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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 interpreterparameters, 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 linenumbers true
  // 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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