diff options
| -rw-r--r-- | python/pyspark/sql.py | 22 | ||||
| -rw-r--r-- | sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala | 29 |
2 files changed, 40 insertions, 11 deletions
diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py index e344610b1f..c31d49ce83 100644 --- a/python/pyspark/sql.py +++ b/python/pyspark/sql.py @@ -77,12 +77,25 @@ class SQLContext: """Infer and apply a schema to an RDD of L{dict}s. We peek at the first row of the RDD to determine the fields names - and types, and then use that to extract all the dictionaries. + and types, and then use that to extract all the dictionaries. Nested + collections are supported, which include array, dict, list, set, and + tuple. >>> srdd = sqlCtx.inferSchema(rdd) >>> srdd.collect() == [{"field1" : 1, "field2" : "row1"}, {"field1" : 2, "field2": "row2"}, ... {"field1" : 3, "field2": "row3"}] True + + >>> from array import array + >>> srdd = sqlCtx.inferSchema(nestedRdd1) + >>> srdd.collect() == [{"f1" : array('i', [1, 2]), "f2" : {"row1" : 1.0}}, + ... {"f1" : array('i', [2, 3]), "f2" : {"row2" : 2.0}}] + True + + >>> srdd = sqlCtx.inferSchema(nestedRdd2) + >>> srdd.collect() == [{"f1" : [[1, 2], [2, 3]], "f2" : set([1, 2]), "f3" : (1, 2)}, + ... {"f1" : [[2, 3], [3, 4]], "f2" : set([2, 3]), "f3" : (2, 3)}] + True """ if (rdd.__class__ is SchemaRDD): raise ValueError("Cannot apply schema to %s" % SchemaRDD.__name__) @@ -413,6 +426,7 @@ class SchemaRDD(RDD): def _test(): import doctest + from array import array from pyspark.context import SparkContext globs = globals().copy() # The small batch size here ensures that we see multiple batches, @@ -422,6 +436,12 @@ def _test(): globs['sqlCtx'] = SQLContext(sc) globs['rdd'] = sc.parallelize([{"field1" : 1, "field2" : "row1"}, {"field1" : 2, "field2": "row2"}, {"field1" : 3, "field2": "row3"}]) + globs['nestedRdd1'] = sc.parallelize([ + {"f1" : array('i', [1, 2]), "f2" : {"row1" : 1.0}}, + {"f1" : array('i', [2, 3]), "f2" : {"row2" : 2.0}}]) + globs['nestedRdd2'] = sc.parallelize([ + {"f1" : [[1, 2], [2, 3]], "f2" : set([1, 2]), "f3" : (1, 2)}, + {"f1" : [[2, 3], [3, 4]], "f2" : set([2, 3]), "f3" : (2, 3)}]) (failure_count, test_count) = doctest.testmod(globs=globs,optionflags=doctest.ELLIPSIS) globs['sc'].stop() if failure_count: diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index 378ff54531..131c130bbb 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -298,19 +298,28 @@ class SQLContext(@transient val sparkContext: SparkContext) /** * Peek at the first row of the RDD and infer its schema. - * TODO: We only support primitive types, add support for nested types. + * TODO: consolidate this with the type system developed in SPARK-2060. */ private[sql] def inferSchema(rdd: RDD[Map[String, _]]): SchemaRDD = { + import scala.collection.JavaConversions._ + def typeFor(obj: Any): DataType = obj match { + case c: java.lang.String => StringType + case c: java.lang.Integer => IntegerType + case c: java.lang.Long => LongType + case c: java.lang.Double => DoubleType + case c: java.lang.Boolean => BooleanType + case c: java.util.List[_] => ArrayType(typeFor(c.head)) + case c: java.util.Set[_] => ArrayType(typeFor(c.head)) + case c: java.util.Map[_, _] => + val (key, value) = c.head + MapType(typeFor(key), typeFor(value)) + case c if c.getClass.isArray => + val elem = c.asInstanceOf[Array[_]].head + ArrayType(typeFor(elem)) + case c => throw new Exception(s"Object of type $c cannot be used") + } val schema = rdd.first().map { case (fieldName, obj) => - val dataType = obj.getClass match { - case c: Class[_] if c == classOf[java.lang.String] => StringType - case c: Class[_] if c == classOf[java.lang.Integer] => IntegerType - case c: Class[_] if c == classOf[java.lang.Long] => LongType - case c: Class[_] if c == classOf[java.lang.Double] => DoubleType - case c: Class[_] if c == classOf[java.lang.Boolean] => BooleanType - case c => throw new Exception(s"Object of type $c cannot be used") - } - AttributeReference(fieldName, dataType, true)() + AttributeReference(fieldName, typeFor(obj), true)() }.toSeq val rowRdd = rdd.mapPartitions { iter => |