如何将ApacheSpark与MySQL集成，以作为Spark数据帧读取数据库表？

我想用Apache Spark和MySQL运行我现有的应用程序。

基于此，尝试以下假设Java或Scala，不确定这将如何与python一起工作：

将添加到spark群集的路径 初始化驱动程序：Class.forNamecom.mysql.jdbc.driver 创建数据源：

---

从pySpark，它对我有用：

dataframe_mysql = mySqlContext.read.format("jdbc").options(
    url="jdbc:mysql://localhost:3306/my_bd_name",
    driver = "com.mysql.jdbc.Driver",
    dbtable = "my_tablename",
    user="root",
    password="root").load()

使用Scala，这对我很有用： 使用以下命令：

sudo -u root spark-shell --jars /mnt/resource/lokeshtest/guava-12.0.1.jar,/mnt/resource/lokeshtest/hadoop-aws-2.6.0.jar,/mnt/resource/lokeshtest/aws-java-sdk-1.7.3.jar,/mnt/resource/lokeshtest/mysql-connector-java-5.1.38/mysql-connector-java-5.1.38/mysql-connector-java-5.1.38-bin.jar --packages com.databricks:spark-csv_2.10:1.2.0

import org.apache.spark.sql.SQLContext

val sqlcontext = new org.apache.spark.sql.SQLContext(sc)

val dataframe_mysql = sqlcontext.read.format("jdbc").option("url", "jdbc:mysql://Public_IP:3306/DB_NAME").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "tblage").option("user", "sqluser").option("password", "sqluser").load()

dataframe_mysql.show()

对于Scala，如果使用sbt，这也会起作用

在build.sbt文件中：

然后您只需要声明您对驱动程序的使用情况

Class.forName("com.mysql.jdbc.Driver").newInstance

val conf = new SparkConf().setAppName("MY_APP_NAME").setMaster("MASTER")

val sc = new SparkContext(conf)

val sqlContext = new SQLContext(sc)

val data = sqlContext.read
.format("jdbc")
.option("url", "jdbc:mysql://<HOST>:3306/<database>")
.option("user", <USERNAME>)
.option("password", <PASSWORD>)
.option("dbtable", "MYSQL_QUERY")
.load()

---

使用spark 2.0.x，您可以使用DataFrameReader和DataFrameWriter。 使用SparkSession.read访问DataFrameReader，使用Dataset.write访问DataFrameWriter

假设使用火花壳

阅读示例 阅读示例2 从

阅读示例3 如果要从查询结果而不是表中读取数据

val sql="""select * from db.your_table where id>1"""
val jdbcDF = spark.read
  .format("jdbc")
  .option("url", "jdbc:mysql:dbserver")
  .option("dbtable",  s"( $sql ) t")
  .option("user", "username")
  .option("password", "password")
  .load()

举例说明

---

对于Windows 7操作系统上的Spark 2.1.0和Scala，以下代码对我来说非常有用：

import org.apache.spark.sql.SparkSession

object MySQL {
  def main(args: Array[String]) {
    //At first create a Spark Session as the entry point of your app
    val spark:SparkSession = SparkSession
      .builder()
      .appName("JDBC")
      .master("local[*]")
      .config("spark.sql.warehouse.dir", "C:/Exp/")
      .getOrCreate();    

    val dataframe_mysql = spark.read.format("jdbc")
                          .option("url", "jdbc:mysql://localhost/feedback")
                          .option("driver", "com.mysql.jdbc.Driver")
                          .option("dbtable", "person") //replace with own
                          .option("user", "root") //replace with own 
                          .option("password", "vertrigo") // replace with own
                          .load()

    dataframe_mysql.show()
  }
}

---

对于Java，这对我很有用：

@Bean
public SparkConf sparkConf() {
    SparkConf sparkConf = new SparkConf()
            .setAppName(appName)
            .setSparkHome(sparkHome)
            .setMaster(masterUri);

    return sparkConf;
}

@Bean
public JavaSparkContext javaSparkContext() {
    return new JavaSparkContext(sparkConf());
}

@Bean
public SparkSession sparkSession() {
    return SparkSession
            .builder()
            .sparkContext(javaSparkContext().sc())
            .appName("Java Spark SQL basic example")
            .getOrCreate();
}

当然，对于MySQL，我需要连接器：

    <!-- https://mvnrepository.com/artifact/mysql/mysql-connector-java -->
    <dependency>
        <groupId>mysql</groupId>
        <artifactId>mysql-connector-java</artifactId>
        <version>6.0.6</version>
    </dependency>

---

对于使用maven的Java，在pom.xml文件中添加spark依赖项和sql驱动程序依赖项

<properties>
    <java.version>1.8</java.version>
    <spark.version>1.6.3</spark.version>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
 <dependencies>
    <dependency>
        <groupId>mysql</groupId>
        <artifactId>mysql-connector-java</artifactId>
        <version>6.0.6</version>
    </dependency>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-sql_2.10</artifactId>
        <version>${spark.version}</version>
    </dependency>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-core_2.10</artifactId>
        <version>${spark.version}</version>
    </dependency>

    <dependency>
        <groupId>junit</groupId>
        <artifactId>junit</artifactId>
        <version>4.11</version>
        <scope>test</scope>
    </dependency>
</dependencies>

---

也许这会有所帮助：现在不鼓励使用JdbcRDD。最好看看Spark 1.4及更高版本中的DataFrame接口。@Mattingenshron这是真的，尽管当问题被提出和回答时，它不可用。是的，理解。我只是在搜索我自己和其他人时发现了这一点，所以我更新了它，以确保新用户找到最新的东西。这段代码将停止连接spark与databaseSQLContext.load，现在已弃用，并将在2.0中删除。看起来像是一个自动完成的错误，com.imf.jdbc.Driver->com.mysql.jdbc.Driver？你说得对！感谢您捕捉到这一点。mySqlContext应该是sqlContext^这只是一个变量。你可以随意命名。如果我使用的是ODBC而不是JDBC，那么在上面的文本中切换这两个名称是否完全相同？对于spark2.x，使用dataframe=spark\u session.read.formatjdbc.options…Load工作得很好，很干净！谢谢，我们如何使用spark从mysql连接中删除记录？我需要在回答中指出驱动程序选项，以使其正常工作

val jdbcDF = spark.read
  .format("jdbc")
  .option("url", "jdbc:mysql:dbserver")
  .option("dbtable", "schema.tablename")
  .option("user", "username")
  .option("password", "password")
  .load()

val sql="""select * from db.your_table where id>1"""
val jdbcDF = spark.read
  .format("jdbc")
  .option("url", "jdbc:mysql:dbserver")
  .option("dbtable",  s"( $sql ) t")
  .option("user", "username")
  .option("password", "password")
  .load()

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)

import org.apache.spark.sql.SparkSession

object MySQL {
  def main(args: Array[String]) {
    //At first create a Spark Session as the entry point of your app
    val spark:SparkSession = SparkSession
      .builder()
      .appName("JDBC")
      .master("local[*]")
      .config("spark.sql.warehouse.dir", "C:/Exp/")
      .getOrCreate();    

    val dataframe_mysql = spark.read.format("jdbc")
                          .option("url", "jdbc:mysql://localhost/feedback")
                          .option("driver", "com.mysql.jdbc.Driver")
                          .option("dbtable", "person") //replace with own
                          .option("user", "root") //replace with own 
                          .option("password", "vertrigo") // replace with own
                          .load()

    dataframe_mysql.show()
  }
}

@Bean
public SparkConf sparkConf() {
    SparkConf sparkConf = new SparkConf()
            .setAppName(appName)
            .setSparkHome(sparkHome)
            .setMaster(masterUri);

    return sparkConf;
}

@Bean
public JavaSparkContext javaSparkContext() {
    return new JavaSparkContext(sparkConf());
}

@Bean
public SparkSession sparkSession() {
    return SparkSession
            .builder()
            .sparkContext(javaSparkContext().sc())
            .appName("Java Spark SQL basic example")
            .getOrCreate();
}

Properties properties = new Properties();
        properties.put("user", "root");
        properties.put("password", "root");
        properties.put("driver", "com.mysql.cj.jdbc.Driver");
        sparkSession.read()
                    .jdbc("jdbc:mysql://localhost:3306/books?useSSL=false", "(SELECT books.BOOK_ID as BOOK_ID, books.BOOK_TITLE as BOOK_TITLE, books.BOOK_AUTHOR as BOOK_AUTHOR, borrowers.BORR_NAME as BORR_NAME FROM books LEFT OUTER JOIN borrowers ON books.BOOK_ID = borrowers.BOOK_ID) as t", properties) // join example
                    .show();

    <!-- https://mvnrepository.com/artifact/mysql/mysql-connector-java -->
    <dependency>
        <groupId>mysql</groupId>
        <artifactId>mysql-connector-java</artifactId>
        <version>6.0.6</version>
    </dependency>

+-------+------------------+--------------+---------------+
|BOOK_ID|        BOOK_TITLE|   BOOK_AUTHOR|      BORR_NAME|
+-------+------------------+--------------+---------------+
|      1|        Gyűrű kúra|J.R.K. Tolkien|   Sára Sarolta|
|      2|     Kecske-eledel|     Mekk Elek|Maláta Melchior|
|      3|      Répás tészta| Vegán Eleazár|           null|
|      4|Krumpli és pityóka| Farmer Emília|           null|
+-------+------------------+--------------+---------------+

<properties>
    <java.version>1.8</java.version>
    <spark.version>1.6.3</spark.version>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>
 <dependencies>
    <dependency>
        <groupId>mysql</groupId>
        <artifactId>mysql-connector-java</artifactId>
        <version>6.0.6</version>
    </dependency>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-sql_2.10</artifactId>
        <version>${spark.version}</version>
    </dependency>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-core_2.10</artifactId>
        <version>${spark.version}</version>
    </dependency>

    <dependency>
        <groupId>junit</groupId>
        <artifactId>junit</artifactId>
        <version>4.11</version>
        <scope>test</scope>
    </dependency>
</dependencies>

SparkConf sparkConf = new SparkConf();
SparkContext sc = new SparkContext("local", "spark-mysql-test", sparkConf);
SQLContext sqlContext = new SQLContext(sc);

// here you can run sql query
String sql = "(select * from table1 join table2 on table1.id=table2.table1_id) as test_table";
// or use an existed table directly
// String sql = "table1";
DataFrame dataFrame = sqlContext
    .read()
    .format("jdbc")
    .option("url", "jdbc:mysql://127.0.0.1:3306/test?useUnicode=true&characterEncoding=UTF-8&autoReconnect=true")
    .option("user", "root")
    .option("password", "password")
    .option("dbtable", sql)
    .load();

// continue your logical code
......