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This diagram shows how Spark Interpreter can query the table generated from JDBC interpreter.

1. An interpreter (`A`A) a newly created table result can be registered as a resource.

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

3. Let’s say JDBCInterpreter created a table result and keep it (`JDBCTableData`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)

3.2. Overview: How an interpreter can handle table resources

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

4. Public Interfaces

4.1. Interfaces for TableData related classes

TableData interface defines methods to handle a table resource. Each interpreter can implement its own TableData. The reason why we can’t make the global TableData class for all interpreters is that each interpreter uses a different storage and a different mechanism to export/import data.

4.1.1. Additional methods for TableData

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);
 

    …
}

Each interpreter can implement its own TableData class. For example,

• 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.

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

4.2. Example Implementation: ZeppelinResourcePool as Spark Data Source