Blosc Compression

In blosc: Compress and Decompress Data Using the 'BLOSC' Library

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  out.width = "50%",
  fig.width = 5,
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Compress with Blosc

Input data

When your input data is of type raw(), it is assumed that it encodes a data type where each element is typesize bytes long. The data type can be any structured form of data and does not necessarily needs to be known. Of course you need to know the structure when you need to interpret the data, but that's not up to the Blosc compressor.

The example below compresses the raw() data assuming that the data type is 2 bytes long.

library(blosc)
data_input <- as.raw(c(1, 2, 3, 4, 1, 2, 3, 4))
blosc_compress(data_input, typesize = 2)

Note that the length of the resulting data is actually longer than that of the input data. This is because the compressor has an overhead. The data set is just too small compared to the overhead.

Can you compress other data types with Blosc? Yes you can. You first have to encode it to a binary form (raw()), with for instance r_to_dtype() or any other method that converts your data into a raw() format. You can also use the dtype argument to encode and compress your data in one go. In that case you need to specify an appropriate data type (vignette("dtypes")).

The example below shows how to encode numeric() values as little-endian 16 bit floating point data ("<f2") and compresses it.

## The line below won't work as the default `typesize` (4) does
## not match with the dtype size (2)
## blosc_compress(iris$Petal.Length, dtype = "<f2")

## Explicitely set the `typesize` to 2
compressed_iris <-
  blosc_compress(iris$Petal.Length, typesize = 2, dtype = "<f2")

Output

The output is always a vector of raw() data. Generally, the output data should be smaller than the input data. There are exceptions. One is seen above, where the data set is too small in comparison with the compressor overhead. Another case is where the data is just too random, where the compressor algorithm simply can't compress the data. In the its compressed form, the data can no longer be interpreted directly. You need to decompress it first (blosc_decompress()).

Compression Algorithms

You can pick from several algorithms to compress your data: "blosclz", "lz4", "lz4hc", "zlib", or "zstd". There is not a single algorithm that always has the best performance (speed and compression level). It really depends on your data and can be tested by trial and error. You can also lower the compression level argument if you prefer speed over compression level.

Decompress with Blosc

Input data

The decompression function (blosc_decompress()) only accepts raw() data that has been compressed with Blosc. It doesn't have to be created in R, it can be generated with any software using the c-blosc library.

You don't have to specify the compression algorithm, typesize or anything else. All that information is embedded in the header of the raw input data. You can even retrieve this information with:

blosc_info( compressed_iris )

Output

If you don't specify the output type, the decompression routine returns raw() data. Do you remember the iris length data that we compressed earlier? We can simply decompress it by calling blosc_decompress().

iris_length1 <- blosc_decompress(compressed_iris)
head(iris_length1)

It works, but we got raw() data as output. This is because the decompressor knows little about the data structure of the decompressed data. Since we know that we have encoded it as little-endian 16 bit floating point values ("<f2"), we can specify it as such. Once specified, the function will automatically decode the data.

iris_length2 <- blosc_decompress(compressed_iris, dtype = "<f2")
hist(iris_length2)

blosc documentation built on Sept. 11, 2025, 5:11 p.m.