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). The question is: why was average performance so much worse during the last hour of the day? To gain deeper insight into the characteristics of the data, we’ll compute statistics (minimum, maximum, mean, and standard deviation) for the raw data in each hour-of-day bin. First, we initialize several Python NumPy arrays…
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Analyzing 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 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 reading7 Ways to Customize API Monitoring Using Global and Local Variables
It is possible to configure API monitors with rigidly fixed parameters. However, using the API Science platform, it is also possible to configure API monitors using both account-wide global variables and monitor-specific local variables, in order to provide your team with a deeper view into what your customers are experiencing. Introduction to API Science’s Global […]
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. […]
Continue readingHow to Create an Automated Custom Web Site that Displays API Performance Data
For your company to succeed, your API-based product must provide maximal performance and uptime to your customers. In order to provide this, you need to monitor the APIs (both internal and external) that are critical for your product’s customer experience. This series of posts illustrates how you can create a custom automated web site that […]
Continue readingHow to Use MatPlotLib to Display API Performance Data
In my previous two posts, I illustrated how your team can Use cron and curl to Regularly Download API Performance Data and How to Use Python to Extract API Performance Data. In this post, I illustrate how you can use the Python MatPlotLib library to create plots of the downloaded data that will be of […]
Continue readingHow to Use Python to Extract JSON API Performance Data
In my last post, I described How to Use cron and curl to Regularly Download API Performance Data. This is the first step toward creating a view of API performance data that is customized for your company’s needs. Once you have the raw data, you can use your company’s preferred programming languages and the custom […]
Continue readingBritish Army Utilizes APIs for Increased Logistic Efficiency
Large organizations that have existed for centuries have employed computer technology since its innovation. 50 years ago, large-scale organizations contracted developers to utilize 1960s software and hardware technology to create systems that advantageously advanced the accomplishment of their missions. In subsequent decades, this software was tuned for evolving situations. Along the way, errors identified in […]
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