Read in large text file in chunks - r

Question

I'm working with limited RAM (AWS free tier EC2 server - 1GB).
I have a relatively large txt file "vectors.txt" (800mb) I'm trying to read into R. Having tried various methods I have failed to read in this vector to memory.
So, I was researching ways of reading it in in chunks. I know that the dim of the resulting data frame should be 300K * 300. If I was able to read in the file e.g. 10K lines at a time and then save each chunk as an RDS file I would be able to loop over the results and get what I need, albeit just a little slower with less convenience than having the whole thing in memory.
To reproduce:
# Get data
url <- 'https://github.com/eyaler/word2vec-slim/blob/master/GoogleNews-vectors-negative300-SLIM.bin.gz?raw=true'
file <- "GoogleNews-vectors-negative300-SLIM.bin.gz"
download.file(url, file) # takes a few minutes
R.utils::gunzip(file)
# word2vec r library
library(rword2vec)
w2v_gnews <- "GoogleNews-vectors-negative300-SLIM.bin"
bin_to_txt(w2v_gnews,"vector.txt")
So far so good. Here's where I struggle:
word_vectors = as.data.frame(read.table("vector.txt",skip = 1, nrows = 10))
Returns "cannot allocate a vector of size [size]" error message.
Tried alternatives:
word_vectors <- ff::read.table.ffdf(file = "vector.txt", header = TRUE)
Same, not enough memory
word_vectors <- readr::read_tsv_chunked("vector.txt",
callback = function(x, i) saveRDS(x, i),
chunk_size = 10000)
Resulted in:
Parsed with column specification:
cols(
`299567 300` = col_character()
)
|=========================================================================================| 100% 817 MB
Error in read_tokens_chunked_(data, callback, chunk_size, tokenizer, col_specs, :
Evaluation error: bad 'file' argument.
Is there any other way to turn vectors.txt into a data frame? Maybe by breaking it into pieces and reading in each piece, saving as a data frame and then to rds? Or any other alternatives?
EDIT:
From Jonathan's answer below, tried:
library(rword2vec)
library(RSQLite)
# Download pre trained Google News word2vec model (Slimmed down version)
# https://github.com/eyaler/word2vec-slim
url <- 'https://github.com/eyaler/word2vec-slim/blob/master/GoogleNews-vectors-negative300-SLIM.bin.gz?raw=true'
file <- "GoogleNews-vectors-negative300-SLIM.bin.gz"
download.file(url, file) # takes a few minutes
R.utils::gunzip(file)
w2v_gnews <- "GoogleNews-vectors-negative300-SLIM.bin"
bin_to_txt(w2v_gnews,"vector.txt")
# from https://privefl.github.io/bigreadr/articles/csv2sqlite.html
csv2sqlite <- function(tsv,
every_nlines,
table_name,
dbname = sub("\\.txt$", ".sqlite", tsv),
...) {
# Prepare reading
con <- RSQLite::dbConnect(RSQLite::SQLite(), dbname)
init <- TRUE
fill_sqlite <- function(df) {
if (init) {
RSQLite::dbCreateTable(con, table_name, df)
init <<- FALSE
}
RSQLite::dbAppendTable(con, table_name, df)
NULL
}
# Read and fill by parts
bigreadr::big_fread1(tsv, every_nlines,
.transform = fill_sqlite,
.combine = unlist,
... = ...)
# Returns
con
}
vectors_data <- csv2sqlite("vector.txt", every_nlines = 1e6, table_name = "vectors")
Resulted in:
Splitting: 12.4 seconds.
Error: nThread >= 1L is not TRUE

Answer 1

Another option would be to do the processing on-disk, e.g. using an SQLite file and dplyr's database functionality. Here's one option: https://stackoverflow.com/a/38651229/4168169
To get the CSV into SQLite you can also use the bigreadr package which has an article on doing just this: https://privefl.github.io/bigreadr/articles/csv2sqlite.html