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Designing Applications for Static Scalability
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To ensure that your application runs correctly in a cluster, you have to take a number of precautions during design and development. If you don t watch out for the following, then your application will appear to run correctly but might produce incorrect results. The most important design and development consideration for scalable applications is to take special care about all in-memory state of your application. More formally speaking, you have to make a distinction between shared storage and local storage. Shared storage, like a database, is used by multiple nodes at the same time so that each node can reasonably assume that the content is correct and current. However, if you keep in-memory copies of your data at each node, then each modification is processed independently so that the node-specific caches can divert, resulting in the situation that each node has a different value in its cache. But let s look at an example. Let s assume that you ve designed a system that manages customer information using an interface containing the following two methods: interface ICustomerManager { Customer GetCustomer(); void StoreCustomer(Customer cust); } To increase performance, you decide that customer information wouldn t necessarily need to be read from the database. Instead, you simply cache this information in memory. Whenever StoreCustomer() is called, you first update the database and, if successful, afterwards also update the server s in-memory cache. This might seem like a reasonable way to approach this problem if your application only runs on a single server and if nobody else directly accesses your database. As soon as you scale out onto a cluster, this application will, however, produce incorrect results. Let s assume that the name of customer #123 is originally John Doe . As soon as a request to change this name
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CHAPTER 9 .NET REMOTING TIPS AND BEST PRACTICES
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reaches one of the nodes, it will update the database and its in-memory cache to reflect the new value of, say, John Moe . If a second request to retrieve the customer information for customer #123 hits a different node, the returned data will, however, still contain the old and meanwhile incorrect value of John Doe . Does this mean that you cannot use any in-memory caches when planning to deploy your application in a cluster No, definitely not. It just means that you have to look quite carefully at the type of data you are working with. Therefore, one of the first steps in creating scalable applications is to analyze the different kinds of data that is used by your application. It can be advantageous to split the list of database tables into three groups: Group 1: Static data. Group 2: Near-static, seldom changed data. Data that is updated in advance or that is only changed during certain time windows (for example, daily at midnight). Group 3: Operative, dynamic data. If you look at an online shop as a sample, static data are usually lists of zip codes, cities, payment terms, or the list of acceptable means of payment (various credit cards, bank transfer, check, cash, etc.). More often than not, this is also data that cannot be changed by the application itself but that is statically defined or altered using specific administrative tools. In the second group, you will find data that usually doesn t change more often than once a day. These are things like a list of articles, prices, etc. This group can also contain data that is changed at certain intervals, like once a day or once every hour. It is only important that you know exactly when and how often this data is changed. The last group of operative data contains all your real transaction data, the meat of your business. This is usually the most heavily changed data in your application, like customer information, inventory levels, and orders. You will see that, in practice, most of the tables in your database will contain data of the first two groups, and only few tables actually contain data that is changed on a regular basis by business transactions. If you notice during the analysis process that some tables in your system actually contain data of different categories, you can change the database model or simply treat the two kinds of data differently in your application. This happens, for example, if you originally decided to store your article s inventory levels (which change all the time) together with the static data in the same table. You would have to separate this (either in the database or just in your object model) into the real product information on one hand and the transactional information (the inventory level) on the other hand. Based on these three categories, you can now quite easily define matching caching strategies. The decision of whether or not to cache static data is made quite easily: you can usually cache everything that fits in terms of memory usage. Equally easy is the decision of whether or not to cache the operative data of the third group: you shouldn t store this data in memory, but instead always read/write the data to shared storage, for example, a database. Of course, there are exceptions for very specific applications, for example, if you can create fixed assignments between a number of customers and a number of hosts. In these applications, for example, you would store the complete customer data for all customers with names from A to G on one server, the data from H to N on a second, and the data from O to Z on a third one. But for most general applications, you should assume that these kinds of data should not be cached. The most complex category in terms of caching strategies is the second group of seldom changed data, as there are two different types. On one hand, you ll find information that is usually very stable for a longer period of time and that will be changed over a longer period of time,
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