A Closer Look at Select in Java

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4 A Closer Look at Select
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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
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Composite Attributes
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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:
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userid='001' and viewcount > '00025'
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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:
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`userid-viewcount` LIKE '001%' and `userid-viewcount` > '00100025'
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Now both comparisons are using the same indexThe actual userid has to be concatenated at the beginning of the constant viewcount valueThis works with the strictly greater-than operator because the first comparison has already restricted the output to only those items with userid beginning with 001 Performance with Composite Attributes
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Generating an extra attribute for each item that must be kept in sync is an advanced technique It has the drawbacks of added complexity, added storage, and added bandwidth You definitely want to be sure it is warranted before you implement it If you do implement it, keep the following in mind:
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You probably will only need to span two columns with this technique It is possible to do more, if there is more than one equality test in your Select, but using two may well be sufficient Although you are using the synthetic field for comparison and maybe even for sorting, you do not need to return that field and then parse it; just return the regular fields as you need them
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Improving Query Performance
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The performance gain from this technique will depend on the correlation between the two values If there are only a few items for each userid or only a few items meeting the viewcount criteria, this technique will not be very helpful If there are many matches to both criteria but very few in common, this method should give a sizeable performance boost In the case where the composite criteria is highly selective, many more comparisons can safely be added to the query without hurting performance, since only a few items remain to be filtered You must be careful to maintain the proper field length and padding when composing a composite attribute from formatted numbers or dates
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Judicious Use of LIKE
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When using the LIKE operator in a query to do wildcard matching, placement of the wildcard character can have a large impact on performance Placing the wildcard at the end, as in this example comparison, allows the index to be used when fetching results:
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`name` LIKE 'Will%'
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This yields excellent performance However, placing a wildcard character at the beginning like the following prevents the index from being used and results in a full table scan that, depending on your data, is likely to be much slower:
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`email` LIKE '%@ykcom'
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This is true with the character data indexed in any type of database, since characters are indexed from left to right In a relational database, you have the option to create an index with the characters reversed and use a reverse function in the SQL queryYou create a similar solution in SimpleDBYou cannot create the reversed index directly and there is no built-in reverse function, but if you include an extra attribute in your data with values that you have reversed yourself, the reversed index will be created for you automatically This leaves you with the task of reversing the literal value in the query yourself, as in the following:
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`email-reversed` LIKE 'mocky@%'
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Now the wildcard is back on the right where the index can be used Of course, you don t want to be in a situation where you have constant string values typed into a hard-coded query in reverse In reality, you don t want to be in a situation where any of the query literals are hard coded; they should be passed in as parameters Publicly available SimpleDB clients today do not have support for a programming model where prepared statement type constructs are used Hopefully, in the near future, such functionality will be available It is conceivable that client-side functions like reverse() could also be implemented in such a layer
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