What is API Observability?

Mission-critical apps that are deployed on the cloud drive today's modern enterprises, which in turn power their businesses. These applications' fundamental units are microservices, which tiny development teams created to enable speedy feature releases to the market.

APIs serve as the ties that bring these microservices together so they can cooperate. However, as the need to provide new features becomes an essential component of running a modern organization, modern systems have grown more complicated, distributed, and challenging to manage.

Today's organizations are searching for new approaches to remove the barriers that slow down or prevent the success that comes with delivering features faster. The tools, methodologies, and processes that are effective for monolithic applications simply cannot meet the challenges of API-driven applications.

Using a new framework called API observability, development, operations, and security teams can collaborate more effectively to manage the applications that power their company's operations. This makes it easier to spot and fix serious problems before they have an adverse effect on the way the application and your company operate.

In this post, we'll examine what API observability is, how it compensates for the shortcomings of traditional API monitoring, and how it even has positive effects on user experience and marketing.

Here’s how it is done:

  1. Introduction
  2. What is API Observability?
  3. The Four Pillars of API Observability
  4. API Monitoring and Its Limitations
  5. Benefits of API Observability
  6. API Observability is a Process

#1 Introduction

Agile development methodologies and the adoption of open-source software, which prevented developing software code from scratch and allowed for the quick delivery of new software features, were the main factors that led to the creation of cloud-native and SaaS apps over the past ten years.

But more importantly, they make it possible for small teams to work independently and dynamically in a CI/CD pipeline to regularly release new features.

In this new world, APIs are the backbone of contemporary applications that allow users to interact with API-driven applications and facilitate crucial communication between internal microservices, which can number in the thousands. This communication ensures that the microservices are operating in lockstep with one another.

Simply said, APIs are essential, and their effective operation is important to making sure that complete applications can operate at scale and provide services to customers and third-party business partners.

How do organizations ensure that their APIs are continuously assessed for their health, ensuring that errors, performance bottlenecks within microservices or security vulnerabilities do not slow down or impair an API and the application as a whole in a world where APIs have become so crucial to the proper functioning of mission-critical applications?

When examined more closely, APIs can be divided into two categories:

  1. Internal APIs
  2. External APIs
  • External APIs are intended for usage by the general public or by anyone outside the company, provided they have been properly verified and approved. External APIs have external URLs that can be called by anyone with access to the URL, giving them access to the application's resources.
  • Internal APIs act as the unifying force that allows hundreds or even thousands of internal microservices to successfully coexist side by side. They jointly control the application's business logic and have direct access to the backend resources.

#2 What is API Observability?

API Observability is a crucial element to effectively carry out APIOps cycles and make sure you're creating something useful for your API users.  It is a development of conventional monitoring that emerged from the control systems theory.

Tracking known unknowns is the main emphasis of traditional monitoring. This indicates that you are already aware of the metrics to use, such as requests per second or errors per second. You already know what to measure or probe, such as a counter to monitor requests into buckets, even though the metric value may be unknown in advance.

This allows for the reporting of a system's health (in the form of Red, Yellow, and Green), but it is a poor tool for resolving technical or commercial problems, which typically require asking illogical questions.

How can you know when traditional monitoring has reached its limit?

It happens when you are looking into a problem and say to a colleague, "We could fix this, but we don't have the correct context."

Although "context" is a loaded term, it simply indicates that your solution cannot address any known unknowns. To acquire that context, you should instead change the metrics you are tracking, redeploy, and then finally observe the outcome.

Control systems theory gave rise to API observability, which aims to infer state and behavior from a system's outputs to observe as much as possible about how the system functions internally.

You can respond to any arbitrary query about your API activity by using a powerful API analytics tool to examine all this data, not just a few specified metrics that are readily observable. This implies that you can respond to any inquiries about how your "black box" arrived at this state or even duplicate it.

API observability tools are placed directly in your system, enabling them to passively gather data on the entire system, to achieve this.

#3 The Four Pillars of API Observability

The four pillars of API observability are as follows.

  1. API Functional Test Automation
  2. API Performance Management
  3. API Security
  4. API User analytics

Pillar 1: API Functional Test Automation

API application software is complicated, requiring greater time and resources from development teams to find, isolate, and fix critical software bugs before they cause problems or degrade the user experience. Finding all the failure modes that can occur throughout a modern API application is one of the hardest challenges.

You can gather metrics, logs, and traces using traditional monitoring or observability tools, which can eventually assist you in identifying the failure's primary cause. However, it raises the question of why the testing framework you have in place was unable to identify the failure modes.

The problem is largely due to the test software that is now in use, which is heavily employed by R&D teams to find defects and issues in software. The typical testing tools used by QA teams, such as static code analysis, are unable to identify quick API changes across large internal microservices and validate those software changes before they are deployed. This method frequently fails.

On the other hand, API functional test automation enables you to model your APIs using data from the input and output of the entire application. With the aid of a framework, you can communicate data in a more organized and understandable manner while also learning why particular microservices interact with one another using particular APIs.

With the help of these generated models, you can validate your APIs with each software update, guaranteeing that your API application performs as you anticipate it catching software updates that break your API models and how the application acts early in the development cycle.

Additionally, you can design automated contract testing processes and more effectively integrated tests that execute as a part of your CI/CD.

As a result of data being collected during the entire process, you could have a historical perspective on how your APIs have changed over time, view changes to parameters, and enable checks to guarantee that new software updates are compatible with both upstream and downstream microservices.

Pillar 2: API Performance Management

When delivering apps to the end user, performance is frequently a crucial consideration. The end-user experience may be negatively impacted by a bottleneck within an application, such as subpar constructed software or an inability to appropriately scale to meet user demand.

The increasing complexity of API applications makes it far more difficult to pinpoint the main source of performance problems when using traditional monitoring tools. These monolithic application-built technologies are unsuitable for today's API-driven apps.

In essence, modern software engineers are uninformed that an issue actually exists because they are unaware of what their software failures are, or, to put it another way, they are unaware of their unknown unknowns.

Traditional monitoring technologies of today can only be useful for monitoring known unknowns, enabling value if you are aware of the issues and can keep an eye on them.

Through MELT (metrics, events, logs, and traces), which make API-driven applications "observable" by responding to questions about a system without penetrating a black box, API Observability can reinvent how you find the cause of issues in your applications.

Metrics and logs were frequently utilized in the past to identify and address performance issues, but they lack context, making it more difficult to piece together events to comprehend why problems arose.

A forklift improvement to examine and diagnose issues across complicated API-driven systems has been made possible by "tracing" becoming available to development teams.

The system can aggregate signals since tracing information is contextually rich, timeline aware, and methodical. It can be used to correlate discrete actions taking place at any given time when combined with event information.

Pillar 3: API Security

Like any complicated software, APIs have bugs that might all be used by cybercriminals to their advantage. Today, hundreds to thousands of microservices collaborate to give the application functionality to the user, replacing the single monolithic application that previously held the business logic and exposed it to less risk (exposing more risk).

Distributed independent software teams are unable to take a step back to assess how API vulnerabilities can spread across numerous different microservices since they are driven by their own requirements and function dynamically. This lays the groundwork for comprehending how API security is turning into an increasingly challenging but crucial challenge to handle.

It is necessary to reestablish observability at the macro level so that it can get a full picture of how an application works from beginning to end and then identify security vulnerabilities that individual software teams working on their respective APIs are blind to.

Critical operating data gathered by application "telemetry" can be extracted with the use of end-to-end instrumentation across the front-end application, inside each internal microservice, and through the backend.

An end-to-end view of the entire application could be constructed using the data that could be kept in a data lake. With the aid of machine learning, it might replicate the application's business logic while looking for irregularities in how users interact with it.

The smallest variations might show up as anomalous behavior, which are malicious efforts to take advantage of API issues or application business logic errors.

This observability-based approach to API security enables businesses to gain a far more in-depth understanding of the applications that are driven by APIs, empowering development teams to proactively identify and address security flaws or problems with business logic before they are exploited.

Pillar 4: User Analytics

Understanding your customers is essential for any organization, no matter how big or little. Information on user behavior, wants, frustrations, and general pleasure is used to inform business and product decisions. This knowledge is essential to ensure that a company can survive and even prosper in extremely competitive marketplaces.

This demand is considerably larger for SaaS and cloud-based apps and calls for comprehensive data on thousands of users all over the world. All data is handled across all application touchpoints in API-driven apps to provide services to the end user.

Making poor product judgments that could cost firms millions of dollars can be prevented by making well-informed choices. Data is the primary determining element in determining how to segment customers based on a specific feature that addresses a pain issue or how people behave generally.

By allowing teams to comprehend the dynamics of the business, spot subtle patterns, and forecast future behavior, user analytics can be a crucial differentiator.

For instance, an e-commerce company that primarily relies on its mobile app would view the following inquiries as being essential for assessing future product decisions:

How many people used my iPhone and Android app every day in the previous week? Which of the two was used most frequently? How many US states have my iOS app had active users in the past 30 minutes? How many reviews did the products that were looked at but never bought have?

Since mobile apps are typically accessed through APIs, gathering useful data from their users and offering user analytics that offers crucial insights can help organizations expand more quickly while making fewer errors.

#4 API Monitoring and its Limitations

Your API endpoints will get scheduled test calls from a typical API monitoring tool. Any unexpected returns will send an email or Slack notification to your engineers.

API monitoring can also gather performance data that can be neatly displayed in a dashboard to give an API health report if necessary.

Since testing is done away from your system, API monitoring is referred to as a "black box" strategy.

As a result, API monitoring tools are limited to what is visible at the API interface. As an automated alert system, this can be quite helpful, but it will completely overlook some important aspects of API health.

Now that we have a better understanding of these limitations, let's look at the drawbacks of API monitoring tools.

1) Synthetic Data

To verify your API, API monitoring tools employ artificial data samples. This data could, for instance, be "lorem ipsum" data produced programmatically using a test data package.

This data might not be able to reveal the same kinds of issues as genuine customer data because it is less diverse and multidimensional. The order and timing of API calls are likewise synthetic, in addition to the data itself.

Synthetic data can be dangerous because it creates a false sense of confidence in the robustness of your API when, in reality, it only reflects a small portion of what a genuine client might throw at it.

2) Response Data Only

API monitoring tools can only gather replies from your API and externally measurable metadata like time since they use measurements taken from outside of your API. As a result, crucial debugging information won't be available and the system's internal state would be entirely invisible.

Engineers will have to make educated guesses about what caused a bug with only an external view of the system, which can significantly slow down root cause analysis.

#5 Benefits of API Observability

Let's examine the benefits that API observability provides.

  • Real User Data
    API observability collects and analyzes real-time traffic data on your API traffic to monitor actual customer data instead of fake data. By doing this, you can make sure that your monitoring is in line with what customers are encountering, increasing the likelihood that you'll spot issues before they have a significant impact on customers.
  • Debugging with System Data
    Using API observability technologies will allow you to get data from the entire system, unlike black box monitoring. When an error occurs, for instance, more information than simply a snapshot of the API response could be given. This means that with API observability tools, your engineers will be able to troubleshoot issues much more quickly.

We have discussed how API observability overcomes the drawbacks of traditional API monitoring techniques. However, observability benefits aren't limited to fixing bugs.

Once you start gathering observability data, you'll have a wealth of information at your disposal that can support everything from product selection to conversion optimization.

Let's examine some of the supplementary uses for API observability data in more detail.

  • Insights Into User Experience
    You can see how your customers are actually using your APIs due to API observability. For instance, you can discover that specific types of errors are frequently made and that this information can help you identify fields to include or simply where to improve documentation.
  • Security
    APIs are susceptible to abuse by hackers, just like all other applications. Engineers can proactively detect vulnerabilities or flaws in the business logic by using the extensive data that API observability provides to identify patterns of misuse.
  • Business Information
    You can leverage API observability for your company's marketing efforts as well. To analyze API observability data and integrate it with additional client data to provide insights on products and operations, go here.

#6 API Observability is a Process

The foundation provided by API Observability paves the way for development, operations, and security to collaborate effectively while creating modern API-driven applications.

The pillars of API Observability can help businesses better understand how their API-driven applications function and address any problems that might affect the availability, performance, and security of the overall program.

An API observability framework enables teams to accelerate innovations by gaining a deeper understanding of their applications, removing unforeseen barriers, and obtaining crucial product insights that can make it possible for businesses to grow and develop more quickly than ever. This is because API applications will continue to become more complex.

The end result is API-driven applications that are no longer created in secrecy but rather through a continuous development process that allows businesses to find important blind spots before they interfere with the functionality of your application, giving teams access to more information and more control than they could have ever imagined.

Wrap Up!!!

API monitoring tools offer a valuable alert system that can notify your team when there is a problem with your API.

Due to their reliance on synthetic data, they can overlook more subtle errors, and as they typically lack the information needed to diagnose API problems, developers must make educated guesses.

The objective of API observability is to gain knowledge of what is occurring within your API so that root cause analysis can be carried out rapidly, enabling your team to remedy issues more quickly and maintain client satisfaction.

Organizations can also benefit from API observability in terms of user experience and use insights.

Atatus API Monitoring and Observability

Atatus provides Powerful API Monitoring to help you debug and prevent API issues. It monitors the consumer experience and is notified when abnormalities or issues arise. You can deeply understand who is using your APIs, how they are used, and the payloads they are sending.

High API Latency
High API Latency

Atatus's user-centric API observability tracks how your actual customers experience your APIs and applications. Customers may easily get metrics on their quota usage, SLAs, and more.

It monitors the functionality, availability, and performance data of your internal, external, and third-party APIs to see how your actual users interact with the API in your application. It also validates rest APIs and keeps track of metrics like latency, response time, and other performance indicators to ensure your application runs smoothly.

Try your 14-day free trial of Atatus.



CMO at Atatus.

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