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Posted to issues@spark.apache.org by "Shenghu Yang (JIRA)" <ji...@apache.org> on 2015/08/04 08:33:04 UTC
[jira] [Updated] (SPARK-9588) spark sql cache: partition level
cache eviction
[ https://issues.apache.org/jira/browse/SPARK-9588?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Shenghu Yang updated SPARK-9588:
--------------------------------
Description:
In spark 1.4, we can only do 'cache table <table_name>'. However, if we have table which will get a new partition periodically, say every 10 minutes, we have to do 'uncache' & then 'cache' the whole table, taking long time.
Things would be much faster if I can do:
(1) cache table <table_name> partition <newest_partition>
(2) uncache table <table_name> partition <oldest_partition>
This way we can always having a sliding window type of cached data.
was:
In spark 1.4, we can only do 'cache table <table_name>'. However, if we have table which will get a new partition periodically, say every 10 minutes, we have to do 'uncache' & then 'cache' the whole table, taking long time.
Things would be much faster if I can do:
(1) cache table <table_name> partition <newest_partition>
(2) uncache table <table_name> partition <oldest_partition>
> spark sql cache: partition level cache eviction
> -----------------------------------------------
>
> Key: SPARK-9588
> URL: https://issues.apache.org/jira/browse/SPARK-9588
> Project: Spark
> Issue Type: Improvement
> Reporter: Shenghu Yang
>
> In spark 1.4, we can only do 'cache table <table_name>'. However, if we have table which will get a new partition periodically, say every 10 minutes, we have to do 'uncache' & then 'cache' the whole table, taking long time.
> Things would be much faster if I can do:
> (1) cache table <table_name> partition <newest_partition>
> (2) uncache table <table_name> partition <oldest_partition>
> This way we can always having a sliding window type of cached data.
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