DDevelopment & Design

Http server with PHP on RaspberryPI

Totally awesome guide is at http://rasberrypibeginnersguide.tumblr.com/post/27283563130/nginx-php5-on-raspberry-pi-debian-wheezyBut instead using provided silex site config file, you should configure root folder of web server to serve php scripts….

Totally awesome guide is at http://rasberrypibeginnersguide.tumblr.com/post/27283563130/nginx-php5-on-raspberry-pi-debian-wheezy
But instead using provided silex site config file, you should configure root folder of web server to serve php scripts. To do so please rm symlink to
silex file and edit

/etc/nginx/sites-available/default

Set root folder to /var/www

#       root /usr/share/nginx/www;
        root /var/www;

Add index.php as a index file
index index.html index.htm index.php;

And configure all php files to be parsed by fastCGI php bridge set up on port 9000. Just put all below somewhere in default file
 ## Parse all .php file in the /var/www directory
            location ~ \.php$ {
                    fastcgi_split_path_info ^(.+\.php)(.*)$;
                    fastcgi_pass   127.0.0.1:9000;
                    fastcgi_index  index.php;
                    fastcgi_param  SCRIPT_FILENAME  /var/www/silex$fastcgi_script_name;
                    include fastcgi_params;
                    fastcgi_param  QUERY_STRING     $query_string;
                    fastcgi_param  REQUEST_METHOD   $request_method;
                    fastcgi_param  CONTENT_TYPE     $content_type;
                    fastcgi_param  CONTENT_LENGTH   $content_length;
                    fastcgi_intercept_errors        on;
                    fastcgi_ignore_client_abort     off;
                    fastcgi_connect_timeout 60;
                    fastcgi_send_timeout 180;
                    fastcgi_read_timeout 180;
                    fastcgi_buffer_size 128k;
                    fastcgi_buffers 4 256k;
                    fastcgi_busy_buffers_size 256k;
                    fastcgi_temp_file_write_size 256k;
            }
Now restart ngix as mentioned in original article and enjoy PHP on RPi!

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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.