Kevin W Monroe has proposed merging lp:~bigdata-dev/charms/trusty/apache-spark/trunk into lp:charms/trusty/apache-spark.
Requested reviews: Kevin W Monroe (kwmonroe) For more details, see: https://code.launchpad.net/~bigdata-dev/charms/trusty/apache-spark/trunk/+merge/268673 -- Your team Juju Big Data Development is subscribed to branch lp:~bigdata-dev/charms/trusty/apache-spark/trunk.
=== modified file 'README.md' --- README.md 2015-06-25 16:47:56 +0000 +++ README.md 2015-08-20 23:14:26 +0000 @@ -28,12 +28,12 @@ This charm leverages our pluggable Hadoop model with the `hadoop-plugin` interface. This means that you will need to deploy a base Apache Hadoop cluster to run Spark. The suggested deployment method is to use the -[apache-hadoop-spark](https://jujucharms.com/u/bigdata-dev/apache-hadoop-spark/) +[apache-hadoop-spark](https://jujucharms.com/apache-hadoop-spark/) bundle. This will deploy the Apache Hadoop platform with a single Apache Spark unit that communicates with the cluster by relating to the `apache-hadoop-plugin` subordinate charm: - juju-quickstart u/bigdata-dev/apache-hadoop-spark + juju-quickstart apache-hadoop-spark Alternatively, you may manually deploy the recommended environment as follows: @@ -75,7 +75,7 @@ Deploy Apache Zeppelin and relate it to the Spark unit: - juju deploy cs:~bigdata-dev/trusty/apache-zeppelin zeppelin + juju deploy apache-zeppelin zeppelin juju add-relation spark zeppelin Once the relation has been made, access the web interface at @@ -87,7 +87,7 @@ can combine code execution, rich text, mathematics, plots and rich media. Deploy IPython Notebook for Spark and relate it to the Spark unit: - juju deploy cs:~bigdata-dev/trusty/apache-spark-notebook notebook + juju deploy apache-spark-notebook notebook juju add-relation spark notebook Once the relation has been made, access the web interface at === modified file 'hooks/callbacks.py' --- hooks/callbacks.py 2015-07-24 16:28:49 +0000 +++ hooks/callbacks.py 2015-08-20 23:14:26 +0000 @@ -80,13 +80,17 @@ def setup_spark_config(self): ''' - copy Spark's default configuration files to spark_conf property defined - in dist.yaml + copy the default configuration files to spark_conf property + defined in dist.yaml ''' - conf_dir = self.dist_config.path('spark') / 'conf' - self.dist_config.path('spark_conf').rmtree_p() - conf_dir.copytree(self.dist_config.path('spark_conf')) - conf_dir.rmtree_p() + default_conf = self.dist_config.path('spark') / 'conf' + spark_conf = self.dist_config.path('spark_conf') + spark_conf.rmtree_p() + default_conf.copytree(spark_conf) + # Now remove the conf included in the tarball and symlink our real conf + default_conf.rmtree_p() + spark_conf.symlink(default_conf) + spark_env = self.dist_config.path('spark_conf') / 'spark-env.sh' if not spark_env.exists(): (self.dist_config.path('spark_conf') / 'spark-env.sh.template').copy(spark_env) === modified file 'resources.yaml' --- resources.yaml 2015-07-24 16:28:49 +0000 +++ resources.yaml 2015-08-20 23:14:26 +0000 @@ -4,7 +4,7 @@ pathlib: pypi: path.py>=7.0 jujubigdata: - pypi: jujubigdata>=2.1.0,<3.0.0 + pypi: jujubigdata>=4.0.0,<5.0.0 optional_resources: spark-ppc64le: url: https://git.launchpad.net/bigdata-data/plain/apache/ppc64le/spark-1.3.1-bin-2.4.0.tgz?id=45f439740a08b93ae72bc48a7103ebf58dbfa60b
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