12/23/2023 0 Comments Database code for us newstream![]() The persistentAggregate function knows how to store and update the results of the aggregation in a source of state. Then, each group is persistently aggregated using the Count aggregator. First the stream is grouped by the “word” field. The rest of the topology computes word count and keeps the results persistently stored. It simply grabs the sentence, splits it on whitespace, and emits a tuple for each word. Here’s the definition of Split:Īs you can see, it’s really simple. Each sentence tuple creates potentially many word tuples - for instance, the sentence “the cow jumped over the moon” creates six “word” tuples. The next line of the topology definition applies the Split function to each tuple in the stream, taking the “sentence” field and splitting it into words. That state could be updated by Trident (like in this example), or it could be an independent source of state.īack to the example, the spout emits a stream containing one field called “sentence”. Finally, Trident has first-class functions for querying sources of realtime state. ![]() Of course, processing each small batch in isolation isn’t that interesting, so Trident provides functions for doing aggregations across batches and persistently storing those aggregations - whether in memory, in Memcached, in Cassandra, or some other store. The API is very similar to what you see in high level abstractions for Hadoop like Pig or Cascading: you can do group by’s, joins, aggregations, run functions, run filters, and so on. Trident provides a fully fledged batch processing API to process those small batches. Generally the size of those small batches will be on the order of thousands or millions of tuples, depending on your incoming throughput. For example, the incoming stream of sentences might be divided into batches like so: Trident processes the stream as small batches of tuples. Trident keeps track of a small amount of state for each input source (metadata about what it has consumed) in Zookeeper, and the “spout1” string here specifies the node in Zookeeper where Trident should keep that metadata. Input sources can also be queue brokers like Kestrel or Kafka. In this case, the input source is just the FixedBatchSpout defined from before. TridentTopology has a method called newStream that creates a new stream of data in the topology reading from an input source. First a TridentTopology object is created, which exposes the interface for constructing Trident computations. Here’s the code to do the streaming word count part of the computation: This spout cycles through that set of sentences over and over to produce the sentence stream. Implement queries to get the sum of the counts for a list of wordsįor the purposes of illustration, this example will read an infinite stream of sentences from the following source: This example will do two things:Ĭompute streaming word count from an input stream of sentences Let’s look at an illustrative example of Trident. It builds upon Storm’s foundation to make realtime computation as easy as batch computation. We’re really excited about Trident and believe it is a major step forward in Big Data processing. Trident has consistent, exactly-once semantics, so it is easy to reason about Trident topologies. In addition to these, Trident adds primitives for doing stateful, incremental processing on top of any database or persistence store. If you’re familiar with high level batch processing tools like Pig or Cascading, the concepts of Trident will be very familiar - Trident has joins, aggregations, grouping, functions, and filters. It allows you to seamlessly mix high throughput (millions of messages per second), stateful stream processing with low latency distributed querying. Trident is a new high-level abstraction for doing realtime computing on top of Twitter Storm, available in Storm 0.8.0 (released today).
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