Partially Read Really Large CSV.Gz in R Using Vroom

Partially read really large csv.gz in R using vroom

I haven't been able to figure out vroom solution for very large more-than-RAM (gzipped) csv files. However, the following approach has worked well for me and I'd be grateful to know about approaches with better querying speed while also saving disk space.

1. Use split sub-command inxsv from https://github.com/BurntSushi/xsv to split the large csv file into comfortably-within-RAM chunks of say, 10^5, lines and save them in a folder.
2. Read all chunks using data.table::fread one-by-one (to avoid low-memory error) using a for loop and save all of them into a folder as compressed parquet files using arrow package which saves space and prepares the large table for fast querying. For even faster operations, it is advisable to re-save the parquet files partitioned by the fields by which you need to frequently filter.
3. Now you can use arrow::open_dataset and query that multi-file parquet folder using dplyr commands. It takes minimum disk space and gives the fastest results in my experience.

I use data.table::fread with explicit definition of column classes of each field for fastest and most reliable parsing of csv files. readr::read_csv has also been accurate but slower. However, auto-assignment of column classes by read_csv as well as the ways in which you can custom-define column classes by read_csv is actually the best - so less human-time but more machine-time - which means that it may be faster overall depending on scenario. Other csv parsers have thrown errors for the kind of csv files that I work with and waste time.

You may now delete the folder containing chunked csv files to save space, unless you want to experiment loop-reading them with other csv parsers.

Other previously successfully approaches: Loop read all csv chunks as mentioned above and save them into:

1. a folder using disk.frame package. Then that folder may be queried using dplyr or data.table commands explained in the documentation. It has facility to save in compressed fst files which saves space, though not as much as parquet files.
2. a table in DuckDB database which allows querying with SQL or dplyr commands. Using database-tables approach won't save you disk space. But DuckDB also allows querying partitioned/un-partitioned parquet files (which saves disk space) with SQL commands.

EDIT: - Improved Method Below

I experimented a little and found a much better way to do the above operations. Using the code below, the large (compressed) csv file will be chunked automatically within R environment (no need to use any external tool like xsv) and all chunks will be written in parquet format in a folder ready for querying.

library(readr)
library(arrow)

fyl <- "...path_to_big_data_file.csv.gz"
pqFolder <- "...path_to_folder_where_chunked_parquet_files_are_to_be_saved"

f <- function(x, pos){
  write_parquet(x,
                file.path(pqFolder, paste0(pos, ".parquet")),
                compression = "gzip",
                compression_level = 9)
}

read_csv_chunked(
  fyl,
  col_types = list(Column1="f", Column2="c", Column3="T", ...), # all column specifications
  callback = SideEffectChunkCallback$new(f),
  chunk_size = 10^6)

If, instead of parquet, you want to use -

1. disk.frame, the callback function may be used to create chunked compressed fst files for dplyr or data.table style querying.
2. DuckDB, the callback function may be used to append the chunks into a database table for SQL or dplyr style querying.

By judiciously choosing the chunk_size parameter of readr::read_csv_chunked command, the computer should never run out of RAM while running queries.

PS: I use gzip compression for parquet files since they can then be previewed with ParquetViewer from https://github.com/mukunku/ParquetViewer. Otherwise, zstd (not currently supported by ParquetViewer) decompresses faster and hence improves reading speed.

EDIT 2:

I got a csv file which was really big for my machine: 20 GB gzipped and expands to about 83 GB, whereas my home laptop has only 16 GB. Turns out that the read_csv_chunked method I mentioned in earlier EDIT fails to complete. It always stops working after some time and does not create all parquet chunks. Using my previous method of splitting the csv file with xsv and then looping over them creating parquet chunks worked. To be fair, I must mention it took multiple attempts this way too and I had programmed a check to create only additional parquet chunks when running the program on successive attempts.

EDIT 3:

VROOM does have difficulty when dealing with huge files since it needs to store the index in memory as well as any data you read from the file. See development thread https://github.com/r-lib/vroom/issues/203

EDIT 4:

Additional tip: The chunked parquet files created by the above mentioned method may be very conveniently queried using SQL with DuckDB method mentioned at
https://duckdb.org/docs/data/parquet
and
https://duckdb.org/2021/06/25/querying-parquet.html

DuckDB method is significant because R Arrow method currently suffers from a very serious limitation which is mentioned in the official documentation page https://arrow.apache.org/docs/r/articles/dataset.html.

Specifically, and I quote: "In the current release, arrow supports the dplyr verbs mutate(), transmute(), select(), rename(), relocate(), filter(), and arrange(). Aggregation is not yet supported, so before you call summarise() or other verbs with aggregate functions, use collect() to pull the selected subset of the data into an in-memory R data frame."

The problem is that if you use collect() on a very big dataset, the RAM usage spikes and the system crashes. Whereas, using SQL statements to do the same aggregation job on the same big-dataset with DuckDB does not cause RAM usage spikes and does not cause system crash. So until Arrow fixes itself for aggregation queries for big-data, SQL from DuckDB provides a nice solution to querying big datasets in chunked parquet format.

Strategies for reading in CSV files in pieces?

You could read it into a database using RSQLite, say, and then use an sql statement to get a portion.

If you need only a single portion then read.csv.sql in the sqldf package will read the data into an sqlite database. First, it creates the database for you and the data does not go through R so limitations of R won't apply (which is primarily RAM in this scenario). Second, after loading the data into the database , sqldf reads the output of a specified sql statement into R and finally destroys the database. Depending on how fast it works with your data you might be able to just repeat the whole process for each portion if you have several.

Only one line of code accomplishes all three steps, so it's a no-brainer to just try it.

DF <- read.csv.sql("myfile.csv", sql=..., ...other args...)

See ?read.csv.sql and ?sqldf and also the sqldf home page.

Read a 20GB file in chunks without exceeding my RAM - R

The LaF package can read in ASCII data in chunks. It can be used directly or if you are using dplyr the chunked package uses it providing an interface for use with dplyr.

The readr package has readr_csv_chunked and related functions.

The section of this web page entitled The Loop as well as subsequent sections of that page describes how to do chunked reads with base R.

It may be that if you remove all but the first three columns that it will be small enough to just read it in and process in one go.

vroom in the vroom package can read in files very quickly and also has the ability to read in just the columns named in the select= argument which may make it small enough to read it in in one go.

fread in the data.table package is a fast reading function that also supports a select= argument which can select only specified columns.

read.csv.sql in the sqldf (also see github page) package can read a file larger than R can handle into a temporary external SQLite database which it creates for you and removes afterwards and reads the result of the SQL statement given into R. If the first three columns are named col1, col2 and col3 then try the code below. See ?read.csv.sql and ?sqldf for the remaining arguments which will depend on your file.

library(sqldf)
DF <- read.csv.sql("myfile", "select col1, col2, col3 from file", 
  dbname = tempfile(), ...)

read.table and read.csv in the base of R have a colClasses=argument which takes a vector of column classes. If the file has nc columns then use colClasses = rep(c(NA, "NULL"), c(3, nc-3)) to only read the first 3 columns.

Another approach is to pre-process the file using cut, sed or awk (available natively in UNIX and in the Rtools bin directory on Windows) or any of a number of free command line utilities such as csvfix outside of R to remove all but the first three columns and then see if that makes it small enough to read in one go.

Also check out the High Performance Computing task view.

How do I import a large (6 Gb) .csv file into R efficiently and quickly, without the R REPL crashing?

R will crash if you try to load a file that is larger than your available memory, so you should see that you have at least 6gb ram free (a 6gb .csv is roughly 6gb in memory also). Python will have the same problem
(apparently someone asked the exact same question for python a few years ago)

For reading large csv files, you should either use readr::read_csv() or data.table::fread(), as both are much faster than base::read.table().

readr::read_csv_chunked supports reading csv files in chunks, so if you don't need your whole data at once, that might help. You could also try just reading the columns of interest, to keep the memory size smaller.

Combining 100 RDS/RData files into one large file - large file too big

Update: using the newer purrr::map_df() function, which combines map and bind_rows and returns a dataframe

https://purrr.tidyverse.org/reference/map.html

library(tidyverse)
my_files = list.files(pattern = "*.rds")
my_all <- map_df(my_files, read_rds)

...

The dplyr::bind_rows() function is explicitly an efficient implementation of the common pattern of do.call(rbind, dfs) for binding many data frames into one.

https://dplyr.tidyverse.org/reference/bind.html

library(tidyverse)
write_rds(iris, "iris1.rds") #write three sample files
write_rds(iris, "iris2.rds")
write_rds(iris, "iris3.rds")
my_files = list.files(pattern = "*.rds")
dat_list = lapply(my_files, function (x) read_rds(x)) #switched to only read_rds()
my_all <- do.call("bind_rows", dat_list) #switched to bind_rows()

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