Optimize queries with range and inequality filters on multiple properties

This page provides examples of indexing strategies that you can use for queries with range and inequality filters on multiple fields to create an efficient query experience.

Before you optimize your queries, read about range and inequality filters on multiple properties concepts .

Optimize queries with Query Explain

To determine if the query and indexes used are optimal, you can create a query using Query Explain and review the execution summary.

Java

...
// Build the query
Query<Entity> query =
    Query.newEntityQueryBuilder()
        .setKind("employees")
        .setFilter(
            CompositeFilter.and(
                PropertyFilter.gt("salary", 100000), PropertyFilter.gt("experience", 0)))
        .setOrderBy(OrderBy("experience"), OrderBy("salary"))
        .build();

// Set the explain options to get back *only* the plan summary
ExplainResults<Entity> explainResults = datastore.run(query, ExplainOptions.newBuilder().build());

// Get the explain metrics
Optional<ExplainMetrics> explainMetrics = results.getExplainMetrics();
if (!explainMetrics.isPresent()) {
  throw new Exception("No explain metrics returned");
}

// Get the plan summary
PlanSummary planSummary = explainMetrics.get().getPlanSummary();
List<Map<String, Object>> indexesUsed = planSummary.getIndexesUsed();
System.out.println("----- Indexes Used -----");
indexesUsed.forEach(map -> map.forEach((s, o) -> System.out.println(s + ": " + o)));

// Get the execution stats
if (!explainMetrics.getExecutionStats().isPresent()) {
  throw new Exception("No execution stats returned");
}

ExecutionStats queryStats = explainMetrics.getExecutionStats().get();
Map<String, Object> debugStats = queryStats.getDebugStats();
System.out.println("----- Debug Stats -----");
debugStats.forEach((s, o) -> System.out.println(s + ": " + o));

The following example shows how the use of correct index ordering saves the number of entities that Firestore in Datastore mode scans.

Simple queries

With the earlier example of a collection of employees, the simple query that runs with the (salary, experience) index is as follows:

GQL

SELECT *
FROM /employees
WHERE salary > 100000 AND experience > 0
ORDER BY experience, salary;

Java

Query<Entity> query =
   Query.newEntityQueryBuilder()
    .setKind("employees")
    .setFilter(
        CompositeFilter.and(
            PropertyFilter.gt("salary", 100000), PropertyFilter.gt("experience", 0)))
    .setOrderBy(OrderBy("experience"), OrderBy("salary"))
    .build();

The query scans 95000 index entries only to return 5 entities. A large number of index entries were read but filtered out because they did not satisfy the query predicate.

// Output query planning info
{
        "indexesUsed": [
            {
                "query_scope": "Collection Group",
                "properties": "(experience ASC, salary ASC, __name__ ASC)"
            }
        ]
    },
    // Output Query Execution Stats
    {
        "resultsReturned": "5",
        "executionDuration": "2.5s",
        "readOperations": "100",
        "debugStats": {
            "index_entries_scanned": "95000",
            "documents_scanned": "5",
            "billing_details": {
                "documents_billable": "5",
                "index_entries_billable": "95000",
                "small_ops": "0",
                "min_query_cost": "0"
            }
        }
    }

As per the earlier example, we can infer that the salary constraint is more selective than the experience constraint.

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();

When you explicitly use the orderBy() clause to add the predicates in the earlier order, Firestore in Datastore mode uses the (salary, experience) index to run the query. Since the selection of the first range filter is better than the earlier query, the query runs faster and is cost-efficient.

    // Output query planning info
{
    "indexesUsed": [
        {
            "query_scope": "Collection Group",
            "properties": "(salary ASC, experience ASC, __name__ ASC)"
        }
    ],
    // Output Query Execution Stats
        "resultsReturned": "5",
        "executionDuration": "0.2s",
        "readOperations": "6",
        "debugStats": {
            "index_entries_scanned": "1000",
            "documents_scanned": "5",
            "billing_details": {
                "documents_billable": "5",
                "index_entries_billable": "1000",
                "small_ops": "0",
                "min_query_cost": "0"
            }
        }
    }

What's next