Application Observability – What It Is For and How You Can Handle It with AWS
Suppose you have a public-facing web application. Suppose the app users report complaints about the sluggish performance of your application. Both your front-end and your back-end teams dive into their dashboards and metrics, but neither can pinpoint the root of the problem. What we as consulting partners typically discover upon analyzing a case is that businesses have plenty of technical metrics and only a scarce, if not none, representation of user experience metrics for an app. That is exactly the issue with observability that generates certain blindness to the origins of user dissatisfaction.
In this article, we delve into the essence of application observability, uncovering its pivotal role in ensuring robust functionality. Join us to find out:
- what application observability stands for
- why application observability matters for performance
- what the three pillars of app observability are
- what application observability tools and services AWS has to offer.
Table of Contents
What is application observability?
Beyond being merely a trendy term, the concept of “observability” has its roots in control theory, a field focused on comprehending self-regulating systems. Up to the present day, observability has evolved to help enhance the operational efficiency of distributed IT systems. Businesses lean on application observability to ensure the continuous functionality of their environments, with a substantial 87% of organizations currently maintaining dedicated specialists for this purpose exclusively.
AWS’s definition of observability
According to Amazon Web Services (AWS), application observability encompasses the extent to which you can comprehend the workings of a system, often achieved through the collection of metrics, logs, or traces. Putting operational excellence and business goals first necessitates a clear understanding of your system’s current performance.
In straightforward terms, observability refers to the ability to watch and understand the interaction between a business and its customer comprehensively, including all its components. This feature enables you to timely address, or even prevent the occurrence of queries related to what matters to your customers or stakeholders.
Application observability is built upon three categories of telemetry information. Metrics, logs, and traces, if tracked and analyzed properly, can provide comprehensive insights into distributed systems, which helps pinpoint the underlying reasons behind various problems and, as a result, enhance performance. In direct connection with this, observability resides on three pillars: monitoring, tracing, and logging, which will be discussed in detail further in this article. Apart from the obvious immediate observability instruments, additional tools, such as code profilers and artificial intelligence for IT operations (AIOps), contribute to establishing app visibility as well.
Why does application observability matter?
With continuous development and improvement in mind, Amazon prioritizes observability as the key thing allowing for informed decision-making when it comes to ensuring smooth performance and user experience that caters to their needs.
Along with that, visibility into the processes within the technical side of the service provider and the user experience side of the service recipient is what allows and fosters innovation. More powerful and sophisticated technology arises to empower organizations to deliver value to their customers.
AWS observability options
Addressing the questions vital to the business requires robust observability. In this regard, AWS offers several observability tools, services and resources that could be of help, including both native app performance monitoring and managed open-source services.
On the native aspect, among the whole array of possibilities for various metrics, logs and traces, we would like to single out real user monitoring for your front-end user experiences and synthetic canaries. On the other hand, the open-source front offers Grafana, Open Search and Prometheus, – to name just a few managed services.
While the CPU, memory and disk metrics are core measurements of any system, they are not necessarily representative of the customer experience of an app’s performance which some organizations take as the ultimate indicator of effectiveness.
Amazon’s customer requirements
This section will discuss Amazon’s perspective on observability, its applications across various areas, and the strategies provided by AWS to improve customer experience and operational efficiency.
As is already evident, AWS focuses on customers, their experience, trust and loyalty. For this reason, Amazon endows observability with the function of conducting indirect but transparent communication between a business and its services’ end consumer.
Such customer-business interaction reveals that within the AWS e-commerce platform, users make prompt delivery, affordable pricing, robust security and privacy their priorities, the list closes with the page loading speed and product discoverability.
Here is where application observability comes in handy, for technicians may not always be aware of the specific customer requirements while the stakeholders have to be in the know of the customer’s experience to identify and leverage the key performance indicators (KPIs).
Observability virtuous cycle
All the talk basically boils down to the following. After pinpointing the KPIs, you determine the metrics to be collected through instrumentation. With these metrics in hand, you can initiate alerts and take action when business outcomes or customer experiences are in jeopardy.
Your initial steps taken towards the improvement of your app’s performance and user experience can be further analyzed with the established metrics and later used as insights to drive the following enhancement effort. This adds up to creating a virtuous cycle of continuous improvement of the customer experience that Amazon is all about. Once again, no precisely targeted action towards UX improvement can be taken without the relevant data.
The said cycle is given momentum by observability systems that call for having the business services instrumented: in order to generate telemetry data, presented through metrics, logs and traces, which communicates the necessary data with alarms and dashboards. Seemingly straightforward in its idea, the process needs to be simple enough to be conducted on a regular basis – on this condition only it does all start to make sense in the long-term perspective.
Whereas we can discover the basics of customer preferences through operating the app systems, more profound questions receive their answers from the investigation, which comes alongside alarms, analyses, and risk mitigation, brought about by observability. Improvement of the latter causes acceleration in both the learning cycle and risk mitigation. To be fair, observability is too tightly linked with operating culture to determine the initial influence.
The observability journey has multiple entry points. It could start with a simple question or while reviewing operations data in a meeting, where an anomaly in a graph prompts curiosity. More often than not, alarms in the observability journey act as signals, notifying that the system requires looking into. However, in a distributed system, the challenge lies in not being able to pinpoint the area that needs attention in the inherent intricacy and overall complexity, hence the lack of a clear starting point.
What is there to aid are dashboards with key metrics and indicators: they can hint at the further investigation vector. Additional application observability tools also include service maps. Constructed from trace data, a service map provides a summarized view of a distributed system’s health through its metrics. A map can also narrow down the system examination since it highlights areas that are performing well and those that are triggering alarms.
Lastly, log analysis comes into play, enabling us to meticulously filter the obtained information. This phase leads us to the specific exception, cause, or log entry that precisely outlines the issue – the object of the search. In the subsequent section, we will go into detail and illustrate a set of specific actions and techniques, aimed at tackling the emergent issue.
Three pillars of observability
According to AWS, observability resides on three pillars as follows:
Amazon CloudWatch caters to the monitoring of metrics – a numeric representation of data observed and collected over periods of time. Metrics monitoring helps identify trends, mathematical modeling, and predictive analysis.
CloudWatch addresses logs – unchangeable, timestamped records detailing specific events occurring chronologically. Logging offers insights into unforeseen and unpredictable behavior that may arise.
AWS X-Ray handles traces – a sequence of interconnected distributed events that encapsulate the complete journey of a request across a distributed system. Tracing is responsible for visibility into aspects like latency, shedding light on the route taken by a request and its underlying structure.
As Amazon CloudWatch provides a consolidated perspective of operational status and visibility across all AWS services, it functions as a comprehensive observability platform. The objective is to offer a unified interface, enabling users to access metrics, logs, and now X-Ray traces, all within the same CloudWatch console.
CloudWatch, however, extends beyond this. It offers an extensive range of features and functionalities that build upon its core components – the previously mentioned metrics, logs, and X-Ray traces. These include solutions like infrastructure monitoring, application monitoring, and synthetic monitoring.
The latter is an especially unusual tool for it can simulate end users’ actions. Browser-based applications benefit from synthetic monitoring through browser behavior mimicking. This simulation can be conducted globally, helping identify issues in specific regions, possibly related to X-Ray latency or other factors.
While it is true that CloudWatch as a service can be overlooked without immediate impact on the application’s performance, inadequate or ineffective CloudWatch utilization can lead to complex debugging and problem resolution. From another perspective, CloudWatch is a unique tool that offers capabilities to route logs from consumed services through log streams, generate performance reports, and analyze data points to identify potential application issues. Let us consider a series of common emergent issues where CloudWatch proves applicable.
To begin with, scalability. When creating an application for a specific audience or user group, its scalability can remain a gray area. Gradual app extension can lead to user-related issues, for many reasons, including yet not limited to initial design considerations, oversight, or limited resources. Numerous issues may actually originate at the point of smooth operation that overshadows potential design flaws or future problems.
AWS suggests making use of such features as auto-scaling, resource scheduling, and batching. It is a valid concern, though, that determining the extent of the expansion or scaling depends on the present environment status, which necessitates gathering data points or logs. CloudWatch fulfills these roles perfectly by aiding in ongoing state monitoring, issue mitigation, and proactive data analysis.
Another common problem pertains to microservices. Frequent issues faced by the microservice architecture, where plenty of APIs collaborate, are linked to services or the said APIs. Authentication failures, excessive CPU usage, memory depletion, etc. trigger app or server breakdowns which can have severe repercussions for both business and users unless prevented by debugging, based on the data supplied by additional application observability services, including CloudWatch.
Core features of Amazon CloudWatch
Now, it has been established that operational data in the shape of logs, metrics, and events are represented in automated dashboards to form a cohesive view of resources, services and apps, whether run in the cloud or in-house. CloudWatch treats this information based on four pillars, which are described in more detail below.
The fundamentals of employing CloudWatch involve forwarding logs to the resources you utilize. Be it service logs, application logs, load balancer logs, or default service logs, their transmission is facilitated by the CloudWatch agent. Collecting logs from various resources forms the prerequisite for a comprehensive observability approach. Practically speaking, this feature is common among resources like EC2, Lambda, and S3.
The data collected through logs can be conveniently visualized with the help of the CloudWatch dashboard. The functionalities of visual representations and alerts for fluctuations in the data points are not limited to specific locations and operate seamlessly across regions.
The compelling function of CloudWatch is that the collected data, which are available for monitoring, can be further used to create and initiate events that will prompt your resources to execute certain actions, aligning with the app’s requirements. For instance, CloudWatch events can trigger actions like EC2 or container auto-scaling, effectively responding to changes.
On top of collecting data, monitoring them for patterns and alarming trends, and creating events to preemptively react to potential risks, CloudWatch enables you to analyze data in real-time, spanning short and extended durations, and offering resolution as precise as one second, for those businesses that cannot afford downtime.
A lot has been said about the importance of app monitoring, so now we will scale up and consider more comprehensive monitoring which provides system-wide visibility. Whether your systems are rooted on AWS or on-premises, all resources within a multi-tier application are to be monitored, inclusive of the database, even if you utilize it particularly for data storage. Insights can be generated by tracking the data from all app tiers so as to ensure a thorough monitoring strategy.
Resource Optimization and Auto-Scaling Instances
A common unified system’s operation health depends on resource optimization, which involves auto-scaling instances. Illustratively, three out of four pillars of CloudWatch are at work in the following case: when CPU usage exceeds 95%, triggers can add instances; when it drops, instances can be decreased. One is at liberty to set triggers and alerts for desired events and receive notifications to an SNS topic, being alerted via phone or email.
Understanding AWS CloudWatch Operations
Turning to AWS CloudWatch operating, one can begin to understand its functioning by recognizing that it serves as a metrics repository. It utilizes metrics from AWS services and allows you to create custom metrics using the “put-metric-data” operation. Additionally, you can generate your own metrics directly within CloudWatch.
Linking Metrics to Evaluation and Alarming
When it comes to evaluating the present condition of a resource, one is in need of a benchmark. For example, if an instance’s CPU usage exceeds 85%, you might consider adding a new resource. Here, the CPU utilization acts as the benchmark, with the threshold set at 85%. Dealing with a metric like CPU utilization enables you to establish alarms if you direct log streams to the relevant metrics tracking an instance’s state. This alarm evaluates whether CPU usage exceeds the set assessment threshold for the resource’s state of 85%. In essence, you’re establishing an alarm state by defining a specific threshold within the CPU utilization metrics. The metrics you generate are region-specific, though CloudWatch offers cross-region statistics to centralize these metrics into one place.
Moreover, you can create a condition to trigger the auto-scaling policy for launching new instances. This setup connects services with CloudWatch for metrics and log streaming, as a result, CloudWatch alarms use these metrics to enable instance scaling via auto-scaling groups. The necessary actions can be performed with the help of SNS (Simple Notification Service), which also distributes messages or alerts.
So how does Amazon CloudWatch help in application observability?
Provides quantitative insights into the state of resources and services by gathering metrics from resources, databases, storage, and custom metrics.
- Sheds light on the execution flow, errors, and interactions within an app through the collection and storage of logs from the app’s services.
- Helps detect and timely respond to anomalies by providing granular visibility into application performance with real-time monitoring and high-resolution metrics.
- Lets you tackle potential issues in due time by triggering notifications through various channels (SNS or AWS Lambda) whenever a set metric threshold is breached.
- Offers a unified perspective of an app’s behavior in the form of visualized metrics, logs, and other data for faster information analysis.
- Identifies problems that elude manual threshold-based monitoring by employing machine learning to spot abnormalities and norm deviations in the metrics.
- Collaborates with AWS X-Ray to provide distributed tracing capabilities: cross-service request tracing, and detection of performance issues across distributed architectures.
- Supplies automation tools for actions tied to particular conditions, like instance scaling or EC2 instance shutdown.
- Aids in pinpointing optimization prospects and improving resource allocation through data collection and thorough analysis.
- Facilitates cross-region data aggregation, enabling the collection of data from various regions into a centralized location (for apps spanning multiple AWS regions).
A service that gathers information about the requests your application handles, AWS X-Ray offers application observability tools to view, filter, and gain insights from this data, helping you identify issues and optimization possibilities. The data one can further process include requests, responses, and interactions with downstream AWS resources, microservices, databases, and web APIs.
Being essentially a distributed tracing system, X-Ray has been designed with a methodology of distributed request tracing in order to profile and monitor apps, especially microservices. Tracing involves following a single request across the distributed stack of multiple components (i.e. API Gateway, Lambda, SQS, DynamoDB), gauging the performance of each of them. Beyond overall performance, tracing delves deeper into code functioning, for instance: specific code line performance, database calls, latency etc. You are certain to find your own answers to many questions with AWS X-Ray.
Distributed tracing has accurate failure points pinpointing and revealing the underlying reasons for subpar performance as its purpose. Failure spots and the causes of performance issues are brought to light by AWS X-Ray.
Another feature worth separate attention is X-Ray’s focus on performance monitoring. The latter essentially entails supervising and maintaining the performance and accessibility of software applications. Application Performance Monitoring (APM) strives to identify and resolve complex problems linked to application performance, ensuring the intended service level is upheld. AWS X-Ray encompasses both performance monitoring and its maintenance on the appropriate level.
The tool in question gathers traces from the application itself. Instrumenting your app, of which we have had a lot to talk, means sending trace data for incoming/outgoing requests and events, along with request information. This may require some configuration changes. As an example, incoming HTTP requests and AWS service calls can be instrumented for tracing in the Java app; there are some SDKs, agents, and tools to help you do that.
AWS services already integrated with X-Ray can enhance tracing in various ways, like appending tracing headers to incoming requests, forwarding trace data to X-Ray, or utilizing the X-Ray daemon. Particularly, to simplify the utilization of the X-Ray SDK, AWS Lambda can transmit trace data for requests to your Lambda functions and use the X-Ray daemon on workers.
Rather than sending trace data directly to X-Ray, client SDKs transmit JSON segment documents to a daemon process that listens for UDP traffic. Then, the X-Ray daemon stores segments in a queue and uploads them to X-Ray in groups. It is accessible for Linux, Windows, and macOS, and comes integrated with AWS Elastic Beanstalk and AWS Lambda platforms.
One can create an in-depth service map, building on the trace data collected by X-Ray from the AWS resources that power your cloud apps. The service map is bound to reflect the front-end service, and back-end services that your front-end service interacts with in handling requests and storing information. Bottlenecks and latency spikes among other issues are easily detected and treated with X-Ray with a view to enhancing your app’s performance.
So how does AWS X-Ray help in application observability?
- Examines and troubleshoots your distributed app’s performance.
- Reflects view latency distribution and detects performance bottlenecks.
- Pinpoints and specifies user impact across your apps.
- Functions across both AWS and non-AWS services.
- Is ready for real-time use in production with minimal latency.