Spark：如何将元组转换为数据帧

Question

我有train_rdd像(('a',1),('b',2),('c',3))。我使用以下方法将其转换为数据帧

from pyspark.sql import Row
train_label_df = train_rdd.map(lambda x: (Row(**dict(x)))).toDF()

但也许某些RDD中缺少一些密钥。所以错误就发生了

File
"/mnt/hadoop/yarn/local/usercache/hdfs/appcache/application_/container_05_000017/pyspark.zip/pyspark/worker.py", line
253, in main
process()
File
"/mnt/hadoop/yarn/local/usercache/hdfs/appcache/application_/container_05_000017/pyspark.zip/pyspark/worker.py", line
248, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File
"/mnt/hadoop/yarn/local/usercache/hdfs/appcache/application_/container_05_000002/pyspark.zip/pyspark/rdd.py", line
2440, in pipeline_func
File
"/mnt/hadoop/yarn/local/usercache/hdfs/appcache/application_/container_05_000002/pyspark.zip/pyspark/rdd.py", line
2440, in pipeline_func
File
"/mnt/hadoop/yarn/local/usercache/hdfs/appcache/application_/container_05_000002/pyspark.zip/pyspark/rdd.py", line
350, in func
File
"/mnt/hadoop/yarn/local/usercache/hdfs/appcache/application_/container_05_000002/pyspark.zip/pyspark/rdd.py", line
1859, in combineLocally
File
"/mnt/hadoop/yarn/local/usercache/hdfs/appcache/application_/container_05_000017/pyspark.zip/pyspark/shuffle.py", line
237, in mergeValues
for k, v in iterator:
    TypeError: cannot unpack non - iterable NoneType object

是否有其他方法将元组类型RDD转换为数据帧

---

更新：

我还尝试使用createDataFrame

 rdd = sc.parallelize([('a',1), (('a',1), ('b',2)), (('a',1), ('b',2), ('c',3) ) ])
schema = StructType([
        StructField("a", StringType(), True),
        StructField("b", StringType(), True),
        StructField("c", StringType(), True),
])
train_label_df = sqlContext.createDataFrame(rdd,  schema)
train_label_df.show()

出现错误

  File "/home/spark/python/pyspark/sql/types.py", line 1400, in verify_struct
    "length of fields (%d)" % (len(obj), len(verifiers))))
ValueError: Length of object (2) does not match with length of fields (3)

Answer 1

您可以将元组映射到dict：

rdd1 = rdd.map(lambda x: dict(x if isinstance(x[0],tuple) else [x]))

然后执行以下操作之一：

from pyspark.sql import Row 

cols = ["a", "b", "c"]

rdd1.map(lambda x: Row(**{c:x.get(c) for c in cols})).toDF().show()
+ -+  +  +
|  a|   b|   c|
+ -+  +  +
|  1|null|null|
|  1|   2|null|
|  1|   2|   3|
+ -+  +  +

或

rdd1.map(lambda x: tuple(x.get(c) for c in cols)).toDF(cols).show()