In today’s fast-paced digital world, ensuring your frontend applications run smoothly is crucial. Monitoring plays a key role in achieving this, providing insights into performance, user experience, and potential issues. One powerful tool for this task is Prometheus, a robust open-source monitoring system and time-series database. In this article, we’ll explore how to use Prometheus to monitor frontend applications, offering practical guidance to help you set up and use it effectively.
Understanding Prometheus
What is Prometheus?
Prometheus is an open-source monitoring and alerting toolkit designed for reliability and scalability. It excels at collecting metrics and providing a flexible query language, which makes it a popular choice for monitoring various types of applications, including frontend ones.
Prometheus works by scraping metrics from configured endpoints at specified intervals. These metrics are then stored in a time-series database, allowing you to query and analyze data over time.
This approach helps in understanding application performance and detecting issues before they impact users.
How Prometheus Fits into the Monitoring Landscape
Prometheus is part of a larger ecosystem of monitoring tools and practices. It integrates well with other components like Grafana for visualization and Alertmanager for handling alerts. Together, these tools provide a comprehensive monitoring solution.
For frontend applications, Prometheus offers detailed insights into user interactions, performance metrics, and application health. This integration enables developers to monitor not just server-side performance but also how their applications perform in real-world scenarios.
Setting Up Prometheus for Frontend Monitoring
Installing Prometheus
To get started with Prometheus, you’ll first need to install it. This process is straightforward and involves downloading the Prometheus binary and configuring it for your environment.
You can download Prometheus from its official website. Once downloaded, extract the binary and run it using the command line. The default configuration file, prometheus.yml, will need to be edited to specify which endpoints to scrape.
Configuring Prometheus for Frontend Applications
Configuring Prometheus involves setting up a configuration file that defines the targets to be monitored. For frontend applications, you will need to configure Prometheus to scrape metrics from the endpoints where your application exposes its performance data.
Here’s a basic example of a configuration for Prometheus:
scrape_configs:
- job_name: 'frontend'
static_configs:
- targets: ['localhost:8080']
In this example, Prometheus is configured to scrape metrics from localhost:8080, where your frontend application’s metrics endpoint would be exposed. You may need to adjust the target based on your application’s deployment setup.
Exposing Metrics from Frontend Applications
Frontend applications typically do not expose metrics out of the box, so you will need to instrument your code to provide this data. For many frontend applications, this involves adding monitoring libraries or using existing integrations.
If you’re using a framework like React or Angular, there are libraries available that help expose performance metrics. For instance, Prometheus client libraries can be used to instrument your frontend application, allowing you to expose metrics in a format that Prometheus can scrape.
Here’s a simplified example of how you might expose a custom metric using JavaScript:
const express = require('express');
const promClient = require('prom-client');
const app = express();
const collectDefaultMetrics = promClient.collectDefaultMetrics;
collectDefaultMetrics();
app.get('/metrics', (req, res) => {
res.set('Content-Type', promClient.register.contentType);
res.end(promClient.register.metrics());
});
app.listen(8080, () => {
console.log('Metrics endpoint available at http://localhost:8080/metrics');
});
In this example, an Express server is set up to serve metrics at the /metrics endpoint, which Prometheus will scrape.
Querying and Analyzing Metrics with Prometheus
Understanding PromQL
PromQL (Prometheus Query Language) is the powerful query language used by Prometheus to retrieve and manipulate time-series data. It allows you to perform complex queries, aggregations, and calculations on your metrics.
Here’s a simple PromQL query example:
rate(http_requests_total[5m])
This query calculates the per-second rate of HTTP requests over the last 5 minutes. Understanding PromQL will enable you to create detailed dashboards and alerts based on your frontend application’s performance.
Creating Dashboards with Grafana
Grafana is a popular open-source platform for visualizing metrics stored in Prometheus. Integrating Grafana with Prometheus allows you to create detailed dashboards that provide insights into your application’s performance.
To set up Grafana, download and install it from the Grafana website. After installation, configure Grafana to use Prometheus as a data source. Once configured, you can create dashboards to visualize metrics like response times, error rates, and user interactions.
Setting Up Alerts
Alerts are a critical component of monitoring, allowing you to detect and respond to issues in real-time. Prometheus Alertmanager is responsible for managing alerts and sending notifications based on your configured rules.
To set up alerts, you need to define alerting rules in Prometheus. Here’s a simple example of an alerting rule:
groups:
- name: frontend_alerts
rules:
- alert: HighErrorRate
expr: rate(http_requests_total{status="500"}[5m]) > 0.1
for: 10m
labels:
severity: critical
annotations:
summary: "High error rate detected"
This rule triggers an alert if the rate of HTTP 500 errors exceeds 0.1 per second over a 5-minute period. Configure Alertmanager to handle these alerts and notify you via email, Slack, or other channels.
Best Practices for Monitoring Frontend Applications with Prometheus
Instrumenting Your Frontend Code Effectively
To get the most out of Prometheus, it’s crucial to instrument your frontend code effectively. This involves selecting the right metrics to monitor and ensuring that your instrumentation doesn’t negatively impact application performance.
When choosing metrics, focus on those that provide meaningful insights into user experience and application performance. Common metrics to monitor include page load times, error rates, and user interactions.
Instrument your code to collect these metrics in a way that provides a clear view of how your application is performing.
Be mindful of the performance overhead introduced by instrumentation. Ensure that the code used for exposing metrics is efficient and does not degrade the user experience.
Regularly review and optimize your instrumentation to balance the level of detail with performance.
Scaling Prometheus for High Traffic Applications
For high-traffic frontend applications, managing Prometheus at scale requires careful planning and configuration. Prometheus is designed to handle large amounts of data, but there are several strategies to ensure it performs well under heavy loads.
One approach is to use Prometheus federation, which allows you to aggregate metrics from multiple Prometheus instances. This technique involves setting up a central Prometheus server that scrapes data from several regional or specialized Prometheus servers.
Federation helps distribute the load and manage large-scale monitoring setups.
Another strategy is to configure data retention policies to manage the volume of stored metrics. Prometheus supports configuring how long data is retained, allowing you to strike a balance between data availability and storage requirements.
Integrating Prometheus with CI/CD Pipelines
Integrating Prometheus with your CI/CD pipelines enhances your monitoring setup by providing insights into the performance and stability of new deployments. By incorporating Prometheus into your CI/CD workflows, you can track the impact of code changes on application performance in real-time.
For instance, configure your CI/CD pipeline to deploy monitoring configurations alongside application updates. Use Prometheus to monitor metrics related to the deployment process, such as build times and deployment success rates.
This integration helps you detect issues early and ensures that new changes don’t negatively impact performance.
Handling Metrics Storage and Retention
Prometheus stores metrics data in a time-series database, which can grow significantly over time. Proper management of this data is essential for maintaining performance and ensuring that historical data remains accessible.
Configure data retention settings to control how long metrics are stored. By default, Prometheus retains data for 15 days, but this can be adjusted based on your needs. Use retention policies to balance between having enough historical data for analysis and managing storage costs.
Consider using external storage solutions for long-term data retention if you need to keep metrics for extended periods. Prometheus can be integrated with external storage systems like Thanos or Cortex to provide scalable and long-term storage options.
Ensuring Data Security and Privacy
Security and privacy are critical aspects of monitoring, especially when dealing with sensitive data. Protecting the data collected by Prometheus and ensuring that access is properly controlled are essential for maintaining security.
Implement access controls to restrict who can view and modify Prometheus configurations and metrics. Use authentication mechanisms to secure access to Prometheus and associated tools like Grafana.
Encrypt sensitive data both in transit and at rest to prevent unauthorized access. Ensure that your monitoring setup complies with relevant data protection regulations and best practices.
Troubleshooting Common Issues with Prometheus
Dealing with Metric Collection Issues
Sometimes, Prometheus might encounter issues with metric collection, such as incomplete or missing data. Common causes include misconfigured targets, network issues, or problems with the metric endpoints.
To troubleshoot metric collection issues, start by verifying the configuration settings in prometheus.yml to ensure that targets are correctly defined and reachable. Check the logs for any errors related to scraping or connectivity issues.
If metrics are not being collected as expected, review the exposed metrics endpoints in your application to ensure they are functioning correctly. Use tools like curl or browser-based inspection to confirm that metrics are available at the specified endpoints.
Handling High Cardinality Metrics
High cardinality metrics, which involve a large number of unique label values, can strain Prometheus and affect performance. High cardinality can lead to increased storage requirements and slower query performance.
To manage high cardinality, avoid using excessively granular labels and focus on metrics that provide actionable insights. Implement aggregation and summarization strategies to reduce the number of unique label values and improve query performance.
Addressing Performance Bottlenecks
Prometheus performance bottlenecks can arise from various sources, such as inefficient queries, high data ingestion rates, or hardware limitations. Identifying and addressing these bottlenecks is crucial for maintaining a responsive monitoring setup.
Monitor Prometheus performance using built-in metrics and external tools. Look for signs of high CPU or memory usage, slow query responses, or excessive disk I/O. Optimize your setup by adjusting scrape intervals, optimizing queries, and scaling hardware resources as needed.
Troubleshooting Alerting Issues
Alerting issues can disrupt your ability to detect and respond to problems effectively. Common alerting issues include misconfigured alert rules, incorrect thresholds, or issues with Alertmanager.
Review your alerting rules to ensure they are correctly defined and aligned with your monitoring objectives. Test alert rules to confirm that they trigger as expected under the appropriate conditions.
Verify that Alertmanager is properly configured to handle and route alerts to the correct channels.
Enhancing Frontend Monitoring with Advanced Techniques
Leveraging Distributed Tracing
Distributed tracing provides detailed insights into the flow of requests through your application, helping you identify performance bottlenecks and issues. Integrating distributed tracing with Prometheus enhances your monitoring setup by providing a comprehensive view of request processing and performance.
Use distributed tracing tools like Jaeger or Zipkin to collect and visualize trace data. Combine this with Prometheus metrics to correlate performance issues with specific requests or components.
Implementing Synthetic Monitoring
Synthetic monitoring involves simulating user interactions with your application to measure performance and availability. This technique complements traditional monitoring by providing proactive insights into how your application performs under various conditions.
Set up synthetic monitoring scripts to simulate user actions and collect performance data. Integrate this data with Prometheus to monitor and analyze synthetic test results alongside real user metrics.
Exploring Custom Metrics and Labels
Custom metrics and labels allow you to tailor your monitoring setup to your specific needs. By defining and collecting custom metrics, you can gain deeper insights into application performance and user behavior.
Identify key metrics relevant to your application and define custom labels to track specific dimensions of performance. Use these metrics to create targeted alerts and dashboards that provide actionable insights for your development and operations teams.
Integrating Prometheus with Other Tools and Platforms
Combining Prometheus with Grafana for Enhanced Visualization
Grafana is an excellent tool for visualizing metrics collected by Prometheus. Its powerful visualization capabilities allow you to create detailed and customizable dashboards, which are crucial for understanding and analyzing frontend application performance.
To integrate Grafana with Prometheus, start by adding Prometheus as a data source in Grafana. Once connected, you can use Grafana’s extensive library of visualization options to create dashboards that display metrics in an insightful manner.
For frontend applications, consider visualizing metrics such as page load times, user interactions, and error rates. Customize your dashboards to show trends, comparisons, and performance indicators, helping you make informed decisions based on real-time data.
Using Prometheus with Alertmanager for Effective Notification Handling
Prometheus Alertmanager is designed to manage alerts, handle notifications, and integrate with various communication channels. By configuring Alertmanager, you can ensure that alerts are effectively routed and delivered to the right teams or individuals.
Set up Alertmanager by configuring routing rules that determine how alerts are processed and where they are sent. You can configure it to send notifications via email, Slack, PagerDuty, or other messaging platforms.
This setup helps ensure that critical issues are addressed promptly.
Integrating Prometheus with CI/CD Pipelines
Integrating Prometheus with your CI/CD pipelines enhances the monitoring of your deployment processes and application performance. By incorporating Prometheus metrics into your CI/CD workflows, you gain valuable insights into how new releases affect your frontend applications.
For example, you can configure your CI/CD pipeline to deploy Prometheus configurations along with application updates. Use Prometheus to monitor build times, deployment success rates, and post-deployment performance. This integration helps you detect and address issues early, improving the overall quality of your releases.
Leveraging Prometheus with Distributed Systems
For large-scale applications running on distributed systems, Prometheus offers capabilities to monitor complex environments. Its federation feature allows you to aggregate metrics from multiple Prometheus instances, providing a unified view of your distributed application’s performance.
Configure Prometheus federation by setting up a central Prometheus server that scrapes metrics from regional or specialized Prometheus instances. This setup helps manage monitoring at scale, ensuring that you have a comprehensive view of performance across different parts of your application.
Enhancing Your Monitoring Strategy
Regularly Reviewing and Updating Metrics
Regularly reviewing and updating your metrics is essential for maintaining effective monitoring. As your application evolves, new metrics may become relevant, or existing ones may need adjustment.
Periodically assess your metrics to ensure they continue to provide valuable insights.
Incorporate feedback from your development and operations teams to refine your monitoring setup. This iterative approach helps ensure that your metrics align with your current needs and provide actionable data for improving application performance.
Documenting and Sharing Monitoring Practices
Documenting your monitoring practices and sharing them with your team is crucial for maintaining consistency and ensuring that everyone understands how to use Prometheus effectively.
Create documentation that outlines your metrics, alerting rules, and dashboard configurations.
Share this documentation with your team and encourage collaboration on monitoring practices. Regularly review and update the documentation to reflect any changes in your monitoring setup or practices.
Staying Informed About New Developments
The monitoring landscape is constantly evolving, with new tools, features, and best practices emerging regularly. Stay informed about developments in the Prometheus ecosystem and related technologies to keep your monitoring strategy up-to-date.
Follow relevant blogs, forums, and industry news to learn about new tools and techniques. Participate in community discussions and conferences to gain insights from experts and peers.
This proactive approach helps you stay ahead of trends and continually enhance your monitoring setup.
Troubleshooting Advanced Issues
Handling Metrics Collection Latency
Metrics collection latency can affect the timeliness of the data you receive from Prometheus. Latency issues may arise due to network delays, high data volumes, or performance bottlenecks.
To address latency, ensure that your Prometheus server is properly optimized and that network configurations are tuned for performance. Consider adjusting scrape intervals and reducing the amount of data collected if latency persists.
Dealing with Metric Data Gaps
Metric data gaps occur when Prometheus fails to collect or store data for certain periods. This issue can result from network issues, endpoint problems, or configuration errors.
Investigate the cause of data gaps by checking Prometheus logs and reviewing your configuration. Ensure that your metric endpoints are reliable and that Prometheus is properly configured to handle data collection.
Managing Resource Utilization
Prometheus requires resources for storing and querying metrics. High resource utilization can impact performance, especially in large-scale setups.
Monitor Prometheus resource usage and adjust your hardware or configuration as needed. Optimize queries, reduce retention periods, and consider scaling your Prometheus deployment to manage resource utilization effectively.
Advanced Use Cases and Techniques
Utilizing Custom Metrics for Business Insights
Prometheus allows you to define and collect custom metrics tailored to your business needs. This capability helps you track specific aspects of user behavior or application performance that are critical to your business goals.
For example, you might create custom metrics to track user interactions such as button clicks, form submissions, or feature usage. By instrumenting your frontend code to emit these metrics, you can gain insights into user engagement and identify areas for improvement.
Custom metrics can also be used to monitor business KPIs, such as transaction volumes or user acquisition rates. Integrating these metrics with Prometheus helps you correlate business performance with technical performance, providing a comprehensive view of how your application supports business objectives.
Implementing Service-Level Objectives (SLOs) and Service-Level Indicators (SLIs)
Service-Level Objectives (SLOs) and Service-Level Indicators (SLIs) are key components of Site Reliability Engineering (SRE) practices that help you define and measure the performance and reliability of your application.
SLOs are performance targets that you set for your application, such as response times or error rates. SLIs are metrics used to measure whether you are meeting these targets.
Prometheus can be used to collect and monitor SLIs, helping you track whether you are achieving your SLOs.
For example, you might define an SLO for page load times, such as ensuring that 95% of page loads complete within 2 seconds. Use Prometheus to track the SLIs related to page load times and create alerts if the SLO is at risk of being breached.
Integrating with Distributed Logging Systems
Distributed logging systems, such as ELK Stack (Elasticsearch, Logstash, Kibana) or Loki, complement Prometheus by providing detailed logs and traces.
Integrating these systems with Prometheus offers a more complete monitoring solution, combining metrics with logs and traces for a deeper understanding of application performance.
For instance, you can use Prometheus for monitoring application metrics and Loki for logging detailed information about errors and events. Combining these data sources allows you to correlate metrics with specific log entries, helping you diagnose issues more effectively.
Monitoring User Experience and Frontend Performance
User experience monitoring is essential for understanding how users interact with your application and how performance affects their experience. Prometheus can be used to collect metrics related to frontend performance, such as page load times, rendering times, and user interactions.
Consider integrating tools like Google Lighthouse or WebPageTest to perform performance audits and gather data on key user experience metrics. Use Prometheus to collect this data and visualize it alongside other metrics to get a complete picture of user experience.
Additionally, implement Real User Monitoring (RUM) to capture metrics from actual users interacting with your application. This approach provides real-time insights into how your application performs under various conditions and helps identify performance issues that impact users.
Managing Multi-Cluster and Multi-Environment Deployments
In complex environments with multiple clusters or deployment environments, managing monitoring setups can become challenging. Prometheus supports multi-cluster and multi-environment monitoring through features like federation and multi-tenancy.
Federation allows you to aggregate metrics from multiple Prometheus instances into a central instance, providing a unified view of metrics across clusters. This approach helps manage monitoring at scale and ensures that you have visibility into performance across different parts of your infrastructure.
Multi-tenancy enables you to isolate metrics and configurations for different environments or teams within the same Prometheus instance. This feature helps you manage monitoring for various applications or teams while maintaining clear boundaries and configurations.
Exploring Prometheus Ecosystem Tools
The Prometheus ecosystem includes various tools and projects that extend its capabilities and integrate with other systems. Exploring these tools can enhance your monitoring setup and provide additional functionalities.
Thanos is an open-source project that adds high availability, scalability, and long-term storage capabilities to Prometheus. It provides a unified view of metrics across multiple Prometheus instances and supports long-term storage of historical data.
Cortex is another project that offers scalable and long-term storage solutions for Prometheus metrics. It enables horizontal scaling and provides features like multi-tenancy and advanced query capabilities.
Kubernetes Metrics Server integrates with Prometheus to provide resource usage metrics for Kubernetes clusters. It allows you to monitor the health and performance of your containerized applications and clusters.
Future Trends in Monitoring Frontend Applications
The Rise of Observability
Observability is an evolution of monitoring that emphasizes understanding the internal state of a system based on the data it produces. It includes metrics, logs, and traces, providing a holistic view of application performance.
As observability becomes more prevalent, integrating Prometheus with observability platforms will be crucial. Combining metrics with logs and traces helps you gain a deeper understanding of application behavior and improve troubleshooting and performance optimization.
Advancements in AI and Machine Learning for Monitoring
Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being used to enhance monitoring and observability. These technologies can analyze large volumes of metrics data, detect anomalies, and provide predictive insights.
AI-driven monitoring solutions can automate anomaly detection, predict potential issues before they occur, and offer actionable recommendations for performance optimization.
Exploring AI and ML tools that integrate with Prometheus can enhance your monitoring strategy and improve your ability to manage complex applications.
The Growing Importance of End-to-End Monitoring
End-to-end monitoring involves tracking the complete lifecycle of user interactions with your application, from frontend performance to backend services and infrastructure. This comprehensive approach helps you understand how different components interact and affect overall performance.
As applications become more complex, end-to-end monitoring will become increasingly important. Integrating Prometheus with tools that provide visibility across the entire application stack will help you manage performance more effectively and ensure a seamless user experience.
Advanced Integration and Optimization Techniques
Integrating Prometheus with Cloud-Native Platforms
As cloud-native architectures become more prevalent, integrating Prometheus with cloud platforms can optimize monitoring and performance management.
Many cloud-native platforms provide built-in support for Prometheus, making it easier to collect and analyze metrics from containerized applications and microservices.
For example, platforms like Kubernetes offer native integration with Prometheus for monitoring containerized applications. You can use Prometheus to collect metrics from Kubernetes clusters, track pod and node performance, and manage resource utilization.
AWS Managed Service for Prometheus provides a fully managed Prometheus-compatible monitoring service, simplifying the deployment and scaling of Prometheus in the cloud. It integrates seamlessly with other AWS services and supports features like long-term storage and high availability.
Leveraging Metrics Aggregation for Improved Performance
Metrics aggregation involves summarizing and consolidating metrics to reduce data volume and improve query performance. Prometheus supports aggregation through its query language, PromQL, which allows you to perform various operations on metrics data.
Use aggregation functions like rate(), avg(), and sum() to aggregate metrics and reduce the granularity of data. This approach helps manage large volumes of data, improves query performance, and makes it easier to visualize trends and patterns.
For instance, you can aggregate metrics to calculate average response times over specific periods or summarize error rates by application components. Aggregated metrics provide high-level insights while reducing the amount of raw data you need to process and store.
Implementing Continuous Improvement with Prometheus
Continuous improvement involves regularly evaluating and enhancing your monitoring setup to ensure it meets evolving needs and supports business objectives. Prometheus facilitates continuous improvement by providing insights into application performance and enabling iterative adjustments.
Regularly review your metrics, alerts, and dashboards to ensure they reflect current performance objectives and user requirements. Use feedback from your development and operations teams to identify areas for improvement and make data-driven decisions to enhance your monitoring strategy.
Consider implementing A/B testing or canary deployments to evaluate the impact of changes on application performance. Use Prometheus to monitor the effects of these changes and make adjustments based on the results.
Exploring Prometheus Ecosystem Extensions
The Prometheus ecosystem includes various extensions and integrations that enhance its capabilities and extend its functionality. Exploring these extensions can help you tailor Prometheus to your specific monitoring needs.
Prometheus Exporters are tools that expose metrics from different systems and services to Prometheus. For example, the Node Exporter collects metrics from hardware and operating systems, while the Blackbox Exporter tests the availability and performance of endpoints.
Alertmanager Integrations allow you to integrate Prometheus alerts with other tools and platforms. For instance, integrating with Opsgenie or VictorOps can enhance incident management and response capabilities.
Grafana Plugins provide additional visualization options and integrations with other data sources. Explore the Grafana plugin repository to find plugins that complement your Prometheus setup and enhance your dashboard capabilities.
Final Insights and Best Practices
Emphasizing Data Security and Privacy
When implementing Prometheus for monitoring frontend applications, it’s essential to consider data security and privacy. Ensure that metrics data is handled securely, especially if it includes sensitive information. Follow best practices for securing Prometheus servers, such as using HTTPS for data transmission and implementing access controls.
Additionally, be mindful of data retention policies. Configure Prometheus to manage data retention according to your organization’s compliance requirements and data privacy policies. This helps protect user data and maintain compliance with regulations.
Regularly Evaluating Performance and Scaling
As your application grows and evolves, regularly evaluate the performance of your Prometheus setup. Monitor the resource usage of Prometheus servers and adjust configurations as needed to handle increased data volumes and query loads.
Consider scaling your Prometheus deployment or using distributed solutions like Thanos or Cortex if necessary.
Evaluate your monitoring setup periodically to ensure it continues to meet your needs. Adjust metrics, alerts, and dashboards based on feedback from your team and changes in application requirements.
Engaging with the Prometheus Community
The Prometheus community is an excellent resource for staying informed about best practices, new features, and emerging trends. Engage with the community by participating in forums, attending meetups or conferences, and contributing to open-source projects.
Join discussions on platforms like the Prometheus User Group, GitHub repositories, and Slack channels. Networking with other Prometheus users and developers can provide valuable insights and help you stay up-to-date with the latest developments in the monitoring ecosystem.
Continuous Learning and Adaptation
Monitoring and observability practices are continually evolving, and staying updated with new techniques and tools is crucial. Invest time in learning about new advancements in monitoring technologies, performance optimization, and best practices.
Explore online resources, attend training sessions, and follow industry blogs to expand your knowledge. Adapt your monitoring strategy based on the latest insights and innovations to keep your frontend applications performing at their best.
Wrapping it up
Using Prometheus to monitor frontend applications provides a robust and flexible approach to understanding and improving performance. By configuring Prometheus effectively, integrating it with other tools like Grafana and Alertmanager, and exploring advanced techniques, you gain valuable insights into application behavior, user experience, and performance.
Ensure data security, regularly evaluate performance, and engage with the Prometheus community to stay informed about best practices and new developments. By continuously refining your monitoring strategy, you can address issues proactively, optimize performance, and deliver exceptional user experiences.
Incorporate these practices to harness the full potential of Prometheus and maintain a high-performing, reliable frontend application.
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