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
[email protected] env:<environment: 0x4fce350>
[email protected] 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$arr_delay)) -> dailyDelayDF; #注意，语法和dplyr中的有所不同，结果还是sparkRDF
> str(dailyDelayDF)
Formal class ‘DataFrame‘ [package "SparkR"] with 2 slots
[email protected] env:<environment: 0x4cd3118>
[email protected] sdf:Class ‘jobj‘ <environment: 0x4cd6968>
> showDF(dailyDelayDF)
+----------+--------------------+--------------------+
| date| AVG(dep_delay)| AVG(arr_delay)|
+----------+--------------------+--------------------+
|2011-01-01| 5.2| 5.8|
|2011-01-02| 1.8333333333333333| -2.0|
................
在39机器上跑的
collect将sparkDF转化成DF
Collects all the elements of a Spark DataFrame and coerces them into an R data.frame.
collect(x, stringsAsFactors = FALSE)，x：A SparkSQL DataFrame
> dist_df<- sql(hiveContext, "SELECT * FROM anjuke_scores where restaurant<=1");
> local_df <- dist_df %>%
groupBy(dist_df$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