DDevelopment & Design

Oracle ODBC dla Windows

Potrzebowałem dostępu do bazy Oracle przez ODBC. Niestety Oracle’owy driver odbc jest inny niż wszystkie :-), bo nie pozwala zdefiniować dostępu do bazy wprost, tylko należy użyć spójnego mechanizmu narzędzi Oracle’owych, definiującego połączenie. Mowa o

TNS (Transparent Network Substrate), co ja bym nazwał definicją połączenia (zamiast Przeźroczystego Substratu Sieciowego ;-)). TNS może pochodzić z kilku źródeł – lokalnego (specjalnego pliku) i globalnego – np. LDAP. Dzięki temu we wszystkich narzędziach bazodanowych Oracle, podajemy tylko nazwę połączenia zamiast każdorazowo określać wszystkie parametry połączenia. Rozwiązanie zmyślne, ale patrząc przez pryzmat problemów z konfiguracją – nieintuicyjne. 

Aby połączyć się przez Oracle ODBC, należy pobrać sterowniki. Ja znalazłem cały pakiet zwany ODAC (Oracle Data Access Component). Po zainstalowaniu należy zdefiniować TNS naszego połączenia. Do tego celu służy plik tnsnames.ora, który zawiera specjalną składnię. Poniżej podstawowa konfiguracja:

my_conn =
 (DESCRIPTION =
   (ADDRESS_LIST =
     (ADDRESS = (PROTOCOL = TCP)(HOST = moj.serwer.pl)(PORT = 1521))
   )
 (CONNECT_DATA =
   (SERVICE_NAME = sid_uslugi)
 )
)

Ponadto należy poinstruować narzędzia Oracle w jaki sposób ma wyszukiwać definicji połączeń. Mowa tu o kolejności przeszukiwania oraz z których źródeł skorzystać (ww. lokalne i/lub globalne). Ustawiamy to w pliku sqlnet.ora. Tu potrzebujemy tylko:

*NAMES.DIRECTORY_PATH= (TNSNAMES) *

Oba pliki należy umieścić w (już istniejącym) katalogu
%KATALOG_INSTALACJI_ODAC%\product\11.2.0\client_1\Network\Admin

Teraz można już korzystać z ODBC. Jeśli mieliście otwarty program korzystający z połączenia, to dla pewności należy go uruchomić ponownie.

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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.