# Tutorials

Using `python-blosc` (or just `blosc`, because we are going to talk always on how to use it in a Python environment) is pretty easy. It basically mimics the API of the `zlib` module included in the standard Python library.

Here are some examples on how to use it. For the full documentation, please refer to the reference section.

Most of the times in this tutorial have been obtained using a VM with 2 cores on top of a Intel(R) Core(TM) i5-3380M CPU @ 2.90GHz.

## Compressing and decompressing with `blosc`

Let’s start creating a NumPy array with 80 MB full of data:

    >>> import numpy as np
    >>> a = np.linspace(0, 100, 1e7)
    >>> bytes_array = a.tostring()  # get an array of bytes

and let’s compare Blosc operation with `zlib` (please note that we are using IPython for leveraging its timing capabilities):

    >>> import zlib
    >>> %timeit zlib.compress(bytes_array)
    1 loops, best of 3: 4.65 s per loop   # ~ 17 MB/s
    >>> import blosc
    >>> %timeit blosc.compress(bytes_array, typesize=8)
    100 loops, best of 3: 17.7 ms per loop # ~ 4.5 GB/s and 260x faster than zlib

but Blosc can use different codecs under the hood. Let’s try the LZ4 codec (instead of the BloscLZ which is the default):

    >>> %timeit blosc.compress(bytes_array, typesize=8, cname='lz4')
    100 loops, best of 3: 8.77 ms per loop  # ~ 9.1 GB/s and 530x faster than zlib

It is important to note that this is quite more than the speed of a memcpy() in this machine:

    >>> %timeit a.copy()
    10 loops, best of 3: 27.2 ms per loop  # ~ 2.9 GB/s

which means that both BloscLZ and LZ4 codecs can be faster than memcpy(), just as the Blosc slogan promises.

Blosc also comes with the ZLib codec too, and it actually runs faster than the naked zlib:

    >>> %timeit blosc.compress(bytes_array, typesize=8, cname='zlib')
    10 loops, best of 3: 139 ms per loop  # ~ 580 MB/s and 33x faster than zlib

The reason why the internal Zlib codec in Blosc is faster than the ‘naked’ one is 1) that Blosc can use multithreading (using 8 threads here) and 2) that Blosc splits the data to compress in smaller blocks that are more friendly to the caches in modern CPUs.

Now, let’s have a look at the compression ratios:

    >>> zpacked = zlib.compress(bytes_array); len(zpacked)
    52994692
    >>> round(len(bytes_array) / float(len(zpacked)), 3)
    1.51     # zlib achieves a 1.5x compression ratio

    >>> blzpacked = blosc.compress(bytes_array, typesize=8, cname='blosclz'); len(blzpacked)
    6986533
    >>> round(len(bytes_array) / float(len(blzpacked)), 3)
    11.451   # BloscLZ codec reaches more than 11x compression ratio

    >>> lz4packed = blosc.compress(bytes_array, typesize=8, cname='lz4'); len(lz4packed)
    3716015
    >>> round(len(bytes_array) / float(len(lz4packed)), 3)
    21.528   # LZ4 codec reaches more than 21x compression ratio

Here we can see how Blosc, with its different codecs, is very efficient compressing this kind of binary data. It is important to note that the codecs alone are not the only responsible for high compression ratios. See what a naked LZ4 codec can do on the same string:

    >>> import lz4
    >>> lz4_packed = lz4.compress(bytes_array); len(lz4_packed)
    80309133
    >>> round(len(bytes_array) / float(len(lz4_packed)), 3)
    0.996

That is, the naked LZ4 codec cannot compress the array of bytes at all, whereas through Blosc LZ4 can compress quite a lot. In fact, this difference in compression also happens with ZLib:

    >>> zlibpacked = blosc.compress(bytes_array, typesize=8, cname='zlib'); len(zlibpacked)
    875274
    >>> round(len(zpacked) / float(len(zlibpacked)), 3)
    60.546   # ZLib codec reaches 60x more compression than naked ZLib

Here it is a plot with the different compression ratios achieved:

The explanation for this apparently shocking result is that Blosc uses filters (`SHUFFLE` and `BITSHUFFLE` currently, but the list can increase more in the future) prior to the compression stage and these allow in general for better compression ratios when using binary data.

How about decompression?:

    >>> %timeit zlib.decompress(zpacked)
    1 loops, best of 3: 275 ms per loop  # ~ 290 MB/s
    >>> %timeit blosc.decompress(blzpacked)
    100 loops, best of 3: 17.3 ms per loop  # ~ 4.3 GB/s and 16x faster than zlib
    >>> %timeit blosc.decompress(lz4packed)
    10 loops, best of 3: 41.9 ms per loop  # ~ 1.9 GB/s and 6.5x faster than zlib
    >>> %timeit blosc.decompress(zlibpacked)
    10 loops, best of 3: 40.7 ms per loop  # ~ 2.0 GB/S and 6.8x faster than zlib

Here we see a couple of things:

- All the internal codecs in Blosc are way faster than naked ZLib
- The fastest codec for decompressing here is BloscLZ (remember that LZ4 was the fastest for compression).

The next plot summarizes the speed benchmarks above:

These results should reinforce the idea that there is not a single codec that wins in all areas (compression ratio, compression speed and decompression speed) and each has its pro’s and con’s. It is up to the user to choose whatever fits better to him (hint: there is no replacement for experimentation).

Finally, here it is the way to discover all the internal codecs inside your Blosc package:

    >>> blosc.cnames
    ['blosclz', 'lz4', 'lz4hc', 'snappy', 'zlib']

Note: the actual list of codecs may change depening on how you have compiled the underlying C-Blosc library.

## Using different filters

In the same way that you can use different codecs inside Blosc, you can use different filters too (currently `SHUFFLE` and `BITSHUFLE`). These allow the integrated compressors to compress more efficiently or not, depending on your datasets.

Here it is an example using the `SHUFFLE` filter:

    >>> %time bpacked = blosc.compress(bytes_array, shuffle=blosc.SHUFFLE)
    CPU times: user 112 ms, sys: 4 ms, total: 116 ms
    Wall time: 29.9 ms
    >>> len(bpacked)
    6986533

Here there is another example using `BITSHUFFLE`:

    >>> %time bpacked = blosc.compress(bytes_array, shuffle=blosc.BITSHUFFLE)
    CPU times: user 120 ms, sys: 8 ms, total: 128 ms
    Wall time: 32.9 ms
    >>> len(bpacked)
    5942257     #  ~ 1.2x smaller than blosclz/shuffle

You can also deactivate filters completely with `NOSHUFFLE`:

    >>> %time bpacked = blosc.compress(bytes_array, shuffle=blosc.NOSHUFFLE)
    CPU times: user 416 ms, sys: 8 ms, total: 424 ms
    Wall time: 107 ms
    >>> len(bpacked)
    74323591    #  just a 7% of compression wrt the original buffer

So you have quite a bit of flexibility on choosing different codecs and filters inside Blosc. Again, depending on the dataset you have and the requeriments on performance, you may want to experiment a bit before sticking with your preferred one.

## Supporting the buffer interface

python-blosc supports compressing and decompressing from any bytes-like object that supports the buffer-interface: this includes `buffer`, `memoryview` and `bytearray`:

    >>> input_bytes = b"abcdefghijklmnopqrstuvwxyz"
    >>> blosc.compress(input_bytes, typesize=1)
    '\x02\x01\x03\x01\x1a\x00\x00\x00\x1a\x00\x00\x00*\x00\x00\x00abcdefghijklmnopqrstuvwxyz'
    >>> blosc.compress(memoryview(input_bytes), typesize=1)
    '\x02\x01\x03\x01\x1a\x00\x00\x00\x1a\x00\x00\x00*\x00\x00\x00abcdefghijklmnopqrstuvwxyz'
    >>> blosc.compress(bytearray(input_bytes), typesize=1)
    '\x02\x01\x03\x01\x1a\x00\x00\x00\x1a\x00\x00\x00*\x00\x00\x00abcdefghijklmnopqrstuvwxyz'
    >>> compressed = blosc.compress(input_bytes, typesize=1)
    >>> blosc.decompress(compressed)
    'abcdefghijklmnopqrstuvwxyz'
    >>> blosc.decompress(memoryview(compressed))
    'abcdefghijklmnopqrstuvwxyz'
    >>> blosc.decompress(bytearray(compressed))
    'abcdefghijklmnopqrstuvwxyz'

Note however, that there are subtle differences between Python 2.x and 3.x. For example, in Python 2.x we can compress/decompress both `str` and `unicode` types, whereas in Python 3.x we can only compress ‘binary’ data which does not include `unicode`.

## Packaging NumPy arrays

Want to use `blosc` to compress and decompress NumPy objects without having to worry about passing the typesize for optimal compression, or having to create the final container for decompression? `blosc` comes with the `pack_array` and `unpack_array` to perform this in a handy way:

    >>> a = np.linspace(0, 100, 1e7)
    >>> packed = blosc.pack_array(a)
    >>> %timeit blosc.pack_array(a)
    10 loops, best of 3: 104 ms per loop  # ~ 770 MB/s
    >>> %timeit blosc.unpack_array(packed)
    10 loops, best of 3: 76.3 ms per loop  # ~ 1 GB/s

Although this is a convenient way for compressing/decompressing NumPy arrays, this method uses pickle/unpickle behind the scenes. This step implies additional copies, which takes both memory and time.

## Compressing from a data pointer

For avoiding the data copy problem in the previous section, `blosc` comes with a couple of lower-level functions: `compress_ptr` and `decompress_ptr`. Here are they in action:

    >>> c = blosc.compress_ptr(a.__array_interface__['data'][0], a.size, a.dtype.itemsize, 9, True)
    >>> %timeit blosc.compress_ptr(a.__array_interface__['data'][0], a.size, a.dtype.itemsize, 9, True)
    10 loops, best of 3: 17.8 ms per loop   # ~ 4.5 GB/s
    >>> a2 = np.empty(a.size, dtype=a.dtype)
    >>> %timeit blosc.decompress_ptr(c, a2.__array_interface__['data'][0])
    100 loops, best of 3: 11 ms per loop   #  ~ 7.3 GB/s
    >>> (a == a2).all()
    True

As you see, these are really low level functions because you should pass actual pointers where the data is, as well as the size and itemsize (for compression). Needless to say, it is very easy to cause a segfault by passing incorrect parameters to the functions (wrong pointer or wrong size).

On the other hand, and contrarily to the `pack_array` / `unpack_array` method, the `compress_ptr` / `decompress_ptr` functions do not need to make internal copies of the data buffers, so they are extremely fast (as much as the C-Blosc library can be), but you have to provide a container when doing the de-serialization.