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Multi-partition data processing

For queries that only touch a single partition, the effort of sending queries through a stream and collecting a stream of responses is perhaps overkill. However, this idea opens the possibility of distributed execution of complex queries that need to combine data from several partitions, taking advantage of the infrastructure for message routing, partitioning, and joining that is already provided by stream processors.

Storm’s distributed RPC feature supports this usage pattern (see “Message passing and RPC” on page 468). For example, it has been used to compute the number of people who have seen a URL on Twitter—i.e., the union of the follower sets of everyone who has tweeted that URL [48]. As the set of Twitter users is partitioned, this computation requires combining results from many partitions.

Another example of this pattern occurs in fraud prevention: in order to assess the risk of whether a particular purchase event is fraudulent, you can examine the reputation scores of the user’s IP address, email address, billing address, shipping address, and so on. Each of these reputation databases is itself partitioned, and so collecting the scores for a particular purchase event requires a sequence of joins with differently partitioned datasets [49].

The internal query execution graphs of MPP databases have similar characteristics (see “Comparing Hadoop to Distributed Databases” on page 414). If you need to perform this kind of multi-partition join, it is probably simpler to use a database that provides this feature than to implement it using a stream processor. However, treating queries as streams provides an option for implementing large-scale applications that run against the limits of conventional off-the-shelf solutions.

 
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