spark相关介绍-提取hive表（一） (3)

多个文件读取for循环

通过for循环遍历读取文件夹里面的文件时，保留文件名信息；

具体示例可以参考文末的

import org.apache.spark.rdd.RDD import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.sql.SparkSession import org.apache.spark.{SparkConf, SparkContext} import org.apache.spark.sql.functions.monotonically_increasing_id import org.apache.log4j.{Level, Logger} import org.apache.spark.sql.types.{StructType, StructField, StringType, IntegerType} import org.apache.hadoop.fs.{FileSystem, Path} Logger.getLogger("org").setLevel(Level.WARN) // val log = Logger.getLogger(this.getClass) @transient lazy val log:Logger = Logger.getLogger(this.getClass) class DataProcess_get_data_byfor (ss: SparkSession) extends java.io.Serializable{ import ss.implicits._ import ss.sql import org.apache.spark.sql.types.DataTypes ... def union_dataframe(df_1:RDD[String], df_2:RDD[String]):RDD[String] ={ val count1 = df_1.map(item=>item.split(sep_line)).take(1)(0).length val count2 = df_2.map(item=>item.split(sep_line)).take(1)(0).length val name2 = df_2.name.split("http://www.likecs.com/").takeRight(1)(0) val arr2 = df_2.map(item=>item.split(sep_line):+name2).map(p => Row(p: _*)) println(s"运行到这儿了") var name1 = "" var arr1 = ss.sparkContext.makeRDD(List().map(p => Row(p: _*))) // var arr1 = Array[org.apache.spark.sql.Row] if (count1 == count2){ name1 = df_1.name.split("http://www.likecs.com/").takeRight(1)(0) arr1 = df_1.map(item=>item.split(sep_line):+name1).map(p => Row(p: _*)) // arr1.foreach(f=>print(s"arr1嘞$f" + f.length + "\n")) println(s"运行到这儿了没？$count1~$count2 $name1/$name2") arr1 } else{ println(s"运行到这儿了不相等哈？$count1~$count2 $name1/$name2") arr1 = df_1.map(item=>item.split(sep_line)).map(p => Row(p: _*)) } var rawRDD = arr1.union(arr2) // arr3.foreach(f=>print(s"$f" + f.length + "\n")) // var rawRDD = sc.parallelize(arr3) var sourceRdd = rawRDD.map(_.mkString(sep_line)) // var count31 = arr1.take(1)(0).length // var count32 = arr2.take(1)(0).length // var count3 = sourceRdd.map(item=>item.split(sep_line)).take(1)(0).length // var nums = sourceRdd.count // print(s"arr1: $count31、arr2: $count32、arr3: $count3, 数据量为:$nums") sourceRdd } } object LoadingData_from_hdfs_text_with_path_byfor extends mylog{// with Logging ... def main(args: Array[String]=Array("tb1", "hdfs:/", "3","\n", "\001", "cols","data1", "test", "")): Unit = { ... val hdfs_address = args(1) ... val pattern = args(6) val pattern_no = args(7) val select_cols = select_col.split("#").toSeq log.warn(s"Loading cols are : \n $select_cols") val files = FileSystem.get(spark.sparkContext.hadoopConfiguration).listStatus(new Path(s"$hdfs_address")) val files_name = files.toList.map(f=> f.getPath.getName) val file_filter = files_name.filter(_.contains(pattern)).filterNot(_.contains(pattern_no)) val df_1 = file_filter.map(item=> sc.textFile(s"$path1$item")) df_1.foreach(f=>{ println(f + "数据量是" + f.count) }) val df2 = df_1.reduce(_ union _) println("合并后的数据量是" + df2.count) val Cs = new DataProcess_get_data_byfor(spark) ... // 将for循环读取的结果合并起来 val result = df_1.reduce((a, b)=>union_dataframe(a, b)) val result2 = result.map(item=>item.split(sep_line)).map(p => Row(p: _*)) val df_data = spark.createDataFrame(result2, new_schema1) val df_desc = select_cols.toDF.join(tb_desc, $"value"===$"col_name", "left") println("\n") //df_desc.show(false) val df_gb_result = df_data.select(select_cols.map(df_data.col(_)): _*) df_gb_result.show(5, false) println("生成的dataframe,依path列groupby的结果如下") df_gb_result.groupBy("path").count().show(false) ... // spark.stop() } } val path1 = "hdfs:202001/" val tb_name = "tb_name" val sep_text = "\n" val sep_line = "\001" val cols = "city#province#etl_date#path" val pattern = "result_copy_1" val pattern_no = "1.csv" // val file_filter = List("file1_10.csv", "file_12.csv", "file_11.csv") // 执行脚本 LoadingData_from_hdfs_text_with_path_byfor.main(Array(tb_name, path1, "4", sep_line, sep_text, cols, pattern, pattern_no, ""))

执行脚本的完整示例 import org.apache.spark.rdd.RDD import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.sql.SparkSession import org.apache.spark.sql.functions.monotonically_increasing_id import org.apache.log4j.{Level, Logger} import org.apache.spark.sql.types.{StructType, StructField, StringType, IntegerType} Logger.getLogger("org").setLevel(Level.WARN) val log = Logger.getLogger(this.getClass) class DataProcess_base (ss: SparkSession) extends java.io.Serializable{ import ss.implicits._ import ss.sql import org.apache.spark.sql.types.DataTypes def get_table_desc(tb_name:String="tb"):DataFrame ={ val gb_sql = s"desc ${tb_name}" val gb_desc = sql(gb_sql) val names = gb_desc.filter(!$"col_name".contains("#")).withColumn("id", monotonically_increasing_id()) names } def get_hdfs_data(hdfs_address:String="hdfs:"):RDD[String]={ val gb_data = ss.sparkContext.textFile(hdfs_address) gb_data.cache() val counts1 = gb_data.count println(f"the rows of origin hdfs data is $counts1%-1d") gb_data } } object LoadingData_from_hdfs_base extends mylog{// with Logging Logger.getLogger("org").setLevel(Level.WARN) val conf = new SparkConf() conf.setMaster("yarn") conf.setAppName("LoadingData_From_hdfs") conf.set("spark.home", System.getenv("SPARK_HOME")) val spark = SparkSession.builder().config(conf).enableHiveSupport().getOrCreate() import spark.implicits._ import spark.sql var UIAddress = spark.conf.getOption("spark.driver.appUIAddress").repr var yarnserver = spark.conf.getOption("spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_URI_BASES").repr println(f"Config as follows: \nUIAddress: $UIAddress, \nyarnserver: $yarnserver") def main(args: Array[String]=Array("tb1", "3", "\001", "cols", "")): Unit = { if (args.length < 2) { println("Usage: LoadingData_from_hdfs <tb_name, parts. sep_line, cols, paths>") System.err.println("Usage: LoadingData_from_hdfs <tb_name, parts, sep_line, cols, paths>") System.exit(1) } log.warn("开始啦调度") val tb_name = args(0) val parts = args(1) val sep_line = args(2) val select_col = args(3) val save_paths = args(4) val select_cols = select_col.split("#").toSeq log.warn(s"Loading cols are : \n $select_cols") val gb_sql = s"DESCRIBE FORMATTED ${tb_name}" val gb_desc = sql(gb_sql) val hdfs_address = gb_desc.filter($"col_name".contains("Location")).take(1)(0).getString(1) println(s"tbname路径是$hdfs_address") val hdfs_address_cha = s"$hdfs_address/*/" val Cs = new DataProcess_base(spark) val tb_desc = Cs.get_table_desc(tb_name) val raw_data = Cs.get_hdfs_data(hdfs_address) val len1 = raw_data.map(item => item.split(sep_line)).first.length val names = tb_desc.filter(!$"col_name".contains("#")).dropDuplicates(Seq("col_name")).sort("id").select("col_name").take(len1).map(_(0)).toSeq.map(_.toString) val schema1 = StructType(names.map(fieldName => StructField(fieldName, StringType))) val rawRDD = raw_data.map(_.split(sep_line).map(_.toString)).map(p => Row(p: _*)).filter(_.length == len1) val df_data = spark.createDataFrame(rawRDD, schema1)//.filter("custommsgtype = '1'") val df_desc = select_cols.toDF.join(tb_desc, $"value"===$"col_name", "left") val df_gb_result = df_data.select(select_cols.map(df_data.col(_)): _*)//.limit(100) df_gb_result.show(5, false) println("生成的dataframe,依path列groupby的结果如下") // val part = parts.toInt // df_gb_result.repartition(part).write.mode("overwrite").option("header","true").option("sep","#").csv(save_paths) // log.warn(f"the rows of origin data compare to mysql results is $ncounts1%-1d VS $ncounts3%-4d") // spark.stop() } } val cols = "area_name#city_name#province_name" val tb_name = "tb1" val sep_line = "\u0001" // 执行脚本 LoadingData_from_hdfs_base.main(Array(tb_name, "4", sep_line, cols, ""))