Louis-Victor Jadavji (or "LV") is a recognized leader in the cloud services industry. He's helped 50+ digital native companies like ModusBox, Later, and NS1 choose the right cloud stack for their applications. His work has been featured in Forbes (30 Under 30 All-Star), HuffPost, The New York Times, The Globe and Mail, and Inc. Magazine.
Let’s say you have a SaaS web application. You may want to instrument your code to get metrics that help you understand backend application performance. As the technical stack grows, this can become more complex to manage, and when outages occur in production, you’re likely panicking to identify bottlenecks and root issues. This process is made more laborious due to poor visibility of the entire application and prolongs disruptions that impact end-users.
With APM tools, you can hook a plugin into your application and expose useful metrics with minimal modifications to the application, like:
It’s easy to collect application performance metrics and get visualizations in a rich APM UI so that developers and DevOps engineers understand performance metrics better.
APM products have evolved beyond application performance monitoring and added infrastructure monitoring, containers/Kubernetes monitoring, alerting, etc. to a “single pane of glass.” Ultimately, this speeds up correlating application data and infrastructure bottlenecks.
We cover the following topics in this post:
APM products come in two flavors: SaaS and on-premises. Each has an agent and agentless version as well.
In the SaaS model, an APM vendor provides the managed service, runs the infrastructure to cater to customer needs, and exposes the service through APIs (typically REST or GraphQL). The vendor is responsible for the platform's security, performance, scaling, high availability, and reliability.
In the agent-based version, the customers need to install and run an APM agent provided by the vendor on the supported OS (e.g., Linux, Windows). The application injects metrics into the agent, which relays them to the SaaS endpoint.
Most vendors support programming languages such as Python, Java, .NET, Node.JS, C/C++, PHP, etc. Depending on the application code, programming language plugins can be integrated into the application for analysis.
In the agentless version, there are no agents to install. You just take the SaaS endpoint and inject the metrics directly. If there are connectivity issues to the SaaS endpoint, there is no way to buffer the lost metrics, so it’s sometimes a good practice to use an agent-based version.
In the on-premises model, customers install the APM product on-premise and are self-charged with maintaining the service typically due to security and compliance concerns. In this model, there can also be an agent-based or agentless installation that the clients use to inject metrics for the endpoint.
Pricing models vary but are usually somewhat related to the number of CPUs/cores, hosts, and requests.
APM products help developers identify issues in code and provide remedies, thereby reducing the time to test and deploy efficient code.
As APM tools reduce the time taken for development and testing, developers can focus on innovative features and can deploy them at a faster pace.
With the help of APM tools, developers can optimize their code thanks to AI-powered algorithms that follow best practices and provide suggestions to developers and automatic code reviews.
With APM recommendations for improving application performance, users experience faster application response times.
Instead of being reactive when issues happen, Ops teams can proactively monitor the visualizations/reports that APM tools provide and prevent application issues or incidents.
Traditionally, the Ops team spends a lot of time trying to find out why issues occur by going through logs manually, which prolongs outages. With APM tools, they can find where the issues are quickly and resolve them faster.
APM tools also help different teams, such as operations teams, infrastructure teams, database teams, etc., quickly run root cause analyses when incidents occur.
Responsive applications are crucial for a good user experience, and APM tools help with this.
APM tools provide better alerts and notifications that can be sent to different devices so that the Ops team can act on issues quickly.
APM tools provide better reports and visualizations for monitoring which helps different teams drill down into issues faster.
Because APM tools provide good reporting for Ops teams to identify issues faster, find root causes, get instant alerts/notifications, and take actions to cut downtime or prevent issues from happening again, they’re likely to enjoy their job just a bit more :)
APM tools can provide alerts and notifications that help Ops teams take a proactive approach to preventing issues. Developers can also proactively deploy optimized code rather than wait for users to complain about slow application response times.
APM tools provide real-time reports and dashboards that can help teams monitor applications in real time.
AppDyamics can monitor cloud-native technologies and traditional infrastructure and understand what drives user experience and business results.
Dynatrace provides infrastructure, application, microservice monitoring, security, digital experience, business analytics, and cloud automation.
New Relic can monitor web and mobile applications in real time.
Datadog is a SaaS-based data analytics platform that can be used for cloud-scale applications. It can monitor servers, databases, tools, and services.
Splunk provides full trace analysis of your production environment, ensures you don't miss an anomaly, helps troubleshoot through AI-powered analytics, and provides code profiling.
SolarWinds can monitor your hybrid applications and bring visibility into your logs, metrics, tracing hosts, and the overall digital experience.
APM helps different teams:
Because organizations are building on more complex architectures and adopting new technologies, Application Performance Monitoring has had to evolve drastically over the last few years.
In the past, when organizations followed a waterfall model for development, implemented monolith architectures, and had only on-prem data centers, different monitoring tools like database monitoring, infrastructure monitoring, etc. were enough to get the job done.
With modern-day architectures comprised of multiple vendors, tools, distributed systems, cloud/SaaS offerings, agile development, and deployments, it’s almost necessary to have Application Performance Monitoring tools in place to debug issues and detect problems quickly.
In complex architectures, it’s important to drill down into the details quickly and find out the root cause of issues, how to fix them, and how to prevent further issues. Speedy work is key in these situations, and sometimes APM tools are insufficient.
APM tools provide visibility only to the “known knowns.” But for organizations that strive for reliable systems, it’s important to have visibility into the “unknown unknowns”. APM tools help in collecting telemetry data from different systems. Still, Ops teams should be able to correlate all data from different services or systems to get the full picture. This is where Observability comes in.
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