Firestore in Datastore mode supports using range and inequality filters on multiple properties in a single query. This feature gives you range and inequality conditions on multiple properties and simplifies your application development by delegating implementation of post-filtering logic to Firestore in Datastore mode.
Range and inequality filters on multiple properties
The following query uses range filters on priority and days to return all tasks with priority greater than four and with less than three days to complete.
Go
query := datastore.NewQuery("Task").
FilterField("priority", ">", 4).
FilterField("days", "<", 3).
GQL
SELECT * FROM /tasks WHERE priority > 4 AND days < 3;
Java
Query<Entity> query =
Query.newEntityQueryBuilder()
.setKind("Task")
.setFilter(
CompositeFilter.and(
PropertyFilter.gt("priority", 4), PropertyFilter.lt("days", 3)))
.build();
Node.js
const query = datastore
.createQuery('Task')
.filter(
and([
new PropertyFilter('priority', '>', 4),
new PropertyFilter('days', '<', 3),
])
);
Python
from google.cloud import datastore
client = datastore.Client()
query = client.query(kind="Task")
query.add_filter(filter=PropertyFilter("priority", ">", 4))
query.add_filter(filter=PropertyFilter("days", "<", 3))
PHP
$query = $datastore->query()
->kind('Task')
->filter('priority', '>', 4)
->filter('days', '<', 3)
C#
Query query = new Query("Task")
{
Filter = Filter.And(Filter.GreaterThan("priority", 4),
Filter.LessThan("days", 3))
};
Ruby
query = datastore.query("Task")
.where("priority", ">", 4)
.where("days", "<", 3)
Indexing considerations
Before you start running queries, make sure you have read about queries.
If an ORDER BY clause isn't specified, Firestore in Datastore mode uses any index that
can satisfy the query's filter condition to serve the query. This approach produces a result
set that is ordered according to the index definition.
To optimize the performance and cost of Firestore in Datastore mode queries, optimize the order of properties in the index. To do this, ensure that your index is ordered from left to right so that the query distills to a dataset that prevents scanning of extraneous index entries.
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 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. An index that reduces the number of index scans is the
(salary [...], experience [...]) index. As a result, a fast and cost-efficient
query orders salary before experience, as shown in the following example:
GQL
SELECT *
FROM /employees
WHERE salary > 100000 AND experience > 0
ORDER BY salary, experience
Java
Query<Entity> query =
Query.newEntityQueryBuilder()
.setKind("employees")
.setFilter(
CompositeFilter.and(
PropertyFilter.gt("salary", 100000), PropertyFilter.gt("experience", 0)))
.setOrderBy(OrderBy("salary"), OrderBy("experience"))
.build();
Node.js
const query = datastore
.createQuery("employees")
.filter(
and([
new PropertyFilter("salary", ">", 100000),
new PropertyFilter("experience", ">", 0),
])
)
.order("salary")
.order("experience");
Python
query = client.query(kind="employees")
query.add_filter("salary", ">", 100000)
query.add_filter("experience", ">", 0)
query.order = ["-salary", "-experience"]
Best practices for optimizing indexes
When optimizing indexes, note the following best practices.
Order queries by equalities followed by most selective range or inequality field
Firestore in Datastore mode uses the leftmost properties of a composite index to satisfy
the equality constraints and the range and inequality constraint, if any, on the
first field of the orderBy() query. These constraints can reduce the number of
index entries that Firestore in Datastore mode scans. Firestore in Datastore mode uses the remaining
properties of the index to satisfy other range and inequality constraints of the
query. These constraints don't reduce the number of index entries that
Firestore in Datastore mode scans, but they filter out unmatched entities so that the number of
entities that are returned to the clients are reduced.
For more information about creating efficient indexes, see index structure and definition and optimizing indexes.
Order properties in decreasing order of query constraint selectivity
To ensure that Firestore in Datastore mode selects the optimal index for your query,
specify an orderBy() clause that orders range and inequality properties based
on how selective their constraints are in your query, starting from the most
selective. Higher selectivity matches fewer entities, while lower selectivity
matches more entities. In your index ordering, put range and inequality
properties with higher selectivity before properties with lower selectivity.
To minimize the number of entities that Firestore in Datastore mode scans and returns over the network, you should always order properties 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, if 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 that only a small number of employees will have salary higher than $100,000, then an efficient way to write the query is as follows:
Java
Query<Entity> query =
Query.newEntityQueryBuilder()
.setKind("employees")
.setFilter(PropertyFilter.gt("salary", 100000))
.setOrderBy(OrderBy("salary"))
.build();
QueryResults<Entity> results = datastore.run(query);
// Order results by `experience`
Node.js
const query = datastore
.createQuery("employees")
.filter(new PropertyFilter("salary", ">", 100000))
.order("salary");
const [entities] = await datastore.runQuery(query);
// Order results by `experience`
Python
query = client.query(kind="employees")
query.add_filter("salary", ">", 100000)
query.order = ["salary"]
results = query.fetch()
// Order results by `experience`
While adding an ordering on experience to the query will yield the same set
of entities 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
Firestore in Datastore mode always prefers an index whose index properties prefix match the
order by clause of the query. If experience were added to the order by clause,
then Firestore in Datastore mode will select the (experience [...], salary [...]) index
for computing query results. Since there are no other constraints on
experience, Firestore in Datastore mode 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 properties are billed based on entities read and index entries read.
For detailed information, see the Pricing page.
Limitations
Apart from the query limitations, note the following limitations before using queries with range and inequality filters on multiple properties:
- To prevent queries from becoming too expensive to run, Firestore in Datastore mode limits the number of range or inequality operators to 10.
What's Next
- Learn about optimizing your queries.
- Learn more about performing simple and compound queries.
- Understand how Firestore in Datastore mode uses indexes.