sparkR 跑通的函数

spark1.4.0的sparkR的思路：用spark从大数据集中抽取小数据（sparkR的DataFrame），然后到R里分析（DataFrame）。

这两个DataFrame是不同的，前者是分布式的，集群上的DF，R里的那些包都不能用；后者是单机版的DF，包里的函数都能用。

sparkR的开发计划，个人觉得是将目前包里的函数，迁移到sparkR的DataFrame里，这样就打开一片天地。

> a<- sql(hiveContext, "SELECT count(*) FROM anjuke_scores where restaurant>=10");

> a<- sql(hiveContext, "SELECT * FROM anjuke_scores limit 5")
> a
DataFrame[city:string, housingname:string, ori_traffic_score:int, ori_traffic_score_normal:double, metro_station:double, metro_station_normal:double,...

> first(a)  #显示Formal Data Frame的第一行

> head(a) ; #列出a的前6行

> columns(a) # 列出全部的列

[1] "city" "housingname" "ori_traffic_score" "ori_traffic_score_normal"

[5] "metro_station" "metro_station_normal" "bus_station" "bus_station_normal" ...

> showDF(a)

> b<-filter(a, a$ori_comfort>8); # 行筛选， ori_comfort_normal:double

> print(a);    #打印列名及类型
DataFrame[city:string, housingname:string, ori_traffic_score:int, ......

> printSchema(a); # 打印列名的树形框架概要 root |-- city: string (nullable = true) |-- housingname: string (nullable = true) |-- ori_traffic_score: integer (nullable = true) |-- ori_traffic_score_normal: double (nullable = true) |-- metro_station: double (nullable = true)
> take(a,10) ; # 提取Formal class DataFrame的前面num行，成为R中普通的 data frame ， take(x, num)

city housingname ori_traffic_score ori_traffic_score_normal metro_station metro_station_normal

1 \t\x9a \xddrw\xb8 NA 0 NA 0

2 \t\x9a \xe4\xf04\u03a2\021~ NA 0 NA 0

3 \t\x9a \xf6\xe3w\xb8 NA 0 NA 0

4 \t\x9a \x8e=\xb0w\xb8 NA 0 NA 0

5 \t\x9a \t\x9a\xe4\xf04\xce\xe4\xf0~ NA 0 NA 0

6 \t\x9a q4\xfdE NA 0 NA 0

7 \t\x9a \xe4\xf04\xce NA 0 NA 0

8 \t\x9a )\xfdVT NA 0 NA 0

9 \t\x9a q\177V NA 0 NA 0

10 \t\x9a \xe4\xf04\xceW\xb8 NA 0 NA 0

> b<-take(a,10)
> dim(b)
[1] 10 41

> aa <- withColumn(a, "ori_comfort_aa", a$ori_comfort * 5)   #用现有的列生成新的列, 新增一列，ori_comfort_aa，结果还是Formal data frame结构
> printSchema(aa)
root
 |-- city: string (nullable = true)
.........
 |-- comfort_normal: double (nullable = true)
 |-- ori_comfort_aa: double (nullable = true)

> aa <- mutate(a, newCol1 = a$commerce_normal * 5, newCol2 = a$bank_normal * 2) ； #与withColumn类似

> printSchema(aa)

root

|-- city: string (nullable = true)

。。。。。。。。。。。。。。。。。。

|-- comfort_normal: double (nullable = true)

|-- newCol1: double (nullable = true)

|-- newCol2: double (nullable = true)

a1<-arrange(a,asc(a$level_tow)); # 按列排序， asc升序，desc降序

a1<-orderBy(a,asc(a$level_tow)); # 按列排序

count(a) ; # 统计 Formal Data Frame有多少行数据

> dtypes(a);  #以list的形式列出Formal Data Frame的全部列名及类型
[[1]]
[1] "city"   "string"

[[2]]
[1] "housingname" "string"

> a<-withColumnRenamed(a,"comfort_normal","AA");  # 更改列名
> printSchema(a)
root
 |-- city: string (nullable = true)
 |-- housingname: string (nullable = true)
..........
 |-- AA: double (nullable = true)

创建sparkR的数据框的函数

createDataFrame

> df<-createDataFrame(sqlContext,a.df);  # a.df是R中的数据框， df是sparkR的数据框，注意：使用sparkR的数据库，需要sqlContext

> str(a.df)

'data.frame': 5 obs. of 41 variables:

> str(df)

Formal class 'DataFrame' [package "SparkR"] with 2 slots

..@ env:<environment: 0x4fce350>

..@ sdf:Class 'jobj' <environment: 0x4fc70b0>

> destDF <- select(SFO_DF, "dest", "cancelled"); #选择列

> showDF(destDF); #显示sparkR的DF

+----+---------+

|dest|cancelled|

+----+---------+

| SFO| 0|

................

> registerTempTable(SFO_DF, "flightsTable"); #要对sparkDF使用SQL语句，首先需要将DF注册成一个table

> wa <- sql(sqlContext, "SELECT dest, cancelled FROM flightsTable"); #在sqlContext下使用SQL语句

> showDF(wa); #查询的结果还是sparkDF

+----+---------+

|dest|cancelled|

+----+---------+

| SFO| 0|

................

> local_df <- collect(wa); #将sparkDF转换成R中的DF

> str(local_df)

'data.frame': 2818 obs. of 2 variables:

$ dest : chr "SFO" "SFO" "SFO" "SFO" ...

$ cancelled: int 0 0 0 0 0 0 0 0 0 0 ...

> wa<-flights_df[1:1000,]; #wa是R中的DF

> flightsDF<-createDataFrame(sqlContext,wa) ; #flightsDF是sparkR的DF

> library(magrittr); #管道函数的包对sparkRDF适用

> groupBy(flightsDF, flightsDF$date) %>%

+ summarize(avg(flightsDF$dep_delay), avg(flightsDF$arr_delay)) -> dailyDelayDF; #注意，语法和dplyr中的有所不同，结果还是sparkRDF

> str(dailyDelayDF)

Formal class 'DataFrame' [package "SparkR"] with 2 slots

..@ env:<environment: 0x4cd3118>

..@ sdf:Class 'jobj' <environment: 0x4cd6968>

> showDF(dailyDelayDF)

+----------+--------------------+--------------------+

| date| AVG(dep_delay)| AVG(arr_delay)|

+----------+--------------------+--------------------+

|2011-01-01| 5.2| 5.8|

|2011-01-02| 1.8333333333333333| -2.0|

................

在39机器上跑的

collect将sparkDF转化成DF

Collects all the elements of a Spark DataFrame and coerces them into an R data.frame.

collect(x, stringsAsFactors = FALSE)，x：A SparkSQL DataFrame

> dist_df<- sql(hiveContext, "SELECT * FROM anjuke_scores where restaurant<=1");

> local_df <- dist_df %>%

groupBy(dist_df$city) %>%

summarize(count = n(dist_df$housingname)) %>%

collect

> local_df

city count

1 \t\x9a 5

2 8\xde 7

3 \xf0\xde 2

..........

..........

take也可将sparkDF转化成DF

Take the first NUM rows of a DataFrame and return a the results as a data.frame

take(x, num)

> local_df <- dist_df %>%

groupBy(dist_df$city) %>%

summarize(count = n(dist_df$housingname))

> a<-take(local_df,100)

[Stage 16:=========================================> (154 + 1) / 199] > View(a)

> a

city count

1 \t\x9a 5

2 8\xde 7

3 \xf0\xde 2

..........

..........

不通的函数：

> describe(a)
Error in x[present, drop = FALSE] :
  object of type 'S4' is not subsettable

> jfkDF <- filter(flightsDF, flightsDF$dest == "DFW")
Error in filter(flightsDF, flightsDF$dest == "DFW") :
  no method for coercing this S4 class to a vector