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https://issues.apache.org/jira/browse/PARQUET-16?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14058231#comment-14058231
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Cheng Lian commented on PARQUET-16:
-----------------------------------

Hi [~dvryaboy], thanks for pointing out that PR, the background information is 
very helpful. Actually I can make my change compatible with that PR. 
Essentially the problem we are facing to is much easier.

Currently, calling {{ParquetInputFormat.getSplits(JobContext jobContext)}} 
result a call chain like this:

{code}
List<InputSplit> getSplits(JobContext jobContext)
 List<Footer> getFooters(JobContext jobContext)
  List<FileStatus> listStatus(JobContext jobContext)                            
        <-- (1)
 List<ParquetInputSplit> getSplits(Configuration configuration, List<Footer> 
footers)
  ...                                                                           
        <-- (2)
{code}

Basically all the {{FileStatus}} objects are already fetched at (1), but 
abandoned immediately, and then fetched again at (2). The bad thing here is 
that (2) fetches all those objects by calling {{getFileStatus()}} on all 
part-files sequentially. Thus we only need to pass those fetched objects from 
(1) to (2), and caching is not required to solve this performance issue.

> Unnecessary getFileStatus() calls on all part-files in 
> ParquetInputFormat.getSplits
> -----------------------------------------------------------------------------------
>
>                 Key: PARQUET-16
>                 URL: https://issues.apache.org/jira/browse/PARQUET-16
>             Project: Parquet
>          Issue Type: Bug
>          Components: parquet-mr
>            Reporter: Cheng Lian
>
> When testing Spark SQL Parquet support, we found that accessing large Parquet 
> files located in S3 can be very slow. To be more specific, we have a S3 
> Parquet file with over 3,000 part-files, calling 
> {{ParquetInputFormat.getSplits}} on it takes several minutes. (We were 
> accessing this file from our office network rather than AWS.)
> After some investigation, we found that {{ParquetInputFormat.getSplits}} is 
> trying to call {{getFileStatus()}} on all part-files one by one sequentially 
> ([here|https://github.com/apache/incubator-parquet-mr/blob/parquet-1.5.0/parquet-hadoop/src/main/java/parquet/hadoop/ParquetInputFormat.java#L370]).
>  And in the case of S3, each {{getFileStatus()}} call issues an HTTP request 
> and wait for the reply in a blocking manner, which is considerably expensive.
> Actually all these {{FileStatus}} objects have already been fetched when 
> footers are retrieved 
> ([here|https://github.com/apache/incubator-parquet-mr/blob/parquet-1.5.0/parquet-hadoop/src/main/java/parquet/hadoop/ParquetInputFormat.java#L443]).
>  Caching these {{FileStatus}} objects can greatly improve our S3 case 
> (reduced from over 5 minutes to about 1.4 minutes).
> Will submit a PR for this issue soon.



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