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https://issues.apache.org/jira/browse/SPARK-10199?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14724438#comment-14724438
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Xiangrui Meng commented on SPARK-10199:
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[~vinodkc] Did yo measure the entire save/load operation or the schema
inference part? If the schema inference only takes 1% of the entire save/load
operation, maybe we shouldn't over-optimize this part.
Also, for micro-benchmarks, you should run the test multiple times and compare
the average to reduce variance. Most of your test runs in less than 1 second.
It is very likely to observe huge variance.
> Avoid using reflections for parquet model save
> ----------------------------------------------
>
> Key: SPARK-10199
> URL: https://issues.apache.org/jira/browse/SPARK-10199
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
> Reporter: Feynman Liang
> Priority: Minor
>
> These items are not high priority since the overhead writing to Parquest is
> much greater than for runtime reflections.
> Multiple model save/load in MLlib use case classes to infer a schema for the
> data frame saved to Parquet. However, inferring a schema from case classes or
> tuples uses [runtime
> reflection|https://github.com/apache/spark/blob/d7b4c095271c36fcc7f9ded267ecf5ec66fac803/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala#L361]
> which is unnecessary since the types are already known at the time `save` is
> called.
> It would be better to just specify the schema for the data frame directly
> using {{sqlContext.createDataFrame(dataRDD, schema)}}
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