Spark学习——DataFrame清洗HDFS日志并存入Hive中-Toy模板网

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1.开启Hadoop集群和Hive元数据、Hive远程连接

2.配置

3.读取日志文件并清洗

4.单独处理第四列的数据——方法一：

5.单独处理第四列的数据——方法二：

6.单独处理第四列的数据——方法三：

7.数据清洗结果展示

8.存入Hive中

9.DataGrip中的代码

HDFS日志文件内容：

2023-02-20 15:19:46 INFO org.apache.hadoop.hdfs.server.namenode.TransferFsImage: Downloaded file edits_tmp_0000000000000030396-0000000000000033312_0000000000025236168 size 0 bytes.
2023-02-20 15:19:46 INFO org.apache.hadoop.hdfs.server.namenode.Checkpointer: Checkpointer about to load edits from 1 stream(s).
2023-02-20 15:19:46 INFO org.apache.hadoop.hdfs.server.namenode.FSImage: Reading /opt/soft/hadoop313/data/dfs/namesecondary/current/edits_0000000000000030396-0000000000000033312 expecting start txid #30396
2023-02-20 15:19:46 INFO org.apache.hadoop.hdfs.server.namenode.FSImage: Start loading edits file /opt/soft/hadoop313/data/dfs/namesecondary/current/edits_0000000000000030396-0000000000000033312

我们要将上面的日志，使用DataFrame API清洗成表格并存入Hive中，清洗后的表格如下：

Spark学习——DataFrame清洗HDFS日志并存入Hive中

1.开启Hadoop集群和Hive元数据、Hive远程连接

Spark学习——DataFrame清洗HDFS日志并存入Hive中

2.配置

 val spark: SparkSession = SparkSession.builder().appName("demo01")
      .master("local[*]")
      .config("hive.metastore.uris", "thrift://lxm147:9083")
      .enableHiveSupport()
      .getOrCreate()
    val sc: SparkContext = spark.sparkContext

    import spark.implicits._
    import org.apache.spark.sql.functions._

3.读取日志文件并清洗

// TODO 读取文件清洗
    val df1: DataFrame = sc.textFile("in/hadoophistory.log")
      .map(_.split(" "))
      .filter(_.length >= 8)
      .map(x => {
        val tuple: (String, String, String, String, String) = (x(0), x(1), x(2), x(3), x(4))
        tuple
      }).toDF()
    df1.show(4,false)
    /*
    +----------+--------+----+-------------------------------------------------------+------------+
    |_1        |_2      |_3  |_4                                                     |_5          |
    +----------+--------+----+-------------------------------------------------------+------------+
    |2023-02-20|15:19:46|INFO|org.apache.hadoop.hdfs.server.namenode.TransferFsImage:|Downloaded  |
    |2023-02-20|15:19:46|INFO|org.apache.hadoop.hdfs.server.namenode.Checkpointer:   |Checkpointer|
    |2023-02-20|15:19:46|INFO|org.apache.hadoop.hdfs.server.namenode.FSImage:        |Reading     |
    |2023-02-20|15:19:46|INFO|org.apache.hadoop.hdfs.server.namenode.FSImage:        |Start       |
    +----------+--------+----+-------------------------------------------------------+------------+
    */

4.单独处理第四列的数据——方法一：

  // TODO 单独处理第四列的数据
    val df2: DataFrame =
      df1.withColumn("test", split(col("_4"), "\\."))
        .select(
          $"_1".as("t1"),
          $"_2".as("t2"),
          $"_3".as("t3"),
          col("test").getItem(0).as("a0"),
          col("test").getItem(1).as("a1"),
          col("test").getItem(2).as("a2"),
          col("test").getItem(3).as("a3"),
          col("test").getItem(4).as("a4"),
          col("test").getItem(5).as("a5"),
          col("test").getItem(6).as("a6"),
          $"_5".as("t5")
        )

5.单独处理第四列的数据——方法二：

val df2: DataFrame = 
      df1.rdd.map(
      line => {
        val strings: Array[String] = line.toString().split(",")
        val value: Array[String] = strings(3).split("\\.")
        (strings(0).replaceAll("\\[", ""), strings(1), strings(2),
          value(0), value(1), value(2), value(3), value(4), value(5), value(6),
          strings(4).replaceAll("]", "")
        )
      }
    ).toDF("t1", "t2", "t3", "a1", "a2", "a3", "a4", "a5", "a6", "a7", "t5")

6.单独处理第四列的数据——方法三：

方法三比较麻烦，但是可以对数据类型做单独处理，可以参考我的另一篇博文《Spark高级操作之json复杂和嵌套数据结构的操作》

另一篇博文中读取的日志数据更换了

7.数据清洗结果展示

df2.show(4, truncate = false)

+----------+--------+----+---+------+------+----+------+--------+----------------+------------+
|t1        |t2      |t3  |a1 |a2    |a3    |a4  |a5    |a6      |a7              |t5          |
+----------+--------+----+---+------+------+----+------+--------+----------------+------------+
|2023-02-20|15:19:46|INFO|org|apache|hadoop|hdfs|server|namenode|TransferFsImage:|Downloaded  |
|2023-02-20|15:19:46|INFO|org|apache|hadoop|hdfs|server|namenode|Checkpointer:   |Checkpointer|
|2023-02-20|15:19:46|INFO|org|apache|hadoop|hdfs|server|namenode|FSImage:        |Reading     |
|2023-02-20|15:19:46|INFO|org|apache|hadoop|hdfs|server|namenode|FSImage:        |Start       |
+----------+--------+----+---+------+------+----+------+--------+----------------+------------+

8.存入Hive中

println("正在存储......")
df2.write.mode(SaveMode.Overwrite).saveAsTable("shopping.dataframe")

spark.close()
sc.stop()
println("存储完毕......")

9.DataGrip中的代码

SET hive.exec.compress.output=true;
SET mapred.output.compression.codec=org.apache.hadoop.io.compress.SnappyCodec;
SET mapred.output.compression.type=BLOCK;

use shopping;
show tables;
select * from dataframe;

Spark学习——DataFrame清洗HDFS日志并存入Hive中