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Description

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 Meta 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.

Programming language: C++
License: Apache License 2.0
Tags: Analytics     Data     Database     Engine    

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README

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](velox/examples)

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 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

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](CONTRIBUTING.md) 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 [email protected] 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.](LICENSE)


*Note that all licence references and agreements mentioned in the Velox README section above are relevant to that project's source code only.