Consider this simple example that uses sparklyr:
library(sparklyr)
library(janeaustenr) # to get some text data
library(stringr)
library(dplyr)
mytext <- austen_books() %>%
mutate(label = as.integer(str_detect(text, 'great'))) #create a fake label variable
mytext_spark <- copy_to(sc, mytext, name = 'mytext_spark', overwrite = TRUE)
# Source: table<mytext_spark> [?? x 3]
# Database: spark_connection
text book label
<chr> <chr> <int>
1 SENSE AND SENSIBILITY Sense & Sensibility 0
2 "" Sense & Sensibility 0
3 by Jane Austen Sense & Sensibility 0
4 "" Sense & Sensibility 0
5 (1811) Sense & Sensibility 0
6 "" Sense & Sensibility 0
7 "" Sense & Sensibility 0
8 "" Sense & Sensibility 0
9 "" Sense & Sensibility 0
10 CHAPTER 1 Sense & Sensibility 0
11 "" Sense & Sensibility 0
12 "" Sense & Sensibility 0
13 The family of Dashwood had long been settled in Sussex. Their estate Sense & Sensibility 0
14 was large, and their residence was at Norland Park, in the centre of Sense & Sensibility 0
15 their property, where, for many generations, they had lived in so Sense & Sensibility 0
16 respectable a manner as to engage the general good opinion of their Sense & Sensibility 0
The dataframe is reasonably tiny in size (about 70k rows and 14k unique words).
Now, training a naive bayes model only takes a few seconds on my cluster.
First, I define the pipeline
pipeline <- ml_pipeline(sc) %>%
ft_regex_tokenizer(input.col='text',
output.col = 'mytoken',
pattern = "\\s+",
gaps =TRUE) %>%
ft_count_vectorizer(input_col = 'mytoken', output_col = 'finaltoken') %>%
ml_naive_bayes( label_col = "label",
features_col = "finaltoken",
prediction_col = "pcol",
probability_col = "prcol",
raw_prediction_col = "rpcol",
model_type = "multinomial",
smoothing = 0,
thresholds = c(1, 1))
then training the naive bayes model
> library(microbenchmark)
> microbenchmark(model <- ml_fit(pipeline, mytext_spark),times = 3)
Unit: seconds
expr min lq mean median uq max neval
model <- ml_fit(pipeline, mytext_spark) 6.718354 6.996424 7.647227 7.274494 8.111663 8.948832 3
Now the problem is that trying to run any tree-based model (random forest, boosted trees, etc) on the same (actually tiny!!) dataset will not work.
pipeline2 <- ml_pipeline(sc) %>%
ft_regex_tokenizer(input.col='text',
output.col = 'mytoken',
pattern = "\\s+",
gaps =TRUE) %>%
ft_count_vectorizer(input_col = 'mytoken', output_col = 'finaltoken') %>%
ml_gbt_classifier( label_col = "label",
features_col = "finaltoken",
prediction_col = "pcol",
probability_col = "prcol",
raw_prediction_col = "rpcol",
max_memory_in_mb = 10240,
cache_node_ids = TRUE)
model2 <- ml_fit(pipeline2, mytext_spark)
# wont work :(
Error: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 69.0 failed 4 times, most recent failure: Lost task 0.3 in stage 69.0 (TID 1580, 1.1.1.1.1, executor 5): java.lang.IllegalArgumentException: Size exceeds Integer.MAX_VALUE
I think this is due to the sparseness of the matrix representation of the tokens, but is there anything that can be done here? Is this a sparklyr problem? A spark problem? Is my code non-efficient?
Thanks!
You are getting this error because you are actually hitting the famous 2G limit that we have in Spark https://issues.apache.org/jira/browse/SPARK-6235
The solution is to repartition your data before feeding it to the algorithm.
This is actually two gotchas in this post :
- Working with local data.
- Tree based models in Spark are memory hungry.
So, let’s review your code which seems harmless;
library(janeaustenr) # to get some text data
library(stringr)
mytext <- austen_books() %>%
mutate(label = as.integer(str_detect(text, 'great'))) # create a fake label variable
mytext_spark <- copy_to(sc, mytext, name = 'mytext_spark', overwrite = TRUE)
So what does the last line do ?
copy_to (not designed for big data sets), actually just copies the local R data frame to a 1 partition Spark DataFrame
So you’ll just need to repartition your data to make sure that once the pipeline prepares your data before feeding into gbt, the partition size is smaller than 2GB.
So you can just do the following to repartition your data :
# 20 is an arbitrary number I chose to test and it seems to work well in this case,
# you might want to reconsider that if you have a bigger dataset.
mytext_spark <-
copy_to(sc, mytext, name = 'mytext_spark', overwrite = TRUE) %>%
sdf_repartition(partitions = 20)
PS1: max_memory_in_mb is the amount of memory you are giving for gbt to computes it's statistics. It's not related directly to the amount of data as input.
PS2: If you didn't set up enough memory to your executors, you might run into a java.lang.OutOfMemoryError : GC overhead limit exceeded
EDIT: What's the meaning of repartitioning data ?
We can always refer to the definition of what a partition is before talking about repartitioning. I'll try to be short.
A partition is a logical chunk of a large distributed data set.
Spark manages data using partitions that helps parallelize distributed data processing with minimal network traffic for sending data between executors. By default, Spark tries to read data into an RDD from the nodes that are close to it. Since Spark usually accesses distributed partitioned data, to optimize transformation operations it creates partitions to hold the data chunks.
Increasing partitions count will make each partition to have less data (or not at all!)
source: excerpt from @JacekLaskowski Mastering Apache Spark book.
But data partitions isn't always right, like in this case. So repartition is needed. (sdf_repartition for sparklyr)
sdf_repartition will scatter and shuffle your data across your nodes. i.e sdf_repartition(20) will create of 20 partitions of your data instead of the 1 you originally have in this case.
I hope this helps.
The whole code :
library(sparklyr)
library(dplyr)
config <- spark_config()
config$`sparklyr.shell.driver-memory` <- "4G"
config$`sparklyr.shell.executor-memory` <- "4G"
Sys.setenv(SPARK_HOME = "/Users/eliasah/server/spark-2.3.1-SNAPSHOT-bin-2.7.3")
sc <- spark_connect(master = "local", config = config)
library(janeaustenr) # to get some text data
library(stringr)
mytext <- austen_books() %>%
mutate(label = as.integer(str_detect(text, 'great'))) #create a fake label variable
mytext_spark <- copy_to(sc, mytext, name = 'mytext_spark', overwrite = TRUE) %>% sdf_repartition(partitions = 20)
pipeline <- ml_pipeline(sc) %>%
ft_regex_tokenizer(input.col='text',
output.col = 'mytoken',
pattern = "\\s+",
gaps =TRUE) %>%
ft_count_vectorizer(input_col = 'mytoken', output_col = 'finaltoken') %>%
ml_naive_bayes( label_col = "label",
features_col = "finaltoken",
prediction_col = "pcol",
probability_col = "prcol",
raw_prediction_col = "rpcol",
model_type = "multinomial",
smoothing = 0,
thresholds = c(1, 1))
library(microbenchmark)
microbenchmark(model <- ml_fit(pipeline, mytext_spark),times = 3)
pipeline2 <- ml_pipeline(sc) %>%
ft_regex_tokenizer(input.col='text',
output.col = 'mytoken',
pattern = "\\s+",
gaps =TRUE) %>%
ft_count_vectorizer(input_col = 'mytoken', output_col = 'finaltoken') %>%
ml_gbt_classifier( label_col = "label",
features_col = "finaltoken",
prediction_col = "pcol",
probability_col = "prcol",
raw_prediction_col = "rpcol",
max_memory_in_mb = 10240, # this is amount of data that can be use for
cache_node_ids = TRUE)
model2 <- ml_fit(pipeline2, mytext_spark)
pipeline3 <- ml_pipeline(sc) %>%
ft_regex_tokenizer(input.col='text',
output.col = 'mytoken',
pattern = "\\s+",
gaps =TRUE) %>%
ft_count_vectorizer(input_col = 'mytoken', output_col = 'finaltoken')
model2
# PipelineModel (Transformer) with 3 stages
# <pipeline_1ce45bb8b7a7>
# Stages
# |--1 RegexTokenizer (Transformer)
# | <regex_tokenizer_1ce4342b543b>
# | (Parameters -- Column Names)
# | input_col: text
# | output_col: mytoken
# |--2 CountVectorizerModel (Transformer)
# | <count_vectorizer_1ce4e0e6489>
# | (Parameters -- Column Names)
# | input_col: mytoken
# | output_col: finaltoken
# | (Transformer Info)
# | vocabulary: <list>
# |--3 GBTClassificationModel (Transformer)
# | <gbt_classifier_1ce41ab30213>
# | (Parameters -- Column Names)
# | features_col: finaltoken
# | label_col: label
# | prediction_col: pcol
# | probability_col: prcol
# | raw_prediction_col: rpcol
# | (Transformer Info)
# | feature_importances: num [1:39158] 6.73e-04 7.20e-04 1.01e-15 1.97e-03 0.00 ...
# | num_classes: int 2
# | num_features: int 39158
# | total_num_nodes: int 540
# | tree_weights: num [1:20] 1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 ...
# | trees: <list>
Can you please provide the full error traceback?
My guess is that you're running out of memory. Random forest and gbt trees are ensemble models, so they require more memory and computational power than naive bayes.
Try repartitioning the data (spark.sparkContext.defaultParallelism value is a good place to start) so that each of your workers gets a smaller and more evenly distributed chunk.
If that doesn't work, try reducing your max_memory_in_mb parameter to 256.
来源：https://stackoverflow.com/questions/50700233/how-can-i-train-a-random-forest-with-a-sparse-matrix-in-spark