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A common mistake in Web page design is not setting the maximum expiration date on relatively static content. This is vital for reducing PLT2 (the second time a user visits a site). If the file has no cache setting, the browser will request an updated version of the file from the server. Even if the file has not changed and is not redownloaded to the browser, time is wasted making the request to the server. Fiddler is a tool developed by Eric Lawrence of Microsoft and is available on the Internet at http://www.fiddler2.com. It is used largely by security testers in Microsoft but also has a number of performance features, as shown in Figure 14-10.
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Figure 14-10: Fiddler loading www.officelive.com. In the detailed results tab of a performance test, Fiddler shows all the files that were loaded with a Web page and the cache setting for them. It is an easy way to find files that could be cached. With the tool, you can also see what percentage of time the page takes to load various bits of content.
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The biggest impact to page load time is the network. As the distance from the source to the client increases, the network becomes an increasingly influential component of page load time. Reducing page weight reduces the amount of data, but it is really the number of round trips that most affects PLT. VRTA3 and Fiddler can help with this analysis, and VRTA3 does have some nice graphical representations that help. VRTA Used to Find Bugs in Internet Explorer VRTA was designed to help engineers visualize the download of a Web page. I have been using this technique inside of Microsoft for the past four years. Presenting this process in a very visual way so that the engineers can see what items are serialized behind each other has helped improve page load times for many services. The tool has also helped to identify some of the more difficult to diagnose issues in browsers. One problem we found in Microsoft Internet Explorer 7 was the JavaScript blocking behavior, which restricted the number of files loading simultaneously. The effect was that parallel TCP ports were limited to only two. VRTA is also in active use for testing our new Internet Explorer 8 browser, but as of the writing of this book, that product hasn't shipped and I can't really share those bugs just yet. Jim Pierson, Perf Architect, MSN and Windows Live The Microsoft Office Live team sets a threshold for the maximum number of round trips (un-cached) that each Web page can have. When the developer makes a check-in to the source control system, a suite of tests for performance run and any page exceeding the round trip threshold is flagged.
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Several Other Critical Thoughts on S+S
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In this section, I want to share a bit more on testing in an S+S world. Although these points didn't really fit well under test techniques, each contains information that any tester on a service should champion.
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Continuous Quality Improvement Program
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In 3, "Engineering Life Cycles," we discussed the concept of Milestone Q (a.k.a. MQ or M0). For larger projects, this milestone is typically about clearing the decks and getting ready for the next major release. Teams invest in infrastructure improvements to help the process of developing and shipping to be smoother and faster. Developers often investigate new technologies and develop new prototypes. In the services world, where teams might go years shipping monthly or quarterly, we don't often take time for an MQ. In the services world, we focus on continuous improvement. All of our production services are very data driven and most use a very Six Sigma-like process we call quality of service (QoS) to drive continuous improvement. This QoS should not be confused with the computer networking concept that gives certain applications priority network access over others. Our version of QoS is about finding the unique insights that will help us to improve customer satisfaction. A successful QoS program must have data from three major categories. It can include other data, but in many cases that can distract from the very clear goal of improving customer satisfaction. The three major categories of data are voice of the customer, product quality, and operational quality, as shown in Figure 14-11.
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Figure 14-11: Three key data sources for QoS. Voice of the customer can be gathered from multiple sources. Direct surveys of customer satisfaction are the most common. Many services teams have shifted from using direct surveys of customer satisfaction to the Net Promoter score. Blog and Twitter mining is another way to see what customers are saying about your product or service. The Net Promoter score is a management tool that can be used to gauge the loyalty of a firm's customer relationships. It serves as an alternative to traditional customer satisfaction research. For services with direct support, call center data is a vital component of voice of the customer. Whether through actual phone calls or online chat help, user requests are categorized. By mining this data, a service team can identify the top customer escalations and prioritize those to improve customer satisfaction but also drive down the support costs. Product quality focuses on bugs and performance. In many cases, bugs might already be known, but how important they are to fix relative to other bugs is often hard to determine. A major area of product quality is the continual measurement and improvement of page load time relative to competitors. What is good performance today is not necessarily a customer satisfaction driver in the future. Operational quality is the same set of data operations that you would use to look for internal
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efficiencies, but in a QoS approach it is used to identify potential dissatisfaction (DSat) drivers. I often like to kid with the operations engineers, saying that there is little they can do to drive up customer satisfaction but a lot they can do to drive it down. This fact doesn't put operations in a very fun position, which is why it can be a challenging role. Here are a few key operational metrics and how they can be used to improve QoS: Root cause analysis (RCA) on any production outage can identify either a process problem or an underlying bug or architecture flaw. A high time to detection for a production issue can show holes in monitoring. A high time to resolution can indicate inadequate logging or diagnostic tools. Percentage of tickets resolved through Tier 1 or a self-healing system can show efficiency of handling simple, high-volume production issues. Resolving at Tier 1 or in an automated selfhealing system also brings down time to resolution. Percentage of false alarms can show errors in monitoring or alert thresholds. The richest area for finding product improvements is tickets by bucket. This is very similar to the call center coding but is the operational view instead of the customer perspective. Any area that has a high count is rich for product improvement or self-healing automation. When all three data sources are brought together, the team can better prioritize the right fixes to maximize improvements to customer satisfaction. Here is an example of how this could work in a very layered service: The call center team has a high number of complaints from users that could not process PayPal payments on the evening of June 23. When enough calls about a common problem have come into the call center an alert is sent to the live site operations team. Upon investigation they discover that PayPal has updated a certificate on their service that requires any other service trying to connect to them to also update to the new version. Certificates are essentially private keys services use to identify trusted partners. Upon review of this incident the call center, operations team, and product engineering team identify a gap in the service monitoring and agree to implement a PayPal monitoring solution and an alert system for when certificates need to be updated. The solution to add an alert will warn the operations team when the certificate is due to expire and thus prevent a future outage. On the off chance that this does happen again, the enhanced monitoring will reduce the time to detection and time to resolution. The key to the successful use of this approach is to bring all the data and all the stakeholders together to identify root cause and optimal improvements. Just Run It Once a Week, The Customers Won't Mind Oops! My own passion for QoS started many years ago after we launched the new version of the billing platform. It is a very embarrassing story, but it happened quite a long time ago and many names have been changed to protect the involved but innocent. The new billing platform was a major internal service that let us track all the users of the subscription services and allowed us to bill them correctly and be paid by the credit card companies. One element of the system was the nightly billing batch job. We ran it at night more out of history than for any design reason. A couple of weeks after launch, we were all in the war room triaging bugs. Operations joined us a bit late and started to share the issues they'd seen with the new system. Zach informed us that the nightly billing batch job was taking about five days to complete. They had only had two successful runs so far.
We were all shocked we knew that the credit card number encryption would add some overhead, but not to this degree. Fixing this bug would mean a complete rewrite of the batch job that would certainly cause us to slip out the next major release by several weeks. Just then someone, and I really can't remember who, said, "Why don't we just charge customers once a week. I mean if they were supposed to pay us on June 1st and we wait until June 6th, who's going to care I know we'll lose interest for Microsoft, but at least the customers will be happy." This really did seem like a brilliant idea. We were only nine months away from the next major release, so the batch job could wait until then. We did decide to add a metric to our QoS scorecard for billing batch job time to completion with a goal of less than 168 hours because that would be more than seven days and that was just unacceptable. Two months later, we all got together for the monthly QoS review. We had Dev, Test, PM, and Operations in the room. The call center team was on the speakerphone; they always seemed more comfortable on the phone than actually being in the room for a meeting. Each team began by going over their section of the scorecard. The product team called out that they'd fixed 15 bugs this month and that page load time for the signup pages had improved by 200 percent. We all felt that would surely have a positive impact on customer satisfaction. Next, operations presented. Zach went through the numbers on availability and ticket volume. He got to his last metric, which was billing batch job completion time. "The batch job is starting to take longer, but we are still under 130 hours. At the rate customers are being added to the system, we could exceed our one week max in about a year." Everyone was actually pleased by this news because we were well on track to have the next major version out long before we ran out of buffer time. The call center team started their presentation by going over call volume and time to resolution. When they got to call by category, we had a new number one category with the temporary name of "you bounced my check." The call center team didn't know quite what to do because customers were calling in and blaming us for overdraft charges on their bank accounts. "This doesn't make sense," Chris said. "We only take credit cards, so we can't cause checks to bounce." Bharat leaned forward and spoke up so that those on the phone could hear him, "I could see us impacting bank accounts. I don't know about all of you, but when I signed up for my test account I used the credit card number from my bank ATM card." Bharat was right. People were just starting to use ATM cards as substitutes for credit cards. This still didn't explain why they were blaming us for overdraft charges. Brett asked, "Are they saying we're overcharging them " "No," said the voice on the phone, "they are saying we are taking money out of their accounts and either causing them to bounce checks, or when we do take the money, they don't have the funds in the account." Most of us in the room were starting to realize what was going on, but it was Ben that first said, "It's the batch job!" Yes, many of our customers had grown used to us taking payments on a specific day of the month. After that, any remaining funds they could spend as they liked. We realized we could not wait to fix the batch job. If not for the QoS process, we would not have had all of these teams and data in the room and would not have been able to realize how a bad design and a bad decision were leading to customer
dissatisfaction. Within a few weeks the new job was put into production and completed in just a few hours. It was a major rewrite with a substantial amount of parallel processing and dedicated equipment for the decryption process. We also changed the metric on the QoS scorecard to be "Days in a row the billing job finished in under 5 hours." I left the team shortly after this, but last I heard they had topped a thousand days and were closing in on two thousand.
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