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docs: replace URIs in interpreter
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@ -246,5 +246,5 @@ Following steps are performed:
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* using sh interpreter it's checked the existence of the new file copied from Alluxio and its content is showed
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<center>
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</center>
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@ -41,9 +41,9 @@ limitations under the License.
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In a notebook, to enable the **Cassandra** interpreter, click on the **Gear** icon and select **Cassandra**
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<center>
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</center>
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## Using the Cassandra Interpreter
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@ -53,7 +53,7 @@ In a paragraph, use **_%cassandra_** to select the **Cassandra** interpreter and
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To access the interactive help, type **HELP;**
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<center>
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</center>
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## Interpreter Commands
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@ -312,7 +312,7 @@ The schema objects (cluster, keyspace, table, type, function and aggregate) are
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There is a drop-down menu on the top left corner to expand objects details. On the top right menu is shown the Icon legend.
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<center>
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</center>
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## Runtime Parameters
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@ -24,7 +24,9 @@ limitations under the License.
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<div id="toc"></div>
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## Overview
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[Elasticsearch](https://www.elastic.co/products/elasticsearch) is a highly scalable open-source full-text search and analytics engine. It allows you to store, search, and analyze big volumes of data quickly and in near real time. It is generally used as the underlying engine/technology that powers applications that have complex search features and requirements.
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[Elasticsearch](https://www.elastic.co/products/elasticsearch) is a highly scalable open-source full-text search and analytics engine.
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It allows you to store, search, and analyze big volumes of data quickly and in near real time.
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It is generally used as the underlying engine/technology that powers applications that have complex search features and requirements.
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## Configuration
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<table class="table-configuration">
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@ -71,7 +73,7 @@ limitations under the License.
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</table>
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<center>
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</center>
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> **Note #1 :** You can add more properties to configure the Elasticsearch client.
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@ -82,7 +84,8 @@ limitations under the License.
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In a notebook, to enable the **Elasticsearch** interpreter, click the **Gear** icon and select **Elasticsearch**.
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## Using the Elasticsearch Interpreter
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In a paragraph, use `%elasticsearch` to select the Elasticsearch interpreter and then input all commands. To get the list of available commands, use `help`.
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In a paragraph, use `%elasticsearch` to select the Elasticsearch interpreter and then input all commands.
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To get the list of available commands, use `help`.
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```bash
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%elasticsearch
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@ -118,7 +121,7 @@ get /index/type/id
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```
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Example:
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### Search
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With the `search` command, you can send a search query to Elasticsearch. There are two formats of query:
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@ -142,7 +145,8 @@ size 50
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search /index1,index2,.../type1,type2,... <JSON document containing the query or query_string elements>
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```
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> A search query can also contain [aggregations](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations.html). If there is at least one aggregation, the result of the first aggregation is shown, otherwise, you get the search hits.
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> A search query can also contain [aggregations](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations.html).
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If there is at least one aggregation, the result of the first aggregation is shown, otherwise, you get the search hits.
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Examples:
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@ -202,25 +206,25 @@ content_length | date | request.headers[0] | request.headers[1] | request.method
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Examples:
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* With a table containing the results:
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* You can also use a predefined diagram:
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* With a JSON query:
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* With a JSON query containing a `fields` parameter (for filtering the fields in the response): in this case, all the fields values in the response are arrays, so, after flattening the result, the format of all the field names is `field_name[x]`
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* With a query string:
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* With a query containing a multi-value metric aggregation:
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* With a query containing a multi-bucket aggregation:
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### Count
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With the `count` command, you can count documents available in some indices and types. You can also provide a query.
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@ -233,10 +237,10 @@ count /index1,index2,.../type1,type2,... <JSON document containing the query OR
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Examples:
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* Without query:
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* With a query:
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### Index
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With the `index` command, you can insert/update a document in Elasticsearch.
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@ -258,7 +262,7 @@ delete /index/type/id
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```
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### Apply Zeppelin Dynamic Forms
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You can leverage [Zeppelin Dynamic Form](../manual/dynamicform.html) inside your queries. You can use both the `text input` and `select form` parameterization features.
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You can leverage [Zeppelin Dynamic Form](../usage/dynamic_form/intro.html) inside your queries. You can use both the `text input` and `select form` parameterization features.
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```bash
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%elasticsearch
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@ -37,7 +37,8 @@ limitations under the License.
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</tr>
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</table>
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This interpreter supports the [Geode](http://geode.incubator.apache.org/) [Object Query Language (OQL)](http://geode-docs.cfapps.io/docs/developing/querying_basics/oql_compared_to_sql.html). With the OQL-based querying language:
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This interpreter supports the [Geode](http://geode.incubator.apache.org/) [Object Query Language (OQL)](http://geode-docs.cfapps.io/docs/developing/querying_basics/oql_compared_to_sql.html).
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With the OQL-based querying language:
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[<img align="right" src="http://img.youtube.com/vi/zvzzA9GXu3Q/3.jpg" alt="zeppelin-view" hspace="10" width="200"></img>](https://www.youtube.com/watch?v=zvzzA9GXu3Q)
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@ -53,17 +54,24 @@ This [Video Tutorial](https://www.youtube.com/watch?v=zvzzA9GXu3Q) illustrates s
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## Create Interpreter
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By default Zeppelin creates one `Geode/OQL` instance. You can remove it or create more instances.
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Multiple Geode instances can be created, each configured to the same or different backend Geode cluster. But over time a `Notebook` can have only one Geode interpreter instance `bound`. That means you _cannot_ connect to different Geode clusters in the same `Notebook`. This is a known Zeppelin limitation.
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Multiple Geode instances can be created, each configured to the same or different backend Geode cluster.
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But over time a `Notebook` can have only one Geode interpreter instance `bound`.
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That means you _cannot_ connect to different Geode clusters in the same `Notebook`.
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This is a known Zeppelin limitation.
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To create new Geode instance open the `Interpreter` section and click the `+Create` button. Pick a `Name` of your choice and from the `Interpreter` drop-down select `geode`. Then follow the configuration instructions and `Save` the new instance.
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To create new Geode instance open the `Interpreter` section and click the `+Create` button.
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Pick a `Name` of your choice and from the `Interpreter` drop-down select `geode`.
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Then follow the configuration instructions and `Save` the new instance.
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> Note: The `Name` of the instance is used only to distinguish the instances while binding them to the `Notebook`. The `Name` is irrelevant inside the `Notebook`. In the `Notebook` you must use `%geode.oql` tag.
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## Bind to Notebook
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In the `Notebook` click on the `settings` icon in the top right corner. The select/deselect the interpreters to be bound with the `Notebook`.
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In the `Notebook` click on the `settings` icon in the top right corner.
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The select/deselect the interpreters to be bound with the `Notebook`.
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## Configuration
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You can modify the configuration of the Geode from the `Interpreter` section. The Geode interpreter expresses the following properties:
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You can modify the configuration of the Geode from the `Interpreter` section.
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The Geode interpreter expresses the following properties:
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<table class="table-configuration">
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<tr>
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@ -94,7 +102,10 @@ You can modify the configuration of the Geode from the `Interpreter` section. T
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> *Tip 2: Always start the paragraphs with the full `%geode.oql` prefix tag! The short notation: `%geode` would still be able run the OQL queries but the syntax highlighting and the auto-completions will be disabled.*
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### Create / Destroy Regions
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The OQL specification does not support [Geode Regions](https://cwiki.apache.org/confluence/display/GEODE/Index#Index-MainConceptsandComponents) mutation operations. To `create`/`destroy` regions one should use the [GFSH](http://geode-docs.cfapps.io/docs/tools_modules/gfsh/chapter_overview.html) shell tool instead. In the following it is assumed that the GFSH is colocated with Zeppelin server.
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The OQL specification does not support [Geode Regions](https://cwiki.apache.org/confluence/display/GEODE/Index#Index-MainConceptsandComponents) mutation operations.
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To `create`/`destroy` regions one should use the [GFSH](http://geode-docs.cfapps.io/docs/tools_modules/gfsh/chapter_overview.html) shell tool instead.
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In the following it is assumed that the GFSH is colocated with Zeppelin server.
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```bash
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%sh
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@ -112,7 +123,10 @@ gfsh << EOF
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EOF
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```
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Above snippet re-creates two regions: `regionEmployee` and `regionCompany`. Note that you have to explicitly specify the locator host and port. The values should match those you have used in the Geode Interpreter configuration. Comprehensive list of [GFSH Commands by Functional Area](http://geode-docs.cfapps.io/docs/tools_modules/gfsh/gfsh_quick_reference.html).
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Above snippet re-creates two regions: `regionEmployee` and `regionCompany`.
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Note that you have to explicitly specify the locator host and port.
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The values should match those you have used in the Geode Interpreter configuration.
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Comprehensive list of [GFSH Commands by Functional Area](http://geode-docs.cfapps.io/docs/tools_modules/gfsh/gfsh_quick_reference.html).
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### Basic OQL
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```sql
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@ -163,7 +177,7 @@ gfsh -e "connect" -e "list members"
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```
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### Apply Zeppelin Dynamic Forms
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You can leverage [Zeppelin Dynamic Form](../manual/dynamicform.html) inside your OQL queries. You can use both the `text input` and `select form` parameterization features
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You can leverage [Zeppelin Dynamic Form](../usage/dynamic_form/intro.html) inside your OQL queries. You can use both the `text input` and `select form` parameterization features
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```sql
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%geode.oql
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@ -24,7 +24,10 @@ limitations under the License.
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<div id="toc"></div>
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## Important Notice
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Hive Interpreter will be deprecated and merged into JDBC Interpreter. You can use Hive Interpreter by using JDBC Interpreter with same functionality. See the example below of settings and dependencies.
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Hive Interpreter will be deprecated and merged into JDBC Interpreter.
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You can use Hive Interpreter by using JDBC Interpreter with same functionality.
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See the example below of settings and dependencies.
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### Properties
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<table class="table-configuration">
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@ -130,7 +133,11 @@ This interpreter provides multiple configuration with `${prefix}`. User can set
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## Overview
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The [Apache Hive](https://hive.apache.org/) ™ data warehouse software facilitates querying and managing large datasets residing in distributed storage. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. At the same time this language also allows traditional map/reduce programmers to plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL.
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The [Apache Hive](https://hive.apache.org/) ™ data warehouse software facilitates querying and managing large datasets
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residing in distributed storage. Hive provides a mechanism to project structure onto
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this data and query the data using a SQL-like language called HiveQL.
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At the same time this language also allows traditional map/reduce programmers to
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plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL.
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## How to use
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Basically, you can use
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@ -151,7 +158,8 @@ select * from my_table;
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You can also run multiple queries up to 10 by default. Changing these settings is not implemented yet.
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### Apply Zeppelin Dynamic Forms
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You can leverage [Zeppelin Dynamic Form](../manual/dynamicform.html) inside your queries. You can use both the `text input` and `select form` parameterization features.
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You can leverage [Zeppelin Dynamic Form](../usage/dynamic_form/intro.html) inside your queries.
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You can use both the `text input` and `select form` parameterization features.
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```sql
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%hive
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@ -26,7 +26,7 @@ limitations under the License.
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## Overview
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[Apache Ignite](https://ignite.apache.org/) In-Memory Data Fabric is a high-performance, integrated and distributed in-memory platform for computing and transacting on large-scale data sets in real-time, orders of magnitude faster than possible with traditional disk-based or flash technologies.
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You can use Zeppelin to retrieve distributed data from cache using Ignite SQL interpreter. Moreover, Ignite interpreter allows you to execute any Scala code in cases when SQL doesn't fit to your requirements. For example, you can populate data into your caches or execute distributed computations.
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@ -82,14 +82,14 @@ At the "Interpreters" menu, you may edit Ignite interpreter or create new one. Z
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</tr>
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</table>
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## How to use
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After configuring Ignite interpreter, create your own notebook. Then you can bind interpreters like below image.
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For more interpreter binding information see [here](../manual/interpreters.html#what-is-interpreter-setting).
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For more interpreter binding information see [here](../usage/interpreter/overview.html#what-is-interpreter-setting).
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### Ignite SQL interpreter
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In order to execute SQL query, use ` %ignite.ignitesql ` prefix. <br>
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@ -101,7 +101,7 @@ For example, you can select top 10 words in the words cache using the following
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select _val, count(_val) as cnt from String group by _val order by cnt desc limit 10
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```
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As long as your Ignite version and Zeppelin Ignite version is same, you can also use scala code. Please check the Zeppelin Ignite version before you download your own Ignite.
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@ -123,6 +123,6 @@ val res = cache.query(qry).getAll()
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collectionAsScalaIterable(res).foreach(println _)
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```
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Apache Ignite also provides a guide docs for Zeppelin ["Ignite with Apache Zeppelin"](https://apacheignite.readme.io/docs/data-analysis-with-apache-zeppelin)
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@ -33,7 +33,7 @@ By now, it has been tested with:
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<div class="row" style="margin: 30px auto;">
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<div class="col-md-6">
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<img src="../assets/themes/zeppelin/img/docs-img/tested_databases.png" width="300px"/>
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<img src="/assets/themes/zeppelin/img/docs-img/tested_databases.png" width="300px"/>
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</div>
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<div class="col-md-6">
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<li style="padding-bottom: 5px; list-style: circle">
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@ -76,12 +76,13 @@ If you are using other databases not in the above list, please feel free to shar
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First, click `+ Create` button at the top-right corner in the interpreter setting page.
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<img src="../assets/themes/zeppelin/img/docs-img/click_create_button.png" width="600px"/>
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<img src="/assets/themes/zeppelin/img/docs-img/click_create_button.png" width="600px"/>
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Fill `Interpreter name` field with whatever you want to use as the alias(e.g. mysql, mysql2, hive, redshift, and etc..). Please note that this alias will be used as `%interpreter_name` to call the interpreter in the paragraph.
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Fill `Interpreter name` field with whatever you want to use as the alias(e.g. mysql, mysql2, hive, redshift, and etc..).
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Please note that this alias will be used as `%interpreter_name` to call the interpreter in the paragraph.
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Then select `jdbc` as an `Interpreter group`.
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<img src="../assets/themes/zeppelin/img/docs-img/select_name_and_group.png" width="200px"/>
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<img src="/assets/themes/zeppelin/img/docs-img/select_name_and_group.png" width="200px"/>
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The default driver of JDBC interpreter is set as `PostgreSQL`. It means Zeppelin includes `PostgreSQL` driver jar in itself.
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So you don't need to add any dependencies(e.g. the artifact name or path for `PostgreSQL` driver jar) for `PostgreSQL` connection.
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@ -121,7 +122,7 @@ The JDBC interpreter properties are defined by default like below.
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<tr>
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<td>default.precode</td>
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<td></td>
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<td>Some SQL which executes every time after initialization of the interpreter (see [Binding mode](../manual/interpreters.md#interpreter-binding-mode))</td>
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<td>Some SQL which executes every time after initialization of the interpreter (see <a href="../usage/interpreter/overview.html#interpreter-binding-mode">Binding mode</a>)</td>
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</tr>
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<tr>
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<td>default.completer.schemaFilters</td>
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@ -141,17 +142,17 @@ The JDBC interpreter properties are defined by default like below.
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</table>
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If you want to connect other databases such as `Mysql`, `Redshift` and `Hive`, you need to edit the property values.
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You can also use [Credential](../security/datasource_authorization.html) for JDBC authentication.
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You can also use [Credential](../setup/security/datasource_authorization.html) for JDBC authentication.
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If `default.user` and `default.password` properties are deleted(using X button) for database connection in the interpreter setting page,
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the JDBC interpreter will get the account information from [Credential](../security/datasource_authorization.html).
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the JDBC interpreter will get the account information from [Credential](../setup/security/datasource_authorization.html).
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The below example is for `Mysql` connection.
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<img src="../assets/themes/zeppelin/img/docs-img/edit_properties.png" width="600px" />
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<img src="/assets/themes/zeppelin/img/docs-img/edit_properties.png" width="600px" />
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The last step is **Dependency Setting**. Since Zeppelin only includes `PostgreSQL` driver jar by default, you need to add each driver's maven coordinates or JDBC driver's jar file path for the other databases.
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<img src="../assets/themes/zeppelin/img/docs-img/edit_dependencies.png" width="600px" />
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<img src="/assets/themes/zeppelin/img/docs-img/edit_dependencies.png" width="600px" />
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That's it. You can find more JDBC connection setting examples([Mysql](#mysql), [MariaDB](#mariadb), [Redshift](#redshift), [Apache Hive](#apache-hive), [Apache Phoenix](#apache-phoenix), and [Apache Tajo](#apache-tajo)) in [this section](#examples).
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@ -210,13 +211,13 @@ For example, if a connection needs a schema parameter, it would have to add the
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## Binding JDBC interpter to notebook
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To bind the interpreters created in the interpreter setting page, click the gear icon at the top-right corner.
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<img src="../assets/themes/zeppelin/img/docs-img/click_interpreter_binding_button.png" width="600px" />
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<img src="/assets/themes/zeppelin/img/docs-img/click_interpreter_binding_button.png" width="600px" />
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Select(blue) or deselect(white) the interpreter buttons depending on your use cases.
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If you need to use more than one interpreter in the notebook, activate several buttons.
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Don't forget to click `Save` button, or you will face `Interpreter *** is not found` error.
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<img src="../assets/themes/zeppelin/img/docs-img/jdbc_interpreter_binding.png" width="550px" />
|
||||
<img src="/assets/themes/zeppelin/img/docs-img/jdbc_interpreter_binding.png" width="550px" />
|
||||
|
||||
## How to use
|
||||
### Run the paragraph with JDBC interpreter
|
||||
|
|
@ -229,11 +230,11 @@ show databases
|
|||
If the paragraph is `FINISHED` without any errors, a new paragraph will be automatically added after the previous one with `%jdbc_interpreter_name`.
|
||||
So you don't need to type this prefix in every paragraphs' header.
|
||||
|
||||
<img src="../assets/themes/zeppelin/img/docs-img/run_paragraph_with_jdbc.png" width="600px" />
|
||||
<img src="/assets/themes/zeppelin/img/docs-img/run_paragraph_with_jdbc.png" width="600px" />
|
||||
|
||||
### Apply Zeppelin Dynamic Forms
|
||||
|
||||
You can leverage [Zeppelin Dynamic Form](../manual/dynamicform.html) inside your queries. You can use both the `text input` and `select form` parametrization features.
|
||||
You can leverage [Zeppelin Dynamic Form](../usage/dynamic_form/intro.html) inside your queries. You can use both the `text input` and `select form` parametrization features.
|
||||
|
||||
```sql
|
||||
%jdbc_interpreter_name
|
||||
|
|
@ -316,7 +317,7 @@ Here are some examples you can refer to. Including the below connectors, you can
|
|||
|
||||
### Postgres
|
||||
|
||||
<img src="../assets/themes/zeppelin/img/docs-img/postgres_setting.png" width="600px" />
|
||||
<img src="/assets/themes/zeppelin/img/docs-img/postgres_setting.png" width="600px" />
|
||||
|
||||
##### Properties
|
||||
<table class="table-configuration">
|
||||
|
|
@ -360,7 +361,7 @@ Here are some examples you can refer to. Including the below connectors, you can
|
|||
|
||||
### Mysql
|
||||
|
||||
<img src="../assets/themes/zeppelin/img/docs-img/mysql_setting.png" width="600px" />
|
||||
<img src="/assets/themes/zeppelin/img/docs-img/mysql_setting.png" width="600px" />
|
||||
|
||||
##### Properties
|
||||
<table class="table-configuration">
|
||||
|
|
@ -404,7 +405,7 @@ Here are some examples you can refer to. Including the below connectors, you can
|
|||
|
||||
### MariaDB
|
||||
|
||||
<img src="../assets/themes/zeppelin/img/docs-img/mariadb_setting.png" width="600px" />
|
||||
<img src="/assets/themes/zeppelin/img/docs-img/mariadb_setting.png" width="600px" />
|
||||
|
||||
##### Properties
|
||||
<table class="table-configuration">
|
||||
|
|
@ -448,7 +449,7 @@ Here are some examples you can refer to. Including the below connectors, you can
|
|||
|
||||
### Redshift
|
||||
|
||||
<img src="../assets/themes/zeppelin/img/docs-img/redshift_setting.png" width="600px" />
|
||||
<img src="/assets/themes/zeppelin/img/docs-img/redshift_setting.png" width="600px" />
|
||||
|
||||
##### Properties
|
||||
<table class="table-configuration">
|
||||
|
|
@ -492,7 +493,7 @@ Here are some examples you can refer to. Including the below connectors, you can
|
|||
|
||||
### Apache Hive
|
||||
|
||||
<img src="../assets/themes/zeppelin/img/docs-img/hive_setting.png" width="600px" />
|
||||
<img src="/assets/themes/zeppelin/img/docs-img/hive_setting.png" width="600px" />
|
||||
|
||||
##### Properties
|
||||
<table class="table-configuration">
|
||||
|
|
@ -544,13 +545,16 @@ Here are some examples you can refer to. Including the below connectors, you can
|
|||
[Maven Repository : org.apache.hive:hive-jdbc](https://mvnrepository.com/artifact/org.apache.hive/hive-jdbc)
|
||||
|
||||
##### Impersonation
|
||||
When Zeppelin server is running with authentication enabled, then the interpreter can utilize Hive's user proxy feature i.e. send extra parameter for creating and running a session ("hive.server2.proxy.user=": "${loggedInUser}"). This is particularly useful when multiple users are sharing a notebook.
|
||||
When Zeppelin server is running with authentication enabled, then the interpreter can utilize Hive's user proxy feature
|
||||
i.e. send extra parameter for creating and running a session ("hive.server2.proxy.user=": "${loggedInUser}").
|
||||
This is particularly useful when multiple users are sharing a notebook.
|
||||
|
||||
To enable this set following:
|
||||
|
||||
- `zeppelin.jdbc.auth.type` as `SIMPLE` or `KERBEROS` (if required) in the interpreter setting.
|
||||
- `${prefix}.proxy.user.property` as `hive.server2.proxy.user`
|
||||
|
||||
|
||||
See [User Impersonation in interpreter](../usage/interpreter/user_impersonation.html) for more information.
|
||||
|
||||
##### Sample configuration
|
||||
<table class="table-configuration">
|
||||
|
|
@ -592,7 +596,7 @@ Use the appropriate `default.driver`, `default.url`, and the dependency artifact
|
|||
|
||||
#### Thick client connection
|
||||
|
||||
<img src="../assets/themes/zeppelin/img/docs-img/phoenix_thick_setting.png" width="600px" />
|
||||
<img src="/assets/themes/zeppelin/img/docs-img/phoenix_thick_setting.png" width="600px" />
|
||||
|
||||
##### Properties
|
||||
<table class="table-configuration">
|
||||
|
|
@ -634,7 +638,7 @@ Use the appropriate `default.driver`, `default.url`, and the dependency artifact
|
|||
|
||||
#### Thin client connection
|
||||
|
||||
<img src="../assets/themes/zeppelin/img/docs-img/phoenix_thin_setting.png" width="600px" />
|
||||
<img src="/assets/themes/zeppelin/img/docs-img/phoenix_thin_setting.png" width="600px" />
|
||||
|
||||
##### Properties
|
||||
<table class="table-configuration">
|
||||
|
|
@ -686,7 +690,7 @@ Before Adding one of the below dependencies, check the Phoenix version first.
|
|||
|
||||
### Apache Tajo
|
||||
|
||||
<img src="../assets/themes/zeppelin/img/docs-img/tajo_setting.png" width="600px" />
|
||||
<img src="/assets/themes/zeppelin/img/docs-img/tajo_setting.png" width="600px" />
|
||||
|
||||
##### Properties
|
||||
<table class="table-configuration">
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ limitations under the License.
|
|||
## Overview
|
||||
[Apache Lens](https://lens.apache.org/) provides an Unified Analytics interface. Lens aims to cut the Data Analytics silos by providing a single view of data across multiple tiered data stores and optimal execution environment for the analytical query. It seamlessly integrates Hadoop with traditional data warehouses to appear like one.
|
||||
|
||||
|
||||
|
||||
|
||||
## Installing and Running Lens
|
||||
In order to use Lens interpreters, you may install Apache Lens in some simple steps:
|
||||
|
|
@ -90,14 +90,14 @@ At the "Interpreters" menu, you can edit Lens interpreter or create new one. Zep
|
|||
</tr>
|
||||
</table>
|
||||
|
||||
|
||||
|
||||
|
||||
### Interpreter Binding for Zeppelin Notebook
|
||||
After configuring Lens interpreter, create your own notebook, then you can bind interpreters like below image.
|
||||
|
||||
|
||||
|
||||
|
||||
For more interpreter binding information see [here](http://zeppelin.apache.org/docs/manual/interpreters.html).
|
||||
For more interpreter binding information see [here](../usage/interpreter/overview.html#interpreter-binding-mode).
|
||||
|
||||
### How to use
|
||||
You can analyze your data by using [OLAP Cube](http://lens.apache.org/user/olap-cube.html) [QL](http://lens.apache.org/user/cli.html) which is a high level SQL like language to query and describe data sets organized in data cubes.
|
||||
|
|
@ -174,11 +174,11 @@ fact add partitions --fact_name sales_raw_fact --storage_name local --path your/
|
|||
query execute cube select customer_city_name, product_details.description, product_details.category, product_details.color, store_sales from sales where time_range_in(delivery_time, '2015-04-11-00', '2015-04-13-00')
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
These are just examples that provided in advance by Lens. If you want to explore whole tutorials of Lens, see the [tutorial video](https://cwiki.apache.org/confluence/display/LENS/2015/07/13/20+Minute+video+demo+of+Apache+Lens+through+examples).
|
||||
|
||||
## Lens UI Service
|
||||
Lens also provides web UI service. Once the server starts up, you can open the service on http://serverhost:19999/index.html and browse. You may also check the structure that you made and use query easily here.
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -197,11 +197,13 @@ hello("livy")
|
|||
```
|
||||
|
||||
## Impersonation
|
||||
When Zeppelin server is running with authentication enabled, then this interpreter utilizes Livy’s user impersonation feature i.e. sends extra parameter for creating and running a session ("proxyUser": "${loggedInUser}"). This is particularly useful when multi users are sharing a Notebook server.
|
||||
|
||||
When Zeppelin server is running with authentication enabled,
|
||||
then this interpreter utilizes Livy’s user impersonation feature
|
||||
i.e. sends extra parameter for creating and running a session ("proxyUser": "${loggedInUser}").
|
||||
This is particularly useful when multi users are sharing a Notebook server.
|
||||
|
||||
## Apply Zeppelin Dynamic Forms
|
||||
You can leverage [Zeppelin Dynamic Form](../manual/dynamicform.html). You can use both the `text input` and `select form` parameterization features.
|
||||
You can leverage [Zeppelin Dynamic Form](../usage/dynamic_form/intro.html). You can use both the `text input` and `select form` parameterization features.
|
||||
|
||||
```
|
||||
%livy.pyspark
|
||||
|
|
|
|||
|
|
@ -31,17 +31,18 @@ In Zeppelin notebook, you can use ` %md ` in the beginning of a paragraph to inv
|
|||
|
||||
In Zeppelin, Markdown interpreter is enabled by default and uses the [pegdown](https://github.com/sirthias/pegdown) parser.
|
||||
|
||||
<img src="../assets/themes/zeppelin/img/docs-img/markdown-interpreter-setting.png" width="60%" />
|
||||
<img src="/assets/themes/zeppelin/img/docs-img/markdown-interpreter-setting.png" width="60%" />
|
||||
|
||||
## Example
|
||||
|
||||
The following example demonstrates the basic usage of Markdown in a Zeppelin notebook.
|
||||
|
||||
<img src="../assets/themes/zeppelin/img/docs-img/markdown-example.png" width="70%" />
|
||||
<img src="/assets/themes/zeppelin/img/docs-img/markdown-example.png" width="70%" />
|
||||
|
||||
## Mathematical expression
|
||||
|
||||
Markdown interpreter leverages %html display system internally. That means you can mix mathematical expressions with markdown syntax. For more information, please see [Mathematical Expression](../displaysystem/basicdisplaysystem.html#mathematical-expressions) section.
|
||||
Markdown interpreter leverages %html display system internally. That means you can mix mathematical expressions with markdown syntax.
|
||||
For more information, please see [Mathematical Expression](../usage/display_system/basic.html#mathematical-expressions) section.
|
||||
|
||||
## Configuration
|
||||
<table class="table-configuration">
|
||||
|
|
@ -62,11 +63,11 @@ Markdown interpreter leverages %html display system internally. That means you c
|
|||
|
||||
`pegdown` parser provides github flavored markdown.
|
||||
|
||||
<img src="../assets/themes/zeppelin/img/docs-img/markdown-example-pegdown-parser.png" width="70%" />
|
||||
<img src="/assets/themes/zeppelin/img/docs-img/markdown-example-pegdown-parser.png" width="70%" />
|
||||
|
||||
`pegdown` parser provides [YUML](http://yuml.me/) and [Websequence](https://www.websequencediagrams.com/) plugins also.
|
||||
|
||||
<img src="../assets/themes/zeppelin/img/docs-img/markdown-example-pegdown-parser-plugins.png" width="70%" />
|
||||
<img src="/assets/themes/zeppelin/img/docs-img/markdown-example-pegdown-parser-plugins.png" width="70%" />
|
||||
|
||||
### Markdown4j Parser
|
||||
|
||||
|
|
|
|||
|
|
@ -12,7 +12,11 @@ group: manual
|
|||
<div id="toc"></div>
|
||||
|
||||
## Overview
|
||||
[Apache Pig](https://pig.apache.org/) is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets.
|
||||
[Apache Pig](https://pig.apache.org/) is a platform for analyzing large data sets that consists of
|
||||
a high-level language for expressing data analysis programs,
|
||||
coupled with infrastructure for evaluating these programs.
|
||||
The salient property of Pig programs is that their structure is amenable to substantial parallelization,
|
||||
which in turns enables them to handle very large data sets.
|
||||
|
||||
## Supported interpreter type
|
||||
- `%pig.script` (default Pig interpreter, so you can use `%pig`)
|
||||
|
|
@ -55,7 +59,8 @@ group: manual
|
|||
|
||||
At the Interpreters menu, you have to create a new Pig interpreter. Pig interpreter has below properties by default.
|
||||
And you can set any Pig properties here which will be passed to Pig engine. (like tez.queue.name & mapred.job.queue.name).
|
||||
Besides, we use paragraph title as job name if it exists, else use the last line of Pig script. So you can use that to find app running in YARN RM UI.
|
||||
Besides, we use paragraph title as job name if it exists, else use the last line of Pig script.
|
||||
So you can use that to find app running in YARN RM UI.
|
||||
|
||||
<table class="table-configuration">
|
||||
<tr>
|
||||
|
|
@ -116,7 +121,8 @@ b = group bank_data by age;
|
|||
foreach b generate group, COUNT($1);
|
||||
```
|
||||
|
||||
The same as above, but use dynamic text form so that use can specify the variable maxAge in textbox. (See screenshot below). Dynamic form is a very cool feature of Zeppelin, you can refer this [link]((../manual/dynamicform.html)) for details.
|
||||
The same as above, but use dynamic text form so that use can specify the variable maxAge in textbox.
|
||||
(See screenshot below). Dynamic form is a very cool feature of Zeppelin, you can refer this [link]((../usage/dynamic_form/intro.html)) for details.
|
||||
|
||||
```
|
||||
%pig.query
|
||||
|
|
@ -126,7 +132,8 @@ b = group bank_data by age;
|
|||
foreach b generate group, COUNT($1) as count;
|
||||
```
|
||||
|
||||
Get the number of each age for specific marital type, also use dynamic form here. User can choose the marital type in the dropdown list (see screenshot below).
|
||||
Get the number of each age for specific marital type,
|
||||
also use dynamic form here. User can choose the marital type in the dropdown list (see screenshot below).
|
||||
|
||||
```
|
||||
%pig.query
|
||||
|
|
@ -138,11 +145,14 @@ foreach b generate group, COUNT($1) as count;
|
|||
|
||||
The above examples are in the Pig tutorial note in Zeppelin, you can check that for details. Here's the screenshot.
|
||||
|
||||
<img class="img-responsive" width="1024px" style="margin:0 auto; padding: 26px;" src="../assets/themes/zeppelin/img/pig_zeppelin_tutorial.png" />
|
||||
<img class="img-responsive" width="1024px" style="margin:0 auto; padding: 26px;" src="/assets/themes/zeppelin/img/pig_zeppelin_tutorial.png" />
|
||||
|
||||
|
||||
Data is shared between `%pig` and `%pig.query`, so that you can do some common work in `%pig`, and do different kinds of query based on the data of `%pig`.
|
||||
Besides, we recommend you to specify alias explicitly so that the visualization can display the column name correctly. In the above example 2 and 3 of `%pig.query`, we name `COUNT($1)` as `count`. If you don't do this,
|
||||
then we will name it using position. E.g. in the above first example of `%pig.query`, we will use `col_1` in chart to represent `COUNT($1)`.
|
||||
Data is shared between `%pig` and `%pig.query`, so that you can do some common work in `%pig`,
|
||||
and do different kinds of query based on the data of `%pig`.
|
||||
Besides, we recommend you to specify alias explicitly so that the visualization can display
|
||||
the column name correctly. In the above example 2 and 3 of `%pig.query`, we name `COUNT($1)` as `count`.
|
||||
If you don't do this, then we will name it using position.
|
||||
E.g. in the above first example of `%pig.query`, we will use `col_1` in chart to represent `COUNT($1)`.
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -25,5 +25,6 @@ limitations under the License.
|
|||
|
||||
## Important Notice
|
||||
|
||||
|
||||
Postgresql interpreter is deprecated and merged into [JDBC Interpreter](./jdbc.html). You can use it with JDBC Interpreter as same functionality. See [Postgresql setting example](./jdbc.html#postgres) for more detailed information.
|
||||
Postgresql interpreter is deprecated and merged into [JDBC Interpreter](./jdbc.html).
|
||||
You can use it with JDBC Interpreter as same functionality.
|
||||
See [Postgresql setting example](./jdbc.html#postgres) for more detailed information.
|
||||
|
|
|
|||
|
|
@ -114,7 +114,7 @@ Example
|
|||
```
|
||||
|
||||
## Using Zeppelin Dynamic Forms
|
||||
You can leverage [Zeppelin Dynamic Form]({{BASE_PATH}}/manual/dynamicform.html) inside your Python code.
|
||||
You can leverage [Zeppelin Dynamic Form]({{BASE_PATH}}/usage/dynamic_form/intro.html) inside your Python code.
|
||||
|
||||
**Zeppelin Dynamic Form can only be used if py4j Python library is installed in your system. If not, you can install it with `pip install py4j`.**
|
||||
|
||||
|
|
@ -148,9 +148,13 @@ z.configure_mpl(width=400, height=300, fmt='svg')
|
|||
plt.plot([1, 2, 3])
|
||||
```
|
||||
|
||||
Will produce a 400x300 image in SVG format, which by default are normally 600x400 and PNG respectively. In the future, another option called `angular` can be used to make it possible to update a plot produced from one paragraph directly from another (the output will be `%angular` instead of `%html`). However, this feature is already available in the `pyspark` interpreter. More details can be found in the included "Zeppelin Tutorial: Python - matplotlib basic" tutorial notebook.
|
||||
Will produce a 400x300 image in SVG format, which by default are normally 600x400 and PNG respectively.
|
||||
In the future, another option called `angular` can be used to make it possible to update a plot produced from one paragraph directly from another
|
||||
(the output will be `%angular` instead of `%html`). However, this feature is already available in the `pyspark` interpreter.
|
||||
More details can be found in the included "Zeppelin Tutorial: Python - matplotlib basic" tutorial notebook.
|
||||
|
||||
If Zeppelin cannot find the matplotlib backend files (which should usually be found in `$ZEPPELIN_HOME/interpreter/lib/python`) in your `PYTHONPATH`, then the backend will automatically be set to agg, and the (otherwise deprecated) instructions below can be used for more limited inline plotting.
|
||||
If Zeppelin cannot find the matplotlib backend files (which should usually be found in `$ZEPPELIN_HOME/interpreter/lib/python`) in your `PYTHONPATH`,
|
||||
then the backend will automatically be set to agg, and the (otherwise deprecated) instructions below can be used for more limited inline plotting.
|
||||
|
||||
If you are unable to load the inline backend, use `z.show(plt)`:
|
||||
```python
|
||||
|
|
@ -168,11 +172,13 @@ The `z.show()` function can take optional parameters to adapt graph dimensions (
|
|||
z.show(plt, width='50px')
|
||||
z.show(plt, height='150px', fmt='svg')
|
||||
```
|
||||
<img class="img-responsive" src="../assets/themes/zeppelin/img/docs-img/pythonMatplotlib.png" />
|
||||
<img class="img-responsive" src="/assets/themes/zeppelin/img/docs-img/pythonMatplotlib.png" />
|
||||
|
||||
|
||||
## Pandas integration
|
||||
Apache Zeppelin [Table Display System](../displaysystem/basicdisplaysystem.html#table) provides built-in data visualization capabilities. Python interpreter leverages it to visualize Pandas DataFrames though similar `z.show()` API, same as with [Matplotlib integration](#matplotlib-integration).
|
||||
Apache Zeppelin [Table Display System](../usage/display_system/basic.html#table) provides built-in data visualization capabilities.
|
||||
Python interpreter leverages it to visualize Pandas DataFrames though similar `z.show()` API,
|
||||
same as with [Matplotlib integration](#matplotlib-integration).
|
||||
|
||||
Example:
|
||||
|
||||
|
|
@ -184,7 +190,9 @@ z.show(rates)
|
|||
|
||||
## SQL over Pandas DataFrames
|
||||
|
||||
There is a convenience `%python.sql` interpreter that matches Apache Spark experience in Zeppelin and enables usage of SQL language to query [Pandas DataFrames](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html) and visualization of results though built-in [Table Display System](../displaysystem/basicdisplaysystem.html#table).
|
||||
There is a convenience `%python.sql` interpreter that matches Apache Spark experience in Zeppelin and
|
||||
enables usage of SQL language to query [Pandas DataFrames](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html) and
|
||||
visualization of results though built-in [Table Display System](../usage/display_system/basic.html#table).
|
||||
|
||||
**Pre-requests**
|
||||
|
||||
|
|
@ -217,7 +225,9 @@ For in-depth technical details on current implementation please refer to [python
|
|||
|
||||
### Some features not yet implemented in the Python Interpreter
|
||||
|
||||
* Interrupt a paragraph execution (`cancel()` method) is currently only supported in Linux and MacOs. If interpreter runs in another operating system (for instance MS Windows) , interrupt a paragraph will close the whole interpreter. A JIRA ticket ([ZEPPELIN-893](https://issues.apache.org/jira/browse/ZEPPELIN-893)) is opened to implement this feature in a next release of the interpreter.
|
||||
* Interrupt a paragraph execution (`cancel()` method) is currently only supported in Linux and MacOs.
|
||||
If interpreter runs in another operating system (for instance MS Windows) , interrupt a paragraph will close the whole interpreter.
|
||||
A JIRA ticket ([ZEPPELIN-893](https://issues.apache.org/jira/browse/ZEPPELIN-893)) is opened to implement this feature in a next release of the interpreter.
|
||||
* Progression bar in webUI (`getProgress()` method) is currently not implemented.
|
||||
* Code-completion is currently not implemented.
|
||||
|
||||
|
|
|
|||
|
|
@ -69,23 +69,23 @@ By default, the R Interpreter appears as two Zeppelin Interpreters, `%r` and `%k
|
|||
|
||||
`%r` will behave like an ordinary REPL. You can execute commands as in the CLI.
|
||||
|
||||
<img class="img-responsive" src="../assets/themes/zeppelin/img/docs-img/repl2plus2.png" width="700px"/>
|
||||
<img class="img-responsive" src="/assets/themes/zeppelin/img/docs-img/repl2plus2.png" width="700px"/>
|
||||
|
||||
R base plotting is fully supported
|
||||
|
||||
<img class="img-responsive" src="../assets/themes/zeppelin/img/docs-img/replhist.png" width="550px"/>
|
||||
<img class="img-responsive" src="/assets/themes/zeppelin/img/docs-img/replhist.png" width="550px"/>
|
||||
|
||||
If you return a data.frame, Zeppelin will attempt to display it using Zeppelin's built-in visualizations.
|
||||
|
||||
<img class="img-responsive" src="../assets/themes/zeppelin/img/docs-img/replhead.png" width="550px"/>
|
||||
<img class="img-responsive" src="/assets/themes/zeppelin/img/docs-img/replhead.png" width="550px"/>
|
||||
|
||||
`%knitr` interfaces directly against `knitr`, with chunk options on the first line:
|
||||
|
||||
<img class="img-responsive" src="../assets/themes/zeppelin/img/docs-img/knitgeo.png" width="550px"/>
|
||||
<img class="img-responsive" src="/assets/themes/zeppelin/img/docs-img/knitgeo.png" width="550px"/>
|
||||
|
||||
<img class="img-responsive" src="../assets/themes/zeppelin/img/docs-img/knitstock.png" width="550px"/>
|
||||
<img class="img-responsive" src="/assets/themes/zeppelin/img/docs-img/knitstock.png" width="550px"/>
|
||||
|
||||
<img class="img-responsive" src="../assets/themes/zeppelin/img/docs-img/knitmotion.png" width="550px"/>
|
||||
<img class="img-responsive" src="/assets/themes/zeppelin/img/docs-img/knitmotion.png" width="550px"/>
|
||||
|
||||
The two interpreters share the same environment. If you define a variable from `%r`, it will be within-scope if you then make a call using `knitr`.
|
||||
|
||||
|
|
@ -93,23 +93,23 @@ The two interpreters share the same environment. If you define a variable from
|
|||
|
||||
If `SPARK_HOME` is set, the `SparkR` package will be loaded automatically:
|
||||
|
||||
<img class="img-responsive" src="../assets/themes/zeppelin/img/docs-img/sparkrfaithful.png" width="550px"/>
|
||||
<img class="img-responsive" src="/assets/themes/zeppelin/img/docs-img/sparkrfaithful.png" width="550px"/>
|
||||
|
||||
The Spark Context and SQL Context are created and injected into the local environment automatically as `sc` and `sql`.
|
||||
|
||||
The same context are shared with the `%spark`, `%sql` and `%pyspark` interpreters:
|
||||
|
||||
<img class="img-responsive" src="../assets/themes/zeppelin/img/docs-img/backtoscala.png" width="700px"/>
|
||||
<img class="img-responsive" src="/assets/themes/zeppelin/img/docs-img/backtoscala.png" width="700px"/>
|
||||
|
||||
You can also make an ordinary R variable accessible in scala and Python:
|
||||
|
||||
<img class="img-responsive" src="../assets/themes/zeppelin/img/docs-img/varr1.png" width="550px"/>
|
||||
<img class="img-responsive" src="/assets/themes/zeppelin/img/docs-img/varr1.png" width="550px"/>
|
||||
|
||||
And vice versa:
|
||||
|
||||
<img class="img-responsive" src="../assets/themes/zeppelin/img/docs-img/varscala.png" width="550px"/>
|
||||
<img class="img-responsive" src="/assets/themes/zeppelin/img/docs-img/varscala.png" width="550px"/>
|
||||
|
||||
<img class="img-responsive" src="../assets/themes/zeppelin/img/docs-img/varr2.png" width="550px"/>
|
||||
<img class="img-responsive" src="/assets/themes/zeppelin/img/docs-img/varr2.png" width="550px"/>
|
||||
|
||||
## Caveats & Troubleshooting
|
||||
|
||||
|
|
|
|||
|
|
@ -37,9 +37,9 @@ In a notebook, to enable the **Scalding** interpreter, click on the **Gear** ico
|
|||
|
||||
<center>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</center>
|
||||
|
||||
|
|
@ -124,7 +124,7 @@ print("%table " + table)
|
|||
```
|
||||
|
||||
If you click on the icon for the pie chart, you should be able to see a chart like this:
|
||||
|
||||
|
||||
|
||||
|
||||
### HDFS mode
|
||||
|
|
|
|||
|
|
@ -63,6 +63,7 @@ At the "Interpreters" menu in Zeppelin dropdown menu, you can set the property v
|
|||
## Example
|
||||
The following example demonstrates the basic usage of Shell in a Zeppelin notebook.
|
||||
|
||||
<img src="../assets/themes/zeppelin/img/docs-img/shell-example.png" />
|
||||
<img src="/assets/themes/zeppelin/img/docs-img/shell-example.png" />
|
||||
|
||||
If you need further information about **Zeppelin Interpreter Setting** for using Shell interpreter, please read [What is interpreter setting?](../manual/interpreters.html#what-is-interpreter-setting) section first.
|
||||
If you need further information about **Zeppelin Interpreter Setting** for using Shell interpreter,
|
||||
please read [What is interpreter setting?](../usage/interpreter/overview.html#what-is-interpreter-setting) section first.
|
||||
|
|
@ -200,7 +200,7 @@ Staring from 0.6.1 SparkSession is available as variable `spark` when you are us
|
|||
There are two ways to load external libraries in Spark interpreter. First is using interpreter setting menu and second is loading Spark properties.
|
||||
|
||||
### 1. Setting Dependencies via Interpreter Setting
|
||||
Please see [Dependency Management](../manual/dependencymanagement.html) for the details.
|
||||
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.
|
||||
|
|
@ -389,23 +389,28 @@ In sql environment, you can create form in simple template.
|
|||
select * from ${table=defaultTableName} where text like '%${search}%'
|
||||
```
|
||||
|
||||
To learn more about dynamic form, checkout [Dynamic Form](../manual/dynamicform.html).
|
||||
To learn more about dynamic form, checkout [Dynamic Form](../usage/dynamic_form/intro.html).
|
||||
|
||||
|
||||
## 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 utilizing Zeppelin's built-in [Angular Display System](../displaysystem/back-end-angular.html), as shown below:
|
||||
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="../assets/themes/zeppelin/img/docs-img/matplotlibAngularExample.gif" />
|
||||
<img class="img-responsive" src="/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). It creates separated SparkContext per each notebook in `isolated` mode.
|
||||
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.
|
||||
|
||||
|
||||
## Setting up Zeppelin with Kerberos
|
||||
Logical setup with Zeppelin, Kerberos Key Distribution Center (KDC), and Spark on YARN:
|
||||
|
||||
<img src="../assets/themes/zeppelin/img/docs-img/kdc_zeppelin.png">
|
||||
<img src="/assets/themes/zeppelin/img/docs-img/kdc_zeppelin.png">
|
||||
|
||||
### Configuration Setup
|
||||
|
||||
|
|
|
|||
|
|
@ -35,6 +35,7 @@ Apache Zeppelin
|
|||
- works with external spark while supporting [Spark Cluster Mode](../setup/deployment/spark_cluster_mode.html#apache-zeppelin-on-spark-cluster-mode)
|
||||
- supports [different context per user / note](../usage/interpreter/interpreter_binding_mode.html)
|
||||
- shares variables among PySpark, SparkR and Spark through [ZeppelinContext](../interpreter/spark.html#zeppelincontext)
|
||||
- has [Livy Interpreter](../interpreter/livy.html)
|
||||
|
||||
<br/>
|
||||
|
||||
|
|
|
|||
|
|
@ -57,6 +57,8 @@ For the further information about SQL support in Zeppelin, please check
|
|||
- [JDBC Interpreter](../interpreter/jdbc.html)
|
||||
- [Spark Interpreter](../interpreter/spark.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/)
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue