我用pyspark写序列文件，键是image filename，值是用bytestring表示的图像
from PIL import Image
def get_image(filename):
s = StringIO()
im=io.imread(filename)
io.imsave(s, im)
return [(filename, s)]
rdd = sc.parallelize(filenames)
rdd.flatMap(get_image).saveAsSequenceFile("/user/myname/output")但是pyspark抛出一个异常，表明pickle不支持该格式
Caused by: net.razorvine.pickle.InvalidOpcodeException: opcode not implemented: OBJ
at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:224)
at net.razorvine.pickle.Unpickler.load(Unpickler.java:85)
at net.razorvine.pickle.Unpickler.loads(Unpickler.java:98)
at org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:151)
at org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:150)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308)
at scala.collection.Iterator$class.foreach(Iterator.scala:727)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
at scala.collection.AbstractIterator.to(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1298)
at org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1298)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1850)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1850)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:88)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
... 1 more
1条答案
按热度按时间s4n0splo1#
pickling的obj操作码在您试图对python类/对象进行编码/解码以进行序列化时使用。在我的例子中，我并不打算将一个对象写入序列文件，所以对我的修复只是修复这个bug。
至于整个生态系统，问题是spark使用的是pyrolite 4.13，但直到4.17版本，obj编码/解码才被引入pyrolite库。至于该怎么办，我想你有几个选择：
通过pull请求或github问题说服spark维护人员使用pyrolite的更高版本。
建立你自己的Spark版本，使用该版本的辉石
不要将类/对象写入序列文件。