spark load data from mysql

code first

本机通过spark-shell.cmd启动一个spark进程

	SparkSession spark = SparkSession.builder().appName("Simple Application").master("local[2]").getOrCreate();

        Map<String, String> map = new HashMap<>();

        map.put("url","jdbc:mysql:xxx");

        map.put("user", "user");

        map.put("password", "pass");

        String tableName = "table";

        map.put("dbtable", tableName);

        map.put("driver", "com.mysql.jdbc.Driver");

        String lowerBound = 1 + "";   //低界限

        String upperBound = 10000 + "";  //高界限

        map.put("fetchsize", "100000");  //实例和mysql服务端单次拉取行数，拉取后才能执行rs.next()

        map.put("numPartitions", "50");  //50个分区区间，将以范围[lowerBound,upperBound]划分成50个分区，每个分区执行一次查询

        map.put("partitionColumn", "id");  //分区条件列

        System.out.println("tableName:" + tableName + ", lowerBound:"+lowerBound+", upperBound:"+upperBound);

        map.put("lowerBound", lowerBound);

        map.put("upperBound", upperBound);

        Dataset dataset = spark.read().format("jdbc").options(map).load(); //transform操作

        dataset.registerTempTable("tmp__");

        Dataset<Row> ds = spark.sql("select * from tmp__"); //transform操作

        ds.cache().show();  //action，触发sql真正执行

执行到show时，任务开始真正执行，此时，我们单机debug，来跟踪partitionColumn的最终实现方式

debug类

org.apache.spark.sql.execution.datasources.jdbc.JDBCRelation.buildScan

此时parts为size=50的分区列表

  override def buildScan(requiredColumns: Array[String], filters: Array[Filter]): RDD[Row] = {

	// Rely on a type erasure hack to pass RDD[InternalRow] back as RDD[Row]

	JDBCRDD.scanTable(

	  sparkSession.sparkContext,

	  schema,

	  requiredColumns,

	  filters,

	  parts,

	  jdbcOptions).asInstanceOf[RDD[Row]]

  }

单个分区内的whereClause值

whereCluase="id < 21 or id is null"

继续往下断点,到单个part的执行逻辑,此时代码应该是在Executor中的某个task线程中

org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD.compute

	val myWhereClause = getWhereClause(part)

	val sqlText = s"SELECT $columnList FROM ${options.table} $myWhereClause"

	stmt = conn.prepareStatement(sqlText,

		ResultSet.TYPE_FORWARD_ONLY, ResultSet.CONCUR_READ_ONLY)

	stmt.setFetchSize(options.fetchSize)

	rs = stmt.executeQuery()

	val rowsIterator = JdbcUtils.resultSetToSparkInternalRows(rs, schema, inputMetrics)

	CompletionIterator[InternalRow, Iterator[InternalRow]](

	  new InterruptibleIterator(context, rowsIterator), close())

此时

myWhereClause=WHERE id < 21 or id is null

最终的sql语句

sqlText=SELECT id,xx FROM tablea WHERE id < 21 or id is null

所有part都会经过compute

Executor执行完任务后，将信息发送回Driver

Executor: Finished task 7.0 in stage 2.0 (TID 12). 1836 bytes result sent to driver

总结

numPartitions、partitionColumn、lowerBound、upperBound结合后，spark将生成很多个parts,每个part对应一个查询whereClause，最终查询数据将分成numPartitions个任务来拉取数据，因此，partitionColumn必须是索引列，否则，效率将大大降低
自动获取table schema,程序会执行类型select * from tablea where 1=0 来获取字段及类型
lowerBound,upperBound仅用来生成parts区间，最终生成的sql中，不会使用它们来作为数据范围的最小或最大值