Not able to connect MysqlDB through python spark
<p>I want to save my processed rdd into mysql table for that i am using SparkDataFrame but i getting follwing error</p>

<pre><code>py4j.protocol.Py4JJavaError: An error occurred while calling o216.jdbc.
: java.sql.SQLException: No suitable driver found for jdbc:mysql://localhost:3306/student?user=root&amp;password=root.
</code></pre>

<p>i added mysql-jar to sparkshell</p>

<p>spark-shell -–driver-class-path /path-to-mysql-jar/mysql-connectorjava-5.1.38-bin.jar. </p>

<pre><code>    from pyspark import SparkContext
    from datetime import datetime
    import os
    import sys
    from pyspark.sql import SQLContext, Row
    sqlContext = SQLContext(sc)
    file1 = sc.textFile("/home/hadoop/text1").cache()
    file2 = sc.textFile("/home/hadoop/text2").cache()
    file3 = file1.union(file2).coalesce(1).map(lambda line: line.split(','))
    file1.unpersist()
    file2.unpersist()
    result = file3.map(lambda x: (x[0]+', '+x[1],float(x[2]))).reduceByKey(lambda a,b:a+b).sortByKey('true').coalesce(1)
    result = result.map(lambda x:x[0]+','+str(x[1]))\
    schema_site = sqlContext.createDataFrame(result)
    schema_site.registerTempTable("table1")
    mysql_url="jdbc:mysql://localhost:3306/test?user=root&amp;password=root&amp;driver=com.mysql.jdbc.Driver"
    schema_site.write.jdbc(url=mysql_url, table="table1", mode="append")
</code></pre>

<p>i am using spark spark-1.5.0-bin-hadoop2.4</p>

<p>also setup hive metastore.</p>

<p>So how could i load my RDD result into Mysql table.</p>

<p>input file is </p>

<pre><code>  file1 contents are

  1234567  65656545  12

  1234567  65675859  11 

  file2 contents are,

  1234567  65656545  12

  1234567  65675859  11

 and the resultnat RDD is like

 1234567  65656545 24

 1234567  65675859  22

i created the table in mysql with three colunm,

std_id  std_code  std_res
</code></pre>

<p>and i want the table output like,</p>

<pre><code>  std_id  std_code  std_res

  1234567  65656545    24

   1234567  65675859   24
</code></pre>