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Spark快速大数据分析(第2版)
book

Spark快速大数据分析(第2版)

by Jules S. Damji, Brooke Wenig, Tathagata Das, Denny Lee
November 2021
Intermediate to advanced
340 pages
10h 46m
Chinese
Posts & Telecom Press
Content preview from Spark快速大数据分析(第2版)
92
4
4-4
DataFrameReader
DataFrameWriter
中的CSV选项
属 性 名 合 法 值 含  义 作用范围
compression
none
bzip2
deflate
gzip
lz4
snappy
使用指定压缩编码写数据
dateFormat
yyyy-MM-dd
等日期时间
格式
使用
Java
DateTimeFormatter
支持的任何格式
/
multiLine
true
false
使用多行模式。默认为
false
(单行模式)
inferSchema
true
false
如果为
true
,那么
Spark
会自动确定字段的数据类型。
默认为
false
sep
任意字符 用该字符分隔一行中各列的值。默认使用逗号(
,
)作
为分隔符
/
escape
任意字符 用该字符转义引号,默认为
\
/
header
true
false
表明第一行是否为各字段名组成的表头。默认为
false
/
4.3.6
 
Avro
Spark 2.4
引入了对
Avro
格式的支持。例如,在序列化和反序列化消息时,
Apache Kafka
就会使用
A
vro
格式。
Avro
格式有很多优点,包括直接映射
JSON
、快速且高效、支持多种
编程语言。
1.
Avro
文件读入
DataFrame
DataFrameReader
Avro
文件读入
DataFrame
与本节介绍的其他数据源的用法一致。
// Scala代码
val
df = spark.read.format("avro")
.load("/databricks-datasets/learning-spark-v2/flights/summary-data/avro/*") ...
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Publisher Resources

ISBN: 9787115576019