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As with gathering read and write data, compiling some data storage requirements early in design will help smooth the physical implementation Even during the design phase, knowing ahead of time how much data you ll be storing can affect some design decisions Let s go back to the work-flow application for those high school teachers Table 52 shows the sample data being input for those attendance records; we ll call this the Attendance entity Table 52 Sample Data Being Input for Attendance Records
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Data Type
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Int Datetime char(20) Int char(200)
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StudentID Date Class TeacherID Note
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Student identifier Date for attendance record Name of the class attended (or not) Teacher identifier Notes about the entry (eg, tardy due to weather )
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Obviously, there are some assumptions being made here concerning StudentID and TeacherID (being foreign keys to other entities) For now, let s focus on the data types that were chosen As discussed in 3, we know the amount of bytes each record in the physical table will occupy Here, we have 8 bytes of int data, 220 bytes of char data, and 8 bytes from the datetime field Altogether, we have 236 bytes per record If we have 1,200 students in the school, for each date we have about 283,200 bytes, or 27656K The average school year is about 180 days; this is roughly 48MB of data for a school year What does this mean to us The attendance data, in and of itself, is not likely to be a storage concern Now, apply this exercise quickly to every entity that you are working on, and you ll find roughly how much data you ll be storing Although this knowledge has huge value during implementation, you may wonder why we re talking about it now Suppose that, during the design phase, you are given a last-minute requirement to change the identity fields because the new student identifiers require a much longer field Now the stakeholders want an alphanumeric ID (for now, we ignore the merits of such a decision) Suddenly, we re storing a 12-character char field
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for both of those two int fields Substituting the new values, we end up with 52MB of data for the same entity and time period Although in this case the difference is negligible, in other entities it could have a huge impact Knowing what the impact will be on those larger entities may drive you to review the decision to change a data type before committing to it, because it could have a significant effect in the physical implementation Again, most of this information will be more useful later in the project Remembering to gather the data (and compile and recompile it during initial design) is the important thing for now
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This might be the most important type of performance-related data to obtain during requirements gathering You need to forecast the kind of transaction load your data model will need to support Although the pure logical design will be completely independent of SQL Server s performance, it s likely that you will be responsible for developing and implementing the physical database as well (or at least asked to provide guidance to the development team) And as we discussed in 4, the degree of normalization, and the number of entities, can lead to bulky physical databases, resulting in poor query performance As with the other types of data being gathered, you glean this information primarily from interviews, observations, and review of the existing system Generally, to start identifying the transaction load on your model, you must identify pieces of information that relate to both transaction speed and transaction load For example, whenever there is a process in place that requires a user to wait for the retrieval of data such as a customer service operator bringing up a customer record you ll need to understand the overall goal for the expediency of that record retrieval Is there a hard-and-fast business rule in place For example, a web application might need to reduce the amount of time a web user must wait for a page to return with data, and therefore it would restrict how much time a database query can take Similarly, you ll want to take notes on how many users are expected to hit the database built from your model at any given time Will there be internal and external users How many on average, and how many during peak times What is the expected number of users a year from now The answers to these questions will give you insight into performance expectations
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