Export Firebase Crashlytics data to BigQuery

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You can export your Firebase Crashlytics data into BigQuery for further analysis. BigQuery lets you analyze the data using BigQuery SQL, export it to another cloud provider, and use it for visualization and custom dashboards with Google Data Studio.

Enable export to BigQuery

1. In the Firebase console, go to the Integrations page.

2. In the BigQuery card, click Link.

3. Follow the on-screen instructions to enable export to BigQuery.

   If you want near real-time access to your Crashlytics data in BigQuery, then consider upgrading to streaming export.

What happens when you enable export?

• You select the dataset location. After the dataset is created, the location can't be changed, but you can copy the dataset to a different location or manually move (recreate) the dataset in a different location. To learn more, see Change the location for existing exports.

  This location is only applicable for the data exported into BigQuery, and it does not impact the location of data stored for use in the Crashlytics dashboard of the Firebase console or in Android Studio.

• By default, all apps in your project are linked to BigQuery and any apps that you later add to the project are automatically linked to BigQuery. You can manage which apps send data.

• Firebase sets up daily syncs of your data to BigQuery.

  • After you link your project, you usually need to wait until the next day's sync for your first set of data to be exported to BigQuery.

  • The daily sync happens once per day, regardless of any scheduled export that you might have set up in BigQuery. Note that the timing and duration of the sync job can change, so we don't recommend scheduling downstream operations or jobs based on a specific timing of the export.

• Firebase exports a copy of your existing data to BigQuery. The initial propagation of data for export may take up to 48 hours.

  • For each linked app, this export includes a batch table containing the data from the daily sync.

  • You can manually schedule data backfills for the batch table up to the past 30 days or for the most recent date when you enabled export to BigQuery (whichever is most recent).

  Note that if you enabled export of Crashlytics data before mid-October 2024, you can also backfill 30 days prior to the day you enabled export.

• If you enable Crashlytics streaming export to BigQuery, all linked apps will also have a realtime table containing constantly updating data.

To deactivate export to BigQuery, unlink your project in the Firebase console.

What data is exported to BigQuery?

Firebase Crashlytics data is exported into a BigQuery dataset named firebase_crashlytics. By default, individual tables will be created inside the Crashlytics dataset for each app in your project. Firebase names the tables based on the app's identifier, with periods converted to underscores, and a platform name appended to the end.

For example, data for an Android app with the package name com.google.test would be in a table named com_google_test_ANDROID. This batch table is updated once every day. If you enable Crashlytics streaming export to BigQuery, then Crashlytics data will also be streamed in realtime to a table named com_google_test_ANDROID_REALTIME.

Each row in a table represents an event that occurred in the app, including crashes, non-fatal errors, and ANRs.

Crashlytics streaming export to BigQuery

You can stream your Crashlytics data in realtime with BigQuery streaming. You can use it for any purpose that requires live data, such as presenting information in a live dashboard, watching a rollout live, or monitoring application problems that trigger alerts and custom workflows.

When you enable Crashlytics streaming export to BigQuery, in addition to the batch table you will also have a realtime table. Here are the differences you should be aware of between the tables:

The batch table is ideal for long-term analysis and identifying trends over time because we durably store events before writing them, and they can be backfilled to the table for up to 30 days*. When we write data to your realtime table, we immediately write it to BigQuery, and so it is ideal for live dashboards and custom alerts. These two tables can be combined with a stitching query to get the benefits of both.

By default, the realtime table has a partition expiration time of 30 days. To learn how to modify this, see Set the partition expiration in the BigQuery documentation.

^{*See details about backfill support in Upgrade to the new export infrastructure.}

Enable Crashlytics streaming export to BigQuery

1. In the Firebase console, go to the Integrations page.

2. In the BigQuery card, click Manage.

3. Select the Include streaming checkbox.

This action enables streaming for all of your linked apps.

What can you do with the exported data?

Exports to BigQuery contain raw crash data including device type, operating system, exceptions (Android apps) or errors (Apple apps), and Crashlytics logs, as well as other data.

Review exactly what Crashlytics data is exported and its table schema later in this page.

Use a Data Studio template

To enable realtime data in your Data Studio template, follow the instructions in Visualizing exported Crashlytics data with Data Studio.

Create views

You can transform queries into views using the BigQuery UI. For detailed instructions, see Create views in the BigQuery documentation.

Run queries

The following examples demonstrate queries that you can run on your Crashlytics data to generate reports that aggregate crash event data into more easily-understood summaries. Since these types of reports aren't available in the Crashlytics dashboard of the Firebase console, they can supplement your analysis and understanding of crash data.

Example 1: Crashes by day

After working to fix as many bugs as possible, you think your team is finally ready to launch your new photo-sharing app. Before you do, you want to check the number of crashes per day for the past month, to be sure your bug-bash made the app more stable over time.

Here's an example query for an Android app. For an iOS app, use its bundle ID and IOS (instead of package name and ANDROID).

SELECTCOUNT(DISTINCTevent_id)ASnumber_of_crashes,FORMAT_TIMESTAMP("%F",event_timestamp)ASdate_of_crashesFROM`PROJECT_ID.firebase_crashlytics.PACKAGE_NAME_ANDROID`GROUPBYdate_of_crashesORDERBYdate_of_crashesDESCLIMIT30;

Example 2: Find the most pervasive crashes

To properly prioritize production plans, you want to find the top 10 most pervasive crashes in your app. You produce a query that provides the pertinent points of data.

Here's an example query for an Android app. For an iOS app, use its bundle ID and IOS (instead of package name and ANDROID).

SELECTDISTINCTissue_id,COUNT(DISTINCTevent_id)ASnumber_of_crashes,COUNT(DISTINCTinstallation_uuid)ASnumber_of_impacted_user,blame_frame.file,blame_frame.lineFROM`PROJECT_ID.firebase_crashlytics.PACKAGE_NAME_ANDROID`WHEREevent_timestamp>=TIMESTAMP_SUB(CURRENT_TIMESTAMP(),INTERVAL168HOUR)ANDevent_timestamp<CURRENT_TIMESTAMP()GROUPBYissue_id,blame_frame.file,blame_frame.lineORDERBYnumber_of_crashesDESCLIMIT10;

Example 3: Top 10 crashing devices

Fall is new phone season! Your company knows that this also means it's new device-specific issues season — especially for Android. To get ahead of the looming compatibility concerns, you put together a query that identifies the 10 devices that experienced the most crashes in the past week (168 hours).

Here's an example query for an Android app. For an iOS app, use its bundle ID and IOS (instead of package name and ANDROID).

SELECTdevice.model,COUNT(DISTINCTevent_id)ASnumber_of_crashesFROM`PROJECT_ID.firebase_crashlytics.PACKAGE_NAME_ANDROID`WHEREevent_timestamp>=TIMESTAMP_SUB(CURRENT_TIMESTAMP(),INTERVAL168HOUR)ANDevent_timestamp<CURRENT_TIMESTAMP()GROUPBYdevice.modelORDERBYnumber_of_crashesDESCLIMIT10;

Example 4: Filter by custom key

You're a game developer who wants to know which level of your game experiences the most crashes.

To help track that stat, you set a custom Crashlytics key called current_level, and update it every time the user reaches a new level.

Crashlytics.sharedInstance().setIntValue(3,forKey:"current_level");

CrashlyticsKitsetIntValue:3forKey:@"current_level";

Crashlytics.setInt("current_level",3);

With that key in your export to BigQuery, you can then write a query to report the distribution of current_level values associated with each crash event.

Here's an example query for an Android app. For an iOS app, use its bundle ID and IOS (instead of package name and ANDROID).

SELECT COUNT(DISTINCT event_id) AS num_of_crashes, value FROM `PROJECT_ID.firebase_crashlytics.PACKAGE_NAME_ANDROID` UNNEST(custom_keys) WHERE key = "current_level" GROUP BY key, value ORDER BY num_of_crashes DESC

Example 5: User ID extraction

You have an Android app in early access. Most of your users love it, but three have experienced an unusual number of crashes. To get to the bottom of the problem, you write a query that pulls all the crash events for those users, using their user IDs.

Here's an example query for an Android app. For an iOS app, use its bundle ID and IOS (instead of package name and ANDROID).

SELECT * FROM `PROJECT_ID.firebase_crashlytics.PACKAGE_NAME_ANDROID` WHERE user.id IN ("USER_ID_1", "USER_ID_2", "USER_ID_3") ORDER BY user.id 

Example 6: Find all users facing a particular crash issue

Your team has accidentally released a critical bug to a group of beta testers. Your team was able to use the query from the "Find most pervasive crashes" example above to identify the specific crash issue ID. Now your team would like to run a query to extract the list of app users who were impacted by this crash.

Here's an example query for an Android app. For an iOS app, use its bundle ID and IOS (instead of package name and ANDROID).

SELECT user.id as user_id FROM `PROJECT_ID.firebase_crashlytics.PACKAGE_NAME_ANDROID` WHERE issue_id = "ISSUE_ID" AND application.display_version = "APP_VERSION" AND user.id != "" ORDER BY user.id;

Example 7: Number of users impacted by a crash issue, broken down by country

Your team has detected a critical bug during the rollout of a new release. You were able to use the query from the "Find most pervasive crashes" example above to identify the specific crash issue ID. Your team would now like to see if this crash has spread to users in different countries around the world.

To write this query, your team will need to do the following:

1. Enable export of Google Analytics data to BigQuery. See Export project data to BigQuery.

2. Update your app to pass a user ID into both the Google Analytics SDK and the Crashlytics SDK.

   Swift

   Crashlytics.sharedInstance().setUserIdentifier("123456789");Analytics.setUserID("123456789");

   Objective-C

   CrashlyticsKitsetUserIdentifier:@"123456789";FIRAnalyticssetUserID:@"12345678 9";

   Java

   Crashlytics.setUserIdentifier("123456789");mFirebaseAnalytics.setUserId("123456789");

3. Write a query that uses the user ID field to join events in the Google Analytics dataset with crashes in the Crashlytics dataset.

   Here's an example query for an Android app. For an iOS app, use its bundle ID and IOS (instead of package name and ANDROID).

   SELECT DISTINCT c.issue_id, a.geo.country, COUNT(DISTINCT c.user.id) as num_users_impacted FROM `PROJECT_ID.firebase_crashlytics.PACKAGE_NAME_ANDROID` c INNER JOIN `PROJECT_ID.analytics_TABLE_NAME.events_*` a on c.user.id = a.user_id WHERE c.issue_id = "ISSUE_ID" AND a._TABLE_SUFFIX BETWEEN '20190101' AND '20200101' GROUP BY c.issue_id, a.geo.country, c.user.id

Example 8: Top 5 issues so far today

Here's an example query for an Android app. For an iOS app, use its bundle ID and IOS (instead of package name and ANDROID).

SELECTissue_id,COUNT(DISTINCTevent_id)ASeventsFROM`PROJECT_ID.firebase_crashlytics.PACKAGE_NAME_ANDROID_REALTIME`WHEREDATE(event_timestamp)=CURRENT_DATE()GROUPBYissue_idORDERBYeventsDESCLIMIT5;

Example 9: Top 5 issues since DATE, including today

You can also combine the batch and realtime tables with a stitching query to add realtime information to the reliable batch data. Since event_id is a primary key, you can use DISTINCT event_id to dedupe any common events from the two tables.

Here's an example query for an Android app. For an iOS app, use its bundle ID and IOS (instead of package name and ANDROID).

SELECTissue_id,COUNT(DISTINCTevent_id)ASeventsFROM(SELECTissue_id,event_id,event_timestampFROM`PROJECT_ID.firebase_crashlytics.PACKAGE_NAME_ANDROID_REALTIME`UNIONALLSELECTissue_id,event_id,event_timestampFROM`PROJECT_ID.firebase_crashlytics.PACKAGE_NAME_ANDROID`)WHEREevent_timestamp>="YYYY_MM_DD"GROUPBYissue_idORDERBYeventsDESCLIMIT5;

Understand the Crashlytics schema in BigQuery

When you set up Crashlytics data export to BigQuery, Firebase exports recent events (crashes, non-fatal errors, and ANRs), including events from up to two days before the link, with the option to backfill up to 30 days.

From that point until you deactivate the export, Firebase exports Crashlytics events on a daily basis. It can take a few minutes for the data to be available in BigQuery after each export.

Datasets

Crashlytics creates a new dataset in BigQuery for Crashlytics data. The dataset covers your entire project, even if it has multiple apps.

Tables

Crashlytics creates a table in the dataset for each app in your project, unless you've opted out of exporting data for that app. Firebase names the tables based on the app's identifier, with periods converted to underscores, and a platform name appended to the end.

For example, data for an Android app with the package name com.google.test would be in a table named com_google_test_ANDROID, and realtime data (if enabled) would be in a table named com_google_test_ANDROID_REALTIME

Tables contain a standard set of Crashlytics data in addition to any custom Crashlytics keys defined by you in your app.

Rows

Each row in a table represents an error the app encountered.

Columns

The columns in a table are identical for crashes, non-fatal errors, and ANRs. If Crashlytics streaming export to BigQuery is enabled, then the realtime table will have the same columns as the batch table. Note that you may have columns in rows that represent events that don't have stack traces.

The columns within the export are listed in this table:

Visualize exported Crashlytics data with Data Studio

Google Data Studio turns your Crashlytics datasets in BigQuery into reports that are easier to read, easier to share, and fully customizable.

To learn more about using Data Studio, try the Data Studio quickstart guide, Welcome to Data Studio.

Use a Crashlytics report template

Data Studio has a sample report for Crashlytics that includes a comprehensive set of dimensions and metrics from the exported CrashlyticsBigQuery schema. If you have enabled Crashlytics streaming export to BigQuery, then you can view that data on the Realtime trends page of the Data Studio template.You can use the sample as a template to quickly create new reports and visualizations based on your own app's raw crash data:

1. Open the Crashlytics Data Studio Dashboard template.

2. Click Use Template in the upper-right corner.

3. In the New Data Source drop down, select Create New Data Source.

4. Click Select on the BigQuery card.

5. Select a table containing exported Crashlytics data by choosing My Projects > PROJECT_ID > firebase_crashlytics > TABLE_NAME.

   Your batch table is always available to select. If Crashlytics streaming export to BigQuery is enabled, then you can select your realtime table instead.

6. Under Configuration, set Crashlytics Template level to Default.

7. Click Connect to create the new data source.

8. Click Add to Report to return to the Crashlytics template.

9. Finally, click Create Report to create your copy of the Crashlytics Data Studio Dashboard template.

Upgrade to the new export infrastructure

In mid-October 2024, Crashlytics launched a new infrastructure for exporting Crashlytics data into BigQuery. For now, upgrading to this new infrastructure is optional.

This new infrastructure supports Crashlytics dataset locations outside the United States.

• If you enabled export before mid-October 2024, you can now optionally change the location for data export to any BigQuery-supported dataset location.

• If you enabled export in mid-October 2024 or later, then you can select any BigQuery-supported dataset location during setup.

Another difference in the new infrastructure is that it doesn't support backfills of data from before you enabled export. (With the old infrastructure, you could backfill up to 30 days prior to the enablement date.) The new infrastructure supports backfills up to the past 30 days or for the most recent date when you enabled export to BigQuery (whichever is most recent).

Prerequisite for upgrading

Before upgrading to the new infrastructure, confirm that you meet the following prerequisite: Your current batch BigQuery tables have matching identifiers to the bundle IDs or package names set for your registered Firebase Apps.

For example:

• If you have a BigQuery table named com_yourcompany_yourproject_IOS, then you should have a Firebase iOS+ App registered in your Firebase project with the bundle ID com.yourcompany.yourproject.

• If you have a BigQuery table named com_yourcompany_yourproject_ANDROID, then you should have a Firebase Android App registered in your Firebase project with the package name com.yourcompany.yourproject.

Here's how to find all the Firebase Apps registered in your Firebase project:

1. In the Firebase console, go to your settingsProject settings.

2. Scroll down to the Your apps card, then click on the desired Firebase App to view the app's information, including its identifier.

The new export infrastructure will export each app's data based on the package name or bundle ID set for the registered Firebase App. In order to not disrupt your BigQuery workflow, it's important to make sure your current batch tables already have the correct names so that the new infrastructure can append all new data to the existing tables. If you have batch table names that do not match your registered Firebase Apps, but you still want to upgrade, reach out to Firebase Support.

How to upgrade to the new infrastructure

If you've already enabled export, you can upgrade to the new infrastructure simply by toggling Crashlytics data export off and then on again in the Firebase console.

Here are the detailed steps:

1. In the Firebase console, go to the Integrations page.

2. In the BigQuery card, click Manage.

3. Toggle off the Crashlytics slider to disable export. When prompted, confirm that you want data export to stop.

4. Immediately toggle on again the Crashlytics slider to re-enable export. When prompted, confirm that you want to export data.

   Your Crashlytics data export to BigQuery is now using the new export infrastructure.