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Posted to commits@zeppelin.apache.org by zj...@apache.org on 2021/08/17 02:34:12 UTC
[zeppelin] branch master updated: [ZEPPELIN-5483] Update spark doc
This is an automated email from the ASF dual-hosted git repository.
zjffdu pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/zeppelin.git
The following commit(s) were added to refs/heads/master by this push:
new 2276573 [ZEPPELIN-5483] Update spark doc
2276573 is described below
commit 227657365193793515bd64aa1678f45fe3502b0f
Author: Jeff Zhang <zj...@apache.org>
AuthorDate: Mon Aug 9 11:53:55 2021 +0800
[ZEPPELIN-5483] Update spark doc
### What is this PR for?
* Update spark doc
* Other docs like install.md are updated in this PR as well
* Add one new tutorial note for how to run pyspark with customized python runtime in yarn
### What type of PR is it?
[Documentation ]
### Todos
* [ ] - Task
### What is the Jira issue?
* https://issues.apache.org/jira/browse/ZEPPELIN-5483
### How should this be tested?
* no tests needed
### Screenshots (if appropriate)
### Questions:
* Does the licenses files need update? no
* Is there breaking changes for older versions? no
* Does this needs documentation? no
Author: Jeff Zhang <zj...@apache.org>
Closes #4203 from zjffdu/ZEPPELIN-5483 and squashes the following commits:
42b6213d47 [Jeff Zhang] [ZEPPELIN-5483] Update spark doc
---
docs/README.md | 2 +-
docs/_includes/themes/zeppelin/_navigation.html | 4 +-
docs/interpreter/ksql.md | 6 +-
docs/interpreter/spark.md | 304 +++--
docs/quickstart/flink_with_zeppelin.md | 42 +
docs/quickstart/install.md | 30 +-
docs/quickstart/python_with_zeppelin.md | 5 +-
docs/quickstart/r_with_zeppelin.md | 42 +
docs/quickstart/spark_with_zeppelin.md | 13 +-
docs/quickstart/sql_with_zeppelin.md | 2 +
docs/setup/deployment/flink_and_spark_cluster.md | 2 +
docs/usage/interpreter/dependency_management.md | 5 +-
.... PySpark Conda Env in Yarn Mode_2GE79Y5FV.zpln | 1397 ++++++++++++++++++++
13 files changed, 1738 insertions(+), 116 deletions(-)
diff --git a/docs/README.md b/docs/README.md
index 7ca822e..c9646e1 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -42,7 +42,7 @@ bundle exec jekyll serve --watch
**Run locally using docker**
```
-docker run --rm -it \
+docker run --rm -it \
-v $PWD:/docs \
-w /docs \
-p '4000:4000' \
diff --git a/docs/_includes/themes/zeppelin/_navigation.html b/docs/_includes/themes/zeppelin/_navigation.html
index 8bbf6b0..3459856 100644
--- a/docs/_includes/themes/zeppelin/_navigation.html
+++ b/docs/_includes/themes/zeppelin/_navigation.html
@@ -34,8 +34,10 @@
<li><a href="{{BASE_PATH}}/quickstart/yarn.html">Yarn</a></li>
<li role="separator" class="divider"></li>
<li><a href="{{BASE_PATH}}/quickstart/spark_with_zeppelin.html">Spark with Zeppelin</a></li>
+ <li><a href="{{BASE_PATH}}/quickstart/flink_with_zeppelin.html">Flink with Zeppelin</a></li>
<li><a href="{{BASE_PATH}}/quickstart/sql_with_zeppelin.html">SQL with Zeppelin</a></li>
<li><a href="{{BASE_PATH}}/quickstart/python_with_zeppelin.html">Python with Zeppelin</a></li>
+ <li><a href="{{BASE_PATH}}/quickstart/r_with_zeppelin.html">R with Zeppelin</a></li>
</ul>
</li>
@@ -131,6 +133,7 @@
<li><a href="{{BASE_PATH}}/usage/interpreter/overview.html">Overview</a></li>
<li role="separator" class="divider"></li>
<li><a href="{{BASE_PATH}}/interpreter/spark.html">Spark</a></li>
+ <li><a href="{{BASE_PATH}}/interpreter/flink.html">Flink</a></li>
<li><a href="{{BASE_PATH}}/interpreter/jdbc.html">JDBC</a></li>
<li><a href="{{BASE_PATH}}/interpreter/python.html">Python</a></li>
<li><a href="{{BASE_PATH}}/interpreter/r.html">R</a></li>
@@ -140,7 +143,6 @@
<li><a href="{{BASE_PATH}}/interpreter/bigquery.html">BigQuery</a></li>
<li><a href="{{BASE_PATH}}/interpreter/cassandra.html">Cassandra</a></li>
<li><a href="{{BASE_PATH}}/interpreter/elasticsearch.html">Elasticsearch</a></li>
- <li><a href="{{BASE_PATH}}/interpreter/flink.html">Flink</a></li>
<li><a href="{{BASE_PATH}}/interpreter/geode.html">Geode</a></li>
<li><a href="{{BASE_PATH}}/interpreter/groovy.html">Groovy</a></li>
<li><a href="{{BASE_PATH}}/interpreter/hazelcastjet.html">Hazelcast Jet</a></li>
diff --git a/docs/interpreter/ksql.md b/docs/interpreter/ksql.md
index bc91ade..2a308be 100644
--- a/docs/interpreter/ksql.md
+++ b/docs/interpreter/ksql.md
@@ -57,7 +57,7 @@ Following some examples:
PRINT 'orders';
```
-
+
```
%ksql
@@ -66,7 +66,7 @@ CREATE STREAM ORDERS WITH
KAFKA_TOPIC ='orders');
```
-
+
```
%ksql
@@ -75,4 +75,4 @@ FROM ORDERS
LIMIT 10
```
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/docs/interpreter/spark.md b/docs/interpreter/spark.md
index fd0356d..12e0560 100644
--- a/docs/interpreter/spark.md
+++ b/docs/interpreter/spark.md
@@ -26,7 +26,7 @@ limitations under the License.
## Overview
[Apache Spark](http://spark.apache.org) is a fast and general-purpose cluster computing system.
It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs.
-Apache Spark is supported in Zeppelin with Spark interpreter group which consists of below six interpreters.
+Apache Spark is supported in Zeppelin with Spark interpreter group which consists of following interpreters.
<table class="table-configuration">
<tr>
@@ -52,7 +52,17 @@ Apache Spark is supported in Zeppelin with Spark interpreter group which consist
<tr>
<td>%spark.r</td>
<td>SparkRInterpreter</td>
- <td>Provides an R environment with SparkR support</td>
+ <td>Provides an vanilla R environment with SparkR support</td>
+ </tr>
+ <tr>
+ <td>%spark.ir</td>
+ <td>SparkIRInterpreter</td>
+ <td>Provides an R environment with SparkR support based on Jupyter IRKernel</td>
+ </tr>
+ <tr>
+ <td>%spark.shiny</td>
+ <td>SparkShinyInterpreter</td>
+ <td>Used to create R shiny app with SparkR support</td>
</tr>
<tr>
<td>%spark.sql</td>
@@ -66,6 +76,69 @@ Apache Spark is supported in Zeppelin with Spark interpreter group which consist
</tr>
</table>
+## Main Features
+
+<table class="table-configuration">
+ <tr>
+ <th>Feature</th>
+ <th>Description</th>
+ </tr>
+ <tr>
+ <td>Support multiple versions of Spark</td>
+ <td>You can run different versions of Spark in one Zeppelin instance</td>
+ </tr>
+ <tr>
+ <td>Support multiple versions of Scala</td>
+ <td>You can run different Scala versions (2.10/2.11/2.12) of Spark in on Zeppelin instance</td>
+ </tr>
+ <tr>
+ <td>Support multiple languages</td>
+ <td>Scala, SQL, Python, R are supported, besides that you can also collaborate across languages, e.g. you can write Scala UDF and use it in PySpark</td>
+ </tr>
+ <tr>
+ <td>Support multiple execution modes</td>
+ <td>Local | Standalone | Yarn | K8s </td>
+ </tr>
+ <tr>
+ <td>Interactive development</td>
+ <td>Interactive development user experience increase your productivity</td>
+ </tr>
+
+ <tr>
+ <td>Inline Visualization</td>
+ <td>You can visualize Spark Dataset/DataFrame vis Python/R's plotting libraries, and even you can make SparkR Shiny app in Zeppelin</td>
+ </tr>
+
+ </tr>
+ <td>Multi-tenancy</td>
+ <td>Multiple user can work in one Zeppelin instance without affecting each other.</td>
+ </tr>
+
+ </tr>
+ <td>Rest API Support</td>
+ <td>You can not only submit Spark job via Zeppelin notebook UI, but also can do that via its rest api (You can use Zeppelin as Spark job server).</td>
+ </tr>
+</table>
+
+## Play Spark in Zeppelin docker
+
+For beginner, we would suggest you to play Spark in Zeppelin docker.
+In the Zeppelin docker image, we have already installed
+miniconda and lots of [useful python and R libraries](https://github.com/apache/zeppelin/blob/branch-0.10/scripts/docker/zeppelin/bin/env_python_3_with_R.yml)
+including IPython and IRkernel prerequisites, so `%spark.pyspark` would use IPython and `%spark.ir` is enabled.
+Without any extra configuration, you can run most of tutorial notes under folder `Spark Tutorial` directly.
+
+First you need to download Spark, because there's no Spark binary distribution shipped with Zeppelin.
+e.g. Here we download Spark 3.1.2 to`/mnt/disk1/spark-3.1.2`,
+and we mount it to Zeppelin docker container and run the following command to start Zeppelin docker container.
+
+```bash
+docker run -u $(id -u) -p 8080:8080 --rm -v /mnt/disk1/spark-3.1.2:/opt/spark -e SPARK_HOME=/opt/spark --name zeppelin apache/zeppelin:0.10.0
+```
+
+After running the above command, you can open `http://localhost:8080` to play Spark in Zeppelin. We only verify the spark local mode in Zeppelin docker, other modes may not work due to network issues.
+
+
## Configuration
The Spark interpreter can be configured with properties provided by Zeppelin.
You can also set other Spark properties which are not listed in the table. For a list of additional properties, refer to [Spark Available Properties](http://spark.apache.org/docs/latest/configuration.html#available-properties).
@@ -201,14 +274,15 @@ You can also set other Spark properties which are not listed in the table. For a
<td></td>
<td>
Overrides Spark UI default URL. Value should be a full URL (ex: http://{hostName}/{uniquePath}.
- In Kubernetes mode, value can be Jinja template string with 3 template variables 'PORT', 'SERVICE_NAME' and 'SERVICE_DOMAIN'.
- (ex: http://{{PORT}}-{{SERVICE_NAME}}.{{SERVICE_DOMAIN}})
+ In Kubernetes mode, value can be Jinja template string with 3 template variables PORT, {% raw %} SERVICE_NAME {% endraw %} and {% raw %} SERVICE_DOMAIN {% endraw %}.
+ (e.g.: {% raw %}http://{{PORT}}-{{SERVICE_NAME}}.{{SERVICE_DOMAIN}} {% endraw %}). In yarn mode, value could be a knox url with {% raw %} {{applicationId}} {% endraw %} as placeholder,
+ (e.g.: {% raw %}https://knox-server:8443/gateway/yarnui/yarn/proxy/{{applicationId}}/{% endraw %})
</td>
</tr>
<tr>
<td>spark.webui.yarn.useProxy</td>
<td>false</td>
- <td>whether use yarn proxy url as spark weburl, e.g. http://localhost:8088/proxy/application_1583396598068_0004</td>
+ <td>whether use yarn proxy url as Spark weburl, e.g. http://localhost:8088/proxy/application_1583396598068_0004</td>
</tr>
<tr>
<td>spark.repl.target</td>
@@ -224,17 +298,21 @@ You can also set other Spark properties which are not listed in the table. For a
Without any configuration, Spark interpreter works out of box in local mode. But if you want to connect to your Spark cluster, you'll need to follow below two simple steps.
-### Export SPARK_HOME
+* Set SPARK_HOME
+* Set master
+
+
+### Set SPARK_HOME
There are several options for setting `SPARK_HOME`.
* Set `SPARK_HOME` in `zeppelin-env.sh`
-* Set `SPARK_HOME` in Interpreter setting page
+* Set `SPARK_HOME` in interpreter setting page
* Set `SPARK_HOME` via [inline generic configuration](../usage/interpreter/overview.html#inline-generic-confinterpreter)
-#### 1. Set `SPARK_HOME` in `zeppelin-env.sh`
+#### Set `SPARK_HOME` in `zeppelin-env.sh`
-If you work with only one version of spark, then you can set `SPARK_HOME` in `zeppelin-env.sh` because any setting in `zeppelin-env.sh` is globally applied.
+If you work with only one version of Spark, then you can set `SPARK_HOME` in `zeppelin-env.sh` because any setting in `zeppelin-env.sh` is globally applied.
e.g.
@@ -251,21 +329,21 @@ export HADOOP_CONF_DIR=/usr/lib/hadoop
```
-#### 2. Set `SPARK_HOME` in Interpreter setting page
+#### Set `SPARK_HOME` in interpreter setting page
-If you want to use multiple versions of spark, then you need create multiple spark interpreters and set `SPARK_HOME` for each of them. e.g.
-Create a new spark interpreter `spark24` for spark 2.4 and set `SPARK_HOME` in interpreter setting page
+If you want to use multiple versions of Spark, then you need to create multiple Spark interpreters and set `SPARK_HOME` separately. e.g.
+Create a new Spark interpreter `spark24` for Spark 2.4 and set its `SPARK_HOME` in interpreter setting page as following,
<center>
<img src="{{BASE_PATH}}/assets/themes/zeppelin/img/docs-img/spark_SPARK_HOME24.png" width="80%">
</center>
-Create a new spark interpreter `spark16` for spark 1.6 and set `SPARK_HOME` in interpreter setting page
+Create a new Spark interpreter `spark16` for Spark 1.6 and set its `SPARK_HOME` in interpreter setting page as following,
<center>
<img src="{{BASE_PATH}}/assets/themes/zeppelin/img/docs-img/spark_SPARK_HOME16.png" width="80%">
</center>
-#### 3. Set `SPARK_HOME` via [inline generic configuration](../usage/interpreter/overview.html#inline-generic-confinterpreter)
+#### Set `SPARK_HOME` via [inline generic configuration](../usage/interpreter/overview.html#inline-generic-confinterpreter)
Besides setting `SPARK_HOME` in interpreter setting page, you can also use inline generic configuration to put the
configuration with code together for more flexibility. e.g.
@@ -273,23 +351,26 @@ configuration with code together for more flexibility. e.g.
<img src="{{BASE_PATH}}/assets/themes/zeppelin/img/docs-img/spark_inline_configuration.png" width="80%">
</center>
-### Set master in Interpreter menu
-After starting Zeppelin, go to **Interpreter** menu and edit **spark.master** property in your Spark interpreter setting. The value may vary depending on your Spark cluster deployment type.
+### Set master
+
+After setting `SPARK_HOME`, you need to set **spark.master** property in either interpreter setting page or inline configuartion. The value may vary depending on your Spark cluster deployment type.
For example,
* **local[*]** in local mode
* **spark://master:7077** in standalone cluster
- * **yarn-client** in Yarn client mode (Not supported in spark 3.x, refer below for how to configure yarn-client in Spark 3.x)
- * **yarn-cluster** in Yarn cluster mode (Not supported in spark 3.x, refer below for how to configure yarn-client in Spark 3.x)
+ * **yarn-client** in Yarn client mode (Not supported in Spark 3.x, refer below for how to configure yarn-client in Spark 3.x)
+ * **yarn-cluster** in Yarn cluster mode (Not supported in Spark 3.x, refer below for how to configure yarn-cluster in Spark 3.x)
* **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.
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.
+Note that without setting `SPARK_HOME`, it's running in local mode with included version of Spark. The included version may vary depending on the build profile. And this included version Spark has limited function, so it
+is always recommended to set `SPARK_HOME`.
-> Yarn client mode and local mode will run driver in the same machine with zeppelin server, this would be dangerous for production. Because it may run out of memory when there's many spark interpreters running at the same time. So we suggest you only allow yarn-cluster mode via setting `zeppelin.spark.only_yarn_cluster` in `zeppelin-site.xml`.
+Yarn client mode and local mode will run driver in the same machine with zeppelin server, this would be dangerous for production. Because it may run out of memory when there's many Spark interpreters running at the same time. So we suggest you
+only allow yarn-cluster mode via setting `zeppelin.spark.only_yarn_cluster` in `zeppelin-site.xml`.
#### Configure yarn mode for Spark 3.x
@@ -314,77 +395,55 @@ Specifying `yarn-client` & `yarn-cluster` in `spark.master` is not supported in
</table>
-## SparkContext, SQLContext, SparkSession, ZeppelinContext
+## Interpreter binding mode
-SparkContext, SQLContext, SparkSession (for spark 2.x) and ZeppelinContext are automatically created and exposed as variable names `sc`, `sqlContext`, `spark` and `z`, respectively, in Scala, Kotlin, Python and R environments.
+The default [interpreter binding mode](../usage/interpreter/interpreter_binding_mode.html) is `globally shared`. That means all notes share the same Spark interpreter.
+So we recommend you to use `isolated per note` which means each note has own Spark interpreter without affecting each other. But it may run out of your machine resource if too many
+Spark interpreters are created, so we recommend to always use yarn-cluster mode in production if you run Spark in hadoop cluster. And you can use [inline configuration](../usage/interpreter/overview.html#inline-generic-configuration) via `%spark.conf` in the first paragraph to customize your spark configuration.
-> Note that Scala/Python/R environment shares the same SparkContext, SQLContext, SparkSession and ZeppelinContext instance.
+You can also choose `scoped` mode. For `scoped` per note mode, Zeppelin creates separated scala compiler/python shell for each note but share a single `SparkContext/SqlContext/SparkSession`.
-## YARN Mode
-Zeppelin support both yarn client and yarn cluster mode (yarn cluster mode is supported from 0.8.0). For yarn mode, you must specify `SPARK_HOME` & `HADOOP_CONF_DIR`.
-Usually you only have one hadoop cluster, so you can set `HADOOP_CONF_DIR` in `zeppelin-env.sh` which is applied to all spark interpreters. If you want to use spark against multiple hadoop cluster, then you need to define
-`HADOOP_CONF_DIR` in interpreter setting or via inline generic configuration.
-## Dependency Management
+## SparkContext, SQLContext, SparkSession, ZeppelinContext
-For spark interpreter, it is not recommended to use Zeppelin's [Dependency Management](../usage/interpreter/dependency_management.html) for managing
-third party dependencies (`%spark.dep` is removed from Zeppelin 0.9 as well). Instead you should set the standard Spark properties.
+SparkContext, SQLContext, SparkSession (for spark 2.x, 3.x) and ZeppelinContext are automatically created and exposed as variable names `sc`, `sqlContext`, `spark` and `z` respectively, in Scala, Kotlin, Python and R environments.
-<table class="table-configuration">
- <tr>
- <th>Spark Property</th>
- <th>Spark Submit Argument</th>
- <th>Description</th>
- </tr>
- <tr>
- <td>spark.files</td>
- <td>--files</td>
- <td>Comma-separated list of files to be placed in the working directory of each executor. Globs are allowed.</td>
- </tr>
- <tr>
- <td>spark.jars</td>
- <td>--jars</td>
- <td>Comma-separated list of jars to include on the driver and executor classpaths. Globs are allowed.</td>
- </tr>
- <tr>
- <td>spark.jars.packages</td>
- <td>--packages</td>
- <td>Comma-separated list of Maven coordinates of jars to include on the driver and executor classpaths. The coordinates should be groupId:artifactId:version. If spark.jars.ivySettings is given artifacts will be resolved according to the configuration in the file, otherwise artifacts will be searched for in the local maven repo, then maven central and finally any additional remote repositories given by the command-line option --repositories.</td>
- </tr>
-</table>
-You can either set Spark properties in interpreter setting page or set Spark submit arguments in `zeppelin-env.sh` via environment variable `SPARK_SUBMIT_OPTIONS`.
-For examples:
+> Note that Scala/Python/R environment shares the same SparkContext, SQLContext, SparkSession and ZeppelinContext instance.
-```bash
-export SPARK_SUBMIT_OPTIONS="--files <my_file> --jars <my_jar> --packages <my_package>"
-```
+## Yarn Mode
+
+Zeppelin support both yarn client and yarn cluster mode (yarn cluster mode is supported from 0.8.0). For yarn mode, you must specify `SPARK_HOME` & `HADOOP_CONF_DIR`.
+Usually you only have one hadoop cluster, so you can set `HADOOP_CONF_DIR` in `zeppelin-env.sh` which is applied to all Spark interpreters. If you want to use spark against multiple hadoop cluster, then you need to define
+`HADOOP_CONF_DIR` in interpreter setting or via inline generic configuration.
+
+## K8s Mode
-But it is not recommended to set them in `SPARK_SUBMIT_OPTIONS`. Because it will be shared by all spark interpreters, which means you can not set different dependencies for different users.
+Regarding how to run Spark on K8s in Zeppelin, please check [this doc](../quickstart/kubernetes.html).
## PySpark
-There're 2 ways to use PySpark in Zeppelin:
+There are 2 ways to use PySpark in Zeppelin:
* Vanilla PySpark
* IPySpark
### Vanilla PySpark (Not Recommended)
-Vanilla PySpark interpreter is almost the same as vanilla Python interpreter except Zeppelin inject SparkContext, SQLContext, SparkSession via variables `sc`, `sqlContext`, `spark`.
-By default, Zeppelin would use IPython in `%spark.pyspark` when IPython is available, Otherwise it would fall back to the original PySpark implementation.
-If you don't want to use IPython, then you can set `zeppelin.pyspark.useIPython` as `false` in interpreter setting. For the IPython features, you can refer doc
-[Python Interpreter](python.html)
+Vanilla PySpark interpreter is almost the same as vanilla Python interpreter except Spark interpreter inject SparkContext, SQLContext, SparkSession via variables `sc`, `sqlContext`, `spark`.
+
+By default, Zeppelin would use IPython in `%spark.pyspark` when IPython is available (Zeppelin would check whether ipython's prerequisites are met), Otherwise it would fall back to the vanilla PySpark implementation.
### IPySpark (Recommended)
-You can use `IPySpark` explicitly via `%spark.ipyspark`. IPySpark interpreter is almost the same as IPython interpreter except Zeppelin inject SparkContext, SQLContext, SparkSession via variables `sc`, `sqlContext`, `spark`.
-For the IPython features, you can refer doc [Python Interpreter](python.html)
+
+You can use `IPySpark` explicitly via `%spark.ipyspark`. IPySpark interpreter is almost the same as IPython interpreter except Spark interpreter inject SparkContext, SQLContext, SparkSession via variables `sc`, `sqlContext`, `spark`.
+For the IPython features, you can refer doc [Python Interpreter](python.html#ipython-interpreter-pythonipython-recommended)
## SparkR
-Zeppelin support SparkR via `%spark.r`. Here's configuration for SparkR Interpreter.
+Zeppelin support SparkR via `%spark.r`, `%spark.ir` and `%spark.shiny`. Here's configuration for SparkR Interpreter.
<table class="table-configuration">
<tr>
@@ -412,12 +471,28 @@ Zeppelin support SparkR via `%spark.r`. Here's configuration for SparkR Interpre
<td>out.format = 'html', comment = NA, echo = FALSE, results = 'asis', message = F, warning = F, fig.retina = 2</td>
<td>R plotting options.</td>
</tr>
+ <tr>
+ <td>zeppelin.R.shiny.iframe_width</td>
+ <td>100%</td>
+ <td>IFrame width of Shiny App</td>
+ </tr>
+ <tr>
+ <td>zeppelin.R.shiny.iframe_height</td>
+ <td>500px</td>
+ <td>IFrame height of Shiny App</td>
+ </tr>
+ <tr>
+ <td>zeppelin.R.shiny.portRange</td>
+ <td>:</td>
+ <td>Shiny app would launch a web app at some port, this property is to specify the portRange via format '<start>:<end>', e.g. '5000:5001'. By default it is ':' which means any port</td>
+ </tr>
</table>
+Refer [R doc](r.html) for how to use R in Zeppelin.
## SparkSql
-Spark Sql Interpreter share the same SparkContext/SparkSession with other Spark interpreter. That means any table registered in scala, python or r code can be accessed by Spark Sql.
+Spark sql interpreter share the same SparkContext/SparkSession with other Spark interpreters. That means any table registered in scala, python or r code can be accessed by Spark sql.
For examples:
```scala
@@ -435,11 +510,13 @@ df.createOrReplaceTempView("people")
select * from people
```
-By default, each sql statement would run sequentially in `%spark.sql`. But you can run them concurrently by following setup.
+You can write multiple sql statements in one paragraph. Each sql statement is separated by semicolon.
+Sql statement in one paragraph would run sequentially.
+But sql statements in different paragraphs can run concurrently by the following configuration.
-1. Set `zeppelin.spark.concurrentSQL` to true to enable the sql concurrent feature, underneath zeppelin will change to use fairscheduler for spark. And also set `zeppelin.spark.concurrentSQL.max` to control the max number of sql statements running concurrently.
+1. Set `zeppelin.spark.concurrentSQL` to true to enable the sql concurrent feature, underneath zeppelin will change to use fairscheduler for Spark. And also set `zeppelin.spark.concurrentSQL.max` to control the max number of sql statements running concurrently.
2. Configure pools by creating `fairscheduler.xml` under your `SPARK_CONF_DIR`, check the official spark doc [Configuring Pool Properties](http://spark.apache.org/docs/latest/job-scheduling.html#configuring-pool-properties)
-3. Set pool property via setting paragraph property. e.g.
+3. Set pool property via setting paragraph local property. e.g.
```
%spark(pool=pool1)
@@ -448,25 +525,61 @@ By default, each sql statement would run sequentially in `%spark.sql`. But you c
```
This pool feature is also available for all versions of scala Spark, PySpark. For SparkR, it is only available starting from 2.3.0.
-
-## Interpreter Setting Option
-You can choose one of `shared`, `scoped` and `isolated` options when you configure Spark interpreter.
-e.g.
+## Dependency Management
+
+For Spark interpreter, it is not recommended to use Zeppelin's [Dependency Management](../usage/interpreter/dependency_management.html) for managing
+third party dependencies (`%spark.dep` is removed from Zeppelin 0.9 as well). Instead, you should set the standard Spark properties as following:
+
+<table class="table-configuration">
+ <tr>
+ <th>Spark Property</th>
+ <th>Spark Submit Argument</th>
+ <th>Description</th>
+ </tr>
+ <tr>
+ <td>spark.files</td>
+ <td>--files</td>
+ <td>Comma-separated list of files to be placed in the working directory of each executor. Globs are allowed.</td>
+ </tr>
+ <tr>
+ <td>spark.jars</td>
+ <td>--jars</td>
+ <td>Comma-separated list of jars to include on the driver and executor classpaths. Globs are allowed.</td>
+ </tr>
+ <tr>
+ <td>spark.jars.packages</td>
+ <td>--packages</td>
+ <td>Comma-separated list of Maven coordinates of jars to include on the driver and executor classpaths. The coordinates should be groupId:artifactId:version. If spark.jars.ivySettings is given artifacts will be resolved according to the configuration in the file, otherwise artifacts will be searched for in the local maven repo, then maven central and finally any additional remote repositories given by the command-line option --repositories.</td>
+ </tr>
+</table>
+
+As general Spark properties, you can set them in via inline configuration or interpreter setting page or in `zeppelin-env.sh` via environment variable `SPARK_SUBMIT_OPTIONS`.
+For examples:
+
+```bash
+export SPARK_SUBMIT_OPTIONS="--files <my_file> --jars <my_jar> --packages <my_package>"
+```
+
+To be noticed, `SPARK_SUBMIT_OPTIONS` is deprecated and will be removed in future release.
-* In `scoped` per user mode, Zeppelin creates separated Scala compiler for each user but share a single SparkContext.
-* In `isolated` per user mode, Zeppelin creates separated SparkContext for each user.
## ZeppelinContext
+
Zeppelin automatically injects `ZeppelinContext` as variable `z` in your Scala/Python environment. `ZeppelinContext` provides some additional functions and utilities.
-See [Zeppelin-Context](../usage/other_features/zeppelin_context.html) for more details.
+See [Zeppelin-Context](../usage/other_features/zeppelin_context.html) for more details. For Spark interpreter, you can use z to display Spark `Dataset/Dataframe`.
+
+
+<img src="{{BASE_PATH}}/assets/themes/zeppelin/img/docs-img/spark_zshow.png">
+
## Setting up Zeppelin with Kerberos
+
Logical setup with Zeppelin, Kerberos Key Distribution Center (KDC), and Spark on YARN:
<img src="{{BASE_PATH}}/assets/themes/zeppelin/img/docs-img/kdc_zeppelin.png">
-There're several ways to make spark work with kerberos enabled hadoop cluster in Zeppelin.
+There are several ways to make Spark work with kerberos enabled hadoop cluster in Zeppelin.
1. Share one single hadoop cluster.
In this case you just need to specify `zeppelin.server.kerberos.keytab` and `zeppelin.server.kerberos.principal` in zeppelin-site.xml, Spark interpreter will use these setting by default.
@@ -474,11 +587,26 @@ In this case you just need to specify `zeppelin.server.kerberos.keytab` and `zep
2. Work with multiple hadoop clusters.
In this case you can specify `spark.yarn.keytab` and `spark.yarn.principal` to override `zeppelin.server.kerberos.keytab` and `zeppelin.server.kerberos.principal`.
+### Configuration Setup
+
+1. On the server that Zeppelin is installed, install Kerberos client modules and configuration, krb5.conf.
+ This is to make the server communicate with KDC.
+
+2. Add the two properties below to Spark configuration (`[SPARK_HOME]/conf/spark-defaults.conf`):
+
+ ```
+ spark.yarn.principal
+ spark.yarn.keytab
+ ```
+
+> **NOTE:** If you do not have permission to access for the above spark-defaults.conf file, optionally, you can add the above lines to the Spark Interpreter setting through the Interpreter tab in the Zeppelin UI.
+
+3. That's it. Play with Zeppelin!
## User Impersonation
-In yarn mode, the user who launch the zeppelin server will be used to launch the spark yarn application. This is not a good practise.
-Most of time, you will enable shiro in Zeppelin and would like to use the login user to submit the spark yarn app. For this purpose,
+In yarn mode, the user who launch the zeppelin server will be used to launch the Spark yarn application. This is not a good practise.
+Most of time, you will enable shiro in Zeppelin and would like to use the login user to submit the Spark yarn app. For this purpose,
you need to enable user impersonation for more security control. In order the enable user impersonation, you need to do the following steps
**Step 1** Enable user impersonation setting hadoop's `core-site.xml`. E.g. if you are using user `zeppelin` to launch Zeppelin, then add the following to `core-site.xml`, then restart both hdfs and yarn.
@@ -508,19 +636,7 @@ You can get rid of this message by setting `zeppelin.spark.deprecatedMsg.show` t
<img src="{{BASE_PATH}}/assets/themes/zeppelin/img/docs-img/spark_deprecate.png">
-### Configuration Setup
-
-1. On the server that Zeppelin is installed, install Kerberos client modules and configuration, krb5.conf.
-This is to make the server communicate with KDC.
-
-2. Add the two properties below to Spark configuration (`[SPARK_HOME]/conf/spark-defaults.conf`):
- ```
- spark.yarn.principal
- spark.yarn.keytab
- ```
-
- > **NOTE:** If you do not have permission to access for the above spark-defaults.conf file, optionally, you can add the above lines to the Spark Interpreter setting through the Interpreter tab in the Zeppelin UI.
-
-3. That's it. Play with Zeppelin!
+## Community
+[Join our community](http://zeppelin.apache.org/community.html) to discuss with others.
diff --git a/docs/quickstart/flink_with_zeppelin.md b/docs/quickstart/flink_with_zeppelin.md
new file mode 100644
index 0000000..70f7970
--- /dev/null
+++ b/docs/quickstart/flink_with_zeppelin.md
@@ -0,0 +1,42 @@
+---
+layout: page
+title: "Flink with Zeppelin"
+description: ""
+group: quickstart
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+{% include JB/setup %}
+
+# Flink support in Zeppelin
+
+<div id="toc"></div>
+
+<br/>
+
+For a brief overview of Apache Flink fundamentals with Apache Zeppelin, see the following guide:
+
+- **built-in** Apache Flink integration.
+- With [Flink Scala Scala](https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/deployment/repls/scala_shell/) [PyFlink Shell](https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/deployment/repls/python_shell/), [Flink SQL](https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/dev/table/sql/overview/)
+- Inject ExecutionEnvironment, StreamExecutionEnvironment, BatchTableEnvironment, StreamTableEnvironment.
+- Canceling job and displaying its progress
+- Supports different modes: local, remote, yarn, yarn-application
+- Dependency management
+- Streaming Visualization
+
+<br/>
+
+For the further information about Flink support in Zeppelin, please check
+
+- [Flink Interpreter](../interpreter/flink.html)
diff --git a/docs/quickstart/install.md b/docs/quickstart/install.md
index aa14d9f..4606e0f 100644
--- a/docs/quickstart/install.md
+++ b/docs/quickstart/install.md
@@ -50,7 +50,7 @@ Two binary packages are available on the [download page](http://zeppelin.apache.
- **all interpreter package**: unpack it in a directory of your choice and you're ready to go.
- **net-install interpreter package**: only spark, python, markdown and shell interpreter included. Unpack and follow [install additional interpreters](../usage/interpreter/installation.html) to install other interpreters. If you're unsure, just run `./bin/install-interpreter.sh --all` and install all interpreters.
-
+
### Building Zeppelin from source
Follow the instructions [How to Build](../setup/basics/how_to_build.html), If you want to build from source instead of using binary package.
@@ -67,9 +67,11 @@ bin/zeppelin-daemon.sh start
After Zeppelin has started successfully, go to [http://localhost:8080](http://localhost:8080) with your web browser.
-By default Zeppelin is listening at `127.0.0.1:8080`, so you can't access it when it is deployed in another remote machine.
+By default Zeppelin is listening at `127.0.0.1:8080`, so you can't access it when it is deployed on another remote machine.
To access a remote Zeppelin, you need to change `zeppelin.server.addr` to `0.0.0.0` in `conf/zeppelin-site.xml`.
+Check log file at `ZEPPELIN_HOME/logs/zeppelin-server-*.log` if you can not open Zeppelin.
+
#### Stopping Zeppelin
```
@@ -84,15 +86,27 @@ Make sure that [docker](https://www.docker.com/community-edition) is installed i
Use this command to launch Apache Zeppelin in a container.
```bash
-docker run -p 8080:8080 --rm --name zeppelin apache/zeppelin:0.9.0
+docker run -p 8080:8080 --rm --name zeppelin apache/zeppelin:0.10.0
```
+
To persist `logs` and `notebook` directories, use the [volume](https://docs.docker.com/engine/reference/commandline/run/#mount-volume--v-read-only) option for docker container.
```bash
-docker run -p 8080:8080 --rm -v $PWD/logs:/logs -v $PWD/notebook:/notebook \
+docker run -u $(id -u) -p 8080:8080 --rm -v $PWD/logs:/logs -v $PWD/notebook:/notebook \
-e ZEPPELIN_LOG_DIR='/logs' -e ZEPPELIN_NOTEBOOK_DIR='/notebook' \
- --name zeppelin apache/zeppelin:0.9.0
+ --name zeppelin apache/zeppelin:0.10.0
+```
+
+`-u $(id -u)` is to make sure you have the permission to write logs and notebooks.
+
+For many interpreters, they require other dependencies, e.g. Spark interpreter requires Spark binary distribution
+and Flink interpreter requires Flink binary distribution. You can also mount them via docker volumn. e.g.
+
+```bash
+docker run -u $(id -u) -p 8080:8080 --rm -v /mnt/disk1/notebook:/notebook \
+-v /usr/lib/spark-current:/opt/spark -v /mnt/disk1/flink-1.12.2:/opt/flink -e FLINK_HOME=/opt/flink \
+-e SPARK_HOME=/opt/spark -e ZEPPELIN_NOTEBOOK_DIR='/notebook' --name zeppelin apache/zeppelin:0.10.0
```
If you have trouble accessing `localhost:8080` in the browser, Please clear browser cache.
@@ -146,13 +160,15 @@ Congratulations, you have successfully installed Apache Zeppelin! Here are a few
#### New to Apache Zeppelin...
* For an in-depth overview, head to [Explore Zeppelin UI](../quickstart/explore_ui.html).
- * And then, try run [Tutorial Notebook](http://localhost:8080/#/notebook/2A94M5J1Z) in your Zeppelin.
+ * And then, try run Tutorial Notebooks shipped with your Zeppelin distribution.
* And see how to change [configurations](../setup/operation/configuration.html) like port number, etc.
-#### Spark, Python, SQL, and more
+#### Spark, Flink, SQL, Python, R and more
* [Spark support in Zeppelin](./spark_with_zeppelin.html), to know more about deep integration with [Apache Spark](http://spark.apache.org/).
+ * [Flink support in Zeppelin](./flink_with_zeppelin.html), to know more about deep integration with [Apache Flink](http://flink.apache.org/).
* [SQL support in Zeppelin](./sql_with_zeppelin.html) for SQL support
* [Python support in Zeppelin](./python_with_zeppelin.html), for Matplotlib, Pandas, Conda/Docker integration.
+ * [R support in Zeppelin](./r_with_zeppelin.html)
* [All Available Interpreters](../#available-interpreters)
#### Multi-user support ...
diff --git a/docs/quickstart/python_with_zeppelin.md b/docs/quickstart/python_with_zeppelin.md
index 80237f8..76b3d58 100644
--- a/docs/quickstart/python_with_zeppelin.md
+++ b/docs/quickstart/python_with_zeppelin.md
@@ -27,16 +27,17 @@ limitations under the License.
The following guides explain how to use Apache Zeppelin that enables you to write in Python:
+- supports [vanilla python](../interpreter/python.html#vanilla-python-interpreter-python) and [ipython](../interpreter/python.html#ipython-interpreter-pythonipython-recommended)
- supports flexible python environments using [conda](../interpreter/python.html#conda), [docker](../interpreter/python.html#docker)
- can query using [PandasSQL](../interpreter/python.html#sql-over-pandas-dataframes)
- also, provides [PySpark](../interpreter/spark.html)
+- [run python interpreter in yarn cluster](../interpreter/python.html#run-python-in-yarn-cluster) with customized conda python environment.
- with [matplotlib integration](../interpreter/python.html#matplotlib-integration)
-- support [ipython](../interpreter/python.html#ipython-interpreter-pythonipython-recommended)
- can create results including **UI widgets** using [Dynamic Form](../interpreter/python.html#using-zeppelin-dynamic-forms)
<br/>
-For the further information about Spark support in Zeppelin, please check
+For the further information about Python support in Zeppelin, please check
- [Python Interpreter](../interpreter/python.html)
diff --git a/docs/quickstart/r_with_zeppelin.md b/docs/quickstart/r_with_zeppelin.md
new file mode 100644
index 0000000..f9b9feb
--- /dev/null
+++ b/docs/quickstart/r_with_zeppelin.md
@@ -0,0 +1,42 @@
+---
+layout: page
+title: "R with Zeppelin"
+description: ""
+group: quickstart
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+{% include JB/setup %}
+
+# R support in Zeppelin
+
+<div id="toc"></div>
+
+<br/>
+
+The following guides explain how to use Apache Zeppelin that enables you to write in R:
+
+- Supports [vanilla R](../interpreter/r.html#how-to-use-r-interpreter) and [IRkernel](../interpreter/r.html#how-to-use-r-interpreter)
+- Visualize R dataframe via [ZeppelinContext](../interpreter/r.html#zshow)
+- [Run R interpreter in yarn cluster](../interpreter/r.html#run-r-in-yarn-cluster) with customized conda R environment.
+- [Make R Shiny App] (../interpreter/r.html#make-shiny-app-in-zeppelin)
+
+<br/>
+
+For the further information about R support in Zeppelin, please check
+
+- [R Interpreter](../interpreter/r.html)
+
+
+
diff --git a/docs/quickstart/spark_with_zeppelin.md b/docs/quickstart/spark_with_zeppelin.md
index 6b35beb..7250f00 100644
--- a/docs/quickstart/spark_with_zeppelin.md
+++ b/docs/quickstart/spark_with_zeppelin.md
@@ -28,12 +28,13 @@ limitations under the License.
For a brief overview of Apache Spark fundamentals with Apache Zeppelin, see the following guide:
- **built-in** Apache Spark integration.
-- with [SparkSQL](http://spark.apache.org/sql/), [PySpark](https://spark.apache.org/docs/latest/api/python/pyspark.html), [SparkR](https://spark.apache.org/docs/latest/sparkr.html)
-- inject [SparkContext](https://spark.apache.org/docs/latest/api/java/org/apache/spark/SparkContext.html), [SQLContext](https://spark.apache.org/docs/latest/sql-programming-guide.html) and [SparkSession](https://spark.apache.org/docs/latest/sql-programming-guide.html) automatically
-- canceling job and displaying its progress
-- supporting [Spark Cluster Mode](../setup/deployment/spark_cluster_mode.html#apache-zeppelin-on-spark-cluster-mode) for external spark clusters
-- supports [different context per user / note](../usage/interpreter/interpreter_binding_mode.html)
-- sharing variables among PySpark, SparkR and Spark through [ZeppelinContext](../interpreter/spark.html#zeppelincontext)
+- With [Spark Scala](https://spark.apache.org/docs/latest/quick-start.html) [SparkSQL](http://spark.apache.org/sql/), [PySpark](https://spark.apache.org/docs/latest/api/python/pyspark.html), [SparkR](https://spark.apache.org/docs/latest/sparkr.html)
+- Inject [SparkContext](https://spark.apache.org/docs/latest/api/java/org/apache/spark/SparkContext.html), [SQLContext](https://spark.apache.org/docs/latest/sql-programming-guide.html) and [SparkSession](https://spark.apache.org/docs/latest/sql-programming-guide.html) automatically
+- Canceling job and displaying its progress
+- Supports different modes: local, standalone, yarn(client & cluster), k8s
+- Dependency management
+- Supports [different context per user / note](../usage/interpreter/interpreter_binding_mode.html)
+- Sharing variables among PySpark, SparkR and Spark through [ZeppelinContext](../interpreter/spark.html#zeppelincontext)
- [Livy Interpreter](../interpreter/livy.html)
<br/>
diff --git a/docs/quickstart/sql_with_zeppelin.md b/docs/quickstart/sql_with_zeppelin.md
index df63ccd..e007f20 100644
--- a/docs/quickstart/sql_with_zeppelin.md
+++ b/docs/quickstart/sql_with_zeppelin.md
@@ -38,6 +38,7 @@ The following guides explain how to use Apache Zeppelin that enables you to writ
* [Apache Tajo](../interpreter/jdbc.html#apache-tajo)
* and so on
- [Spark Interpreter](../interpreter/spark.html) supports [SparkSQL](http://spark.apache.org/sql/)
+- [Flink Interpreter](../interpreter/flink.html) supports [Flink SQL](https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/dev/table/sql/overview/)
- [Python Interpreter](../interpreter/python.html) supports [pandasSQL](../interpreter/python.html#sql-over-pandas-dataframes)
- can create query result including **UI widgets** using [Dynamic Form](../usage/dynamic_form/intro.html)
@@ -56,6 +57,7 @@ For the further information about SQL support in Zeppelin, please check
- [JDBC Interpreter](../interpreter/jdbc.html)
- [Spark Interpreter](../interpreter/spark.html)
+- [Flink Interpreter](../interpreter/flink.html)
- [Python Interpreter](../interpreter/python.html)
- [IgniteSQL Interpreter](../interpreter/ignite.html#ignite-sql-interpreter) for [Apache Ignite](https://ignite.apache.org/)
- [Kylin Interpreter](../interpreter/kylin.html) for [Apache Kylin](http://kylin.apache.org/)
diff --git a/docs/setup/deployment/flink_and_spark_cluster.md b/docs/setup/deployment/flink_and_spark_cluster.md
index c793651..8aaa495 100644
--- a/docs/setup/deployment/flink_and_spark_cluster.md
+++ b/docs/setup/deployment/flink_and_spark_cluster.md
@@ -20,6 +20,8 @@ limitations under the License.
{% include JB/setup %}
+<font color=red>This document is outdated, it is not verified in the latest Zeppelin.</font>
+
# Install with Flink and Spark cluster
<div id="toc"></div>
diff --git a/docs/usage/interpreter/dependency_management.md b/docs/usage/interpreter/dependency_management.md
index f4aeb44..d616d4e 100644
--- a/docs/usage/interpreter/dependency_management.md
+++ b/docs/usage/interpreter/dependency_management.md
@@ -24,13 +24,14 @@ limitations under the License.
You can include external libraries to interpreter by setting dependencies in interpreter menu.
+To be noticed, this approach doesn't work for spark and flink interpreters. They have their own dependency management, please refer their doc for details.
+
When your code requires external library, instead of doing download/copy/restart Zeppelin, you can easily do following jobs in this menu.
* Load libraries recursively from Maven repository
* Load libraries from local filesystem
* Add additional maven repository
- * Automatically add libraries to SparkCluster
-
+
<hr>
<div class="row">
<div class="col-md-6">
diff --git a/notebook/Spark Tutorial/8. PySpark Conda Env in Yarn Mode_2GE79Y5FV.zpln b/notebook/Spark Tutorial/8. PySpark Conda Env in Yarn Mode_2GE79Y5FV.zpln
new file mode 100644
index 0000000..7532541
--- /dev/null
+++ b/notebook/Spark Tutorial/8. PySpark Conda Env in Yarn Mode_2GE79Y5FV.zpln
@@ -0,0 +1,1397 @@
+{
+ "paragraphs": [
+ {
+ "text": "%md\n\nThis tutorial is for how to customize pyspark runtime environment via conda in yarn-cluster mode.\nIn this approach, the spark interpreter (driver) and spark executor all run in yarn containers. \nAnd remmeber this approach only works when ipython is enabled, so make sure you include the following python packages in your conda env which are required for ipython.\n\n* jupyter\n* grpcio\n* protobuf\n\nThis turorial is only verified with spark 3.1.2, other versions of [...]
+ "user": "anonymous",
+ "dateUpdated": "2021-08-09 20:25:07.164",
+ "progress": 0,
+ "config": {
+ "tableHide": false,
+ "editorSetting": {
+ "language": "markdown",
+ "editOnDblClick": true,
+ "completionKey": "TAB",
+ "completionSupport": false
+ },
+ "colWidth": 12.0,
+ "editorMode": "ace/mode/markdown",
+ "fontSize": 9.0,
+ "editorHide": true,
+ "title": false,
+ "results": {},
+ "enabled": true
+ },
+ "settings": {
+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "HTML",
+ "data": "\u003cdiv class\u003d\"markdown-body\"\u003e\n\u003cp\u003eThis tutorial is for how to customize pyspark runtime environment via conda in yarn-cluster mode.\u003cbr /\u003e\nIn this approach, the spark interpreter (driver) and spark executor all run in yarn containers.\u003cbr /\u003e\nAnd remmeber this approach only works when ipython is enabled, so make sure you include the following python packages in your conda env which are required for ipython.\u003c/p\u003e\n\ [...]
+ }
+ ]
+ },
+ "apps": [],
+ "runtimeInfos": {},
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1628499052501_412639221",
+ "id": "paragraph_1616510705826_532544979",
+ "dateCreated": "2021-08-09 16:50:52.501",
+ "dateStarted": "2021-08-09 20:25:07.190",
+ "dateFinished": "2021-08-09 20:25:09.774",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Create Python conda env",
+ "text": "%sh\n\n# make sure you have conda and momba installed.\n# install miniconda: https://docs.conda.io/en/latest/miniconda.html\n# install mamba: https://github.com/mamba-org/mamba\n\necho \"name: pyspark_env\nchannels:\n - conda-forge\n - defaults\ndependencies:\n - python\u003d3.8 \n - jupyter\n - grpcio\n - protobuf\n - pandasql\n - pycodestyle\n # use numpy \u003c 1.20, otherwise the following pandas udf example will fail, see https://github.com/Azure/MachineLearn [...]
+ "user": "anonymous",
+ "dateUpdated": "2021-08-09 20:25:09.790",
+ "progress": 0,
+ "config": {
+ "editorSetting": {
+ "language": "sh",
+ "editOnDblClick": false,
+ "completionKey": "TAB",
+ "completionSupport": false
+ },
+ "colWidth": 12.0,
+ "editorMode": "ace/mode/sh",
+ "fontSize": 9.0,
+ "title": true,
+ "results": {},
+ "enabled": true
+ },
+ "settings": {
+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "TEXT",
+ "data": "\nRemove all packages in environment /mnt/disk1/jzhang/miniconda3/envs/pyspark_env:\n\npkgs/r/linux-64 \npkgs/main/noarch \npkgs/main/linux-64 \npkgs/r/noarch \nconda-forge/noarch \nconda-forge/linux-64 \nTransaction\n\n Prefix: /mnt/disk1/jzhang/miniconda3/envs/pyspark_env\n\n Updating specs:\n\n - python\u003d3.8\n - jupyter\n - grpcio\n - protobuf\n - pandasql\n - pycodestyle\n - numpy\u003d\u003d1. [...]
+ }
+ ]
+ },
+ "apps": [],
+ "runtimeInfos": {},
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1628499052502_43002557",
+ "id": "paragraph_1617163651950_276096757",
+ "dateCreated": "2021-08-09 16:50:52.502",
+ "dateStarted": "2021-08-09 20:25:09.796",
+ "dateFinished": "2021-08-09 20:26:01.854",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Create Python conda tar",
+ "text": "%sh\n\nrm -rf pyspark_env.tar.gz\nconda pack -n pyspark_env\n",
+ "user": "anonymous",
+ "dateUpdated": "2021-08-09 20:26:01.935",
+ "progress": 0,
+ "config": {
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+ "title": true,
+ "results": {},
+ "enabled": true
+ },
+ "settings": {
+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "TEXT",
+ "data": "Collecting packages...\nPacking environment at \u0027/mnt/disk1/jzhang/miniconda3/envs/pyspark_env\u0027 to \u0027pyspark_env.tar.gz\u0027\n\r[ ] | 0% Completed | 0.0s\r[ ] | 0% Completed | 0.1s\r[ ] | 0% Completed | 0.2s\r[ ] | 0% Completed | 0.3s\r[ ] | 1% Completed | [...]
+ }
+ ]
+ },
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+ "runtimeInfos": {},
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+ "id": "paragraph_1617170106834_1523620028",
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+ "dateFinished": "2021-08-09 20:27:03.580",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Upload Python conda tar to hdfs",
+ "text": "%sh\n\nhadoop fs -rmr /tmp/pyspark_env.tar.gz\nhadoop fs -put pyspark_env.tar.gz /tmp\n# The python conda tar should be public accessible, so need to change permission here.\nhadoop fs -chmod 644 /tmp/pyspark_env.tar.gz\n",
+ "user": "anonymous",
+ "dateUpdated": "2021-08-09 20:27:03.588",
+ "progress": 0,
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+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "TEXT",
+ "data": "rmr: DEPRECATED: Please use \u0027-rm -r\u0027 instead.\n21/08/09 20:27:05 INFO fs.TrashPolicyDefault: Moved: \u0027hdfs://emr-header-1.cluster-46718:9000/tmp/pyspark_env.tar.gz\u0027 to trash at: hdfs://emr-header-1.cluster-46718:9000/user/hadoop/.Trash/Current/tmp/pyspark_env.tar.gz\n"
+ }
+ ]
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+ "id": "paragraph_1617163700271_1335210825",
+ "dateCreated": "2021-08-09 16:50:52.503",
+ "dateStarted": "2021-08-09 20:27:03.591",
+ "dateFinished": "2021-08-09 20:27:10.407",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Configure Spark Interpreter",
+ "text": "%spark.conf\n\n# set the following 2 properties to run spark in yarn-cluster mode\nspark.master yarn\nspark.submit.deployMode cluster\n\nspark.driver.memory 4g\nspark.executor.memory 4g\n\n# spark.yarn.dist.archives can be either local file or hdfs file\nspark.yarn.dist.archives hdfs:///tmp/pyspark_env.tar.gz#environment\n# spark.yarn.dist.archives pyspark_env.tar.gz#environment\n\nzeppelin.interpreter.conda.env.name environment\n\nspark.sql.execution.arrow.pyspark.enabled [...]
+ "user": "anonymous",
+ "dateUpdated": "2021-08-09 20:27:10.499",
+ "progress": 0,
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+ "code": "SUCCESS",
+ "msg": []
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+ "dateStarted": "2021-08-09 20:27:10.506",
+ "dateFinished": "2021-08-09 20:27:10.517",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Use Matplotlib",
+ "text": "%md\n\nThe following example use matplotlib in pyspark. Here the matplotlib is only used in spark driver.\n",
+ "user": "anonymous",
+ "dateUpdated": "2021-08-09 20:27:10.603",
+ "progress": 0,
+ "config": {
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+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "HTML",
+ "data": "\u003cdiv class\u003d\"markdown-body\"\u003e\n\u003cp\u003eThe following example use matplotlib in pyspark. Here the matplotlib is only used in spark driver.\u003c/p\u003e\n\n\u003c/div\u003e"
+ }
+ ]
+ },
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+ "dateFinished": "2021-08-09 20:27:10.614",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Use Matplotlib",
+ "text": "%spark.pyspark\n\n%matplotlib inline\n\nimport matplotlib.pyplot as plt\n\nplt.plot([1,2,3,4])\nplt.ylabel(\u0027some numbers\u0027)\nplt.show()\n",
+ "user": "anonymous",
+ "dateUpdated": "2021-08-09 20:27:10.707",
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+ "results": {
+ "code": "SUCCESS",
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+ "type": "IMG",
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+ "status": "FINISHED"
+ },
+ {
+ "title": "PySpark UDF using Pandas and PyArrow",
+ "text": "%md\n\nFollowing are examples of using pandas and pyarrow in udf. Here we use python packages in both spark driver and executors. All the examples are from [apache spark official document](https://spark.apache.org/docs/latest/api/python/user_guide/arrow_pandas.html#recommended-pandas-and-pyarrow-versions)",
+ "user": "anonymous",
+ "dateUpdated": "2021-08-09 20:28:22.458",
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+ "code": "SUCCESS",
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+ {
+ "type": "HTML",
+ "data": "\u003cdiv class\u003d\"markdown-body\"\u003e\n\u003cp\u003eFollowing are examples of using pandas and pyarrow in udf. Here we use python packages in both spark driver and executors. All the examples are from \u003ca href\u003d\"https://spark.apache.org/docs/latest/api/python/user_guide/arrow_pandas.html#recommended-pandas-and-pyarrow-versions\"\u003eapache spark official document\u003c/a\u003e\u003c/p\u003e\n\n\u003c/div\u003e"
+ }
+ ]
+ },
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+ "jobName": "paragraph_1628502428567_60098788",
+ "id": "paragraph_1628502428567_60098788",
+ "dateCreated": "2021-08-09 17:47:08.568",
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+ "dateFinished": "2021-08-09 20:28:22.478",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Enabling for Conversion to/from Pandas",
+ "text": "%md\n\nArrow is available as an optimization when converting a Spark DataFrame to a Pandas DataFrame using the call `DataFrame.toPandas()` and when creating a Spark DataFrame from a Pandas DataFrame with `SparkSession.createDataFrame()`. To use Arrow when executing these calls, users need to first set the Spark configuration `spark.sql.execution.arrow.pyspark.enabled` to true. This is disabled by default.\n\nIn addition, optimizations enabled by `spark.sql.execution.arrow. [...]
+ "user": "anonymous",
+ "dateUpdated": "2021-08-09 20:28:22.561",
+ "progress": 0,
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+ "title": true,
+ "results": {},
+ "enabled": true
+ },
+ "settings": {
+ "params": {},
+ "forms": {}
+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "HTML",
+ "data": "\u003cdiv class\u003d\"markdown-body\"\u003e\n\u003cp\u003eArrow is available as an optimization when converting a Spark DataFrame to a Pandas DataFrame using the call \u003ccode\u003eDataFrame.toPandas()\u003c/code\u003e and when creating a Spark DataFrame from a Pandas DataFrame with \u003ccode\u003eSparkSession.createDataFrame()\u003c/code\u003e. To use Arrow when executing these calls, users need to first set the Spark configuration \u003ccode\u003espark.sql.exec [...]
+ }
+ ]
+ },
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+ "runtimeInfos": {},
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1628503042999_590218180",
+ "id": "paragraph_1628503042999_590218180",
+ "dateCreated": "2021-08-09 17:57:22.999",
+ "dateStarted": "2021-08-09 20:28:22.565",
+ "dateFinished": "2021-08-09 20:28:22.574",
+ "status": "FINISHED"
+ },
+ {
+ "text": "%spark.pyspark\n\nimport pandas as pd\nimport numpy as np\n\n# Generate a Pandas DataFrame\npdf \u003d pd.DataFrame(np.random.rand(100, 3))\n\n# Create a Spark DataFrame from a Pandas DataFrame using Arrow\ndf \u003d spark.createDataFrame(pdf)\n\n# Convert the Spark DataFrame back to a Pandas DataFrame using Arrow\nresult_pdf \u003d df.select(\"*\").toPandas()\n",
+ "user": "anonymous",
+ "dateUpdated": "2021-08-09 20:28:22.664",
+ "progress": 0,
+ "config": {
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+ "results": {
+ "code": "SUCCESS",
+ "msg": []
+ },
+ "apps": [],
+ "runtimeInfos": {
+ "jobUrl": {
+ "propertyName": "jobUrl",
+ "label": "SPARK JOB",
+ "tooltip": "View in Spark web UI",
+ "group": "spark",
+ "values": [
+ {
+ "jobUrl": "http://emr-worker-2.cluster-46718:37989/jobs/job?id\u003d0"
+ }
+ ],
+ "interpreterSettingId": "spark"
+ }
+ },
+ "progressUpdateIntervalMs": 500,
+ "jobName": "paragraph_1628499052504_328504071",
+ "id": "paragraph_1628487947468_761461400",
+ "dateCreated": "2021-08-09 16:50:52.504",
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+ "dateFinished": "2021-08-09 20:28:27.045",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Pandas UDFs (a.k.a. Vectorized UDFs)",
+ "text": "%md\n\nPandas UDFs are user defined functions that are executed by Spark using Arrow to transfer data and Pandas to work with the data, which allows vectorized operations. A Pandas UDF is defined using the `pandas_udf()` as a decorator or to wrap the function, and no additional configuration is required. A Pandas UDF behaves as a regular PySpark function API in general.\n\nBefore Spark 3.0, Pandas UDFs used to be defined with `pyspark.sql.functions.PandasUDFType`. From Spa [...]
+ "user": "anonymous",
+ "dateUpdated": "2021-08-09 20:28:27.071",
+ "progress": 0,
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+ "enabled": true
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+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "HTML",
+ "data": "\u003cdiv class\u003d\"markdown-body\"\u003e\n\u003cp\u003ePandas UDFs are user defined functions that are executed by Spark using Arrow to transfer data and Pandas to work with the data, which allows vectorized operations. A Pandas UDF is defined using the \u003ccode\u003epandas_udf()\u003c/code\u003e as a decorator or to wrap the function, and no additional configuration is required. A Pandas UDF behaves as a regular PySpark function API in general.\u003c/p\u003e\n [...]
+ }
+ ]
+ },
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+ "jobName": "paragraph_1628503123247_32503996",
+ "id": "paragraph_1628503123247_32503996",
+ "dateCreated": "2021-08-09 17:58:43.248",
+ "dateStarted": "2021-08-09 20:28:27.075",
+ "dateFinished": "2021-08-09 20:28:27.083",
+ "status": "FINISHED"
+ },
+ {
+ "text": "%spark.pyspark\n\nimport pandas as pd\n\nfrom pyspark.sql.functions import pandas_udf\n\n@pandas_udf(\"col1 string, col2 long\")\ndef func(s1: pd.Series, s2: pd.Series, s3: pd.DataFrame) -\u003e pd.DataFrame:\n s3[\u0027col2\u0027] \u003d s1 + s2.str.len()\n return s3\n\n# Create a Spark DataFrame that has three columns including a struct column.\ndf \u003d spark.createDataFrame(\n [[1, \"a string\", (\"a nested string\",)]],\n \"long_col long, string_col strin [...]
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+ "dateUpdated": "2021-08-09 20:28:27.174",
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+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "TEXT",
+ "data": "root\n |-- long_col: long (nullable \u003d true)\n |-- string_col: string (nullable \u003d true)\n |-- struct_col: struct (nullable \u003d true)\n | |-- col1: string (nullable \u003d true)\n\nroot\n |-- func(long_col, string_col, struct_col): struct (nullable \u003d true)\n | |-- col1: string (nullable \u003d true)\n | |-- col2: long (nullable \u003d true)\n\n"
+ }
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+ "id": "paragraph_1628499507315_836384477",
+ "dateCreated": "2021-08-09 16:58:27.315",
+ "dateStarted": "2021-08-09 20:28:27.177",
+ "dateFinished": "2021-08-09 20:28:27.797",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Series to Series",
+ "text": "%md\n\nThe type hint can be expressed as `pandas.Series`, … -\u003e `pandas.Series`.\n\nBy using `pandas_udf()` with the function having such type hints above, it creates a Pandas UDF where the given function takes one or more `pandas.Series` and outputs one `pandas.Series`. The output of the function should always be of the same length as the input. Internally, PySpark will execute a Pandas UDF by splitting columns into batches and calling the function for each batch as a [...]
+ "user": "anonymous",
+ "dateUpdated": "2021-08-09 20:28:27.878",
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+ {
+ "type": "HTML",
+ "data": "\u003cdiv class\u003d\"markdown-body\"\u003e\n\u003cp\u003eThe type hint can be expressed as \u003ccode\u003epandas.Series\u003c/code\u003e, … -\u0026gt; \u003ccode\u003epandas.Series\u003c/code\u003e.\u003c/p\u003e\n\u003cp\u003eBy using \u003ccode\u003epandas_udf()\u003c/code\u003e with the function having such type hints above, it creates a Pandas UDF where the given function takes one or more \u003ccode\u003epandas.Series\u003c/code\u003e and outputs one \u003cco [...]
+ }
+ ]
+ },
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+ "dateFinished": "2021-08-09 20:28:27.889",
+ "status": "FINISHED"
+ },
+ {
+ "title": "Series to Series",
+ "text": "%spark.pyspark\n\nimport pandas as pd\n\nfrom pyspark.sql.functions import col, pandas_udf\nfrom pyspark.sql.types import LongType\n\n# Declare the function and create the UDF\ndef multiply_func(a: pd.Series, b: pd.Series) -\u003e pd.Series:\n return a * b\n\nmultiply \u003d pandas_udf(multiply_func, returnType\u003dLongType())\n\n# The function for a pandas_udf should be able to execute with local Pandas data\nx \u003d pd.Series([1, 2, 3])\nprint(multiply_func(x, x))\n [...]
+ "user": "anonymous",
+ "dateUpdated": "2021-08-09 20:28:27.981",
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+ },
+ "results": {
+ "code": "SUCCESS",
+ "msg": [
+ {
+ "type": "TEXT",
+ "data": "0 1\n1 4\n2 9\ndtype: int64\n+-------------------+\n|multiply_func(x, x)|\n+-------------------+\n| 1|\n| 4|\n| 9|\n+-------------------+\n\n"
+ }
+ ]
+ },
+ "apps": [],
+ "runtimeInfos": {
+ "jobUrl": {
+ "propertyName": "jobUrl",
+ "label": "SPARK JOB",
+ "tooltip": "View in Spark web UI",
+ "group": "spark",
+ "values": [
+ {
+ "jobUrl": "http://emr-worker-2.cluster-46718:37989/jobs/job?id\u003d1"
+ },
+ {
+ "jobUrl": "http://emr-worker-2.cluster-46718:37989/jobs/job?id\u003d2"
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+ "dateFinished": "2021-08-09 20:28:29.754",
+ "status": "FINISHED"
+ },
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\ No newline at end of file