How API Monitoring Works to Improve Website Reliability

API is the de facto standard used by software developers to enable communication between incompatible software components from different platforms. In order for API to continue to run well, API monitoring is needed to ensure that its performance remains good and consistent, even in the midst of high traffic and demand. Find out how API monitoring works below.

What is API?

API is a set of functions or procedures that regulate access points for a particular system, service, or application on a website. API is a mechanism that allows two software components to communicate with each other using a set of definitions and protocols.

For example, there is an API software system from the Meteorology, Climatology, and Geophysics Agency (BMKG) which contains daily, even hourly weather. Then on our cellphones there is an application that informs us about the weather. Well, this application communicates with the BMKG system via API.

What is API Monitoring?

API monitoring is the process of constantly monitoring both the availability of API endpoints and the validity of their data exchange. While monitoring the API, we can also increase visibility into how the API operates and its performance.

APIs need to be continuously monitored to detect failures or slow applications, and to:

Ensure successful multi-step transactions
Validate returned data and resolve errors
Find viruses or problems that occur in third-party API integrations.

How Does API Monitoring Improve API Performance?

API monitoring allows API performance evaluation from various perspectives, including the scalability of request loading performance and multiple requests at once. In addition, data exchange performance for structure validation and expected results is also checked. In this way, API monitoring can be used to maximize tasks in various companies.

Here are how API monitoring improves website reliability:

1. Go Beyond API Availability, Validate Functional Uptime

API availability or uptime is the standard in API monitoring. At the same time, availability monitoring alone is not enough for API transactions that involve data exchange. In other words, we must test various verbs, such as Create, Read, Update, and Delete (CRUD) services across all application sources exposed via the API to ensure that the service is indeed operational. Using synthetic monitoring tools with multi-step API monitoring is one way to get API availability by relying on data. Synthetic monitoring only uses a set of pre-defined APIs.

2. Don’t Forget API Dependencies

There may be other internal APIs and/or external APIs that depend on the input and output of our application APIs. Even though we have implemented an API monitoring strategy, other APIs may also use it, but they may not. Because APIs are interdependent, we must also monitor the behavior of third-party APIs that our application relies on.

3. Adopt Test Automation into API Monitoring

Test automation will simplify our performance so that we can determine the API monitoring strategy. Testing in API monitoring that is set automatically will make it easier to monitor routinely at regular intervals. This cycle will improve API performance at every stage of the code release process.

4. Choose a Tool that Has a Strong Alerting Function

There are monitoring tools that are only strong in metric visualization. Of course this function is also important, but it would be better if it had a strong alerting function. Visualizing metrics requires constant human intervention to find out if something is wrong with the API error. Therefore, a tool with strong alerting capabilities should be a priority when choosing an API monitoring tool.

API Metrics to Monitor

Here are the API metrics to monitor:

1. Availability or Uptime

API availability or uptime is a measure expressed in percentages represented as 99.9% or 99.99%. Sometimes, the same is calculated as downtime per year as an overall average.

2. CPU and Memory Usage

The CPU and memory usage of the API host server is a sign that the virtual machine, container, or API gateway node is overloaded. If it is overloaded, the API performance will slow down. We can measure CPU usage across the cluster hosting the API code in our application, as well as the number of processes waiting to run which is also known as CPU load or process queue size. Meanwhile, memory can be simply measured as a percentage of available and used memory.

3. API Consumption

PI consumption measures requests per minute and requests per second. We can group multiple API calls into a single API call with a flexible pagination scheme to lower API consumption.

It should be noted that synthetic monitoring is not intended to calculate average consumption, as it emulates individual transactions rather than monitoring aggregate transaction volume. Telemetry instrumentation to measure and report consumption rates is usually built into the API design from the start. Or it can be monitored with Application Performance Monitoring (APM) tools.

4. Response Time

Response time is a tricky calculation to measure with third-party APIs due to problematic recording latencies and possibly issues with the network itself. The best approach to monitoring latency is to use an API monitoring tool that can report network latency and API response times separately.

5. Error Rate

Error rates, such as errors per minute and error codes, provide granular details in tracking down issues with each individual API. For example, an error code in the range of 400 – 500 implies a problematic API or web service provider.

However, there may also be API errors with a status of 200OK that are not designed using the correct HTTP status code. Synthetic monitoring tools can compare test results with expected values ​​to confirm the accuracy of API responses outside of status codes.

6. Unique API Consumers

Unique API consumer metrics provide insight into the overall growth and condition of new consumers based on the number of monthly active users. A sudden drop in consumers during peak operating hours is interpreted as a symptom of underlying issues in the application platform.

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