My previous post used CURL component metrics to illustrate performance in calling the World Bank Countries API (located in Washington, D.C.) from Oregon. The curl resolve, connect, processing, and transfer times for calls from Oregon over a period of one week were plotted and analyzed. Here is the plotted Oregon data: The conclusion from studying […]
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Using CURL Component Data to Illustrate API Call Performance Patterns
My last post analyzed performance component results produced by the curl Internet data transfer utility in an effort to understand what typically causes slow API call performance. Four instances of unusually slow but successful calls to the World Bank Countries API were studied. In three instances (calls from Oregon, Ireland, and Japan), curl’s Processing Time […]
Continue readingAnalyzing 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 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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