DDevelopment & Design

Recently at storm-users

I’ve been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa’s post “Football zero, Apache Pig hero”. Since I’ve encountered a lot of insightful and very interesting information I’ve decided to describe some of those in this post. nimbus will work in HA mode – There’s a pull request open for it already… but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we’ll look at reworking the HA pull request. (storm’s pull request) pig on storm – Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal. implementing topologies in pure python with petrel looks like this: class Bolt(storm.BasicBolt): def initialize(self, conf, context): ''' This method executed only once ''' storm.log('initializing bolt') def process(self, tup): ''' This method executed every time a new tuple arrived ''' msg = tup.values[0] storm.log('Got tuple %s' %msg) if __name__ == "__main__": Bolt().run() Fliptop is happy with storm – see their presentation here topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics. storm vs flume – some users’ point of view: I use Storm and Flume and find that they are better at different things – it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don’t necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors) That’s all for this day – however, I’ll keep on reading through storm-users, so watch this space for more info on storm development. I’ve been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa’s post “Football zero, Apache Pig hero”. Since I’ve encountered a lot of insightful and very interesting information I’ve decided to describe some of those in this post. nimbus will work in HA mode – There’s a pull request open for it already… but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we’ll look at reworking the HA pull request. (storm’s pull request) pig on storm – Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal. implementing topologies in pure python with petrel looks like this: class Bolt(storm.BasicBolt): def initialize(self, conf, context): ''' This method executed only once ''' storm.log('initializing bolt') def process(self, tup): ''' This method executed every time a new tuple arrived ''' msg = tup.values[0] storm.log('Got tuple %s' %msg) if __name__ == "__main__": Bolt().run() Fliptop is happy with storm – see their presentation here topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics. storm vs flume – some users’ point of view: I use Storm and Flume and find that they are better at different things – it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don’t necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors) That’s all for this day – however, I’ll keep on reading through storm-users, so watch this space for more info on storm development.

I’ve been reading through storm-users Google Group recently. This
resolution was heavily inspired by Adam Kawa’s post “Football zero, Apache Pig hero”. Since I’ve encountered a lot of insightful and very interesting information I’ve decided to describe some of those in this post.

  • nimbus will work in HA mode – There’s a pull request open for it already… but some
    recent work (distributing topology files via Bittorrent) will greatly
    simplify the implementation. Once the Bittorrent work is done we’ll look
    at reworking the HA pull request. (storm’s pull request)

  • pig on storm – Pig on Trident would be a cool and welcome project. Join
    and groupBy have very clear semantics there, as those concepts exist
    directly in Trident. The extensions needed to Pig are the concept of
    incremental, persistent state across batches (mirroring those concepts
    in Trident). You can read a complete proposal.

  • implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()

  • Fliptop is happy with storm – see their presentation here

  • topology metrics in 0.9.0: The new metrics feature allows you to collect
    arbitrarily custom metrics over fixed windows. Those metrics are
    exported to a metrics stream that you can consume by implementing
    IMetricsConsumer and configure with Config.java#L473.
    Use TopologyContext#registerMetric to register new metrics.

  • storm vs flume – some users’ point of view: I use Storm and Flume and find that they are better at
    different things – it really depends on your use case as to which one is
    better suited. First and foremost, they were originally designed to do
    different things: Flume is a reliable service for collecting,
    aggregating, and moving large amounts of data from source to destination
    (e.g. log data from many web servers to HDFS). Storm is more for
    real-time computation (e.g. streaming analytics) where you analyse data
    in flight and don’t necessarily land it anywhere. Having said that,
    Storm is also fault-tolerant and can write to external data stores (e.g.
    HBase) and you can do real-time computation in Flume (using
    interceptors)

That’s all for this day – however, I’ll keep on reading through storm-users, so watch this space for more info on storm development.

— Previous article

Integration tests with Maven and JUnit

Next article —

Dostawcy Trailer

You May Also Like
DDevelopment & Design

Need to make a quick json fixes – JSONPath for rescue

From time to time I have a need to do some fixes in my json data. In a world of flat files I do this with grep/sed/awk tool chain. How to handle it for JSON? Searching for a solution I came across the JSONPath. It quite mature tool (from 2007) but I haven't hear about it so I decided to share my experience with others.

First of all you can try it without pain online: http://jsonpath.curiousconcept.com/. Full syntax is described at http://goessner.net/articles/JsonPath/



But also you can download python binding and run it from command line:
$ sudo apt-get install python-jsonpath-rw
$ sudo apt-get install python-setuptools
$ sudo easy_install -U jsonpath

After that you can use inside python or with simple cli wrapper:
#!/usr/bin/python
import sys, json, jsonpath

path = sys.argv[1]

result = jsonpath.jsonpath(json.load(sys.stdin), path)
print json.dumps(result, indent=2)

… you can use it in your shell e.g. for json:
{
"store": {
"book": [
      {
"category": "reference",
"author": "Nigel Rees",
"title": "Sayings of the Century",
"price": 8.95
      },
      {
"category": "fiction",
"author": "Evelyn Waugh",
"title": "Sword of Honour",
"price": 12.99
      },
      {
"category": "fiction",
"author": "Herman Melville",
"title": "Moby Dick",
"isbn": "0-553-21311-3",
"price": 8.99
      },
      {
"category": "fiction",
"author": "J. R. R. Tolkien",
"title": "The Lord of the Rings",
"isbn": "0-395-19395-8",
"price": 22.99
      }
    ],
"bicycle": {
"color": "red",
"price": 19.95
    }
  }
}

You can print only book nodes with price lower than 10 by:
$ jsonpath '$..book[?(@.price

Result:
[
  {
"category": "reference",
"price": 8.95,
"title": "Sayings of the Century",
"author": "Nigel Rees"
  },
  {
"category": "fiction",
"price": 8.99,
"title": "Moby Dick",
"isbn": "0-553-21311-3",
"author": "Herman Melville"
  }
]

Have a nice JSON hacking!From time to time I have a need to do some fixes in my json data. In a world of flat files I do this with grep/sed/awk tool chain. How to handle it for JSON? Searching for a solution I came across the JSONPath. It quite mature tool (from 2007) but I haven't hear about it so I decided to share my experience with others.