# Compression Benchmarks: brotli, gzip, xz, bz2

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Brotli is a new compression algorithm optimized for the web, in particular small text documents. Brotli decompression is at least as fast as for gzip while significantly improving the compression ratio. The price we pay is that compression is much slower than gzip. Brotli is therefore most effective for serving static content such as fonts and html pages.
The brotli package is now on CRAN and supports both compression and decompression of the brotli format. Let’s benchmark the available compression formats in R using a some example text data from the COPYING file.
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```
library(brotli)
library(ggplot2)
# Example data
myfile <- file.path(R.home(), "COPYING")
x <- readBin(myfile, raw(), file.info(myfile)$size)
# The usual suspects
y1 <- memCompress(x, "gzip")
y2 <- memCompress(x, "bzip2")
y3 <- memCompress(x, "xz")
y4 <- brotli_compress(x)
```

```
stopifnot(identical(x, memDecompress(y1, "gzip")))
stopifnot(identical(x, memDecompress(y2, "bzip2")))
stopifnot(identical(x, memDecompress(y3, "xz")))
stopifnot(identical(x, brotli_decompress(y4)))
```

## Compression ratio

If we compare compression ratios, we can see Brotli significantly outperformes the competition for this example.```
# Combine data
alldata <- data.frame (
algo = c("gzip", "bzip2", "xz (lzma2)", "brotli"),
ratio = c(length(y1), length(y2), length(y3), length(y4)) / length(x)
)
ggplot(alldata, aes(x = algo, fill = algo, y = ratio)) +
geom_bar(color = "white", stat = "identity") +
xlab("") + ylab("Compressed ratio (less is better)")
```

## Decompression speed

Perhaps the most important performance dimension for internet formats is decompression speed. Clients should be able to decompress quickly, even with limited resources such as on browsers and mobile devices.```
library(microbenchmark)
bm <- microbenchmark(
memDecompress(y1, "gzip"),
memDecompress(y2, "bzip2"),
memDecompress(y3, "xz"),
brotli_decompress(y4),
times = 1000
)
alldata$decompression <- summary(bm)$median
ggplot(alldata, aes(x = algo, fill = algo, y = decompression)) +
geom_bar(color = "white", stat = "identity") +
xlab("") + ylab("Decompression time (less is better)")
```

## Compression speed

So far Brotli showed the best compression ratio, with decompression performance comparable to gzip. But there is no such thing as a free pastry in Switzerland. Here is the caveat: compressing data with brotli is complex and slow:```
library(microbenchmark)
bm <- microbenchmark(
memCompress(x, "gzip"),
memCompress(x, "bzip2"),
memCompress(x, "xz"),
brotli_compress(x),
times = 20
)
alldata$compression <- summary(bm)$median
ggplot(alldata, aes(x = algo, fill = algo, y = compression)) +
geom_bar(color = "white", stat = "identity") +
xlab("") + ylab("Compression time (less is better)")
```

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