# Timing hash functions with the bench package

This article is originally published at https://www.jumpingrivers.com

This blog post has two goals

- Investigate the
**bench**package for timing R functions - Consequently explore the different algorithms in the
**digest**package using**bench**

### What is **digest**?

The **digest** package provides a hash function to summarise R objects. Standard hashes are available, such as md5, crc32, sha-1, and sha-256.

The key function in the package is `digest()`

that applies a cryptographical hash function to arbitrary R objects. By default, the objects are internally serialized using `md5`

. For example,

```
library("digest")
digest(1234)
## [1] "37c3db57937cc950b924e1dccb76f051"
digest(1234, algo = "sha256")
## [1] "01b3680722a3f3a3094c9845956b6b8eba07f0b938e6a0238ed62b8b4065b538"
```

The **digest** package is fairly popular and has a large number of reverse dependencies

```
length(tools::package_dependencies("digest", reverse = TRUE)$digest)
## [1] 186
```

The number of available hashing algorithms has grown over the years, and as a little side project, we decided to test the speed of the various algorithms. To be clear, I’m not considering any security aspects or the potential of hash clashes, just pure speed.

### Timing in R

There are numerous ways of timing R functions. A recent addition to this list is the **bench** package. The main function bench::mark() has a number of useful features over other timing functions.

To time and compare two functions, we load the relevant packages

```
library("bench")
library("digest")
library("tidyverse")
```

then we call the `mark()`

function and compare the `md5`

with the `sha1`

hash

```
value = 1234
mark(check = FALSE,
md5 = digest(value, algo = "md5"),
sha1 = digest(value, algo = "sha256")) %>%
select(expression, median)
## # A tibble: 2 x 2
## expression median
## <bch:expr> <bch:tm>
## 1 md5 50.2µs
## 2 sha1 50.9µs
```

The resulting `tibble`

object, contains all the timing information. For simplicity, we’ve just selected the expression and median time.

### More advanced **bench**

Of course, it’s more likely that you’ll want to compare more than two things. You can compare as many function calls as you want with `mark()`

, as we’ll demonstrate in the following example. It’s probably more likely that you’ll want to compare these function calls against more than one value. For example, in the **digest** package there are eight different algorithms. Ranging from the standard `md5`

to the newer `xxhash64`

methods. To compare times, we’ll generate `n = 20`

random character strings of length `N = 10,000`

. This can all be wrapped up in the single function `press()`

function call from the **bench** package:

```
N = 1e4
results = suppressMessages(
bench::press(
value = replicate(n = 20, paste0(sample(LETTERS, N, replace = TRUE), collapse = "")),
{
bench::mark(
iterations = 1, check = FALSE,
md5 = digest(value, algo = "md5"),
sha1 = digest(value, algo = "sha1"),
crc32 = digest(value, algo = "crc32"),
sha256 = digest(value, algo = "sha256"),
sha512 = digest(value, algo = "sha512"),
xxhash32 = digest(value, algo = "xxhash32"),
xxhash64 = digest(value, algo = "xxhash64"),
murmur32 = digest(value, algo = "murmur32")
)
}
)
)
```

The tibble `results`

contain timing results. But it’s easier to work with relative timings. So we’ll rescale

```
rel_timings = results %>%
unnest() %>%
select(expression, median) %>%
mutate(expression = names(expression)) %>%
distinct() %>%
mutate(median_rel = unclass(median/min(median)))
```

Then plot the results, ordered by slowed to largest

```
ggplot(rel_timings) +
geom_boxplot(aes(x = fct_reorder(expression, median_rel),
y = median_rel)) +
theme_minimal() +
ylab("Relative timings") + xlab(NULL) +
ggtitle("N = 10,000") + coord_flip()
```

The `sha256`

algorithm is about three times slower than the `xxhash32`

method. However, it’s worth bearing in mind that although it’s relatively slower, the absolute times are very small

```
rel_timings %>%
group_by(expression) %>%
summarise(median = median(median)) %>%
arrange(desc(median))
## # A tibble: 8 x 2
## expression median
## <chr> <bch:tm>
## 1 sha256 171.2µs
## 2 sha1 112.6µs
## 3 md5 109.5µs
## 4 sha512 108.4µs
## 5 crc32 91µs
## 6 xxhash64 85.1µs
## 7 murmur32 82.1µs
## 8 xxhash32 77.6µs
```

It’s also worth seeing how the results vary according to the size of the character string `N`

.

Regardless of the value of `N`

, the `sha256`

algorithm is consistently in the slowest.

## Conclusion

R is going the way of “tidy” data. Though it wasn’t the focus of this blog post, I think that the **bench** package is as good as other timing packages out there. Not only that, but it fits in with the whole “tidy” data thing. Two birds, one stone.

The post Timing hash functions with the bench package appeared first on Jumping Rivers.

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This article is originally published at https://www.jumpingrivers.com

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