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In the next phase, there is only one task because there are only two input blocks. The following table shows the results from this reduction phase.
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AggregationID SurveySlug Number of responses Histogram of first multiple choice question CCDE ADD AD E C B Average of first range question
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1.1 1.2 1.3 1.4 1.5 1.6
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travelfeedback bookreview productwishlist servicesatisfaction purchaseexperience brandrating
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4 3 1 1 1 1
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At this point, it s not possible to reduce the data any further, and the summary statistics for all the survey data that the application read during the original map phase have been calculated. It s now possible to update the summary data based on survey responses received after the initial map phase ran. You process all new survey data using MapReduce and then combine the results from the new data with the old in the final step.
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Scaling the Surveys Application
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This section describes how Tailspin designed one functional area of the Surveys application for scalability. Tailspin anticipates that some surveys may have thousands, or even hundreds of thousands of respondents, and Tailspin wants to make sure that the public website remains responsive for all users at all times. At the same time, survey owners want to be able to view summary statistics calculated from the survey responses to date. In 3, Accessing the Surveys Application, you saw how Tailspin uses two websites for the Surveys application: one where subscribers design and administer their surveys, and one where users fill out their survey responses. The Surveys application currently supports three question types: free text, numeric range (values from one to five), and multiple choice. Survey owners must be able to view some basic summary statistics that the application calculates for each survey, such as the total number of responses received, histograms of the multiple-choice results, and aggregations such as averages of the range results. The Surveys application provides a pre-determined set of summary statistics that cannot be customized by subscribers. Subscribers who want to perform a more sophisticated analysis of their survey responses can export the survey data to a SQL Azure instance.
Calculating summary statistics is an expensive operation if there are a large number of responses to process.
goals and requirements
There are also integration tests that verify the end-to-end behavior of the application using Windows Azure storage.
Because of the expected volume of survey response data, Tailspin anticipates that generating the summary statistics will be an expensive operation because of the large number of storage transactions that must occur when the application reads the survey responses. However, Tailspin does not require the summary statistics to be always up to date and based on all of the available survey responses. Tailspin is willing to accept a delay while the application calculates the summary data if this reduces the cost of generating them. The public site where respondents fill out surveys must always have fast response times when users save their responses, and it must record the responses accurately so that there is no risk of any errors in the data when a subscriber comes to analyze the results. The developers at Tailspin also want to be able to run comprehensive unit tests on the components that calculate the summary statistics without any dependencies on Windows Azure storage.
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To meet the requirements, the developers at Tailspin decided to use a worker role to handle the task of generating the summary statistics from the survey results. Using a worker role enables the application to perform this resource-intensive process as a background task, ensuring that the web role responsible for collecting survey answers is not blocked while the application calculates the summary statistics. Based on the framework for worker roles that the previous section outlined, this asynchronous task is one that will by triggered on a schedule, and it must be run as a single instance process because it updates a single set of results. The application can use additional tasks in the same worker role to perform any additional processing on the response data; for example, it can generate a list of ordered answers to enable paging through the response data. To calculate the survey statistics, Tailspin considered two basic approaches. The first approach is for the task in the worker role to retrieve all the survey responses to date at a fixed time interval, recalculate the summary statistics, and then save the summary data over the top of the existing summary data. The second approach is for the task in the worker role to retrieve all the survey response data that the application has saved since the last time the task ran, and use this data to adjust the summary statistics to reflect the new survey results. The first approach is the simplest to implement, because the second approach requires a mechanism for tracking which survey results are new. The second approach also depends on it being possible to calculate the new summary data from the old summary data and the new survey results without re-reading all the original survey results. Note: You can recalculate all the summary data in the Surveys application using the second approach. However, suppose you want one of your pieces of summary data to be a list of the 10 most popular words used in answering a free-text question. In this case, you would always have to process all of the survey answers, unless you also maintained a separate list of all the words used and a count of how often they appeared. This adds to the complexity of the second approach. The key difference between the two approaches is cost. The graph in Figure 4 shows the result of an analysis that compares the costs of the two approaches for three different daily volumes of survey answers. The graph shows the first approach on the upper line with the Recalculate label, and the second approach on the lower line with the Merge label.
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