[jira] [Assigned] (SPARK-19846) Add a flag to disable constraint propagation
[
https://issues.apache.org/jira/browse/SPARK-19846?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-19846:
Assignee: (was: Apache Spark)
> Add a flag to disable constraint propagation
>
>
> Key: SPARK-19846
> URL: https://issues.apache.org/jira/browse/SPARK-19846
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Affects Versions: 2.1.0
>Reporter: Liang-Chi Hsieh
>
> Constraint propagation can be computation expensive and block the driver
> execution for long time. For example, the below benchmark needs 30mins.
> Compared with other attempts to modify how constraints propagation works,
> this is a much simpler option: add a flag to disable constraint propagation.
> {code}
> import org.apache.spark.ml.{Pipeline, PipelineStage}
> import org.apache.spark.ml.feature.{OneHotEncoder, StringIndexer,
> VectorAssembler}
> spark.conf.set(SQLConf.CONSTRAINT_PROPAGATION_ENABLED.key, false)
> val df = (1 to 40).foldLeft(Seq((1, "foo"), (2, "bar"), (3,
> "baz")).toDF("id", "x0"))((df, i) => df.withColumn(s"x$i", $"x0"))
> val indexers = df.columns.tail.map(c => new StringIndexer()
> .setInputCol(c)
> .setOutputCol(s"${c}_indexed")
> .setHandleInvalid("skip"))
> val encoders = indexers.map(indexer => new OneHotEncoder()
> .setInputCol(indexer.getOutputCol)
> .setOutputCol(s"${indexer.getOutputCol}_encoded")
> .setDropLast(true))
> val stages: Array[PipelineStage] = indexers ++ encoders
> val pipeline = new Pipeline().setStages(stages)
> val startTime = System.nanoTime
> pipeline.fit(df).transform(df).show
> val runningTime = System.nanoTime - startTime
> {code}
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[jira] [Assigned] (SPARK-19846) Add a flag to disable constraint propagation
[
https://issues.apache.org/jira/browse/SPARK-19846?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-19846:
Assignee: Apache Spark
> Add a flag to disable constraint propagation
>
>
> Key: SPARK-19846
> URL: https://issues.apache.org/jira/browse/SPARK-19846
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Affects Versions: 2.1.0
>Reporter: Liang-Chi Hsieh
>Assignee: Apache Spark
>
> Constraint propagation can be computation expensive and block the driver
> execution for long time. For example, the below benchmark needs 30mins.
> Compared with other attempts to modify how constraints propagation works,
> this is a much simpler option: add a flag to disable constraint propagation.
> {code}
> import org.apache.spark.ml.{Pipeline, PipelineStage}
> import org.apache.spark.ml.feature.{OneHotEncoder, StringIndexer,
> VectorAssembler}
> spark.conf.set(SQLConf.CONSTRAINT_PROPAGATION_ENABLED.key, false)
> val df = (1 to 40).foldLeft(Seq((1, "foo"), (2, "bar"), (3,
> "baz")).toDF("id", "x0"))((df, i) => df.withColumn(s"x$i", $"x0"))
> val indexers = df.columns.tail.map(c => new StringIndexer()
> .setInputCol(c)
> .setOutputCol(s"${c}_indexed")
> .setHandleInvalid("skip"))
> val encoders = indexers.map(indexer => new OneHotEncoder()
> .setInputCol(indexer.getOutputCol)
> .setOutputCol(s"${indexer.getOutputCol}_encoded")
> .setDropLast(true))
> val stages: Array[PipelineStage] = indexers ++ encoders
> val pipeline = new Pipeline().setStages(stages)
> val startTime = System.nanoTime
> pipeline.fit(df).transform(df).show
> val runningTime = System.nanoTime - startTime
> {code}
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