Partitioning Functionality into Separate Services in Java

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Partitioning Functionality into Separate Services
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It could be argued that the reading list belongs in the same service as the user data It is data owned by the user Logically, it makes sense to put it with the user data However, the reading list has a high write frequency, while the other user data probably has different characteristics Thus, it makes sense to split the reading list out into a separate service that can be run on its own It might make sense to keep the vote data with the user data or the entry data From the perspective of when the data will most likely be read, it might belong with the entry The list of the user s previous votes also needs to be pulled up later There are a few approaches to storing and exposing the vote data The first is to create a single-purpose service to answer all queries that have to do with votes This service would be responsible for storing aggregate statistics for entries as well as for individual users Thus, given a user, it could return the votes It could also return the aggregate stats for a list of entries Finally, another service call could be exposed to return the aggregate stats for a list of entries along with the user s vote when given a user ID This kind of service is very specific but has the advantage of being optimizable Vote data in this application is an example of high-read and high-write data Keeping it behind a single service gives you the ability to try out multiple approaches and iterate on the best solution The other method is to replicate or duplicate data where it is needed For example, the entries need to know the aggregate voting statistics Thus, when a new vote is created, the entry service should be notified of this either through the messaging system or through a direct HTTP call The vote itself could reside with the regular user data since the individual votes are always pulled up in reference to a specific user In the old ActiveRecord models, this method was taken through a counter cache 11, Messaging, shows how to enable this kind of cross-service replication with minimal effort and without slowing down the saving of the primary data Which approach to take really depends on the other needs Both are legitimate ways to solve the problem
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Partitioning on Join Frequency
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One of the most tempting methods of partitioning services is to minimize cross-service joins For a developer familiar with relational databases, this makes sense Joins are expensive, and you don t want to have to cross over to another service to join necessary data You ll make fewer requests if you can perform joins inside a service boundary The caveat is that almost all data in an application is joined to something else So which joins should you minimize If you took the concept of minimizing joins to its
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4 Service and API Design
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logical conclusion, you d end up with a single monolithic application If you re going to separate into services, you need to make some tough decisions about joins The previous two ways of partitioning by read/write frequency and logical separation are a good starting point First, data should be grouped together using those criteria For joins, it is best to consider how often particular joins occur Take, as an example, the activity stream part of the social feed reader Whenever a user takes an action such as voting, commenting, or following, all the followers must be notified in the stream If separate services were created for the activities and the users, the data about which users are following others would need to be accessible from both places If the follows information is not replicated to both places, then it should be put in the single place where it is needed most frequently Thus, you could minimize the joins that have to occur from the other service The other option for minimizing joins is to replicate data across services In normal relational database jargon, this is referred to as denormalization This is the more sensible course of action when looking at the activity stream and users services The information about which users are following each other should be replicated to the activity stream service This data represents a good candidate for replication because it has a much higher read frequency than write frequency High writes would mean duplicating all those messages and getting little return However, with a smaller number of writes than reads, the replicated data can make a difference Further, the write of the follow can be pushed and replicated through a messaging system, and the data will be replicated to both services seamlessly This method is covered in detail in 11, Messaging
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