[Video](https://www.youtube.com/watch?v=qMZ_q-iHqsU)

Cloud Firestore supports using range and inequality filters on multiple fields in a single query. You can have range and inequality conditions on multiple fields and simplify your
application development by delegating implementation of post-filtering logic to
Cloud Firestore.

## Range and inequality filters on multiple fields

The following query uses range filters on population and density to return all
cities where population is higher than 1,000,000 people and population density
is less than 10,000 people per unit of area.

### Web version 9 modular

```javascript
const q = query(
    collection(db, "cities"),
    where('population', '>', 1000000),
    where('density', '<', 10000),
  );
```

### Swift

```swift
let query = db.collection("cities")
  .whereField("population", isGreaterThan: 1000000)
  .whereField("density", isLessThan: 10000)
```

### Objective-C

```objective-c
FIRQuery *query =
 [[[[self.db collectionWithPath:@"cities"]
queryWhereField:@"population" isGreaterThan:@1000000]
   queryWhereField:@"density" isLessThan:@10000];
```

### Java Android

```java
Query query = db.collection("cities")
 .whereGreaterThan("population", 1000000)
 .whereLessThan("density", 10000);
```

### Kotlin+KTX Android

```java
val query = db.collection("cities")
 .whereGreaterThan("population", 1000000)
 .whereLessThan("density", 10000)
```

### Go

```go
   query := client.Collection("cities").
      Where("population", ">", 1000000).
      Where("density", "<", 10000)
```

### Java

```java
db.collection("cities")
  .whereGreaterThan("population", 1000000)
  .whereLessThan("density", 10000);
```

### Node.js

```javascript
db.collection("cities")
  .where('population', '>', 1000000),
  .where('density', '<', 10000)
```

### Python

```python
from google.cloud import firestore

db = firestore.Client()
query = db.collection("cities")
.where("population", ">", 1000000)
.where("density", "<", 10000)
```

### PHP

```php
$collection = $db->collection('samples/php/cities');
$chainedQuery = $collection
    ->where('population', '>', 1000000)
    ->where('density', '<', 10000);
```

### C#

```c#
CollectionReference citiesRef = db.Collection("cities");
Query query = citiesRef
    .WhereGreaterThan("Population", 1000000)
    .WhereLessThan("Density", 10000);
QuerySnapshot querySnapshot = await query.GetSnapshotAsync();
foreach (DocumentSnapshot documentSnapshot in querySnapshot)
{
    var name = documentSnapshot.GetValue<string>("Name");
    var population = documentSnapshot.GetValue<int>("Population");
    var density = documentSnapshot.GetValue<int>("Density");
    Console.WriteLine($"City '{name}' returned by query. Population={population}; Density={density}");
}
```

### Ruby

```ruby
query = cities_ref.where("population", ">", "1000000")
                  .where("density", "<", 10000)
```

### C++

```c++
CollectionReference cities_ref = db->Collection("cities");
Query query = cities_ref.WhereGreaterThan("population", FieldValue::Integer(1000000))
                       .WhereLessThan("density", FieldValue::Integer(10000));
```

### Unity

```c#
CollectionReference citiesRef = db.Collection("cities");
Query query = citiesRef.WhereGreaterThan("population", 1000000)
                      .WhereLessThan("density", 10000);
```

### Dart

```dart
final citiesRef = FirebaseFirestore.instance.collection('cities')
final query = citiesRef.where("population", isGreaterThan: 1000000)
                  .where("density", isLessThan: 10000);
```

## Indexing considerations

Before you run your queries, read
about \[queries\]\[1\] and the Cloud Firestore \[data model\]\[2\].

In Cloud Firestore, the `ORDER BY` clause of a query determines which indexes
can be used to serve the query. For example, an `ORDER BY a ASC, b ASC` query
requires a composite index on the `a ASC, b ASC` fields.

To optimize the performance and cost of Cloud Firestore queries,
optimize the order of fields in the index. To do this, ensure that your index is
ordered from left to right such that the query distills to a dataset that
prevents scanning of unnecessary index entries.

Suppose you want to search through a collection of employees and find United States
employees whose salary is more than $100,000 and whose number of years of experience is
greater than 0. Based on your understanding of the dataset, you know that the
salary constraint is more selective than the experience constraint. The ideal
index that would reduce the number of index scans would be the
`(salary [...], experience [...])`. Thus, the query that would be fast and
cost-efficient would order `salary` before `experience` and look as follows:

### Java

```java
db.collection("employees")
  .whereGreaterThan("salary", 100000)
  .whereGreaterThan("experience", 0)
  .orderBy("salary")
  .orderBy("experience");
```

### Node.js

```javascript
db.collection("employees")
  .where("salary", ">", 100000)
  .where("experience", ">", 0)
  .orderBy("salary")
  .orderBy("experience");
```

### Python

```python
db.collection("employees")
  .where("salary", ">", 100000)
  .where("experience", ">", 0)
  .order_by("salary")
  .order_by("experience");
```

## Best practices for optimizing indexes

When optimizing indexes, note the following best practices.

#### Order index fields by equalities followed by most selective range or inequality field

Cloud Firestore uses the leftmost fields of a composite index to satisfy the
equality constraints and the range or inequality constraint, if any, on the first field
of the `orderBy()` query. These constraints can reduce the number of index
entries that Cloud Firestore scans. Cloud Firestore uses the remaining fields
of the index to satisfy other range or inequality constraints of the query. These
constraints don't reduce the number of index entries that Cloud Firestore scans
but filter out unmatched documents so that the number of documents that are
returned to the clients are reduced.

For more information about creating efficient indexes, see \[index properties\]\[4\].

#### Order fields in decreasing order of query constraint selectivity

To ensure that Cloud Firestore selects the optimal index for your query,
specify an `orderBy()` clause that orders fields in decreasing order of query
constraint selectivity. Higher selectivity matches a smaller subset of
documents, while lower selectivity matches a larger subset of documents. Ensure that
you select range or inequality fields with higher selectivity earlier in the index
ordering than fields with lower selectivity.

To minimize the number of documents that Cloud Firestore scans and returns over
the network, you should always order fields in the decreasing order of query
constraint selectivity. If the result set is not in the required order and the
result set is expected to be small, you can implement client-side logic to
reorder it as per your ordering expectation.

For example, suppose you want to search through a collection of employees to find
United States employees whose salary is more than $100,000 and order the results by the year of
experience of the employee. If you expect only a small number of employees will have salaries
greater than $100,000, then the most efficient way to write the query is as follows:

### Java

```java
db.collection("employees")
  .whereGreaterThan("salary", 100000)
  .orderBy("salary")
  .get()
  .addOnSuccessListener(new OnSuccessListener<QuerySnapshot>() {
        @Override
        public void onSuccess(QuerySnapshot queryDocumentSnapshots) {
          // Order results by `experience`
        }
    });;
```

### Node.js

```javascript
const querySnapshot = await db.collection('employees')
                              .where("salary", ">", 100000)
                              .orderBy("salary")
                              .get();

// Order results by `experience`
```

### Python

```python
results = db.collection("employees")
            .where("salary", ">", 100000)
            .order_by("salary")
            .stream()

// Order results by `experience`
```

While adding an ordering on `experience` to the query will yield the same set
of documents and obviate re-ordering the results on the clients, the query may
read many more extraneous index entries than the earlier query. This is because
Cloud Firestore always prefers an index whose index fields prefix match the
order by clause of the query. If `experience` were added to the order by clause,
then Cloud Firestore will select the `(experience [...], salary [...])` index
for computing query results. Since there are no other constraints on
`experience`, Cloud Firestore will read **all** index entries of the
`employees` collection before applying the `salary` filter to find the final
result set. This means that index entries which don't satisfy the `salary`
filter are still read, thus increasing the latency and cost of the query.

## Pricing

Queries with range and inequality filters on multiple fields are billed based on
documents read and index entries read.

For detailed information, see the \[Pricing\]\[5\] page.

## Limitations

Apart from the \[query limitations\]\[6\], note the following limitations before
using queries with range and inequality filters on multiple fields:

- Queries with range or inequality filters on document fields and only equality constraints on the document key `(__name__)` aren't supported.
- Cloud Firestore limits the number of range or inequality fields to 10. This is to prevent queries from becoming too expensive to run.

## What's next

- Learn about \[optimizing your queries\]\[3\].
- Learn more about \[performing simple and compound queries\]\[8\].
- Understand how \[Cloud Firestore uses indexes\]\[9\].

\[1\]: /docs/firestore/query-data/get-data
\[2\]: /docs/firestore/data-model
\[3\]: /docs/firestore/query-data/multiple-range-optimize-indexes
\[4\]: /docs/firestore/concepts/index-overview#index_properties
\[5\]: /docs/firestore/pricing
\[6\]: /docs/firestore/query-data/queries#query_limitations
\[7\]: https://en.wikipedia.org/wiki/Disjunctive_normal_form
\[8\]: /docs/firestore/query-data/queries
\[9\]: /docs/firestore/concepts/index-overview
\[10\]: /docs/firestore/query-data/multiple-range-optimize-indexes