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Tuplex is a parallel big data processing framework that runs data science pipelines written in Python at the speed of compiled code. Tuplex has similar Python APIs to Apache Spark or Dask, but rather than invoking the Python interpreter, Tuplex generates optimized LLVM bytecode for the given pipeline and input data set.

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Tuplex: Blazing Fast Python Data Science

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Tuplex is a parallel big data processing framework that runs data science pipelines written in Python at the speed of compiled code. Tuplex has similar Python APIs to Apache Spark or Dask, but rather than invoking the Python interpreter, Tuplex generates optimized LLVM bytecode for the given pipeline and input data set. Under the hood, Tuplex is based on data-driven compilation and dual-mode processing, two key techniques that make it possible for Tuplex to provide speed comparable to a pipeline written in hand-optimized C++.

You can join the discussion on Tuplex on our Gitter community or read up more on the background of Tuplex in our SIGMOD'21 paper.

Contributions welcome!

Contents

Example

Tuplex can be used in python interactive mode, a jupyter notebook or by copying the below code to a file. To try it out, run the following example:

from tuplex import *
c = Context()
res = c.parallelize([1, 2, None, 4]).map(lambda x: (x, x * x)).collect()
# this prints [(1, 1), (2, 4), (4, 16)]
print(res)

Quickstart

To try out Tuplex, simply try out the following starter notebooks using Google Colab:

Name Link Description
(01) Intro to Tuplex Google Colab Basic commands to manipulate columns and modify data with user code
(02) Working with Files Google Colab Loading and saving files, detecting types.

More examples can be found here.

Installation

To install Tuplex, you can use a PyPi package for Linux or MacOS(Intel), or a Docker container which will launch a jupyter notebook with Tuplex preinstalled.

Docker

docker run -p 8888:8888 tuplex/tuplex

PyPI

pip install tuplex

Building

Tuplex is available for MacOS and Linux. The current version has been tested under MacOS 10.13-12.0 and Ubuntu 18.04/20.04/22.04 LTS. To install Tuplex, simply install the dependencies first and then build the package.

MacOS build from source

To build Tuplex, you need several other packages first which can be easily installed via brew. If you want to build Tuplex with AWS support, you need macOS 10.13+.

brew install [email protected] boost boost-python3 aws-sdk-cpp pcre2 antlr4-cpp-runtime googletest gflags yaml-cpp celero protobuf libmagic
python3 -m pip install 'cloudpickle<2.0' numpy
python3 setup.py install --user

Ubuntu build from source

To faciliate installing the dependencies for Ubuntu, we do provide two scripts (scripts/ubuntu1804/install_reqs.sh for Ubuntu 18.04, or scripts/ubuntu2004/install_reqs.sh for Ubuntu 20.04). To create an up to date version of Tuplex, simply run

./scripts/ubuntu1804/install_reqs.sh
python3 -m pip install 'cloudpickle<2.0' numpy
python3 setup.py install --user

Customizing the build

Besides building a pip package, especially for development it may be more useful to invoke cmake directly. To create a development version of Tuplex and work with it like a regular cmake project, go to the folder tuplex and then use the standard workflow to compile the package via cmake (and not the top-level setup.py file):

mkdir build
cd build
cmake ..
make -j$(nproc)

The python package corresponding to Tuplex can be then found in build/dist/python with C++ test executables based on googletest in build/dist/bin. If you'd like to use a cmake-compatible IDE like CLion or VSCode you can simply open the tuplex/ folder and import the CMakeLists.txt contained there.

To customize the cmake build, the following options are available to be passed via -D<option>=<value>:

option values description
CMAKE_BUILD_TYPE Release (default), Debug, RelWithDebInfo, tsan, asan, ubsan select compile mode. Tsan/Asan/Ubsan correspond to Google Sanitizers.
BUILD_WITH_AWS ON (default), OFF build with AWS SDK or not. On Ubuntu this will build the Lambda executor.
BUILD_WITH_ORC ON, OFF (default) build with ORC file format support.
BUILD_NATIVE ON, OFF (default) build with -march=native to target platform architecture.
SKIP_AWS_TESTS ON (default), OFF skip aws tests, helpful when no AWS credentials/AWS Tuplex chain is setup.
GENERATE_PDFS ON, OFF (default) output in Debug mode PDF files if graphviz is installed (e.g., brew install graphviz) for ASTs of UDFs, query plans, ...
PYTHON3_VERSION 3.6, ... when trying to select a python3 version to build against, use this by specifying major.minor. To specify the python executable, use the options provided by cmake.
LLVM_ROOT_DIR e.g. /usr/lib/llvm-9 specify which LLVM version to use
BOOST_DIR e.g. /opt/boost specify which Boost version to use. Note that the python component of boost has to be built against the python version used to build Tuplex

For example, to create a debug build which outputs PDFs use the following snippet:

cmake -DCMAKE_BUILD_TYPE=Debug -DGENERATE_PDFS=ON ..

License

Tuplex is available under Apache 2.0 License, to cite the paper use:

@inproceedings{10.1145/3448016.3457244,
author = {Spiegelberg, Leonhard and Yesantharao, Rahul and Schwarzkopf, Malte and Kraska, Tim},
title = {Tuplex: Data Science in Python at Native Code Speed},
year = {2021},
isbn = {9781450383431},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3448016.3457244},
doi = {10.1145/3448016.3457244},
booktitle = {Proceedings of the 2021 International Conference on Management of Data},
pages = {1718–1731},
numpages = {14},
location = {Virtual Event, China},
series = {SIGMOD/PODS '21}
}

(c) 2017-2022 Tuplex contributors

Open Source Agenda is not affiliated with "Tuplex" Project. README Source: tuplex/tuplex
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