Etsy has maintained a sharded MySQL architecture since around 2010. This database cluster contains most of Etsy’s online data and is made up of ~1,000 tables distributed across ~1,000 shards. Over the last 16 years, it has grown significantly: combined, these tables have over 425 TB of data and receive roughly 1.7 million requests per second.
Etsy engineers access our MySQL data through a proprietary object-relational mapping (ORM). The ORM has a corresponding model for each MySQL table. W...
When buyers search on Etsy, they need to quickly and easily find the perfect item. At the same time, sellers need to be confident their unique products are being seen by the right customers. Our Ads Search ranking model, which is built on a multitask learning foundation, is the critical link in this connection. Recently, we identified an opportunity to drive more meaningful buyer engagement by enhancing our model’s ability to predict purchase intent. We achieved this via a dual-pronged impr...
Etsy’s marketplace is defined by the creativity and craftsmanship of our sellers and the hundreds of millions of highly diverse products they offer. You can find silversmiths who cold-forge recycled sterling silver, weavers who dye raw fleece with indigo and black walnut, and ceramicists who throw stoneware on a kick wheel.
These details define each product and often determine whether it matches a buyer’s taste, style, and interests. Sometimes buyers know exactly what they want, searching...
I have been working on a project called NanoTDB, designed specifically for environments where resources are constrained. I wanted a database solution that is small, simple to deploy, and maintains a minimal memory and CPU footprint, particularly for performance efficiency when running on SD storage. NanoTDB addresses these needs by offering: Key Features: Lightweight Architecture: Optimized for low-power hardware and minimal overhead. Embedded Web Dashboard: Provides real-time visibility int...
Etsy has maintained a sharded MySQL architecture since around 2010. This database cluster contains most of Etsy’s online data and is made up of ~1,000 tables distributed across ~1,000 shards. Over the last 16 years, it has grown significantly: combined, these tables have over 425 TB of data and receive roughly 1.7 million requests per second.
Etsy engineers access our MySQL data through a proprietary object-relational mapping (ORM). The ORM has a corresponding model for each MySQL table. W...
When buyers search on Etsy, they need to quickly and easily find the perfect item. At the same time, sellers need to be confident their unique products are being seen by the right customers. Our Ads Search ranking model, which is built on a multitask learning foundation, is the critical link in this connection. Recently, we identified an opportunity to drive more meaningful buyer engagement by enhancing our model’s ability to predict purchase intent. We achieved this via a dual-pronged impr...
Etsy’s marketplace is defined by the creativity and craftsmanship of our sellers and the hundreds of millions of highly diverse products they offer. You can find silversmiths who cold-forge recycled sterling silver, weavers who dye raw fleece with indigo and black walnut, and ceramicists who throw stoneware on a kick wheel.
These details define each product and often determine whether it matches a buyer’s taste, style, and interests. Sometimes buyers know exactly what they want, searching...
Etsy has maintained a sharded MySQL architecture since around 2010. This database cluster contains most of Etsy’s online data and is made up of ~1,000 tables distributed across ~1,000 shards. Over the last 16 years, it has grown significantly: combined, these tables have over 425 TB of data and receive roughly 1.7 million requests per second.
Etsy engineers access our MySQL data through a proprietary object-relational mapping (ORM). The ORM has a corresponding model for each MySQL table. W...
When buyers search on Etsy, they need to quickly and easily find the perfect item. At the same time, sellers need to be confident their unique products are being seen by the right customers. Our Ads Search ranking model, which is built on a multitask learning foundation, is the critical link in this connection. Recently, we identified an opportunity to drive more meaningful buyer engagement by enhancing our model’s ability to predict purchase intent. We achieved this via a dual-pronged impr...
Etsy’s marketplace is defined by the creativity and craftsmanship of our sellers and the hundreds of millions of highly diverse products they offer. You can find silversmiths who cold-forge recycled sterling silver, weavers who dye raw fleece with indigo and black walnut, and ceramicists who throw stoneware on a kick wheel.
These details define each product and often determine whether it matches a buyer’s taste, style, and interests. Sometimes buyers know exactly what they want, searching...
In our Engineering Energizers Q&A series, we highlight the engineering minds driving innovation across Salesforce. Today, we spotlight Elijah Ben Izzy, Software Engineering Architect at Salesforce. Elijah is building AgentScript — an open source programming language and control plane for Agentforce that makes enterprise AI agents easier to create, simpler to control, and safer to […]
The post Agentforce’s AgentScript: Building Deterministic Control for Enterprise AI Workflows appeared f...
By Oleksii Tkachuk, Kartik Sathyanarayanan, Rajiv ShringiIntroductionNetflix has a diverse range of graph use cases, each serving specific business needs with unique functionality and performance requirements. These use cases fall into two broad categories:OLAP: These use cases typically involve open-ended and algorithmic exploration of large graph datasets. They often utilize industry-standard models and languages such as RDF with SPARQL, Property Graphs with Gremlin or openCypher, and even...
MySQL
