惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Hacker News: Ask HN
Hacker News: Ask HN
H
Help Net Security
Microsoft Azure Blog
Microsoft Azure Blog
B
Blog RSS Feed
Jina AI
Jina AI
Stack Overflow Blog
Stack Overflow Blog
量子位
博客园_首页
Vercel News
Vercel News
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
Forbes - Security
Forbes - Security
IT之家
IT之家
N
News and Events Feed by Topic
S
Security Affairs
Recent Commits to openclaw:main
Recent Commits to openclaw:main
Webroot Blog
Webroot Blog
Recorded Future
Recorded Future
L
LangChain Blog
Y
Y Combinator Blog
AI
AI
MyScale Blog
MyScale Blog
大猫的无限游戏
大猫的无限游戏
小众软件
小众软件
Know Your Adversary
Know Your Adversary
AWS News Blog
AWS News Blog
Help Net Security
Help Net Security
Cyberwarzone
Cyberwarzone
L
Lohrmann on Cybersecurity
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Google Online Security Blog
Google Online Security Blog
V2EX - 技术
V2EX - 技术
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
PCI Perspectives
PCI Perspectives
I
Intezer
T
Tenable Blog
G
Google Developers Blog
Application and Cybersecurity Blog
Application and Cybersecurity Blog
T
Troy Hunt's Blog
L
LINUX DO - 最新话题
云风的 BLOG
云风的 BLOG
C
CXSECURITY Database RSS Feed - CXSecurity.com
有赞技术团队
有赞技术团队
O
OpenAI News
P
Proofpoint News Feed
TaoSecurity Blog
TaoSecurity Blog
C
Check Point Blog
Last Week in AI
Last Week in AI
S
Schneier on Security
Simon Willison's Weblog
Simon Willison's Weblog
Blog — PlanetScale
Blog — PlanetScale

Datadog | The Monitor blog

Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - February 2026 Amazon EC2 security: How misconfigured and public AMIs expand your cloud attack surface Enable end-to-end visibility into your Java apps with a single command Measure and improve mobile app startup performance with Datadog RUM Evaluating our AI Guard application to improve quality and control cost Identify untested code across every level of your codebase Make use of guardrail metrics and stop babysitting your releases Monitor Versa Networks SD-WAN performance in Datadog Improve performance and reliability with APM Recommendations Remediate transitive vulnerabilities faster with Datadog Software Composition Analysis Generate audit-ready vulnerability and compliance reports with Datadog Sheets Monitor Fortinet FortiManager performance in Datadog Improve test coverage across codebases with Datadog Code Coverage Move fast, don’t break things: Consistent testing standards at scale Enrich logs with ServiceNow CMDB context before routing to any SIEM or logging tool Monitor Lustre with Datadog Make faster, better product decisions with Datadog Product Analytics Surface and remediate runtime posture issues with Workload Protection Findings Protect agentic AI applications with Datadog AI Guard How to optimize JavaScript code with CSS Trace Google Pub/Sub workloads in Cloud Run with Datadog Detect human names in logs with ML in Sensitive Data Scanner How we cut our NLQ agent debugging time from hours to minutes with LLM Observability Debug PostgreSQL query latency faster with EXPLAIN ANALYZE in Datadog Database Monitoring Datadog acquires Propolis Unify and correlate frontend and backend data with retention filters Scale compliance across global frameworks with Datadog Cloud Security Monitor Arista VeloCloud SD-WAN performance with Datadog Building reliable dashboard agents with Datadog LLM Observability Simplify log collection and aggregation for MSSPs with Datadog Observability Pipelines Mitigation for Node.js denial-of-service vulnerability affecting Datadog APM Automate flaky test fixes with the Bits AI Dev Agent and Test Optimization How we built an AI SRE agent that investigates like a team of engineers Datadog integrations 2025 recap: Observability for AI, security, and hybrid cloud Design effective executive dashboards with Datadog Implement dbt data quality checks with dbt-expectations Bring faster visibility into AWS Lambda functions with remote instrumentation Troubleshoot faster with the GitLab Source Code integration in Datadog How Cambia Health Solutions saved $30,000 monthly with Cloud Cost Management and the Datadog Resource Catalog Normalize any logs for Cloud SIEM with Datadog's OCSF processor Optimizing Datadog at scale: Cost-efficient observability at Zendesk Detect, diagnose, and resolve network issues easily with CNM Network Health Connect engineering errors to user impact in early-stage products Cilium configuration for Kubernetes operations at scale Designing feedback loops for progressive delivery Ship features faster and safer with Datadog Feature Flags Choosing the right OpenTelemetry Collector distribution Route your monitor alerts with Datadog monitor notification rules Automate Cloud SIEM investigations with Bits AI Security Analyst Cloud threat detection: How to identify risky activity across control and data planes Collecting Kafka performance metrics Monitoring Kafka with Datadog Monitoring Kafka performance metrics
Debug Android crashes faster with Datadog
2021-04-20 · via Datadog | The Monitor blog

Technical issues, such as fatal crashes, are one of the biggest reasons why users uninstall mobile applications, so quickly identifying and resolving issues is vital for user retention. This can be challenging, particularly in the Android market, which has a wide variety of mobile devices and versions of the Android operating system. You need visibility into every issue so you can determine which crashes impact your application the most and efficiently resolve them.

In this post, we’ll look at how Datadog helps you:

Collect crash data from your users’ Android devices

Understanding how users interact with your application can help you troubleshoot issues more effectively, so collecting application usage data like errors and user actions is a crucial first step. Datadog’s Real User Monitoring (RUM) Android SDK lets you collect RUM data from your application, giving you real-time insights into all user activity. This data includes:

  • user sessions, which contain metadata such as each user’s device and geolocation
  • view events, generated each time a user navigates to a different part of your application’s UI
  • action events, which are any user interactions within your application (e.g., taps, scrolls)
  • any errors triggered during a user’s interaction with your application

To start using Datadog’s RUM SDK, follow the in-app instructions to generate an application ID and a client token for a new application.

Set up the RUM SDK to debug Android applications with our agentless onboarding process
Set up the RUM SDK to debug Android applications with our agentless onboarding process

Then you can initialize your configuration to start reporting crashes by setting crashReportsEnabled to true in your Configuration.Builder class.

val config = Configuration.Builder(

crashReportsEnabled = true,

rumEnabled = true

)

Once you’ve registered your application in Datadog, see our documentation for more information about completing the setup process.

Whenever a crash occurs, the Android SDK captures it, and sends the data to Datadog. And, in cases where the user’s device is offline or inaccessible due to low battery or network issues, the SDK buffers and locally stores the crash data and then sends it to Datadog the next time the application is used.

Now that you’re collecting crash data, you need to ensure that it is in a human-readable format before you can view and analyze it in the Datadog UI, which we’ll look at in more detail next.

Decode your Android applications

Most Android applications are obfuscated when they are published in order to reduce their size for faster downloads and prevent other users from repurposing their source code. This means that any data collected from an application will also be obfuscated and unusable in that state. In order to use your application’s data, you will need to deobfuscate it, a process that restores an application’s source code to a more understandable form—similar to reversing the minification process for JavaScript in web applications.

When your compiler runs, tools such as ProGuard and R8 create a mapping file that maps line numbers from obfuscated code back to line numbers from the original source file. This mapping file is necessary for deobfuscation, which Datadog’s Gradle plugin simplifies by automatically retrieving the mapping file and uploading it to Datadog.

As with other types of RUM data, Datadog enriches crash data with meaningful metadata you can use to identify its root cause, such as information about the device and operating system, network availability, user actions, and the user flow that led to the crash. Datadog provides built-in RUM dashboards that you can use to visualize all of this data, giving you more context for troubleshooting problems with your application.

Debug Android crashes with Datadog's RUM Mobile dashboard
Debug Android crashes with Datadog's RUM Mobile dashboard

You can also build crash reports using Datadog’s RUM error dashboard in order to view all crashes or non-fatal errors at a glance, enabling you to get a better understanding of where and how they impact users the most.

Create custom crash reports to debug Android crashes
Create custom crash reports to debug Android crashes

Your application provides a wealth of RUM data, but knowing how to prioritize it is necessary for fixing crashes efficiently. Next, we’ll show you how Datadog helps you organize RUM data in order to quickly triage and respond to the most urgent application issues.

Fix Android crashes, starting with the most important ones

During heavy usage, your application may generate a large volume of errors that cause it to crash, such as I/O exceptions, runtime exceptions, and out-of-memory errors. With the wide range of Android devices and operating systems running different versions and build variants of your application, understanding the root cause of these errors is more challenging.

Datadog Error Tracking helps you reduce the noise and prioritize the most important errors captured by the Android SDK by grouping similar errors together into issues so that you can see which issues are impacting your users the most. Error Tracking also allows you to filter issues by different dimensions, such as a device’s operating system or the version of your application. For example, if multiple customers report a bug for the latest version of your application, you can filter by that specific version and quickly view all of the issues impacting it.

Use Error Tracking to find and debug Android crashes
Use Error Tracking to find and debug Android crashes

You can select any issue to see its frequency over time, which versions of your application were affected, the full stack trace of the error, and more details about user activity (e.g., views, device information, affected application versions). In the screenshot below, you can see that a runtime exception triggered a crash at the “Check-out” path group.

View a stack trace for an Android application crash
View a stack trace for an Android application crash

For more details about what led up to the crash, you can also view the full session timeline, as seen in the screenshot below. This gives you a complete history of the events before the crash, so you can better understand how to resolve the problem.

View the full session timeline to debug an Android application crash
View the full session timeline to debug an Android application crash

Issues like these severely impact your users’ experience, so you need to know when they happen as soon as possible. You can use alerts to instantly notify you of new issues, enabling you to address them before they become more critical or widespread.

Create alerts to immediately notify you of significant issues

Datadog offers intelligent alerting capabilities that you can customize to fit your needs in order to prioritize the most important issues while reducing alert fatigue. For example, you can create an alert that will notify you of every new issue that causes a crash on the application’s check-out path—the path attribute references the application’s view and is automatically captured by the SDK.

Create intelligent alerts to debug Android crashes
Create intelligent alerts to debug Android crashes

Alerts provide all of the context you need to troubleshoot the issue, enabling you, for example, to quickly remove the affected version from Google Play before more users install it.

Monitoring Android crashes with Datadog

Fatal crashes impact not only your users’ experience but also your application’s reach in a highly competitive mobile market. To ensure that your Android applications are performing optimally, you can use Datadog RUM and the Android SDK to collect, analyze, and resolve errors and fatal crashes—Error Tracking is built into RUM with no additional setup required. You can also use Datadog’s built-in RUM dashboards and Datadog APM to get end-to-end visibility across your entire application stack. Check out our documentation to learn more about collecting Android crash data, or sign up for a free 14-day trial today if you don’t already have a Datadog account.