Web Hooks and External Services in Java

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The code pulls one message at a time off the queue and ships it over to the Twitter API, using the method described in the previous section If it is successful, the queue is sent an acknowledgment, which permanently removes the message from the queue:
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# Create a new Servolux::Server and augment it with the # WorkerPool methods The run loop will be executed every # 30 seconds by this server server = Servolux::Servernew('WorkerPool', :logger => Loggernew($stdout), :interval => 30) serverextend WorkerPool # Startup the server The "before_starting" method will be # called and the run loop will begin executing This method # will not return until a SIGINT or SIGTERM is sent to the # server process serverstartup
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Here you are simply loading your custom WorkerPool module, setting the interval at which the run method will be executed, and starting up the server As you might imagine, implementing queues and worker pools to support scaling an application and taking slow writes out of the user s request lifecycle is a significant change and addition to the infrastructure that a team is supporting These worker pools should be managed and instrumented in much the same way as the actual application servers in order to ensure that they are running and healthy at all times The example here is relatively generic and would require that additional considerations be dealt with in a live application This is left for you as an exercise because the actual requirements are unique to the project at hand
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Ensuring Performance and Reliability
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Using messaging and work queues to bring external services into in an application is a necessary step in increasing performance and bringing a service-oriented design to fruition You need to give some thought to how the connections to external services are managed They should be isolated to ensure that the performance of one service does not have a negative impact on others For your own APIs that are exposed to the public, you should be careful to ensure their reliable operation The following sections
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cover some of the factors you need to take into account when working with and exposing services to the public
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Imagine that there is a shipping company that has one warehouse All trucks deliver packages to this warehouse and pick them up there for delivery elsewhere in a huband-spoke system One day a hurricane destroys the warehouse All shipping is halted Queuing systems operate in much the same way as this warehouse and shipping system Therefore, queues should be segregated to prevent failure Some common ways that queues can be separated are by feature, by current state of the work, by region, and by customer When integrating with multiple external services, queues should always be segregated by service Segregating queues by service allows work to continue even if one service is down For example, if an application pushes status updates to both Facebook and Twitter, and the work for these tasks both go through the same queue and worker pool, an outage at Twitter will affect the application s ability to send messages to Facebook Maintaining separate queues for each service ensures that a single failure does not cascade to the rest
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Metrics
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Metrics are a window into the operational health of a system Basic metrics that are important in queuing systems include the size of each queue, the length of time any item spends in the queue before being picked up by a worker, and the rates of ingress and egress Because queues can sometimes abstract away pain points that customers would normally feel immediately, knowing these simple metrics can put an operations team ahead of issues that may be happening in the system but that customers have not yet noticed For instance, if the queue of status updates going to Twitter is growing, it may be because the marketing plan is finally working, and users are joining en masse But a quick comparison with the total time that messages are staying in the queue can help a team instantly distinguish between this situation and something being broken Having metrics for services that are exposed within an application is just as important However, the determination of what to measure is likely to be quite unique Some API metrics that may matter in an application are usage by geography, usage by customer, usage by time of day, usage by application feature, and internal usage versus external usage Performance in any of these cases is also important, as is general usage Keeping track of these metrics can help determine when one part of an application is
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