如何使用dplyr(1.0)、sparkyr(1.4)和spark(3.0)/hadoop(2.7)的最新版本从sparkDataframe中提取每组的前n行？

9rnv2umw  于 2021-05-18  发布在  Spark
关注(0)|答案(1)|浏览(428)

我的尝试 top_n() 以及 scale_head() 两个都失败并出现错误。
与…有关的问题 top_n() 报告于https://github.com/tidyverse/dplyr/issues/4467 哈德利在结束发言时说：
这将由#4687+tidyverse/dbplyr#394通过引入新的 slice_min() 以及 slice_max() 函数，它还允许我们解决与 top_n() .
尽管我已经更新了所有的软件包 top_n() 失败原因：

Error: org.apache.spark.sql.AnalysisException: Undefined function: 'top_n_rank'. This function is neither a registered temporary function nor a permanent function registered in the database 'default'.; line 3 pos 7

（请参阅下面的完整代码和日志）
好 啊， top_n() 现在在中被取代 dplyr 1.0，所以我试过了 slice_head() . 这也失败了，因为：

Error in UseMethod("slice_head") : 
  no applicable method for 'slice_head' applied to an object of class "c('tbl_spark', 'tbl_sql', 'tbl_lazy', 'tbl')"

我刚开始用SparkyR。。。有人能重现这些问题吗？或者我应该查找一些软件包的安装问题/不兼容版本吗？
如果问题得到确认，我如何从sparkDataframe中提取每组的前n行？
代码示例：

library(sparklyr)
library(dplyr)

# Just in case already connected

spark_disconnect_all()

# Connect to local Spark cluster

sc <- spark_connect(master = "local")

# Print the version of Spark

spark_version(sc = sc)

# Copy data frame to Spark

iris_tbl <- copy_to(sc, iris)

# List the data frames available in Spark

src_tbls(sc)

# Get some info from the data frame

dim(iris_tbl)
glimpse(iris_tbl)

# Return the first 10 rows for each Species

# Using top_n()

top_10 <- iris_tbl %>%
  group_by(Species) %>%
  top_n(10)
glimpse(top_10)

# Using slice_head()

slice_head_10 <- iris_tbl %>%
  group_by(Species) %>%
  slice_head(n = 10)
slice_head_10
glimpse(slice_head_10)

# Disconnect from Spark

spark_disconnect(sc = sc)

# session Info

sessionInfo()

完整日志（在rmarkdown中运行）：

Restarting R session...

> # Chunk 1: setup
> knitr::opts_chunk$set(echo = TRUE)
> 
> # Chunk 2
> library(sparklyr)
> library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

> 
> # Just in case already connected
> spark_disconnect_all()
[1] 0
> 
> # Connect to local Spark cluster
> sc <- spark_connect(master = "local")

* Using Spark: 3.0.0

> 
> # Print the version of Spark
> spark_version(sc = sc)
[1] ‘3.0.0’
> 
> # Copy data frame to Spark
> iris_tbl <- copy_to(sc, iris)
> 
> # List the data frames available in Spark
> src_tbls(sc)
[1] "iris"
> 
> # Get some info from the data frame
> dim(iris_tbl)
[1] NA  5
> glimpse(iris_tbl)
Rows: ??
Columns: 5
Database: spark_connection
$ Sepal_Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8…
$ Sepal_Width  <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4…
$ Petal_Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6…
$ Petal_Width  <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2…
$ Species      <chr> "setosa", "setosa", "setosa", "setosa", "setosa", "setosa"…
> 
> # Return the first 10 rows for each Species 
> # Using top_n()
> top_10 <- iris_tbl %>%
+   group_by(Species) %>%
+   top_n(10)
Selecting by Species
> glimpse(top_10)
Error: org.apache.spark.sql.AnalysisException: Undefined function: 'top_n_rank'. This function is neither a registered temporary function nor a permanent function registered in the database 'default'.; line 3 pos 7
    at org.apache.spark.sql.catalyst.analysis.Analyzer$LookupFunctions$$anonfun$apply$15.$anonfun$applyOrElse$102(Analyzer.scala:1852)
    at org.apache.spark.sql.catalyst.analysis.package$.withPosition(package.scala:53)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$LookupFunctions$$anonfun$apply$15.applyOrElse(Analyzer.scala:1852)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$LookupFunctions$$anonfun$apply$15.applyOrElse(Analyzer.scala:1843)
    at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$1(TreeNode.scala:309)
    at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:72)
    at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:309)
    at org.apache.spark.sql.catalyst.plans.QueryPlan.$anonfun$transformExpressionsDown$1(QueryPlan.scala:96)
    at org.apache.spark.sql.catalyst.plans.QueryPlan.$anonfun$mapExpressions$1(QueryPlan.scala:118)
    at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:72)
    at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpression$1(QueryPlan.scala:118)
    at org.apache.spark.sql.catalyst.plans.QueryPlan.recursiveTransform$1(QueryPlan.scala:129)
    at org.apache.spark.sql.catalyst.plans.QueryPlan.$anonfun$mapExpressions$4(QueryPlan.scala:139)
    at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:237)
    at org.apache.spark.sql.catalyst.plans.QueryPlan.mapExpressions(QueryPlan.scala:139)
    at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsDown(QueryPlan.scala:96)
    at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressions(QueryPlan.scala:87)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$$anonfun$resolveExpressions$1.applyOrElse(AnalysisHelper.scala:129)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$$anonfun$resolveExpressions$1.applyOrElse(AnalysisHelper.scala:128)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$2(AnalysisHelper.scala:108)
    at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:72)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$1(AnalysisHelper.scala:108)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:194)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown(AnalysisHelper.scala:106)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown$(AnalysisHelper.scala:104)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsDown(LogicalPlan.scala:29)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$4(AnalysisHelper.scala:113)
    at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$mapChildren$1(TreeNode.scala:399)
    at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:237)
    at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:397)
    at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:350)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$1(AnalysisHelper.scala:113)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:194)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown(AnalysisHelper.scala:106)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown$(AnalysisHelper.scala:104)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsDown(LogicalPlan.scala:29)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperators(AnalysisHelper.scala:73)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperators$(AnalysisHelper.scala:72)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:29)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveExpressions(AnalysisHelper.scala:128)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveExpressions$(AnalysisHelper.scala:127)
    at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveExpressions(LogicalPlan.scala:29)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$LookupFunctions$.apply(Analyzer.scala:1843)
    at org.apache.spark.sql.catalyst.analysis.Analyzer$LookupFunctions$.apply(Analyzer.scala:1840)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:149)
    at scala.collection.IndexedSeqOptimized.foldLeft(IndexedSeqOptimized.scala:60)
    at scala.collection.IndexedSeqOptimized.foldLeft$(IndexedSeqOptimized.scala:68)
    at scala.collection.mutable.WrappedArray.foldLeft(WrappedArray.scala:38)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:146)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:138)
    at scala.collection.immutable.List.foreach(List.scala:392)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:138)
    at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:176)
    at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:170)
    at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:130)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:116)
    at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:88)
    at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:116)
    at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:154)
    at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:201)
    at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:153)
    at org.apache.spark.sql.execution.QueryExecution.$anonfun$analyzed$1(QueryExecution.scala:68)
    at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
    at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:133)
    at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:763)
    at org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:133)
    at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:68)
    at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:66)
    at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:58)
    at org.apache.spark.sql.Dataset$.$anonfun$ofRows$2(Dataset.scala:99)
    at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:763)
    at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:97)
    at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:606)
    at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:763)
    at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:601)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at sparklyr.Invoke.invoke(invoke.scala:147)
    at sparklyr.StreamHandler.handleMethodCall(stream.scala:136)
    at sparklyr.StreamHandler.read(stream.scala:61)
    at sparklyr.BackendHandler.$anonfun$channelRead0$1(handler.scala:58)
    at scala.util.control.Brea
> 
> # Using slice_head()
> slice_head_10 <- iris_tbl %>%
+   group_by(Species) %>%
+   slice_head(n = 10)
Error in UseMethod("slice_head") : 
  no applicable method for 'slice_head' applied to an object of class "c('tbl_spark', 'tbl_sql', 'tbl_lazy', 'tbl')"
> slice_head_10
Error: object 'slice_head_10' not found
> glimpse(slice_head_10)
Error in glimpse(slice_head_10) : object 'slice_head_10' not found
> 
> # Disconnect from Spark
> spark_disconnect(sc = sc)
> 
> # session Info
> sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.1 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] dplyr_1.0.2.9000 sparklyr_1.4.0  

loaded via a namespace (and not attached):
 [1] pillar_1.4.6      compiler_4.0.3    dbplyr_1.4.4      r2d3_0.2.3       
 [5] base64enc_0.1-3   tools_4.0.3       digest_0.6.27     jsonlite_1.7.1   
 [9] lifecycle_0.2.0   tibble_3.0.4      pkgconfig_2.0.3   rlang_0.4.8      
[13] DBI_1.1.0         cli_2.1.0         rstudioapi_0.11   yaml_2.2.1       
[17] parallel_4.0.3    xfun_0.18         withr_2.3.0       httr_1.4.2       
[21] knitr_1.30        generics_0.0.2    htmlwidgets_1.5.2 vctrs_0.3.4      
[25] askpass_1.1       rappdirs_0.3.1    rprojroot_1.3-2   tidyselect_1.1.0 
[29] glue_1.4.2        forge_0.2.0       R6_2.4.1          fansi_0.4.1      
[33] purrr_0.3.4       tidyr_1.1.2       blob_1.2.1        magrittr_1.5     
[37] backports_1.1.10  ellipsis_0.3.1    htmltools_0.5.0   assertthat_0.2.1 
[41] config_0.3        utf8_1.1.4        openssl_1.4.3     crayon_1.3.4     
>
apache-sparktop-nrsparklyrdplyr

1条答案

按热度按时间
44u64gxh

44u64gxh1#

使用 filter 以及 row_number . 请注意，您需要指定 arrange 第一个 row_number 在…工作 sparklyr .

iris_tbl %>%
  group_by(Species) %>%
  arrange(Sepal_Length) %>%
  filter(row_number() <= 3)

# > # Source:     spark<?> [?? x 5]

# > # Groups:     Species

# > # Ordered by: Sepal_Length

# >   Sepal_Length Sepal_Width Petal_Length Petal_Width Species

# >          <dbl>       <dbl>        <dbl>       <dbl> <chr>

# > 1          4.9         2.4          3.3         1   versicolor

# > 2          5           2            3.5         1   versicolor

# > 3          5           2.3          3.3         1   versicolor

# > 4          4.9         2.5          4.5         1.7 virginica

# > 5          5.6         2.8          4.9         2   virginica

# > 6          5.7         2.5          5           2   virginica

# > 7          4.3         3            1.1         0.1 setosa

# > 8          4.4         2.9          1.4         0.2 setosa

# > 9          4.4         3            1.3         0.2 setosa
赞(0）分享  回复(0）举报  2021-05-19
我来回答

相关问题

    查看更多

    热门标签

    更多
    JavaquerypythonNode开发语言requestUtil数据库Table后端算法LoggerMessageElementParser

    最新问答

    更多
    • 蜀ICP备2022004421号-2 NULL123 https://www.null123.com © All rights reserved
    • 本站内容来源互联网,如果侵犯您的权益请联系我们删除, 联系方式：448109455@qq.com