Saturday, May 3, 2014

Best Practices for Storing Joda-Money Values in the Google App Engine Datastore

This article describes some best practices for storing Joda-Money currency values in App Engine using the objectify-appengine library.  It is based upon knowledge outlined in a previous article called, "Best Practices for Storing BigDecimal Values in the Google App Engine Datastore."


-------------------------------------------------------------------------------------

One of the best libraries around for manipulating currency values in Java is called Joda Money, which supplies two nice classes to represent currency: org.joda.money.Money and org.Joda.money.BigMoney.  If you're not familiar with them, the biggest difference is that the BigMoney class is "...not restricted to the standard decimal places of a Money object, and can represent an amount to any precision that a BigDecimal can represent."  

See the Javadoc for more details, but it's worth reiterating that both of these Joda classes are backed by BigDecimal, so before continuing you might want to explore some potential issues with storing BigDecimal values in the App Engine Datastore here.

Existing Options: Objectify
The objectify-appengine library is an all-around excellent tool in any App Engine developer's tool-kit.  It handles all of the typical Java types in the Datastore, and also has the ability to store custom data-types via its Translator framework.  This is a powerful feature of Objectify, and to this end the library comes with two optional translators that can handle joda-money values: MoneyStringTranslatorFactory and BigMoneyStringTranslatorFactory.

MoneyStringTranslatorFactory & BigMoneyStringTranslatorFactory
The MoneyStringTranslatorFactory and BigMoneyStringTranslatorFactory classes are great alternatives for storing joda-money values.  Each essentially translates a BigMoney class into it's .toString() equivalent when storing to the datastore, and then creates a new BigMoney object from this String when loading the value from the datastore.  For example, a BigMoney object with the "USD" currency code and a value of "35.751" would be stored to the Datastore as the String: "USD $35.751".  

As the Javadoc states in the class definition, however, this implementation is not ideal for all use-cases, and can be problematic in certain cases.  

Improper Native Datastore Comparison
For example, in Java a BigMoney object with a value of "USD $11.25" would be "less-than" a comparable BigMoney object with a value of "USD $100.25", which makes sense from a currency and number perspective.

However, when translated to their String-equivalents, and then compared, the value “USD $11.25” is lexicographically “greater than” the value “USD $100.25”, which is somewhat counter-intuitive if you're not aware of how lexicographic String comparison works.

While technically accurate (the String “USD $11.25” is greater than "USD $100.25"), it's wildly incorrect from a currency perspective.  Negative values and divergent currency codes will tend to exacerbate this problem.  For example, “USD $35.00” is lexicographically "greater-than" “AUD $500.00” while numerically the $500 value is greater than the $35 value.

A Potential Improvement: JodaMoneyTranslatorFactory
A library called objectify-utils provides a potential improvement over the default translator when it comes to storing joda-money fields into the Datastore.  This new translator is called JodaMoneyTranslatorFactory, and is build upon the lexicographic encoding libraries discussed in another post here, which are used to ensure the proper storage of BigDecimal values in the App Engine Datastore.

From a high-level, the main benefits to using JodaMoneyTranslatorFactory are that developers can use arbitrary precision money values (just like with BigDecimals) and can rely on consistent greater-than/less-than filtering as well as sorting of Money values natively via the Datastore. 

Additionally, JodaMoneyTranslatorFactory separates the Currency code from the currency amount in the Datastore, allowing for finer-grained control over each data point, as well as potential sorting across money values with different currency codes (though the use-case for this is somewhat dubious since it is difficult to envision the usefulness of sorting money values in different currency codes).

A Final Note About Potentially Inconsistent Money/BigMoney Indexing
Another area that can be the source of stubborn inconsistencies when dealing with Money types in general is that of Money/BigMoney object instantiation.  

Since these classes are based upon a BigDecimal, it can be very easy to create two numbers with the same "amount", but with different decimal-place precision values.  An example can be seen in the number "$35.990" and the equivalent number "$35.99".  While numerically equivalent (depending on who you ask), these two numbers would be considered "not equivalent" both per the BigDecimal .equals() contract, and lexicographically when stored as an encoded String in the Datastore.

This may or may not make sense, depending on your point of view, but luckily Joda provides the #isEqual method which allows us to perform money comparisons independent of scale.  In this case, "$35.990" and "$35.99" would be considered equal.

In the Datastore, however, we have no such mechanism, even when lexicographically encoding our Money values.  Thus, for purposes of native datastore indexing, the value "35.990" would still be considered "greater-than" "$35.99".

In practice, this is probably not a big deal, because these two values would still be returned "next to" each other in a sorted result.  However, it's something to be aware of when using either Translator.

No comments:

Post a Comment