Month: August 2022

Meta is introducing Velox, an open source unified execution engine aimed at accelerating data management systems and streamlining their development. Velox is under active development. Experimental results from our paper published at the International Conference on Very Large Data Bases (VLDB) 2022 show how Velox improves efficiency and consistency in data management systems. Velox helps… Continue reading Introducing Velox: An open source unified execution engine

At Salesforce we regularly use our products and services to scale our own business. One example is Buildpacks, which we created nearly a decade ago and is now a part of Hyperforce. Hyperpacks are an innovative new way of using Cloud Native Buildpacks (CNB) to manage our public cloud infrastructure.  Buildpacks were created to help… Continue reading Hyperpacks: Using Buildpacks to Build Hyperforce

When we focus on minimizing errors and downtime here at Meta, we place a lot of attention on service-level indicators (SLIs) and service-level objectives (SLOs). Consider Instagram, for example. There, SLIs represent metrics from different product surfaces, like the volume of error response codes to certain endpoints, or the number of successful media uploads. Based… Continue reading Improving Meta’s SLO workflows with data annotations

A touring alien from a galaxy far, far away is an avid Instagram user. Her Instagram Feed is dominated by: Friends and family posts Some space travel magazines A few general news accounts Lots of science fiction blogs She logs in, scrolls through her feed gently — catching up with friends and family, keeping pace… Continue reading How Instagram suggests new content

Infrastructure and software failures will happen. We idolize four 9s (99.99%) availability. We know we need to optimize and improve Recovery-Time-Objective (RTO, the time it takes to restore service after a service disruption) and Recovery-Point-Objective (RPO, the acceptable data loss measured in time). But how can we actually deliver high availability for our customers? One… Continue reading Architectural Principles for High Availability on Hyperforce

Many of Meta’s products, such as search and language translations, utilize AI models to continuously improve user experiences. As the performance of hardware we use to support training infrastructure increases, we need to scale our data ingestion infrastructure accordingly to handle workloads more efficiently. GPUs, which are used for training infrastructure, tend to double in… Continue reading Scaling data ingestion for machine learning training at Meta