Candice Chisset, Author at FusionReactor Observability & APM

How has observability transitioned from optional to indispensable in the AI era?

Candice Chisset — Thu, 08 Aug 2024 11:37:01 +0000

Observability: From optional to indispensable in the AI era

A significant paradigm shift is underway in the rapidly evolving landscape of IT operations. Once relegated to the realm of “nice-to-have” features, observability has emerged as an absolute necessity in modern systems. This transformation is driven by the convergence of two powerful forces: the increasing complexity of our digital infrastructure and the rise of artificial intelligence in troubleshooting.

The advent of distributed systems, microservices architectures, and cloud-native applications has created a level of complexity that traditional monitoring approaches cannot handle. In this intricate web of interconnected services, pinpointing the root cause of an issue has become akin to finding a needle in a haystack. Enter observability – the ability to infer a system’s internal state from its external outputs.

Observability goes beyond mere monitoring. It provides a depth of insight and context that is crucial for understanding the behavior of complex systems. Observability tools offer a holistic view of an application’s performance and health by collecting and analyzing metrics, logs, and traces. This comprehensive perspective is invaluable for human operators and increasingly for AI-driven troubleshooting systems.

As AI becomes more prominent in IT operations, particularly in areas like anomaly detection and initial problem identification, the importance of high-quality observability data cannot be overstated. AI algorithms are only as good as the data they’re trained on and fed. Robust, comprehensive observability data is the foundation upon which these AI models are built and refined.

Moreover, observability data provides the raw material human experts need to verify and contextualize AI-generated insights. In a world where AI is increasingly making initial assessments, the ability of human operators to drill down into the underlying data is crucial for maintaining oversight and accountability.

The synergy between AI and observability is reshaping the troubleshooting landscape. AI can sift through vast amounts of observability data at speeds impossible for humans, identifying patterns and anomalies that might otherwise go unnoticed. Meanwhile, observability provides the context and granularity for AI systems and human experts to make informed decisions about system health and performance.

As we move further into the AI era, the role of observability will only grow in importance. It’s becoming the bedrock upon which effective IT operations are built, enabling proactive problem-solving rather than reactive firefighting. Organizations that invest in robust observability practices today are not just solving current operational challenges – they’re future-proofing their IT operations for the AI-driven landscape of tomorrow.

In conclusion, the journey of observability from an optional extra to an indispensable component of IT operations reflects the broader evolution of our digital infrastructure. As our systems become more complex and AI plays an increasingly central role in managing them, the ability to observe, understand, and act upon system behavior in real-time using tools like FusionReactor is no longer a luxury – it’s a necessity for staying competitive and ensuring reliability in the digital age.

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The future of Observability: Leveraging AI for predictive Insights

Candice Chisset — Wed, 07 Aug 2024 12:03:24 +0000

The future of Observability: Leveraging AI for predictive Insights

In the digital age, where businesses increasingly rely on complex technological ecosystems, maintaining seamless operations and preemptively addressing issues has become a cornerstone of competitive advantage. Observability, the practice of monitoring and understanding the state of systems, has evolved significantly with the advent of artificial intelligence (AI). This thought piece explores the transformative impact of AI-powered predictive insights on observability, highlighting its key benefits, operational mechanisms, and future trends.

Introduction: The role of AI in observability

As systems grow more intricate and data volumes soar, traditional monitoring techniques fall short of providing the depth and speed of insights required. AI enhances observability by enabling real-time data analysis, anomaly detection, and predictive analytics. AI-driven observability tools sift through vast amounts of data to identify patterns and irregularities that would be impossible for humans to detect manually. This shift improves system reliability and empowers organizations to make informed, proactive decisions.

Key benefits: Predictive analytics, automated responses, deeper data insights

Predictive analytics: One of the most significant advantages of AI in observability is predictive analytics. By analyzing historical data, AI models can forecast potential issues before they manifest into critical problems. This foresight allows teams to address vulnerabilities proactively, reducing downtime and enhancing system stability.

Automated responses: AI cannot just predict problems but also automate responses. When anomalies are detected, AI systems can trigger predefined actions to mitigate the issue. This automation speeds up incident response times and minimizes the impact on operations, ensuring business continuity.

Deeper data insights: AI-driven observability tools provide deeper insights into system performance. By continuously analyzing data from various sources, these tools offer a comprehensive view of the system’s health, performance trends, and potential risks. This granular visibility helps organizations optimize their infrastructure and improve overall efficiency.

How it works: AI algorithms, real-time data processing, anomaly detection

AI algorithms: At the heart of AI-powered observability are sophisticated algorithms that process and analyze data. These algorithms are trained on historical data to recognize standard patterns and detect deviations. Machine learning models continuously learn and adapt, improving their accuracy over time.

Real-time data processing: AI-powered observability tools operate in real-time, processing data as it flows through the system. This real-time capability is crucial for identifying and addressing issues promptly, minimizing the window of vulnerability, and reducing the likelihood of significant disruptions.

Anomaly detection: AI excels at anomaly detection, identifying unusual patterns that might indicate potential problems. By flagging these anomalies early, AI enables teams to investigate and resolve issues before they escalate, ensuring smoother operations and improved system reliability.

Impact: Improved system performance, reduced downtime, smarter decisions

The impact of AI on observability is profound. AI-powered tools significantly enhance system performance by providing predictive insights and automating responses. Organizations experience reduced downtime as potential issues are addressed before they become critical. Moreover, AI’s deeper data insights enable smarter decision-making, allowing businesses to optimize their operations, allocate resources more effectively, and drive continuous improvement.

Future trends: AI advancements, integration with other technologies

The future of AI in observability is bright, with continuous advancements on the horizon. As AI technologies evolve, observability tools will become even more sophisticated, offering greater accuracy and deeper insights. Integration with other emerging technologies, such as edge computing and the Internet of Things (IoT), will expand the scope and capabilities of AI-powered observability. These integrations will enable organizations to monitor and manage increasingly complex and distributed systems easily.

Conclusion – The future of Observability

In conclusion, AI-powered predictive insight tools like FusionReactor revolutionize observability, providing unparalleled visibility into system health and performance. The benefits of predictive analytics, automated responses, and deeper data insights are transforming how organizations operate, leading to improved system performance, reduced downtime, and more intelligent decision-making. As AI advances, the future of observability promises even more significant innovations, driving efficiency and resilience in the digital age.

By understanding and leveraging these advancements, organizations can stay ahead of the curve, ensuring their systems are robust, reliable, and ready to meet the challenges of tomorrow.

Get the infographic

For a summary of these insights, download our comprehensive infographic on “The Future of Observability: AI-Powered Predictive Insights.”

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AI and troubleshooting: Evolution, not replacement

Candice Chisset — Mon, 05 Aug 2024 10:00:08 +0000

Why Are AI and troubleshooting evolving together instead of replacing each other?

As artificial intelligence advances, its impact on Application Performance Monitoring (APM) and observability is undeniable. However, whether AI will entirely replace human troubleshooting is more nuanced than a simple yes or no.

AI excels at:

Pattern recognition across vast datasets
Rapid analysis of complex system interactions
Predictive anomaly detection
Automated root cause analysis

These capabilities are transforming how we approach troubleshooting, but they’re not rendering human expertise obsolete. Instead, AI is becoming a powerful augmentation tool that enhances human decision-making.

The future of troubleshooting likely involves a symbiosis between AI and human experts:

AI handles initial triage, filtering out noise and identifying potential issues.
Human experts interpret AI findings, considering broader context and business impact.
AI suggests potential solutions based on historical data and the system’s current state.
Humans make final decisions on corrective actions, especially in high-stakes situations.

This collaboration allows teams to focus on strategic problem-solving rather than getting bogged down in routine diagnostics. It also addresses AI’s current limitations, such as:

Difficulty adapting to novel situations outside its training data
Lack of intuition for subtle environmental or organizational factors
Potential for bias or errors in edge cases

As AI evolves, the balance will shift, with machines handling increasingly complex troubleshooting tasks. However, the need for human oversight, creativity, and accountability in critical systems means that AI is more likely to redefine troubleshooting roles rather than eliminate them.

The key for IT professionals is to embrace AI as a powerful ally in the quest for system reliability and performance while continuously developing higher-level skills that will remain uniquely human.

AI and Troubleshooting: Evolution, Not Replacement

In the rapidly evolving information technology landscape, artificial intelligence (AI) has emerged as a transformative force, reshaping numerous aspects of developing, maintaining, and optimizing systems. Nowhere is this impact more evident than in Application Performance Monitoring (APM) and observability. As AI advances at an unprecedented pace, it raises a critical question: Will AI eventually replace human troubleshooting entirely? As we’ll explore, the answer is far more nuanced than a simple yes or no.

The rise of AI in IT operations

To understand the role of AI in troubleshooting, we must first acknowledge its remarkable capabilities. AI excels in several key areas that are crucial for effective system monitoring and problem resolution:

Pattern Recognition Across Vast Datasets: AI can analyze enormous volumes of log data, metrics, and system events, identifying patterns and correlations that would be impossible for humans to discern manually.
Rapid Analysis of Complex System Interactions: Modern IT environments are increasingly complex, with intricate webs of microservices, cloud resources, and distributed systems. AI can quickly map and analyze these interactions, providing insights into system behavior.
Predictive Anomaly Detection: By learning from historical data, AI can predict potential issues before they occur, enabling proactive maintenance and reducing downtime.
Automated Root Cause Analysis: When problems arise, AI can swiftly sift through the data to pinpoint the root cause, significantly reducing the Mean Time to Resolution (MTTR).

These capabilities are undoubtedly transforming the landscape of IT operations and troubleshooting. However, it’s crucial to recognize that they are not rendering human expertise obsolete. Instead, AI is emerging as a powerful augmentation tool that enhances human decision-making and problem-solving capabilities.

The symbiosis of AI and human expertise

The future of troubleshooting is not one of replacement but instead of symbiosis between AI and human experts. This collaborative approach leverages the strengths of both artificial and human intelligence:

AI-Driven Initial Triage: AI systems can continuously monitor vast amounts of data, filtering out noise and identifying potential issues. This allows human experts to focus on genuinely problematic situations rather than being overwhelmed by false positives.
Human Interpretation and Contextualization: While AI can identify anomalies and suggest potential causes, human experts play a crucial role in interpreting these findings within the broader context of business operations, organizational goals, and subtle environmental factors that may not be captured in data.
AI-Suggested Solutions: AI can propose potential solutions or remediation strategies based on historical data and the current system state. This can include everything from configuration changes to resource allocation adjustments.
Human Decision-Making and Implementation: In high-stakes situations, human experts remain essential for final decisions on corrective actions. They can weigh the AI’s suggestions against other factors, including potential business impacts, regulatory considerations, and long-term strategic goals.

This collaborative approach allows IT teams to focus on strategic problem-solving and system optimization rather than getting bogged down in routine diagnostics and alert fatigue. It combines AI’s unparalleled data processing capabilities with human experts’ nuanced understanding and creative problem-solving skills.

Addressing AI’s current limitations

While AI has made remarkable strides in the field of IT operations, it’s essential to acknowledge its current limitations:

Adaptability to Novel Situations: AI systems are trained on historical data and may struggle when confronted with new scenarios or unprecedented system behaviors. Human experts, on the other hand, can draw on broader experience and creative thinking to address novel challenges.
Contextual Understanding: AI may lack the intuition for subtle environmental or organizational factors that can influence system behavior. Things like upcoming product launches, marketing campaigns, or even local events can impact system performance in ways that may not be immediately apparent to an AI.
Bias and Edge Cases: AI systems can inadvertently perpetuate biases in their training data or struggle with edge cases not well-represented in their learning sets. Human oversight is crucial for identifying and mitigating these issues.
Ethical and Strategic Decision-Making: While AI can provide data-driven insights, it lacks the capacity for moral reasoning and strategic thinking often required in complex troubleshooting scenarios, especially when trade-offs between different business priorities are involved.

The evolving role of IT professionals

As AI continues to evolve, the balance between machine and human involvement in troubleshooting will undoubtedly shift. We can expect AI systems to handle increasingly complex tasks and make more nuanced recommendations. However, the need for human oversight, creativity, and accountability in critical systems means that AI is more likely to redefine troubleshooting roles rather than eliminate them.

IT professionals of the future will need to develop a new set of skills to thrive in this AI-augmented environment:

AI Literacy: Understanding the capabilities and limitations of AI systems will be crucial for effective collaboration and oversight.
Data Interpretation: The ability to critically analyze and interpret AI-generated insights will become increasingly important.
Strategic Thinking: As routine tasks are automated, IT professionals must focus more on strategic planning and optimization.
Interdisciplinary Knowledge: Understanding the intersection of technology with business strategy, user experience, and even psychology will become more valuable.
Ethical Reasoning: As AI systems play a more significant role in decision-making, navigating complex ethical considerations will be essential.

Embracing the future of troubleshooting

The key for IT professionals is to embrace AI as a powerful ally in the quest for system reliability and performance. Rather than viewing AI as a threat, it should be seen as a tool that allows human experts to operate at a higher level, focusing on more complex, strategic, and creative system management and optimization aspects.

As we move forward, the most successful IT operations will be those that effectively blend the strengths of AI and human expertise. This symbiotic relationship will drive unprecedented system reliability, performance, and innovation levels.

In conclusion, while AI is undoubtedly reshaping the landscape of troubleshooting and IT operations, it is not a story of replacement but one of evolution and augmentation. Tools like FusionReactor exemplify this shift, integrating AI to enhance, rather than replace, traditional methods. The future belongs to those who can harness the power of AI while continuing to develop the higher-level skills that remain uniquely human. As we navigate this transformative era, we should focus on building collaborative systems that leverage the best of artificial and human intelligence, ushering in a new age of IT operations that is more proactive, efficient, and capable than ever.

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How to store FusionReactor logs persistently with Docker volumes

Candice Chisset — Tue, 23 Jul 2024 11:53:22 +0000

How to store FusionReactor logs persistently with Docker volumes

As a CFML developer using FusionReactor, ensuring your logs are accessible and secure is crucial. When running FusionReactor in a Docker container, storing logs in a persistent location becomes essential. This guide will walk you through using Docker volumes to ensure your FusionReactor logs are safely stored outside the ephemeral container environment and show you how to store FusionReactor logs.

Understanding the challenge

By default, FusionReactor stores its logs within the container. This poses a problem for ephemeral instances, as the logs disappear when the container is removed. The solution? Docker volumes.

Benefits

Storing FusionReactor (FR) logs persistently with Docker volumes offers several important benefits. Here’s an overview of the key advantages:

Data persistence: – Logs are preserved even when containers are stopped, removed, or replaced.
More accessible log analysis: – Logs can be accessed and analyzed without entering the container.

Improved backup and recovery: – Logs can be easily backed up along with other persistent data.
Debugging and troubleshooting:- Persistent logs make it easier to debug issues across container restarts or updates.
Resource optimization: – Prevents unnecessary storage usage inside containers.

By leveraging Docker volumes for FR log storage, you create a more robust, flexible, and maintainable logging infrastructure for your CFML applications.

Step-by-step guide

1. Locate default log folders

First, identify the default locations for your instance’s logs and archives:

– Check the Log Engine Settings for the default log folder

– Review the Log Archive Settings for the archive folder location

> Info: These folders are located within the container for ephemeral instances, so they cannot be changed to a location outside of the container via these fields.

2. Use Docker volume

Docker volumes allow you to specify persistent local folders for logs and archives in your `docker-compose.yaml` file. Here’s how it works:

– Define volume mappings in your `docker-compose.yaml`

– Docker will check if the local folders you specify exist when you run the file

– If the folders don’t exist, Docker will create them for you

– This allows you to create uniquely named local log and archive folders for each container

> Note: If necessary, you can manually create and name the local folders you wish to use for persistent log storage.

3. Configure your docker-compose.yaml

Here’s an example of how to set up your `docker-compose.yaml`:

```yaml
app:
  image: app
  stdin_open: true
  tty: true
  links:
     - mysql
  ports:
     - "8089:8088"
     - "8080:8080"
     - "8182:8888"
  volumes:
     - "./logs:/opt/fusionreactor/instance/app/log"
     - "./archive:/opt/fusionreactor/instance/app/archive"

In this example, local `logs` and `archive` folders have been specified under `volumes`. These folders will be created in the same directory as the `docker-compose.yaml` file.

4. Launch your Container

With your `docker-compose.yaml` file configured, bring up your container:

```bash
docker-compose up -d
```

FusionReactor will now write logs and archives to the persistent local folders outside the container.

Important considerations

> Warning: You must ensure that each instance is written to unique log and archive folders. If multiple instances are pointing to the same log and archive folders, this can cause logging issues.

– This method works for both logs and archived logs

– The specified local folders will be created if they don’t already exist

– You can create uniquely named folders for each container to avoid conflicts

Conclusion – how to store FusionReactor logs

By following these steps, you’ll have a robust logging setup for your FusionReactor instances running in Docker, ensuring no valuable data is lost when containers are cycled.

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Exciting new features coming to FusionReactor

Candice Chisset — Mon, 15 Jul 2024 09:36:21 +0000

Custom Detectors, FusionReactor 12.1.0, the Slack OpsPilot integration, and much more!

We’re thrilled to announce a series of groundbreaking enhancements to FusionReactor, designed to revolutionize how you monitor and optimize your applications. Let’s dive into these exciting new features:

Custom Detectors: Advanced anomaly detection

FusionReactor now offers Custom Detectors, a powerful tool for advanced anomaly detection. While setting up Custom Detectors is a manual process, it provides unparalleled flexibility in monitoring your application’s performance.

Key features:

Define specific conditions or thresholds tailored to your unique needs
Monitor CPU, memory, and other critical resources
Three default detectors are included:
- Online instance count: Tracks the number of active server instances, ensuring proper load distribution and system availability.
- Heap memory usage: Monitors the application’s memory consumption, helping prevent memory leaks and optimize resource allocation.
- CPU utilization: Measures processor usage, identifying performance bottlenecks and capacity issues.

Benefits:

Enhanced flexibility: Customize monitoring to your application’s specific requirements
Deeper insights: Gain more detailed and relevant performance information
Precise problem identification: Accurately pinpoint bottlenecks and potential issues

Exciting new features that are coming soon:

On-Premise UI tunnel

Get ready for the On-Premise UI tunnel, a game-changing feature that bridges FusionReactor Cloud and On-Premises Agents. This integration embeds the on-prem agent’s user interface directly within the FusionReactor Cloud platform. Users must upgrade to version 12.1 in the next major release to take advantage of this exciting new feature. A dark theme option is also being developed for this feature, offering enhanced visual comfort and aesthetics.

Key benefits:

Unified interface: Access all on-premises agent features within the Cloud UI
Familiar transition: Ease the move to cloud-based monitoring with a familiar environment
Full feature set: Utilize the complete suite of on-premises capabilities in the cloud, including the Debugger and Settings Management

The On-Premise UI tunnel combines the robust features of FusionReactor On-Premises with the convenience of the Cloud platform, enhancing your ColdFusion application monitoring and optimization capabilities.

Slack OpsPilot Integration: Bringing monitoring to your workspace

We’re also introducing Slack OpsPilot integration, directly bringing FusionReactor’s powerful monitoring and troubleshooting capabilities into your team’s Slack workspace. Similar capabilities are also coming soon for MS Teams.

Key benefits:

Seamless access to data: Retrieve Machine Learning Telemetry (MLT) data and critical system information without leaving Slack
Enhanced collaboration: Utilize a multi-user chat workflow for real-time discussion of OpsPilot insights
Efficiency in troubleshooting: Significantly reduce time to access vital data, especially crucial during intense troubleshooting sessions

FusionReactor 12.1.0

FusionReactor 12.1.0 is on the horizon, bringing exciting new features and improvements. This upcoming release introduces the On-Premise UI tunnel, a groundbreaking integration that embeds the on-premises agent’s user interface directly within the FusionReactor Cloud platform. FR 12.1.0 also expands its language support, offering compatibility with Java 21 and introducing support for Box Lang. Importantly, this update includes crucial security enhancements and bug fixes, addressing potential vulnerabilities and improving overall stability. These enhancements further solidify FusionReactor’s position as a versatile, secure, and powerful application performance monitoring solution.

In development

OpsPilot Knowledge

OpsPilot aims to seamlessly integrate with customer teams, gaining a deep understanding of their products and services. This insight allows OpsPilot to offer tailored assistance and advice specific to each customer’s unique situation. The new OpsPilot Knowledge feature enables customers to onboard OpsPilot as a virtual team member by providing crucial information that it retains indefinitely. When customers share knowledge sources with OpsPilot, it leverages this information to answer questions more accurately and provide the context for effective problem-solving and investigation.

Exciting new features in FusionReactor Cloud

These upcoming features represent our commitment to continually improving FusionReactor, providing you with the most advanced application monitoring and optimization tools. Stay tuned for more updates as we roll out these exciting enhancements!

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Enhanced anomaly detection with FusionReactor’s new Custom Detectors

Candice Chisset — Mon, 08 Jul 2024 12:48:37 +0000

Enhanced anomaly detection

FusionReactor Cloud has been upgraded with new Custom Detectors, enhancing its anomaly detection capabilities. This feature allows for more precise monitoring and diagnostics of your application’s performance. Setting up Custom Detectors requires manual input and some knowledge of PromQL, but they offer exceptional customization, letting you set specific conditions or thresholds that match your application’s particular requirements.

Benefits of using Custom Detectors

Enhanced customization: Custom Detectors allow you to set specific conditions or thresholds that match your application’s requirements. This level of customization enables more precise monitoring tailored to your unique environment.

Flexibility across technologies: While there are pre-configured detectors for Java and ColdFusion, you can create custom detectors for any technology stack. This allows you to monitor CPU, memory, or any other crucial resources relevant to your application, regardless of the underlying technology.

Precise anomaly detection: The ability to define exact metrics and conditions relevant to your application allows for more accurate identification of performance bottlenecks and potential issue

Pre-configured detectors for Java and ColdFusion

FusionReactor Cloud offers three pre-configured detectors specifically designed for Java and ColdFusion environments to jumpstart your custom anomaly detection. While these may not apply directly to other technology stacks, they are excellent templates for creating custom detectors.

Monitor what matters

With FusionReactor, you can create custom detectors for monitoring CPU, memory, or any other crucial resources relevant to your application. This flexibility accurately identifies performance bottlenecks and potential issues across various technologies, helping maintain your application’s efficiency and reliability.

Steps to add a Custom Detector

Navigate to FR > Alerting > Anomaly Detection (Beta) > Custom Detector
Select the ADD DETECTOR button and configure the Custom Detector settings as follows:
1. Enter a unique query label.
2. Set the aggregate.
3. Enter a PromQL expression.
4. Adjust the anomaly probability threshold according to your needs.
5. Select the Time Range and Pending For intervals.
6. Select from predefined subscriptions to determine where alerts for anomalies should be sent.
Click the APPLY CHANGES button.
Toggle the Anomaly Detection check to enable the Custom Detector.

How are Custom Detectors different from the RED method of anomaly detection?

Custom Detectors and Service Detectors (RED) serve different purposes in anomaly detection, each suited for specific monitoring needs. Custom detectors offer high flexibility, allowing you to create tailored conditions or thresholds for your application’s unique requirements. These can incorporate complex logic to target particular anomalies of concern. In contrast, Service Detectors focus on preconfigured anomaly detection for a given service’s Rate, Errors, and Duration metrics.

While custom detectors excel at identifying specific, non-standard issues, Service Detectors provide efficient, out-of-the-box monitoring for these key service health indicators. Custom detectors offer precision and customization, whereas Service Detectors provide simplicity and quick setup.

It’s important to note that Custom Detectors and Service Detectors typically require some level of user adjustment or tuning to achieve optimal results. Users should expect to refine detector configurations based on their application behavior and performance patterns. This fine-tuning process helps ensure the detectors accurately identify relevant anomalies while minimizing false positives.

Both approaches have their merits, depending on the monitoring scenario and specific needs of your application. The choice between them often involves balancing the desire for customization with the need for quick implementation and ease of use.

Enhanced anomaly detection with Custom Detectors

FusionReactor’s new Custom Detectors feature significantly advances application performance monitoring. By offering granular control over anomaly detection, teams can create highly targeted monitoring solutions to identify issues before they impact users.

Whether you’re running Java, ColdFusion, or any other technology stack, Custom Detectors provide the flexibility to monitor CPU, memory, or any other crucial resources relevant to your specific application. This level of customization ensures that you can maintain your application’s efficiency and reliability with unprecedented precision.

As you implement Custom Detectors, remember that the key to success lies in understanding your application’s normal behavior and fine-tuning your detectors accordingly. You can create a robust anomaly detection system with practice and iteration that keeps your application running at peak performance.

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How to copy FusionReactor configuration between instances

Candice Chisset — Wed, 03 Jul 2024 09:43:09 +0000

How to copy FusionReactor configuration between instances

Configuring FusionReactor for multiple ColdFusion instances on your server can be time-consuming if done individually. As a CFML developer, you’ll be pleased to know that FusionReactor simplifies this process by storing its configuration in a single file. This allows you to easily replicate settings across your CF instances, saving valuable development time.

All FusionReactor configuration settings are stored in a file named reactor.conf. It’s completely safe to copy this file between your ColdFusion instances, enabling you to configure one instance exactly how you need it for your CFML applications and then quickly apply those same optimized settings to other instances of FusionReactor. This streamlined approach ensures consistent monitoring and performance across all your CF environments.

You can find this file at: {FusionReactor Directory}/instance/{Instance Name}/conf/reactor.conf

This guide will walk you through copying the FusionReactor configuration between instances to streamline your setup.

Why would a user want to copy FusionReactor configuration between instances?

Copying FusionReactor configuration between instances offers several benefits that can significantly streamline the management and deployment of FusionReactor across multiple servers or environments. Here are the primary reasons a user might want to do this:

Consistency in configuration: Ensuring all instances have the same uniform management and monitoring settings.

Avoiding manual configuration: Saves time and reduces the risk of human error by not having to configure each instance manually.

Steps to copy configuration

Important note: Before making any changes to the configuration, it is crucial to stop the application server. This prevents the old configuration from overwriting any changes you make.

Step 1: Navigate to the config directory of the preconfigured instance:

Go to {FusionReactor Directory}/instance/{Instance Name}/conf.

Step 2: Copy the reactor.conf file:

Take a copy of the reactor.conf file from the preconfigured instance.

Step 3: Navigate to the config directory of the target instance:

Go to {FusionReactor Directory}/instance/{Instance Name}/conf for the instance you want to configure.

Step 4: Stop the application server:

Stop the application server on the target server to prevent any configuration conflicts.

Step 4: Backup the current configuration:

Make a backup copy of the current reactor.conf file in the target instance’s config directory.

Step 5: Place the preconfigured reactor.conf file:

Replace the existing reactor.conf file with the copied file from the preconfigured instance.

Step 6: Start the application server:

When the application server and FusionReactor start, the configuration should reflect the settings of the preconfigured instance.

Warning: Ensure that the read/write permissions of the reactor.conf file is correctly set. Incorrect permissions may prevent the application server user from reading or writing to the configuration file on startup. Proper permissions are crucial for the application server’s and FusionReactor’s smooth functioning.

Following these straightforward steps, you can efficiently replicate FusionReactor configurations across multiple ColdFusion instances, significantly reducing setup time and ensuring consistency in your CFML application monitoring. This approach streamlines your workflow and guarantees all your CF environments benefit from the same optimized FusionReactor settings.

For CFML developers and administrators, you can fine-tune FusionReactor once and easily apply those configurations across your entire CF infrastructure. Whether you’re managing development, staging, or production environments, you’ll have confidence that your FusionReactor monitoring is uniform and optimized for your specific CFML needs.

This consistency is crucial for accurate performance comparisons between environments and maintaining a reliable monitoring strategy across your ColdFusion ecosystem. Ultimately, this efficient configuration process allows you to focus more on developing and improving your CFML applications rather than spending excessive time setting up your monitoring.

Learn more

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How to view historical data in FusionReactor Cloud

Candice Chisset — Tue, 02 Jul 2024 10:45:45 +0000

How to view historical data in FusionReactor Cloud

FusionReactor Cloud provides powerful tools to view and analyze historical data, allowing you to maintain visibility over your application’s performance and troubleshoot issues effectively. Up to 13 months of metric data and up to 30 days of trace and log data are stored in the Cloud. Here’s a step-by-step guide on how to access and use historical data.

Servers view

The Servers view overviews the servers running a FusionReactor agent within your infrastructure. You can see a broad overview of all servers or more detailed information about a specific subset. This view is unique as it supports both live and historical data modes.

Live mode, immediate, and historical data

Live mode:

The Servers screen operates in Live mode by default, indicated by an orange clock icon.

In Live mode, data is streamed in real-time from your instances, which is helpful for monitoring and resolving immediate issues.

Switching to historical data:

To view historical data, toggle the Live mode clock. Use the time picker to select the desired timeframe. You can filter historical data by default times (e.g., the last hour) or customize the time frame to meet your needs.

Applications view

The Applications list view provides an overview of all your applications running on servers with FusionReactor. Selecting an application will take you to the application details screen.

Application details

The Transaction List list displays all transactions associated with the selected application for the set timeframe.

Transactions can be filtered by:

Time Taken: This shows the percentage of time the application spends running each transaction.

Average Time: Displays the average time for each transaction, with a line indicating the average across all transactions.

Slowest: Lists the longest response time for each transaction.

Throughput: Indicates the number of requests for each transaction.

Errors: Shows the number of errors for each transaction that encountered issues.

Explore view

The Explore feature allows you to query and analyze all metrics, logs, and traces ingested into your cloud account. This feature is ideal for creating new data views and filtering data as needed.

Datasource selection

In the Explore view, you can select from three datasource options using the dropdown menu on the left of the screen:

Metrics: All metrics are sent from a FusionReactor agent, and additional metrics are created within the ingest engine.

Traces: Includes any slow or error transactions sent from a FusionReactor agent. Note that requests viewed in the recent or running tabs of the server view do not appear here as they are not ingested.

Logs: Contains logs sent to FusionReactor, either from a FusionReactor agent or log shipper.

Why analyze historical data?

Analyzing historical data in FusionReactor Cloud offers significant benefits for maintaining and optimizing application performance.

Key advantages include:

Visualizing data: Enables diagnosis of live and historical issues, providing insights into application performance over time.

Anomaly detection: Examining historical data facilitates setting optimal anomaly detection thresholds, helping distinguish true anomalies from normal fluctuations.

Viewing historical data: FusiopnReactor offers up to 13 months of metric data and 30 days of trace and log data storage, allowing for a review of past performance to diagnose issues that are not immediately apparent.

Live mode vs. historical data: This option allows users to toggle between live and historical data views for comprehensive analysis, facilitating both immediate issue resolution and long-term trend analysis.

Why use FusionReactor Cloud to view historical data

FusionReactor Cloud offers robust capabilities for viewing historical data, enabling you to monitor, analyze, and troubleshoot your applications effectively. By leveraging these features, you can ensure your applications’ smooth and efficient operation and quickly identify and resolve issues as they arise. Whether using the Servers, Applications, or Explore views, you can access detailed historical metrics, traces, and logs, providing comprehensive insights into your application’s performance over time.

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Introducing DEEP: Transforming real-time application monitoring

Candice Chisset — Thu, 16 May 2024 09:58:56 +0000

FusionReactor introduces DEEP, a dynamic monitoring tool

In today’s agile technological environment, ensuring the seamless performance and reliability of applications is paramount. To address this need, FusionReactor introduces DEEP, a dynamic monitoring tool designed to enhance existing monitoring capabilities in real-time. DEEP empowers users with application data collection precisely when it’s needed, facilitating proactive decision-making, effective debugging, and heightened system visibility.

Enhancing monitoring capabilities with DEEP

DEEP shakes up monitoring with its user-friendly interface that dynamically instruments applications, ensuring seamless runtime application observability. At the core of DEEP lies its powerful query language, DeepQL, which draws inspiration from PromQL. This monitoring-centric language streamlines the real-time addition and manipulation of logs, metrics, traces, and live data snapshots, empowering users with more effective debugging and improved performance monitoring capabilities.

Benefits of DEEP

Dynamic monitoring: DEEP enables dynamic monitoring of applications, allowing users to gather data precisely when it’s needed, facilitating proactive decision-making and issue resolution.

Enhanced observability: DEEP introduces DeepQL, a powerful query language that simplifies the real-time manipulation of logs, metrics, traces, and live data snapshots, facilitating comprehensive observability.

Effective debugging: By facilitating the real-time addition of data, DEEP streamlines the debugging process, allowing users to identify and address issues promptly, reducing downtime and improving system reliability.

DEEP features

Search

Users can search for snapshots triggered by entering a query in the Deep datasource and further filter results by service name or tags.

Create Tracepoint

DEEP allows the creation of tracepoints, defining actions to be taken at specific code locations. Users can configure different types of tracepoints, including Line Tracepoints and Dynamic Monitoring options such as Logging, Spans, and Metrics.

Accessing DEEP in FusionReactor Cloud

To use DEEP in FusionReactor Cloud, navigate to the Explore page and select the Deep datasource. For more information be sure to check out our Dynamic Monitoring (DEEP) documentation.

Why DEEP revolutionizes application monitoring

DEEP revolutionizes application monitoring by providing users with dynamic monitoring capabilities, enhanced observability, and effective debugging tools. With its powerful features and seamless integration with FusionReactor, DEEP empowers organizations to optimize their application performance and deliver exceptional user experiences in today’s competitive digital landscape.

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Optimizing monitoring and management: OpsPilot 1.2.0 unveils enhanced features for FusionReactor Cloud users

Candice Chisset — Mon, 15 Apr 2024 10:33:00 +0000

OpsPilot 1.2.0 released

OpsPilot 1.2.0 brings several new features and improvements designed to optimize user experience, streamline workflows, and further empower users in their monitoring and management endeavors within FusionReactor Cloud.

New features:

OpsPilot Vision

OpsPilot introduces OpsPilot Vision, a groundbreaking feature that enriches its capabilities by allowing users to upload images to add context to their inquiries. With OpsPilot Vision, users can now provide supplementary visual information alongside their questions, enabling OpsPilot to deliver more comprehensive and tailored responses. This integration enhances the overall user experience, fostering greater clarity and effectiveness in communication.

Improved FR knowledge base

OpsPilot has significantly upgraded its FusionReactor knowledge base, enhancing proficiency in understanding and addressing issues. With this improvement, OpsPilot can now provide more informed and effective responses when dealing with FusionReactor-related tasks. This advancement promises smoother operations and quicker resolutions, optimizing system performance and minimizing downtime.

Continue on error

In the event that OpsPilot encounters an issue while responding, users now have the option to choose between retrying the operation or continuing the conversation seamlessly without interruption.

Improvements

Alarm refinement

In response to user feedback, OpsPilot has optimized its alarm notification system. Specifically, OpsPilot will no longer repeat identical alarm notifications during its task list processing. This refinement eliminates redundancy and ensures a more focused and efficient user experience. Users can now rely on clear, actionable notifications without encountering unnecessary repetition.

Time ranges

OpsPilot now features automatic time range determination, simplifying data querying within FusionReactor Cloud. OpsPilot automatically determines the most relevant time range to address user queries by intelligently analyzing data. This enhancement saves users time, eliminates guesswork, and ensures they receive actionable insights based on the most pertinent data.

Graph resolutions

OpsPilot has enhanced the resolution of graphs displayed within the platform, ensuring clearer and more detailed visual representations of data.

OpsPilot 1.2.0 marks a significant advancement for FusionReactor Cloud users

OpsPilot 1.2.0 delivers a suite of enhancements that not only improve the functionality and efficiency of the platform but also enrich the user experience with innovative features like OpsPilot Vision and the enhanced FusionReactor knowledge base. These improvements are tailored to meet the evolving needs of users, ensuring that the monitoring and management of applications are more effective and intuitive. With these developments, OpsPilot continues solidifying its role as an essential tool for IT professionals, providing powerful, precise, and user-friendly solutions that drive better decision-making and operational efficiency. As OpsPilot evolves, it remains committed to delivering excellence and innovation in the dynamic field of IT management, proving once again that it is at the forefront of technological advancement and customer satisfaction.

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