Github user gzm0 commented on a diff in the pull request:
https://github.com/apache/spark/pull/1759#discussion_r15922943
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/TypedSql.scala ---
@@ -0,0 +1,202 @@
+package org.apache.spark.sql
+
+import org.apache.spark.sql.catalyst.analysis._
+import org.apache.spark.sql.catalyst.expressions.{Expression, ScalaUdf,
AttributeReference}
+import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
+import org.apache.spark.sql.catalyst.types._
+
+import scala.language.experimental.macros
+import scala.language.existentials
+
+import records._
+import Macros.RecordMacros
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.{SqlParser, ScalaReflection}
+
+/**
+ * A collection of Scala macros for working with SQL in a type-safe way.
+ */
+private[sql] object SQLMacros {
+ import scala.reflect.macros._
+
+ def sqlImpl(c: Context)(args: c.Expr[Any]*) =
+ new Macros[c.type](c).sql(args)
+
+ case class Schema(dataType: DataType, nullable: Boolean)
+
+ class Macros[C <: Context](val c: C) extends ScalaReflection {
+ val universe: c.universe.type = c.universe
+
+ import c.universe._
+
+ val rowTpe = tq"_root_.org.apache.spark.sql.catalyst.expressions.Row"
+
+ val rMacros = new RecordMacros[c.type](c)
+
+ trait InterpolatedItem {
+ def placeholderName: String
+ def registerCode: Tree
+ def localRegister(catalog: Catalog, registry: FunctionRegistry)
+ }
+
+ case class InterpolatedUDF(index: Int, expr: c.Expr[Any], returnType:
DataType)
+ extends InterpolatedItem{
+
+ val placeholderName = s"func$index"
+
+ def registerCode = q"""registerFunction($placeholderName, $expr)"""
+
+ def localRegister(catalog: Catalog, registry: FunctionRegistry) = {
+ registry.registerFunction(
+ placeholderName, (_: Seq[Expression]) => ScalaUdf(null,
returnType, Nil))
+ }
+ }
+
+ case class InterpolatedTable(index: Int, expr: c.Expr[Any], schema:
StructType)
+ extends InterpolatedItem{
+
+ val placeholderName = s"table$index"
+
+ def registerCode = q"""$expr.registerTempTable($placeholderName)"""
+
+ def localRegister(catalog: Catalog, registry: FunctionRegistry) = {
+ catalog.registerTable(None, placeholderName,
LocalRelation(schema.toAttributes :_*))
+ }
+ }
+
+ case class RecSchema(name: String, index: Int, cType: DataType, tpe:
Type)
+
+ def sql(args: Seq[c.Expr[Any]]) = {
+
+ val q"""
+ $interpName(
+ scala.StringContext.apply(..$rawParts))""" = c.prefix.tree
+
+ //rawParts.map(_.toString).foreach(println)
+
+ val parts =
+ rawParts.map(
+ _.toString.stripPrefix("\"")
+ .replaceAll("\\\\", "")
+ .stripSuffix("\""))
+
+ val interpolatedArguments = args.zipWithIndex.map { case (arg, i) =>
+ // println(arg + " " + arg.actualType)
+ arg.actualType match {
+ case TypeRef(_, _, Seq(schemaType)) =>
+ InterpolatedTable(i, arg,
schemaFor(schemaType).dataType.asInstanceOf[StructType])
+ case TypeRef(_, _, Seq(inputType, outputType)) =>
+ InterpolatedUDF(i, arg, schemaFor(outputType).dataType)
+ }
+ }
+
+ val query = parts(0) + args.indices.map { i =>
+ interpolatedArguments(i).placeholderName + parts(i + 1)
+ }.mkString("")
+
+ val parser = new SqlParser()
+ val logicalPlan = parser(query)
+ val catalog = new SimpleCatalog(true)
+ val functionRegistry = new SimpleFunctionRegistry
+ val analyzer = new Analyzer(catalog, functionRegistry, true)
+
+ interpolatedArguments.foreach(_.localRegister(catalog,
functionRegistry))
+ val analyzedPlan = analyzer(logicalPlan)
+
+ val fields = analyzedPlan.output.map(attr => (attr.name,
attr.dataType))
+ val record = genRecord(q"row", fields)
+
+ val tree = q"""
+ ..${interpolatedArguments.map(_.registerCode)}
+ val result = sql($query)
+ result.map(row => $record)
+ """
+
+ // println(tree)
+ c.Expr(tree)
+ }
+
+ // TODO: Handle nullable fields
--- End diff --
I was more talking about things like (all schema values non-nullable)
```sql
SELECT p1.name, AVG(p2.height) as average
FROM players AS p1 LEFT JOIN players AS p2 ON p2.score > p1.score
GROUP BY p1.id, p1.name
HAVING average - p1.height > 0 -- implicitly filters average for null
```
versus
```sql
SELECT p1.name, AVG(p2.height) as average
FROM players AS p1 INNER JOIN players AS p2 ON p2.score > p1.score
GROUP BY p1.id, p1.name
HAVING average - p1.height > 0 -- average is NOT NULL since we inner join
```
what should the return type of `average` be in the first example? Would we
require the programmer to explicitly add `average IS NOT NULL`?
I don't have a more flagrant example at hand, but I'm just concerned that
these kind of things could lead to tons of "useless" `IS NOT NULL` clauses...
On the other hand, I see that not using boxing (through `Option`) in the
front end only pushes the problem farther to the backend, since it needs
nullability to make storage decisions I suppose. (However, this is transparent
to the user, so can be changed easily)
Let me know what you think..
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