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Operations with Builders

On this page

  • Overview
  • Sample Class
  • Construct a Filter
  • Array Operators
  • Create a Projection
  • Lambda Expressions
  • Define a Sort
  • Define an Update
  • Define Index Keys
  • Build an Aggregation Pipeline
  • Write Pipeline Results to a Collection
  • Build an Atlas Search Query
  • Perform an Atlas Vector Search
  • Additional Information
  • API Documentation

In this guide, you can learn about the helper classes, or builders, that the .NET/C# Driver provides to create types used in your operations. Using builders helps you identify errors at compile time and avoid them at runtime. This guide provides information on builder classes that you can use for the following tasks:

  • Creating a filter definition

  • Creating a projection

  • Defining a sort order

  • Defining an update operation

  • Selecting index keys

Tip

MongoDB C# Analyzer

The C# Analzyer is a tool that helps you analyze your builders definitions and understand how your .NET/C# code translates into the MongoDB Query API. For more information and installation instructions, see the C# Analzyer reference page.

You should read this guide if you want to learn more about how to construct definitions and build up syntax using builders.

The code examples in this guide demonstrate how you can use builders to create types to interact with documents in the sample collection plants.flowers. Documents in this collection are modeled by the following Flower class:

public class Flower
{
public ObjectId Id { get; set; }
public string Name { get; set; }
public string Category { get; set; }
public double Price { get; set; }
public List<string> Season { get; set; }
public double Stock { get; set; }
public string Description { get; set; }
}

Each builder class takes a generic type parameter TDocument which represents the type of document that you are working with. In this guide, the Flower class is the document type used in each builder class example.

The FilterDefinitionBuilder class provides a type-safe interface for building up queries. Suppose you want to query your collection for documents matching the following criteria:

  • Price field value less than 20

  • Category field value is "Perennial"

Use builders to create the filter definition with the typed variant:

var builder = Builders<Flower>.Filter;
var filter = builder.Lt(f => f.Price, 20) & builder.Eq(f => f.Category, "Perennial");

Using the typed variant form provides compile-time safety. Additionally, your IDE can provide refactoring support.

Alternatively, you can use string-based field names to contruct the filter:

var builder = Builders<Flower>.Filter;
var filter = builder.Lt("Price", 20) & builder.Eq("Category", "Perennial");

If your document has properties or fields that serialize to arrays, you can use the methods beginning with Any, such as AnyEq() or AnyLt(), to compare the entire array against a single item.

Use builders to check which documents in the collection have a Season array that includes "winter":

var builder = Builders<Flower>.Filter;
var filter = builder.AnyEq(f => f.Season, "winter");

The ProjectionDefinitionBuilder class provides a type-safe interface for defining a projection. Suppose you want to create a projection on the Name and Price fields, but exclude the Id field.

Use builders to create the projection definition with the typed variant:

var builder = Builders<Flower>.Projection;
var projection = builder.Include(f => f.Name).Include(f => f.Price).Exclude(f => f.Id);

You can also use string-based field names to define the projection:

var builder = Builders<Flower>.Projection;
var projection = builder.Include("Name").Include("Price").Exclude("Id");

Finally, you can use the Expression() method to define the projection:

var builder = Builders<Flower>.Projection;
var projection = builder.Expression(f => new { Name = f.Name, Price = f.Price });

This definition has a return type of ProjectionDefinition<TDocument, TProjection> whereas the others return a ProjectionDefinition<TDocument>.

The driver supports using lambda expressions to render projections. When you define a Find() projection with the Expression() method to create a lambda expression, the driver inspects the expression to determine which fields are referenced and automatically constructs a server-side projection to return only those fields.

You can also use lambda expressions to create new fields by performing operations on values in your documents. The following example shows how you can use a lambda expression to project a new Profit field using the Price and Stock fields:

var builder = Builders<Flower>.Projection;
var projection = builder.Expression(f => new { Profit = f.Price * f.Stock });

Note

Id Field Exclusion

When you create a projection using a lambda expression, the output automatically excludes the Id field unless you explicitly include is as a projection field.

The SortDefinitionBuilder class provides a type-safe interface for building up sort syntax. Suppose you want to define a sort with the following order:

  • Ascending on Price

  • Descending on Category

Use builders to create the sort definition with the typed variant:

var builder = Builders<Flower>.Sort;
var sort = builder.Ascending(f => f.Price).Descending(f => f.Category);

Alternatively, you can use string-based field names to define the sort:

var builder = Builders<Flower>.Sort;
var sort = builder.Ascending("Price").Descending("Category");

The UpdateDefinitionBuilder class provides a type-safe interface for building up an update specification. Suppose you want to create an update specification with the following criteria:

  • Create the new field SunRequirement

  • Multiply the Price field value by 0.9

Use builders to create the update specification with the typed variant:

var builder = Builders<Flower>.Update;
var update = builder.Set(f => f.SunRequirement, "Full sun").Mul(f => f.Price, 0.9);

Alternatively, you can use string-based field names to define the update:

var builder = Builders<Flower>.Update;
var update = builder.Set("SunRequirement", "Full sun").Mul("Price", 0.9);

The IndexKeysDefinitionBuilder class provides a type-safe interface for defining index keys. Suppose you want to select Category as an ascending index key.

Use builders to select the index key with the typed variant:

var builder = Builders<Flower>.IndexKeys;
var keys = builder.Ascending(f => f.Category);

Alternatively, you can use string-based field names to select the index key:

var builder = Builders<BsonDocument>.IndexKeys;
var keys = builder.Ascending("Category");

The IndexKeysDefinitionBuilder class also provides methods to build a wildcard index. You can create a wildcard index using All field paths or A single field path, in this case using Category:

For more information about how to use wildcard indexes, see Wildcard Indexes.

The PipelineDefinitionBuilder class provides a type-safe interface for defining an aggregation pipeline. An aggregation pipeline is a series of stages that are used to transform a document. Suppose you want to create a pipeline that performs the following operations:

  • Matches all documents with "spring" in the Season field

  • Sorts the results by the Category field

  • Groups the documents by category and shows the average price and total available for all documents in that category

Use PipelineDefinitionBuilder classes to build the pipeline:

var sortBuilder = Builders<Flower>.Sort.Ascending(f => f.Category);
var matchFilter = Builders<Flower>.Filter.AnyEq(f => f.Season, "spring");
var pipeline = new EmptyPipelineDefinition<Flower>()
.Match(matchFilter)
.Sort(sortBuilder)
.Group(f => f.Category,
g => new
{
name = g.Key,
avgPrice = g.Average(f => f.Price),
totalAvailable = g.Sum(f => f.Stock)
}
);

The preceding example creates the following pipeline:

[{ "$match" : { "season" : "spring" } }, { "$sort" : { "category" : 1 } }, { "$group" : { "_id" : "$category", "avgPrice" : { "$avg" : "$price" }, "totalAvailable" : { "$sum" : "$stock" } } }]

You can add stages to your pipeline that don't have corresponding type-safe methods in the PipelineDefinitionBuilder interface by providing your query as a BsonDocument to the AppendStage() method.

var pipeline = new EmptyPipelineDefinition<BsonDocument>().AppendStage<BsonDocument, BsonDocument, BsonDocument>("{ $set: { field1: '$field2' } }");

Note

When using a BsonDocument to define your pipeline stage, the driver does not take into account any BsonClassMap, serialization attributes or serialization conventions. The field names used in the BsonDocument must match those stored on the server.

For more information on providing a query as a BsonDocument, see our FAQ page.

To learn more about the Aggregation Pipeline, see the Aggregation Pipeline server manual page.

You can write the documents returned from an aggregation pipeline to a collection by creating an $out stage at the end of your aggregation pipeline. To create an $out stage, call the Out() method on a PipelineStageDefinitionBuilder. The Out() method requires the name of the collection you want to write the documents to.

The following example builds an aggregation pipeline that matches all documents with a season field value of "Spring" and outputs them to a springFlowers collection:

var outputCollection = database.GetCollection("springFlowers");
var matchFilter = Builders<Flower>.Filter.AnyEq(f => f.Season, "spring");
// Creates an aggregation pipeline and outputs resulting documents to a new collection.
var pipeline = new EmptyPipelineDefinition<Flower>()
.Match(matchFilter)
.Out(outputCollection);

You can write the results of an aggregation pipeline to a time series collection by specifying a TimeSeriesOption object and passing it as the second parameter to the Out() method.

Imagine that the documents in the plants.flowers collection contain a datePlanted field that holds BSON date values. You can store the documents in this collection in a time series collection by using the datePlanted field as the time field.

The following example creates a TimeSeriesOptions object and specifies datePlanted as the timeField. It then builds an aggregation pipeline that matches all documents with a season field value of "Spring" and outputs them to a time series collection called springFlowerTimes.

var timeSeriesOptions = new TimeSeriesOptions("datePlanted");
var collectionName = "springFlowerTimes"
var matchFilter = Builders<Flower>.Filter.AnyEq(f => f.Season, "spring");
// Creates an aggregation pipeline and outputs resulting documents to a time series collection.
var pipeline = new EmptyPipelineDefinition<Flower>()
.Match(matchFilter)
.Out(collectionName, timeSeriesOptions);

To learn more about time series collections, see Time Series Collections.

The Search class provides a type-safe interface for creating a $search pipeline stage.

To learn how to construct search queries with the Search class, see Atlas Search.

You can use builders to create a $vectorSearch aggregation pipeline stage to perform an approximate nearest neighbor search on a vector in the specified field. Your collection must have a defined Atlas Vector Search index before you can perform a vector search on your data.

Tip

To obtain the sample dataset used in the following example, see Quick Start. To create the sample Atlas Vector Search index used in the following example, see Create an Atlas Vector Search Index in the Atlas manual.

Consider the embedded_movies collection in the sample_mflix database. You can use a $vectorSearch stage to perform a semantic search on the plot_embedding field of the documents in the collection.

The following example shows how to use builders to generate an aggregation pipeline to perform the following operations:

  • Performs a vector search on the Atlas Vector Search index of the plot_embedding field using vector embeddings for the string "time travel"

  • Fetches the Title and Plot fields from documents found in the vector search

// Defines vector embeddings for the string "time travel"
var vector = new[] 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// Specifies that the vector search will consider the 150 nearest neighbors
// in the specified index
var options = new VectorSearchOptions<EmbeddedMovie>()
{
IndexName = "vector_index",
NumberOfCandidates = 150
};
// Builds aggregation pipeline and specifies that the $vectorSearch stage
// returns 10 results
var pipeline = new EmptyPipelineDefinition<EmbeddedMovie>()
.VectorSearch(m => m.Embedding, vector, 10, options)
.Project(Builders<EmbeddedMovie>.Projection
.Include(m => m.Title)
.Include(m => m.Plot));

The results of the preeding example contain the following documents:

{ "_id" : ObjectId("573a13a0f29313caabd04a4f"), "plot" : "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.", "title" : "Thrill Seekers" }
{ "_id" : ObjectId("573a13d8f29313caabda6557"), "plot" : "At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.", "title" : "About Time" }
{ "_id" : ObjectId("573a13a5f29313caabd13b4b"), "plot" : "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.", "title" : "The Time Machine" }
{ "_id" : ObjectId("573a13aef29313caabd2e2d7"), "plot" : "After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", "title" : "Crusade in Jeans" }
{ "_id" : ObjectId("573a1399f29313caabceec0e"), "plot" : "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.", "title" : "Timecop" }
{ "_id" : ObjectId("573a1399f29313caabcee36f"), "plot" : "A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...", "title" : "A.P.E.X." }
{ "_id" : ObjectId("573a13c6f29313caabd715d3"), "plot" : "Agent J travels in time to M.I.B.'s early days in 1969 to stop an alien from assassinating his friend Agent K and changing history.", "title" : "Men in Black 3" }
{ "_id" : ObjectId("573a13d4f29313caabd98c13"), "plot" : "Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.", "title" : "Tomorrowland" }
{ "_id" : ObjectId("573a13b6f29313caabd477fa"), "plot" : "With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.", "title" : "Love Story 2050" }
{ "_id" : ObjectId("573a13e5f29313caabdc40c9"), "plot" : "A dimension-traveling wizard gets stuck in the 21st century because cell-phone radiation interferes with his magic. With his home world on the brink of war, he seeks help from a jaded ...", "title" : "The Portal" }

To learn more about Atlas Vector Search, see Atlas Vector Search Overview in the Atlas manual.

Find runnable examples using builders for various operations under Usage Examples.

To learn more about any of the methods or types discussed in this guide, see the following API Documentation:

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