from pyspark import SparkContext # 创建SparkContext对象 sc = SparkContext("local", "Compression and Concatenation in PySpark") # 创建键值对RDD data = [("key1", "value1"), ("key2", "value2"), ("key1", "value3"), ("key2", "value4")] # 将键值对RDD转换为键值对列表RDD rdd ...
values:选中的列(LIST)variableColumnName: 列名valueColumnName:对应列的值宽表转长表,一行变多行,除了选中的ids是不变的,但是会把选中的values中的列由列变成行记录,variableColumnName记录了反转前的列名,valueColumnName 对应 variableColumnName 存储值。 data.show()+---+---+---+---+---+| name|age...
AI代码解释 defnewAPIHadoopFile(self,path,inputFormatClass,keyClass,valueClass,keyConverter=None,valueConverter=None,conf=None,batchSize=0):jconf=self._dictToJavaMap(conf)jrdd=self._jvm.PythonRDD.newAPIHadoopFile(self._jsc,path,inputFormatClass,keyClass,valueClass,keyConverter,valueConverter,jconf...
scn=SparkContext(conf=conf) list=[1,2,3,4,5,6] #出入数据 rdd1=scn.parallelize(list) rdd2=scn.parallelize((1,2,3,4,5,6)) rdd3=scn.parallelize("abcdef") rdd4=scn.parallelize({"key1": "value1", "key2": "value2"}) #通过collect()查看RDD中的内容 print(rdd1.collect()) prin...
dict_row[key] = value_in columns = dict_row.keys() v = dict_row.values() row = Row(*columns) return row(*v) 查: 行元素查询操作: 像SQL那样打印列表前20元素(show函数内可用int类型指定要打印的行数): df.show() df.show(30)
listValue = broadcastList.value if x in listValue: #acc_count.add(1) acc_count +=1 return 1 else: return 0 #1)、过滤数据,去除空行数据 #2)、分割单词 #3)、过滤字典数据:符号数据 line__filter = fileRDD \ .filter(lambda line: (len(line.strip()) > 0)) \ ...
Row(value='# Apache Spark') 现在,我们可以通过以下方式计算包含单词Spark的行数: lines_with_spark = text_file.filter(text_file.value.contains("Spark")) 在这里,我们使用filter()函数过滤了行,并在filter()函数内部指定了text_file_value.contains包含单词"Spark",然后将这些结果放入了lines_with_spark变量...
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 requested assert(path is not None) self._path = path 原创声明:本文系作者授权腾讯云开发者社区发表,未经...
SparkConf,SparkContextfrom pyspark.sql import SparkSessionimport jsonimport pandas as pdimport numpy as npimport osfrom pyspark.sql import SQLContextfrom pyspark.sql import Rowfrom pyspark.sql.types import DoubleType,IntegerType,StringType,DateType,StructType,StructField#from common_value import valList...
# reduce方法对rdd进行合并rdd.reduce(lambdax,y:x+y)('a',7,'a',2,'b',2)# reduceByKey方法根据key对value进行合并rdd.reduceByKey(lambdav1,v2:v1+v2).collect()[('a',9),('b',2)] 分组/groupBy # groupBy方法对rdd的元素分组rdd1.groupBy(lambdax:x%2).mapValues(list).collect()[(0...