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[ZEPPELIN-2949] Updated documentation

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Nelson Costa 2017-10-11 13:51:36 +01:00
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docs/interpreter/spark.md
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@ -148,7 +148,7 @@ You can also set other Spark properties which are not listed in the table. For a
<tr>
<td>zeppelin.spark.uiWebUrl</td>
<td></td>
<td>Override Spark UI default URL</td>
<td>Overrides Spark UI default URL (ex: http://{hostName}/{uniquePath)</td>
</tr>
</table>
@ -188,7 +188,7 @@ For example,
* **yarn-client** in Yarn client mode
* **mesos://host:5050** in Mesos cluster
That's it. Zeppelin will work with any version of Spark and any deployment type without rebuilding Zeppelin in this way.
That's it. Zeppelin will work with any version of Spark and any deployment type without rebuilding Zeppelin in this way.
For the further information about Spark & Zeppelin version compatibility, please refer to "Available Interpreters" section in [Zeppelin download page](https://zeppelin.apache.org/download.html).
> Note that without exporting `SPARK_HOME`, it's running in local mode with included version of Spark. The included version may vary depending on the build profile.
@ -215,7 +215,7 @@ There are two ways to load external libraries in Spark interpreter. First is usi
Please see [Dependency Management](../usage/interpreter/dependency_management.html) for the details.
### 2. Loading Spark Properties
Once `SPARK_HOME` is set in `conf/zeppelin-env.sh`, Zeppelin uses `spark-submit` as spark interpreter runner. `spark-submit` supports two ways to load configurations.
Once `SPARK_HOME` is set in `conf/zeppelin-env.sh`, Zeppelin uses `spark-submit` as spark interpreter runner. `spark-submit` supports two ways to load configurations.
The first is command line options such as --master and Zeppelin can pass these options to `spark-submit` by exporting `SPARK_SUBMIT_OPTIONS` in `conf/zeppelin-env.sh`. Second is reading configuration options from `SPARK_HOME/conf/spark-defaults.conf`. Spark properties that user can set to distribute libraries are:
<table class="table-configuration">
@ -248,7 +248,7 @@ Here are few examples:
```bash
export SPARK_SUBMIT_OPTIONS="--packages com.databricks:spark-csv_2.10:1.2.0 --jars /path/mylib1.jar,/path/mylib2.jar --files /path/mylib1.py,/path/mylib2.zip,/path/mylib3.egg"
```
* `SPARK_HOME/conf/spark-defaults.conf`
```
@ -413,17 +413,17 @@ To learn more about dynamic form, checkout [Dynamic Form](../usage/dynamic_form/
## Matplotlib Integration (pyspark)
Both the `python` and `pyspark` interpreters have built-in support for inline visualization using `matplotlib`,
a popular plotting library for python. More details can be found in the [python interpreter documentation](../interpreter/python.html),
since matplotlib support is identical. More advanced interactive plotting can be done with pyspark through
Both the `python` and `pyspark` interpreters have built-in support for inline visualization using `matplotlib`,
a popular plotting library for python. More details can be found in the [python interpreter documentation](../interpreter/python.html),
since matplotlib support is identical. More advanced interactive plotting can be done with pyspark through
utilizing Zeppelin's built-in [Angular Display System](../usage/display_system/angular_backend.html), as shown below:
<img class="img-responsive" src="{{BASE_PATH}}/assets/themes/zeppelin/img/docs-img/matplotlibAngularExample.gif" />
## Interpreter setting option
You can choose one of `shared`, `scoped` and `isolated` options wheh you configure Spark interpreter.
Spark interpreter creates separated Scala compiler per each notebook but share a single SparkContext in `scoped` mode (experimental).
You can choose one of `shared`, `scoped` and `isolated` options wheh you configure Spark interpreter.
Spark interpreter creates separated Scala compiler per each notebook but share a single SparkContext in `scoped` mode (experimental).
It creates separated SparkContext per each notebook in `isolated` mode.
## IPython support