Elastic APM: A Key to Unlocking Business Growth and Customer Loyalty
Introduction
In today’s competitive market, delivering a seamless and responsive user experience is crucial for retaining customers and driving business growth. Elastic APM (Application Performance Monitoring) has emerged as a vital tool for achieving these objectives. By providing deep insights into application performance and pinpointing bottlenecks, Elastic APM empowers businesses to optimize their applications, enhance user satisfaction, and build long-term customer loyalty. In this blog, we will explore how Elastic APM can be a game-changer for your business, helping you unlock new levels of efficiency and customer engagement. We will also guide you through the setup process of Elasticsearch APM and highlight its key features.
Apm
What is Elasticsearch APM?
Elasticsearch APM is a part of the Elastic Stack, designed to collect in-depth performance metrics and error logs from your applications. It helps you identify bottlenecks, monitor response times, and detect issues in real time, enabling you to maintain a high-performance application environment.
Why Use Elasticsearch APM?
- Real-time Monitoring: Get instant insights into your application’s performance, allowing you to respond quickly to issues.
- Detailed Metrics: Collect and analyze detailed metrics about your application’s behavior, such as response times, throughput, and error rates.
- Error Tracking: Automatically capture and analyze errors and exceptions, providing stack traces and contextual information to help diagnose problems.
- Distributed Tracing: Track requests across distributed systems to pinpoint performance issues, visualize request flows, and identify slow components.
- User Experience Insights: Understand how your end-users experience your application by tracking key performance indicators (KPIs) such as page load times and transaction durations.
Problem Statement
Our team encountered several critical challenges in maintaining the performance and reliability of our applications:
- Identifying Bottlenecks: With increasing user traffic and complex application workflows, pinpointing performance bottlenecks became difficult. Traditional tools lacked the granularity needed to isolate and diagnose slow components effectively.
- Error Tracking and Resolution: Capturing and analyzing errors and exceptions in real-time was challenging. We needed a solution that could provide detailed stack traces and contextual information to speed up the debugging process.
- Resource Utilization: Our applications were experiencing suboptimal resource utilization, leading to increased operational costs and potential scalability issues. Monitoring tools at our disposal did not offer insights into CPU, memory usage, and garbage collection metrics required for optimizing resource usage.
- Distributed System Monitoring: As our system architecture evolved into a microservices-based approach, tracking requests across distributed components became increasingly complex. We needed a way to visualize request flows and identify slow components within the distributed system.
Traditional monitoring tools were insufficient to meet these needs due to their limited scope, lack of real-time insights, and inability to handle the complexity of modern distributed systems. To overcome these challenges, we implemented Elastic APM, which provided a comprehensive and effective solution for monitoring and improving our application’s performance.
How Elastic APM helped us?
Our application was experiencing slow response times during peak traffic hours. By leveraging Elastic APM’s distributed tracing capabilities, we traced the slow requests across multiple microservices as shown below image:
API Tracing to Check Latency
We could trace the slow requests across multiple microservices and identify a specific database query causing the bottleneck. The APM’s detailed metrics and span breakdowns showed that this query was taking significantly longer than expected due to inefficient indexing as shown below image. With this insight, we optimized the database indexing strategy, which resulted in a substantial improvement in response times.
Database Query Latency
Additionally, Elastic APM’s real-time monitoring allowed us to verify the effectiveness of the changes immediately, ensuring that the issue was resolved and the application’s performance was restored.
Application’s Performance
Key Concepts of Elasticsearch APM
- Transactions: Represent the work done by your application in response to a request. For example, an HTTP request to a web server or a job run by a background worker.
- Spans: Represent a unit of work within a transaction, such as a database query or an external HTTP request. Spans help break down the transaction into smaller components.
- Errors: Capture exceptions and other errors that occur within your application. Errors provide stack traces and other contextual information to aid in debugging.
- Metrics: Gather performance metrics such as CPU usage, memory usage, and garbage collection statistics from your application.
Prerequisites
Before we dive into the setup, make sure you have the following:
- An Elasticsearch.
- Kibana instance for visualization.
- An application to monitor (e.g., Node.js, Java, Python, etc.).
Step-by-Step Setup Guide
1. Set Up Elasticsearch and Kibana
Ensure your Elasticsearch and Kibana instances are up and running. If not, follow the official Elasticsearch Installation Guide and Kibana Installation Guide.
2. Install the APM Server
The APM Server receives data from your application and sends it to Elasticsearch. Install the APM Server using the following commands:
On Linux:
$ wget https://artifacts.elastic.co/downloads/apm-server/apm-server-8.4.2-amd64.deb $ sudo dpkg -i apm-server-8.4.2-amd64.deb
On Windows and other platform:
Download and install the APM Server from Elastic’s Download Page.
3. Configure the APM Server
- Start the APM Server:
$ sudo service apm-server start
- Use below curl command for check APM is in running state
$ curl -XGET http://localhost:8200
- Download APM Agent jar file for Java
$ curl -o ‘elastic-apm-agent.jar’ -L ‘https://oss.sonatype.org/service/local/artifact/maven/redirect?r=releases&g=co.elastic.apm&a=elastic-apm-agent&v=LATEST‘
- Copy the APM jar file into your application S3 bucket
- Update Java init.sh file.
$AWS s3 cp s3://"apm-jar-bucket"/apm/apm.jar /apm/apm.jar $JAVA \ -javaagent:/apm/apm.jar \ -Delastic.apm.server_urls=http://10.239.167.0:8200 \ -Delastic.apm.service_name=qa-java-app
4. Open APM in Kibana
Once your APM Server is set up and receiving data, navigate to the APM section in Kibana to start monitoring your application.
apm integration
App Service
Below is an example of dependencies monitored by Elasticsearch APM.
Monitoring App Dependencies
Conclusion
Elasticsearch APM is a powerful tool for monitoring and optimizing application performance. By following this guide, you can set up APM, instrument your application, and start gaining valuable insights into its performance. Whether you’re troubleshooting issues or optimizing for a better user experience, Elasticsearch APM provides the data and tools you need to maintain high-performing applications.
