HDInsight群集中的UTF8文本出现spark结果编码错误“ascii”编解码器无法在位置编码字符：序号不在范围内（128）

Question

在Linux上使用spark在HDInsight集群中尝试使用希伯来语字符UTF-8TSV文件，我得到了编码错误，有什么建议吗？在

这是我的pyspark笔记本代码：

from pyspark.sql import *
# Create an RDD from sample data
transactionsText = sc.textFile("/people.txt")

header = transactionsText.first()

# Create a schema for our data
Entry = Row('id','name','age')

# Parse the data and create a schema
transactionsParts = transactionsText.filter(lambda x:x !=header) .map(lambda l: l.encode('utf-8').split("\t"))
transactions = transactionsParts.map(lambda p: Entry(str(p[0]),str(p[1]),int(p[2])))

# Infer the schema and create a table       
transactionsTable = sqlContext.createDataFrame(transactions)

# SQL can be run over DataFrames that have been registered as a table.
results = sqlContext.sql("SELECT name FROM transactionsTempTable")

# The results of SQL queries are RDDs and support all the normal RDD operations.
names = results.map(lambda p: "name: " + p.name)

for name in names.collect():
  print(name)

错误：

'ascii' codec can't encode characters in position 6-11: ordinal not in range(128) Traceback (most recent call last): UnicodeEncodeError: 'ascii' codec can't encode characters in position 6-11: ordinal not in range(128)

希伯来文文本文件内容：

^{pr2}$

当我尝试使用英语文件时，效果很好：

英文文本文件内容：

id  name    age
1   guy     37
2   maor    32
3   danny   55

输出：

name: guy
name: maor
name: danny

Answer 1

如果使用希伯来语文本运行以下代码：

from pyspark.sql import *

path = "/people.txt"
transactionsText = sc.textFile(path)

header = transactionsText.first()

# Create a schema for our data
Entry = Row('id','name','age')

# Parse the data and create a schema
transactionsParts = transactionsText.filter(lambda x:x !=header).map(lambda l: l.split("\t"))

transactions = transactionsParts.map(lambda p: Entry(unicode(p[0]), unicode(p[1]), unicode(p[2])))

transactions.collect()

您会注意到，这些名称是unicode类型的列表：

^{pr2}$

现在，我们用事务RDD注册一个表：

table_name = "transactionsTempTable"

# Infer the schema and create a table       
transactionsDf = sqlContext.createDataFrame(transactions)
transactionsDf.registerTempTable(table_name)

# SQL can be run over DataFrames that have been registered as a table.
results = sqlContext.sql("SELECT name FROM {}".format(table_name))

results.collect()

您会注意到，从sqlContext.sql(...返回的PysparkDataFrame中的所有字符串都是Pythonunicode类型：

[Row(name=u'\u05d2\u05d9\u05d0'), Row(name=u'maor'), Row(name=u'danny')]

正在运行：

%%sql
SELECT * FROM transactionsTempTable

将获得预期结果：

name: גיא
name: maor
name: danny

---

请注意，如果您想对这些名称进行一些处理，您应该将它们作为unicode字符串来处理。来自this article：

When you’re dealing with text manipulations (finding the number of characters in a string or cutting a string on word boundaries) you should be dealing with unicode strings as they abstract characters in a manner that’s appropriate for thinking of them as a sequence of letters that you will see on a page. When dealing with I/O, reading to and from the disk, printing to a terminal, sending something over a network link, etc, you should be dealing with byte str as those devices are going to need to deal with concrete implementations of what bytes represent your abstract characters.