To visualize the relationship between overall API call performance and the performance of each individual curl performance component, the curl data was downloaded in JSON format. A Python program reads the JSON data into a dictionary (named perf) and creates a scatter plot where the x-axis is total performance time and the y-axis is the performance time for each curl component. Here’s the Python code…
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Analyzing the Causes of Slow API Call Performance
In recent posts I’ve been analyzing the data for a week of API monitoring from four different locations around the globe (Washington, DC, Oregon, Ireland, and Japan). The hourly performance for calls over the week looks like this: Since the World Bank Countries API that’s being called is located in Washington, DC, it’s not surprising […]
Continue readingAnalyzing API Performance Hour-of-Day Statistics
A recent post analyzed API performance by hour of day over a one-week period. The average performance of calls to the API was fairly consistent, except for calls made in the last hour of the day (the hour before Midnight Universal Time). This plot presents the analysis results: The question is: why was average performance […]
Continue readingAnalyzing API Performance by Day of Week
My last post showed how the API Science API can be utilized to create a graphical analysis of API performance binned by hour of day. In this post, we use data extracted from the API Science API to analyze the performance of an API by day of week. This type of analysis would be useful […]
Continue readingAnalyzing API Performance Binned by Hour of Day
Performance data from the API Science API can be analyzed in many different ways. For example, a recent post presents A Graphical View of API Performance Based on Call Location. The analysis uses cURL statistics to compare the performance of monitors that call the World Bank Countries API from various locations around the globe over […]
Continue readingAnalyzing API Call Performance from Different Global Locations Based on cURL Metrics
My previous post presented “A Graphical View of API Performance Based on Call Location.” In that post, we analyzed the performance of a week of calls to the World Bank Countries API (which is served from Washington DC) from four different locations around the globe: Washington DC USA, Oregon USA, Ireland, and Tokyo Japan. The […]
Continue readingA Graphical View of API Performance Based on Call Location
The performance of APIs is dependent on both the processing time from when the API receives a request and delivers a response, and the time it takes for the request and response data packets to traverse the Internet distance between the calling system and the system that hosts the API. The timings for calls to […]
Continue readingHow to Create an Automated Custom Web Site that Displays API Uptime Data
Previous posts described how to use curl, cron, JSON, Python, matplotlib, and HTML to create an automatically-updated custom API performance web page. In addition to providing API performance data, the API Science API also provides uptime data for the APIs you monitor. In this post, I’ll demonstrate how to download API uptime data and create […]
Continue readingHow to Validate Your API Using API Science’s API Analysis Platform
You’ve got a product that requires many nines of uptime. If an API (external or internal) that your product requires is down, your own product is down, or key aspects of it are down. This article describes how you can utilize the API Science API to assess problems as they occur, and alert your team […]
Continue readingWith APIs and Software Libraries, How Much Code Is Needed to Create Something Immensely Useful? Not Much…
Your business likely requires customized views of the data that is core to the creation of your product. Decades ago, accomplishing this involved development of large data analysis software libraries that were customized to a company’s particular needs, along with user interfaces that enabled employees to view the data, so they could respond to anomalies. […]
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