spark sql 小样

package dev.spark.sql

import java.util.Properties

import org.apache.spark.sql.{Row, SQLContext, SaveMode}
import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
import org.apache.spark.{SparkConf, SparkContext}

object DataFrame {

  val num = 0
  val map = scala.collection.immutable.Map("url" -> "jdbc:mysql://192.168.0.1:3306/spark",
    "dbtable"-> "tmp_table3",
    "user"-> "spark",
    "password"->"spark")
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf()
    conf.setMaster("local")
    conf.setAppName("dataFrame")
    val sc = new SparkContext(conf)
    val ssc = new SQLContext(sc)
    val df = ssc.read.json()
    ssc.read.format("json").load(".json")
    // dataFrame.show 直接查看数据集 按条件查看数据集
    df.show()
    df.filter(df.col("col")<= num).show()

    // 将dataFrame注册为临时表 按照SQL方式访问数据集
    df.registerTempTable("tmp_table0")
    // 返回的结果是将每行包装为ROW的数据集集
    val dataSet0 = ssc.sql("SELECT col FROM tmp_table WHERE col <="+ num)
    // dataSet属性方法很多
    dataSet0.collect()foreach(println)
    dataSet0.columns.foreach(println)
    dataSet0.rdd.foreach(println)
    dataSet0.explain()
    dataSet0.alias("")
    dataSet0.cache()
    dataSet0.na

    // SQLContext格式化读取文件
    // parquet
    val pssc = new SQLContext(sc)
    pssc.read.format("parquet")load(".parquet")
    // jdbc
    val dataSet3 = ssc.read.format("jdbc").options(map).load()

    dataSet3.write.jdbc("jdbc:mysql://192.168.0.1:3306/spark","tmp_table3",new Properties())
    // HiveSQLContext在resources中配置hive-site.xml后对hive仓库进行查询 注意：优先从临时表中查询，可以通过数据库.表名的方式完全限定避免歧义,默认仓库是default
    val hssc = new HiveContext(sc)

    val dataSet1 = hssc.sql("SELECT col FROM database.table")
    dataSet1.registerTempTable("tmp_table1")
    // 相同sparkContext上下文可以进行联表操作
    hssc.sql("SELECT * FROM tmp_table0 t0 inner join tmp_table1 t1 on t0.col = t1.col")

    // 数据映射为表
    dataSet0.rdd.map(line=>Row(line.size))
    val rowkeyStructField = new StructField("rowkey", IntegerType,true)
    val tableStructType = new StructType(Array(rowkeyStructField))
    val dataSet2 = hssc.createDataFrame(dataSet0.rdd, tableStructType)
    dataSet2.registerTempTable("tmp_table2")
    dataSet2.write.mode(SaveMode.Append).saveAsTable("hive_spark.tmp_table2")

    // rdd转dataframe需要隐式转换
    import ssc.implicits._
    case class RowKeyClass (rowkey:Int)
    dataSet0.rdd.map(x => new RowKeyClass(x.size)).toDF()
  }
}

spark sql 小样

标签：方式   mys   tap   for   ack   user   表名   select   数据集