[
https://issues.apache.org/jira/browse/SPARK-14083?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15353969#comment-15353969
]
Sean Zhong commented on SPARK-14083:
------------------------------------
For typed operation like map, it will first de-serialize InternalRow to type T,
apply the operation, and then serialize T back to InternalRow.
For un-typed operation like select("column"), it directly operates on
InternalRow.
If end user defines a custom serializer like Kryo, then it is not possible to
map typed operation to un-typed operation.
```
scala> case class C(c: Int)
scala> val ds: Dataset[C] = Seq(C(1)).toDS
scala> ds.select("c") // <- Return correct result when using default encoder.
res1: org.apache.spark.sql.DataFrame = [c: int]
scala> implicit val encoder: Encoder[C] = Encoders.kryo[C] // <- Define a
Kryo encoder
scala> val ds2: Dataset[C] = Seq(C(1)).toDS
ds2: org.apache.spark.sql.Dataset[C] = [value: binary] // <- Row is encoded as
binary by using Kryo encoder
scala> ds2.select("c") // <- Fails even if "c" is an existing field in class C!
org.apache.spark.sql.AnalysisException: cannot resolve '`c`' given input
columns: [value];
...
```
> Analyze JVM bytecode and turn closures into Catalyst expressions
> ----------------------------------------------------------------
>
> Key: SPARK-14083
> URL: https://issues.apache.org/jira/browse/SPARK-14083
> Project: Spark
> Issue Type: New Feature
> Components: SQL
> Reporter: Reynold Xin
>
> One big advantage of the Dataset API is the type safety, at the cost of
> performance due to heavy reliance on user-defined closures/lambdas. These
> closures are typically slower than expressions because we have more
> flexibility to optimize expressions (known data types, no virtual function
> calls, etc). In many cases, it's actually not going to be very difficult to
> look into the byte code of these closures and figure out what they are trying
> to do. If we can understand them, then we can turn them directly into
> Catalyst expressions for more optimized executions.
> Some examples are:
> {code}
> df.map(_.name) // equivalent to expression col("name")
> ds.groupBy(_.gender) // equivalent to expression col("gender")
> df.filter(_.age > 18) // equivalent to expression GreaterThan(col("age"),
> lit(18)
> df.map(_.id + 1) // equivalent to Add(col("age"), lit(1))
> {code}
> The goal of this ticket is to design a small framework for byte code analysis
> and use that to convert closures/lambdas into Catalyst expressions in order
> to speed up Dataset execution. It is a little bit futuristic, but I believe
> it is very doable. The framework should be easy to reason about (e.g. similar
> to Catalyst).
> Note that a big emphasis on "small" and "easy to reason about". A patch
> should be rejected if it is too complicated or difficult to reason about.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
