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https://issues.apache.org/jira/browse/SPARK-16164?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiangrui Meng updated SPARK-16164:
----------------------------------
Description:
[~cmccubbin] reported a bug when he used StringIndexer in an ML pipeline with
additional filters. It seems that during filter pushdown, we changed the
ordering in the logical plan. I'm not sure whether we should treat this as a
bug.
{code}
val df1 = (0 until 3).map(_.toString).toDF
val indexer = new StringIndexer()
.setInputCol("value")
.setOutputCol("idx")
.setHandleInvalid("skip")
.fit(df1)
val df2 = (0 until 5).map(_.toString).toDF
val predictions = indexer.transform(df2)
predictions.where('idx > 2).show()
{code}
Please see the notebook at
https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/1233855/2159162931615821/588180/latest.html
for error messages.
was:
[~cmccubbin] reported a bug when he used StringIndexer in an ML pipeline with
additional filters. It seems that during filter pushdown, we changed the
ordering in the logical plan. I'm not sure whether we should treat this as a
bug.
{code}
val df1 = (0 until 3).map(_.toString).toDF
val indexer = new StringIndexer()
.setInputCol("value")
.setOutputCol("idx")
.setHandleInvalid("skip")
.fit(df1)
val df2 = (0 until 5).map(_.toString).toDF
val predictions = indexer.transform(df2)
predictions.where('idx > 2).show()
{code}
> Filter pushdown should keep the ordering in the logical plan
> ------------------------------------------------------------
>
> Key: SPARK-16164
> URL: https://issues.apache.org/jira/browse/SPARK-16164
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.0
> Reporter: Xiangrui Meng
>
> [~cmccubbin] reported a bug when he used StringIndexer in an ML pipeline with
> additional filters. It seems that during filter pushdown, we changed the
> ordering in the logical plan. I'm not sure whether we should treat this as a
> bug.
> {code}
> val df1 = (0 until 3).map(_.toString).toDF
> val indexer = new StringIndexer()
> .setInputCol("value")
> .setOutputCol("idx")
> .setHandleInvalid("skip")
> .fit(df1)
> val df2 = (0 until 5).map(_.toString).toDF
> val predictions = indexer.transform(df2)
> predictions.where('idx > 2).show()
> {code}
> Please see the notebook at
> https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/1233855/2159162931615821/588180/latest.html
> for error messages.
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