在 pyspark 中执行 NLTK

Question

我是 pyspark 的新手，我已经开发了一个在 HDFS 文件上执行 NLTK 的程序，以下是它的步骤。我使用的是 spark 2.3.1

1.从 HDFS 获取文件

2。执行词形还原

3.删除标点符号。

4.将RDD转换为DataFrame

5.执行分词器

6.删除停用词

7.分解列数据为每条记录创建唯一的行

8.我想将所有文件数据保存到一个文件中，因此我将输出与旧文件合并

9.现在将整个合并后的输出写入 HDFS

10.然后删除旧文件并将 spark 创建的文件重命名为不同的名称

11.我正在为所有二元和三元文件执行此操作。

这是我的 pyspark 代码。

%pyspark

import os
import pyspark
import csv
import nltk
import json
import string
import re

from pyspark.ml.feature import Tokenizer, StopWordsRemover
from pyspark.ml.feature import NGram
from pyspark import SparkContext, SparkConf as sc
from pyspark.sql.types import StringType

from nltk.corpus import stopwords
nltk.download('stopwords')

from pyspark.sql import SQLContext
from pyspark.sql.functions import explode,regexp_replace

import pandas
import hdfs



nltk.download('punkt')
from nltk.stem import WordNetLemmatizer
nltk.download('wordnet')


from pyspark import SparkContext, SparkConf
# conf = SparkConf().setAppName("PySpark App")
sc = SparkContext.getOrCreate()
sqlContext = SQLContext(sc)

hdfs_dst_dir = "/user/zeppelin/achyuttest.csv/"
counter=0

#Lemmatizen

def lemma(x):

    lemmatizer = WordNetLemmatizer()
    return lemmatizer.lemmatize(x)



for i in range(1,50001):
    data = sc.textFile('hdfs:///user/spark/Patentdata/ElectronicsPatents/Link\ {}/abstract.txt'.format(i), use_unicode=False)

    print(type(data))

    if data.isEmpty():
        continue


    else:
        lem_words = data.map(lemma)


        list_punct=list(string.punctuation)


        len_list = lem_words.collect()


        test_str = len_list[0]
        test_df = test_str.split(' ')


        data_df = data.map(lambda x: (x, )).toDF(['lem_words'])






# Perform Tokenizer

        tokenizer =  Tokenizer(inputCol="lem_words", outputCol="tokenized_data")
        outputdata = tokenizer.transform(data_df)
        outputdata = outputdata.select('tokenized_data')




    # Remove stop words

        remover = StopWordsRemover(inputCol='tokenized_data', outputCol='words_clean')
        outputdata = remover.transform(outputdata).select('words_clean')


#Explode one Row into multiple Row with value

        result_df = outputdata.withColumn("exploded", explode("words_clean")).select("exploded")

        result_df=result_df.select(regexp_replace('exploded',"[^a-zA-Z\\s]",""))



        print("Link  ========>",i)
#Merge with old output

        if counter>0:
            old_data = sc.textFile('hdfs:///user/zeppelin/achyuttest.csv/unigram.csv', use_unicode=False)
            old_data_df = old_data.map(lambda x: (x, )).toDF(['words_clean'])


            result_df = old_data_df.union(result_df)

        else:
            pass

#Write DataFrame to HDFS

        result_df.coalesce(1).write.mode('append').csv(hdfs_dst_dir)

        fs = spark._jvm.org.apache.hadoop.fs.FileSystem.get(spark._jsc.hadoopConfiguration())

    # Rename file

    #list files in the directory


        list_status = fs.listStatus(spark._jvm.org.apache.hadoop.fs.Path(hdfs_dst_dir))


    #filter name of the file starts with part-

        print("Get FileName")
        file_name = [file.getPath().getName() for file in list_status if file.getPath().getName().startswith('part-')][0]

        print(file_name)
    #rename the file


        new_filename = "unigram.csv"

    # Remove Old file

        fs.delete(spark._jvm.org.apache.hadoop.fs.Path(hdfs_dst_dir+''+new_filename))
        fs.rename(spark._jvm.org.apache.hadoop.fs.Path(hdfs_dst_dir+''+file_name),spark._jvm.org.apache.hadoop.fs.Path(hdfs_dst_dir+''+new_filename))



## Bigrams

        bigram = NGram(n=2, inputCol="words_clean", outputCol="bigrams")

        bigramDataFrame = bigram.transform(outputdata)




    #Explode one Row into multiple Row with value

        result_df = bigramDataFrame.withColumn("exploded", explode("bigrams")).select("exploded")
        result_df=result_df.select(regexp_replace('exploded',"[^a-zA-Z\\s]",""))


    #Merge with old output

        if counter>0:
            old_data = sc.textFile('hdfs:///user/zeppelin/achyuttest.csv/bigram.csv', use_unicode=False)
            old_data_df = old_data.map(lambda x: (x, )).toDF(["exploded"])


            result_df = old_data_df.union(result_df)

        else:
            pass


    # Write Output in file

        result_df.coalesce(1).write.mode('append').csv('hdfs:///user/zeppelin/achyuttest.csv')

    # Rename file

    #list files in the directory

        list_status = fs.listStatus(spark._jvm.org.apache.hadoop.fs.Path(hdfs_dst_dir))

    #filter name of the file starts with part-

        file_name = [file.getPath().getName() for file in list_status if file.getPath().getName().startswith('part-')][0]

    #rename the file

        new_filename = "bigram.csv"

        fs.delete(spark._jvm.org.apache.hadoop.fs.Path(hdfs_dst_dir+''+new_filename))
        fs.rename(spark._jvm.org.apache.hadoop.fs.Path(hdfs_dst_dir+''+file_name),spark._jvm.org.apache.hadoop.fs.Path(hdfs_dst_dir+''+new_filename))





## TriGram

        trigram = NGram(n=3, inputCol="words_clean", outputCol="trigrams")

        trigramDataFrame = trigram.transform(outputdata)


    #Explode one Row into multiple Row with value

        result_df = trigramDataFrame.withColumn("exploded", explode("trigrams")).select("exploded")
        result_df=result_df.select(regexp_replace('exploded',"[^a-zA-Z\\s]",""))

    #Merge with old output

        if counter>0:
            old_data = sc.textFile('hdfs:///user/zeppelin/achyuttest.csv/trigram.csv', use_unicode=False)
            old_data_df = old_data.map(lambda x: (x, )).toDF(["exploded"])


            result_df = old_data_df.union(result_df)

        else:
            pass


#Save DataFrame in HDFS
        result_df.coalesce(1).write.mode('append').csv('hdfs:///user/zeppelin/achyuttest.csv')

    # Rename file

    #list files in the directory

        list_status = fs.listStatus(spark._jvm.org.apache.hadoop.fs.Path(hdfs_dst_dir))

    #filter name of the file starts with part-

        file_name = [file.getPath().getName() for file in list_status if file.getPath().getName().startswith('part-')][0]

    #rename the file

        new_filename = "trigram.csv"

        fs.delete(spark._jvm.org.apache.hadoop.fs.Path(hdfs_dst_dir+''+new_filename))
        fs.rename(spark._jvm.org.apache.hadoop.fs.Path(hdfs_dst_dir+''+file_name),spark._jvm.org.apache.hadoop.fs.Path(hdfs_dst_dir+''+new_filename))

        counter = counter+1

我正在对 50K 文件执行此代码，而我的 spark 花费了太多时间来执行此程序。 （2天过去了，还在继续……）

我正在虚拟机中运行 HDP（运行一个节点 HDP 沙箱）这是我的系统规范...

====> 来宾操作系统::

1. 内存：12930 MB

2. CPU：6CPU

===> 纱线规格::

1.内存：4608 MB

1. 最大容器内存：4608 MB

2. 最大容器大小（Vcores）：4

3. 虚拟核心数：4

===> Zeppelin Pyspark 解释器规范:: 1. spark.executor.memory: 空白（文档中指定的意思是1g）

所以我有两个问题。

1. 我的代码是否正确？
2. 我必须在 YARN 和 Zeppelin Interpreter 中指定哪个值，这样它才能快速高效地工作。

谢谢。

Answer 1

我正在回答我的第一个问题。

根据旧代码，我正在为文件夹中的每个文件制作一个 RDD，因此花费了太多时间（处理 3K 文件需要 19 小时。）

但现在我所做的是在 Single RDD 操作中读取所有输入文件，并对其执行所有操作。 （现在新代码需要大约 15 分钟来处理 3K 文件。）

---

最终代码

评论用于额外理解

专利检测-local.py

"""
To Run this code
Set Pyspark_python 

$ export PYSPARK_PYTHON=/usr/bin/python3
$ pip install nltk


RUN ON Spark::

$ ./bin/spark-submit file_path/Patentdetect-local.py
"""



import pyspark
import nltk
import string
import os
import re

from pyspark import SparkContext
from nltk.stem import WordNetLemmatizer

from pyspark.ml.feature import NGram
from pyspark.sql.types import ArrayType,StructType,StructField,StringType
from pyspark.sql.functions import explode,array,split,collect_list
from pyspark.sql.window import Window
from pyspark.sql import SparkSession


sc = SparkContext.getOrCreate()

spark = SparkSession.builder.appName('Spark Example').getOrCreate()


Source_path="<path>/*/abstract.txt"

Destination_path="<path>/spark-outputs/parquet/Electronics-50/"



data=sc.textFile(Source_path)


data.persist()
lower_casetext = data.map(lambda x:x.lower())



# splitting_rdd = lower_casetext.map(lambda x:x.split(" "))
# print(splitting_rdd.collect())


# Function to perform Sentence tokeniaztion
def sent_TokenizeFunct(x):
    return nltk.sent_tokenize(x)

sentencetokenization_rdd = lower_casetext.map(sent_TokenizeFunct)

# Function to perform Word tokenization

def word_TokenizeFunct(x):
    splitted = [word for line in x for word in line.split()]
    return splitted

wordtokenization_rdd = sentencetokenization_rdd.map(word_TokenizeFunct)


# Remove Stop Words

def removeStopWordsFunct(x):
    from nltk.corpus import stopwords
    stop_words=set(stopwords.words('english'))
    filteredSentence = [w for w in x if not w in stop_words]
    return filteredSentence
stopwordRDD = wordtokenization_rdd.map(removeStopWordsFunct)


# Remove Punctuation marks

def removePunctuationsFunct(x):
    list_punct=list(string.punctuation)
    filtered = [''.join(c for c in s if c not in list_punct) for s in x] 
    filtered_space = [s for s in filtered if s] #remove empty space 
    return filtered
rmvPunctRDD = stopwordRDD.map(removePunctuationsFunct)

# Perform Lemmatization

def lemma(x):

    lemmatizer = WordNetLemmatizer()

    final_rdd = [lemmatizer.lemmatize(s) for s in x]
    return final_rdd

lem_wordsRDD = rmvPunctRDD.map(lemma)

# Join tokens

# def joinTokensFunct(x):
#     joinedTokens_list = []
#     x = " ".join(x)
#     return x

# joinedTokensRDD = lem_wordsRDD.map(joinTokensFunct)


##Create DataFrame from RDD

df = lem_wordsRDD.map(lambda x: (x, )).toDF(["features"])

tokenized_df = df.withColumn("values", explode("features")).select("values")


## Write DataFrame Output

# tokenized_df.write.mode('append').csv(Destination_path)

## Change File-name

# for old_file_name in os.listdir(Destination_path):
#   src = Destination_path+old_file_name
#   dst = Destination_path+"unigram.csv"
    
#   if old_file_name.startswith("part-"):
#       os.rename(src, dst)
        # break


## For Bigrams following commented line is enough
# # tokenized_df.select(F.concat_ws(" ",F.col("values"),F.lead("values").over(Window.orderBy(F.lit(None))))).show()



## Create Final DataFrme 

final_df = tokenized_df.select(collect_list("values").alias("listed_data"))

# final_df.show(truncate=False)

final_df.persist()


## Unigram

unigram = NGram(n=1, inputCol="listed_data", outputCol="unigrams")

unigramDataFrame = unigram.transform(final_df)

unigram_FinalDataFrame = unigramDataFrame.withColumn("unigram_final",explode("unigrams")).select("unigram_final")


## Write DataFrame Outputs

unigram_FinalDataFrame.write.mode('append').parquet(Destination_path)

# Change filename
for old_file_name in os.listdir(Destination_path):
    src = Destination_path+old_file_name
    dst = Destination_path+"unigram.parquet"
    
    if old_file_name.startswith("part-"):
        os.rename(src, dst)


## Bigram

bigram = NGram(n=2, inputCol="listed_data", outputCol="bigrams")

bigramDataFrame = bigram.transform(final_df)

bigram_FinalDataFrame = bigramDataFrame.withColumn("bigram_final",explode("bigrams")).select("bigram_final")


## Write DataFrame Outputs

bigram_FinalDataFrame.write.mode('append').parquet(Destination_path)

## Change filename
for old_file_name in os.listdir(Destination_path):
    src = Destination_path+old_file_name
    dst = Destination_path+"bigram.parquet"
    
    if old_file_name.startswith("part-"):
        os.rename(src, dst)
        # break

## Trigram 

trigram = NGram(n=3, inputCol="listed_data", outputCol="trigram")

trigramDataFrame = trigram.transform(final_df)

trigram_FinalDataFrame = trigramDataFrame.withColumn("trigram_final",explode("trigram")).select("trigram_final")

## Write DataFrame Outputs
trigram_FinalDataFrame.write.mode('append').parquet(Destination_path)

# Change Filename
for old_file_name in os.listdir(Destination_path):
    src = Destination_path+old_file_name
    dst = Destination_path+"trigram.parquet"
    
    if old_file_name.startswith("part-"):
        os.rename(src, dst)
        # break

final_df.unpersist()
data.unpersist()