Question

使用SparklyR和Spark 2.0.2调用逻辑回归后，我在Spark上遇到以下错误。

ml_logistic_regression(Data, ml_formula)

我读入Spark的数据集相对较大（2.2GB）。以下是错误消息：

Error: org.apache.spark.SparkException: Job aborted due to stage failure: Task 
13 in stage 64.0 failed 1 times, most recent failure: 
Lost task 13.0 in stage 64.0 (TID 1132, localhost):    
java.util.concurrent.ExecutionException: 
java.lang.Exception: 
failed to compile: org.codehaus.janino.JaninoRuntimeException: 
Code of method "(Lorg/apache/spark/sql/catalyst/InternalRow;)Z" 
of class "org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificPredicate" 
grows beyond 64 KB

其他人也遇到过类似的问题：https://github.com/rstudio/sparklyr/issues/298但我无法找到解决方案。有什么想法吗？

Answer 1

当您对数据进行子集并尝试运行模型时会发生什么？您可能需要更改配置设置以处理数据大小：

library(dplyr)
library(sparklyr)
#configure the spark session and connect
config <- spark_config()
config$`sparklyr.shell.driver-memory` <- "XXG" #change depending on the size of the data
config$`sparklyr.shell.executor-memory` <- "XXG"

sc <-  spark_connect(master='yarn-client', spark_home='/XXXX/XXXX/XXXX',config = config)

spark_config()中还有其他设置，您可以更改以处理效果。这只是一对夫妇的一个例子。

Spark和SparklyR错误“超过64 KB”

1 个答案: