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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Spark SQL
与
DataFrame
：
读写外部数据源
｜
117
# 写数据方式1：用save方法将数据写入JDBC源
(jdbcDF1
.write
.format("jdbc")
.option("url", "jdbc:postgresql://[DBSERVER]")
.option("dbtable", "[SCHEMA].[TABLENAME]")
.option("user", "[USERNAME]")
.option("password", "[PASSWORD]")
.save())
# 写数据方式2：用jdbc方法将数据写入JDBC源
(jdbcDF2
.write
.jdbc("jdbc:postgresql:[DBSERVER]", "[SCHEMA].[TABLENAME]",
properties={"user": "[USERNAME]", "password": "[PASSWORD]"}))
5.3.3
MySQL
为了连接到
MySQL
数据库，需要构建或者从
Maven
或
MySQL
下载用于
JDBC
连接的
jar
包
（下载更简单），接着将其添加到
classpath
。然后，以指定
jar
的方式启动
Spark shell
（
spark-shell
或
pyspark
）。
bin/spark-shell --jars mysql-connector-java_8.0.16-bin.jar
以下示例展示了通过
Spark SQL
的数据源
API
和
JDBC ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.