spark-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Anbu Cheeralan (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-18917) Dataframe - Time Out Issues / Taking long time in append mode on object stores
Date Tue, 10 Jan 2017 19:31:58 GMT

     [ https://issues.apache.org/jira/browse/SPARK-18917?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Anbu Cheeralan updated SPARK-18917:
-----------------------------------
    Affects Version/s: 2.1.0

> Dataframe - Time Out Issues / Taking long time in append mode on object stores
> ------------------------------------------------------------------------------
>
>                 Key: SPARK-18917
>                 URL: https://issues.apache.org/jira/browse/SPARK-18917
>             Project: Spark
>          Issue Type: Improvement
>          Components: EC2, SQL, YARN
>    Affects Versions: 2.0.2, 2.1.0
>            Reporter: Anbu Cheeralan
>            Priority: Minor
>   Original Estimate: 72h
>  Remaining Estimate: 72h
>
> When using Dataframe write in append mode on object stores (S3 / Google Storage), the
writes are taking long time to write/ getting read time out. This is because dataframe.write
lists all leaf folders in the target directory. If there are lot of subfolders due to partitions,
this is taking for ever.
> The code is In org.apache.spark.sql.execution.datasources.DataSource.write() following
code causes huge number of RPC calls when the file system is an Object Store (S3, GS).
> if (mode == SaveMode.Append) {
> val existingPartitionColumns = Try {
> resolveRelation()
> .asInstanceOf[HadoopFsRelation]
> .location
> .partitionSpec()
> .partitionColumns
> .fieldNames
> .toSeq
> }.getOrElse(Seq.empty[String])
> There should be a flag to skip Partition Match Check in append mode. I can work on the
patch.



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org


Mime
View raw message