Comparison with `{hash}`
In r2r: R-Object to R-Object Hash Maps

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

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")
packageVersion("r2r")

Features

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

knitr::kable(
    data.frame(
        Feature = c(
            "Basic data structure",
            "Arbitrary type keys", 
            "Arbitrary type values",
            "Arbitrary hash function",
            "Arbitrary key comparison function",
            "Throw or return default on missing keys",
            "Hash table inversion"
        ),
        r2r = c("R environment", "X", "X", "X", "X", "X", ""),
        hash = c("R environment", "", "X", "", "", "", "X")
        ),
    align = "c",
    caption = "Features supported by {r2r} and {hash}"
    )

Performance tests

We will perform our benchmark tests using the CRAN package microbenchmark.

library(microbenchmark)

Key insertion

We start by timing the insertion of:

N <- 1e4

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() }
)

As 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.

Key query

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
    }
)

For 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
    }
)

The 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
    }
)

The results are comparable for the two packages, r2r being slightly more performant in this particular case.

Key deletion

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
    }
)

The 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].

Conclusions

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.

[^1]: This is due to complications introduced by the internal hash collision handling system of r2r.