Select

Description

Generates new documents, either by referencing a subset of existing fields, or by assigning a field to the result of a given expression.

Syntax

Node.js

const names = await db.pipeline()
  .collection("/cities")
  .select(stringConcat(field("name"), ", ", field("location.country")).as("name"), "population")
  .execute();

Client examples

Web

const result = await execute(db.pipeline()
  .collection("books")
  .select(field("soldBooks").multiply(field("price")).round().as("partialRevenue"))
  .aggregate(field("partialRevenue").sum().as("totalRevenue"))
  );
test.firestore.js
Swift
let result = try await db.pipeline()
  .collection("books")
  .select([Field("soldBooks").multiply(Field("price")).round().as("partialRevenue")])
  .aggregate([Field("partialRevenue").sum().as("totalRevenue")])
  .execute()
PipelineSnippets.swift

Kotlin

val result = db.pipeline()
    .collection("books")
    .select(Expression.multiply(field("soldBooks"), field("price")).round().alias("partialRevenue"))
    .aggregate(AggregateFunction.sum("partialRevenue").alias("totalRevenue"))
    .execute()
DocSnippets.kt

Java

Task<Pipeline.Snapshot> result = db.pipeline()
    .collection("books")
    .select(Expression.multiply(field("soldBooks"), field("price")).round().alias("partialRevenue"))
    .aggregate(AggregateFunction.sum("partialRevenue").alias("totalRevenue"))
    .execute();
DocSnippets.java
Python
from google.cloud.firestore_v1.pipeline_expressions import Field

result = (
    client.pipeline()
    .collection("books")
    .select(
        Field.of("soldBooks")
        .multiply(Field.of("price"))
        .round()
        .as_("partialRevenue")
    )
    .aggregate(Field.of("partialRevenue").sum().as_("totalRevenue"))
    .execute()
)
firestore_pipelines.py
Java
Pipeline.Snapshot result =
    firestore
        .pipeline()
        .collection("books")
        .select(round(multiply(field("soldBooks"), field("price"))).as("partialRevenue"))
        .aggregate(sum("partialRevenue").as("totalRevenue"))
        .execute()
        .get();
PipelineSnippets.java

Behavior

Position of a Select Stage

There are no restrictions on when a select stage can be used, but any fields not included in a select stage won't be accessible to subsequent stages in a pipeline.

For example, to select only the name and location fields of all cities in Canada from the following dataset:

Node.js

await db.collection('cities').doc('SF').set({name: 'San Francisco', population: 800000, location: {country: 'USA', state: 'California'}});
await db.collection('cities').doc('TO').set({name: 'Toronto', population: 3000000, location: {country: 'Canada', province: 'Ontario'}});

The following pipeline can be used:

Node.js

const names = await db.pipeline()
  .collection("/cities")
  .where(equal(field("location.country"), "Canada"))
  .select(stringConcat(field("name"), ", ", field("location.country")).as("name"), "population")
  .execute();

Which produces the following documents:

{name: 'Toronto, Canada', population: 3000000},

However, if the select stage is instead placed before the where stage, like:

Node.js

const names = await db.pipeline()
  .collection("/cities")
  .select(stringConcat(field("name"), ",", field("location.country")).as("name"), "population")
  .where(equal(field("location.country"), "Canada"))
  .execute();

No documents will be produced, because location.country has been removed from the document before the execution of the where stage.

Select Nested Fields

The Select stage can be used to select nested fields from both maps and arrays.

For example, to select the nested country field and first entry of the landmarks array from the following documents:

Node.js

await db.collection('cities').doc('SF').set({name: 'San Francisco', population: 800000, location: {country: 'USA', state: 'California'}, landmarks: ['Golden Gate Bridge', 'Alcatraz']});
await db.collection('cities').doc('TO').set({name: 'Toronto', population:  3000000, province: 'ON', location: {country: 'Canada', province: 'Ontario'}, landmarks: ['CN Tower', 'Casa Loma']});
await db.collection('cities').doc('AT').set({name: 'Atlantis', population: null});

The following pipeline can be used:

Node.js

const locations = await db.pipeline()
  .collection("/cities")
  .select(field("name").as("city"), field("location.country").as("country"), field("landmarks").arrayGet(0).as("topLandmark"))
  .execute();

Which produces the following documents:

{city: 'San Francisco', country: 'USA', topLandmark: 'Golden Gate Bridge'},
{city: 'Toronto', country: 'Canada', topLandmark: 'CN Tower'},
{city: 'Atlantis'}

If a nested map value or array value does not exist, it is not included in the resulting document. Array and map access in the select stage behaves identically to the array_get and map_get functions, respectively.