问题描述
我正在尝试按照此处提到的说明进行操作...
I am trying to follow the instructions mentioned here...
https://www.percona.com/blog/2016/08/17/apache-spark-makes-slow-mysql-queries-10x-faster/
这里...
https://www.percona.com/blog/2015/10/07/using-apache-spark-mysql-data-analysis/
我正在使用 sparkdocker 镜像.
I am using sparkdocker image.
docker run -it -p 8088:8088 -p 8042:8042 -p 4040:4040 -h sandbox sequenceiq/spark:1.6.0 bash
cd /usr/local/spark/
./sbin/start-master.sh
./bin/spark-shell --driver-memory 1G --executor-memory 1g --executor-cores 1 --master local
这按预期工作:
scala> sc.parallelize(1 to 1000).count()
但这显示了一个错误:
val jdbcDF = spark.read.format("jdbc").options(
Map("url" -> "jdbc:mysql://1.2.3.4:3306/test?user=dba&password=dba123",
"dbtable" -> "ontime.ontime_part",
"fetchSize" -> "10000",
"partitionColumn" -> "yeard", "lowerBound" -> "1988", "upperBound" -> "2016", "numPartitions" -> "28"
)).load()
这里是错误:
<console>:25: error: not found: value spark
val jdbcDF = spark.read.format("jdbc").options(
如何从 spark shell 中连接到 MySQL?
How do I connect to MySQL from within spark shell?
推荐答案
在 spark 2.0.x 中,您可以使用 DataFrameReader 和 DataFrameWriter.使用 SparkSession.read 访问 DataFrameReader,使用 Dataset.write 访问 DataFrameWriter.
With spark 2.0.x,you can use DataFrameReader and DataFrameWriter.Use SparkSession.read to access DataFrameReader and use Dataset.write to access DataFrameWriter.
假设使用 spark-shell.
Suppose using spark-shell.
val prop=new java.util.Properties()
prop.put("user","username")
prop.put("password","yourpassword")
val url="jdbc:mysql://host:port/db_name"
val df=spark.read.jdbc(url,"table_name",prop)
df.show()
阅读示例 2
val jdbcDF = spark.read
.format("jdbc")
.option("url", "jdbc:mysql:dbserver")
.option("dbtable", "schema.tablename")
.option("user", "username")
.option("password", "password")
.load()
来自 spark 文档一个>
import org.apache.spark.sql.SaveMode
val prop=new java.util.Properties()
prop.put("user","username")
prop.put("password","yourpassword")
val url="jdbc:mysql://host:port/db_name"
//df is a dataframe contains the data which you want to write.
df.write.mode(SaveMode.Append).jdbc(url,"table_name",prop)
这篇关于从 spark 连接到 mysql的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持!