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author | Kan Zhang <kzhang@apache.org> | 2014-06-16 11:11:29 -0700 |
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committer | Reynold Xin <rxin@apache.org> | 2014-06-16 11:11:29 -0700 |
commit | 4fdb491775bb9c4afa40477dc0069ff6fcadfe25 (patch) | |
tree | c996de6ecbf6f913b3e7bc8a45f7801aa58266ac | |
parent | 716c88aa147762f7f617adf34a17edd681d9a4ff (diff) | |
download | spark-4fdb491775bb9c4afa40477dc0069ff6fcadfe25.tar.gz spark-4fdb491775bb9c4afa40477dc0069ff6fcadfe25.tar.bz2 spark-4fdb491775bb9c4afa40477dc0069ff6fcadfe25.zip |
[SPARK-2010] Support for nested data in PySpark SQL
JIRA issue https://issues.apache.org/jira/browse/SPARK-2010
This PR adds support for nested collection types in PySpark SQL, including
array, dict, list, set, and tuple. Example,
```
>>> from array import array
>>> from pyspark.sql import SQLContext
>>> sqlCtx = SQLContext(sc)
>>> rdd = sc.parallelize([
... {"f1" : array('i', [1, 2]), "f2" : {"row1" : 1.0}},
... {"f1" : array('i', [2, 3]), "f2" : {"row2" : 2.0}}])
>>> srdd = sqlCtx.inferSchema(rdd)
>>> srdd.collect() == [{"f1" : array('i', [1, 2]), "f2" : {"row1" : 1.0}},
... {"f1" : array('i', [2, 3]), "f2" : {"row2" : 2.0}}]
True
>>> rdd = 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)}])
>>> srdd = sqlCtx.inferSchema(rdd)
>>> 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
```
Author: Kan Zhang <kzhang@apache.org>
Closes #1041 from kanzhang/SPARK-2010 and squashes the following commits:
1b2891d [Kan Zhang] [SPARK-2010] minor doc change and adding a TODO
504f27e [Kan Zhang] [SPARK-2010] Support for nested data in PySpark SQL
-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 => |