AGFeldman/velox

Velox is a C++ database acceleration library which provides reusable,
extensible, and high-performance data processing components. These components
can be reused to build compute engines focused on different analytical
workloads, including batch, interactive, stream processing, and AI/ML.
Velox was created by Facebook and it is currently developed in partnership with
Intel, ByteDance, and Ahana.

In common usage scenarios, Velox takes a fully optimized query plan as input
and performs the described computation. Considering Velox does not provide a
SQL parser, a dataframe layer, or a query optimizer, it is usually not meant
to be used directly by end-users; rather, it is mostly used by developers
integrating and optimizing their compute engines.

Velox provides the following high-level components:

• Type: a generic typing system that supports scalar, complex, and nested
  types, such as structs, maps, arrays, tensors, etc.
• Vector: an Arrow-compatible columnar memory layout
  module,
  which provides multiple encodings, such as Flat, Dictionary, Constant,
  Sequence/RLE, and Bias, in addition to a lazy materialization pattern and
  support for out-of-order writes.
• Expression Eval: a fully vectorized expression evaluation
  engine
  that allows expressions to be efficiently executed on top of Vector/Arrow
  encoded data.
• Function Packages: sets of vectorized function implementations following
  the Presto and Spark semantic.
• Operators: implementation of common data processing operators such as
  scans, projection, filtering, groupBy, orderBy, shuffle, hash
  join, unnest,
  and more.
• I/O: a generic connector interface that allows different file formats
  (ORC/DWRF and Parquet) and storage adapters (S3, HDFS, local files) to be
  used.
• Network Serializers: an interface where different wire protocols can be
  implemented, used for network communication, supporting
  PrestoPage
  and Spark's UnsafeRow.
• Resource Management: a collection of primitives for handling
  computational resources, such as memory
  arenas and
  buffer management, tasks, drivers, and thread pools for CPU and thread
  execution, spilling, and caching.

Velox is extensible and allows developers to define their own engine-specific
specializations, including:

1. Custom types
2. Simple and vectorized functions
3. Aggregate functions
4. Operators
5. File formats
6. Storage adapters
7. Network serializers

Examples

Examples of extensibility and integration with different component APIs can be
found here

Documentation

Developer guides detailing many aspects of the library, in addition to the list
of available functions can be found here.

Getting Started

We provide scripts to help developers setup and install Velox dependencies.

Get the Velox Source

git clone --recursive https://github.com/facebookincubator/velox.git
cd velox
# if you are updating an existing checkout
git submodule sync --recursive
git submodule update --init --recursive

Setting up on macOS

Once you have checked out Velox, on an Intel MacOS machine you can setup and then build like so:

$ ./scripts/setup-macos.sh 
$ make

On an M1 MacOS machine you can build like so:

$ CPU_TARGET="arm64" ./scripts/setup-macos.sh
$ CPU_TARGET="arm64" make

You can also produce intel binaries on an M1, use CPU_TARGET="sse" for the above.

Setting up on aarch64 Linux (Ubuntu 20.04 or later)

On an aarch64 based machine, you can build like so:

$ CPU_TARGET="aarch64" ./scripts/setup-ubuntu.sh
$ CPU_TARGET="aarch64" make

Setting up on x86_64 Linux (Ubuntu 20.04 or later)

Once you have checked out Velox, you can setup and build like so:

$ ./scripts/setup-ubuntu.sh 
$ make

Building Velox

Run make in the root directory to compile the sources. For development, use
make debug to build a non-optimized debug version, or make release to build
an optimized version. Use make unittest to build and run tests.

Note that,

• Velox requires C++17 , thus minimum supported compiler is GCC 5.0 and Clang 5.0.
• Velox requires the CPU to support instruction sets:
  • bmi
  • bmi2
  • f16c
• Velox tries to use the following (or equivalent) instruction sets where available:
  • On Intel CPUs
    • avx
    • avx2
    • sse
  • On ARM
    • Neon
    • Neon64

Building Velox with docker-compose

If you don't want to install the system dependencies required to build Velox,
you can also build and run tests for Velox on a docker container
using docker-compose.
Use the following commands:

$ docker-compose build ubuntu-cpp
$ docker-compose run --rm ubuntu-cpp

If you want to increase or decrease the number of threads used when building Velox
you can override the NUM_THREADS environment variable by doing:

$ docker-compose run -e NUM_THREADS=<NUM_THREADS_TO_USE> --rm ubuntu-cpp

Contributing

Check our contributing guide to learn about how to
contribute to the project.

Community

The main communication channel with the Velox OSS community is through the
the Velox-OSS Slack workspace.
Please reach out to velox@meta.com to get access to Velox Slack Channel.

License

Velox is licensed under the Apache 2.0 License. A copy of the license
can be found here.