Subqueries

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Subqueries

JayeshLalwani

I have a table, and I want to find the latest records in the table. The table has a column called instnc_id that is incremented everyday. So, I want to find the records that have the max instnc_id.

 

I am trying to do this using subqueries, but it gives me an error. For example, when I try this

 

select ACCT_ID, CR_RVKD_STAT_CD, ACCT_SFX_NUM, SCURT_FRD_STAT_CD, CLSD_REAS_CD from (select *, max(instnc_id) as max_inst_id FROM Stat_hist) where instnc_id=max_inst_id

 

the error I get is

 

Caused by: org.apache.spark.sql.AnalysisException: cannot resolve '`max_inst_id`' given input columns: [CR_RVKD_STAT_CD, ACCT_SFX_NUM, CLSD_REAS_CD, ACCT_ID, instnc_id, SCURT_FRD_STAT_CD]; line 1 pos 172;

'Project ['ACCT_ID, 'CR_RVKD_STAT_CD, 'ACCT_SFX_NUM, 'SCURT_FRD_STAT_CD, CLSD_REAS_CD, scalar-subquery#298 [] AS max_inst_id#299]

:  +- 'Project [unresolvedalias('max('instnc_id), None)]

:     +- 'UnresolvedRelation `Stat_hist`

+- 'Filter (instnc_id#92 = 'max_inst_id)

   +- SubqueryAlias stat_hist

      +- Project [ACCT_ID#0, ACCT_SFX_NUM#1, CR_RVKD_STAT_CD#23, SCURT_FRD_STAT_CD#34, CLSD_REAS_CD#19, instnc_id#92]

 

I have tried various combinations but I keep getting into the same problem: It doesn’t recognize max_inst_id as a column.

 

The only thing that works is if I get max_inst_id in a dataframe and then inner join it with the original table



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Re: Subqueries

Nicholas Hakobian
This sounds like a perfect example of using windowing functions. Have you tried something like the following:

select ACCT_ID, CR_RVKD_STAT_CD, ACCT_SFX_NUM, SCURT_FRD_STAT_CD, CLSD_REAS_CD from (select *, max(instnc_id) over () as max_inst_id FROM Stat_hist) where instnc_id=max_inst_id

However, I have seen instances where window functions without partitioning clauses will cause all partitions to be executed on one task (and spark usually warns about this condition) and this will be very slow. It might actually be more performant to use the inner join which, even though it is scanning through the raw data twice, is more parallelizable.

If you have your data stored in a columnar compressed data format like parquet or orc, the query on the right side of the join should only have a single column, so I/O on that column would be significantly less than the full table; you might even be able to squeeze some more performance out of it (depending on the size of the table), by caching it beforehand.

Nicholas Szandor Hakobian, Ph.D.
Staff Data Scientist
Rally Health

On Fri, Dec 29, 2017 at 1:02 PM, Lalwani, Jayesh <[hidden email]> wrote:

I have a table, and I want to find the latest records in the table. The table has a column called instnc_id that is incremented everyday. So, I want to find the records that have the max instnc_id.

 

I am trying to do this using subqueries, but it gives me an error. For example, when I try this

 

select ACCT_ID, CR_RVKD_STAT_CD, ACCT_SFX_NUM, SCURT_FRD_STAT_CD, CLSD_REAS_CD from (select *, max(instnc_id) as max_inst_id FROM Stat_hist) where instnc_id=max_inst_id

 

the error I get is

 

Caused by: org.apache.spark.sql.AnalysisException: cannot resolve '`max_inst_id`' given input columns: [CR_RVKD_STAT_CD, ACCT_SFX_NUM, CLSD_REAS_CD, ACCT_ID, instnc_id, SCURT_FRD_STAT_CD]; line 1 pos 172;

'Project ['ACCT_ID, 'CR_RVKD_STAT_CD, 'ACCT_SFX_NUM, 'SCURT_FRD_STAT_CD, CLSD_REAS_CD, scalar-subquery#298 [] AS max_inst_id#299]

:  +- 'Project [unresolvedalias('max('instnc_id), None)]

:     +- 'UnresolvedRelation `Stat_hist`

+- 'Filter (instnc_id#92 = 'max_inst_id)

   +- SubqueryAlias stat_hist

      +- Project [ACCT_ID#0, ACCT_SFX_NUM#1, CR_RVKD_STAT_CD#23, SCURT_FRD_STAT_CD#34, CLSD_REAS_CD#19, instnc_id#92]

 

I have tried various combinations but I keep getting into the same problem: It doesn’t recognize max_inst_id as a column.

 

The only thing that works is if I get max_inst_id in a dataframe and then inner join it with the original table



The information contained in this e-mail is confidential and/or proprietary to Capital One and/or its affiliates and may only be used solely in performance of work or services for Capital One. The information transmitted herewith is intended only for use by the individual or entity to which it is addressed. If the reader of this message is not the intended recipient, you are hereby notified that any review, retransmission, dissemination, distribution, copying or other use of, or taking of any action in reliance upon this information is strictly prohibited. If you have received this communication in error, please contact the sender and delete the material from your computer.


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Re: Subqueries

JayeshLalwani

 

Thanks. You are right on both counts

 

  1. Doing max(instnc_id) over () works. I thought that Spark would automatically treat max(instnc_id) as max(instnc_id) over ()
  2. Spark tries to do max function in one task, and it runs out of memory

 

I’ll revert back to join. Thanks again

From: Nicholas Hakobian <[hidden email]>
Date: Friday, December 29, 2017 at 8:10 PM
To: "Lalwani, Jayesh" <[hidden email]>
Cc: "[hidden email]" <[hidden email]>
Subject: Re: Subqueries

 

This sounds like a perfect example of using windowing functions. Have you tried something like the following:

 

select ACCT_ID, CR_RVKD_STAT_CD, ACCT_SFX_NUM, SCURT_FRD_STAT_CD, CLSD_REAS_CD from (select *, max(instnc_id) over () as max_inst_id FROM Stat_hist) where instnc_id=max_inst_id

 

However, I have seen instances where window functions without partitioning clauses will cause all partitions to be executed on one task (and spark usually warns about this condition) and this will be very slow. It might actually be more performant to use the inner join which, even though it is scanning through the raw data twice, is more parallelizable.

 

If you have your data stored in a columnar compressed data format like parquet or orc, the query on the right side of the join should only have a single column, so I/O on that column would be significantly less than the full table; you might even be able to squeeze some more performance out of it (depending on the size of the table), by caching it beforehand.


Nicholas Szandor Hakobian, Ph.D.

Staff Data Scientist

Rally Health

 

On Fri, Dec 29, 2017 at 1:02 PM, Lalwani, Jayesh <[hidden email]> wrote:

I have a table, and I want to find the latest records in the table. The table has a column called instnc_id that is incremented everyday. So, I want to find the records that have the max instnc_id.

 

I am trying to do this using subqueries, but it gives me an error. For example, when I try this

 

select ACCT_ID, CR_RVKD_STAT_CD, ACCT_SFX_NUM, SCURT_FRD_STAT_CD, CLSD_REAS_CD from (select *, max(instnc_id) as max_inst_id FROM Stat_hist) where instnc_id=max_inst_id

 

the error I get is

 

Caused by: org.apache.spark.sql.AnalysisException: cannot resolve '`max_inst_id`' given input columns: [CR_RVKD_STAT_CD, ACCT_SFX_NUM, CLSD_REAS_CD, ACCT_ID, instnc_id, SCURT_FRD_STAT_CD]; line 1 pos 172;

'Project ['ACCT_ID, 'CR_RVKD_STAT_CD, 'ACCT_SFX_NUM, 'SCURT_FRD_STAT_CD, CLSD_REAS_CD, scalar-subquery#298 [] AS max_inst_id#299]

:  +- 'Project [unresolvedalias('max('instnc_id), None)]

:     +- 'UnresolvedRelation `Stat_hist`

+- 'Filter (instnc_id#92 = 'max_inst_id)

   +- SubqueryAlias stat_hist

      +- Project [ACCT_ID#0, ACCT_SFX_NUM#1, CR_RVKD_STAT_CD#23, SCURT_FRD_STAT_CD#34, CLSD_REAS_CD#19, instnc_id#92]

 

I have tried various combinations but I keep getting into the same problem: It doesn’t recognize max_inst_id as a column.

 

The only thing that works is if I get max_inst_id in a dataframe and then inner join it with the original table

 


The information contained in this e-mail is confidential and/or proprietary to Capital One and/or its affiliates and may only be used solely in performance of work or services for Capital One. The information transmitted herewith is intended only for use by the individual or entity to which it is addressed. If the reader of this message is not the intended recipient, you are hereby notified that any review, retransmission, dissemination, distribution, copying or other use of, or taking of any action in reliance upon this information is strictly prohibited. If you have received this communication in error, please contact the sender and delete the material from your computer.

 



The information contained in this e-mail is confidential and/or proprietary to Capital One and/or its affiliates and may only be used solely in performance of work or services for Capital One. The information transmitted herewith is intended only for use by the individual or entity to which it is addressed. If the reader of this message is not the intended recipient, you are hereby notified that any review, retransmission, dissemination, distribution, copying or other use of, or taking of any action in reliance upon this information is strictly prohibited. If you have received this communication in error, please contact the sender and delete the material from your computer.