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From "Liang-Chi Hsieh (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-22231) Support of map, filter, withColumn, dropColumn in nested list of structures
Date Tue, 10 Oct 2017 03:19:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-22231?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16198082#comment-16198082
] 

Liang-Chi Hsieh commented on SPARK-22231:
-----------------------------------------

I think there is a typo in the second example to add a new column:
{code:scala}
val result = df.mapItems("items") {
  item => item.withColumn(item("b") + 1 as "c")
}
{code}

> Support of map, filter, withColumn, dropColumn in nested list of structures
> ---------------------------------------------------------------------------
>
>                 Key: SPARK-22231
>                 URL: https://issues.apache.org/jira/browse/SPARK-22231
>             Project: Spark
>          Issue Type: New Feature
>          Components: SQL
>    Affects Versions: 2.2.0
>            Reporter: DB Tsai
>
> At Netflix's algorithm team, we work on ranking problems to find the great content to
fulfill the unique tastes of our members. Before building a recommendation algorithms, we
need to prepare the training, testing, and validation datasets in Apache Spark. Due to the
nature of ranking problems, we have a nested list of items to be ranked in one column, and
the top level is the contexts describing the setting for where a model is to be used (e.g.
profiles, country, time, device, etc.)  Here is a blog post describing the details, [Distributed
Time Travel for Feature Generation|https://medium.com/netflix-techblog/distributed-time-travel-for-feature-generation-389cccdd3907].
>  
> To be more concrete, for the ranks of videos for a given profile_id at a given country,
our data schema can be looked like this,
> {code:java}
> root
>  |-- profile_id: long (nullable = true)
>  |-- country_iso_code: string (nullable = true)
>  |-- items: array (nullable = false)
>  |    |-- element: struct (containsNull = false)
>  |    |    |-- title_id: integer (nullable = true)
>  |    |    |-- scores: double (nullable = true)
> ...
> {code}
> We oftentimes need to work on the nested list of structs by applying some functions on
them. Sometimes, we're dropping or adding new columns in the nested list of structs. Currently,
there is no easy solution in open source Apache Spark to perform those operations using SQL
primitives; many people just convert the data into RDD to work on the nested level of data,
and then reconstruct the new dataframe as workaround. This is extremely inefficient because
all the optimizations like predicate pushdown in SQL can not be performed, we can not leverage
on the columnar format, and the serialization and deserialization cost becomes really huge
even we just want to add a new column in the nested level.
> We built a solution internally at Netflix which we're very happy with. We plan to make
it open source in Spark upstream. We would like to socialize the API design to see if we miss
any use-case.  
> The first API we added is *mapItems* on dataframe which take a function from *Column*
to *Column*, and then apply the function on nested dataframe. Here is an example,
> {code:java}
> case class Data(foo: Int, bar: Double, items: Seq[Double])
> val df: Dataset[Data] = spark.createDataset(Seq(
>   Data(10, 10.0, Seq(10.1, 10.2, 10.3, 10.4)),
>   Data(20, 20.0, Seq(20.1, 20.2, 20.3, 20.4))
> ))
> val result = df.mapItems("items") {
>   item => item * 2.0
> }
> result.printSchema()
> // root
> // |-- foo: integer (nullable = false)
> // |-- bar: double (nullable = false)
> // |-- items: array (nullable = true)
> // |    |-- element: double (containsNull = true)
> result.show()
> // +---+----+--------------------+
> // |foo| bar|               items|
> // +---+----+--------------------+
> // | 10|10.0|[20.2, 20.4, 20.6...|
> // | 20|20.0|[40.2, 40.4, 40.6...|
> // +---+----+--------------------+
> {code}
> Now, with the ability of applying a function in the nested dataframe, we can add a new
function, *withColumn* in *Column* to add or replace the existing column that has the same
name in the nested list of struct. Here is two examples demonstrating the API together with
*mapItems*; the first one replaces the existing column,
> {code:java}
> case class Item(a: Int, b: Double)
> case class Data(foo: Int, bar: Double, items: Seq[Item])
> val df: Dataset[Data] = spark.createDataset(Seq(
>   Data(10, 10.0, Seq(Item(10, 10.0), Item(11, 11.0))),
>   Data(20, 20.0, Seq(Item(20, 20.0), Item(21, 21.0)))
> ))
> val result = df.mapItems("items") {
>   item => item.withColumn(item("b") + 1 as "b")
> }
> result.printSchema
> root
> // |-- foo: integer (nullable = false)
> // |-- bar: double (nullable = false)
> // |-- items: array (nullable = true)
> // |    |-- element: struct (containsNull = true)
> // |    |    |-- a: integer (nullable = true)
> // |    |    |-- b: double (nullable = true)
> result.show(false)
> // +---+----+----------------------+
> // |foo|bar |items                 |
> // +---+----+----------------------+
> // |10 |10.0|[[10,11.0], [11,12.0]]|
> // |20 |20.0|[[20,21.0], [21,22.0]]|
> // +---+----+----------------------+
> {code}
> and the second one adds a new column in the nested dataframe.
> {code:java}
> val df: Dataset[Data] = spark.createDataset(Seq(
>   Data(10, 10.0, Seq(Item(10, 10.0), Item(11, 11.0))),
>   Data(20, 20.0, Seq(Item(20, 20.0), Item(21, 21.0)))
> ))
> val result = df.mapItems("items") {
>   item => item.withColumn(item("b") + 1 as "b")
> }
> result.printSchema
> root
> // |-- foo: integer (nullable = false)
> // |-- bar: double (nullable = false)
> // |-- items: array (nullable = true)
> // |    |-- element: struct (containsNull = true)
> // |    |    |-- a: integer (nullable = true)
> // |    |    |-- b: double (nullable = true)
> // |    |    |-- c: double (nullable = true)
> result.show(false)
> // +---+----+--------------------------------+
> // |foo|bar |items                           |
> // +---+----+--------------------------------+
> // |10 |10.0|[[10,10.0,11.0], [11,11.0,12.0]]|
> // |20 |20.0|[[20,20.0,21.0], [21,21.0,22.0]]|
> // +---+----+--------------------------------+
> {code}
> We also implement a filter predicate to nested list of struct, and it will return those
items which matched the predicate. The following is the API example,
> {code:java}
> val df: Dataset[Data] = spark.createDataset(Seq(
>   Data(10, 10.0, Seq(Item(10, 10.0), Item(11, 11.0))),
>   Data(20, 20.0, Seq(Item(20, 20.0), Item(21, 21.0)))
> ))
> val result = df.filterItems("items") {
>   item => item("a") < 20
> }
> // +---+----+----------------------+
> // |foo|bar |items                 |
> // +---+----+----------------------+
> // |10 |10.0|[[10,10.0], [11,11.0]]|
> // |20 |20.0|[]                    |
> // +---+----+----------------------+
> {code}
> Dropping a column in the nested list of struct can be achieved by similar API to *withColumn*.
We add *drop* method to *Column* to implement this. Here is an example,
> {code:java}
> val df: Dataset[Data] = spark.createDataset(Seq(
>   Data(10, 10.0, Seq(Item(10, 10.0), Item(11, 11.0))),
>   Data(20, 20.0, Seq(Item(20, 20.0), Item(21, 21.0)))
> ))
> val result = df.mapItems("items") {
>   item => item.drop("b")
> }
> result.printSchema
> root
> // |-- foo: integer (nullable = false)
> // |-- bar: double (nullable = false)
> // |-- items: array (nullable = true)
> // |    |-- element: struct (containsNull = true)
> // |    |    |-- a: integer (nullable = true)
> result.show(false)
> // +---+----+------------+
> // |foo|bar |items       |
> // +---+----+------------+
> // |10 |10.0|[[10], [11]]|
> // |20 |20.0|[[20], [21]]|
> // +---+----+------------+
> {code}
> As you can see, those APIs are not opaque to Spark optimizers, and can fully take advantage
of columnar data structure. 
> We're looking forward to the community feedback and suggestion! Thanks.



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