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import os
import sys
sys.path.append(os.path.dirname(sys.path[0]))
from utils.common_util import CommonUtil
from utils.hdfs_utils import HdfsUtils
from utils.spark_util import SparkUtil
from pyspark.sql import functions as F
from pyspark.sql.types import ArrayType, StringType, StructType, StructField, BooleanType, MapType
"""
merchantwords 搜索词分词词频
"""
def is_number(str):
"""
判断一个字符是否是数字
:param str:
:return:
"""
import re
return re.match(r"^-?\d+\.?\d+$", str) is not None
def word_tokenize(keyword: str):
import re
keyword = re.sub(r'(\d+\.?\d*|-|\"|,|，|？|\?|/|、|)', '', keyword).strip()
from nltk.tokenize import word_tokenize
result = word_tokenize(keyword, "english")
# 过滤标点如下
filter_arr = [
" ", "\t", "\r", "\n", "(", ")", ",", "，", "[", "]", "、", "-", ":", "&", "|", "+", "``", "'", "'", "\""
]
return list(filter(lambda x: not is_number(x) and x not in filter_arr, result))
def run():
spark = SparkUtil.get_spark_session("app_name")
udf_word_tokenize = F.udf(word_tokenize, ArrayType(StringType()))
keywords_all = spark.sql("select keyword from dwt_merchantwords_st_detail where site_name='us'").cache()
df_all = keywords_all.withColumn("word", F.explode(udf_word_tokenize(F.col("keyword"))))
df_all = df_all.groupby(F.col("word")) \
.agg(F.count("word").alias("frequency")) \
.orderBy(F.col("frequency").desc()) \
.select(
F.col("word"),
F.col("frequency"),
F.lit("us").alias("site_name")
)
hive_tb = 'tmp_word_frequency'
# # 去重
partition_dict = {
"site_name": "us"
}
hdfs_path = CommonUtil.build_hdfs_path(hive_tb, partition_dict)
HdfsUtils.delete_hdfs_file(hdfs_path)
partition_by = list(partition_dict.keys())
print(f"当前存储的表名为：{hive_tb},分区为{partition_by}", )
df_all.write.saveAsTable(name=hive_tb, format='hive', mode='append', partitionBy=partition_by)
def word_pluralize(keyword: str):
from textblob import Word
# 单数形式
singularize = Word(keyword).singularize().string
# 复数形式
pluralize = Word(singularize).pluralize().string
result = {
"text": keyword,
"singularize": singularize,
"pluralize": pluralize,
"pluralizeFlag": keyword == pluralize,
"not_regular": keyword not in [singularize, pluralize]
}
return result
def word_stem(keyword: str):
from nltk.stem.snowball import SnowballStemmer
stemmer = SnowballStemmer("english", ignore_stopwords=False)
return stemmer.stem(keyword)
def word_test():
spark = SparkUtil.get_spark_session("word_test")
udf_word_pluralize = F.udf(word_pluralize, StructType(
[
StructField('text', StringType(), True),
StructField('singularize', StringType(), True),
StructField('pluralize', StringType(), True),
StructField('pluralizeFlag', BooleanType(), True),
StructField('not_regular', BooleanType(), True),
]
))
udf_word_stem = F.udf(word_stem, StringType())
keywords_all = spark.sql("select word,frequency from tmp_word_frequency").cache()
keywords_all = keywords_all.withColumn("resultMap", udf_word_pluralize(F.col("word"))).select(
F.col("word"),
F.col("frequency"),
F.col("resultMap").getField("singularize").alias("singularize"),
F.col("resultMap").getField("pluralize").alias("pluralize"),
F.col("resultMap").getField("pluralizeFlag").alias("pluralizeFlag"),
F.col("resultMap").getField("not_regular").alias("not_regular"),
).where("(pluralizeFlag == true) or (not_regular == true)")
# 计算词根
keywords_all = keywords_all.withColumn("word_stem", udf_word_stem(F.col("word")))
keywords_all = keywords_all.withColumn("singularize_stem", udf_word_stem(F.col("singularize")))
keywords_all = keywords_all.withColumn("pluralize_stem", udf_word_stem(F.col("pluralize")))
hive_tb = 'tmp_word_not_regular_v2'
keywords_all.write.saveAsTable(name=hive_tb, format='hive', mode='append')
print("success")
def word_for_calc():
spark = SparkUtil.get_spark_session("word_for_calc")
keywords_all = spark.sql("""
select *
from (
select word, sum(volume) as volume
from (
select regexp_extract(keyword, 'for (.*)', 0) as word, volume
from big_data_selection.dwt_merchantwords_st_detail
) tmp
where word != ''
group by word
) tmp
order by volume desc
""")
keywords_all.write.saveAsTable(name="tmp_for_market", format='hive', mode='append')
print("success")
def word_for_download():
spark = SparkUtil.get_spark_session("word_for_calc")
keywords_all = spark.sql("""
select word
from tmp_for_market
order by volume desc
""")
CommonUtil.df_export_csv(spark, keywords_all, csv_name='word_for_calc', limit=200 * 10000)
print("success")
pass
if __name__ == '__main__':
# word_for_calc()
word_for_download()
print("success")