Which pyspark methods should I use for this table join?











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Article

|------|-----------|-------|

|  ID  | PARENT_ID | _data |

|------|-----------|-------|

|  12  |    34     |  mom  |

|------|-----------|-------|

|  5   |    34     |  dad  |

|------|-----------|-------|





Article_Meta

|-------|---------|------------|

|  ID   | USER_ID | COMMENT_ID |

|-------|---------|------------|

|  12   |  [3]    |  [ 7, 8]   |

|-------|---------|------------|

|  34   |  [6]    |  [ 1, 2]   |

|-------|---------|------------|



Result: Article + Article_Metadata

        ID 12 has User ID 3 and 6 because 

        ID = Article_Meta#12 has User_ID 3 AND

        ParentID = Article_Meta#34 has USER_ID 6         



|------|-----------|-------|---------|------------|

|  ID  | PARENT_ID | _data | USER_ID | COMMENT_ID |

|------|-----------|-------|---------|------------|

|  12  |    34     |  mom  | [ 3, 6] |[7, 8, 1, 2]|

|------|-----------|-------|---------|------------|

|  5   |    34     |  dad  |  [6]    |  [ 1, 2]   |

|------|-----------|-------|---------|------------|


I have a table Article and I would like to join it with Article_Meta.



As you can see Article has an ID and a ParentID. Both this columns belong to the Article_Meta ID column.



How should I join Article with Article_Meta so that the USER_ID and COMMENT_ID are the combined result of the Article_PARENT_ID AND Article_ID in the MetaData Table? (Wich pyspark methods should I use?)



More Explanation:
In the Result Table Article #12 has USER_ID [3, 6] that's because Article #12 belongs to Article_Meta #12 and #34 (Parent ID)






apache-spark pyspark apache-spark-sql







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    Article
    
|------|-----------|-------|
    
|  ID  | PARENT_ID | _data |
    
|------|-----------|-------|
    
|  12  |    34     |  mom  |
    
|------|-----------|-------|
    
|  5   |    34     |  dad  |
    
|------|-----------|-------|
    

    

    
Article_Meta
    
|-------|---------|------------|
    
|  ID   | USER_ID | COMMENT_ID |
    
|-------|---------|------------|
    
|  12   |  [3]    |  [ 7, 8]   |
    
|-------|---------|------------|
    
|  34   |  [6]    |  [ 1, 2]   |
    
|-------|---------|------------|
    

    
Result: Article + Article_Metadata
    
        ID 12 has User ID 3 and 6 because 
    
        ID = Article_Meta#12 has User_ID 3 AND
    
        ParentID = Article_Meta#34 has USER_ID 6         
    

    
|------|-----------|-------|---------|------------|
    
|  ID  | PARENT_ID | _data | USER_ID | COMMENT_ID |
    
|------|-----------|-------|---------|------------|
    
|  12  |    34     |  mom  | [ 3, 6] |[7, 8, 1, 2]|
    
|------|-----------|-------|---------|------------|
    
|  5   |    34     |  dad  |  [6]    |  [ 1, 2]   |
    
|------|-----------|-------|---------|------------|


    I have a table Article and I would like to join it with Article_Meta.



    As you can see Article has an ID and a ParentID. Both this columns belong to the Article_Meta ID column.



    How should I join Article with Article_Meta so that the USER_ID and COMMENT_ID are the combined result of the Article_PARENT_ID AND Article_ID in the MetaData Table? (Wich pyspark methods should I use?)



    More Explanation:
    In the Result Table Article #12 has USER_ID [3, 6] that's because Article #12 belongs to Article_Meta #12 and #34 (Parent ID)






    apache-spark pyspark apache-spark-sql







    share|improve this question
























      up vote
      -2
      down vote

      favorite









      up vote
      -2
      down vote

      favorite











      Article
      
|------|-----------|-------|
      
|  ID  | PARENT_ID | _data |
      
|------|-----------|-------|
      
|  12  |    34     |  mom  |
      
|------|-----------|-------|
      
|  5   |    34     |  dad  |
      
|------|-----------|-------|
      

      

      
Article_Meta
      
|-------|---------|------------|
      
|  ID   | USER_ID | COMMENT_ID |
      
|-------|---------|------------|
      
|  12   |  [3]    |  [ 7, 8]   |
      
|-------|---------|------------|
      
|  34   |  [6]    |  [ 1, 2]   |
      
|-------|---------|------------|
      

      
Result: Article + Article_Metadata
      
        ID 12 has User ID 3 and 6 because 
      
        ID = Article_Meta#12 has User_ID 3 AND
      
        ParentID = Article_Meta#34 has USER_ID 6         
      

      
|------|-----------|-------|---------|------------|
      
|  ID  | PARENT_ID | _data | USER_ID | COMMENT_ID |
      
|------|-----------|-------|---------|------------|
      
|  12  |    34     |  mom  | [ 3, 6] |[7, 8, 1, 2]|
      
|------|-----------|-------|---------|------------|
      
|  5   |    34     |  dad  |  [6]    |  [ 1, 2]   |
      
|------|-----------|-------|---------|------------|


      I have a table Article and I would like to join it with Article_Meta.



      As you can see Article has an ID and a ParentID. Both this columns belong to the Article_Meta ID column.



      How should I join Article with Article_Meta so that the USER_ID and COMMENT_ID are the combined result of the Article_PARENT_ID AND Article_ID in the MetaData Table? (Wich pyspark methods should I use?)



      More Explanation:
      In the Result Table Article #12 has USER_ID [3, 6] that's because Article #12 belongs to Article_Meta #12 and #34 (Parent ID)






      apache-spark pyspark apache-spark-sql







      share|improve this question













      Article
      
|------|-----------|-------|
      
|  ID  | PARENT_ID | _data |
      
|------|-----------|-------|
      
|  12  |    34     |  mom  |
      
|------|-----------|-------|
      
|  5   |    34     |  dad  |
      
|------|-----------|-------|
      

      

      
Article_Meta
      
|-------|---------|------------|
      
|  ID   | USER_ID | COMMENT_ID |
      
|-------|---------|------------|
      
|  12   |  [3]    |  [ 7, 8]   |
      
|-------|---------|------------|
      
|  34   |  [6]    |  [ 1, 2]   |
      
|-------|---------|------------|
      

      
Result: Article + Article_Metadata
      
        ID 12 has User ID 3 and 6 because 
      
        ID = Article_Meta#12 has User_ID 3 AND
      
        ParentID = Article_Meta#34 has USER_ID 6         
      

      
|------|-----------|-------|---------|------------|
      
|  ID  | PARENT_ID | _data | USER_ID | COMMENT_ID |
      
|------|-----------|-------|---------|------------|
      
|  12  |    34     |  mom  | [ 3, 6] |[7, 8, 1, 2]|
      
|------|-----------|-------|---------|------------|
      
|  5   |    34     |  dad  |  [6]    |  [ 1, 2]   |
      
|------|-----------|-------|---------|------------|


      I have a table Article and I would like to join it with Article_Meta.



      As you can see Article has an ID and a ParentID. Both this columns belong to the Article_Meta ID column.



      How should I join Article with Article_Meta so that the USER_ID and COMMENT_ID are the combined result of the Article_PARENT_ID AND Article_ID in the MetaData Table? (Wich pyspark methods should I use?)



      More Explanation:
      In the Result Table Article #12 has USER_ID [3, 6] that's because Article #12 belongs to Article_Meta #12 and #34 (Parent ID)






      apache-spark pyspark apache-spark-sql




      apache-spark pyspark apache-spark-sql






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      asked yesterday









      John Smith

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