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.../static/img/undraw_docusaurus_react.svg | 170 - website/static/img/undraw_docusaurus_tree.svg | 40 - website/static/img/velox-favicon.png | Bin 79916 -> 0 bytes website/static/img/velox-logo-white.png | Bin 79118 -> 0 bytes website/static/img/velox-logo.svg | 1 - website/static/img/velox_blog_pic.png | Bin 79812 -> 0 bytes 51 files changed, 23310 deletions(-) delete mode 100644 website/.gitignore delete mode 100644 website/README.md delete mode 100644 website/babel.config.js delete mode 100644 website/blog/2022-07-01-june-2022-updates.mdx delete mode 100644 website/blog/2022-07-31-public-announcement.mdx delete mode 100644 website/blog/2022-10-31-simple-1.mdx delete mode 100644 website/blog/2023-03-15-build-experience.mdx delete mode 100644 website/blog/2023-05-01-simple-2.mdx delete mode 100644 website/blog/2023-07-19-array-sort.mdx delete mode 100644 website/blog/2023-09-06-optimize-try-cast.mdx delete mode 100644 website/blog/2023-10-02-reduce-agg.mdx delete mode 100644 website/blog/2024-01-27-like-optimization.mdx delete mode 100644 website/blog/authors.yml delete mode 100644 website/docs/intro.md delete mode 100644 website/docusaurus.config.js delete mode 100644 website/package-lock.json delete mode 100644 website/package.json delete mode 100644 website/sidebars.js delete mode 100644 website/src/components/HomepageFeatures/index.js delete mode 100644 website/src/components/HomepageFeatures/styles.module.css delete mode 100644 website/src/css/custom.css delete mode 100644 website/src/pages/index.js delete mode 100644 website/src/pages/index.module.css delete mode 100644 website/static/.nojekyll delete mode 100644 website/static/img/dark_blue_dots.jpg delete mode 100644 website/static/img/favicon.ico delete mode 100644 website/static/img/logo.svg delete mode 100644 website/static/img/simple1_1.png delete mode 100644 website/static/img/simple1_10.png delete mode 100644 website/static/img/simple1_11.png delete mode 100644 website/static/img/simple1_12.png delete mode 100644 website/static/img/simple1_13.png delete mode 100644 website/static/img/simple1_14.png delete mode 100644 website/static/img/simple1_2.png delete mode 100644 website/static/img/simple1_3.png delete mode 100644 website/static/img/simple1_4.png delete mode 100644 website/static/img/simple1_5.png delete mode 100644 website/static/img/simple1_6.png delete mode 100644 website/static/img/simple1_7.png delete mode 100644 website/static/img/simple1_8.png delete mode 100644 website/static/img/simple1_9.png delete mode 100644 website/static/img/simple1_code1.png delete mode 100644 website/static/img/simple1_code2.png delete mode 100644 website/static/img/stack_transform.png delete mode 100644 website/static/img/undraw_docusaurus_mountain.svg delete mode 100644 website/static/img/undraw_docusaurus_react.svg delete mode 100644 website/static/img/undraw_docusaurus_tree.svg delete mode 100644 website/static/img/velox-favicon.png delete mode 100644 website/static/img/velox-logo-white.png delete mode 100644 website/static/img/velox-logo.svg delete mode 100644 website/static/img/velox_blog_pic.png diff --git a/website/.gitignore b/website/.gitignore deleted file mode 100644 index b2d6de30624f..000000000000 --- a/website/.gitignore +++ /dev/null @@ -1,20 +0,0 @@ -# Dependencies -/node_modules - -# Production -/build - -# Generated files -.docusaurus -.cache-loader - -# Misc -.DS_Store -.env.local -.env.development.local -.env.test.local -.env.production.local - -npm-debug.log* -yarn-debug.log* -yarn-error.log* diff --git a/website/README.md b/website/README.md deleted file mode 100644 index aaba2fa1e16e..000000000000 --- a/website/README.md +++ /dev/null @@ -1,41 +0,0 @@ -# Website - -This website is built using [Docusaurus 2](https://docusaurus.io/), a modern static website generator. - -### Installation - -``` -$ yarn -``` - -### Local Development - -``` -$ yarn start -``` - -This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server. - -### Build - -``` -$ yarn build -``` - -This command generates static content into the `build` directory and can be served using any static contents hosting service. - -### Deployment - -Using SSH: - -``` -$ USE_SSH=true yarn deploy -``` - -Not using SSH: - -``` -$ GIT_USER= yarn deploy -``` - -If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch. diff --git a/website/babel.config.js b/website/babel.config.js deleted file mode 100644 index e00595dae7d6..000000000000 --- a/website/babel.config.js +++ /dev/null @@ -1,3 +0,0 @@ -module.exports = { - presets: [require.resolve('@docusaurus/core/lib/babel/preset')], -}; diff --git a/website/blog/2022-07-01-june-2022-updates.mdx b/website/blog/2022-07-01-june-2022-updates.mdx deleted file mode 100644 index a3f528534ffc..000000000000 --- a/website/blog/2022-07-01-june-2022-updates.mdx +++ /dev/null @@ -1,62 +0,0 @@ ---- -slug: june-2022-updates -title: June 2022 Update -authors: [majetideepak] -tags: [monthly-updates] ---- - - -## Documentation - -* Add documentation for :ref:`complex types writers`. - -## Core Library - -* Add support for INTERVAL DAY TO SECOND Presto type. -* Allow cast between DATE and TIMESTAMP types. -* Allow cast from JSON to scalar, ARRAY, and MAP types. -* Add :ref:`GroupIdNode` and GroupId operator to support aggregations over grouping sets. -* Add support for function signatures with DECIMAL input and return types using flex and bison to evaluate formulas for calculating the return precision and scale based on input precisions and scales. -* Add support for conversion of DuckDB DECIMALS to Velox DECIMALS. -* Add support for running tasks on the caller's thread. -* Fix expression evaluation to disable sub-expression optimization for non-deterministic functions. - -## Presto Functions - -* Add :func:`degrees`, :func:`e`, and :func:`sha512` functions. -* Add aggregate function :func:`map_union`. -* Optimize :func:`zip` for the case when all arrays are flat and the same size. -* Extend :func:`plus`, :func:`minus` functions to support DATE, INTERVAL DAY TO SECOND argument types. - -## Hive Connector - -* Add support for reading files from HDFS. -* Add limited ORC support. -* Optimize `NOT IN ()` filters pushed down into DWRF reader. - -## TPC-H Connector - -* Add `totalParts` and `partNumber` to TpchSplit. - -## Performance and Correctness - -* Add q3 to TPC-H benchmark. -* Add utility to benchmark dataset generation speed to TPC-H connector. -* Optimize constant aggregation mask. -* Optimize VectorWriter for a subset of simple functions that return strings. -* Optimize DictionaryVector wrapping LazyVector to load only necessary rows. - -## Debugging Experience - -* Separate the user exception stack from the runtime exception stack trace collection control. - -## Credits - -Adam Simpkins, Aditi Pandit, Amit Dutta, Behnam Robatmili, Chad Austin, -Connor Devlin, Daniel Ng, Dark Knight, Deepak Majeti, Denis Yaroshevskiy, -Huameng Jiang, Jake Jung, Jialiang Tan, Jie1 Zhang, Jimmy Lu, Karteek Murthy, -Katie Mancini, Ke Jia, Kevin Wilfong, Krishna Pai, Laith Sakka, Masha Basmanova, -Michael Shang, Mindaugas Rukas, Orri Erling, Patrick Stuedi, Paul Saab, -Pedro Eugenio Rocha Pedreira, Pramod Sathyanarayana, Sahana CB, Sergey Pershin, -Wei He, Xavier Deguillard, Xiaoxuan Meng, Yating Zhou, Yoav Helfman, Zeyi (Rice) Fan, -Zhenyuan Zhao, artem.malyshev, benitakbritto, frankobe, usurai, yingsu00, zhaozhenhui diff --git a/website/blog/2022-07-31-public-announcement.mdx b/website/blog/2022-07-31-public-announcement.mdx deleted file mode 100644 index 340c50bac89c..000000000000 --- a/website/blog/2022-07-31-public-announcement.mdx +++ /dev/null @@ -1,23 +0,0 @@ ---- -slug: public-announcement -title: 'Velox: The Unified Execution Engine Now Available' -tags: [public-announcement] ---- -![img alt](/img/velox_blog_pic.png) - -We are extremely excited to introduce Velox as an [open-source project](https://engineering.fb.com/2022/08/31/open-source/velox/), with a mission to define common standards for modular data processing systems. Velox provides reusable, extensible, high-performance, and dialect-agnostic data processing components for building execution engines, and enhancing data management systems. We envision Velox to be the defacto execution engine for [Arrow-compatible](https://www.globenewswire.com/en/news-release/2022/06/23/2468271/0/en/Voltron-Data-Announces-Commitment-to-Improve-Interoperability-Between-Apache-Arrow-and-the-Velox-Open-Source-Project-Created-by-Meta.html) data format powering ML and Analytical workloads. - -The Velox team has been partnering with companies such as [Ahana, Intel, and Voltron Data](https://www.techtarget.com/searchdatamanagement/news/252524446/Meta-and-partners-build-Velox-open-source-execution-engine?amp=1) as well as various academic institutions to accelerate innovation and development in the data management industry. - -Looking at the future, we believe Velox’s unified and modular nature has the potential to disrupt the data management industry. It will allow us to deepen our partnership with hardware vendors and proactively adapt our unified software stack as hardware advances. We believe that modularity and reusability are the future of database system development and hope a vibrant open source community will help us in this journey. - -Quick Introduction - - - -If you are excited to learn what’s under the hood, refer to the Velox research [paper](https://research.facebook.com/publications/velox-metas-unified-execution-engine/). - -If you are interested in contributing, visit our [Contributing](https://github.com/facebookincubator/velox/blob/main/CONTRIBUTING.md) guide on Github. For technical discussions and exploring what’s happening within our community, refer to the [discussions](https://github.com/facebookincubator/velox/discussions) section. - -We will be publishing a series of technical blogs on various topics on our [website](https://velox-lib.io/blog) soon! - -For more updates and exciting news follow us on [Twitter](https://twitter.com/velox_lib). diff --git a/website/blog/2022-10-31-simple-1.mdx b/website/blog/2022-10-31-simple-1.mdx deleted file mode 100644 index 3b711f61acc4..000000000000 --- a/website/blog/2022-10-31-simple-1.mdx +++ /dev/null @@ -1,109 +0,0 @@ ---- -slug: simple-functions-1 -title: "Simple Functions: Introduction and Basic Optimizations" -authors: [lsakka] -tags: [tech-blog, simple-functions] ---- - -_This blogpost is part of a series of blog posts that discuss different features and optimizations of the simple function interface in Velox_. - - -## Introduction to Simple Functions - -Scalar functions are one of the most used extension points in Velox. Since Velox is a vectorized engine, by nature functions are "vector functions" that consume Velox vectors (batches of data) and produce vectors. Velox allows users to write functions as vector functions or as single-row operations "simple functions" that are converted to vector functions using template expansion through SimpleFunctionAdapter. - -
- -
- -Writing functions as vector functions directly gives the user `complete` control over the function implementations and optimizations, however it comes with some cost that can be summarized in three points: - -- **Complexity** : Requires an understanding of Velox vectorized data representation and encodings, which requires additional work for our customers, specially those without DB background. Moreover, Writing optimized vector functions requires even deeper understanding. -- **Repetition** : Involves repeated efforts and code; in each function, authors have to decode the input vectors, apply the same optimizations, and build the output vectors. For example, most arithmetic functions need benefits from a fast path when all the inputs are flat-encoded, authors need to implement that for every function that benefits from it. -- **Reliability** : More code means more bugs, especially in such a complex context. - -Writing functions through the simple interface mitigates the previously mentioned drawbacks, and significantly simplifies the function authoring process. For example, to add the function plus the user only needs to implement the PlusFunction struct shown in the graph above , which is then expanded using template expansion to a vector function. - -However, the simple function interface does not give the user full control over the authoring and has its own limitations, for example the function map_keys can be implemented in O(1) as a vector function by moving the keys vector; this is not possible to express as a simple function. - -Another limitation is that when using the simple interface, authors do not have access to the encodings of the input vectors, nor control over the encoding of the result vector. Hence, do not have the power to optimize the code for specific input encodings or optimize it by generating specific output encodings. The array_sort function for instance does not need to re-order the elements and copy them during sorting; instead it can generate a dictionary vector as an output, which is something not expressible as a simple function. - - -In the ideal world we would like to add most of the optimization that someone can do in a vector function to the simple functions adapter, so it would be enabled automatically. We have identified a number of optimizations that apply to all functions and implemented these generically in the SimpleFunctionAdapter. In this way, we can achieve the best of the two worlds and gain **Simplicity**, **Efficiency** and **Reliability** for most functions. - -In the past year, we have been working on several improvements to the simple function interface on both the `expressivity` and `performance` axes that we will discuss in this series of notes. - -In this blog post, we will talk about some of the general optimizations that we have in the adapter, the optimizations discussed in this post make the performance of most simple functions that operates on primitive types matches their counter optimized vector function implementations. In the next blog post, we will discuss complex types in simple functions. - -## General Optimizations - -#### Vector Reuse -If the output type matches one of the input types, and the input vector is to die after the function invocation, then it is possible to reuse it for the results instead of allocating a new vector. For example, in the expression plus(a, b), if a is stored in a flat vector that is not used after the invocation of the plus function, then that vevtor can be used to store the reults of the computation instead of allocating a new vevtor for the results. - -#### Bulk Null Setting -Nulls are represented in a bit vector, hence, writing each bit can be expensive specially for primitive operations (like plus and minus). One optimization is -to optimize for the `not null` case, and bulk setting the nulls to not null. After that during the computation, only if the results are null, the null bit is set to null. - -#### Null Setting Avoidance -The adapter can statically infer if a function never generates null; In the simple function interface if the `call` function return's type is `void`, it means the output is never null, and if it's `bool`, then the function returns true for not null and false for null). - -When the function does not generate nulls, then null setting is **completely avoided** during the computation (only the previous bulk setting is needed). The consequence of that is that the hot loop applying the function becomes simdizable triggering a huge boost in performance for primitive operations. - -Worth to note also that if the simple function happens to be inlined in the adapter, then even if its return type is not void, but it always returns `true` then the compiler will be able to infer that setting nulls is never executed and would remove the null setting code. - - -#### Encoding Based Fast Path -Vectors in Velox can have different encodings (flat, constant..etc). The generic way of reading a vector of arbitrary encoding is to use a decoded vector to guarantee correct data access. Even though decoded vectors provide a consistent API and make it easier to handle arbitrarily encoded input data, they translate into an overhead each time an input value is accessed (we need to check the encoding of the vector to know how to read the value for every row). - -When the function is a primitive operation like plus or minus, such overhead is expensive! To avoid that, encoding based fast paths can be added, the code snippet below illustrates the idea. -
- -
- -In the code above, the overhead of checking the encoding is switched outside the loop that applies the functions (the plus operation here). And the inner loops are simple operations that are potentially simdizable and free of encoding checks. -One issue with this optimization is that the core loop is replicated many times. In general, the numbers of times it will be replicated -is `n^m` where `n` is the number of args, and `m` is the number of encodings. - -To avoid code size blowing, we only apply this optimization when all input arguments are primitives and the number of input arguments is <=3. -The figure below shows the effect of this optimization on the processing time of a query of primitive operations (the expression is a common pattern in ML use cases). - -
- -
- -To compromise for both (performance and code size) when the conditions for specializing for all encodings are not met, we have a pseudo specialization mode that does not blow up the code size, but still reduce the overhead of decoding to a single multiplication per argument. This mode is enabled when all the primitive arguments are either flat or constant. The code below illustrates the idea: - -
- -
- -When the input vector is constant we can read the value always from index 0 of the values buffer, and when it is flat we can read it from the index row; this can be achieved by assigning a factor to either 0 or 1 and reducing the decoding operation per row into a multiplication with that factor Note that such a multiplication does not prevent simd. The graph above shows that the psudeo specialization makes the program 1.6X fatser wi, while the complete specialization makes the program 2.5X faster. - -#### ASCII Fast Path -Functions with string inputs can be optimized when the inputs are known to be ascii. For example the length function for ascii strings is the size of the StringView O(1). But for non-ascii inputs the computation is a more complicated O(n) operation. -Users can define a function `callAscii()` that will be called when all the string input arguments are ascii. - -
- -
- -#### Zero-Copy Optimization -When an input string (or portion of it, reaches the output as is) it does not need to be deep copied. Instead only a StringView needs to be set. Substring is an example of a function that benefits from this. This can be done in the simple function interface in two simple steps. - -1. Using setNoCopy(); to set the output results without copying string vectors. -2. Inform the function to make the output vector share ownership of input string buffers, this can be by setting the field reuse_strings_from_arg. - -The graph below shows the effect of the previous two optimizations on the performance of the substring function. -
- -
Runtime of function substring with different optimizations.
-
- - -#### Constant Inputs Pre-processing - -Users can pre-process constant inputs of functions to avoid repeated computation by defining `initialize` function which is called once during query compilations and receives the constant inputs. For example, a regex function with constant pattern would only needs to compile the pattern expressions only once when its constant. - - - -For more information about how to write simple functions check the documentation and the examples. diff --git a/website/blog/2023-03-15-build-experience.mdx b/website/blog/2023-03-15-build-experience.mdx deleted file mode 100644 index 164c893265e9..000000000000 --- a/website/blog/2023-03-15-build-experience.mdx +++ /dev/null @@ -1,50 +0,0 @@ ---- -slug: velox-build-experience -title: "Improving the Velox Build Experience" -authors: [jwujciak, raulcd, kgpai] -tags: [tech-blog, packaging] ---- - - -When Velox was open sourced in August 2021, it was not nearly as easily usable and portable as it is today. In order for Velox to become the unified execution engine blurring the boundaries for data analytics and ML, we needed Velox to be easy to build and package on multiple platforms, and support a wide range of hardware architectures. If we are supporting all these platforms, we also need to ensure that Velox remains fast and regressions are caught early. - -To improve the Velox experience for users and community developers, Velox has partnered with Voltron Data to help make Velox more accessible and user-friendly. In this blog post, we will examine the challenges we faced, the improvements that have already been made, and the ones yet to come. - -## Enhancements & Improvements - -Velox was a product of the mono repo and required installation of dependencies on the system via a script. Any change in the state of the host system could cause a build failure and introduce version conflicts of dependencies. Fixing these challenges was a big focus to help the Velox Community and we worked in collaboration with the Voltron Data Team. We wanted to improve the overall Velox user experience by making Velox easy to consume across a wide range of platforms to accelerate its adoption. - -We choose hermetic builds as a solution to the aforementioned problems, as they provide a number of benefits. Hermetic builds[^1] improve reproducibility by providing isolation from the state of the host machine and produce the same result for any given commit in the Velox repository. This requires precise dependency management. - -[^1]: [Hermeticity](https://bazel.build/basics/hermeticity) - why hermetic builds are recommended - -The first major step in moving towards hermetic builds was the integration of a new [dependency management system](https://github.com/facebookincubator/velox/tree/main/CMake/resolve_dependency_modules) that is able to download, configure and build the necessary dependencies within the Velox build process. This new system also gives users the option to use already installed system dependencies. We hope this work will increase adoption of Velox in downstream projects and make troubleshooting of build issues easier, as well as improve overall reliability and stability. - -We also wanted to lower the barrier to entry for contributions to Velox. Therefore, we created Docker [Development images](https://github.com/facebookincubator/velox/pkgs/container/velox-dev) for both Ubuntu and CentOS, and we now publish them automatically when changes are merged. We hope this work will help speed up the development process by allowing developers to stand up a development environment quickly, without the requirement of installing third-party dependencies locally. We also use these images in the Velox CI to lower build times and speed up the feedback loop for proposing a PR. - -```bash -# Run the development image from the root of the Velox repository -# to build and test Velox -docker compose run --rm ubuntu-cpp -``` - -An important non-technical improvement is the introduction of new issue templates and utility scripts. These will help guide troubleshooting and getting support from the relevant Velox developers via Github. This helps to improve the experience for the community and make it easier for users and contributors to get help and support when they need it. - -Lastly, we implemented new [nightly builds](https://app.circleci.com/pipelines/github/facebookincubator/velox?branch=main) to increase the overall reliability and stability of Velox, as well as test the integration with downstream community projects. - -To enable easy access to Velox from Python, we built CI infrastructure to generate and publish pre-built binary wheels for [PyVelox](https://pypi.org/project/pyvelox/) (the Velox Python Bindings). We also improved Conda support by contributing to upstream feedstocks. - -```bash -# Try PyVelox today! -pip install pyvelox -``` - -## Future Goals - -We will continue the work of moving all dependencies to the new dependency management system to move closer to hermetic builds and make development and usage as smooth as possible. - -In the same theme, the next major goal is the refactoring of the existing CMake build system to use a target based "modern" style. This will allow us to properly install Velox as a system library to be used by other projects. This project will improve the development experience overall by creating a stable, reliable build infrastructure, but also allows us to publish Velox as a conda-forge package and make it easier to further improve support for non x86_64 architectures like Apple Silicon, arm64 systems, various compilers and older CPUs that don’t support the currently obligatory instructions sets like BMI2 and make Velox available to an even larger community. - -Confidence in the stability and reliability of a project are key when you want to deploy it as part of your stack. Therefore, we are working on a release process and versioning scheme for Velox so that you can deploy with confidence! - -In conclusion, the collaboration between Velox and Voltron Data has led to several key improvements in Velox's packaging and CI. Setting up a new environment with Velox has never been this easy! With the new improvements, this new broader community of developers and contributors can expect a smoother and more user-friendly experience when using Velox. The Velox team is continuously working towards further improving the developer and user experience, and we invite you to [join us](https://github.com/facebookincubator/velox/blob/main/CONTRIBUTING.md) in building the next generation unified execution engine! diff --git a/website/blog/2023-05-01-simple-2.mdx b/website/blog/2023-05-01-simple-2.mdx deleted file mode 100644 index 1f8ce94a8e88..000000000000 --- a/website/blog/2023-05-01-simple-2.mdx +++ /dev/null @@ -1,111 +0,0 @@ ---- -slug: simple-functions-2 -title: "Simple Functions: Efficient Complex Types" -authors: [lsakka] -tags: [tech-blog, simple-functions] ---- -_This blogpost is part of a series of blog posts that discuss different features and optimizations of the simple function interface_. - - -## Efficient Complex Types -In this blogpost, we will discuss two major recent changes to the simple function interface to make its performance comparable to the vector function implementations for functions that produce or consume complex types (Arrays, Maps and Rows). - -To show how much simpler simple functions are. The figure below shows a function _NestedMapSum_ written in both the simple and vector interfaces. The function consumes a nested map and computes the summations of all values and keys. -Note that the vector function implementation is minimal without any special optimization (ex: vector reuse, fast path for flat inputs ..etc). Adding optimizations will make it even longer. - -
- -
NestedMapSum function implemented using vector(left) and simple(right) interfaces.
-
- - -### View types for inputs -The previous representations of input complex types in the simple function interface were computationally expensive. Data from vectors used to be copied into std containers and passed to simple functions to process it. Arrays, Maps and Structs used to be materialized into std::vectors, folly::F14FastMap and std::tuples. - The graph below illustrates the previous approach. -
- -
- -The previous approach has two key inefficiencies; **Eager materialization** : For each row, all the data in the input vector is decoded and read before calling the function. And **Double reading**, the data is read twice once when the input is constructed, and again in the function when it's used. - -In order to mitigate those regressions, Velox introduced View types: ArraViews, MapViews ...etc. The goal is to keep the authoring simple but achieve at least the performance of a basic vector implementation that decodes input and applies some logic for every row without any special optimizations. - -The view types are **Lazy**, very cheap to construct and do not materialize the underlying data unless the code accesses it.For example, the function _array_first_ only needs to read the first element in every array, moreover the _cardinality_ function does not need to read any elements in the array. They view types have interfaces similar to those of std::containers. - -In a simplified form, an ArrayView stores the length and the offset of the array within the vector, in addition to a pointer to the elements array. Only when an element is accessed then an OptionalAccessor is created, which contains the index of the accessed element and a pointer -to the containing vector. Only when the user calls value() or has_value() on that accessor then the nullity or the value is read. Other view types are implemented in a similar way, -The graph below illustrates the process. - -
- -
- - -The graph below compares the runtime of some functions written in the simple interface before and after the introduction of the view types. The speedup for arrays is around 2X, for maps the speed is much higher > `10X` because materializing the intermediate representation previously involves hashing the elements while constructing the hashmap. Furthermore, the overhead of materialization for nested complex types is very high as well, as reflected in _row_arrays_sum_. - -
- -
Runtimes of functions before and after the introduction of view types, normalized to the runtime of the version that uses the view types.
-
- - -The graph below compares the runtimes of some functions written using the simple interface, a basic vector function implementation with no special optimizations for the non general case, and a vector implementation that is specialized for flat and null free. The bars are annotated with the line of codes (LOC) used to implement each function. - - -
- -
- -We can see that LOC are significantly lower for simple functions. ArraySum with flat and null free optimization is faster because the summation can be optimized much better when it's performed over a sequential array of data. -The reason the simple is faster than the vector for some benchmarks is because we have several optimizations in the simple interface that are not implemented in the basic vector versions. - -### Writer types for outputs - - -A similar pattern of inefficiency existed for functions with complex output types. The graph below shows the previous path of writing complex types through the simple function interface. In the previous path, for each row, the result is first written to a temporary object (std::vector, folly::f14FastMap<\>, etc.), then serialized into the Velox vector. -
- -
- -We changed the writing path so that the data is written directly into the Velox vector during the function evaluation. By introducing writer types: ArrayWriter, MapWriter, RowWriter. This avoids the double materialization and the unnecessary sorting and hashing for maps. - -Consider the function below for example that constructs an array [0, n-1). - -
- -
- - -outerArray is an array writer and whenever push_back is called, the underlying vector array is updated directly and a new element is written to it. - -**In order & final elements writing**: Unlike the previous interface, the new writer interface needs to write things in order, since it directly serializes elements into Velox vector buffers. Written elements also can not be modified. - -For example, for a function with an Array output , we can't add three maps, and write to them concurrently. The new interface should enforce that one map is written completely before the next one starts. This is because we are serializing things directly in the map vector, and to determine the offset of the new map we need first to know the end offset of the previous one. - -The code below shows a function with Array output: -
- -
- -**Compatibility with std::like containers.**: Unfortunately, the new interface is not completely compatible with std::like interfaces, in fact, it deviates syntactically and semantically (for example a std::map enforces unique keys and ordering of elements) while map writer does not. -When the element type is primitive (ex: Array) we enable std::like APIs (push_back, emplace()). - -But we can not do that for nested complex types (ex:Array\>) since it breaks the in-order & final elements writing rule mentioned above. - -The figure below shows the performance gain achieved by this change, functions' performance is evaluated before and after the change. -
- -
- - -The chart below compares the performance of those functions with vector functions implementations, a vector function with an optimization that precomputes the total size needed for the output vector and a single resize is also added. -Note that those functions do almost no computation other than constructing the output map. Hence the resize cost becomes very critical, if those were doing more work, then its effect would be less. -Furthermore, the gap indicates that it might be worth it to add a way in the simple interface that enables pre-computing/resizing the output vector size. -
- -
- - -### Examples: - -For full documentation of the view and writer types, APIs, and how to write simple functions follow thelink. diff --git a/website/blog/2023-07-19-array-sort.mdx b/website/blog/2023-07-19-array-sort.mdx deleted file mode 100644 index 1320c0055d4c..000000000000 --- a/website/blog/2023-07-19-array-sort.mdx +++ /dev/null @@ -1,203 +0,0 @@ ---- -slug: array-sort -title: array_sort lambda function -authors: [mbasmanova] -tags: [tech-blog,functions] ---- - -Presto provides an array_sort -function to sort arrays in ascending order with nulls placed at the end. - -``` -presto> select array_sort(array[2, 5, null, 1, -1]); - _col0 ---------------------- - [-1, 1, 2, 5, null] -``` - -There is also an array_sort_desc -function that sorts arrays in descending order with nulls placed at the end. - -``` -presto> select array_sort_desc(array[2, 5, null, 1, -1]); - _col0 ---------------------- - [5, 2, 1, -1, null] -``` - -Both array_sort and array_sort_desc place nulls at the end of the array. - -There is also a version of array_sort -function that takes a comparator lambda function and uses it to sort the array. - -``` -array_sort(array(T), function(T, T, int)) -> array(T) -``` - -A common use case is to sort an array of structs using one of the struct fields as the sorting key. - -``` -presto> select array_sort(array[row('apples', 23), row('bananas', 12), row('grapes', 44)], - -> (x, y) -> if (x[2] < y[2], -1, if(x[2] > y[2], 1, 0))); - - _col0 ---------------------------------------------------------------------------------------- - [{f0=bananas, f1=12}, {f0=apples, f1=23}, {f0=grapes, f1=44}] -``` - -This is all very nice and convenient, but there is a catch. - -The documentation says that the "comparator will take two nullable arguments representing two -nullable elements of the array."" Did you notice the word "nullable" in "nullable arguments" -and "nullable elements"? Do you think it is important? It is Ok if the answer is No or Not Really. -Turns out this "nullable" thing is very important. The comparator is expected to handle null -inputs and should not assume that inputs are not null or that nulls are handled automatically. - -Why is it important to handle null inputs? Let’s see what happens if the comparator doesn’t -handle nulls. - -``` -presto> select array_sort(array[2, 3, null, 1], - (x, y) -> if (x < y, -1, if (x > y, 1, 0))); - _col0 ------------------ - [2, 3, null, 1] -``` - -The result array is not sorted! If subsequent logic relies on the array to be sorted the query -will silently return wrong results. And if there is no logic that relies on the sortedness of -the array then why waste CPU cycles on sorting? - -Why is the array not sorted? That’s because the comparator returns 0 whenever x or y is null. - -``` - x < y returns null if x or y is null, then - x > y returns null if x or y is null, then - result is 0 -``` - -This confuses the sorting algorithm as it sees that 1 == null, 2 == null, 3 == null, -but 1 != 2 and 1 != 3. The algorithm assumes that the comparator function is written correctly, -e.g. if a < b then b > a and if a == b and b == c then a == c. Comparator function that doesn’t -handle nulls does not satisfy these rules and causes unpredictable results. - -To handle null inputs, the comparator function needs to be modified, for example, like so: - -``` - (x, y) -> CASE WHEN x IS NULL THEN 1 - WHEN y IS NULL THEN -1 - WHEN x < y THEN -1 - WHEN x > y THEN 1 - ELSE 0 END -``` - -``` -presto> select array_sort(array[2, 3, null, 1], - -> (x, y) -> CASE WHEN x IS NULL THEN 1 - -> WHEN y IS NULL THEN -1 - -> WHEN x < y THEN -1 - -> WHEN x > y THEN 1 - -> ELSE 0 END - -> ); - _col0 ------------------ - [1, 2, 3, null] -``` - -This is longer and harder to read, but the result array is sorted properly. The new -comparator says that null is greater than any other value, so null is placed at the -end of the array. - -Note: When (x, y) return -1, the algorithm assumes that x <= y. - - -Writing comparators correctly is not easy. Writing comparators that handle null inputs -is even harder. Having no feedback when a comparator is written incorrectly makes it -yet harder to spot bugs and fix them before a query lands in production and starts -producing wrong results. - -I feel that Presto’s array_sort function with a custom comparator is dangerous and hard -to use and I wonder if it makes sense to replace it with a safer, easier to use alternative. - -``` -array_sort(array(T), function(T, U)) -> array(T) -``` - -This version takes an array and a transform lambda function that specifies how to compute -sorting keys from the array elements. - -To sort array of structs by one of the struct fields, one would write - -``` -presto> select array_sort(array[row('apples', 23), row('bananas', 12), row('grapes', 44)], - x -> x[2]) - - _col0 ---------------------------------------------------------------------------------------- - [{f0=bananas, f1=12}, {f0=apples, f1=23}, {f0=grapes, f1=44}] -``` - -This version would sort the array by the sorting keys computed using the specified lambda -in ascending order placing nulls at the end of the array. - -A matching array_sort_desc function would sort in descending order placing nulls at the end -of the array. - -These functions would be easier to write and read and null handling will happen automatically. - -We implemented these functions in Velox. - - -We also added partial support for array_sort with a comparator lambda function. Expression -compiler in Velox analyzes the comparator expression to determine whether it can be re-written -to the alternative version of array_sort. If so, it re-writes the expression and evaluates it. -Otherwise, it throws an unsupported exception. - -For example, - -``` - array_sort(a, (x, y) -> if (x[2] < y[2], -1, if(x[2] > y[2], 1, 0))) -``` - -is re-written to - -``` - array_sort(a, x -> x[2]) -``` - -This rewrite allows Prestissimo and Presto-on-Spark-on-Velox to support common use cases -and do so efficiently. - -The rewrite handles a few different ways to express the same comparator. Some examples: - -``` - // becomes array_sort(a, f(x)) - (x, y) -> if(f(x) < f(y), -1, if(f(x) > f(y), 1, 0)) - - // becomes array_sort_desc(a, f(x)) - (x, y) -> if(f(x) < f(y), 1, if(f(x) > f(y), -1, 0)) - - // becomes array_sort(a, f(x)) - (x, y) -> if(f(x) < f(y), -1, if(f(x) = f(y), 0, 1)) - - // becomes array_sort(a, f(x)) - (x, y) -> if(f(x) = f(y), 0, if(f(x) < f(y), -1, 1)) - - // becomes array_sort(a, f(x)) - (x, y) -> if(f(y) < f(x), 1, if(f(x) < f(y), -1, 0)) -``` - -Why didn’t we implement full support for comparator lambda functions in array_sort? Actually, -we couldn’t think of an efficient way to do that in a vectorized engine. Velox doesn’t use code -generation and interprets expressions. It can do that efficiently if it can process data in large -batches. array_sort with custom comparator doesn’t lend itself well to such processing. - -array_sort with a transform lambda works well in a vectorized engine. To process a batch of arrays, -Velox first evaluates the transform lambda on all the elements of the arrays, then sorts the results. - - -For further reading, consider the Vectorized and performance-portable Quicksort -blog post from Google. - -Thank you Orri Erling for brainstorming -and Xiaoxuan Meng for reviewing the code. diff --git a/website/blog/2023-09-06-optimize-try-cast.mdx b/website/blog/2023-09-06-optimize-try-cast.mdx deleted file mode 100644 index 8a823e7467cd..000000000000 --- a/website/blog/2023-09-06-optimize-try-cast.mdx +++ /dev/null @@ -1,111 +0,0 @@ ---- -slug: optimize-try_cast. -title: "Learnings from optimizing try_cast" -authors: [lsakka] -tags: [tech-blog, performance] ---- - -One of the queries shadowed internally at Meta was much slower in Velox compared to presto(2 CPU days vs. 4.5 CPU hours). Initial investigation identified that the overhead is related to casting empty strings inside a try_cast. - -In this blogpost I summarize my learnings from investigating and optimizing try_cast. - --------------------------------------------------------------------------------------------- - -### Start and end results - -Initial benchmark: -``` -name total time -try_cast(empty_string_col as int) 4.88s -try_cast(valid_string_col as int) 2.15ms -``` - -The difference between casting a valid and invalid input is huge (>1000X), although ideally casting an invalid string should be -just setting a null and should not be that expensive. - -Benchmark results after optimization: -``` -name total time -try_cast(empty_string_col as int) 1.24ms -try_cast(valid_string_col as int) 2.15ms -``` --------------------------------------------------------------------------------------------- - -### Sources of regression -The investigation revealed several factors that contributed to the huge gap, summarized in the points below in addition to -their approximate significance. - - -**Error logs overhead.** - -Whenever a VeloxUserError is thrown an error log used to be generated, however those errors are expected to, (1) either get converted to null if is -thrown from within a try, (2) or show up to the user otherwise. Hence, no need for that expensive logging . - -Moreover, each failing row used to generate two log message because VELOX_USER_FAIL was called twice. Disabling logging for user error helped save 2.6s of the 4.88s. - - -**Throwing overhead.** - -Each time a row is casted **four** exception were thrown: - -1. From within Folly library. -2. From Cast in Conversions.h, the function catch the exception thrown by Folly and convert it to Velox exception and throw it. -3. From castToPrimitive function, which catch the exception and threw a new exception with more context. -4. Finally, a forth throw came from applyToSelectedNoThrow which caught an exception and called toVeloxException -to check exception type and re-throw. - -Those are addressed and avoided using the following: - -1. When the input is an empty string, avoid calling folly by directly checking if the input is empty. -2. Remove the catch and re-throw from Conversions.h -3. Introduce setVeloxExceptionError, which can be used to set the error directly in evalContext without throwing (does not call toVeloxException). -4. Optimize applyToSelectedNoThrow to call setVeloxExceptionError if it catches Velox exception. - -With all those changes throwing exceptions is completely avoided when casting empty strings. This takes the runtime down to 382.07ms, -but its still much higher than 2.15ms. - - -** Velox exception construction overhead.** - -Constructing Velox exception is expensive, even when there is no throw at all! Luckily this can be avoided for try_cast, since -the output can be directly set to null without having to use the errorVector to track errors. By doing so the benchmark runtime goes -down to 1.24ms. - --------------------------------------------------------------------------------------------- - - -### Follow up tasks -After all the changes we have the following performance numbers for other patterns of similar expressions -(much better than before but still can be optimized a lot). -``` -try_cast(empty_string_col as int) 1.24ms 808.79 - -try_cast(invalid_string_col as int) 393.61ms 2.54 - -try(cast(empty_string_col as int)) 375.82ms 2.66 - -try(cast(invalid_string_col as int)) 767.74ms 1.30 -``` - -All these can be optimized to have the same runtime cost of the first expression 1.24ms. - -To do that two thing are needed: - - -**1) Tracking errors for try, should not require constructing exceptions** - -The way errors are tracked when evaluating a try expression is by setting values in an ErrorVector; which is a vector of VeloxException pointers. -This forces the construction of a Velox exception for each row, but that is not needed (for try expressions) since only row numbers need to be -tracked to be converted eventually to nulls, but not the actual errors. - -This can be changed such that errors are tracked using a selectivity vector. Its worth noting that for other expressions such as conjunct -expressions this tracking is needed, hence we need to distinguish between both. - -This would help optimize any try(x) expression where x throws for large number of rows. - -**2)Use throw-free cast library** - -Avoiding throw and instead returning a boolean should allow us to directly set null in try_cast and avoid construction of exceptions completely. - -While this is done now for empty strings, its not done for all other types of errors. Folly provides a non-throwing API (folly::tryTo) that can be tried for that purpose. -folly::tryTo. According to the folly documentation *On the error path, you can expect tryTo to be roughly three orders of magnitude faster than the throwing to and to completely avoid any lock contention arising from stack unwinding*. diff --git a/website/blog/2023-10-02-reduce-agg.mdx b/website/blog/2023-10-02-reduce-agg.mdx deleted file mode 100644 index 1e7d2dda745f..000000000000 --- a/website/blog/2023-10-02-reduce-agg.mdx +++ /dev/null @@ -1,178 +0,0 @@ ---- -slug: reduce-agg -title: reduce_agg lambda aggregate function -authors: [mbasmanova] -tags: [tech-blog,functions] ---- - -## Definition - -Reduce_agg -is the only lambda aggregate Presto function. It allows users to define arbitrary aggregation -logic using 2 lambda functions. - -``` -reduce_agg(inputValue T, initialState S, inputFunction(S, T, S), combineFunction(S, S, S)) → S - -Reduces all non-NULL input values into a single value. inputFunction will be invoked for -each non-NULL input value. If all inputs are NULL, the result is NULL. In addition to taking -the input value, inputFunction takes the current state, initially initialState, and returns the -new state. combineFunction will be invoked to combine two states into a new state. The final -state is returned. Throws an error if initialState is NULL or inputFunction or combineFunction -returns a NULL. -``` - -Once can think of reduce_agg as using inputFunction to implement partial aggregation and -combineFunction to implement final aggregation. Partial aggregation processes a list of -input values and produces an intermediate state: - -``` -auto s = initialState; -for (auto x : input) { - s = inputFunction(s, x); -} - -return s; -``` - -Final aggregation processes a list of intermediate states and computes the final state. - -``` -auto s = intermediates[0]; -for (auto i = 1; i < intermediates.size(); ++i) - s = combineFunction(s, intermediates[i]); -} - -return s; -``` - -For example, one can implement SUM aggregation using reduce_agg as follows: - -``` -reduce_agg(c, 0, (s, x) -> s + x, (s, s2) -> s + s2) -``` - -Implementation of AVG aggregation is a bit trickier. For AVG, state is a tuple of sum and -count. Hence, reduce_agg can be used to compute (sum, count) pair, but it cannot compute -the actual average. One needs to apply a scalar function on top of reduce_agg to get the -average. - -``` -SELECT id, sum_and_count.sum / sum_and_count.count FROM ( - SELECT id, reduce_agg(value, CAST(row(0, 0) AS row(sum double, count bigint)), - (s, x) -> CAST(row(s.sum + x, s.count + 1) AS row(sum double, count bigint)), - (s, s2) -> CAST(row(s.sum + s2.sum, s.count + s2.count) AS row(sum double, count bigint))) AS sum_and_count - FROM t - GROUP BY id -); -``` - -The examples of using reduce_agg to compute SUM and AVG are for illustrative purposes. -One should not use reduce_agg if a specialized aggregation function is available. - -One use case for reduce_agg we see in production is to compute a product of input values. - -``` -reduce_agg(c, 1.0, (s, x) -> s * x, (s, s2) -> s * s2) -``` - -Another example is to compute a list of top N distinct values from all input arrays. - -``` -reduce_agg(x, array[], - (a, b) -> slice(reverse(array_sort(array_distinct(concat(a, b)))), 1, 1000), - (a, b) -> slice(reverse(array_sort(array_distinct(concat(a, b)))), 1, 1000)) -``` - -Note that this is equivalent to the following query: - -``` -SELECT array_agg(v) FROM ( - SELECT DISTINCT v - FROM t, UNNEST(x) AS u(v) - ORDER BY v DESC - LIMIT 1000 -) -``` - -## Implementation - -Efficient implementation of reduce_agg lambda function is not straightforward. Let’s -consider the logic for partial aggregation. - -``` -auto s = initialState; -for (auto x : input) { - s = inputFunction(s, x); -} -``` - -This is a data-dependent loop, i.e. the next loop iteration depends on the results of -the previous iteration. inputFunction needs to be invoked on each input value `x` -separately. Since inputFunction is a user-defined lambda, invoking inputFunction means -evaluating an expression. And since expression evaluation in Velox is optimized for -processing large batches of values at a time, evaluating expressions on one value at -a time is very inefficient. To optimize the implementation of reduce_agg we need to -reduce the number of times we evaluate user-defined lambdas and increase the number -of values we process each time. - -One approach is to - -1. convert all input values into states by evaluating inputFunction(initialState, x); -1. split states into pairs and evaluate combineFunction on all pairs; -1. repeat step (2) until we have only one state left. - -Let’s say we have 1024 values to process. Step 1 evaluates inputFunction expression -on 1024 values at once. Step 2 evaluates combineFunction on 512 pairs, then on 256 -pairs, then on 128 pairs, 64, 32, 16, 8, 4, 2, finally producing a single state. -Step 2 evaluates combineFunction 9 times. In total, this implementation evaluates -user-defined expressions 10 times on multiple values each time. This is a lot more -efficient than the original implementation that evaluates user-defined expressions -1024 times. - -In general, given N inputs, the original implementation evaluates expressions N times -while the new one log2(N) times. - -Note that in case when N is not a power of two, splitting states into pairs may leave -an extra state. For example, splitting 11 states produces 5 pairs + one extra state. -In this case, we set aside the extra state, evaluate combineFunction on 5 pairs, then -bring extra state back to a total of 6 states and continue. - -A benchmark, velox/functions/prestosql/aggregates/benchmarks/ReduceAgg.cpp, shows that -initial implementation of reduce_agg is 60x slower than SUM, while the optimized - implementation is only 3x slower. A specialized aggregation function will always be - more efficient than generic reduce_agg, hence, reduce_agg should be used only when - specialized aggregation function is not available. - -Finally, a side effect of the optimized implementation is that it doesn't support -applying reduce_agg to sorted inputs. I.e. one cannot use reduce_agg to compute an -equivalent of - -``` - SELECT a, array_agg(b ORDER BY b) FROM t GROUP BY 1 -``` - -The array_agg computation depends on order of inputs. A comparable implementation -using reduce_agg would look like this: - -``` - SELECT a, - reduce_agg(b, array[], - (s, x) -> concat(s, array[x]), - (s, s2) -> concat(s, s2) - ORDER BY b) - FROM t GROUP BY 1 -``` - -To respect ORDER BY b, the reduce_agg would have to apply inputFunction to each -input value one at a time using a data-dependent loop from above. As we saw, this -is very expensive. The optimization we apply does not preserve the order of inputs, -hence, cannot support the query above. Note that -Presto doesn't -support applying reduce_agg to sorted inputs either. - - -Thank you Orri Erling for brainstorming -and Xiaoxuan Meng and -Pedro Eugênio Rocha Pedreira for -reviewing the code. diff --git a/website/blog/2024-01-27-like-optimization.mdx b/website/blog/2024-01-27-like-optimization.mdx deleted file mode 100644 index 57ba15f647c7..000000000000 --- a/website/blog/2024-01-27-like-optimization.mdx +++ /dev/null @@ -1,147 +0,0 @@ ---- -slug: like -title: "Improve LIKE's performance" -authors: [xumingming] -tags: [tech-blog,performance] ---- - -## What is LIKE? - -LIKE is a very useful SQL operator. -It is used to do string pattern matching. The following examples for LIKE usage are from the Presto doc: - -``` -SELECT * FROM (VALUES ('abc'), ('bcd'), ('cde')) AS t (name) -WHERE name LIKE '%b%' ---returns 'abc' and 'bcd' - -SELECT * FROM (VALUES ('abc'), ('bcd'), ('cde')) AS t (name) -WHERE name LIKE '_b%' ---returns 'abc' - -SELECT * FROM (VALUES ('a_c'), ('_cd'), ('cde')) AS t (name) -WHERE name LIKE '%#_%' ESCAPE '#' ---returns 'a_c' and '_cd' -``` - -These examples show the basic usage of LIKE: - -- Use `%` to match zero or more characters. -- Use `_` to match exactly one character. -- If we need to match `%` and `_` literally, we can specify an escape char to escape them. - -When we use Velox as the backend to evaluate Presto's query, LIKE operation is translated -into Velox's function call, e.g. `name LIKE '%b%'` is translated to -`like(name, '%b%')`. Internally Velox converts the pattern string into a regular -expression and then uses regular expression library RE2 -to do the pattern matching. RE2 is a very good regular expression library. It is fast -and safe, which gives Velox LIKE function a good performance. But some popularly used simple patterns -can be optimized using direct simple C++ string functions instead of regex. -e.g. Pattern `hello%` matches inputs that start with `hello`, which can be implemented by direct memory -comparison of prefix ('hello' in this case) bytes of input: - -``` -// Match the first 'length' characters of string 'input' and prefix pattern. -bool matchPrefixPattern( - StringView input, - const std::string& pattern, - size_t length) { - return input.size() >= length && - std::memcmp(input.data(), pattern.data(), length) == 0; -} -``` - -It is much faster than using RE2. Benchmark shows it gives us a 750x speedup. We can do similar -optimizations for some other patterns: - -- `%hello`: matches inputs that end with `hello`. It can be optimized by direct memory comparison of suffix bytes of the inputs. -- `%hello%`: matches inputs that contain `hello`. It can be optimized by using `std::string_view::find` to check whether inputs contain `hello`. - -These simple patterns are straightforward to optimize. There are some more relaxed patterns that -are not so straightforward: - -- `hello_velox%`: matches inputs that start with 'hello', followed by any character, then followed by 'velox'. -- `%hello_velox`: matches inputs that end with 'hello', followed by any character, then followed by 'velox'. -- `%hello_velox%`: matches inputs that contain both 'hello' and 'velox', and there is a single character separating them. - -Although these patterns look similar to previous ones, but they are not so straightforward -to optimize, `_` here matches any single character, we can not simply use memory comparison to -do the matching. And if user's input is not pure ASCII, `_` might match more than one byte which -makes the implementation even more complex. Also note that the above patterns are just for -illustrative purpose. Actual patterns can be more complex. e.g. `h_e_l_l_o`, so trivial algorithm -will not work. - -## Optimizing Relaxed Patterns - -We optimized these patterns as follows. First, we split the patterns into a list of sub patterns, e.g. -`hello_velox%` is split into sub-patterns: `hello`, `_`, `velox`, `%`, because there is -a `%` at the end, we determine it as a `kRelaxedPrefix` pattern, which means we need to do some prefix -matching, but it is not a trivial prefix matching, we need to match three sub-patterns: - -- kLiteralString: hello -- kSingleCharWildcard: _ -- kLiteralString: velox - -For `kLiteralString` we simply do a memory comparison: - -``` -if (subPattern.kind == SubPatternKind::kLiteralString && - std::memcmp( - input.data() + start + subPattern.start, - patternMetadata.fixedPattern().data() + subPattern.start, - subPattern.length) != 0) { - return false; -} -``` - -Note that since it is a memory comparison, it handles both pure ASCII inputs and inputs that -contain Unicode characters. - -Matching `_` is more complex considering that there are variable length multi-bytes character in -unicode inputs. Fortunately there are existing libraries which provides unicode related operations: utf8proc. -It provides functions that tells us whether a byte in input is the start of a character or not, -how many bytes current character consists of etc. So to match a sequence of `_` our algorithm is: - -``` -if (subPattern.kind == SubPatternKind::kSingleCharWildcard) { - // Match every single char wildcard. - for (auto i = 0; i < subPattern.length; i++) { - if (cursor >= input.size()) { - return false; - } - - auto numBytes = unicodeCharLength(input.data() + cursor); - cursor += numBytes; - } -} -``` - -Here: - -- `cursor` is the index in the input we are trying to match. -- `unicodeCharLength` is a function which wraps utf8proc function to determine how many bytes current character consists of. - -So the logic is basically repeatedly calculate size of current character and skip it. - -It seems not that complex, but we should note that this logic is not effective for pure ASCII input. -Every character is one byte in pure ASCII input. So to match a sequence of `_`, we don't need to calculate the size -of each character and compare in a for-loop. In fact, we don't need to explicitly match `_` for pure ASCII input as well. -We can use the following logic instead: -``` -for (const auto& subPattern : patternMetadata.subPatterns()) { - if (subPattern.kind == SubPatternKind::kLiteralString && - std::memcmp( - input.data() + start + subPattern.start, - patternMetadata.fixedPattern().data() + subPattern.start, - subPattern.length) != 0) { - return false; - } -} -``` - -It only matches the kLiteralString pattern at the right position of the inputs, `_` is automatically -matched(actually skipped). No need to match it explicitly. With this optimization we get 40x speedup -for kRelaxedPrefix patterns, 100x speedup for kRelaxedSuffix patterns. - -Thank you Maria Basmanova for spending a lot of time -reviewing the code. diff --git a/website/blog/authors.yml b/website/blog/authors.yml deleted file mode 100644 index 82ac28b151b3..000000000000 --- a/website/blog/authors.yml +++ /dev/null @@ -1,61 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -pedroerp: - name: Pedro Pedreira - title: Software Engineer @ Meta - url: https://github.com/pedroerp - image_url: https://github.com/pedroerp.png - -mbasmanova: - name: Masha Basmanova - title: Software Engineer @ Meta - url: https://github.com/mbasmanova - image_url: https://github.com/mbasmanova.png - -majetideepak: - name: Deepak Majeti - title: Principal Engineer @ Ahana - url: https://github.com/majetideepak - image_url: https://github.com/majetideepak.png - -lsakka: - name: Laith Sakka - title: Software Engineer @ Meta - url: https://github.com/laithsakka - image_url: https://github.com/laithsakka.png - -kgpai: - name: Krishna Pai - title: Software Engineer @ Meta - url: https://github.com/kgpai - image_url: https://github.com/kgpai.png - -jwujciak: - name: Jacob Wujciak-Jens - title: Software Engineer @ Voltron Data - url: https://github.com/assignUser - image_url: https://github.com/assignuser.png - -raulcd: - name: Raúl Cumplido - title: Software Engineer @ Voltron Data - url: https://github.com/raulcd - image_url: https://github.com/raulcd.png - -xumingming: - name: James Xu - title: Software Engineer @ Alibaba - url: https://github.com/xumingming - image_url: https://github.com/xumingming.png diff --git a/website/docs/intro.md b/website/docs/intro.md deleted file mode 100644 index 6ce1c895db58..000000000000 --- a/website/docs/intro.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -id: roadmap -title: Roadmap -slug: / ---- -- Add support for DECIMAL type. -- Add support for window functions. -- Add support for converting Velox plan fragments to and from Substrait representation. -- Make it easier to build Velox on a variety of platforms. -- Improve reliability by setting up performance testing for each PR. -- Make it easier to debug failures, crashes and incorrect results. -- Introduce simple aggregate function API for better authoring experience. -- Expand test coverage for vectors and expression evaluation. diff --git a/website/docusaurus.config.js b/website/docusaurus.config.js deleted file mode 100644 index 26f9d3d664ce..000000000000 --- a/website/docusaurus.config.js +++ /dev/null @@ -1,154 +0,0 @@ -// @ts-check -// Note: type annotations allow type checking and IDEs autocompletion - -const lightCodeTheme = require('prism-react-renderer/themes/github'); -const darkCodeTheme = require('prism-react-renderer/themes/dracula'); - -/** @type {import('@docusaurus/types').Config} */ -const config = { - title: 'Velox', - tagline: 'The open source unified execution engine.', - url: 'https://velox-lib.io', - baseUrl: '/', - onBrokenLinks: 'throw', - onBrokenMarkdownLinks: 'warn', - favicon: 'img/favicon.ico', - - // GitHub pages deployment config. - // If you aren't using GitHub pages, you don't need these. - organizationName: 'facebook', // Usually your GitHub org/user name. - projectName: 'docusaurus', // Usually your repo name. - - // Even if you don't use internalization, you can use this field to set useful - // metadata like html lang. For example, if your site is Chinese, you may want - // to replace "en" with "zh-Hans". - i18n: { - defaultLocale: 'en', - locales: ['en'], - }, - - presets: [ - [ - 'classic', - /** @type {import('@docusaurus/preset-classic').Options} */ - ({ - docs: { - sidebarPath: require.resolve('./sidebars.js'), - // Please change this to your repo. - // Remove this to remove the "edit this page" links. - }, - blog: { - showReadingTime: true, - // Please change this to your repo. - // Remove this to remove the "edit this page" links. - }, - theme: { - customCss: require.resolve('./src/css/custom.css'), - }, - gtag: { - trackingID: 'G-C6M8TCJBRM', - anonymizeIP: true, - }, - }), - ], - ], - - themeConfig: - /** @type {import('@docusaurus/preset-classic').ThemeConfig} */ - ({ - image: 'img/velox_blog_pic.png', - navbar: { - title: 'Velox', - logo: { - alt: 'Velox Logo', - src: 'img/logo.svg', - }, - items: [ - { - href: 'https://facebookincubator.github.io/velox/', - label: 'Docs', - position: 'left', - }, - { - href: '/docs', - label: 'Roadmap', - position: 'left', - }, - { - to: '/blog', - label: 'Blog', - position: 'left', - }, - { - href: 'https://github.com/facebookincubator/velox/blob/main/CONTRIBUTING.md', - label: 'Contributing', - position: 'right', - }, - { - href: 'https://github.com/facebookincubator/velox', - label: 'GitHub', - position: 'right', - }, - ], - }, - footer: { - style: 'dark', - links: [ - { - title: 'Documentation', - items: [ - { - label: 'Velox in 10 Minutes', - href: 'https://facebookincubator.github.io/velox/velox-in-10-min.html', - }, - { - label: 'Developer Guides', - href: 'https://facebookincubator.github.io/velox/develop.html', - }, - { - label: 'Presto Functions', - href: 'https://facebookincubator.github.io/velox/functions.html', - }, - { - label: 'Monthly Updates', - href: 'https://facebookincubator.github.io/velox/monthly-updates.html', - }, - ], - }, - { - title: 'Community', - items: [ - { - label: 'Slack', - href: 'https://velox-oss.slack.com', - }, - { - label: 'Contributing', - href: 'https://github.com/facebookincubator/velox/blob/main/CONTRIBUTING.md', - }, - ], - }, - { - title: 'More', - items: [ - { - label: 'Blog', - to: '/blog', - }, - { - label: 'GitHub', - href: 'https://github.com/facebookincubator/velox', - }, - ], - }, - ], - copyright: `Copyright © ${new Date().getFullYear()} Meta Platforms, Inc.`, - }, - prism: { - theme: lightCodeTheme, - darkTheme: darkCodeTheme, - }, - }), -}; - -module.exports = config; diff --git a/website/package-lock.json b/website/package-lock.json deleted file mode 100644 index fd6513482173..000000000000 --- a/website/package-lock.json +++ /dev/null @@ -1,21355 +0,0 @@ -{ - "name": "website", - "version": "0.0.0", - "lockfileVersion": 2, - "requires": true, - "packages": { - "": { - "name": "website", - "version": "0.0.0", - "dependencies": { - "@docusaurus/core": "2.0.1", - "@docusaurus/plugin-google-gtag": "^2.0.1", - "@docusaurus/preset-classic": "2.0.1", - "@mdx-js/react": "^1.6.22", - 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"name": "website", - "version": "0.0.0", - "private": true, - "scripts": { - "docusaurus": "docusaurus", - "start": "docusaurus start", - "build": "docusaurus build", - "swizzle": "docusaurus swizzle", - "deploy": "docusaurus deploy", - "clear": "docusaurus clear", - "serve": "docusaurus serve", - "write-translations": "docusaurus write-translations", - "write-heading-ids": "docusaurus write-heading-ids" - }, - "dependencies": { - "@docusaurus/core": "2.0.1", - "@docusaurus/plugin-google-gtag": "^2.0.1", - "@docusaurus/preset-classic": "2.0.1", - "@mdx-js/react": "^1.6.22", - "axios": "^0.28.0", - "clsx": "^1.2.1", - "prism-react-renderer": "^1.3.5", - "react": "^17.0.2", - "react-dom": "^17.0.2" - }, - "devDependencies": { - "@docusaurus/module-type-aliases": "2.0.1" - }, - "browserslist": { - "production": [ - ">0.5%", - "not dead", - "not op_mini all" - ], - "development": [ - "last 1 chrome version", - "last 1 firefox version", - "last 1 safari version" - ] - }, - "engines": { - "node": ">=16.14" - } -} diff --git a/website/sidebars.js b/website/sidebars.js deleted file mode 100644 index 7757dc2809c6..000000000000 --- a/website/sidebars.js +++ /dev/null @@ -1,20 +0,0 @@ -/** - * Creating a sidebar enables you to: - - create an ordered group of docs - - render a sidebar for each doc of that group - - provide next/previous navigation - - The sidebars can be generated from the filesystem, or explicitly defined here. - - Create as many sidebars as you want. - */ - -// @ts-check - -/** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */ -const sidebars = { - // By default, Docusaurus generates a sidebar from the docs folder structure - tutorialSidebar: [{type: 'autogenerated', dirName: '.'}], -}; - -module.exports = sidebars; diff --git a/website/src/components/HomepageFeatures/index.js b/website/src/components/HomepageFeatures/index.js deleted file mode 100644 index 1f589f964067..000000000000 --- a/website/src/components/HomepageFeatures/index.js +++ /dev/null @@ -1,62 +0,0 @@ -import React from 'react'; -import clsx from 'clsx'; -import styles from './styles.module.css'; -import useBaseUrl from "@docusaurus/useBaseUrl"; - -const FeatureList = [ - { - title: 'Efficiency', - description: ( - <> - Velox democratizes runtime optimizations previously only implemented - in individual engines. - - ), - }, - { - title: 'Consistency', - description: ( - <> - By leveraging the same execution library, compute engines can expose - the exact same functions, data types, and semantics to their users. - - ), - }, - { - title: 'Reusability', - description: ( - <> - Features and runtime optimizations available in Velox are developed - and maintained once, reducing engineering duplication and promoting reusability.  - - ), - }, -]; - -function Feature({Svg, title, description}) { - return ( -
-
-

{title}

-

{description}

-
-
- ); -} - -export default function HomepageFeatures() { - return ( -
-
-
- {FeatureList.map((props, idx) => ( - - ))} -
-
- -
-
-
- ); -} diff --git a/website/src/components/HomepageFeatures/styles.module.css b/website/src/components/HomepageFeatures/styles.module.css deleted file mode 100644 index b248eb2e5dee..000000000000 --- a/website/src/components/HomepageFeatures/styles.module.css +++ /dev/null @@ -1,11 +0,0 @@ -.features { - display: flex; - align-items: center; - padding: 2rem 0; - width: 100%; -} - -.featureSvg { - height: 200px; - width: 200px; -} diff --git a/website/src/css/custom.css b/website/src/css/custom.css deleted file mode 100644 index b1973ab451fc..000000000000 --- a/website/src/css/custom.css +++ /dev/null @@ -1,45 +0,0 @@ -/** - * Any CSS included here will be global. The classic template - * bundles Infima by default. Infima is a CSS framework designed to - * work well for content-centric websites. - */ - -:root { - --ifm-color-primary: #284294; - --ifm-color-primary-dark: #243b85; - --ifm-color-primary-darker: #22387e; - --ifm-color-primary-darkest: #1c2e68; - --ifm-color-primary-light: #2c49a3; - --ifm-color-primary-lighter: #2e4caa; - --ifm-color-primary-lightest: #3456c2; -} - -[data-theme='dark'] { - --ifm-color-primary: #6897e0; - --ifm-color-primary-dark: #4d84da; - --ifm-color-primary-darker: #3f7bd8; - --ifm-color-primary-darkest: #2762be; - --ifm-color-primary-light: #83aae6; - --ifm-color-primary-lighter: #91b3e8; - --ifm-color-primary-lightest: #bcd1f1; -} - -.jumbotron { - background: url("../../static/img/dark_blue_dots.jpg") no-repeat center center; - background-size: cover; - color: white; -} - -.hero__title { - text-align: left; - margin-top: 2rem !important; -} - -.hero__subtitle { - text-align: left; -} - -.button-wrapper { - text-align: left; - margin-top: 2rem !important; -} diff --git a/website/src/pages/index.js b/website/src/pages/index.js deleted file mode 100644 index ba4660e44b70..000000000000 --- a/website/src/pages/index.js +++ /dev/null @@ -1,73 +0,0 @@ -import React from 'react'; -import clsx from 'clsx'; -import Link from '@docusaurus/Link'; -import useDocusaurusContext from '@docusaurus/useDocusaurusContext'; -import Layout from '@theme/Layout'; -import HomepageFeatures from '@site/src/components/HomepageFeatures'; -import useBaseUrl from '@docusaurus/useBaseUrl'; - -import styles from './index.module.css'; - -function HomepageHeader() { - const {siteConfig} = useDocusaurusContext(); - return ( -
-
- -
- -
- -
- -
-

{siteConfig.title}

-

- {`${siteConfig.tagline}`} -

- -
- - Join Us - -
-
-
-
-
- ); -} - -export default function Home() { - const {siteConfig} = useDocusaurusContext(); - return ( - - -
-
-
-
-
-
- -
-
-
-
-
- -
- - ); -} diff --git a/website/src/pages/index.module.css b/website/src/pages/index.module.css deleted file mode 100644 index bb225c16c370..000000000000 --- a/website/src/pages/index.module.css +++ /dev/null @@ -1,30 +0,0 @@ -/** - * CSS files with the .module.css suffix will be treated as CSS modules - * and scoped locally. - */ - -.heroBanner { - padding: 4rem 0; - text-align: center; - position: relative; - overflow: hidden; -} - -@media screen and (max-width: 996px) { - .heroBanner { - padding: 2rem; - } -} - -.buttons { - display: flex; - align-items: center; - justify-content: center; -} - -.features { - display: flex; - align-items: center; - padding: 2rem 0; - width: 100%; -} diff --git a/website/static/.nojekyll b/website/static/.nojekyll deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/website/static/img/dark_blue_dots.jpg b/website/static/img/dark_blue_dots.jpg deleted file mode 100644 index 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