As we build Crashlytics and talk to our developers, we've found that the way they use our dashboards is often nuanced and specific to their team. We've done our best to incorporate the themes we hear most often into the dashboard you see in the Firebase console, but one dashboard solution simply isn't enough.
That's why we launched the Crashlytics integration with BigQuery, giving you the freedom to deeply explore your data. And, using Data Studio (a free tool that sits on top of BigQuery), you can make custom dashboards from your Crashlytics data that fit the unique way your team works. Data Studio allows your team members who aren't comfortable with SQL to easily work with the BigQuery data set. Data Studio dashboards are also easy to collaborate on and share, so your team can work more efficiently.
Today, we're launching a Data Studio template that gives you a preview of what's possible with Crashlytics and BigQuery. Let's take a closer look at the template.
The overview section of our template provides an overview of which OS versions crash the most, which devices crash the most, and how it's trending over time. You can customize each section to display the results of the exact queries you want and display how you need based on your business logic. If you want to keep an eye on the deprecation of an old operating system you can change or filter directly in the queries that back the dashboard.
Up until now, exploring your crash reports by custom metadata like Experiment ID or an Analytics breadcrumb has been limited, making it tough to identify which variant in an experiment is least stable or which level in a game has the most crashes. Now, when you export your data to BigQuery, it's easy to run any deep analysis you want, and then visualize your report with Data Studio or any other business system you use.
As an example, say that you set up your Android game so that you log what level a crash occurs with:
Crashlytics.setInt("current_level", 3);
Now you can filter by the presence of a key and its values. We've created a sample dashboard for filtering these in our Data Studio template.
We know our largest apps have different teams that specialize in specific areas of the code. For that, we've made it easy to filter by specific files in our Data Studio template.
Our data studio template is totally customizable, meaning if you'd rather filter on a different part of our scheme or make more advanced tools, you can easily adapt based on your needs. You can adjust the template using the Data Studio UI or you can edit the backing BigQuery queries.
Your team can all work together by sharing the dashboard in DataStudio. This means team members don't need to learn SQL to get the benefits of the Crashlytics integration with BigQuery.
You'll also be able to select date ranges longer than 90 days, if you set up retention in BigQuery. The Crashlytics dashboard currently retains data for 90 days. With BigQuery you own the retention and deletion policies, making it much simpler for your team to track year-over-year trends in stability data. This means you'll be able to customize your dashboard to display data over the exact period you are interested in.
With just a few clicks from the Firebase Crashlytics dashboard you can enable daily exports of all raw crash data on a per-app or per-project basis. This includes your stack traces, logs, keys and any other crash data. You can also use the new BigQuery sandbox to get started for free.
Once you link to Crashlytics to BigQuery, follow these instructions to connect this template with your Crashlytics dataset.
If you are a current Fabric user, you can gain access to BigQuery export and all the other features of Firebase by linking your app in the Fabric dashboard. Check out this link for details and documentation.
We hope this improvement makes it even easier for you to dig into your crash reporting data and efficiently debug your app! As always, if you have any questions, you can find us on Twitter (@firebase) and on Stack Overflow. Happy debugging!
No one likes litter, so why do we live with it? Litter directly impacts a community's health, safety, and economic potential. At Rubbish, we believe people should love where they live. That's why we created the Rubbish app, which empowers neighborhoods to tackle litter at the local level by photographing and reporting litter, sharing and analyzing the data, and engaging community partners to clean up together. Our mission is to build stronger, healthier communities with less trash, more beautiful streets, and happier residents and we can't do it without Firebase.
Here's a quick video of how Rubbish works
The concept for Rubbish resulted from a moment of panic and frustration: while we (Elena and Emin, co-founders of Rubbish) were walking the streets of New York City with Elena's dog Larsen, he choked on a chicken bone. Luckily, he was ok, but the two of us were not. Why was litter an unfortunate part of city living, with no effective solution to address it?
This is Larsen. He's a good boy.
We decided to tackle this issue and find an innovative solution together. We started to document litter daily, taking pictures and noting problem areas in our communities, which quickly accumulated into thousands of photos sitting in a stagnant shared album. We needed a better way to store and organize the information we were collecting so we could use it to make a difference. We also needed a way to share the photos and their metadata with several audiences (governments, community partners) and on several social media channels through our app. Each platform had its own set of requirements and specifications, and the idea of creating the infrastructure to accomplish this was daunting, until we discovered Firebase.
To combat the litter problem and make real change, we needed a quick, seamless way to gather, process, and share all the information surrounding each documented piece of trash.
We evaluated lots of options, but Firebase stood out because it provided a comprehensive set of tools that allowed us to quickly build the backend infrastructure of the Rubbish app and address the challenges of storage, data validation, processing, and distribution.
For example, we faced the challenge of quickly storing and tracking user-generated photos. Cloud Storage and Firestore allow us to keep track of what is being reported and where. Another challenge was verifying user submissions, especially ones requiring priority attention from third parties, like reports that need local agency involvement. With the help of Cloud Functions for Firebase, we set up a dashboard to summarize the data and generate reports in one place. We also instrumented Cloud Functions to act as a safety net and help us with quality control. For instance, before reports are automatically formatted and sent to local government agencies like San Francisco 311 for follow-up, the functions check that the submissions came from validated users with good track records, and are in the correct vicinity of the agency. We use Cloud Functions to trigger a validation review via our backend and via email whenever a photo is uploaded. Then, a member of our team evaluates the uploaded image to make sure it's clear and relevant. This makes an otherwise complicated process easy and automated.
Additionally, we use Firebase Authentication and Security Rules to ensure that only the intended information gets shared, and to protect each user's privacy and security. Firebase allows us to seamlessly integrate our data with APIs from local governments, social networks, and our own app in a few lines of code. With Firebase, Rubbish can effectively store, share, and process the data to create real insights and impact. In addition to Firebase, we also use some of Google Cloud Platform's APIs, such as the Google Sheets API, Maps SDK for iOS, Places API, Geocoding API, and Cloud Runtime Configuration API.
Firebase-powered dashboard that allows us to manage user submissions.
This is one of our dashboards for tracking neighborhood trends.
As we grew our software development team, we were concerned about the time and resources it takes a new team member to get up to speed and become productive. Firebase provided easy onboarding of new members with user-friendly training resources, like robust sample projects, fun developer videos, straightforward technical documentation, and more. In fact, our new engineers are onboarded and ready to contribute three times faster, saving us significant time and resources that can now be focused elsewhere. We reduced development time on new features, as well as the time needed for maintenance, security handling, and developer onboarding, which maximizes our productivity.
In short, Firebase enables start-up teams like us to communicate effectively, share information, and grow. It's a huge value for us that Firebase allowed us to effectively engage such a variety of talented, passionate individuals.
Our team and their favorite Firebase product or their favorite snack.
Since Firebase covers the backend infrastructure behind the app and facilitates collaboration on our team, we can focus on expanding our field testing and cultivating relationships with important partners. We launched a pilot program on San Francisco's Polk Street in August 2018, working with the community to sponsor resident-led street cleanings. We use the data we collect to inform local sponsors and residents about the progress, including summaries of the number and types of trash collected - all that wouldn't be possible without Firebase.
We've also been collaborating with the San Francisco Community Benefit Districts and the local San Francisco government to optimize and track improvements through Rubbish. For example, we pinpointed the largest source of cigarette butts (customers at bars and restaurants) and worked with these businesses to install cigarette receptacles. We're excited to find even deeper trends and new ways to analyze and address the litter problem.
As Rubbish continues to map and track litter, we are finding that trash patterns on the street can be as dynamic as traffic patterns. Local events, the weather, and time of day all play a role in determining what your street will look like when you step out for your morning walk. The data we collect is providing insight into important trends like these and is being used to help local communities sponsor and track clean-up efforts in a meaningful way. By relying on Firebase to store, process, and analyze an increasing amount of data, we feel confident that we can engage and empower individuals, communities, and governments to tackle extensive, seemingly unsolvable problems like litter.
Firebase Performance Monitoring provides detailed insights into how your app performs in the hands of real users, giving visibility into bottlenecks that could be causing churn and revenue loss.
We've received positive feedback about the richness of performance data Firebase Performance Monitoring surfaces. However, a common complaint has been that it's difficult to determine the cause of these issues from the data we surface, forcing developers to spend a lot of time investigating the root cause of performance issues.
For example, it's great to know that your app launches slowly for 40% of your users, but why is this happening? Even though developers can use attributes such as app version, OS version, and geography to filter data in the dashboard, the data still may not give enough detail to pinpoint the exact issue at hand.
To address the need for actionable insights, we are pleased to launch the ability to dig deeper into an individual session of a trace, so you see attributes and events that happened leading up to a performance issue. With this feature, developers can see three new categories of information:
Surfacing these extra details in the context of a trace will help improve debuggability and issue resolution for performance issues.
Let's see how sessions works with a concrete example. Imagine you are an e-retail app developer using a custom trace, productImageLoading, to measure how long it takes to load an image of an item in your catalogue. You notice that an issue appears in the Firebase Performance Monitoring console for this trace because the product images are loading slower than the defined threshold of 200ms.
Performance Monitoring surfaces emerging issues.
Previously, if you clicked on the issue to get more details, you would see information like the median time for the trace, and you could slice the data by various segmentations, such as country, device, etc.
The issue details page shows more information and allows for data slicing
While the details page is helpful, it shows the information aggregated among all trace samples, which doesn't give enough context about other factors that may have contributed to the issue.
This is where drilling down into a session of a trace becomes powerful. With this new feature, you can now examine device properties, system usage, traces and network requests that happened around the same time as the specific trace instance being investigated. You can access all sessions for the metric from the top bar of the metrics detail page. If you're already segmenting your data by an attribute like app version or country, then you can click through to a pre-filtered view of sessions.
Sessions has 2 entry points
In the sessions view, you can narrow down sessions corresponding to a particular percentile range of the trace duration and look at the details of the trace instances for that range. The percentile range groups sessions into cohorts based on their performance making it easier to find the sessions with the worst performance:
Sessions view showing CPU, memory, traces and network requests for a percentile range
Looking at a product image loading session in the 90-95 percentile range, you can see the following:
Based on the above trace session data, you can see that requesting a large image impacted memory and CPU, and subsequently slowed down loading of the product image. This helps you pinpoint where in your code to investigate the issue further.
This is just an example of the powerful debuggability that comes with the new feature. We hope that developers are able to use this new feature in a myriad of use cases to bridge the gap between cause and effect and greatly reduce time spent debugging trace issues.
To get started on iOS or Android, please see our docs here. If you have any feedback, feel free to share with us through our support channel. Happy building!
Notifications are one of the most powerful ways of bringing latent users back to your app. Properly timed and targeted notifications can be vital in increasing engagement. That's why we've redesigned the Firebase notifications dashboard to support much more sophisticated and powerful notification campaigns.
The old notifications dashboard let you set up notification campaigns as one-time alerts that could go out immediately, or be scheduled for a later date. For example, with a few clicks, you could set up a notification campaign that would remind new users who failed to complete onboarding to do so on Monday. However, it was not possible to automate this reminder to go out every Monday - unless you did it manually.
In the new Firebase notifications dashboard, we have added the ability to create recurring campaigns. Recurring campaigns are notification campaigns that run automatically, whenever a user meets the targeting conditions. Now, it's easy to set-up that weekly reminder to encourage new users to complete onboarding. Or, perhaps you want to offer bi-weekly discounts on in-app purchases to spenders to nudge them towards a purchase - that's also possible!
The new notifications dashboard allows you to set user-level message frequency caps, so you can limit the number of times a user gets a message to prevent spamming them. You can limit messages to only be sent once per user, or allow one message over a specified number of days.
Perhaps you want to send a welcome message once to each new user. Use a single message to target every new user once. Or, perhaps you want to encourage users to check out a tutorial on how to use the app. You can send a notification once every few days to guide them towards the action until it's been completed. And since targeted segments are dynamic, users who meet the criteria will automatically start receiving notifications, and users who no longer meet the criteria will stop receiving the targeted notifications. This means your notification will only be delivered to users who find it relevant.
Users can receive a notification just once
Users can receive notifications at a custom interval
Untargeted batch and blast notifications are annoying and can cause churn. It's vital to carefully segment the right users so your notification appears welcome and relevant to their recent interaction with your app - not out of place and random. The new notifications dashboard includes a more sophisticated segment builder that gives you the ability to target prevalent user characteristics, like last app engagement and the number of days since they first opened the app. This targeting is built into the dashboard, so you don't have to add any code to get these new parameters.
Finally, we also improved the results section of the notifications dashboard so you can better monitor the performance of your campaigns and make adjustments as needed. In the new notifications dashboard, you can now track the effectiveness of recurring campaigns day-by-day. Here, you can see daily data points for notification sends, opens, and conversions. You'll also notice that the graphs have been updated from a bar chart to a time-series graph, which are more intuitive and easier to interpret.
The redesigned Firebase notifications dashboard offers new, powerful campaign options, sophisticated targeting, and rich analytics to track the progress of your notifications campaigns. If you're new to Firebase notifications, get started with the Firebase Cloud Messaging guides.
Check out the Firebase console to set up your notification campaigns today!
Last year at Firebase Summit, we introduced you to Predictions, a machine learning product that helps you smartly segment your users based on their predicted future behavior. Without requiring anyone on your app team to have ML expertise, Predictions gives you insight into which segments of users are likely to churn or spend (or complete another conversion event) so you can make informed product decisions and grow your app.
As of today, Predictions makes more than 6 billion predictions per day for our developers and allows them to take meaningful actions by making predictive segments available for targeting in Remote Config, Cloud Messaging, In-App Messaging, and A/B Testing.
This year at Firebase Summit, we announced that Predictions has graduated out of beta and into general availability with a host of new features that we added based on your feedback.
Since Predictions continuously update based on actual user behavior inside your app, we heard from many of you that you wanted to know how stable a prediction was before you integrate it into your app.
To help answer this question, we created a health indicator at the bottom of each predictive segment card that gives you a snapshot of how a certain prediction is performing:
Image 1: Green means it has been performing consistently well over the last two weeks
Image 2: Yellow means it is performing well today but did not meet the quality threshold some time in the past two weeks
Image 3: Red means it is not performing well today and had other performance issues over the last two weeks
It is worth mentioning that actions targeted with Predictions have a fail-safe mechanism, so if a predictive segment is performing poorly, it simply turns inactive. That means, if you are using Remote Config to deliver a set of values to users in that predicted group, Remote Config will gracefully fall back to your default values if the predictive segment decreases in reliability. Any notifications or in-app messages directed at that predictive segment will also not trigger until the predictive segment increases in accuracy.
To help you understand how we assess the quality of a prediction, we are now exposing our evaluation criteria. For every predictive segment, we use a portion of your historical data from the last 28 days that we hold out during the model training phase.
We then compare the results of the prediction to what actually happened. This gives us two ways to score the prediction: how many of the users in the predictive segment actually behaved in the predicted way (we call that true positive rate) and how many users in the predictive segment were incorrectly classified (or in more technical terms, the false positive rate).
You can access this data from the bottom of the prediction card
Tapping on the health indicator exposes these values.
By exposing these two scores to you, you can now make a better determination about which risk profile to choose for your action.
Another common question we received during our beta phase is what went into creating a predictive segment. We now offer a details page that gives you the ingredient list! You can click through and see what data our model makes use of. This includes event frequency, volume, and parameters as well as other data like device language, freshness of app install and more.
The last thing we are excited to announce is that now, you can export your raw predictions data into BigQuery. This will give you access to the raw prediction score, the thresholds we used for each risk profile, as well as the final result. You can use this data to create your own risk profiles or if you supply your own user_id property in analytics, to do sophisticated analysis with your analytics data. For example, you can find out which countries exhibit the highest potential to churn or spend!
We are humbled to have gained your trust over the past year and hope these improvements make it easier for you to make the most out of Predictions in your mobile apps and games. As always, if you have any questions, you can find us on Twitter (@firebase) and on Stack Overflow.
For more information on these updates, check out our docs below!
Predictions risk tolerance and performance
Predictions model inputs and details page
Predictions data export to BigQuery
