# bcolz: columnar and compressed data containers

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bcolz provides columnar, chunked data containers that can be compressed
either in-memory and on-disk. Column storage allows for efficiently
querying tables, as well as for cheap column addition and removal. It is
based on [NumPy](http://www.numpy.org), and uses it as the standard data
container to communicate with bcolz objects, but it also comes with
support for import/export facilities to/from [HDF5/PyTables
tables](http://www.pytables.org) and [pandas
dataframes](http://pandas.pydata.org).

bcolz objects are compressed by default not only for reducing memory/disk storage, but also to improve I/O speed. The
compression process is carried out internally by [Blosc](http://blosc.org), a high-performance, multithreaded
meta-compressor that is optimized for binary data (although it works with text data just fine too).

bcolz can also use [numexpr](https://github.com/pydata/numexpr)
internally (it does that by default if it detects numexpr installed) or
[dask](https://github.com/dask/dask) so as to accelerate many vector and query operations (although it can use pure
NumPy for doing so too). numexpr/dask can optimize the memory usage and use multithreading for doing the computations,
so it is blazing fast. This, in combination with carray/ctable disk-based, compressed containers, can be used for
performing out-of-core computations efficiently, but most importantly
*transparently*.

Just to whet your
appetite, [here is an example](http://nbviewer.ipython.org/github/Blosc/movielens-bench/blob/master/querying-ep14.ipynb)
with real data, where bcolz is already fulfilling the promise of accelerating memory I/O by using compression.

## Rationale

By using compression, you can deal with more data using the same amount of memory, which is very good on itself. But in
case you are wondering about the price to pay in terms of performance, you should know that nowadays memory access is
the most common bottleneck in many computational scenarios, and that CPUs spend most of its time waiting for data.
Hence, having data compressed in memory can reduce the stress of the memory subsystem as well.

Furthermore, columnar means that the tabular datasets are stored column-wise order, and this turns out to offer better
opportunities to improve compression ratio. This is because data tends to expose more similarity in elements that sit in
the same column rather than those in the same row, so compressors generally do a much better job when data is aligned in
such column-wise order. In addition, when you have to deal with tables with a large number of columns and your
operations only involve some of them, a columnar-wise storage tends to be much more effective because minimizes the
amount of data that travels to CPU caches.

So, the ultimate goal for bcolz is not only reducing the memory needs of large arrays/tables, but also making bcolz
operations to go faster than using a traditional data container like those in NumPy or Pandas. That is actually already
the case in some real-life scenarios (see the notebook above) but that will become pretty more noticeable in combination
with forthcoming, faster CPUs integrating more cores and wider vector units.

## Requisites

- Python >= 3.7
- NumPy >= 1.16.5
- Cython >= 0.22 (just for compiling the beast)
- C-Blosc >= 1.8.0 (optional, as the internal Blosc will be used by default)

Optional:

- numexpr >= 2.5.2
- dask >= 0.9.0
- pandas
- tables (pytables)

## Installing as wheel

There are wheels for Linux and Mac OS X that you can install with
```python
pip install bcolz-zipline
```
Then also install NumPy with 
```python
pip install numpy
```
and test your installation with 
```python
python -c 'import bcolz;bcolz.test()'
```

## Building

There are different ways to compile bcolz, depending if you want to link with an already installed Blosc library or not.

### Compiling with an installed Blosc library (recommended)

Python and Blosc-powered extensions have a difficult relationship when compiled using GCC, so this is why using an
external C-Blosc library is recommended for maximum performance (for details, see
<https://github.com/Blosc/python-blosc/issues/110>).

Go to <https://github.com/Blosc/c-blosc/releases> and download and install the C-Blosc library. Then, you can tell bcolz
where is the C-Blosc library in a couple of ways:

Using an environment variable:

``` {.sourceCode .console}
$ BLOSC_DIR=/usr/local     (or "set BLOSC_DIR=\blosc" on Win)
$ export BLOSC_DIR         (not needed on Win)
$ python setup.py build_ext --inplace
```

Using a flag:

``` {.sourceCode .console}
$ python setup.py build_ext --inplace --blosc=/usr/local
```

### Compiling without an installed Blosc library

bcolz also comes with the Blosc sources with it so, assuming that you have a C++ compiler installed, do:

``` {.sourceCode .console}
$ python setup.py build_ext --inplace
```

That\'s all. You can proceed with testing section now.

Note: The requirement for the C++ compiler is just for the Snappy dependency. The rest of the other components of Blosc
are pure C
(including the LZ4 and Zlib libraries).

## Testing

After compiling, you can quickly check that the package is sane by running:

    $ PYTHONPATH=.   (or "set PYTHONPATH=." on Windows)
    $ export PYTHONPATH    (not needed on Windows)
    $ python -c"import bcolz; bcolz.test()"  # add `heavy=True` if desired

## Installing

Install it as a typical Python package:

    $ pip install -U .

Optionally Install the additional dependencies:

    $ pip install .[optional]

## Documentation

You can find the online manual at:

<http://bcolz.blosc.org>

but of course, you can always access docstrings from the console (i.e.
`help(bcolz.ctable)`).

Also, you may want to look at the bench/ directory for some examples of use.

## Resources

Visit the main bcolz site repository at: <http://github.com/Blosc/bcolz>

Home of Blosc compressor: <http://blosc.org>

User\'s mail list: <http://groups.google.com/group/bcolz>
(<bcolz@googlegroups.com>)

An [introductory talk (20 min)](https://www.youtube.com/watch?v=-lKV4zC1gss) about bcolz at EuroPython

2014. [Slides here](http://blosc.org/docs/bcolz-EuroPython-2014.pdf).

## License

Please see `BCOLZ.txt` in `LICENSES/` directory.

## Share your experience

Let us know of any bugs, suggestions, gripes, kudos, etc. you may have.

**Enjoy Data!**
