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https://issues.apache.org/jira/browse/SPARK-15632?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-15632:
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Assignee: Apache Spark (was: Xiang Zhong)
> Dataset typed filter operation changes query plan schema
> --------------------------------------------------------
>
> Key: SPARK-15632
> URL: https://issues.apache.org/jira/browse/SPARK-15632
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
> Affects Versions: 2.0.0
> Reporter: Cheng Lian
> Assignee: Apache Spark
>
> h1. Overview
> Filter operations should never change query plan schema. However, Dataset
> typed filter operation does introduce schema change in some cases.
> Furthermore, all the following aspects of the schema may be changed:
> # field order,
> # field number,
> # field data type,
> # field name, and
> # field nullability
> This is mostly because we wrap the actual {{Filter}} operator with a
> {{SerializeFromObject}}/{{DeserializeToObject}} pair (query plan fragment
> illustrated as following), which performs a bunch of magic tricks.
> {noformat}
> SerializeFromObject
> Filter
> DeserializeToObject
> <child-plan>
> {noformat}
> h1. Reproduction
> h2. Field order, field number, and field data type change
> {code}
> case class A(b: Double, a: String)
> val data = Seq(
> "{ 'a': 'foo', 'b': 1, 'c': 'extra' }",
> "{ 'a': 'bar', 'b': 2, 'c': 'extra' }",
> "{ 'a': 'bar', 'c': 'extra' }"
> )
> val df1 = spark.read.json(sc.parallelize(data))
> df1.printSchema()
> // root
> // |-- a: string (nullable = true)
> // |-- b: long (nullable = true)
> // |-- c: string (nullable = true)
> val ds1 = df1.as[A]
> ds1.printSchema()
> // root
> // |-- a: string (nullable = true)
> // |-- b: long (nullable = true)
> // |-- c: string (nullable = true)
> val ds2 = ds1.filter(_.b > 1) // <- Here comes the trouble maker
> ds2.printSchema()
> // root <- 1. Reordered `a` and `b`, and
> // |-- b: double (nullable = true) 2. dropped `c`, and
> // |-- a: string (nullable = true) 3. up-casted `b` from long to double
> val df2 = ds2.toDF()
> df2.printSchema()
> // root <- (Same as above)
> // |-- b: double (nullable = true)
> // |-- a: string (nullable = true)
> {code}
> h3. Field order change
> {{DeserializeToObject}} resolves the encoder deserializer expression by
> *name*. Thus field order in input query plan doesn't matter.
> h3. Field number change
> Same as above, fields not referred by the encoder are silently dropped while
> resolving deserializer expressions by name.
> h3. Field data type change
> When generating deserializer expressions, we allows "sane" implicit coercions
> (e.g. integer to long, and long to double) by inserting {{UpCast}} operators.
> Thus actual field data types in input query plan don't matter either as long
> as there are valid implicit coercions.
> h2. Field name and nullability change
> {code}
> val ds3 = spark.range(10)
> ds3.printSchema()
> // root
> // |-- id: long (nullable = false)
> val ds4 = ds3.filter(_ > 3)
> ds4.printSchema()
> // root
> // |-- value: long (nullable = true) 4. Name changed from `id` to `value`,
> and
> // 5. nullability changed from false to
> true
> {code}
> h3. Field name change
> Primitive encoders like {{Encoder\[Long\]}} doesn't have a named field, thus
> they always has only a single field with hard-coded name "value". On the
> other hand, when serializing domain objects back to rows, schema of
> {{SerializeFromObject}} is solely determined by the encoder. Thus the
> original name "id" becomes "value".
> h3. Nullability change
> [PR #11880|https://github.com/apache/spark/pull/11880] updated return type of
> {{SparkSession.range}} from {{Dataset\[Long\]}} to
> {{Dataset\[java.lang.Long\]}} due to
> [SI-4388|https://issues.scala-lang.org/browse/SI-4388]. As a consequence,
> although the underlying {{Range}} operator produces non-nullable output, the
> result encoder is nullable since {{java.lang.Long}} is nullable. Thus, we
> observe nullability change after typed filtering because serializer
> expression is derived from encoder rather than the query plan.
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