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https://issues.apache.org/jira/browse/SPARK-24974?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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[email protected] updated SPARK-24974:
-------------------------------------------------
Description:
SharedInMemoryCache has all filestatus no matter whether you specify partition
columns or not. It causes long load time for queries that use only couple
partitions because Spark loads file's paths for files from all partitions.
I partitioned files by *type* and i has directory structure like
{code:java}
report_date=2018-07-24/type=A/file_1
{code}
I am trying to execute
{code:java}
val count = spark.read.parquet("/custom_path/report_date=2018-07-24").filter(
"type == 'A'").count
{code}
In my query i need to load only files of type *A* and it is just a couple of
files. But spark load all 19K of files from all partitions into
SharedInMemoryCache which takes about 60 secs and only after that throws unused
partitions.
was:
SharedInMemoryCache has all filestatus no matter whether you specify partition
columns or not. It causes long load time for queries that use only couple
partitions because Spark loads file's paths for files from all partitions.
I partitioned files by *type* and i has directory structure like
{code:java}
report_date=2018-07-24/type=A/file_1
{code}
I am trying to execute
{code:java}
val count = spark.read.parquet("/custom_path/report_date=2018-07-24").filter(
"type == 'A'").count
{code}
In my query i need to load only files of type *A* and it is just a couple of
files. But spark load all 19K of files from all partitions into
SharedInMemoryCache which takes about 60 secs and only after that throws unused
partitions.
> Spark put all file's paths into SharedInMemoryCache even for unused
> partitions.
> -------------------------------------------------------------------------------
>
> Key: SPARK-24974
> URL: https://issues.apache.org/jira/browse/SPARK-24974
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.2.1
> Reporter: [email protected]
> Priority: Major
>
> SharedInMemoryCache has all filestatus no matter whether you specify
> partition columns or not. It causes long load time for queries that use only
> couple partitions because Spark loads file's paths for files from all
> partitions.
> I partitioned files by *type* and i has directory structure like
> {code:java}
> report_date=2018-07-24/type=A/file_1
> {code}
>
> I am trying to execute
> {code:java}
> val count = spark.read.parquet("/custom_path/report_date=2018-07-24").filter(
> "type == 'A'").count
> {code}
>
> In my query i need to load only files of type *A* and it is just a couple of
> files. But spark load all 19K of files from all partitions into
> SharedInMemoryCache which takes about 60 secs and only after that throws
> unused partitions.
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