Weld is a language and runtime for improving the performance of data-intensive applications. It optimizes across libraries and functions by expressing the core computations in libraries using a common intermediate representation, and optimizing across each framework.

Modern analytics applications combine multiple functions from different libraries and frameworks to build complex workflows. Even though individual functions can achieve high performance in isolation, the performance of the combined workflow is often an order of magnitude below hardware limits due to extensive data movement across the functions. Weld’s take on solving this problem is to lazily build up a computation for the entire workflow, and then optimizing and evaluating it only when a result is needed.

• Building
  • MacOS LLVM Installation
  • Ubuntu LLVM Installation
  • Building Weld
• Documentation
• Grizzly (Pandas on Weld)
• Running an Interactive REPL
• Benchmarking

To build Weld, you need Rust 1.13 or higher and LLVM 3.8.

To install Rust, follow the steps here. You can verify that Rust was installed correctly on your system by typing rustc into your shell.

To install LLVM on macOS, first install brew. Then:

$ brew install llvm38
$ export PATH=$PATH:/usr/local/bin

Weld's dependencies require llvm-config, so you may need to create a symbolic link so the correct llvm-config is picked up:

$ ln -s /usr/local/bin/llvm-config-3.8 /usr/local/bin/llvm-config

To make sure this worked correctly, run llvm-config --version. You should see 3.8.x.

To install LLVM on Ubuntu :

$ sudo apt install llvm-3.8
$ sudo apt install clang-3.8

Weld's dependencies require llvm-config, so you may need to create a symbolic link so the correct llvm-config is picked up:

$ ln -s /usr/bin/llvm-config-3.8 /usr/local/bin/llvm-config

To make sure this worked correctly, run llvm-config --version. You should see 3.8.x.

With LLVM and Rust installed, you can build Weld. Clone this repository, set the WELD_HOME environment variable, and build using cargo:

$ git clone https://www.github.com/weld-project/weld
$ cd weld/
$ export WELD_HOME=`pwd`
$ cargo build --release

Weld builds two dynamically linked libraries (.so files on Linux and .dylib files on macOS): libweld and libweldrt. Both of these libraries must be on the LD_LIBRARY_PATH. By default, the libraries are in $WELD_HOME/target/release and $WELD_HOME/weld_rt/target/release. Set up the LD_LIBRARY_PATH as follows:

$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$WELD_HOME/weld_rt/target/release:$WELD_HOME/target/release

Finally, run the unit and integration tests:

$ cargo test

The docs/ directory contains documentation for the different components of Weld.

• language.md describes the syntax of the Weld IR.
• api.md describes the low-level C API for interfacing with Weld.
• python.md gives an overview of the Python API.
• tutorial.md contains a tutorial for how to build a small vector library using Weld.

Grizzly is a subset of Pandas integrated with Weld. Details on how to use Grizzly are in python/grizzly. Some example workloads that make use of Grizzly are in examples/python/grizzly.

• cargo test runs unit and integration tests. A test name substring filter can be used to run a subset of the tests:

  cargo test <substring to match in test name>
  

• The target/release/repl program is a simple "shell" where one can type Weld programs and see the results of parsing, macro substitution and type inference.

Example repl session:

> let a = 5 + 2; a + a
Raw structure: [...]

After macro substitution:
let a=((5+2));(a+a)

After inlining applies:
let a=((5+2));(a+a)

After type inference:
let a:i32=((5+2));(a:i32+a:i32)

Expression type: i32

> map([1, 2], |x| x+1)
Raw structure: [...]

After macro substitution:
result(for([1,2],appender[?],|b,x|merge(b,(|x|(x+1))(x))))

After inlining applies:
result(for([1,2],appender[?],|b,x|merge(b,(x+1))))

After type inference:
result(for([1,2],appender[i32],|b:appender[i32],x:i32|merge(b:appender[i32],(x:i32+1))))

Expression type: vec[i32]

cargo bench runs benchmarks under the benches/ directory. The results of the benchmarks are written to a file called benches.csv. To specify specific benchmarks to run:

$ cargo bench [benchmark-name]

If a benchmark name is not provided, all benchmarks are run.