另外这里有个小技巧,pandas读取csv很慢,例如我自己会经常读取5-10G左右的csv文件,这时在第一次读取后使用topickle保存成pickle文件,在以后加载时用readpickle读取pickle文件,不仅速度上会快10几倍,文件的大小也会有2-5倍的减小(减小程度取决于你dataframe的内容和数据类型) 最后总结还是那句话,当数据能全部加载到内存...
eval_type=read_int(infile)ifeval_type==PythonEvalType.NON_UDF:func,profiler,deserializer,serializer=read_command(pickleSer,infile)else:func,profiler,deserializer,serializer=read_udfs(pickleSer,infile,eval_type) 在read_udfs 中,如果是 PANDAS 类的 UDF,会创建 ArrowStreamPandasUDFSerializer,其余的 UDF ...
logFile="file:///opt/modules/hadoop-2.8.5/README.txt"sc=SparkContext("local","first app")logData=sc.textFile(logFile).cache()numAs=logData.filter(lambda s:'a'ins).count()numBs=logData.filter(lambda s:'b'ins).count()print("Line with a:%i,lines with b :%i"%(numAs,numBs)) 然...
_python_broadcast = None if sock_file is not None: # the jvm is doing decryption for us. Read the value # immediately from the sock_file self._value = self.load(sock_file) else: # the jvm just dumps the pickled data in path -- we'll unpickle lazily when # the value is ...
AI代码解释 if__name__=='__main__':# Read information about how to connect back to theJVMfrom the environment.java_port=int(os.environ["PYTHON_WORKER_FACTORY_PORT"])auth_secret=os.environ["PYTHON_WORKER_FACTORY_SECRET"](sock_file,_)=local_connect_and_auth(java_port,auth_secret)main(so...
81 0.001 0.000 20.194 0.249 serializers.py:160(_read_with_length) 80 0.000 0.000 20.167 0.252 serializers.py:470(loads) 80 3.280 0.041 20.167 0.252 {cPickle.loads} 4194304 1.024 0.000 16.295 0.000 types.py:1532(<lambda>) 4194304 2.048 0.000 15.270 0.000 types.py:610(fromInternal) ...
main(sock_file, sock_file) 这里会去向 JVM 建立连接,并从 socket 中读取指令和数据。对于如何进行序列化、反序列化,是通过 UDF 的类型来区分: eval_type = read_int(infile) ifeval_type == PythonEvalType.NON_UDF: func, profiler, deserializer, serializer = read_command(pickleSer, infile) ...
原因:shuffle分为shuffle write和shuffle read两部分。 shuffle write的分区数由上一阶段的RDD分区数控制,shuffle read的分区数则是由Spark提供的一些参数控制。shuffle write可以简单理解为类似于saveAsLocalDiskFile的操作,将计算的中间结果按某种规则临时放到各个executor所在的本地磁盘上。
in <module> from pyspark.serializers import read_int, PickleSerializer File "/xxx/xxx/lib/python3.8/site-packages/pyspark/serializers.py", line 72, in <module> from pyspark import cloudpickle File "/xxx/xxx/lib/python3.8/site-packages/pyspark/cloudpickle.py", line 145, in <module> _cell_...
py--- from pyspark import SparkContext logFile = "file:///home/hadoop/spark-2.1.0-bin-hadoop2.7/README.md" sc = SparkContext("local", "first app") logData = sc.textFile(logFile).cache() numAs = logData.filter(lambda s: 'a' in s).count() numBs = logData.filter(lambda s: '...