implementing `data.table` when performing joins on large datasets in R 4.3
I'm working with important performance optimization when trying to perform joins using the `data.table` package on two large datasets in R 4.3... I have two tables, `dt1` and `dt2`, with approximately 1 million rows each. I expect the join operation to be fast due to `data.table`'s efficiency, but it seems to take a very long time and occasionally throws memory allocation errors when trying to run the following code: ```R library(data.table) dt1 <- data.table(id = 1:1000000, value = rnorm(1000000)) dt2 <- data.table(id = sample(1:1000000, 1000000, replace = TRUE), score = rnorm(1000000)) # Performing the join result <- merge(dt1, dt2, by = "id", all = TRUE) ``` I've tried optimizing it by setting keys on both data.tables: ```R dt1[, key := .(id)] dt2[, key := .(id)] setkey(dt1, id) setkey(dt2, id) ``` Then I reran the join: ```R result <- dt1[dt2, on = "id", nomatch = 0] ``` While this did improve performance slightly, I still find that it takes an inordinate amount of time (over 10 minutes). I also received a `behavior: want to allocate vector of size 1000000 MB` warning. I'm running this on a machine with 16GB of RAM and using R version 4.3. Any suggestions on how I can optimize the join further or manage memory more effectively? Are there specific `data.table` configurations or practices to follow for large datasets that I might be missing? The project is a microservice built with R. Has anyone dealt with something similar?