July 17, 2024
An open supply unified execution engine
  • Meta is introducing Velox, an open supply unified execution engine aimed toward accelerating knowledge administration methods and streamlining their improvement.
  • Velox is underneath lively improvement. Experimental outcomes from our paper printed on the Worldwide Convention on Very Massive Knowledge Bases (VLDB) 2022 present how Velox improves effectivity and consistency in knowledge administration methods.
  • Velox helps consolidate and unify knowledge administration methods in a way we consider shall be of profit to the business. We’re hoping the bigger open supply neighborhood will be part of us in contributing to the mission.

Meta’s infrastructure performs an vital position in supporting our services. Our knowledge infrastructure ecosystem consists of dozens of specialised knowledge computation engines, all targeted on totally different workloads for quite a lot of use instances starting from SQL analytics (batch and interactive) to transactional workloads, stream processing, knowledge ingestion, and extra. Not too long ago, the fast progress of synthetic intelligence (AI) and machine studying (ML) use instances inside Meta’s infrastructure has led to extra engines and libraries focused at function engineering, knowledge preprocessing, and different workloads for ML coaching and serving pipelines. 

Nonetheless, regardless of the similarities, these engines have largely advanced independently. This fragmentation has made sustaining and enhancing them tough, particularly contemplating that as workloads evolve, the {hardware} that executes these workloads additionally adjustments. Finally, this fragmentation leads to methods with totally different function units and inconsistent semantics — decreasing the productiveness of information customers that must work together with a number of engines to complete duties.

With a purpose to handle these challenges and to create a stronger, extra environment friendly knowledge infrastructure for our personal merchandise and the world, Meta has created and open sourced Velox. It’s a novel, state-of-the-art unified execution engine that goals to hurry up knowledge administration methods in addition to streamline their improvement. Velox unifies the widespread data-intensive elements of information computation engines whereas nonetheless being extensible and adaptable to totally different computation engines. It democratizes optimizations that had been beforehand carried out solely in particular person engines, offering a framework wherein constant semantics will be carried out. This reduces work duplication, promotes reusability, and improves general effectivity and consistency.  

Velox is underneath lively improvement, but it surely’s already in numerous phases of integration with greater than a dozen knowledge methods at Meta, together with Presto, Spark, and PyTorch (the latter via an information preprocessing library known as TorchArrow), in addition to different inside stream processing platforms, transactional engines, knowledge ingestion methods and infrastructure, ML methods for function engineering, and others. 

Because it was first uploaded to GitHub, the Velox open supply mission has attracted greater than 150 code contributors, together with key collaborators equivalent to Ahana, Intel, and Voltron Knowledge, in addition to numerous tutorial establishments. By open-sourcing and fostering a neighborhood for Velox, we consider we will speed up the tempo of innovation within the knowledge administration system’s improvement business. We hope extra people and firms will be part of us on this effort. 

An summary of Velox

Whereas knowledge computation engines could seem distinct at first, they’re all composed of an analogous set of logical elements: a language entrance finish, an intermediate illustration (IR), an optimizer, an execution runtime, and an execution engine. Velox gives the constructing blocks required to implement execution engines, consisting of all data-intensive operations executed inside a single host, equivalent to expression analysis, aggregation, sorting, becoming a member of, and extra — additionally generally known as the info aircraft. Due to this fact, Velox expects an optimized plan as enter and effectively executes it utilizing the sources obtainable within the native host.

Knowledge administration methods like Presto and Spark usually have their very own execution engines and different elements. Velox can operate as a typical execution engine throughout totally different knowledge administration methods. (Diagram by Philip Bell.)

Velox leverages quite a few runtime optimizations, equivalent to filter and conjunct reordering, key normalization for array and hash-based aggregations and joins, dynamic filter pushdown, and adaptive column prefetching. These optimizations present optimum native effectivity given the obtainable information and statistics extracted from incoming batches of information. Velox can be designed from the bottom as much as effectively assist advanced knowledge sorts as a consequence of their ubiquity in fashionable workloads, and therefore extensively depends on dictionary encoding for cardinality-increasing and cardinality-reducing operations equivalent to joins and filtering, whereas nonetheless offering quick paths for primitive knowledge sorts.

The principle elements offered by Velox are:

  • Sort: a generic sort system that enables builders to signify scalar, advanced, and nested knowledge sorts, together with structs, maps, arrays, capabilities (lambdas), decimals, tensors, and extra.
  • Vector: an Apache Arrow–suitable columnar reminiscence structure module supporting a number of encodings, equivalent to flat, dictionary, fixed, sequence/RLE, and body of reference, along with a lazy materialization sample and assist for out-of-order consequence buffer inhabitants.
  • Expression Eval: a state-of-the-art vectorized expression analysis engine constructed primarily based on vector-encoded knowledge, leveraging methods equivalent to widespread subexpression elimination, fixed folding, environment friendly null propagation, encoding-aware analysis, dictionary peeling, and memoization.
  • Features: APIs that can be utilized by builders to construct customized capabilities, offering a easy (row by row) and vectorized (batch by batch) interface for scalar capabilities and an API for combination capabilities. 
    • A operate bundle suitable with the favored PrestoSQL dialect can be offered as a part of the library.
  • Operators: implementation of widespread SQL operators equivalent to TableScan, Challenge, Filter, Aggregation, Trade/Merge, OrderBy, TopN, HashJoin, MergeJoin, Unnest, and extra.
  • I/O: a set of APIs that enables Velox to be built-in within the context of different engines and runtimes, equivalent to:
    • Connectors: allows builders to specialize knowledge sources and sinks for TableScan and TableWrite operators.
    • DWIO: an extensible interface offering assist for encoding/decoding widespread file codecs equivalent to Parquet, ORC, and DWRF.
    • Storage adapters: a byte-based extensible interface that enables Velox to hook up with storage methods equivalent to Tectonic, S3, HDFS, and extra. 
    • Serializers: a serialization interface concentrating on community communication the place totally different wire protocols will be carried out, supporting PrestoPage and Spark’s UnsafeRow codecs.
  • Useful resource administration: a set of primitives for dealing with computational sources, equivalent to CPU and reminiscence administration, spilling, and reminiscence and SSD caching.

Velox’s essential integrations and experimental outcomes

Past effectivity good points, Velox gives worth by unifying the execution engines throughout totally different knowledge computation engines. The three hottest integrations are Presto, Spark, and TorchArrow/PyTorch.

Presto — Prestissimo 

Velox is being built-in into Presto as a part of the Prestissimo mission, the place Presto Java employees are changed by a C++ course of primarily based on Velox. The mission was initially created by Meta in 2020 and is underneath continued improvement in collaboration with Ahana, together with different open supply contributors.

Prestissimo gives a C++ implementation of Presto’s HTTP REST interface, together with worker-to-worker alternate serialization protocol, coordinator-to-worker orchestration, and standing reporting endpoints, thereby offering a drop-in C++ alternative for Presto employees. The principle question workflow consists of receiving a Presto plan fragment from a Java coordinator, translating it right into a Velox question plan, and handing it off to Velox for execution.

We carried out two totally different experiments to discover the speedup offered by Velox in Presto. Our first experiment used the TPC-H benchmark and measured near an order of magnitude speedup in some CPU-bound queries. We noticed a extra modest speedup (averaging 3-6x) for shuffle-bound queries.

Though the TPC-H dataset is a normal benchmark, it’s not consultant of actual workloads. To discover how Velox may carry out in these eventualities, we created an experiment the place we executed manufacturing site visitors generated by quite a lot of interactive analytical instruments discovered at Meta. On this experiment, we noticed a median of 6-7x speedups in knowledge querying, with some outcomes growing speedups by over an order of magnitude. You may study extra concerning the particulars of the experiments and their leads to our research paper.

Prestissimo outcomes on actual analytic workloads. The histogram above reveals relative speedup of Prestissimo over Presto Java. The y-axis signifies the variety of queries (in 1000’s [K]). Zero on the x-axis means Presto Java is quicker; 10 signifies that Prestissimo is not less than 10 instances quicker than Presto Java.

Prestissimo’s codebase is accessible on GitHub.  

Spark — Gluten

Velox can be being built-in into Spark as a part of the Gluten project created by Intel. Gluten permits C++ execution engines (equivalent to Velox) for use throughout the Spark setting whereas executing Spark SQL queries. Gluten decouples the Spark JVM and execution engine by making a JNI API primarily based on the Apache Arrow knowledge format and Substrait question plans, thus permitting Velox for use inside Spark by merely integrating with Gluten’s JNI API.

Gluten’s codebase is accessible on GitHub.  


TorchArrow is a dataframe Python library for knowledge preprocessing in deep studying, and a part of the PyTorch mission. TorchArrow internally interprets the dataframe illustration right into a Velox plan and delegates it to Velox for execution. Along with converging the in any other case fragmented area of ML knowledge preprocessing libraries, this integration permits Meta to consolidate execution-engine code between analytic engines and ML infrastructure. It gives a extra constant expertise for ML finish customers, who’re generally required to work together with totally different computation engines to finish a specific process, by exposing the identical set of capabilities/UDFs and making certain constant habits throughout engines.

TorchArrow was lately launched in beta mode on GitHub.

The way forward for database system improvement

Velox demonstrates that it’s doable to make knowledge computation methods extra adaptable by consolidating their execution engines right into a single unified library. As we proceed to combine Velox into our personal methods, we’re dedicated to constructing a sustainable open supply neighborhood to assist the mission in addition to to hurry up library improvement and business adoption. We’re additionally curious about persevering with to blur the boundaries between ML infrastructure and conventional knowledge administration methods by unifying operate packages and semantics between these silos.

Wanting on the future, we consider Velox’s unified and modular nature has the potential to be useful to industries that make the most of, and particularly people who develop, knowledge administration methods. It can enable us to associate with {hardware} distributors and proactively adapt our unified software program stack as {hardware} advances. Reusing unified and extremely environment friendly elements will even enable us to innovate quicker as knowledge workloads evolve. We consider that modularity and reusability are the way forward for database system improvement, and we hope that knowledge corporations, academia, and particular person database practitioners alike will be part of us on this effort. 

In-depth documentation about Velox and these elements will be discovered on our website and in our analysis paper “Velox: Meta’s unified execution engine.”


We wish to thank all contributors to the Velox mission. A particular thank-you to Sridhar Anumandla, Philip Bell, Biswapesh Chattopadhyay, Naveen Cherukuri, Wei He, Jiju John, Jimmy Lu, Xiaoxuang Meng, Krishna Pai, Laith Sakka, Bikramjeet Vigand, Kevin Wilfong from the Meta group, and to numerous neighborhood contributors, together with Frank Hu, Deepak Majeti, Aditi Pandit, and Ying Su.