Python ValueError:对象（3）的长度与字段的长度不匹配

我手动创建PySpark DataFrame，如下所示：

acdata = sc.parallelize([ 
[('timestamp', 1506340019), ('pk', 111), ('product_pk', 123), ('country_id', 'FR'), ('channel', 'web')]
])
# Convert to tuple
acdata_converted = acdata.map(lambda x: (x[0][1], x[1][1], x[2][1]))

# Define schema
acschema = StructType([
    StructField("timestamp", LongType(), True),
    StructField("pk", LongType(), True),
    StructField("product_pk", LongType(), True),
    StructField("country_id", StringType(), True),
    StructField("channel", StringType(), True)
])

df = sqlContext.createDataFrame(acdata_converted, acschema)

但是当我编写

df.head（）

并执行

spark submit

时，我得到以下错误：

org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/mnt/yarn/usercache/hdfs/appcache/application_1510134261242_0002/container_1510134261242_0002_01_000003/pyspark.zip/pyspark/worker.py", line 177, in main
    process()
  File "/mnt/yarn/usercache/hdfs/appcache/application_1510134261242_0002/container_1510134261242_0002_01_000003/pyspark.zip/pyspark/worker.py", line 172, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/mnt/yarn/usercache/hdfs/appcache/application_1510134261242_0002/container_1510134261242_0002_01_000003/pyspark.zip/pyspark/serializers.py", line 268, in dump_stream
    vs = list(itertools.islice(iterator, batch))
  File "/mnt/yarn/usercache/hdfs/appcache/application_1510134261242_0002/container_1510134261242_0002_01_000001/pyspark.zip/pyspark/sql/session.py", line 520, in prepare
  File "/mnt/yarn/usercache/hdfs/appcache/application_1510134261242_0002/container_1510134261242_0002_01_000003/pyspark.zip/pyspark/sql/types.py", line 1358, in _verify_type
    "length of fields (%d)" % (len(obj), len(dataType.fields)))
ValueError: Length of object (3) does not match with length of fields (12)

    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
    at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
    at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
    at org.apache.spark.scheduler.Task.run(Task.scala:108)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)

org.apache.spark.api.python.python异常：回溯（最近一次调用上次）：
文件“/mnt/thread/usercache/hdfs/appcache/application_1510134261242_0002/container_1510134261242_0002_01_000003/pyspark.zip/pyspark/worker.py”，主文件第177行
过程（）
文件“/mnt/thread/usercache/hdfs/appcache/application_1510134261242_0002/container_1510134261242_0002_01_000003/pyspark.zip/pyspark/worker.py”，第172行，处理中
serializer.dump_流（func（拆分索引，迭代器），outfile）
文件“/mnt/thread/usercache/hdfs/appcache/application_1510134261242_0002/container_1510134261242_0002_01_000003/pyspark.zip/pyspark/serializers.py”，第268行，位于转储流中
vs=列表（itertools.islice（迭代器，批处理））
文件“/mnt/thread/usercache/hdfs/appcache/application_1510134261242_0002/container_1510134261242_0002_01_000001/pyspark.zip/pyspark/sql/session.py”，第520行，准备中
文件“/mnt/thread/usercache/hdfs/appcache/application_1510134261242_0002/container_1510134261242_0002_01_000003/pyspark.zip/pyspark/sql/types.py”，第1358行，在验证类型中
“字段长度（%d）”%（len（obj），len（dataType.fields）））
ValueError:对象（3）的长度与字段（12）的长度不匹配
位于org.apache.spark.api.python.PythonRunner$$anon$1.read（PythonRDD.scala:193）
位于org.apache.spark.api.python.PythonRunner$$anon$1。（PythonRDD.scala:234）
位于org.apache.spark.api.python.PythonRunner.compute（PythonRDD.scala:152）
位于org.apache.spark.api.python.PythonRDD.compute（PythonRDD.scala:63）
在org.apache.spark.rdd.rdd.computeOrReadCheckpoint（rdd.scala:323）上
位于org.apache.spark.rdd.rdd.iterator（rdd.scala:287）
在org.apache.spark.rdd.MapPartitionsRDD.compute上（MapPartitionsRDD.scala:38）
在org.apache.spark.rdd.rdd.computeOrReadCheckpoint（rdd.scala:323）上
位于org.apache.spark.rdd.rdd.iterator（rdd.scala:287）
在org.apache.spark.rdd.MapPartitionsRDD.compute上（MapPartitionsRDD.scala:38）
在org.apache.spark.rdd.rdd.computeOrReadCheckpoint（rdd.scala:323）上
位于org.apache.spark.rdd.rdd.iterator（rdd.scala:287）
在org.apache.spark.rdd.MapPartitionsRDD.compute上（MapPartitionsRDD.scala:38）
在org.apache.spark.rdd.rdd.computeOrReadCheckpoint（rdd.scala:323）上
位于org.apache.spark.rdd.rdd.iterator（rdd.scala:287）
在org.apache.spark.rdd.MapPartitionsRDD.compute上（MapPartitionsRDD.scala:38）
在org.apache.spark.rdd.rdd.computeOrReadCheckpoint（rdd.scala:323）上
位于org.apache.spark.rdd.rdd.iterator（rdd.scala:287）
在org.apache.spark.rdd.MapPartitionsRDD.compute上（MapPartitionsRDD.scala:38）
在org.apache.spark.rdd.rdd.computeOrReadCheckpoint（rdd.scala:323）上
位于org.apache.spark.rdd.rdd.iterator（rdd.scala:287）
位于org.apache.spark.scheduler.ResultTask.runTask（ResultTask.scala:87）
位于org.apache.spark.scheduler.Task.run（Task.scala:108）
位于org.apache.spark.executor.executor$TaskRunner.run（executor.scala:335）
位于java.util.concurrent.ThreadPoolExecutor.runWorker（ThreadPoolExecutor.java:1149）
位于java.util.concurrent.ThreadPoolExecutor$Worker.run（ThreadPoolExecutor.java:624）
运行（Thread.java:748）

---

这意味着什么以及如何解决它？

您需要映射所有5个字段以与定义的模式匹配

    acdata_converted = acdata.map(lambda x: (x[0][1], x[1][1], x[2][1], x[3][1], x[4][1]))

我会这样做：

acdata = sc.parallelize([{'timestamp': 1506340019, 'pk': 111, 'product_pk': 123, 'country_id': 'FR', 'channel': 'web'}, {...}])

# Define schema
acschema = StructType([
    StructField("timestamp", LongType(), True),
    StructField("pk", LongType(), True),
    StructField("product_pk", LongType(), True),
    StructField("country_id", StringType(), True),
    StructField("channel", StringType(), True)
])

df = sqlContext.createDataFrame(acdata_converted, acschema)

---

如果您真的需要并行化数据，也要考虑一下。也可以从字典创建数据帧。

是否只需要第一行和5列？执行此操作或使用字典列表发送到

sc.parallelize