Vroom Benchmarks

vroom is a new approach to reading delimited and fixed width data into R.

It stems from the observation that when parsing files reading data from disk and finding the delimiters is generally not the main bottle neck. Instead (re)-allocating memory and parsing the values into R data types (particularly for characters) takes the bulk of the time.

Therefore you can obtain very rapid input by first performing a fast indexing step and then using the Altrep framework available in R versions 3.5+ to access the values in a lazy / delayed fashion.

How it works

The initial reading of the file simply records the locations of each individual record, the actual values are not read into R. Altrep vectors are created for each column in the data which hold a pointer to the index and the memory mapped file. When these vectors are indexed the value is read from the memory mapping.

This means initial reading is extremely fast, in the real world dataset below it is ~ 1/4 the time of the multi-threaded data.table::fread(). Sampling operations are likewise extremely fast, as only the data actually included in the sample is read. This means things like the tibble print method, calling head(), tail() x[sample(), ] etc. have very low overhead. Filtering also can be fast, only the columns included in the filter selection have to be fully read and only the data in the filtered rows needs to be read from the remaining columns. Grouped aggregations likewise only need to read the grouping variables and the variables aggregated.

Once a particular vector is fully materialized the speed for all subsequent operations should be identical to a normal R vector.

This approach potentially also allows you to work with data that is larger than memory. As long as you are careful to avoid materializing the entire dataset at once it can be efficiently queried and subset.

Reading delimited files

The following benchmarks all measure reading delimited files of various sizes and data types. Because vroom delays reading the benchmarks also do some manipulation of the data afterwards to try and provide a more realistic performance comparison.

Because the read.delim results are so much slower than the others they are excluded from the plots, but are retained in the tables.

Taxi Trip Dataset

This real world dataset is from Freedom of Information Law (FOIL) Taxi Trip Data from the NYC Taxi and Limousine Commission 2013, originally posted at https://chriswhong.com/open-data/foil_nyc_taxi/. It is also hosted on archive.org.

The first table trip_fare_1.csv is 1.55G in size.

#> Observations: 14,776,615
#> Variables: 11
#> $ medallion       <chr> "89D227B655E5C82AECF13C3F540D4CF4", "0BD7C8F5B...
#> $ hack_license    <chr> "BA96DE419E711691B9445D6A6307C170", "9FD8F69F0...
#> $ vendor_id       <chr> "CMT", "CMT", "CMT", "CMT", "CMT", "CMT", "CMT...
#> $ pickup_datetime <chr> "2013-01-01 15:11:48", "2013-01-06 00:18:35", ...
#> $ payment_type    <chr> "CSH", "CSH", "CSH", "CSH", "CSH", "CSH", "CSH...
#> $ fare_amount     <dbl> 6.5, 6.0, 5.5, 5.0, 9.5, 9.5, 6.0, 34.0, 5.5, ...
#> $ surcharge       <dbl> 0.0, 0.5, 1.0, 0.5, 0.5, 0.0, 0.0, 0.0, 1.0, 0...
#> $ mta_tax         <dbl> 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0...
#> $ tip_amount      <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
#> $ tolls_amount    <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.8, 0.0, 0...
#> $ total_amount    <dbl> 7.0, 7.0, 7.0, 6.0, 10.5, 10.0, 6.5, 39.3, 7.0...

Taxi Benchmarks

code: bench/taxi

All benchmarks were run on a Amazon EC2 m5.4xlarge instance with 16 vCPUs and an EBS volume type.

The benchmarks labeled vroom_base uses vroom with base functions for manipulation. vroom_dplyr uses vroom to read the file and dplyr functions to manipulate. data.table uses fread() to read the file and data.table functions to manipulate and readr uses readr to read the file and dplyr to manipulate. By default vroom only uses Altrep for character vectors, these are labeled vroom(altrep: normal). The benchmarks labeled vroom(altrep: full) instead use Altrep vectors for all supported types and vroom(altrep: none) disable Altrep entirely.

The following operations are performed.

  • The data is read
  • print() - N.B. read.delim uses print(head(x, 10)) because printing the whole dataset takes > 10 minutes
  • head()
  • tail()
  • Sampling 100 random rows
  • Filtering for “UNK” payment, this is 6434 rows (0.0435% of total).
  • Aggregation of mean fare amount per payment type.
reading package manipulating package altrep memory read print head tail sample filter aggregate total
read.delim base 6.18GB 1m 12.3s 6ms 1ms 1ms 1ms 1.3s 895ms 1m 14.5s
readr dplyr 6.91GB 37.3s 147ms 2ms 1ms 17ms 249ms 538ms 38.3s
vroom dplyr FALSE 6.55GB 18.4s 117ms 2ms 1ms 14ms 961ms 1.2s 20.7s
vroom base TRUE 6.35GB 1.4s 158ms 3ms 1ms 1ms 1.1s 7.4s 10s
data.table data.table 6.38GB 5.8s 12ms 1ms 1ms 1ms 104ms 764ms 6.7s
vroom dplyr TRUE 6.41GB 1.3s 76ms 2ms 1ms 11ms 1.3s 4s 6.7s

(N.B. Rcpp used in the dplyr implementation fully materializes all the Altrep numeric vectors when using filter() or sample_n(), which is why the first of these cases have additional overhead when using full Altrep.).

All numeric data

All numeric data is really a worst case scenario for vroom. The index takes about as much memory as the parsed data. Also because parsing doubles can be done quickly in parallel and text representations of doubles are only ~25 characters at most there isn’t a great deal of savings for delayed parsing.

For these reasons (and because the data.table implementation is very fast) vroom is a bit slower than fread for pure numeric data.

However because vroom is multi-threaded it is a bit quicker than readr and read.delim for this type of data.


code: bench/all_numeric-long

reading package manipulating package altrep memory read print head tail sample filter aggregate total
read.delim base 4.79GB 1m 51.4s 1.4s 1ms 1ms 2ms 4.5s 37ms 1m 57.3s
readr dplyr 2.82GB 13.1s 64ms 2ms 1ms 16ms 18ms 55ms 13.3s
vroom dplyr FALSE 2.75GB 1.3s 48ms 1ms 1ms 14ms 18ms 46ms 1.5s
vroom base FALSE 2.69GB 1.3s 48ms 1ms 1ms 3ms 6ms 55ms 1.4s
vroom dplyr TRUE 3.29GB 604ms 64ms 1ms 1ms 14ms 42ms 235ms 959ms
vroom base TRUE 3.28GB 581ms 55ms 1ms 1ms 3ms 29ms 251ms 920ms
data.table data.table 2.72GB 256ms 13ms 1ms 1ms 4ms 6ms 25ms 302ms


code: bench/all_numeric-wide

reading package manipulating package altrep memory read print head tail sample filter aggregate total
read.delim base 14.41GB 8m 41s 131ms 7ms 7ms 9ms 75ms 5ms 8m 41.2s
readr dplyr 5.46GB 56.1s 96ms 3ms 3ms 26ms 18ms 39ms 56.3s
vroom dplyr FALSE 5.35GB 6.9s 63ms 3ms 3ms 95ms 14ms 31ms 7.1s
vroom base FALSE 5.34GB 6.9s 61ms 3ms 3ms 5ms 6ms 7ms 7s
vroom dplyr TRUE 7.26GB 3s 68ms 4ms 14ms 23ms 20ms 77ms 3.2s
vroom base TRUE 7.26GB 3s 68ms 4ms 4ms 5ms 11ms 42ms 3.1s
data.table data.table 5.48GB 1.3s 100ms 1ms 1ms 3ms 4ms 4ms 1.4s

All character data

code: bench/all_character-long

All character data is a best case scenario for vroom when using Altrep, as it takes full advantage of the lazy reading.


reading package manipulating package altrep memory read print head tail sample filter aggregate total
read.delim base 4.53GB 1m 43.1s 8ms 1ms 1ms 2ms 28ms 293ms 1m 43.4s
readr dplyr 4.35GB 1m 2.6s 102ms 2ms 1ms 17ms 20ms 215ms 1m 2.9s
vroom dplyr FALSE 4.3GB 50.5s 50ms 2ms 1ms 16ms 21ms 150ms 50.7s
data.table data.table 4.73GB 42.8s 16ms 1ms 1ms 4ms 16ms 149ms 43s
vroom base TRUE 3.22GB 595ms 46ms 1ms 1ms 3ms 163ms 2.1s 2.9s
vroom dplyr TRUE 3.21GB 640ms 58ms 2ms 1ms 16ms 185ms 1.2s 2.1s


code: bench/all_character-wide

reading package manipulating package altrep memory read print head tail sample filter aggregate total
read.delim base 13.09GB 8m 30.4s 149ms 7ms 8ms 26ms 224ms 59ms 8m 30.9s
readr dplyr 12.21GB 7m 39.4s 217ms 4ms 3ms 29ms 38ms 57ms 7m 39.8s
vroom dplyr FALSE 12.14GB 4m 7.3s 67ms 3ms 3ms 28ms 35ms 37ms 4m 7.5s
data.table data.table 12.66GB 3m 21.8s 135ms 2ms 2ms 33ms 168ms 15ms 3m 22.1s
vroom base TRUE 6.57GB 3.1s 62ms 5ms 4ms 5ms 55ms 252ms 3.5s
vroom dplyr TRUE 6.57GB 3.1s 64ms 5ms 4ms 27ms 82ms 160ms 3.4s

Reading multiple delimited files

code: bench/taxi_multiple

The benchmark reads all 12 files in the taxi trip fare data, totaling 173,179,759 rows and 11 columns for a total file size of 18.4G.

reading package manipulating package altrep memory read print head tail sample filter aggregate total
readr dplyr 63.5GB 7m 55s 837ms 1ms 1ms 15ms 4.2s 13.5s 8m 13.6s
vroom dplyr FALSE 63.1GB 3m 52.3s 2.2s 2ms 1ms 14ms 10.5s 7.2s 4m 12.2s
vroom base TRUE 88.3GB 20.3s 3s 1ms 1ms 1ms 21.5s 2m 22.6s 3m 7.5s
vroom dplyr TRUE 88GB 20.4s 2.8s 1ms 1ms 13ms 23.9s 1m 5.6s 1m 52.7s
data.table data.table 59.6GB 1m 35.3s 7ms 1ms 1ms 1ms 1.1s 4.7s 1m 41.1s

Reading fixed width files

United States Census 5-Percent Public Use Microdata Sample files

This fixed width dataset contains individual records of the characteristics of a 5 percent sample of people and housing units from the year 2000 and is freely available at https://web.archive.org/web/20150908055439/https://www2.census.gov/census_2000/datasets/PUMS/FivePercent/California/all_California.zip. The data is split into files by state, and the state of California was used in this benchmark.

The data totals 2,342,339 rows and 37 columns with a total file size of 677M.

Census data benchmarks

code: bench/fwf

reading package manipulating package altrep memory read print head tail sample filter aggregate total
read.delim base 6.17GB 18m 9.6s 16ms 1ms 2ms 3ms 492ms 90ms 18m 10.2s
readr dplyr 6.19GB 32.6s 48ms 2ms 1ms 17ms 95ms 94ms 32.8s
vroom dplyr FALSE 5.96GB 14.7s 44ms 1ms 1ms 15ms 468ms 91ms 15.3s
vroom base TRUE 4.65GB 164ms 56ms 1ms 1ms 7ms 285ms 1.8s 2.3s
vroom dplyr TRUE 4.62GB 163ms 48ms 2ms 1ms 16ms 306ms 1.3s 1.8s

Writing delimited files

code: bench/taxi_writing

The benchmarks write out the taxi trip dataset in a few different ways.

compression base data.table readr vroom
gzip 3m 17.1s 1m 7.8s 2m 0.2s 1m 14.4s
multithreaded_gzip 1m 37.8s 8.9s 53.4s 8.1s
zstandard 1m 39.9s NA 54.2s 12.4s
uncompressed 1m 37.4s 1.5s 52.2s 1.7s

Session and package information

package version date source
base 4.1.0 2021-05-18 local
data.table 1.14.0 2021-02-21 RSPM (R 4.1.0)
dplyr 1.0.7 2021-06-18 RSPM (R 4.1.0)
readr 1.4.0 2020-10-05 RSPM (R 4.1.0)
vroom 1.5.1 2021-06-22 local