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Getting Started with Storm: Continuous streaming computation by Jonathan Leibiusky

By Jonathan Leibiusky

Whilst sizeable information is popping the realm the wrong way up, the following section of the revolution is already taking form: real-time facts research. This hands-on consultant introduces you to hurricane, a allotted, JVM-based process for processing streaming info. via easy tutorials, pattern Java code, and an entire real-world situation, you are going to methods to construct quickly, fault-tolerant suggestions that technique effects once the knowledge arrives. detect how effortless it truly is to establish typhoon clusters for fixing quite a few difficulties, together with non-stop facts computation, allotted distant technique calls, and information circulate processing.

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Extra resources for Getting Started with Storm: Continuous streaming computation with Twitter's cluster technology

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Analytics; ... public class ProductCategoriesCounterBolt extends BaseRichBolt { // ITEM:CATEGORY -> COUNT HashMap counter = new HashMap(); // ITEM:CATEGORY -> COUNT HashMap pendingToSave = new HashMap(); ...

Info Here you are locking the nextTuple method, so you never execute the ack and fail methods. In a real application, we recommend that you do the locking into a separate thread and use an internal queue to exchange information (you’ll learn how to do that in the next example, “Enqueued Messages” on page 34). This is great! You’re reading the Twitter stream with a single spout. If you parallelize the topology, you’ll have several spouts reading different partitions of the same stream, which doesn’t make sense.

The same technique can be applied in other situations—for example, for collecting log files from web servers. See Figure 4-2. info Figure 4-2. Direct connection hashing In the previous example, you connected the spout to a well-known device. You can use the same approach to connect to unknown devices using a coordinating system to maintain the device list. The coordinator detects changes to the list and creates and destroys connections. For example, when collecting log files from web servers, the list of web servers may change over time.

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