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One of the primary areas of contention around SimpleDB and other NoSQL solutions centers on the lack of a database schema Database schemas turn out to be very important in the relational modelThe formalism of predefining your data model into a schema provides a number of specific benefits, but it also imposes restrictions SimpleDB has no notion of a schema at all Many of the structures defined in a typical database schema do not even exist in SimpleDBThis includes things such as stored procedures, triggers, relationships, and views Other elements of a database schema like fields and types do exist in SimpleDB but are flexible and are not enforced on the server Still other features, like indexes, require no formal definition because the SimpleDB service creates and manages them behind the scenes However, the lack of a schema requirement in SimpleDB does not prevent you from gaining the benefits of a schemaYou can create your own schema for whatever portion of your data model that is appropriateThis allows you to cherry-pick the benefits that are helpful to your application without the unneeded restrictions One of the most important things you gain from codifying your data layout is a separation between it and the applicationThis is an enabling feature for tools and application plug-insThird-party tools can query your data, convert your data from one format to another, and analyze and report on your data based solely on the schema definitionThe alternative is less attractiveTools and extensions are more limited in what they can do without knowledge of the formats For example, you cannot compute the sum of values in a numeric column if you do not know the format of that column In the degenerate case, developers must search through your source code to infer data types In SimpleDB, many of the most common database features are not available Query, however, is one important feature that is present and has some bearing on your data formatting Because all the data you store in SimpleDB is variable length character data, you must apply padding to numeric data in order for queries to work properly For example, if you want to store an attribute named price with a value of 26994, you must first add leading zeros to make it 0000026994 This is required because greater-than and lessthan comparisons within SimpleDB compare each character from left to right Padding with zeros allows you to line up the decimal point so the comparisons will be correct for all possible values of that attribute Relational database products handle this for you behind the scenes when you declare a column type is a numeric type like int This is a case in SimpleDB where a schema is beneficialThe code that initially imports records into SimpleDB, the code that writes records as your app runs, and any code that uses a numeric attribute in a query all need to use the exact same format Explicitly storing the schema externally is a much less error-prone approach than implicitly defining the format in duplicated code across various modules Another benefit of the predefined schema in the relational model is that it forces you to think through the data relationships and make unambiguous decisions about your data layout Sometimes, however, the data is simple, there are no relationships, and creating a data model is overkill Sometimes you may still be in the process of defining the data.

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Before an application can read UDP packets sent to it by remote machines, it must bind a socket to a local UDP port using DatagramSocket, and create a DatagramPacket that will act as a container for the UDP packet's data Figure 5-3 shows the relationship between a UDP packet, the various Java classes used to process it, and the actual application Figure 5-3 UDP packets are received by a DatagramSocket and translated into a DatagramPacket object

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What we have done is transform the original sequential calculation (find roots for nodes one "hop" from a root, then find roots for nodes two "hops" from a root, etc) into a calculation that computes a partial result (successor) for each node and then repeatedly combines these partial results, first with neighboring results, then with results from nodes two hops away, then with results from nodes four hops away, and so on This strategy can be applied to other problems that at first appear unavoidably sequential; the Examples section presents other examples This technique is sometimes referred to as pointer jumping or recursive doubling An interesting aspect of this restructuring is that the new algorithm involves substantially more total work than the original sequential one (O(N log N) versus O(N)), but the restructured algorithm contains potential concurrency that if fully exploited reduces total running time to O(log N) (versus O(N)) Most strategies and algorithms based on this pattern similarly trade off an increase in total work for a potential decrease in execution time Notice also that the exploitable concurrency can be extremely fine grained (as in the previous example), which may limit the situations in which this pattern yields an efficient algorithm Nevertheless, the pattern can still serve as an inspiration for lateral thinking about how to parallelize problems that at first glance appear to be inherently sequential Forces

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When an application wishes to read UDP packets, it calls the DatagramSocketreceive method, which copies a UDP packet into the specified DatagramPacket The contents of the DatagramPacket are processed, and the process is repeated as needed The following code snippet illustrates this process:

SimpleDB, on the other hand, handles all the indexing automaticallyAn index is created for each unique attribute name in a domain, and every attribute value is added to the index at the time you store itThere is no maintenance work of any kind for you to do with SimpleDB indexesAlthough you do not have any direct control over the indexing, having an understanding of it can help you get the best performance from your queries

Recasting the problem to transform an inherently sequential traversal of the recursive data structure into one that allows all elements to be operated upon concurrently does so at the cost of increasing the total work of the computation This must be balanced against the improved performance available from running in parallel

DatagramPacket packet = new DatagramPacket (new byte[256], 256); DatagramSocket socket = new DatagramSocket(2000); boolean finished = false; while (! finished ) { socketreceive (packet);

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