not sure if this exists right now, but i would love to see a good tutorial/article on storm showing how to use it to solve real life problems instead of "hey here's a way to split words and count them".
It is a fair criticism of the state of Storm documentation and examples. The book Storm Applied by Manning details tons of Storm usage examples, ranging from the technically advanced to the mundane. I recommend that book to learn more about real world usage (disclosure: I wrote the foreword).
Also, my team is working on an open source example project called birding that uses streamparse and pykafka together to build filtered firehose tweet streams from Twitter's public API. We think this will illustrate both Storm and Kafka really well, and be beyond the typical word count examples. See https://github.com/Parsely/birding to track that effort.
We're using Storm to handle high volume Kafka feeds to process advertising bid requests and other user data in machine learning models. Most of it is proprietary, unfortunately, so I can't write the article you'd like (and that I would have liked before starting this).
So far Storm is working as advertised. Frankly, what it does isn't that terribly difficult, but it's good to have a well-tested implementation of it so we can focus on our business logic.
Comments
not sure if this exists right now, but i would love to see a good tutorial/article on storm showing how to use it to solve real life problems instead of "hey here's a way to split words and count them".
It is a fair criticism of the state of Storm documentation and examples. The book Storm Applied by Manning details tons of Storm usage examples, ranging from the technically advanced to the mundane. I recommend that book to learn more about real world usage (disclosure: I wrote the foreword).
Also, my team is working on an open source example project called birding that uses streamparse and pykafka together to build filtered firehose tweet streams from Twitter's public API. We think this will illustrate both Storm and Kafka really well, and be beyond the typical word count examples. See https://github.com/Parsely/birding to track that effort.
We're using Storm to handle high volume Kafka feeds to process advertising bid requests and other user data in machine learning models. Most of it is proprietary, unfortunately, so I can't write the article you'd like (and that I would have liked before starting this).
So far Storm is working as advertised. Frankly, what it does isn't that terribly difficult, but it's good to have a well-tested implementation of it so we can focus on our business logic.