<br />

This page describes how to retrieve query execution information when you execute a query.

## Use Query Explain

You can use Query Explain to understand how your queries are being executed.
This provides details that you can use to [optimize your queries](https://firebase.google.com/docs/firestore/pipelines/enterprise-optimize-query-performance).

You can use Query Explain through the Google Cloud console.  

##### Console

Execute a query in the Query Editor and open the **Explanation** tab:

1. In the Google Cloud console, go to the **Databases** page.

   [Go to Databases](https://console.cloud.google.com/firestore/databases)
2. From the list of databases, select a Cloud Firestore database. The Google Cloud console opens the **Firestore Explorer** for that database.
3. Enter a query in the query editor and click **Run**.
4. Click the **Explanation** tab to view the query analysis output.

## Analysis

The output of Query Explain contains two main components-the Summary Statistics and Execution Tree.
Consider this query as an example:  

    db.pipeline().collection('/users').sort(field("status").ascending()).limit(100)

## Summary Statistics

The top of the explained output contains a summary of the execution statistics.
Use these statistics to determine if a query has high latency or cost. It also
contains memory statistics which let you know how close your query is
to [memory limits](https://firebase.google.com/docs/firestore/enterprise/quotas).  

    Execution:
     results returned: 2
     request peak memory usage: 20.25 KiB (20,736 B)
     data bytes read: 148 B
     entity row scanned: 2

    Billing:
     read units: 1

## Execution Tree

The execution tree describes the query execution as a series of nodes. The
bottom nodes (leaf nodes) retrieve data from the storage layer which traverses
up the tree to generate a query response.

For details about each execution node,
refer to the [Execution reference](https://firebase.google.com/docs/firestore/pipelines/enterprise-query-explain-reference).

For details on how to use this information to optimize your queries,
see [Optimize query execution](https://firebase.google.com/docs/firestore/pipelines/optimize-query-performance).

The following is an example of an execution tree:  

    Tree:
    • Compute
    |  $out_1: map_set($record_1, "__name__", $__name___1, "__key__", unset)
    |  is query result: true
    |
    |  Execution:
    |   records returned: 2
    |   latency: 5.96 ms (local <1 ms)
    |
    └── • Compute
        |  $__name___1: map_get($record_1, "__key__")
        |
        |  Execution:
        |   records returned: 2
        |   latency: 5.88 ms (local <1 ms)
        |
        └── • MajorSort
            |  fields: [$v_1 ASC]
            |  output: [$record_1]
            |  limit: 100
            |
            |  Execution:
            |   records returned: 2
            |   latency: 5.86 ms (local <1 ms)
            |   peak memory usage: 20.25 KiB (20,736 B)
            |
            └── • Compute
                |  $v_1: map_get($record_1, "status")
                |
                |  Execution:
                |   records returned: 2
                |   latency: 5.23 ms (local <1 ms)
                |
                └── • TableScan
                       source: /users
                       order: UNDEFINED
                       properties: *
                       row range: (-∞..+∞)
                       output record: $record_1
                       variables: [$record_1]

                       Execution:
                        records returned: 2
                        latency: 4.68 ms
                        records scanned: 2
                        data bytes read: 148 B

## What's next

- To learn about the execution tree nodes, see the [Query execution reference](https://firebase.google.com/docs/firestore/pipelines/enterprise-query-explain-reference).
- To learn how to optimize your queries, see [Optimize query execution](https://firebase.google.com/docs/firestore/pipelines/enterprise-optimize-query-performance).