Using Generative AI with Elastic AI Assistant for Observability
Science & Technology
Introduction
In the rapidly evolving landscape of technology, maintaining optimal application performance and reliability has never been more important. The Elastic AI Assistant, an integration with OpenAI and Azure OpenAI, serves as a powerful tool to enhance observability in application performance management (APM) and help troubleshoot various issues. This article is intended to outline several use cases of the Elastic AI Assistant and demonstrate how it assists in understanding application behaviors, troubleshooting errors, and optimizing performance.
Real-World Observability Use Cases
Troubleshooting Errors in APM
Imagine managing an application monitored by Elastic APM. Within the service map, you might notice that multiple services, particularly the product catalog service, are facing issues. Anomaly detection powered by machine learning alerts you to these issues. Detailed insights reveal that latencies have spiked, and a set of errors has been flagged against the product catalog service—a service dependent on PostgreSQL.
Upon investigating the error details, you may come across exception messages with occurrences, but the technical jargon can be confusing. This is where the Elastic AI Assistant comes into play. By inquiring about the error, you learn that it results from a missing database table, indicating possible issues related either to data loading or to the database itself.
Analyzing Alerts
Alerts are a standard part of any operational framework. When an alert arises regarding a production application indicating a threshold has been exceeded, the Elastic AI Assistant aids your analysis. The details not only highlight the threshold breach but also identify a log spike corresponding to the alert.
The AI Assistant leverages machine learning to analyze log data and determine potential causes for the spike. In this instance, it identifies a connection to PG Bench, a benchmarking tool typically utilized in development environments, which raises suspicions about whether it was inadvertently left running in production. The AI Assistant offers remediation suggestions such as optimizing database queries or considering horizontal scaling of the cluster.
Log Investigations
Logs remain a fundamental signal in diagnosing technical issues. If you decide to delve into PostgreSQL logs after discovering a log regarding disconnections, the Elastic AI Assistant can clarify the cause of the log message and its relevance. You can also request a specific query to extract additional log information using the Elasticsearch querying capabilities, resulting in better insights.
Understanding Compute Hosts
When applications run on cloud providers like AWS, GCP, or Azure, it’s crucial to monitor the underlying compute nodes. In a scenario where you identify a high CPU usage process on a Google Kubernetes Engine (GKE) host, you may be uncertain about the process's nature. By engaging the Elastic AI Assistant, you might discover this process is linked to running a headless Chromium browser triggered by Playwright, an open-source test automation tool. This knowledge aids in addressing potential resource issues linked to the functioning of your application.
Profiling Function Performance
Finally, the Elastic AI Assistant adds value to performance profiling by providing contextual information for specific functions within an application. For instance, if you're profiling a function that allocates memory buffers, you can seek insights on its optimization. The AI Assistant suggests reducing the number of calls made to the function, considering an alternative allocator, or exploring optimized memory usage through different data structures.
Conclusion
With its ability to provide contextual insights, remediation suggestions, and operational clarity, the Elastic AI Assistant significantly enhances your observability toolset. Whether dealing with APM errors, log messages, alerts, host processes, or function profiling, this integration proves to be invaluable for Site Reliability Engineers (SREs) and DevOps professionals alike.
For more information on how to utilize the Elastic AI Assistant, check out the documentation through the link provided below.
Keywords
Elastic AI Assistant, observability, application performance management, troubleshooting errors, anomaly detection, log analysis, PostgreSQL, alerts, remediation, function profiling.
FAQ
What is the Elastic AI Assistant? The Elastic AI Assistant is an integration with OpenAI and Azure OpenAI designed to enhance observability and provide context for various use cases in application performance management (APM).
How does the Elastic AI Assistant help with troubleshooting errors? It provides contextual information about application errors, helping users understand the root cause, such as missing database tables or service dependencies.
What can the Elastic AI Assistant do for log analysis? The Assistant can explain log messages and suggest queries to help users find additional relevant log information for further investigation.
Can the Elastic AI Assistant assist with alerts? Yes, it analyzes alert data to identify potential causes and provides remediation suggestions to address issues.
How does the Assistant enhance function profiling? It offers recommendations for optimizing specific functions within an application, including reducing function calls or adjusting memory usage strategies.
