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tional writesWith eventual consistency, you may not see the change immediately, but subsequent reads always reflect the update either fully or not at all; it is never seen partially When splitting a write across requests, each partial write remains atomic, but the write as a whole is notThe loss of atomicity should be carefully considered The other downside to request splitting is the impact on request volume If you are able to maintain 100 writes per second normally, splitting each write into four smaller writes quadruples the request volume, and you may only be able to write data at a net rate of 25 items per secondThis can be alleviated in the same way as throughput issues, spreading the data over multiple domains Avoiding high-attribute counts are more likely to be an issue for inserts Updates and deletes frequently do not require passing as many attributesWhen PutAttributes is used to update an item, only the changed attributes need to be passedThere is no reason to pass the unchanged values, and high cost is a good reason not to Deletes are typically rarer than inserts and updates, and it is, in fact, difficult to rack up high charges using deletes If you want to delete specific attributes, those attributes must be passed specifically, and deleting the whole item is done by passing the item name and no attributesThe main thing that can get you in trouble with deletes is deleting items by passing every attribute Automatic Attribute Merging and Request Splitting The one drawback that applies to all of these workarounds is the fact that you have to somehow code the knowledge of when to do them and when not It is undesirable to write a lot of application code just to get around the database pricingThis is something best done declaratively in the application, in combination with a smart client providing the implementation This declarative information is just the sort of thing that fits into application-level metadata Having a way to declare an attribute as an integer could allow a smart SimpleDB client to read, write, and query for it using a format suitable to the task In the same way, a mechanism could be provided to specify which attributes can be merged and which attributes cannot be split up among multiple requests The value of having a way to define this information declaratively and then storing it somewhere accessible is that tools will also be able to use this information
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Sending a write to SimpleDB with 256 attributes is expensive at upwards of $472 per million It is especially expensive compared to writing one attribute for $3 per million However, it isn t nearly as expensive as getting back a QueryTimeout response A QueryTimeout can come back as a response from any query Complex queries are more likely to experience this, but it can happen with queries using a single comparison It is difficult to predict when they will happen, but often they are easy to reproduce once they do happen
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11 Improving the SimpleDB Client
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One example is a domain I have that is close to the 10GB limit with 124 million items in it Each item has an attribute named tags containing one or more tag valuesA frequent query for this domain fetches the 20 most recently created items for a given tag The query looks like this:
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SELECT title, description, dateCreated, userCreated, tags FROM `domain_024` WHERE tags = '%s' AND dateCreated IS NOT NULL ORDER BY dateCreated DESC LIMIT 20
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To run the query, I replace %s with the name of the tag I want to queryThis query runs all the time, each time with one given tag Some tags are very common, being present in more than 200,000 items Other tags are much more rare Initially, I expected the popular tags to be most likely to have problems, because of the combination of a sort and a where clause that is not very selective However, this did not turn out to be the case at all Popular tags like family matching 203,878 items, travel matching 201,416 items, and friends matching 37,699 items had no problems at all Performance is always good, and they never see QueryTimeoutAt the other end of the spectrum are the tags set in very few itemsThese rare tags also have flawless performance and no QueryTimeout errors, from tags with only one match up to tags like autumn with 544 matches However, some tags in the middle almost always return with the QueryTimeout error From party matching 11,694 items to dance with 5378, all of the problem tags are middle of the road with regard to selectivity I have been unable to discern any pattern to it, other than that they mostly happen in domains with millions of items When you get the QueryTimeout, it is bad in many ways First of all, it takes the full five seconds to return, which is far too long to wait when building a web page, for exampleThen when it finally does come back, you get an error message and no dataYou don t even get a NextToken; you get nothingWell, you don t get nothing you get the maximum box usage charge of all operations The box usage charge for a QueryTimeout is a constant value of $77826 per million, or $85609 for Singapore, N California and IrelandThat is 65% higher than a fully loaded write operation, and equal to the two rightfully expensive operations CreateDomain and DeleteDomainWhen creating and deleting domains, at least you know ahead of time that they will be expensive and can use them in a strictly administrative capacity QueryTimeout error could come back from any query If you have millions of attributes in a domain that gets regular use, you will want to have something in place to alert you to a high incidence of these errors If you get 42 of them per day, it adds $100 to your monthly billThis is a useful feature in a smart SimpleDB client the ability to track QueryTimeout errorsThe seriousness comes from the fact that the same queries tend to bring out that error at a high rate
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