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From "ant_nebula (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-24727) The cache 100 in CodeGenerator is too small for streaming
Date Tue, 03 Jul 2018 01:40:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-24727?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

ant_nebula updated SPARK-24727:
-------------------------------
    Description: 
private val cache = CacheBuilder.newBuilder().maximumSize(100).build

The cache 100 in org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator is too small
for realtime streaming calculation, although is ok for offline calculation. Because realtime streaming
calculation is mostly more complex is one driver, and performance sensitive.

I suggest spark support configging for user with default 100, such as spark.codegen.cache=1000

 

  was:
private val cache = CacheBuilder.newBuilder().maximumSize(100).build

The cache 100 in org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator is too small
for realtime streaming calculation, although is ok for offline calculation. Because realtime streaming
calculation is mostly more complex is one driver, and performance sensitive.

I suggest spark support configging for user with default 100, such as spark.codegen.cache.

 


> The cache 100 in CodeGenerator is too small for streaming
> ---------------------------------------------------------
>
>                 Key: SPARK-24727
>                 URL: https://issues.apache.org/jira/browse/SPARK-24727
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 2.3.1
>            Reporter: ant_nebula
>            Priority: Major
>
> private val cache = CacheBuilder.newBuilder().maximumSize(100).build
> The cache 100 in org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator is too
small for realtime streaming calculation, although is ok for offline calculation. Because realtime streaming
calculation is mostly more complex is one driver, and performance sensitive.
> I suggest spark support configging for user with default 100, such as spark.codegen.cache=1000
>  



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