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本文环境阐明
centos 服务器
jupyter 的 scala 核 spylon-kernel
spark-2.4.0
scala-2.11.12
hadoop-2.6.0本文次要内容
- spark 读取 hive 表的数据，次要包含间接 sql 读取 hive 表；通过 hdfs 文件读取 hive 表，以及 hive 分区表的读取。
- 通过 jupyter 上的 cell 来初始化 sparksession。
- 文末还有通过 spark 提取 hdfs 文件的残缺示例
jupyter 配置文件
- 咱们能够在 jupyter 的 cell 框外面，对 spark 的 session 做出对应的初始化，具体能够见上面的示例。
%%init_spark
launcher.master = "local[*]"
launcher.conf.spark.app.name = "BDP-xw"
launcher.conf.spark.driver.cores = 2
launcher.conf.spark.num_executors = 3
launcher.conf.spark.executor.cores = 4
launcher.conf.spark.driver.memory = '4g'
launcher.conf.spark.executor.memory = '4g'// launcher.conf.spark.serializer = "org.apache.spark.serializer.KryoSerializer"
// launcher.conf.spark.kryoserializer.buffer.max = '4g'import org.apache.spark.sql.SparkSession
var NumExecutors = spark.conf.getOption("spark.num_executors").repr
var ExecutorMemory = spark.conf.getOption("spark.executor.memory").repr
var AppName = spark.conf.getOption("spark.app.name").repr
var max_buffer = spark.conf.getOption("spark.kryoserializer.buffer.max").reprprintln(f"Config as follows: \nNumExecutors: $NumExecutors, \nAppName: $AppName,\nmax_buffer:$max_buffer")
- 间接查看咱们初始化的 sparksession 对应的环境各变量
从 hive 中取数
间接 sparksql 走起
import org.apache.spark.sql.SparkSession
val sql_1 = """select * from tbs limit 4"""
var df = sql(sql_1)
df.show(5, false)
通过 hdfs 取数
- 具体示例能够参考文末的从 hdfs 取数残缺脚本示例
object LoadingData_from_hdfs_base extends mylog{// with Logging
...
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)
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)
...
// 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, ""))
)
判断门路是否为文件夹
- 形式 1
def pathIsExist(spark: SparkSession, path: String): Boolean = {val filePath = new org.apache.hadoop.fs.Path( path)
val fileSystem = filePath.getFileSystem(spark.sparkContext.hadoopConfiguration)
fileSystem.exists(filePath)
}
pathIsExist(spark, hdfs_address)
// 失去后果如下：// pathIsExist: (spark: org.apache.spark.sql.SparkSession, path: String)Boolean
// res4: Boolean = true- 形式 2
import java.io.File
val d = new File("/usr/local/xw")
d.isDirectory
// 失去后果如下：// d: java.io.File = /usr/local/xw
// res3: Boolean = true分区表读取源数据
-
对分区文件须要留神下，须要保障原始的 hdfs 上的 raw 文件外面是否有对应的分区字段值
- 如果分区字段在 hdfs 中的原始文件中，则能够间接通过通过 hdfs 取数
- 若原始文件中，不包含分区字段信息，则须要依照以下形式取数啦
- 具体示例能够参考文末的从 hdfs 取数残缺脚本示例
单个文件读取
object LoadingData_from_hdfs_onefile_with_path extends mylog{def main(args: Array[String]=Array("tb_name", "hdfs:/", "3","\n", "\001", "cols", "")): Unit = {
...
val hdfs_address = args(1)
val len1 = raw_data.map(item => item.split(sep_line)).first.length
val rawRDD = raw_data.flatMap(line => line.split(sep_text)).map(word => (word.split(sep_line):+hdfs_address)).map(p => Row(p: _*))
println(rawRDD.take(2))
val names = tb_desc.filter(!$"col_name".contains("#")).dropDuplicates(Seq("col_name")).sort("id").select("col_name").take(len1).map(_(0)).toSeq.map(_.toString)
import org.apache.spark.sql.types.StructType
val schema1 = StructType(names.map(fieldName => StructField(fieldName, StringType)))
val new_schema1 = schema1.add(StructField("path", StringType))
val df_data = spark.createDataFrame(rawRDD, new_schema1)
val df_desc = select_cols.toDF.join(tb_desc, $"value"===$"col_name", "left")
// df_desc.show(false)
val df_gb_result = df_data.select(select_cols.map(df_data.col(_)): _*)//.limit(100)
df_gb_result.show(5, false)
...
// spark.stop()}
}val file1 = "hdfs:file1.csv"
val tb_name = "tb_name"
val sep_text = "\n"
val sep_line = "\001"
val cols = "city#province#etl_date#path"
// 执行脚本
LoadingData_from_hdfs_onefile_with_path.main(Array(tb_name, file1, "4", sep_line, sep_text, cols, ""))
多个文件读取尝试 1
- 通过 wholeTextFiles 读取文件夹外面的文件时，保留文件名信息；
- 具体示例能够参考文末的从 hdfs 取数残缺脚本示例
object LoadingData_from_hdfs_wholetext_with_path extends mylog{// with Logging
...
def main(args: Array[String]=Array("tb1", "hdfs:/", "3","\n", "\001", "cols", "")): Unit = {
...
val tb_name = args(0)
val hdfs_address = args(1)
val parts = args(2)
val sep_line = args(3)
val sep_text = args(4)
val select_col = args(5)
val save_paths = args(6)
val select_cols = select_col.split("#").toSeq
val Cs = new DataProcess_get_data(spark)
val tb_desc = Cs.get_table_desc(tb_name)
val rddWhole = spark.sparkContext.wholeTextFiles(s"$hdfs_address", 10)
rddWhole.foreach(f=>{println(f._1+"数据量是 =>"+f._2.split("\n").length)
})
val files = rddWhole.collect
val len1 = files.flatMap(item => item._2.split(sep_text)).take(1).flatMap(items=>items.split(sep_line)).length
val names = tb_desc.filter(!$"col_name".contains("#")).dropDuplicates(Seq("col_name")).sort("id").select("col_name").take(len1).map(_(0)).toSeq.map(_.toString)
import org.apache.spark.sql.types.StructType
// 解析 wholeTextFiles 读取的后果并转化成 dataframe
val wordCount = files.map(f=>f._2.split(sep_text).map(g=>g.split(sep_line):+f._1.split("/").takeRight(1)(0))).flatMap(h=>h).map(p => Row(p: _*))
val schema1 = StructType(names.map(fieldName => StructField(fieldName, StringType)))
val new_schema1 = schema1.add(StructField("path", StringType))
val rawRDD = sc.parallelize(wordCount)
val df_data = spark.createDataFrame(rawRDD, new_schema1)
val df_desc = select_cols.toDF.join(tb_desc, $"value"===$"col_name", "left")
//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 的后果如下")
df_gb_result.groupBy("path").count().show(false)
...
// spark.stop()}
}val file1 = "hdfs:file1_1[01].csv"
val tb_name = "tb_name"
val sep_text = "\n"
val sep_line = "\001"
val cols = "city#province#etl_date#path"
// 执行脚本
LoadingData_from_hdfs_wholetext_with_path.main(Array(tb_name, file1, "4", sep_line, sep_text, cols, ""))
读取多文件且保留文件名为列名技术实现
-
以下实现性能
- 将
Array[(String, String)]类型的按 (String, String) 拆成多行； - 将 (String, String) 中的第 2 个元素，依照
\n宰割符分成多行，按\?分隔符分成多列； - 将 (String, String) 中的第 1 个元素，别离加到 2 中的每行前面。在 dataframe 中出现的就是新增一列啦
- 将
-
业务场景
- 如果要一次读取多个文件，且绝对合并后的数据集中，对数据来源于哪一个文件作出辨别。
<!– #endregion –>
- 如果要一次读取多个文件，且绝对合并后的数据集中，对数据来源于哪一个文件作出辨别。
// 测试用例，次要是把 wholetextfile 失去的后果转化为 DataFrame
val test1 = Array(("abasdfsdf", "a?b?c?d\nc?d?d?e"), ("sdfasdf", "b?d?a?e\nc?d?e?f"))
val test2 = test1.map(line=>line._2.split("\n").map(line1=>line1.split("\\?"):+line._1)).flatMap(line2=>line2).map(p => Row(p: _*))
val cols = "cn1#cn2#cn3#cn4#path"
val names = cols.split("#")
val schema1 = StructType(names.map(fieldName => StructField(fieldName, StringType)))
val rawRDD = sc.parallelize(test2)
val df_data = spark.createDataFrame(rawRDD, schema1)
df_data.show(4, false)
test1
多个文件读取 for 循环
- 通过 for 循环遍历读取文件夹外面的文件时，保留文件名信息；
- 具体示例能够参考文末的从 hdfs 取数残缺脚本示例
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Row}
import org.apache.spark.sql.SparkSession
import org.apache.spark.{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("/").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("/").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 循环读取的后果合并起来
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 的后果如下")
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, ""))
执行脚本的残缺示例
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 门路是 $hdfs_address")
val hdfs_address_cha = s"$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 的后果如下")
// 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, ""))正文完