Merge pull request #11 from heathermiller/typedSql

Adding array support (waiting on merge in scala-records)

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala

@@ -74,8 +74,17 @@ trait ScalaReflection {
         }), nullable = true)
     // Need to decide if we actually need a special type here.
     case t if t <:< typeOf[Array[Byte]] => Schema(BinaryType, nullable = true)
+    case t if t <:< typeOf[Array[Int]] => Schema(ArrayType(IntegerType, false), nullable = true)
+    case t if t <:< typeOf[Array[Long]] => Schema(ArrayType(LongType, false), nullable = true)
+    case t if t <:< typeOf[Array[Double]] => Schema(ArrayType(DoubleType, false), nullable = true)
+    case t if t <:< typeOf[Array[Short]] => Schema(ArrayType(ShortType, false), nullable = true)
+    case t if t <:< typeOf[Array[Boolean]] => Schema(ArrayType(BooleanType, false), nullable = true)
+    case t if t <:< typeOf[Array[Float]] => Schema(ArrayType(FloatType, false), nullable = true)
+    case t if t <:< typeOf[Array[String]] => Schema(ArrayType(StringType, false), nullable = true)
     case t if t <:< typeOf[Array[_]] =>
-      sys.error(s"Only Array[Byte] supported now, use Seq instead of $t")
+      val TypeRef(_, _, Seq(elementType)) = t
+      val Schema(dataType, nullable) = schemaFor(elementType)
+      Schema(ArrayType(dataType, containsNull = nullable), nullable = true)
     case t if t <:< typeOf[Seq[_]] =>
       val TypeRef(_, _, Seq(elementType)) = t
       val Schema(dataType, nullable) = schemaFor(elementType)

sql/core/src/main/scala/org/apache/spark/sql/TypedSql.scala

@@ -18,9 +18,11 @@ 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 {
+object SQLMacros {
   import scala.reflect.macros._
 
+  var currentContext: SQLContext = _
+
   def sqlImpl(c: Context)(args: c.Expr[Any]*) =
     new Macros[c.type](c).sql(args)
 
@@ -68,10 +70,29 @@ private[sql] object SQLMacros {
 
     case class RecSchema(name: String, index: Int, cType: DataType, tpe: Type)
 
+    def getSchema(sqlQuery: String, interpolatedArguments: Seq[InterpolatedItem]) = {
+      if (currentContext == null) {
+        val parser = new SqlParser()
+        val logicalPlan = parser(sqlQuery)
+        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)
+
+        analyzedPlan.output.map(attr => (attr.name, attr.dataType))
+      } else {
+        interpolatedArguments.foreach(
+            _.localRegister(currentContext.catalog, currentContext.functionRegistry))
+        currentContext.sql(sqlQuery).schema.fields.map(attr => (attr.name, attr.dataType))
+      }
+    }
+
     def sql(args: Seq[c.Expr[Any]]) = {
 
       val q"""
-        org.apache.spark.sql.test.TestSQLContext.SqlInterpolator(
+        $path.SQLInterpolation(
           scala.StringContext.apply(..$rawParts))""" = c.prefix.tree
 
       //rawParts.map(_.toString).foreach(println)
@@ -96,16 +117,7 @@ private[sql] object SQLMacros {
         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 fields = getSchema(query, interpolatedArguments)
       val record = genRecord(q"row", fields)
 
       val tree = q"""
@@ -157,16 +169,50 @@ private[sql] object SQLMacros {
      * Constructs a nested record if necessary
      */
     def genGetField(row: Tree, index: Int, t: DataType): Tree = t match {
+      case BinaryType =>
+        q"$row($index).asInstanceOf[Array[Byte]]"
+      case DecimalType =>
+        q"$row($index).asInstanceOf[scala.math.BigDecimal]"
       case t: PrimitiveType =>
+        // this case doesn't work for DecimalType or BinaryType,
+        // note that they both extend PrimitiveType
         val methodName = newTermName("get" + primitiveForType(t))
         q"$row.$methodName($index)"
+      case ArrayType(elementType, _) =>
+        val tpe = typeOfDataType(elementType)
+        q"$row($index).asInstanceOf[Array[$tpe]]"        
       case StructType(structFields) =>
         val fields = structFields.map(f => (f.name, f.dataType))
         genRecord(q"$row($index).asInstanceOf[$rowTpe]", fields)
       case _ =>
         c.abort(NoPosition, s"Query returns currently unhandled field type: $t")
     }
-  }
+
+    private def typeOfDataType(dt: DataType): Type = dt match {
+      case ArrayType(elementType, _) =>
+        val elemTpe = typeOfDataType(elementType)
+        appliedType(definitions.ArrayClass.toType, List(elemTpe))
+      case TimestampType =>
+        typeOf[java.sql.Timestamp]
+      case DecimalType =>
+        typeOf[BigDecimal]
+      case BinaryType =>
+        typeOf[Array[Byte]]
+      case _ if dt.isPrimitive =>
+        typeOfPrimitive(dt.asInstanceOf[PrimitiveType])
+    }
+
+    private def typeOfPrimitive(dt: PrimitiveType): Type = dt match {
+      case IntegerType => typeOf[Int]
+      case LongType => typeOf[Long]
+      case ShortType => typeOf[Short]
+      case ByteType => typeOf[Byte]
+      case DoubleType => typeOf[Double]
+      case FloatType => typeOf[Float]
+      case BooleanType => typeOf[Boolean]
+      case StringType => typeOf[String]
+    }
+  } // end of class Macros
 
   // TODO: Duplicated from codegen PR...
   protected def primitiveForType(dt: PrimitiveType) = dt match {

sql/core/src/test/scala/org/apache/spark/sql/TypedSqlSuite.scala

@@ -21,10 +21,28 @@ import org.scalatest.FunSuite
 
 import org.apache.spark.sql.test.TestSQLContext
 
+import scala.math.BigDecimal
+import scala.language.reflectiveCalls
+
+import java.sql.Timestamp
+
 case class Person(name: String, age: Int)
 
 case class Car(owner: Person, model: String)
 
+case class Garage(cars: Array[Car])
+
+case class DataInt(arr: Array[Int])
+case class DataDouble(arr: Array[Double])
+case class DataFloat(arr: Array[Float])
+case class DataString(arr: Array[String])
+case class DataByte(arr: Array[Byte])
+case class DataLong(arr: Array[Long])
+case class DataShort(arr: Array[Short])
+case class DataArrayShort(arr: Array[Array[Short]])
+case class DataBigDecimal(arr: Array[BigDecimal])
+case class DataTimestamp(arr: Array[Timestamp])
+
 class TypedSqlSuite extends FunSuite {
   import TestSQLContext._
 
@@ -35,11 +53,19 @@ class TypedSqlSuite extends FunSuite {
   val cars = sparkContext.parallelize(
     Car(Person("Michael", 30), "GrandAm") :: Nil)
 
+  val garage = sparkContext.parallelize(
+    Array(Car(Person("Michael", 30), "GrandAm"), Car(Person("Mary", 52), "Buick")))
+
   test("typed query") {
     val results = sql"SELECT name FROM $people WHERE age = 30"
     assert(results.first().name == "Michael")
   }
 
+  test("typed query with array") {
+    val results = sql"SELECT * FROM $garage"
+    assert(results.first().owner == "Michael")
+  }
+
   test("int results") {
     val results = sql"SELECT * FROM $people WHERE age = 30"
     assert(results.first().name == "Michael")
@@ -73,4 +99,68 @@ class TypedSqlSuite extends FunSuite {
   //  def addOne(i: Int) = i + 1
   //  assert(sql"SELECT $addOne(1) as two".first.two === 2)
   }
+
+
+  // tests for different configurations of arrays, primitive and nested
+  val sqlContext = new org.apache.spark.sql.SQLContext(sparkContext)
+
+  test("array int results") {
+    val data = sparkContext.parallelize(1 to 10).map(x => DataInt(Array(1, 2, 3)))
+    val ai = sql"SELECT arr FROM $data"
+    assert(ai.take(1).head.arr === Array(1, 2, 3))
+  }
+
+  test("array double results") {
+    val data = sparkContext.parallelize(1 to 10).map(x => DataDouble(Array(1.0, 2.0, 3.0)))
+    val ad = sql"SELECT arr FROM $data"
+    assert(ad.take(1).head.arr === Array(1.0, 2.0, 3.0))
+  }
+
+  test("array float results") {
+    val data = sparkContext.parallelize(1 to 10).map(x => DataFloat(Array(1F, 2F, 3F)))
+    val af = sql"SELECT arr FROM $data"
+    assert(af.take(1).head.arr === Array(1F, 2F, 3F))
+  }
+
+  test("array string results") {
+    val data = sparkContext.parallelize(1 to 10).map(x => DataString(Array("hey","yes","no")))
+    val as = sql"SELECT arr FROM $data"
+    assert(as.take(1).head.arr === Array("hey","yes","no"))
+  }
+
+  test("array byte results") {
+    val data = sparkContext.parallelize(1 to 10).map(x => DataByte(Array(1.toByte, 2.toByte, 3.toByte)))
+    val ab = sql"SELECT arr FROM $data"
+    assert(ab.take(1).head.arr === Array(1.toByte, 2.toByte, 3.toByte))
+  }
+
+  test("array long results") {
+    val data = sparkContext.parallelize(1 to 10).map(x => DataLong(Array(1L, 2L, 3L)))
+    val al = sql"SELECT arr FROM $data"
+    assert(al.take(1).head.arr === Array(1L, 2L, 3L))
+  }
+
+  test("array short results") {
+    val data = sparkContext.parallelize(1 to 10).map(x => DataShort(Array(1.toShort, 2.toShort, 3.toShort)))
+    val ash = sql"SELECT arr FROM $data"
+    assert(ash.take(1).head.arr === Array(1.toShort, 2.toShort, 3.toShort))
+  }
+
+  test("array of array of short results") {
+    val data = sparkContext.parallelize(1 to 10).map(x => DataArrayShort(Array(Array(1.toShort, 2.toShort, 3.toShort))))
+    val aash = sql"SELECT arr FROM $data"
+    assert(aash.take(1).head.arr === Array(Array(1.toShort, 2.toShort, 3.toShort)))
+  }
+
+  test("array bigdecimal results") {
+    val data = sparkContext.parallelize(1 to 10).map(x => DataBigDecimal(Array(new java.math.BigDecimal(1), new java.math.BigDecimal(2), new java.math.BigDecimal(3))))
+    val abd = sql"SELECT arr FROM $data"
+    assert(abd.take(1).head.arr === Array(new java.math.BigDecimal(1), new java.math.BigDecimal(2), new java.math.BigDecimal(3)))
+  }
+
+  test("array timestamp results") {
+    val data = sparkContext.parallelize(1 to 10).map(x => DataTimestamp(Array(new Timestamp(1L), new Timestamp(2L), new Timestamp(3L))))
+    val ats = sql"SELECT arr FROM $data"
+    assert(ats.take(1).head.arr === Array(new Timestamp(1L), new Timestamp(2L), new Timestamp(3L)))
+  }
 }