1. 獲取數(shù)據(jù)

本文通過將github上的Spark項(xiàng)目Git日志作為數(shù)據(jù)，對(duì)SparkSQL的內(nèi)容進(jìn)行詳細(xì)介紹
數(shù)據(jù)獲取命令如下：

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

格式化日志內(nèi)容輸出如下：

[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"}

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然后使用命令將sparktest.json文件上傳到HDFS上

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

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2. 創(chuàng)建DataFrame

使用數(shù)據(jù)創(chuàng)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

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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方法實(shí)戰(zhàn)

（1）顯式前兩行數(shù)據(jù)

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...| +----------------+--------------------+--------------------+--------------------+--------------------+

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（2）計(jì)算總提交次數(shù)

scala> df.count res4: Long = 13507 下圖給出的是我github上的commits次數(shù)，可以看到，其結(jié)束是一致的

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（3）按提交次數(shù)進(jìn)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

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4. DataFrame注冊(cè)成臨時(shí)表使用實(shí)戰(zhàn)

使用下列語句將DataFrame注冊(cè)成表

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

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（1）顯示前2行數(shù)據(jù)

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...| +----------------+--------------------+--------------------+--------------------+--------------------+

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（2）計(jì)算總提交次數(shù)

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

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（3）按提交次數(shù)進(jìn)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| +--------------------+-----------+

更多復(fù)雜的玩法，大家可以自己去嘗試，這里給出的只是DataFrame方法與臨時(shí)表SQL語句的用法差異，以便于有整體的認(rèn)知。

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