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https://issues.apache.org/jira/browse/SPARK-10731?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14901092#comment-14901092
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Yin Huai edited comment on SPARK-10731 at 9/21/15 6:05 PM:
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Looks like the problem is df.collect does not work well with limit. In Scala,
{{df.limit(1).rdd.count()}} will also trigger the problem. When we call
{{df.limit(1).rdd}}, we will launch a job to get 1 record for every partition.
was (Author: yhuai):
Looks like the problem is df.collect does not work well with limit. In Scala,
{{df.limit(1).rdd.count()}} will also trigger the problem.
> The head() implementation of dataframe is very slow
> ---------------------------------------------------
>
> Key: SPARK-10731
> URL: https://issues.apache.org/jira/browse/SPARK-10731
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 1.4.1, 1.5.0
> Reporter: Jerry Lam
> Labels: pyspark
>
> {code}
> df=sqlContext.read.parquet("someparquetfiles")
> df.head()
> {code}
> The above lines take over 15 minutes. It seems the dataframe requires 3
> stages to return the first row. It reads all data (which is about 1 billion
> rows) and run Limit twice. The take(1) implementation in the RDD performs
> much better.
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