In high-performance software systems, detecting performance anomalies and taking corrective or preventive actions are important requirements. API gateways are key component of enterprise integration architectures with the advent of the API Economy. As a result, detecting performance anomalies in such high-performance API-Gateway systems is critical.
What exactly is data validation?
Data validation is simply defined as the process or set of processes of verifying and validating data before it is being made used of. All data task which involves data handling such as (collating, analyzing and structuring) for presentation purposes must include data validation. This is to ensure that accurate data results are been arrived at. For any API project, data validation can though take time it is very important.
In a business setting, how is data validation used?
In API projects, it is important to include data validation into the code process. Unless the Developer has complete trust in the integrity of the data, there is no assurance that when analysis is done, the end result would be accurate. It is important to mention that in API projects, API data are only as good because the output is as good as the input.
Anomaly Detection in API: What is Anomaly Detector?
An anomaly detector, “A.D”, is an AI software service that uses API technology to help you monitor and detect anomalies in time series data of simple and complex systems without necessarily having background in machine learning.
We refer to anomaly as an unexpected pattern of an API data structure. Alternatively, to detect anomaly in APIs you need to set your own API threshold if you don’t have access to an anomaly detector. A threshold simply means a value that an API code quantity must reach (e.g error rate) before an anomaly can occur. It is necessary for you to constantly update your threshold with new values as recent data are being fetched by the API. AAPI-Ops select the best anomaly threshold based on the pattern or recent data. If an AAPI-Ops detect an anomaly, the fully details of the anomaly are displayed on the Anomaly Events Dashboard so you can check the Anomaly in the API Monitoring dashboard and if needed make corrections to your code.
A detected anomaly contains the following data:
- An API metric, such as proxy latency or an HTTP error code, that caused the anomaly.
- The magnitude of the anomaly
Capabilities of the Anomaly Detector
Using an anomaly detector like Univariate Anomaly detector or Multivariate Anomaly detector, you can be able to detect and handle anomalies of univariate and multivariate variables.
Detection of Univariate Anomalies
With the help of the Univariate Detection API you can access, monitor and detect API anomalies in API projects under the time series data without having a background knowledge of what machine learning is . This anomaly detect works irrespective of the industry scenario or data volume. The algorithms also automatically identify models that are best-fitted for your data. It can also make use of time series data to automatically detect anomalies boundaries, expected error values and at which point of the data an anomaly occurred.
REST APIs allows you to integrate the Univariate anomaly detector to your application and work flow processes. This also helps you to detect anomalies as they occur in real time.
Detection of Multivariate Anomalies
Unlike the Univariate anomaly detector, the multivariate anomaly detector helps Developers to futhrr integrate advanced Artificial intelligence (AI) that helps determine anomalies for group for metrics. Like the Univariate anomaly detector, you don’t need to have an experience in machine learning to be able to do this. Key metrics of data can be automatically calculated based on inter-correlations and data dependencies that occur between up to 400 different signals. This help prevent failure in complex systems like software servers and applications, Factory machines, spacecraft etc.
How Do You Detect Anomalies in APIs
Step 1: You need to Create an external test to hit the data check endpoint at a preset frequency from multiple locations to validate (ensure) that the API response code is both expected and the also the correct data is returned in the correct format.
Step 2: Next, you have to request a real browser check data from the SPLUNK API endpoint with the use of an API key
Step 3: Let’s assume the response code returned has a value of “200”
Step 4: Then, use the JSON path to extract the check ID from JSON (python experience needed here)
Step 5: Assert that the check ID obtained from the JSON path corresponds to the expected value.
Simple User Flow
This simple user flow will assist you in testing your API
Availability: If the API returns a response code other than 200 OK, the check will fail.
Data Structure: The step to extract a value using the JSON path will fail the check if the API returns data in a format other than JSON.
Data Accuracy: The check will fail if you are able to extract a value for the ID but it differs from the expected value.
As earlier mentioned, any workflow process that involves collating, storing and analyzing data must include data validation. This helps fat check the authenticity of the datasets and ensure that the chances of an anomaly occurring us greatly reduced. There are several types of data validation techniques that can be used in API projects. They have their pros and cons of usage. As a Developer, you must be careful when choosing the data validation techniques, you want to use to validate your data. Because the accuracy of the data has a significant impact on the results’ quality.