spark解析csv文件

我发现自己经常使用大型CSV文件，并且意识到我现有的工具集不能让我快速浏览它们，我以为我会花一些时间在Spark上看看是否有帮助。

我正在使用芝加哥市发布的犯罪数据集 ：它的大小为1GB，其中包含400万种犯罪的详细信息：

$ ls -alh ~/Downloads/Crimes_-_2001_to_present.csv
-rw-r--r--@ 1 markneedham  staff   1.0G 16 Nov 12:14 /Users/markneedham/Downloads/Crimes_-_2001_to_present.csv$ wc -l ~/Downloads/Crimes_-_2001_to_present.csv4193441 /Users/markneedham/Downloads/Crimes_-_2001_to_present.csv

通过查看第一行和标题，我们可以大致了解文件的内容：

$ head -n 2 ~/Downloads/Crimes_-_2001_to_present.csv
ID,Case Number,Date,Block,IUCR,Primary Type,Description,Location Description,Arrest,Domestic,Beat,District,Ward,Community Area,FBI Code,X Coordinate,Y Coordinate,Year,Updated On,Latitude,Longitude,Location
9464711,HX114160,01/14/2014 05:00:00 AM,028XX E 80TH ST,0560,ASSAULT,SIMPLE,APARTMENT,false,true,0422,004,7,46,08A,1196652,1852516,2014,01/20/2014 12:40:05 AM,41.75017626412204,-87.55494559131228,"(41.75017626412204, -87.55494559131228)"

我想对“主要类型”列进行计数，以了解我们每种犯罪有多少种。 仅使用Unix命令行工具，这就是我们的方法：

$ time tail +2 ~/Downloads/Crimes_-_2001_to_present.csv | cut -d, -f6  | sort | uniq -c | sort -rn
859197 THEFT
757530 BATTERY
489528 NARCOTICS
488209 CRIMINAL DAMAGE
257310 BURGLARY
253964 OTHER OFFENSE
247386 ASSAULT
197404 MOTOR VEHICLE THEFT
157706 ROBBERY
137538 DECEPTIVE PRACTICE
124974 CRIMINAL TRESPASS
47245 PROSTITUTION
40361 WEAPONS VIOLATION
31585 PUBLIC PEACE VIOLATION
26524 OFFENSE INVOLVING CHILDREN
14788 CRIM SEXUAL ASSAULT
14283 SEX OFFENSE
10632 GAMBLING
8847 LIQUOR LAW VIOLATION
6443 ARSON
5178 INTERFERE WITH PUBLIC OFFICER
4846 HOMICIDE
3585 KIDNAPPING
3147 INTERFERENCE WITH PUBLIC OFFICER
2471 INTIMIDATION
1985 STALKING355 OFFENSES INVOLVING CHILDREN219 OBSCENITY86 PUBLIC INDECENCY80 OTHER NARCOTIC VIOLATION12 RITUALISM12 NON-CRIMINAL6 OTHER OFFENSE2 NON-CRIMINAL (SUBJECT SPECIFIED)2 NON - CRIMINALreal 2m37.495s
user    3m0.337s
sys 0m1.471s

这还不错，但似乎是Spark进行的计算类型，因此我了解了如何进行此操作。 首先，我使用以下构建文件创建了一个SBT项目：

name := "playground"version := "1.0"scalaVersion := "2.10.4"libraryDependencies += "org.apache.spark" %% "spark-core" % "1.1.0"libraryDependencies += "net.sf.opencsv" % "opencsv" % "2.3"ideaExcludeFolders += ".idea"ideaExcludeFolders += ".idea_modules"

我下载了Spark，并在解压缩后启动了Spark shell：

$ pwd
/Users/markneedham/projects/spark-play/spark-1.1.0/spark-1.1.0-bin-hadoop1$ ./bin/spark-shell
...
Welcome to____              __/ __/__  ___ _____/ /___\ \/ _ \/ _ `/ __/  '_//___/ .__/\_,_/_/ /_/\_\   version 1.1.0/_/Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_51)
...
Spark context available as sc.scala>

我首先导入一些我需要的类：

scala> import au.com.bytecode.opencsv.CSVParser
import au.com.bytecode.opencsv.CSVParserscala> import org.apache.spark.rdd.RDD
import org.apache.spark.rdd.RDD

现在，在快速入门示例之后 ，我们将根据犯罪CSV文件创建弹性分布式数据集（RDD）：

scala> val crimeFile = "/Users/markneedham/Downloads/Crimes_-_2001_to_present.csv"
crimeFile: String = /Users/markneedham/Downloads/Crimes_-_2001_to_present.csvscala> val crimeData = sc.textFile(crimeFile).cache()
14/11/16 22:31:16 INFO MemoryStore: ensureFreeSpace(32768) called with curMem=0, maxMem=278302556
14/11/16 22:31:16 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 32.0 KB, free 265.4 MB)
crimeData: org.apache.spark.rdd.RDD[String] = /Users/markneedham/Downloads/Crimes_-_2001_to_present.csv MappedRDD[1] at textFile at <console>:17

下一步是使用CSV解析器处理文件的每一行。 一种简单的方法是为每行创建一个新的CSVParser ：

scala> crimeData.map(line => {val parser = new CSVParser(',')parser.parseLine(line).mkString(",")}).take(5).foreach(println)
14/11/16 22:35:49 INFO SparkContext: Starting job: take at <console>:23
...
4/11/16 22:35:49 INFO SparkContext: Job finished: take at <console>:23, took 0.013904 s
ID,Case Number,Date,Block,IUCR,Primary Type,Description,Location Description,Arrest,Domestic,Beat,District,Ward,Community Area,FBI Code,X Coordinate,Y Coordinate,Year,Updated On,Latitude,Longitude,Location
9464711,HX114160,01/14/2014 05:00:00 AM,028XX E 80TH ST,0560,ASSAULT,SIMPLE,APARTMENT,false,true,0422,004,7,46,08A,1196652,1852516,2014,01/20/2014 12:40:05 AM,41.75017626412204,-87.55494559131228,(41.75017626412204, -87.55494559131228)
9460704,HX113741,01/14/2014 04:55:00 AM,091XX S JEFFERY AVE,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0413,004,8,48,03,1191060,1844959,2014,01/18/2014 12:39:56 AM,41.729576153145636,-87.57568059471686,(41.729576153145636, -87.57568059471686)
9460339,HX113740,01/14/2014 04:44:00 AM,040XX W MAYPOLE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,1114,011,28,26,14,1149075,1901099,2014,01/16/2014 12:40:00 AM,41.884543798701515,-87.72803579358926,(41.884543798701515, -87.72803579358926)
9461467,HX114463,01/14/2014 04:43:00 AM,059XX S CICERO AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0813,008,13,64,06,1145661,1865031,2014,01/16/2014 12:40:00 AM,41.785633535413176,-87.74148516669783,(41.785633535413176, -87.74148516669783)

可以，但是每次都创建一个新的CSVParser有点浪费，所以让我们为每个将Spark分割成文件的分区创建一个：

scala> crimeData.mapPartitions(lines => {val parser = new CSVParser(',')lines.map(line => {parser.parseLine(line).mkString(",")})}).take(5).foreach(println)
14/11/16 22:38:44 INFO SparkContext: Starting job: take at <console>:25
...
14/11/16 22:38:44 INFO SparkContext: Job finished: take at <console>:25, took 0.015216 s
ID,Case Number,Date,Block,IUCR,Primary Type,Description,Location Description,Arrest,Domestic,Beat,District,Ward,Community Area,FBI Code,X Coordinate,Y Coordinate,Year,Updated On,Latitude,Longitude,Location
9464711,HX114160,01/14/2014 05:00:00 AM,028XX E 80TH ST,0560,ASSAULT,SIMPLE,APARTMENT,false,true,0422,004,7,46,08A,1196652,1852516,2014,01/20/2014 12:40:05 AM,41.75017626412204,-87.55494559131228,(41.75017626412204, -87.55494559131228)
9460704,HX113741,01/14/2014 04:55:00 AM,091XX S JEFFERY AVE,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0413,004,8,48,03,1191060,1844959,2014,01/18/2014 12:39:56 AM,41.729576153145636,-87.57568059471686,(41.729576153145636, -87.57568059471686)
9460339,HX113740,01/14/2014 04:44:00 AM,040XX W MAYPOLE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,1114,011,28,26,14,1149075,1901099,2014,01/16/2014 12:40:00 AM,41.884543798701515,-87.72803579358926,(41.884543798701515, -87.72803579358926)
9461467,HX114463,01/14/2014 04:43:00 AM,059XX S CICERO AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0813,008,13,64,06,1145661,1865031,2014,01/16/2014 12:40:00 AM,41.785633535413176,-87.74148516669783,(41.785633535413176, -87.74148516669783)

您会注意到我们仍然在打印不理想的标题–让我们摆脱它！

我希望有一个“放置”功能，该功能可以使我做到这一点，但实际上没有。 相反，我们可以利用我们的知识，即第一个分区将包含第一行并以这种方式将其删除 ：

scala> def dropHeader(data: RDD[String]): RDD[String] = {data.mapPartitionsWithIndex((idx, lines) => {if (idx == 0) {lines.drop(1)}lines})}
dropHeader: (data: org.apache.spark.rdd.RDD[String])org.apache.spark.rdd.RDD[String]

现在，让我们再次获取前5行并打印出来：

scala> val withoutHeader: RDD[String] = dropHeader(crimeData)
withoutHeader: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[7] at mapPartitionsWithIndex at <console>:14scala> withoutHeader.mapPartitions(lines => {val parser = new CSVParser(',')lines.map(line => {parser.parseLine(line).mkString(",")})}).take(5).foreach(println)
14/11/16 22:43:27 INFO SparkContext: Starting job: take at <console>:29
...
14/11/16 22:43:27 INFO SparkContext: Job finished: take at <console>:29, took 0.018557 s
9464711,HX114160,01/14/2014 05:00:00 AM,028XX E 80TH ST,0560,ASSAULT,SIMPLE,APARTMENT,false,true,0422,004,7,46,08A,1196652,1852516,2014,01/20/2014 12:40:05 AM,41.75017626412204,-87.55494559131228,(41.75017626412204, -87.55494559131228)
9460704,HX113741,01/14/2014 04:55:00 AM,091XX S JEFFERY AVE,031A,ROBBERY,ARMED: HANDGUN,SIDEWALK,false,false,0413,004,8,48,03,1191060,1844959,2014,01/18/2014 12:39:56 AM,41.729576153145636,-87.57568059471686,(41.729576153145636, -87.57568059471686)
9460339,HX113740,01/14/2014 04:44:00 AM,040XX W MAYPOLE AVE,1310,CRIMINAL DAMAGE,TO PROPERTY,RESIDENCE,false,true,1114,011,28,26,14,1149075,1901099,2014,01/16/2014 12:40:00 AM,41.884543798701515,-87.72803579358926,(41.884543798701515, -87.72803579358926)
9461467,HX114463,01/14/2014 04:43:00 AM,059XX S CICERO AVE,0820,THEFT,$500 AND UNDER,PARKING LOT/GARAGE(NON.RESID.),false,false,0813,008,13,64,06,1145661,1865031,2014,01/16/2014 12:40:00 AM,41.785633535413176,-87.74148516669783,(41.785633535413176, -87.74148516669783)
9460355,HX113738,01/14/2014 04:21:00 AM,070XX S PEORIA ST,0820,THEFT,$500 AND UNDER,STREET,true,false,0733,007,17,68,06,1171480,1858195,2014,01/16/2014 12:40:00 AM,41.766348042591375,-87.64702037047671,(41.766348042591375, -87.64702037047671)

我们终于可以从“主要类型”列中提取值，并计算每个值出现在数据集中的次数了：

scala> withoutHeader.mapPartitions(lines => {val parser=new CSVParser(',')lines.map(line => {val columns = parser.parseLine(line)Array(columns(5)).mkString(",")})}).countByValue().toList.sortBy(-_._2).foreach(println)
14/11/16 22:45:20 INFO SparkContext: Starting job: countByValue at <console>:30
14/11/16 22:45:20 INFO DAGScheduler: Got job 7 (countByValue at <console>:30) with 32 output partitions (allowLocal=false)
...
14/11/16 22:45:30 INFO SparkContext: Job finished: countByValue at <console>:30, took 9.796565 s
(THEFT,859197)
(BATTERY,757530)
(NARCOTICS,489528)
(CRIMINAL DAMAGE,488209)
(BURGLARY,257310)
(OTHER OFFENSE,253964)
(ASSAULT,247386)
(MOTOR VEHICLE THEFT,197404)
(ROBBERY,157706)
(DECEPTIVE PRACTICE,137538)
(CRIMINAL TRESPASS,124974)
(PROSTITUTION,47245)
(WEAPONS VIOLATION,40361)
(PUBLIC PEACE VIOLATION,31585)
(OFFENSE INVOLVING CHILDREN,26524)
(CRIM SEXUAL ASSAULT,14788)
(SEX OFFENSE,14283)
(GAMBLING,10632)
(LIQUOR LAW VIOLATION,8847)
(ARSON,6443)
(INTERFERE WITH PUBLIC OFFICER,5178)
(HOMICIDE,4846)
(KIDNAPPING,3585)
(INTERFERENCE WITH PUBLIC OFFICER,3147)
(INTIMIDATION,2471)
(STALKING,1985)
(OFFENSES INVOLVING CHILDREN,355)
(OBSCENITY,219)
(PUBLIC INDECENCY,86)
(OTHER NARCOTIC VIOLATION,80)
(NON-CRIMINAL,12)
(RITUALISM,12)
(OTHER OFFENSE ,6)
(NON-CRIMINAL (SUBJECT SPECIFIED),2)
(NON - CRIMINAL,2)

我们得到了与Unix命令相同的结果，只是计算不到10秒钟就很酷！

翻译自: https://www.javacodegeeks.com/2014/11/spark-parse-csv-file-and-group-by-column-value.html

spark解析csv文件