Background​

The C++ standard used in a project determines what built-in functionality developers can use to ease development and extended capabilities.

Since its inception in August of 2021 Velox used the C++17 standard.

TL;DR​

This post describes the design principles and software components for extending Velox with hardware acceleration libraries like NVIDIA's cuDF. Velox provides a flexible execution model for hardware accelerators, and cuDF's data structures and algorithms align well with core components in Velox.

Background​

Velox depends on several libraries. Some of these dependencies include open-source libraries from Meta, including Folly and Facebook Thrift. These libraries are in active development and also depend on each other, so they all have to be updated to the same version at the same time.

Updating these dependencies typically involves modifying the Velox code to align with any public API or semantic changes in these dependencies. However, a recent upgrade of Folly and Facebook Thrift to version v2025.04.28.00 caused a SEGFAULT only in one unit test in Velox named velox_functions_remote_client_test.

At the end of the previous article, we were halfway through running our first distributed query:

SELECT l_partkey, count(*) FROM lineitem GROUP BY l_partkey;

We discussed how a query starts, how tasks are set up, and the interactions between plans, operators, and drivers. We have also presented how the first stage of the query is executed, from table scan to partitioned output - or the producer side of the shuffle.  

In this article, we will discuss the second query stage, or the consumer side of the shuffle.

In this article, we will discuss how a distributed compute engine executes a query similar to the one presented in our first article:

SELECT l_partkey, count(*) FROM lineitem GROUP BY l_partkey;

We use the TPC-H schema to illustrate the example, and Prestissimo as the compute engine orchestrating distributed query execution. Prestissimo is responsible for the query engine frontend (parsing, resolving metadata, planning, optimizing) and distributed execution (allocating resources and shipping query fragments), and Velox is responsible for the execution of plan fragments within a single worker node. Throughout this article, we will present which functions are performed by Velox and which by the distributed engine - Prestissimo, in this example.

This is the first part of a series of short articles that will take you through Velox’s internal structures and concepts. In this first part, we will discuss how distributed queries are executed, how data is shuffled among different stages, and present Velox concepts such as Tasks, Splits, Pipelines, Drivers, and Operators that enable such functionality.

TL;DR​

The query trace tool helps analyze and debug query performance and correctness issues. It helps prevent interference from external noise in a production environment (such as storage, network, etc.) by allowing replay of a part of the query plan and dataset in an isolated environment, such as a local machine. This is much more efficient for query performance analysis and issue debugging, as it eliminates the need to replay the whole query in a production environment.

TL;DR​

In late 2023, the Meta OSS (Open Source Software) Team requested all Meta teams to move the CI deployments from CircleCI to Github Actions. Voltron Data and Meta in collaboration migrated all the deployed Velox CI jobs. For the year 2024, Velox CI spend was on track to overshoot the allocated resources by a considerable amount of money. As part of this migration effort, the CI workloads were consolidated and optimized by Q2 2024, bringing down the projected 2024 CI spend by 51%.

TL;DR​

Queries that use TRY or TRY_CAST may experience poor performance and high CPU usage due to excessive exception throwing. We optimized CAST to indicate failure without throwing and introduced a mechanism for scalar functions to do the same. Microbenchmark measuring worst case performance of CAST improved 100x. Samples of production queries show 30x cpu time improvement.

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'