:07.000Z"),
  transaction_curncy_code: "GBP",
  transaction_amount: NumberDecimal("501.98"),
  reported: true,
  card_type: "CREDIT"                               // New added literal value field
}

Here, shown by the // comments, there was a requirement to modify each document's structure slightly, to convert the card_expiry text field into a proper date field, and add a new card_type field, set to the value "CREDIT", for every record.

Naively you might decide to build an aggregation pipeline using a $project stage to achieve this transformation, which would probably look similar to the following:

// BAD
[
  {"$project": {
    // Modify a field + add a new field
    "card_expiry": {"$dateFromString": {"dateString": "$card_expiry"}},
    "card_type": "CREDIT",        

    // Must now name all the other fields for those fields to be retained
    "card_name": 1,
    "card_num": 1,
    "card_sec_code": 1,
    "card_provider_name": 1,
    "transaction_id": 1,
    "transaction_date": 1,
    "transaction_curncy_code": 1,
    "transaction_amount": 1,
    "reported": 1,                
    
    // Remove _id field
    "_id": 0,
  }},
]

As you can see, the pipeline's stage is quite lengthy, and because you use a $project stage to modify/add two fields, you must also explicitly name each other existing field from the source records for inclusion. Otherwise, you will lose those fields during the transformation. Imagine if each payment document has hundreds of possible fields, rather than just ten!

A better approach to building the aggregation pipeline, to achieve the same results, would be to use $set and $unset instead, as shown below:

// GOOD
[
  {"$set": {
    // Modified + new field
    "card_expiry": {"$dateFromString": {"dateString": "$card_expiry"}},
    "card_type": "CREDIT",        
  }},
  
  {"$unset": [
    // Remove _id field
    "_id",
  ]},
]

This time, when you need to add new documents to the collection of existing payments, which include additional new fields, e.g. settlement_date & settlement_curncy_code, no changes are required. The existing aggregation pipeline allows these new fields to appear in the results automatically. However, when using $project, each time the possibility of a new field arises, a developer must first refactor the pipeline to incorporate an additional inclusion declaration (e.g. "settlement_date": 1, or "settlement_curncy_code": 1).

When To Use $project

It is best to use a $project stage when the required shape of output documents is very different from the input documents' shape. This situation often arises when you do not need to include most of the original fields.

This time for the same input payments collection, let us imagine you require a new aggregation pipeline to produce result documents. You need each output document's structure to be very different from the input structure, and you need to retain far fewer original fields, similar to the following:

// OUTPUT  (a record in the results of the executed aggregation)
{
  transaction_info: { 
    date: ISODate("2021-01-13T09:32:07.000Z"),
    amount: NumberDecimal("501.98")
  },
  status: "REPORTED"
}

Using $set/$unset in the pipeline to achieve this output structure would be verbose and would require naming all the fields (for exclusion this time), as shown below:

// BAD
[
  {"$set": {
    // Add some fields
    "transaction_info.date": "$transaction_date",
    "transaction_info.amount": "$transaction_amount",
    "status": {"$cond": {"if": "$reported", "then": "REPORTED", "else": "UNREPORTED"}},
  }},
  
  {"$unset": [
    // Remove _id field
    "_id",

    // Must name all other existing fields to be omitted
    "card_name",
    "card_num",
    "card_expiry",
    "card_sec_code",
    "card_provider_name",
    "transaction_id",
    "transaction_date",
    "transaction_curncy_code",
    "transaction_amount",
    "reported",         
  ]}, 
]

However, by using $project for this specific aggregation, as shown below, to achieve the same results, the pipeline will be less verbose. The pipeline will have the flexibility of not requiring modification if you ever make subsequent additions to the data model, with new previously unknown fields:

// GOOD
[
  {"$project": {
    // Add some fields
    "transaction_info.date": "$transaction_date",
    "transaction_info.amount": "$transaction_amount",
    "status": {"$cond": {"if": "$reported", "then": "REPORTED", "else": "UNREPORTED"}},
    
    // Remove _id field
    "_id": 0,
  }},
]

Another potential downside can occur when using $project to define field inclusion, rather than using $set (or $addFields). When using $project to declare all required fields for inclusion, it can be easy for you to carelessly specify more fields from the source data than intended. Later on, if the pipeline contains something like a $group stage, this will cover up your mistake. The final aggregation's output will not include the erroneous field in the output. You might ask, "Why is this a problem?". Well, what happens if you intended for the aggregation to take advantage of a covered index query for the few fields it requires, to avoid unnecessarily accessing the raw documents. In most cases, MongoDB's aggregation engine can track fields' dependencies throughout a pipeline and, left to its own devices, can understand which fields are not required. However, you would be overriding this capability by explicitly asking for the extra field. A common error is to forget to exclude the _id field in the projection inclusion stage, and so it will be included by default. This mistake will silently kill the potential optimisation. If you must use a $project stage, try to use it as late as possible in the pipeline because it is then clear to you precisely what you are asking for as the aggregation's final output. Also, unnecessary fields like _id may already have been identified by the aggregation engine as no longer required, due to the occurrence of an earlier $group stage, for example.

Main Takeaway

In summary, you should always look to use $set (or $addFields) and $unset for field inclusion and exclusion, rather than $project. The main exception is if you have an obvious requirement for a very different structure for result documents, where you only need to retain a small subset of the input fields.