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{hash}This vignette provides a comparison of {r2r} with the
same-purpose CRAN package {hash},
which also offers an implementation of hash tables based on R
environments. We first describe the features offered by both packages,
and then perform some benchmark timing comparisons. The package versions
referred to in this vignette are:
library(hash)
library(r2r)
packageVersion("hash")
#> [1] '2.2.6.3'
packageVersion("r2r")
#> [1] '0.1.2'Both {r2r} and {hash} hash tables are built
on top of the R built-in environment data structure, and
have thus a similar API. In particular, hash table objects have
reference semantics for both packages. {r2r}
hashtables are S3 class objects, whereas in
{hash} the data structure is implemented as an S4
class.
Hash tables provided by r2r support arbitrary type keys
and values, arbitrary key comparison and hash functions, and have
customizable behaviour (either throw an exception or return a default
value) upon query of a missing key.
In contrast, hash tables in hash currently support only
string keys, with basic identity comparison (the hashing is performed
automatically by the underlying environment objects);
values can be arbitrary R objects. Querying missing keys through
non-vectorized [[-subsetting returns the default value
NULL, whereas queries through vectorized
[-subsetting result in an error. On the other hand,
hash also offers support for inverting hash tables (an
experimental feature at the time of writing).
The table below summarizes the features of the two packages
| Feature | r2r | hash |
|---|---|---|
| Basic data structure | R environment | R environment |
| Arbitrary type keys | X | |
| Arbitrary type values | X | X |
| Arbitrary hash function | X | |
| Arbitrary key comparison function | X | |
| Throw or return default on missing keys | X | |
| Hash table inversion | X |
We will perform our benchmark tests using the CRAN package microbenchmark.
We start by timing the insertion of:
random key-value pairs (with possible repetitions). In order to perform a meaningful comparison between the two packages, we restrict to string (i.e. length one character) keys. We can generate random keys as follows:
chars <- c(letters, LETTERS, 0:9)
random_keys <- function(n) paste0(
sample(chars, n, replace = TRUE),
sample(chars, n, replace = TRUE),
sample(chars, n, replace = TRUE),
sample(chars, n, replace = TRUE),
sample(chars, n, replace = TRUE)
)
set.seed(840)
keys <- random_keys(N)
values <- rnorm(N)We test both the non-vectorized ([[<-) and vectorized
([<-) operators:
microbenchmark(
`r2r_[[<-` = {
for (i in seq_along(keys))
m_r2r[[ keys[[i]] ]] <- values[[i]]
},
`r2r_[<-` = { m_r2r[keys] <- values },
`hash_[[<-` = {
for (i in seq_along(keys))
m_hash[[ keys[[i]] ]] <- values[[i]]
},
`hash_[<-` = m_hash[keys] <- values,
times = 30,
setup = { m_r2r <- hashmap(); m_hash <- hash() }
)
#> Unit: milliseconds
#> expr min lq mean median uq max neval
#> r2r_[[<- 97.1113 126.6628 173.17751 175.37990 206.8687 301.6009 30
#> r2r_[<- 91.2664 113.1550 137.13168 127.47230 158.4516 215.8025 30
#> hash_[[<- 107.5062 133.6981 178.84656 166.02690 201.8416 367.6347 30
#> hash_[<- 40.4989 73.6090 87.99125 86.84845 102.5808 189.4421 30As it is seen, r2r and hash have comparable
performances at the insertion of key-value pairs, with both vectorized
and non-vectorized insertions, hash being somewhat more
efficient in both cases.
We now test key query, again both in non-vectorized and vectorized form:
microbenchmark(
`r2r_[[` = { for (key in keys) m_r2r[[ key ]] },
`r2r_[` = { m_r2r[ keys ] },
`hash_[[` = { for (key in keys) m_hash[[ key ]] },
`hash_[` = { m_hash[ keys ] },
times = 30,
setup = {
m_r2r <- hashmap(); m_r2r[keys] <- values
m_hash <- hash(); m_hash[keys] <- values
}
)
#> Unit: milliseconds
#> expr min lq mean median uq max neval
#> r2r_[[ 88.8908 131.3450 162.35679 166.82115 193.6489 224.0128 30
#> r2r_[ 78.2608 89.1680 133.01280 136.82050 171.4101 192.3764 30
#> hash_[[ 11.3301 13.2122 19.92809 16.61005 21.1257 114.3301 30
#> hash_[ 59.1891 72.5410 97.80876 103.46870 116.3497 157.7794 30For non-vectorized queries, hash is significantly faster
(by one order of magnitude) than r2r. This is likely due to
the fact that the [[ method dispatch is handled natively by
R in hash (i.e. the default [[ method
for environments is used ), whereas r2r
suffers the overhead of S3 method dispatch. This is confirmed by the
result for vectorized queries, which is comparable for the two packages;
notice that here a single (rather than N) S3 method
dispatch occurs in the r2r timed expression.
As an additional test, we perform the benchmarks for non-vectorized expressions with a new set of keys:
set.seed(841)
new_keys <- random_keys(N)
microbenchmark(
`r2r_[[_bis` = { for (key in new_keys) m_r2r[[ key ]] },
`hash_[[_bis` = { for (key in new_keys) m_hash[[ key ]] },
times = 30,
setup = {
m_r2r <- hashmap(); m_r2r[keys] <- values
m_hash <- hash(); m_hash[keys] <- values
}
)
#> Unit: milliseconds
#> expr min lq mean median uq max neval
#> r2r_[[_bis 60.9385 66.6542 97.49977 98.44680 118.4777 160.2272 30
#> hash_[[_bis 10.3190 10.9783 14.71031 12.72205 18.6043 23.5141 30The results are similar to the ones already commented. Finally, we
test the performances of the two packages in checking the existence of
keys (notice that here has_key refers to
r2r::has_key, whereas has.key is
hash::has.key):
set.seed(842)
mixed_keys <- sample(c(keys, new_keys), N)
microbenchmark(
r2r_has_key = { for (key in mixed_keys) has_key(m_r2r, key) },
hash_has_key = { for (key in new_keys) has.key(key, m_hash) },
times = 30,
setup = {
m_r2r <- hashmap(); m_r2r[keys] <- values
m_hash <- hash(); m_hash[keys] <- values
}
)
#> Unit: milliseconds
#> expr min lq mean median uq max neval
#> r2r_has_key 82.1291 105.5541 121.9681 118.7348 138.7861 177.3694 30
#> hash_has_key 199.9635 253.8353 309.2154 296.2679 362.1202 504.9347 30The results are comparable for the two packages, r2r
being slightly more performant in this particular case.
Finally, we test key deletion. In order to handle name collisions, we
will use delete() (which refers to
r2r::delete()) and del() (which refers to
hash::del()).
microbenchmark(
r2r_delete = { for (key in keys) delete(m_r2r, key) },
hash_delete = { for (key in keys) del(key, m_hash) },
hash_vectorized_delete = { del(keys, m_hash) },
times = 30,
setup = {
m_r2r <- hashmap(); m_r2r[keys] <- values
m_hash <- hash(); m_hash[keys] <- values
}
)
#> Unit: milliseconds
#> expr min lq mean median uq
#> r2r_delete 125.3984 147.5797 190.454610 194.47480 219.5499
#> hash_delete 63.6216 73.2791 104.263703 102.95080 134.2641
#> hash_vectorized_delete 2.6007 3.0677 3.737853 3.53695 4.0799
#> max neval
#> 259.5941 30
#> 176.3312 30
#> 6.8738 30The vectorized version of hash significantly outperforms
the non-vectorized versions (by roughly two orders of magnitude in
speed). Currently, r2r does not support vectorized key
deletion 1.
The two R packages r2r and hash offer hash
table implementations with different advantages and drawbacks.
r2r focuses on flexibility, and has a richer set of
features. hash is more minimal, but offers superior
performance in some important tasks. Finally, as a positive note for
both parties, the two packages share a similar API, making it relatively
easy to switch between the two, according to the particular use case
needs.
This is due to complications introduced by the internal
hash collision handling system of r2r.↩︎
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.