MySQL Business Continuity Planning

This document discusses using <IFRAME> tags to improve the performance of third party scripts. It describes how third party scripts normally block page loading and proposes using an iframe to load scripts asynchronously in parallel without blocking. It provides code for creating an iframe targeted to load scripts, handling cross-domain issues, and modifying the Method Queue Pattern to support iframes. The approach allows third party scripts to load without blocking the main page load.

The document discusses Boomerang, an open source tool for measuring real user performance on websites. It measures load times, bandwidth usage, latency and other metrics. Additional functionality can be added through plugins. The presentation encourages developers to use Boomerang to analyze user behavior, identify performance issues, and continuously improve sites based on real user data. It provides several examples of insights that can be gained, such as how performance varies by country, browser, and internet connection speed.

The document is a presentation about abusing JavaScript to measure web performance. It discusses using JavaScript to measure network latency, TCP handshake time, network throughput, DNS lookup time, IPv6 support and latency, and other performance metrics. It provides code examples for measuring each metric in JavaScript and notes challenges to consider. The presentation encourages the use of the open source Boomerang library for accurate performance measurement.

If you're interested in measuring real user web performance, you'll find tools like boomerang or episodes quite handy. Some popular web frameworks even have modules that make it easy to add them to your site. However, what does one do once one has collected the data? How do you filter out the noise and get meaningful insights from the data? In this talk, I'll go over the techniques we've picked up by analyzing millions of datapoints daily. I'll cover some simple rules to filter out invalid data, and the statistics to analyze and make sense of what's left. Do you use the mean, median or mode? What about the geometric mean and standard deviation? How confident are we in the results? And finally, why should we care? This talk should help you gain useful insights from a histogram, or at the very least point you in the right direction for further analysis.

While building boomerang, we developed many interesting methods to measure network performance characteristics using JavaScript running in the browser. While the W3C's NavigationTiming API provides access to many performance metrics, there's far more you can get at with some creative tweaking and analysis of how the browser reacts to certain requests. In this talk, I'll go into the details of how boomerang works to measure network throughput, latency, TCP connect time, DNS time and IPv6 connectivity. I'll also touch upon some of the other performance related browser APIs we use to gather useful information. I will NOT be covering the W3C Navigation Timing API since that's been covered by Alois Reitbauer in a previous Boston Web Perf talk.

The document discusses analyzing real user monitoring (RUM) data to gain insights into website performance and user behavior. It describes building plugins to collect navigation and timing data from browsers. Various statistical techniques for analyzing the data are covered, including log-normal distributions, filtering outliers, sampling, and correlating metrics like page load time and bounce rates. The analysis of an example 8 million page dataset suggests very fast or slow page loads are associated with higher bounce rates, and thresholds for user-unfriendly performance are proposed based on bounce rates exceeding 50%.

This document contains slides from a presentation about using JavaScript to analyze network performance. It discusses how to measure latency, TCP handshake time, network throughput, DNS lookup time, IPv6 support and latency, and private network scanning using JavaScript. Code examples are provided for measuring each of these network metrics by making image requests and timing the responses. The presentation emphasizes that accurately measuring network throughput requires requesting resources of different sizes and accounting for TCP slow start. It also notes some challenges around caching and geo-located DNS results.

This document is a presentation about analyzing web traffic using Node.js modules. It introduces Node.js and the npm package manager. It then discusses modules for parsing HTTP logs, including parsing user agents, handling IP addresses, geolocation, and date formatting. It also covers modules for statistical analysis like fast-stats, gauss, and statsd. The presentation provides code examples for using these modules and takes questions at the end.

The document discusses input validation and output encoding to prevent vulnerabilities like XSS and SQL injection. It provides examples of how unexpected input can enable attacks, like special characters or invalid data types being passed to endpoints and rendered unencoded. The key lessons are that input validation is needed to receive clean, expected data, while output encoding is crucial to prevent exploits when displaying data to users. Both techniques are important defenses that address different but related issues.