DDevelopment & Design

mount.ntfs high cpu ubuntu

My computer suffers from sudden and continous hard drive load strokes. Sometimes it lasts for a few minutes and hence work is impossible because everything goes very slow.I’m trying to locate the cause because it makes me nervous :)Today I found one of…

My computer suffers from sudden and continous hard drive load strokes. Sometimes it lasts for a few minutes and hence work is impossible because everything goes very slow.
I’m trying to locate the cause because it makes me nervous :)

Today I found one of the causes. It’s updatedb.mlocate script which is responsible for scanning hard drives and build locate (a location of files) database. But due to some NTFS driver limitations that poor thing thinks that NTFS share is always new and needs to be rescan at every scheduled scan.

How to make sure that you have the same problem? When hard drive starts to choke check all IO processes with

sudo iotop

and look for updatedb.mlocate or mount.ntfs processes with high IO load. If this is the case try to fix it.
The solution is to point NTFS shares as non-scannable. To do this edit

/etc/updatedb.conf

and add your NTFS mount paths to PRUNEPATHS list. Although NTFS is listed in PRUNEFS entry that dummy mlocate script still would scan NTFS shares.

All that I found on Ubuntu forum.

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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.

DDevelopment & Design

Grails render as JSON catch

One of a reasons your controller doesn't render a proper response in JSON format might be wrong package name that you use. It is easy to overlook. Import are on top of a file, you look at your code and everything seems to be fine. Except response is still not in JSON format.

Consider this simple controller:

class RestJsonCatchController {
def grailsJson() {
render([first: 'foo', second: 5] as grails.converters.JSON)
    }

def netSfJson() {
render([first: 'foo', second: 5] as net.sf.json.JSON)
    }
}

And now, with finger crossed... We have a winner!

$ curl localhost:8080/example/restJsonCatch/grailsJson
{"first":"foo","second":5}
$ curl localhost:8080/example/restJsonCatch/netSfJson
{first=foo, second=5}

As you can see only grails.converters.JSON converts your response to JSON format. There is no such converter for net.sf.json.JSON, so Grails has no converter to apply and it renders Map normally.

Conclusion: always carefully look at your imports if you're working with JSON in Grails!

Edit: Burt suggested that this is a bug. I've submitted JIRA issue here: GRAILS-9622 render as class that is not a codec should throw exception

DDevelopment & Design

Grails with Spock unit test + IntelliJ IDEA = No thread-bound request found

During my work with Grails project using Spock test in IntelliJ IDEA I've encountered this error:

java.lang.IllegalStateException: No thread-bound request found: Are you referring to request attributes outside of an actual web request, or processing a request outside of the originally receiving thread? If you are actually operating within a web request and still receive this message, your code is probably running outside of DispatcherServlet/DispatcherPortlet: In this case, use RequestContextListener or RequestContextFilter to expose the current request.
 at org.springframework.web.context.request.RequestContextHolder.currentRequestAttributes(RequestContextHolder.java:131)
 at org.codehaus.groovy.grails.plugins.web.api.CommonWebApi.currentRequestAttributes(CommonWebApi.java:205)
 at org.codehaus.groovy.grails.plugins.web.api.CommonWebApi.getParams(CommonWebApi.java:65)
... // and few more lines of stacktrace ;)

It occurred when I tried to debug one of test from IDEA level. What is interesting, this error does not happen when I'm running all test using grails test-app for instance.

So what was the issue? With little of reading and tip from Tomek Kalkosiński (http://refaktor.blogspot.com/) it turned out that our test was missing @TestFor annotation and adding it solved all problems.

This annotation, according to Grails docs (link), indicates Spock what class is being tested and implicitly creates field with given type in test class. It is somehow strange as problematic test had explicitly and "manually" created field with proper controller type. Maybe there is a problem with mocking servlet requests?