Partitioning Functionality into Separate Services in Java

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Partitioning Functionality into Separate Services
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updating the rest of the application The first version of this service may use a MySQL database to store the feeds Later, you can change this to a more appropriate document database or key value store without touching any of the other application code Thus, the application reaps the benefits of services in some areas while still maintaining the flexibility of the Rails rapid development style where appropriate
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Partitioning on Logical Function
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Generally, you want to partition services based on their logical function This means you have to consider each piece of functionality in an application and determine which parts fit logically together For some boundaries, this is a straightforward decision In the social feed reader, one service decision stands out as being driven by logical function: updating feeds This service really sits apart from the application Its only purpose is to download XML files, parse them, and insert the new entries into the data store It doesn t make sense to include this in the application server, user service, or any other service Even as part of the Rails application, the updating of feeds exists as a process that runs outside the request/response life cycle of the user In some cases, a feed update may be triggered by user interaction, while that update still occurs outside the request by getting routed to the Delayed Job queue Given that this separation already exists, it makes sense to pull the feed updater into its own service so that it can remain isolated In this way, you can optimize how and when feeds get updated without any other part of the system knowing about these changes The feed updater service highlights the advantages of isolated testability When the application is updated, the updater service need not be tested; conversely, when the feed update system is improved or updated, the application layer doesn t need to be redeployed or tested The feed and feed entry storage service is also separated logically Users can subscribe to feeds, but they don t own them All users of the system share feed and entry data, so this data can be seen as logically separate from users Other examples of services that are logically separated in applications are those for sending email, monitoring systems, collecting statistics, or connecting with external data entities (such as feed updaters, crawlers, or other external APIs) Lower-level services are also partitioned based on logical function As part of your infrastructure, you may decide that a shared key value store is useful for multiple services Similarly, a shared messaging system or queue system might be useful for many
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4 Service and API Design
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services These types of services have a single function that they serve that can be shared across all your other services Amazon provides these in the form of S3 for key value storage and SQS for message queues The higher-level parts of an application, such as views and controllers, can also be partitioned based on logical function For example, a comment thread or discussion could exist across many pages of a site Instead of using a partial in the application to render HTML, a call could be made to a comment service that renders the full HTML snippet for the comments section of the page Generally, services are responsible for data and business logic However, in applications that contain many pages, it may be necessary to split the partial page rendering out into services that can be run and tested independently
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Partitioning on Read/Write Frequencies
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Looking at the read and write frequency of the data in an application is another method that can help determine the best way to partition services There may be data that gets updated very frequently and read less often, while other data may be inserted once and read many times A good reason to partition along these lines is that different data stores are optimized for different behavior Ideally, a service will have to work only with a single data store to manage all its data Future implementation choices should partly drive your partitioning choices For data with a high read frequency, the service should optimize a caching strategy For high-write-frequency data, the service may not want to bother with caching Splitting these out to different services makes the implementation of each individual service easier later Instead of having to optimize both reads and writes in a single place, you can simply make one service handle the read case really well while the other service handles the write case Handling both reads and writes usually requires a trade-off of some sort Services that are designed to primarily handle one case need not make this trade-off The choice is made in advance by designating that data be stored in one location instead of another The social feed reader involves data that crosses all ends of the spectrum: high read and low write, high write and low read, and high read and high write For instance, user metadata would probably not be updated very frequently Meanwhile, the user reading list, voting information, and social stream all have frequent updates The reading list is probably the most frequently updated data in the system As a user reads each entry, it must be removed from his or her list Further, when each feed is updated, the new entries must be added to every subscriber s reading list
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