如何使用 MySQL 和 Apache Spark？

How to work with MySQL and Apache Spark?

提问by pangkaj paul

I want to run my existing application with Apache Spark and MySQL.

我想用 Apache Spark 和 MySQL 运行我现有的应用程序。

回答by cherah30

From pySpark, it work for me :

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()

回答by Liam

With spark 2.0.x,you can use DataFrameReader and DataFrameWriter. Use SparkSession.read to access DataFrameReader and use Dataset.write to access DataFrameWriter.

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。

Suppose using spark-shell.

假设使用 spark-shell。

read example

阅读示例

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()

read example 2

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()

from spark doc

来自火花文档

read example3

阅读示例3

If you want to read data from a query result rather than a table.

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()

write example

写例子

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)

中文版戳我

中文版圆点我

回答by Lokesh

Using Scala, this worked for me :Use the commands below:

使用 Scala，这对我有用：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()

回答by jstuartmill

For Scala if you use the sbtthis will also work.

对于 Scala，如果您使用它，sbt这也将起作用。

In your build.sbtfile:

在您的build.sbt文件中：

libraryDependencies ++= Seq(
    "org.apache.spark" %% "spark-core" % "1.6.2",
    "org.apache.spark" %% "spark-sql" % "1.6.2",
    "org.apache.spark" %% "spark-mllib" % "1.6.2",
    "mysql" % "mysql-connector-java" % "5.1.12"
)

Then you just need to declare your usage of the driver.

然后你只需要声明你对驱动程序的使用。

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()

回答by Jatin

public static void main(String[] args) {
    Map<String, String> options = new HashMap<String, String>();
    options.put("url","jdbc:postgresql://<DBURL>:<PORT>/<Database>?user=<UserName>&password=<Password>");
    options.put("dbtable", "<TableName>");
    JavaSparkContext sc = new JavaSparkContext(new SparkConf().setAppName("DBConnection").setMaster("local[*]"));
    SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc);
    // DataFrame jdbcDF = sqlContext.load("jdbc", options).cache();
    DataFrame jdbcDF = sqlContext.jdbc(options.get("url"),options.get("dbtable"));
    System.out.println("Data------------------->" + jdbcDF.toJSON().first());
    Row[] rows = jdbcDF.collect();
    System.out.println("Without Filter \n ------------------------------------------------- ");
    for (Row row2 : rows) {
        System.out.println(row2.toString());
    }
    System.out.println("Filter Data\n ------------------------------------------------- ");
    jdbcDF = jdbcDF.select("agency_id","route_id").where(jdbcDF.col("route_id").$less$eq(3));
    rows = jdbcDF.collect();
    for (Row row2 : rows) {
        System.out.println(row2.toString());
    }
}

回答by D Qiang

For Java(using maven), add spark dependencies and sql driver dependencies in your pom.xml file,

对于 Java（使用 maven），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>

Sample code, suppose your mysql locates at local, database nameis test, user nameis rootand passwordis password, and two tables in test db are table1and table2

示例代码，在本地假设你的mysql所处，数据库名是测试，用户名是根和密码是密码，并在测试数据库的两个表是表1和表2

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
......

回答by EpicPandaForce

For Java, this worked for me:

对于 Java，这对我有用：

@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();
}

---

---

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();

of course, for MySQL, I needed the connector:

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>

And I get

我得到

+-------+------------------+--------------+---------------+
|BOOK_ID|        BOOK_TITLE|   BOOK_AUTHOR|      BORR_NAME|
+-------+------------------+--------------+---------------+
|      1|        Gy?r? 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|
+-------+------------------+--------------+---------------+

回答by Gábor Bakos

Based on this infoobjects articletry the following (assuming Java or Scala, not sure how this would work with python):

基于这篇infoobjects 文章，请尝试以下操作（假设使用 Java 或 Scala，不确定这将如何与 python 一起使用）：

• add the mysql-connector-javato the path of your spark cluster
• initialize the driver: Class.forName("com.mysql.jdbc.Driver")
• create a JdbcRDDdata source:

• 将mysql-connector-java添加到Spark集群的路径中
• 初始化驱动程序： Class.forName("com.mysql.jdbc.Driver")
• 创建一个JdbcRDD数据源：

---

---

val myRDD = new JdbcRDD( sc, () => 
                               DriverManager.getConnection(url,username,password),
                        "select first_name,last_name,gender from person limit ?, ?",
                        1,//lower bound
                        5,//upper bound
                        2,//number of partitions
                        r =>
                          r.getString("last_name") + ", " + r.getString("first_name"))

回答by Liran Brimer

   val query: String =
    "select col1, col2 from schema.table_name where condition"

  val url= "jdbc:mysql://<ip>:3306/<schema>"
  val username = ""
  val password = ""
  val sqlContext = new org.apache.spark.sql.SQLContext(sc)
  val df = sqlContext.load("jdbc", Map(
    "url" -> (url + "/?user=" + username + "&password=" + password),
    "dbtable" -> s"($query) as tbl",
    "driver" -> "com.mysql.jdbc.Driver"))

df.show()

回答by Martin

For Spark 2.1.0 and Scala (On Windows 7 OS), below code works pretty fine for me:

对于 Spark 2.1.0 和 Scala（在 Windows 7 操作系统上），下面的代码对我来说效果很好：

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()
  }
}