Deepansh created SPARK-23650:
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Summary: Slow SparkR udf (dapply)
Key: SPARK-23650
URL: https://issues.apache.org/jira/browse/SPARK-23650
Project: Spark
Issue Type: Improvement
Components: Spark Shell, SparkR, Structured Streaming
Affects Versions: 2.2.0
Reporter: Deepansh
For eg, I am getting streams from Kafka and I want to implement a model made in
R for those streams. For this, I am using dapply.
My code is:
iris_model <- readRDS("./iris_model.rds")
randomBr <- SparkR:::broadcast(sc, iris_model)
kafka <- read.stream("kafka",subscribe = "source", kafka.bootstrap.servers =
"localhost:9092", topic = "source")
lines<- select(kafka, cast(kafka$value, "string"))
schema<-schema(lines)
df1<-dapply(lines,function(x){
i_model<-SparkR:::value(randomMatBr)
for (row in 1:nrow(x)) {
y<-fromJSON(as.character(x[row,"value"]))
y$predict=predict(iris_model,y)
y<-toJSON(y)
x[row,"value"] = y
}
x
},schema)
Every time when Kafka streams are fetched the dapply method creates new runner
thread and ships the variables again, which causes a huge lag(~2s for shipping
model) every time. I even tried without broadcast variables but it takes same
time to ship variables. Can some other techniques be applied to improve its
performance?
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