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https://issues.apache.org/jira/browse/SPARK-23074?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16460137#comment-16460137
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Ruslan Dautkhanov edited comment on SPARK-23074 at 5/1/18 8:56 PM:
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That's . When we have a "record" that spans multiple text lines, and it happens
that some lines are in one partitions, and rest of lines are in another
partition.. what would monotonically_increasing_id() return? -It wouldn't be
consequential, right?- I just fond answer to this question in the [code
|https://github.com/apache/spark/blob/7c1654e2159662e7e663ba141719d755002f770a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/MonotonicallyIncreasingID.scala#L27].
See [https://stackoverflow.com/a/48454000/470583] - people are creating quite
expensive workaround
{code}
def zipWithIndex(df: DataFrame, offset: Long = 1, indexName: String = "index")
= {
val dfWithPartitionId = df.withColumn("partition_id",
spark_partition_id()).withColumn("inc_id", monotonically_increasing_id())
val partitionOffsets = dfWithPartitionId
.groupBy("partition_id")
.agg(count(lit(1)) as "cnt", first("inc_id") as "inc_id")
.orderBy("partition_id")
.select(sum("cnt").over(Window.orderBy("partition_id")) - col("cnt") -
col("inc_id") + lit(offset) as "cnt" )
.collect()
.map(_.getLong(0))
.toArray
dfWithPartitionId
.withColumn("partition_offset", udf((partitionId: Int) =>
partitionOffsets(partitionId), LongType)(col("partition_id")))
.withColumn(indexName, col("partition_offset") + col("inc_id"))
.drop("partition_id", "partition_offset", "inc_id")
}
{code}
Do you see an easier way to do this? Thanks!
was (Author: tagar):
That's . When we have a "record" that spans multiple text lines, and it happens
that some lines are in one partitions, and rest of lines are in another
partition.. what would monotonically_increasing_id() return? It wouldn't be
consequential, right?
See [https://stackoverflow.com/a/48454000/470583] - people are creating quite
expensive workaround
{code:scala}
def zipWithIndex(df: DataFrame, offset: Long = 1, indexName: String = "index")
= {
val dfWithPartitionId = df.withColumn("partition_id",
spark_partition_id()).withColumn("inc_id", monotonically_increasing_id())
val partitionOffsets = dfWithPartitionId
.groupBy("partition_id")
.agg(count(lit(1)) as "cnt", first("inc_id") as "inc_id")
.orderBy("partition_id")
.select(sum("cnt").over(Window.orderBy("partition_id")) - col("cnt") -
col("inc_id") + lit(offset) as "cnt" )
.collect()
.map(_.getLong(0))
.toArray
dfWithPartitionId
.withColumn("partition_offset", udf((partitionId: Int) =>
partitionOffsets(partitionId), LongType)(col("partition_id")))
.withColumn(indexName, col("partition_offset") + col("inc_id"))
.drop("partition_id", "partition_offset", "inc_id")
}
{code}
Do you see an easier way to do this? Thanks!
> Dataframe-ified zipwithindex
> ----------------------------
>
> Key: SPARK-23074
> URL: https://issues.apache.org/jira/browse/SPARK-23074
> Project: Spark
> Issue Type: New Feature
> Components: Spark Core
> Affects Versions: 2.3.0
> Reporter: Ruslan Dautkhanov
> Priority: Minor
> Labels: dataframe, rdd
>
> Would be great to have a daraframe-friendly equivalent of rdd.zipWithIndex():
> {code:java}
> import org.apache.spark.sql.DataFrame
> import org.apache.spark.sql.types.{LongType, StructField, StructType}
> import org.apache.spark.sql.Row
> def dfZipWithIndex(
> df: DataFrame,
> offset: Int = 1,
> colName: String = "id",
> inFront: Boolean = true
> ) : DataFrame = {
> df.sqlContext.createDataFrame(
> df.rdd.zipWithIndex.map(ln =>
> Row.fromSeq(
> (if (inFront) Seq(ln._2 + offset) else Seq())
> ++ ln._1.toSeq ++
> (if (inFront) Seq() else Seq(ln._2 + offset))
> )
> ),
> StructType(
> (if (inFront) Array(StructField(colName,LongType,false)) else
> Array[StructField]())
> ++ df.schema.fields ++
> (if (inFront) Array[StructField]() else
> Array(StructField(colName,LongType,false)))
> )
> )
> }
> {code}
> credits:
> [https://stackoverflow.com/questions/30304810/dataframe-ified-zipwithindex]
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