Spark 中 Dataset.show 如何使用？有哪些值得注意的地方？

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前言

本文隶属于专栏《大数据技术体系》，该专栏为笔者原创，引用请注明来源，不足和错误之处请在评论区帮忙指出，谢谢！

本专栏目录结构和参考文献请见大数据技术体系

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WHAT

Dataset.show 在我们平常 Spark 开发测试中经常使用。

它可以用来展示 Dataset。

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重载方法

def show(numRows: Int): Unitdef show(): Unitdef show(truncate: Boolean): Unitdef show(numRows: Int, truncate: Boolean): Unitdef show(numRows: Int, truncate: Int): Unitdef show(numRows: Int, truncate: Int, vertical: Boolean): Unit

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使用注意点

上面的 6 个重载方法有哪些值得注意的地方呢？

1. vertical

show() 方法存在 2 种打印模式

默认是横式的，如下所示：

  year  month AVG('Adj Close) MAX('Adj Close)1980  12    0.503218        0.5951031981  01    0.523289        0.5703071982  02    0.436504        0.4752561983  03    0.410516        0.4421941984  04    0.450090        0.483521

另一种是竖式的，如下所示：

-RECORD 0-------------------year            | 1980month           | 12AVG('Adj Close) | 0.503218AVG('Adj Close) | 0.595103
-RECORD 1-------------------year            | 1981month           | 01AVG('Adj Close) | 0.523289AVG('Adj Close) | 0.570307
-RECORD 2-------------------year            | 1982month           | 02AVG('Adj Close) | 0.436504AVG('Adj Close) | 0.475256
-RECORD 3-------------------year            | 1983month           | 03AVG('Adj Close) | 0.410516AVG('Adj Close) | 0.442194
-RECORD 4-------------------year            | 1984month           | 04AVG('Adj Close) | 0.450090AVG('Adj Close) | 0.483521

2. numRows

show() 方法可以通过设置 numRows 来控制最终返回多少行数据，默认 20。

3. truncate

1. show() 方法可以通过设置 truncate 参数来控制单个数据列字符串最长显示的长度，并且所有列都会靠右对齐。
2. 字符串如果超过 truncate（默认是 20），将会截取前面的 truncate - 3 长度，后面再加上 ...

str.substring(0, truncate - 3) + "..."

3. 对于数据类型是 Array[Byte] 的数据列，会用"[", " ", "]" 的格式输出

binary.map("%02X".format(_)).mkString("[", " ", "]")

Dataset.show 具体的源码解析请参考我的这篇博客——Spark SQL 工作流程源码解析（四）optimization 阶段（基于 Spark 3.3.0）

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实践

源码下载

spark-examples 代码已开源，本项目致力于提供最具实践性的 Apache Spark 代码开发学习指南。

点击链接前往 github 下载源码：spark-examples

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数据

{"name": "Alice","age": 18,"sex": "Female","addr": ["address_1","address_2", " address_3"]}
{"name": "Thomas","age": 20, "sex": "Male","addr": ["address_1"]}
{"name": "Tom","age": 50, "sex": "Male","addr": ["address_1","address_2","address_3"]}
{"name": "Catalina","age": 30, "sex": "Female","addr": ["address_1","address_2"]}

代码

package com.shockang.study.spark.sql.showimport com.shockang.study.spark.SQL_DATA_DIR
import com.shockang.study.spark.util.Utils.formatPrint
import org.apache.spark.sql.SparkSession/**** @author Shockang*/
object ShowExample {val DATA_PATH: String = SQL_DATA_DIR + "user.json"def main(args: Array[String]): Unit = {val spark = SparkSession.builder.master("local[*]").appName("ShowExample").getOrCreate()spark.sparkContext.setLogLevel("ERROR")spark.read.json(DATA_PATH).createTempView("t_user")val df = spark.sql("SELECT * FROM t_user")formatPrint("""df.show""")df.showformatPrint("""df.show(2)""")df.show(2)formatPrint("""df.show(true)""")df.show(true)formatPrint("""df.show(false)""")df.show(false)formatPrint("""df.show(2, truncate = true)""")df.show(2, truncate = true)formatPrint("""df.show(2, truncate = false)""")df.show(2, truncate = false)formatPrint("""df.show(2, truncate = 0)""")df.show(2, truncate = 0)formatPrint("""df.show(2, truncate = 20)""")df.show(2, truncate = 20)formatPrint("""df.show(2, truncate = 0, vertical = true)""")df.show(2, truncate = 0, vertical = true)formatPrint("""df.show(2, truncate = 20, vertical = true)""")df.show(2, truncate = 20, vertical = true)formatPrint("""df.show(2, truncate = 0, vertical = false)""")df.show(2, truncate = 0, vertical = false)formatPrint("""df.show(2, truncate = 20, vertical = false)""")df.show(2, truncate = 20, vertical = false)spark.stop()}
}

打印

========== df.show ==========
+--------------------+---+--------+------+
|                addr|age|    name|   sex|
+--------------------+---+--------+------+
|[address_1, addre...| 18|   Alice|Female|
|         [address_1]| 20|  Thomas|  Male|
|[address_1, addre...| 50|     Tom|  Male|
|[address_1, addre...| 30|Catalina|Female|
+--------------------+---+--------+------+========== df.show(2) ==========
+--------------------+---+------+------+
|                addr|age|  name|   sex|
+--------------------+---+------+------+
|[address_1, addre...| 18| Alice|Female|
|         [address_1]| 20|Thomas|  Male|
+--------------------+---+------+------+
only showing top 2 rows========== df.show(true) ==========
+--------------------+---+--------+------+
|                addr|age|    name|   sex|
+--------------------+---+--------+------+
|[address_1, addre...| 18|   Alice|Female|
|         [address_1]| 20|  Thomas|  Male|
|[address_1, addre...| 50|     Tom|  Male|
|[address_1, addre...| 30|Catalina|Female|
+--------------------+---+--------+------+========== df.show(false) ==========
+----------------------------------+---+--------+------+
|addr                              |age|name    |sex   |
+----------------------------------+---+--------+------+
|[address_1, address_2,  address_3]|18 |Alice   |Female|
|[address_1]                       |20 |Thomas  |Male  |
|[address_1, address_2, address_3] |50 |Tom     |Male  |
|[address_1, address_2]            |30 |Catalina|Female|
+----------------------------------+---+--------+------+========== df.show(2, truncate = true) ==========
+--------------------+---+------+------+
|                addr|age|  name|   sex|
+--------------------+---+------+------+
|[address_1, addre...| 18| Alice|Female|
|         [address_1]| 20|Thomas|  Male|
+--------------------+---+------+------+
only showing top 2 rows========== df.show(2, truncate = false) ==========
+----------------------------------+---+------+------+
|addr                              |age|name  |sex   |
+----------------------------------+---+------+------+
|[address_1, address_2,  address_3]|18 |Alice |Female|
|[address_1]                       |20 |Thomas|Male  |
+----------------------------------+---+------+------+
only showing top 2 rows========== df.show(2, truncate = 0) ==========
+----------------------------------+---+------+------+
|addr                              |age|name  |sex   |
+----------------------------------+---+------+------+
|[address_1, address_2,  address_3]|18 |Alice |Female|
|[address_1]                       |20 |Thomas|Male  |
+----------------------------------+---+------+------+
only showing top 2 rows========== df.show(2, truncate = 20) ==========
+--------------------+---+------+------+
|                addr|age|  name|   sex|
+--------------------+---+------+------+
|[address_1, addre...| 18| Alice|Female|
|         [address_1]| 20|Thomas|  Male|
+--------------------+---+------+------+
only showing top 2 rows========== df.show(2, truncate = 0, vertical = true) ==========
-RECORD 0----------------------------------addr | [address_1, address_2,  address_3] age  | 18                                 name | Alice                              sex  | Female                             
-RECORD 1----------------------------------addr | [address_1]                        age  | 20                                 name | Thomas                             sex  | Male                               
only showing top 2 rows========== df.show(2, truncate = 20, vertical = true) ==========
-RECORD 0--------------------addr | [address_1, addre... age  | 18                   name | Alice                sex  | Female               
-RECORD 1--------------------addr | [address_1]          age  | 20                   name | Thomas               sex  | Male                 
only showing top 2 rows========== df.show(2, truncate = 0, vertical = false) ==========
+----------------------------------+---+------+------+
|addr                              |age|name  |sex   |
+----------------------------------+---+------+------+
|[address_1, address_2,  address_3]|18 |Alice |Female|
|[address_1]                       |20 |Thomas|Male  |
+----------------------------------+---+------+------+
only showing top 2 rows========== df.show(2, truncate = 20, vertical = false) ==========
+--------------------+---+------+------+
|                addr|age|  name|   sex|
+--------------------+---+------+------+
|[address_1, addre...| 18| Alice|Female|
|         [address_1]| 20|Thomas|  Male|
+--------------------+---+------+------+
only showing top 2 rows

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