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1. A newly created table result can be registered as a resource in an interpreter.

2. Since every resource registered in a resource pool in an interpreter can be searched via `DisbitrubedResourcePool` and supports remote method invocation, other interpreters can use it.

3. Let’s say JDBCInterpreter created a table result and keep it (JDBCTableData) into its resource pool.

4. Then, SparkInterpreter can fetch rows, columns via remote method invocation. if Zeppelin registers the distributed resource pool as Spark Data Source, SparkInterpreter can use all table resources in Zeppelin smoothly. (e.g Querying the table in SparkSQL as like a normal table)

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Here are is a more detailed view to explain how one interpreter can handle its TableData implementation with the resource pool. 

4. Public Interfaces

4.1. Interfaces for TableData related classes

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4.1.1. Additional methods for TableData

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Code Block
language java theme Eclipse linenumbers true
public interface TableData { … /** * filter the input `TableData` based on columns. */ public TableData filter(List<String> columnNames); /** * Pivot the input `TableData` for visualizations */ public TableData pivot(List<String> keyColumns, List<String> groupColumns, List<String> valueColumns); … }

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• SparkInterpreter can have SparkTableData which

• points RDD to get the table result

• filter and pivot can be written by using Spark RDD APIs

• JDBCInterpreter can have JDBCTableData which

• keeps query to reproduce the table result

• filter and pivot can be written using a query that has additional `where` and `group by` statements.

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Some interpreters (e.g ShellInterpreter) might not be connected with external storage. In this case, those interpreters can use the InterpreterResultTableData class.

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Code Block
language java theme Eclipse linenumbers true
public class DefaultSource implements RelationProvider, SchemaRelationProvider { Logger logger = LoggerFactory.getLogger(DefaultSource.class); public static ResourcePool resourcePool; public DefaultSource() { } @Override public BaseRelation createRelation(SQLContext sqlContext, Map<String, String> parameters) { return createRelation(sqlContext, parameters, null); } @Override public BaseRelation createRelation( SQLContext sqlContext, Map<String, String> parameters, StructType schema) { String path = parameters.get("path").get(); String [] noteIdAndParagraphId = path.split("\\|"); ResourceSet rs = ResourcePoolUtils.getAllResources(); Resource resource = resourcePool.get( noteIdAndParagraphId[0], noteIdAndParagraphId[1], WellKnownResourceName.ZeppelinTableResult.toString()); InterpreterResultMessage message = (InterpreterResultMessage) resource.get(); TableData tableData = new InterpreterResultTableData(message); return new TableDataRelation(sqlContext, tableData); } }

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4.3. ResourceRegistry Class

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• For interpreters which use SQL

• provide an interpreter option: create TableData whenever executing a paragraph

• or provide new interpreter magic for it: %spark.sql_share, %jdbc.mysql_share, …

• or automatically put all table results into the resource pool if they are not heavy (e.g keeping query only, or just reference for RDD)

• If interpreter supports runtime interpreter, we can use this syntax: %jdbc(share=true) to specify whether share the  table result or not
  
  

• For interpreters which use programming language (e.g python)

• provide API like z.put()
  

  Code Block
  language scala themelinenumbers Eclipsetrue
  // infer instance type and convert it to predefined the `TableData` subclass such as `SparkDataFrameTableData` z.put (“myTable01”, myDataFrame01) // or force user to put the `TableData` subclass val myTableData01 = new SparkRDDTableData(myRdd01) z.put(“myTable01”, myTableData01)

   

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• ZEPPELIN-TBD: Adding `pivot`, `filter` to TableData

• ZEPPELIN-TBD: ResourceRegistry

• ZEPPELIN-TBD: ZEPPELIN-TBD: Rest API for resource pool

• ZEPPELIN-TBD: UI for `Table` page

• ZEPPELIN_TBD: Apply `pivot`, `filter` methods for built-in visualisations

• ZEPPELIN-TBD: SparkTableData, SparkSQLTableData, JDBCTableData, etc.

• ZEPPELIN-2029: ACL for `ResourcePool`

• ZEPPELIN-2022: Zeppelin resource pool as Spark DataSource

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7. Potential Future Work

• Watch / Unwatch: for automatic paragraph updating for Streaming Data Representation.

• Ability to construct table result from the resource pool in language interpreters (e.g python)

• Let’s assume that we can build a pandas dataframe using TableData

  Code Block
  language py linenumbers true
  # in python interpreter t = z.get("tableResourceName") # will return object that has `hasNext` and `next` p = new PandasTableData(t) # use p.pandasInstance …

• ZEPPELIN-1494: Bind JDBC result to a dataset on the Zeppelin context