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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.

By looking at the color in Figure 116, you can see the value of elementPosition For example, the red color in the top-right corner indicates elementPositionx is 1 and elementPositiony is 0

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

bits 5-0

To de ne a oating-point based parameter to an effect, you can de ne a constant register in HLSL:

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Although SimpleDB indexes every value, at the time of this writing, each index is single column onlyAWS has suggested that they may implement multi-column indexes in the future, but not the immediate future In the meantime,AWS suggests a way to simulate a multi-column index with an additional synthetic attributeThis technique is specifically applicable to queries where the main two attributes being searched are in the following configuration: one of them is being tested for equality, the other one is a being tested for a rangeThese types of queries are actually quite common, depending on the type of data you are storingAn example is searching for items with the following:

sampler2D inputBrush : register(s0); // sampler 0 = rendered input float inputOpacity : register(c0); // constant 0 = Opacity property float4 main(float2 elementPosition : TEXCOORD0) : COLOR { // // Multiply the opacity by the rendered content // return inputOpacity*tex2D(inputBrush, elementPosition); }

userid='001' and viewcount > '00025'

The bytes represent the character with the value ((x & 0x1f) << 6) + (y & 0x3f) Characters in the range \u0800 to \uFFFF are represented by three bytes, x, y, and z:

If this query becomes too slow, you can simulate an index that spans both the userid and viewcount columns by creating a synthetic attributeThe synthetic attribute is just a new attribute created programmatically that is a duplication of the two field values concatenated togetherThis value must be set for all items, and it must be kept up to date any time one of the values gets a PutAttributes call In this example, the new attribute might be called `userid-viewcount` and the Select statement to take advantage of it would have this comparison in it:

You can bind the parameter we de ned in the HLSL le to a dependency property by setting a property callback method to the PixelShaderConstantCallback method:

x: 1 1 1 0 bits 15-12 y: 1 0

`userid-viewcount` LIKE '001%' and `userid-viewcount` > '00100025'

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