November 2021
Intermediate to advanced
340 pages
10h 46m
Chinese
Content preview from Spark快速大数据分析（第2版）
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94
｜
第
4
章
-rw-r--r-- 1 jules wheel 0 May 17 11:54 _SUCCESS
-rw-r--r-- 1 jules wheel 526 May 17 11:54 part-00000-ffdf70f4-<...>-c000.avro
4. Avro
数据源选项
表
4-5
列出了
DataFrameReader
和
DataFrameWriter
的常用选项。完整的选项列表请参见
文档。
表4-5：
DataFrameReader
和
DataFrameWriter
中的Avro选项
属 性 名 默 认 值 含 义 作用范围
avroSchema
无 可选参数，用户可以使用
JSON
格式提供
Avro
的字
段定义。
Avro
记录的字段名和数据类型必须与
Avro
输入数据或
Catalyst
数据（
Spark
内部数据类型）保
持一致，否则会导致操作失败
读
/
写
recordName topLevelRecord
所写结果的顶层记录名，这是
Avro
记录规范要求的 写
recordNamespace ""
所写结果的命名空间 写
ignoreExtension true
如果开启本选项，则会加载所有文件（不论扩展名
是否为
.avro
），否则会忽略没有
.avro
扩展名的文件
读
compression snappy
允许指定写文件时使用的压缩编码。目前支持设为
uncompressed
、
snappy
、
deflate
、
bzip2
和
xz
。如
果没有设置这个选项，那么会使用
spark.sql.avro.
compression
对应的值 ...
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