与 sparklyr 一起使用时，sample_n 真的是随机样本吗？

Question

我在 spark 数据框中有 5 亿行。我有兴趣使用来自dplyr 的sample_n，因为它可以让我明确指定我想要的样本量。如果我要使用sparklyr::sdf_sample()，我首先必须计算sdf_nrow()，然后创建指定的数据部分sample_size / nrow，然后将此部分传递给sdf_sample。这没什么大不了的，但sdf_nrow() 可能需要一段时间才能完成。

因此，最好直接使用dplyr::sample_n()。但是，经过一些测试，sample_n() 看起来并不是随机的。其实结果和head()是一样的！如果函数不是随机采样行，而是只返回第一行 n 行，这将是一个主要问题。

其他人可以证实这一点吗？ sdf_sample() 是我的最佳选择吗？

# install.packages("gapminder")

library(gapminder)
library(sparklyr)
library(purrr)

sc <- spark_connect(master = "yarn-client")

spark_data <- sdf_import(gapminder, sc, "gapminder")


> # Appears to be random
> spark_data %>% sdf_sample(fraction = 0.20, replace = FALSE) %>% summarise(sample_mean = mean(lifeExp))
# Source:   lazy query [?? x 1]
# Database: spark_connection
  sample_mean
        <dbl>
1    58.83397


> spark_data %>% sdf_sample(fraction = 0.20, replace = FALSE) %>% summarise(sample_mean = mean(lifeExp))
# Source:   lazy query [?? x 1]
# Database: spark_connection
  sample_mean
        <dbl>
1    60.31693


> spark_data %>% sdf_sample(fraction = 0.20, replace = FALSE) %>% summarise(sample_mean = mean(lifeExp))
# Source:   lazy query [?? x 1]
# Database: spark_connection
  sample_mean
        <dbl>
1    59.38692
> 
> 
> # Appears to be random
> spark_data %>% sample_frac(0.20) %>% summarise(sample_mean = mean(lifeExp))
# Source:   lazy query [?? x 1]
# Database: spark_connection
  sample_mean
        <dbl>
1    60.48903


> spark_data %>% sample_frac(0.20) %>% summarise(sample_mean = mean(lifeExp))
# Source:   lazy query [?? x 1]
# Database: spark_connection
  sample_mean
        <dbl>
1    59.44187


> spark_data %>% sample_frac(0.20) %>% summarise(sample_mean = mean(lifeExp))
# Source:   lazy query [?? x 1]
# Database: spark_connection
  sample_mean
        <dbl>
1    59.27986
> 
> 
> # Does not appear to be random
> spark_data %>% sample_n(300) %>% summarise(sample_mean = mean(lifeExp))
# Source:   lazy query [?? x 1]
# Database: spark_connection
  sample_mean
        <dbl>
1    57.78434


> spark_data %>% sample_n(300) %>% summarise(sample_mean = mean(lifeExp))
# Source:   lazy query [?? x 1]
# Database: spark_connection
  sample_mean
        <dbl>
1    57.78434


> spark_data %>% sample_n(300) %>% summarise(sample_mean = mean(lifeExp))
# Source:   lazy query [?? x 1]
# Database: spark_connection
  sample_mean
        <dbl>
1    57.78434
> 
> 
> 
> # === Test sample_n() ===
> sample_mean <- list()
> 
> for(i in 1:20){
+   
+   sample_mean[i] <- spark_data %>% sample_n(300) %>% summarise(sample_mean = mean(lifeExp)) %>% collect() %>% pull()
+   
+ }
> 
> 
> sample_mean %>% flatten_dbl() %>% mean()
[1] 57.78434
> sample_mean %>% flatten_dbl() %>% sd()
[1] 0
> 
> 
> # === Test head() ===
> spark_data %>% 
+   head(300) %>% 
+   pull(lifeExp) %>% 
+   mean()
[1] 57.78434

Answer 1

事实并非如此。如果您检查执行计划（optimizedPlan 定义的函数 here），您会发现它只是一个限制：

spark_data %>% sample_n(300) %>% optimizedPlan()

<jobj[168]>
  org.apache.spark.sql.catalyst.plans.logical.GlobalLimit
  GlobalLimit 300
+- LocalLimit 300
   +- InMemoryRelation [country#151, continent#152, year#153, lifeExp#154, pop#155, gdpPercap#156], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas), `gapminder`
         +- Scan ExistingRDD[country#151,continent#152,year#153,lifeExp#154,pop#155,gdpPercap#156] 

show_query进一步证实了这一点：

spark_data %>% sample_n(300) %>% show_query()

<SQL>
SELECT *
FROM (SELECT *
FROM `gapminder` TABLESAMPLE (300 rows) ) `hntcybtgns`

以及可视化的执行计划：

最后，如果你检查Spark source，你会发现这个案例是用简单的LIMIT实现的：

case ctx: SampleByRowsContext =>
  Limit(expression(ctx.expression), query)

我相信这种语义是从 Hive where equivalent query takes n first rows from each input split 继承而来的。

在实践中，获取精确尺寸的样本非常昂贵，除非绝对必要，否则应避免使用（与大 LIMITS 相同）。