No Code API automation – API Monkey

How to Measure Downtime for your REST API?

A dependable uptime monitoring system lies at the heart of every great API. While 100% uptime is ideal, no engineer expects it to happen. An API may experience downtime for a variety of reasons. Not only may downtime be caused by server difficulties that bring the entire system down, but it can also be caused by bugs in your application that cause a specific endpoint or API transaction to fail.

Simply put, the more APIs you utilize, the more likely you are to experience downtime that will affect the speed of your service.

A REST API’s availability can be represented as either downtime or uptime. Both are based on the same data, but they can portray a different tale depending on the context. The most straightforward statistic to track is availability.

Downtime can be reported as a series of incidents or as an average over a period of time. Because a REST API provider will notify scheduled outages, downtime faults are recognizable and sometimes expected. Even the most dependable APIs, however, are subject to unplanned outages. External providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Services are used by many APIs. As a result, each web service provider’s downtime is also reliant on a third party with whom your application does not directly do business. It could be due to user error, API downtime, rate limiting, or a variety of network-related issues when a call to a third-party API or online service fails.

The uptime of an API can provide insight into business decisions, and it is assessed similarly to downtime. Some stakeholders may be more concerned with downtime when deciding which API provider to remove, while others may be more concerned with uptime when deciding which to choose.

Consider the following scenario: Assume we have 100 ABC API users who all want to know when new data is available. For the sake of simplicity, we’ll suppose that everyone wants to know every five minutes and that we’re in an ideal polling situation where we’re checking for one-fifth of the users every minute (20). Even if we could only open one connection at a time, it would give each user five seconds to get results before the next round of polls started.

However, here’s what it looks like when an API is down:

  • Minute one: 20 connections, all time out
  • Minute two: 20 new connections, plus 20 retries, all time out
  • Minute three: 20 new connections, plus 40 retries, all time out
  • Minute four: 20 new connections, plus 60 retries, all time out
  • Minute five: 20 new connections, plus 80 retries, all time out
  • Minutes six and beyond: 100 retries, all time out until the API comes back up

You can certainly understand how the API downtime snowball effect if allowed, can take over an entire system. No longer calling APIs that are suffering downtime is the solution to the REST API downtime problem. However, you must first determine which APIs are unavailable.

Tips to detect the Downtime at the Initial Stages

Monitor Multiple Endpoints: Keeping track of a large number of crucial endpoints is the first step in avoiding errors. Each endpoint would have a simple monitor to examine the status of a basic HTTP request in an ideal world. You can begin by monitoring one endpoint and expand to multiple endpoints later.

Functional API Checks: Functional API tests are a great way to recreate real-world user scenarios and confirm that a certain transaction is available on a given endpoint (s). After you’ve built up some basic monitoring checks, think about your API’s most important functional needs and set up tests to watch those operations.

Eliminate Flaky Checks: A flaky check occurs when you receive an alert or a downtime notice yet there is no problem with your API. Unreliable checks that regularly fail with false positives create a lot of noise, distract you and your team, and perhaps cause team members to miss important downtime alarms.

Actionable alert notifications: API downtime alerts that require you to first click on a link to get the critical details of the failure are a step backward. API downtime notifications should be actionable straight immediately; otherwise, you’ll waste time looking at the dashboard of another web app instead of responding to the problem with your web service. 

Monitoring Non-production Environments: API problems in non-production environments can be caught before they reach production. You’ll be able to reuse the same tests to monitor each environment if you use a good API monitoring solution. After your API has been deployed to a staging or development environment, several monitoring systems allow you to smoke-test it.

REST API Monitoring Tools

Choosing the correct REST API Monitoring Tool can mean the difference between service uptime and service outage. Some of the greatest REST API Monitoring Tools are listed here.

  1. Loggly: At a price that is hard to surpass, Loggly combines a fantastic user interface with enterprise-level changing and suitable monitoring features.
  2. Sematext Synthetics: A system that tests APIs on a regular basis to ensure that they are operational.
  3. Site24x7 Website Monitoring: This is a cloud-based service that examines website performance and availability, as well as the performance of supporting applications and frameworks, as well as APIs.
  4. ManageEngine Applications Manager: This is a full-featured application manager that unifies all of your company’s systems, cloud applications, and services into a single, easy-to-use dashboard.
  5. Dotcom-Monitor: This is a cloud-based website and web service testing software that incorporates REST and SOAP API availability tests.

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