Open cmh14 opened 4 years ago
cmh14
commented
4 years ago Someone can give me a help? I could not search any similiar problem in Google and I could provide any message may help to solve this problem.
the row_generator() code is like this: def row_generator(x): feature, label = x return { 'label': label, 'feature': np.expand_dims(np.array(feature, dtype=np.float32), axis=0) }
selitvin
commented
4 years ago I see Python worker exited unexpectedly in your call stack.
This would occur if python daemon running your executor code crashes.
If you are running on a cluster (e.g. spark on kubernetes), this may happen if you violate maximal memory allocated for the pod.
If you are running locally, this could result from some non-python error, e.g. running out of memory on your machine; a segfault or some other abnormal termination issue.
Curious, are you able to run dict_to_spark_row(schema,x) on the driver (instead of executor), e.g.:
x:dict_to_spark_row(schema,rdd.map(row_generator).first())Hello sir,Thanks for your help. With your suggestion, I have changed my last code just like that
rows_rdd = rows_rdd.map(lambda x:dict_to_spark_row(schema,spark_df1.rdd.map(row_generator).first()))
or
rows_test = (lambda x:dict_to_spark_row(schema,spark_df1.rdd.map(row_generator).first()))
They both run well. What other meesage do you want to get in my environment?
cmh14
commented
4 years ago But I failed in the codeprint(rows_rdd.first()),so this change may not solve my problem totally. I run these codes locally in a Windows10 System. I still have one other problem .In my case, The data I deal with in pyspark and petastorm is about 14G, and the processed data I want to save is about 100m, But every time I run the code, It may cost about 100g in my C driver. although they disappper when the code finishs its work, If I meet a bug and the code finishs unexpected, I have to clean my C driver manually.
selitvin
commented
4 years ago The data leftover is not entirely surprising as spark executor is terminated abnormally. The abnormal failure (Python worker exited unexpectedly) maybe caused by a variety of reasons. If a core dump is left somewhere (perhaps need to be enabled - don't remember how this is done on Windows), looking at the call stack during crash may hint at the component that causes the process to crash.
I use pyshark to work with my datas and I want to save it for the future use with pytorch, but I face a bug in the code after: rows_rdd = ( spark_df1 .rdd .map(row_generator) .map(lambda x: dict_to_spark_row(schema, x)) ) I try to run the code step by step.Firstly: rows_rdd = spark_df1.rdd print(rows_rdd.first()) The result is like this: Row(feature=[0.27058823529411763, 0.0, 0.0, 0.6666666666666666, 0.13725490196078433, 0.09411764705882353, 0.25098039215686274, 0.0, 0.48627450980392156, 0.023529411764705882, 0.8666666666666667, 0.1843137254901961, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.19607843137254902, 0.7843137254901961, 0.9882352941176471, 0.8117647058823529, 0.4392156862745098, 0.023529411764705882, 0.0, 0.2235294117647059, 0.9019607843137255, 0.8117647058823529, 0.5372549019607843, 0.403921568627451, 0.3137254901960784, 0.09411764705882353, 0.49019607843137253, 0.8313725490196079, 0.9529411764705882, 0.25882352941176473, 0.0, 0.0, 0.592156862745098, 0.984313725490196, 0.9764705882352941, 0.03137254901960784, 0.7450980392156863, 0.6352941176470588, 0.592156862745098, 0.11764705882352941, 0.49411764705882355, 0.1607843137254902, 0.7058823529411765, 0.0196078431372549, 0.49019607843137253, 0.9450980392156862, 0.058823529411764705, 0.5333333333333333, 0.24313725490196078, 0.4392156862745098, 0.9294117647058824, 0.08627450980392157, 0.8156862745098039, 0.17254901960784313, 0.8352941176470589, 0.5215686274509804, 0.9529411764705882, 0.8509803921568627, 0.15294117647058825, 0.23137254901960785, 0.7803921568627451, 0.18823529411764706, 0.7333333333333333, 0.9058823529411765, 0.06274509803921569, 0.2235294117647059, 0.5176470588235295, 0.10196078431372549, 0.803921568627451, 0.9098039215686274, 0.16862745098039217, 0.25882352941176473, 0.38823529411764707, 0.807843137254902, 0.9372549019607843, 0.8117647058823529, 0.2901960784313726, 0.29411764705882354, 0.6313725490196078, 0.7372549019607844, 0.6313725490196078, 0.9803921568627451, 0.49411764705882355, 0.9490196078431372, 0.26666666666666666, 0.592156862745098, 0.3058823529411765, 0.17254901960784313, 0.6627450980392157, 0.18823529411764706, 0.596078431372549, 0.9019607843137255, 0.6549019607843137, 0.32941176470588235, 0.06274509803921569, 0.7529411764705882, 0.6352941176470588, 0.43529411764705883, 0.1843137254901961, 0.7764705882352941, 0.4745098039215686, 0.592156862745098, 0.13333333333333333, 0.6705882352941176, 0.7843137254901961, 0.3215686274509804, 0.6, 0.8313725490196079, 0.6901960784313725, 0.5137254901960784, 0.9764705882352941, 0.12156862745098039, 0.5725490196078431, 0.44313725490196076, 0.5019607843137255, 0.1450980392156863, 0.24313725490196078, 0.4823529411764706, 0.8666666666666667, 0.047058823529411764, 0.6862745098039216, 0.4, 0.9098039215686274, 0.35294117647058826, 0.9098039215686274, 0.6078431372549019, 0.9176470588235294, 0.8666666666666667, 0.9529411764705882, 0.30980392156862746, 0.21568627450980393, 0.6941176470588235, 0.27450980392156865, 0.33725490196078434, 0.7803921568627451, 0.28627450980392155, 0.8235294117647058, 0.7411764705882353, 0.9764705882352941, 0.8117647058823529, 0.2196078431372549, 0.7764705882352941, 0.8901960784313725, 0.9137254901960784, 0.49019607843137253, 0.8431372549019608, 0.7294117647058823, 0.9058823529411765, 0.1803921568627451, 0.9372549019607843, 0.396078431372549, 0.7333333333333333, 0.7294117647058823, 0.4235294117647059, 0.27058823529411763, 0.11764705882352941, 0.00392156862745098, 0.611764705882353, 0.34901960784313724, 0.8, 0.2549019607843137, 0.4235294117647059, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], label=0)
Secondly: rows_rdd = rows_rdd.map(row_generator) print(rows_rdd.first()) the result is like this: {'label': 0, 'feature': array([[0.27058825, 0. , 0. , ..., 0. , 0. ,
Thirdly, I run the code nextline. rows_rdd = rows_rdd.map(lambda x:dict_to_spark_row(schema,x)) print(rows_rdd.first()) But it gives me a error: Traceback (most recent call last): File "D:/pythonproject/deep_traffic_cmh14/create_train_test_set.py", line 270, in
print(rows_rdd.first())
File "C:\Users\DELL\Anaconda3\envs\pytorch\lib\site-packages\pyspark\rdd.py", line 1378, in first
rs = self.take(1)
File "C:\Users\DELL\Anaconda3\envs\pytorch\lib\site-packages\pyspark\rdd.py", line 1360, in take
res = self.context.runJob(self, takeUpToNumLeft, p)
File "C:\Users\DELL\Anaconda3\envs\pytorch\lib\site-packages\pyspark\context.py", line 1069, in runJob
sock_info = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)
File "C:\Users\DELL\Anaconda3\envs\pytorch\lib\site-packages\py4j\java_gateway.py", line 1257, in call
answer, self.gateway_client, self.target_id, self.name)
File "C:\Users\DELL\Anaconda3\envs\pytorch\lib\site-packages\pyspark\sql\utils.py", line 63, in deco
return f(*a, **kw)
File "C:\Users\DELL\Anaconda3\envs\pytorch\lib\site-packages\py4j\protocol.py", line 328, in get_return_value
format(target_id, ".", name), value)
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.runJob.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 33.0 failed 1 times, most recent failure: Lost task 0.0 in stage 33.0 (TID 698, localhost, executor driver): org.apache.spark.SparkException: Python worker exited unexpectedly (crashed)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator$$anonfun$3.applyOrElse(PythonRunner.scala:490)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator$$anonfun$3.applyOrElse(PythonRunner.scala:479)
at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:36)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:597)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:575)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$class.foreach(Iterator.scala:891)
at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
at org.apache.spark.api.python.PythonRDD$$anonfun$3.apply(PythonRDD.scala:153)
at org.apache.spark.api.python.PythonRDD$$anonfun$3.apply(PythonRDD.scala:153)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:123)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.io.EOFException
at java.io.DataInputStream.readInt(DataInputStream.java:392)
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:582)
... 26 more
Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1891) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1879) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1878) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1878) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:927) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:927) at scala.Option.foreach(Option.scala:257) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:927) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2112) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2061) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2050) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:738) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101) at org.apache.spark.api.python.PythonRDD$.runJob(PythonRDD.scala:153) at org.apache.spark.api.python.PythonRDD.runJob(PythonRDD.scala) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at py4j.Gateway.invoke(Gateway.java:282) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:238) at java.lang.Thread.run(Thread.java:748) Caused by: org.apache.spark.SparkException: Python worker exited unexpectedly (crashed) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator$$anonfun$3.applyOrElse(PythonRunner.scala:490) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator$$anonfun$3.applyOrElse(PythonRunner.scala:479) at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:36) at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:597) at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:575) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410) at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) at scala.collection.Iterator$class.foreach(Iterator.scala:891) at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28) at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48) at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310) at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302) at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289) at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28) at org.apache.spark.api.python.PythonRDD$$anonfun$3.apply(PythonRDD.scala:153) at org.apache.spark.api.python.PythonRDD$$anonfun$3.apply(PythonRDD.scala:153) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2101) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at org.apache.spark.scheduler.Task.run(Task.scala:123) at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) ... 1 more Caused by: java.io.EOFException at java.io.DataInputStream.readInt(DataInputStream.java:392) at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:582) ... 26 more
I build the environment by the env in the anaconda and I am sure that I have install the prtastorm using pip.