Spark修炼之道(进阶篇)——Spark入门到精通:第十节 Spark SQL案例实战(一)

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Spark修炼之道(进阶篇)——Spark入门到精通:第十节 Spark SQL案例实战(一)

周志湖 2016-02-05 23:56:00 浏览3366
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作者:周志湖

放假了,终于能抽出时间更新博客了…….

1. 获取数据

本文通过将github上的Spark项目git日志作为数据,对SparkSQL的内容进行详细介绍
数据获取命令如下:

[root@master spark]# git log  --pretty=format:'{"commit":"%H","author":"%an","author_email":"%ae","date":"%ad","message":"%f"}' > sparktest.json

格式化日志内容输出如下:

[root@master spark]# head -1 sparktest.json
{"commit":"30b706b7b36482921ec04145a0121ca147984fa8","author":"Josh Rosen","author_email":"joshrosen@databricks.com","date":"Fri Nov 6 18:17:34 2015 -0800","message":"SPARK-11389-CORE-Add-support-for-off-heap-memory-to-MemoryManager"}

然后使用命令将sparktest.json文件上传到HDFS上


[root@master spark]#hadoop dfs -put sparktest.json /data/

2. 创建DataFrame

使用数据创建DataFrame

scala> val df = sqlContext.read.json("/data/sparktest.json")
16/02/05 09:59:56 INFO json.JSONRelation: Listing hdfs://ns1/data/sparktest.json on driver

查看其模式:

scala> df.printSchema()
root
 |-- author: string (nullable = true)
 |-- author_email: string (nullable = true)
 |-- commit: string (nullable = true)
 |-- date: string (nullable = true)
 |-- message: string (nullable = true)

3. DataFrame方法实战

(1)显式前两行数据

scala> df.show(2)

+----------------+--------------------+--------------------+--------------------+--------------------+
|          author|        author_email|              commit|                date|             message|
+----------------+--------------------+--------------------+--------------------+--------------------+
|      Josh Rosen|joshrosen@databri...|30b706b7b36482921...|Fri Nov 6 18:17:3...|SPARK-11389-CORE-...|
|Michael Armbrust|michael@databrick...|105732dcc6b651b97...|Fri Nov 6 17:22:3...|HOTFIX-Fix-python...|
+----------------+--------------------+--------------------+--------------------+--------------------+

(2)计算总提交次数


scala> df.count
res4: Long = 13507
下图给出的是我github上的commits次数,可以看到,其结束是一致的

这里写图片描述

(3)按提交次数进行降序排序

scala>df.groupBy("author").count.sort($"count".desc).show

+--------------------+-----+
|              author|count|
+--------------------+-----+
|       Matei Zaharia| 1590|
|         Reynold Xin| 1071|
|     Patrick Wendell|  857|
|       Tathagata Das|  416|
|          Josh Rosen|  348|
|  Mosharaf Chowdhury|  290|
|           Andrew Or|  287|
|       Xiangrui Meng|  285|
|          Davies Liu|  281|
|          Ankur Dave|  265|
|          Cheng Lian|  251|
|    Michael Armbrust|  243|
|             zsxwing|  200|
|           Sean Owen|  197|
|     Prashant Sharma|  186|
|  Joseph E. Gonzalez|  185|
|            Yin Huai|  177|
|Shivaram Venkatar...|  173|
|      Aaron Davidson|  164|
|      Marcelo Vanzin|  142|
+--------------------+-----+
only showing top 20 rows

4. DataFrame注册成临时表使用实战

使用下列语句将DataFrame注册成表

scala> val commitLog=df.registerTempTable("commitlog")

(1)显示前2行数据

scala> sqlContext.sql("SELECT * FROM commitlog").show(2)
+----------------+--------------------+--------------------+--------------------+--------------------+
|          author|        author_email|              commit|                date|             message|
+----------------+--------------------+--------------------+--------------------+--------------------+
|      Josh Rosen|joshrosen@databri...|30b706b7b36482921...|Fri Nov 6 18:17:3...|SPARK-11389-CORE-...|
|Michael Armbrust|michael@databrick...|105732dcc6b651b97...|Fri Nov 6 17:22:3...|HOTFIX-Fix-python...|
+----------------+--------------------+--------------------+--------------------+--------------------+

(2)计算总提交次数

scala> sqlContext.sql("SELECT count(*) as TotalCommitNumber  FROM commitlog").show
+-----------------+
|TotalCommitNumber|
+-----------------+
|            13507|
+-----------------+

(3)按提交次数进行降序排序

scala> sqlContext.sql("SELECT author,count(*) as CountNumber  FROM commitlog GROUP BY author ORDER BY CountNumber DESC").show

+--------------------+-----------+
|              author|CountNumber|
+--------------------+-----------+
|       Matei Zaharia|       1590|
|         Reynold Xin|       1071|
|     Patrick Wendell|        857|
|       Tathagata Das|        416|
|          Josh Rosen|        348|
|  Mosharaf Chowdhury|        290|
|           Andrew Or|        287|
|       Xiangrui Meng|        285|
|          Davies Liu|        281|
|          Ankur Dave|        265|
|          Cheng Lian|        251|
|    Michael Armbrust|        243|
|             zsxwing|        200|
|           Sean Owen|        197|
|     Prashant Sharma|        186|
|  Joseph E. Gonzalez|        185|
|            Yin Huai|        177|
|Shivaram Venkatar...|        173|
|      Aaron Davidson|        164|
|      Marcelo Vanzin|        142|
+--------------------+-----------+

更多复杂的玩法,大家可以自己去尝试,这里给出的只是DataFrame方法与临时表SQL语句的用法差异,以便于有整体的认知。

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