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Compress Me, Stupid!

Summary

Compression is a general technique for reducing the size of datasets that normally lie on disk or that should be sent remotely. But time has come to use it as a means to accelerate applications that uses in-memory data too.

Blosc is a high-performance meta-compressor that is meant to do that.

Description

Compression is a technique to reduce the number of bits needed to represent a given dataset. A very common use-case in the distributed digital age is to reduce the size of files in order to reduce the time and bandwidth requirements of sending a file from one location to another.

There are a large variety of different algorithms and implementations of so called "codecs" - a term is derived from the fact that programs that implement a compression algorithm commonly constitute of both a compressor and a corresponding decompressor. There are many different special purpose compressors that exploit specifics in the structure of the input data, for example: MP3, Ogg and FLAC for audio data such as music, GIF, JPEG and PNG for images and MPEG for encoding video. Also, there are many general purpose codecs that make no assumptions about the structure of the data, for example: Zlib(DEFLATE), Bzip2(BWT) and LZMA.

However, and due to the ever growing divide between memory access latency and CPU clock speed a new use-case beyond faster file transfers and more efficient use of disk-space has emerged: "in-memory compression".

Keeping data in RAM that is compressed also means that the CPU has to do more work in order to make use of it. However, if the compressor is fast enough, this decompression overhead could pay off, and applications could work with compressed data transparently, and so not even noticing the slowdown due to the extra effort for compression/decompression.

This technique can be very beneficial in a variety of scenarios where RAM availability is critical. For example, in-memory caching systems like Memcached or Redis could store more data using the same resources thereby optimizing resource usage. Another use case is to use compression for in-memory data containers, à la NumPy's ndarray or Pandas' DataFrame, allowing for improved memory usage and potentially allow for accelerated computations.

In our talk, we will explain first why we are in a moment of computer history that in-memory compression can be beneficial for modern applications.

Then, we will introduce Blosc, a high speed meta-compressor, allowing other existing compressors (BloscLZ, LZ4, Snappy or even Zlib) to leverage the SIMD and multithreading framework that it provides and help achieving extremely fast operation (frequently faster than a plain memcpy() system call).

Finally, we will show some existing data handling libraries (Bloscpack, PyTables, BLZ) -- all written in Python -- that already use Blosc today for fulfilling the promise of faster operations by doing in-memory compressing.

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