# scylladb

Published articles for scylladb.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Bringing QUIC to Seastar

DevFeed: [Bringing QUIC to Seastar](<https://devfeed.tech/articles/bringing-quic-to-seastar-17377.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/09/14/bringing-quic-to-seastar/>)

Author: Cynthia Dunlop

Published: 2026-09-14T13:02:07Z

Content type: article

Language: en

Sources: [ScyllaDB](<https://devfeed.tech/sources/scylladb.md>)

Topics: [Seastar](<https://devfeed.tech/topics/seastar.md>), [networking](<https://devfeed.tech/topics/networking.md>), [Remote Procedure Call (RPC)](<https://devfeed.tech/topics/rpc.md>), [C++](<https://devfeed.tech/topics/c-plus-plus.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Protocol (disambiguation)](<https://devfeed.tech/topics/protocol.md>)

Tags: [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [c-plus-plus](<https://devfeed.tech/tags/c-plus-plus.md>), [collaboration](<https://devfeed.tech/tags/collaboration.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [latency](<https://devfeed.tech/tags/latency.md>), [network](<https://devfeed.tech/tags/network.md>), [networking](<https://devfeed.tech/tags/networking.md>), [rpc](<https://devfeed.tech/tags/rpc.md>), [scylladb](<https://devfeed.tech/tags/scylladb.md>), [seastar](<https://devfeed.tech/tags/seastar.md>), [udp](<https://devfeed.tech/tags/udp.md>)

### AI overview

This article describes a University of Warsaw student project conducted with ScyllaDB to implement a QUIC transport for Seastar using the sans-I/O ngtcp2 library. It also adapts Seastar's RPC layer to operate over QUIC and reports benchmark results showing predictable overhead on a lossless loopback and benefits when packets are dropped.

### Source excerpt

We built a QUIC transport for Seastar on top of ngtcp2's sans-I/O state machine, then adapted RPC to it twice: 1) as a one-to-one socket replacement, and 2) a QUIC-aware approach that opens a fresh stream per call.

## Chip Huyen explains how to cut inference costs without new hardware

DevFeed: [Chip Huyen explains how to cut inference costs without new hardware](<https://devfeed.tech/articles/chip-huyen-explains-how-to-cut-inference-costs-without-new-hardware-10830.md>)

Original publisher: [Read original article](<https://thenewstack.io/pg-99-conf-2026-inference-costs/>)

Author: Tim Koopmans

Published: 2026-09-13T15:00:00Z

Content type: article

Language: en

Sources: [The New Stack](<https://devfeed.tech/sources/the-new-stack.md>)

Topics: [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Low-Latency Inference](<https://devfeed.tech/topics/low-latency-inference.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Frontier Model](<https://devfeed.tech/topics/frontier-model.md>), [AI Engineering](<https://devfeed.tech/topics/ai-engineering.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [math](<https://devfeed.tech/topics/math.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [ai-engineering](<https://devfeed.tech/tags/ai-engineering.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [frontier-model](<https://devfeed.tech/tags/frontier-model.md>), [inference](<https://devfeed.tech/tags/inference.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [performance](<https://devfeed.tech/tags/performance.md>), [post-contributed](<https://devfeed.tech/tags/post-contributed.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [scylladb](<https://devfeed.tech/tags/scylladb.md>), [sponsor-scylladb](<https://devfeed.tech/tags/sponsor-scylladb.md>), [sponsored](<https://devfeed.tech/tags/sponsored.md>), [sponsored-post-contributed](<https://devfeed.tech/tags/sponsored-post-contributed.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

### AI overview

Chip Huyen explains why inference costs can outweigh one-time frontier-model training costs and outlines ways to optimize inference without new hardware. The article emphasizes latency metrics such as time to first token, time per output token, end-to-end latency, and goodput, especially for reasoning models.

### Source excerpt

Last October, the P99 conference -- the online gathering for developers focused on high-performance, low-latency applications -- featured a cracking The post Chip Huyen explains how to cut inference costs without new hardware appeared first on The New Stack.

## A Self-Baked Async FFI Framework for Rust C# Interop

DevFeed: [A Self-Baked Async FFI Framework for Rust C# Interop](<https://devfeed.tech/articles/a-self-baked-async-ffi-framework-for-rust-c-interop-4882.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/08/31/async-ffi-framework-for-rust-c-interop/>)

Author: Cynthia Dunlop

Published: 2026-08-31T13:00:34Z

Content type: article

Language: en

Sources: [ScyllaDB](<https://devfeed.tech/sources/scylladb.md>)

Topics: [Rust](<https://devfeed.tech/topics/rust.md>), [Tokio](<https://devfeed.tech/topics/tokio.md>), [.NET](<https://devfeed.tech/topics/net.md>), [C#](<https://devfeed.tech/topics/csharp.md>), [scylladb](<https://devfeed.tech/topics/scylladb.md>), [For the Love of Code](<https://devfeed.tech/topics/for-the-love-of-code.md>)

Tags: [c-sharp](<https://devfeed.tech/tags/c-sharp.md>), [drivers](<https://devfeed.tech/tags/drivers.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [hackathon](<https://devfeed.tech/tags/hackathon.md>), [interop](<https://devfeed.tech/tags/interop.md>), [rust](<https://devfeed.tech/tags/rust.md>), [scylladb](<https://devfeed.tech/tags/scylladb.md>)

### AI overview

The article describes ScyllaDB's effort to build a Rust-based driver architecture with thin language-binding layers, focusing on a C# driver. It explains the motivation for creating a custom asynchronous FFI framework to connect Rust's Tokio runtime with .NET's async runtime over the C ABI, after existing binding-generation tools proved insufficient for Rust-C# async interoperation.

### Source excerpt

How we got tokio and .NET's async runtime talking to each other, over the C ABI

## How Medium Powers Real-Time Recommendations at 1M OPS

DevFeed: [How Medium Powers Real-Time Recommendations at 1M OPS](<https://devfeed.tech/articles/how-medium-powers-real-time-recommendations-at-1m-ops-4879.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/08/11/medium-real-time-recommendations/>)

Author: Cynthia Dunlop

Published: 2026-08-11T16:40:45Z

Content type: article

Language: en

Sources: [ScyllaDB](<https://devfeed.tech/sources/scylladb.md>)

Topics: [real-time](<https://devfeed.tech/topics/real-time.md>), [scylladb](<https://devfeed.tech/topics/scylladb.md>), [data](<https://devfeed.tech/topics/data.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Software](<https://devfeed.tech/topics/software.md>)

Tags: [architecture](<https://devfeed.tech/tags/architecture.md>), [data](<https://devfeed.tech/tags/data.md>), [inference](<https://devfeed.tech/tags/inference.md>), [models](<https://devfeed.tech/tags/models.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [recommendation-system](<https://devfeed.tech/tags/recommendation-system.md>), [scale](<https://devfeed.tech/tags/scale.md>), [scylladb](<https://devfeed.tech/tags/scylladb.md>), [user-stories](<https://devfeed.tech/tags/user-stories.md>)

### AI overview

The article explains how Medium's ScyllaDB-based feature store supports real-time recommendations. It describes processing user activity and new articles, storing features for recommendation models, and addressing data-modeling challenges as the system scaled to 1 million operations per second.

### Source excerpt

Inside Medium's move from relational features to list features in its ScyllaDB-based feature store "Keep readers reading" is the not-so-simple goal of Medium's recommendations system. To predict what's most likely to appeal to a particular reader at any given time, Medium continuously processes user activity signals (stories read, recommendations shown, follows, likes, etc.). It then immediately correlates that with the steady stream of new articles, which is estimated at millions per month. Smart models and good inference logic are required, but that's not enough. The data must be stored and retrieved quickly enough to remain relevant while the user is [...]

## ScyllaDB Is Now Supported in MCP Toolbox for Databases

DevFeed: [ScyllaDB Is Now Supported in MCP Toolbox for Databases](<https://devfeed.tech/articles/scylladb-is-now-supported-in-mcp-toolbox-for-databases-4872.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/07/08/scylladb-is-now-supported-in-mcp-toolbox-for-databases/>)

Author: Cynthia Dunlop

Published: 2026-07-08T12:01:04Z

Content type: article

Language: en

Sources: [ScyllaDB](<https://devfeed.tech/sources/scylladb.md>)

Topics: [scylladb](<https://devfeed.tech/topics/scylladb.md>), [Model Context Protocol](<https://devfeed.tech/topics/model-context-protocol.md>), [MCP Server](<https://devfeed.tech/topics/mcp-server.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai-ml](<https://devfeed.tech/tags/ai-ml.md>), [databases](<https://devfeed.tech/tags/databases.md>), [ecosystem](<https://devfeed.tech/tags/ecosystem.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [integration](<https://devfeed.tech/tags/integration.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [mcp-server](<https://devfeed.tech/tags/mcp-server.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [scylladb](<https://devfeed.tech/tags/scylladb.md>), [sdks](<https://devfeed.tech/tags/sdks.md>), [uncategorized](<https://devfeed.tech/tags/uncategorized.md>)

### AI overview

This article explains the ScyllaDB integration added in version 1.5.0 of MCP Toolbox for Databases. It describes how the open-source MCP server connects AI agents and applications to self-hosted ScyllaDB clusters and ScyllaDB Cloud, and outlines installation, configuration, and CQL tool setup.

### Source excerpt

Connect your AI agents to ScyllaDB using the new ScyllaDB integration in MCP Toolbox for Databases

## ScyllaDB 2026.2: DynamoDB Streams and Vector Search, Trie Indexes, and Strongly Consistent Tables

DevFeed: [ScyllaDB 2026.2: DynamoDB Streams and Vector Search, Trie Indexes, and Strongly Consistent Tables](<https://devfeed.tech/articles/scylladb-2026-2-dynamodb-streams-and-vector-search-trie-indexes-and-strongly-consistent-tables-4867.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/06/29/scylladb-2026-2/>)

Author: Tzach Livyatan

Published: 2026-06-29T19:18:20Z

Content type: release

Language: en

Sources: [ScyllaDB](<https://devfeed.tech/sources/scylladb.md>)

Topics: [2026.2](<https://devfeed.tech/topics/2026-2.md>), [Alternator (for DynamoDB)](<https://devfeed.tech/topics/alternator-for-dynamodb.md>), [API](<https://devfeed.tech/topics/api.md>), [Amazon DynamoDB](<https://devfeed.tech/topics/amazon-dynamodb.md>), [ScyllaDB Cloud](<https://devfeed.tech/topics/scylladb-cloud.md>), [AI search](<https://devfeed.tech/topics/ai-search.md>), [Streams](<https://devfeed.tech/topics/streams.md>), [Replication](<https://devfeed.tech/topics/replication.md>), [event driven](<https://devfeed.tech/topics/event-driven.md>), [Latency](<https://devfeed.tech/topics/latency.md>)

Tags: [2026-2](<https://devfeed.tech/tags/2026-2.md>), [amazon-dynamodb](<https://devfeed.tech/tags/amazon-dynamodb.md>), [api](<https://devfeed.tech/tags/api.md>), [event](<https://devfeed.tech/tags/event.md>), [event-driven](<https://devfeed.tech/tags/event-driven.md>), [features](<https://devfeed.tech/tags/features.md>), [latency](<https://devfeed.tech/tags/latency.md>), [performance](<https://devfeed.tech/tags/performance.md>), [product](<https://devfeed.tech/tags/product.md>), [replication](<https://devfeed.tech/tags/replication.md>), [scylladb](<https://devfeed.tech/tags/scylladb.md>), [scylladb-cloud](<https://devfeed.tech/tags/scylladb-cloud.md>), [search](<https://devfeed.tech/tags/search.md>), [streams](<https://devfeed.tech/tags/streams.md>), [updates](<https://devfeed.tech/tags/updates.md>), [vector](<https://devfeed.tech/tags/vector.md>)

### AI overview

ScyllaDB 2026.2 adds generally available DynamoDB Streams compatibility and a Vector Search extension to Alternator, alongside ScyllaDB Cloud integrations, Trie index performance improvements, stability updates, and experimental strongly consistent tables and online migration.

### Source excerpt

ScyllaDB 2026.2 brings a combination of GA new features, exciting experimental features, and multiple stability and external use case improvements.

## Automate ScyllaDB X Cloud Clusters with Terraform

DevFeed: [Automate ScyllaDB X Cloud Clusters with Terraform](<https://devfeed.tech/articles/automate-scylladb-x-cloud-clusters-with-terraform-4865.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/06/16/automate-scylladb-x-cloud-clusters-with-terraform/>)

Author: Cynthia Dunlop

Published: 2026-06-16T12:27:30Z

Content type: tutorial

Language: en

Sources: [ScyllaDB](<https://devfeed.tech/sources/scylladb.md>)

Topics: [ScyllaDB Cloud](<https://devfeed.tech/topics/scylladb-cloud.md>), [Terraform](<https://devfeed.tech/topics/terraform.md>), [Infrastructure as code](<https://devfeed.tech/topics/infrastructure-as-code.md>), [autoscaling](<https://devfeed.tech/topics/autoscaling.md>), [Provisioning](<https://devfeed.tech/topics/provisioning.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>)

Tags: [autoscaling](<https://devfeed.tech/tags/autoscaling.md>), [data](<https://devfeed.tech/tags/data.md>), [ecosystem](<https://devfeed.tech/tags/ecosystem.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [infrastructure-as-code](<https://devfeed.tech/tags/infrastructure-as-code.md>), [platform](<https://devfeed.tech/tags/platform.md>), [product](<https://devfeed.tech/tags/product.md>), [scale](<https://devfeed.tech/tags/scale.md>), [scylladb](<https://devfeed.tech/tags/scylladb.md>), [scylladb-cloud](<https://devfeed.tech/tags/scylladb-cloud.md>), [terraform](<https://devfeed.tech/tags/terraform.md>), [uncategorized](<https://devfeed.tech/tags/uncategorized.md>)

### AI overview

This tutorial explains how to use the ScyllaDB Cloud Terraform provider to provision and manage elastic ScyllaDB X Cloud clusters. It covers the tablets-based architecture, autoscaling behavior, Terraform configuration differences from standard clusters, and key scaling parameters such as instance families, minimum storage, and target utilization.

### Source excerpt

The ScyllaDB Cloud Terraform provider gives you infrastructure-as-code control over your clusters.

## How Discord Automates ScyllaDB Clusters at Scale

DevFeed: [How Discord Automates ScyllaDB Clusters at Scale](<https://devfeed.tech/articles/how-discord-automates-scylladb-clusters-at-scale-17933.md>)

Original publisher: [Read original article](<https://read.bytesizeddesign.com/p/how-discord-automates-scylladb-clusters>)

Author: Byte-Sized Design

Published: 2026-06-15T06:30:00Z

Content type: article

Language: en

Sources: [Byte-Sized Design](<https://devfeed.tech/sources/byte-sized-design.md>)

Topics: [Discord](<https://devfeed.tech/topics/discord.md>), [scylladb](<https://devfeed.tech/topics/scylladb.md>), [Framework](<https://devfeed.tech/topics/framework.md>), [Database](<https://devfeed.tech/topics/database.md>)

Tags: [database](<https://devfeed.tech/tags/database.md>), [discord](<https://devfeed.tech/tags/discord.md>), [framework](<https://devfeed.tech/tags/framework.md>), [scale](<https://devfeed.tech/tags/scale.md>), [scylladb](<https://devfeed.tech/tags/scylladb.md>)

### AI overview

An article about how Discord automates ScyllaDB cluster operations at scale, describing a framework that reduced a 36-hour database operation to two hours of mostly waiting.

### Source excerpt

The framework that turned a 36-hour database operation into two hours of mostly waiting

## ScyllaDB Customer Experience Spotlight: Faisal Saeed

DevFeed: [ScyllaDB Customer Experience Spotlight: Faisal Saeed](<https://devfeed.tech/articles/scylladb-customer-experience-spotlight-faisal-saeed-4864.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/06/11/cx-spotlight-faisal-saeed/>)

Author: Cynthia Dunlop

Published: 2026-06-11T12:01:56Z

Content type: article

Language: en

Sources: [ScyllaDB](<https://devfeed.tech/sources/scylladb.md>)

Topics: [ScyllaDB Cloud](<https://devfeed.tech/topics/scylladb-cloud.md>), [data-architecture](<https://devfeed.tech/topics/data-architecture.md>), [migration](<https://devfeed.tech/topics/migration.md>), [Automation](<https://devfeed.tech/topics/automation.md>), [Database](<https://devfeed.tech/topics/database.md>), [on-prem](<https://devfeed.tech/topics/on-prem.md>), [MariaDB](<https://devfeed.tech/topics/mariadb.md>)

Tags: [app](<https://devfeed.tech/tags/app.md>), [automation](<https://devfeed.tech/tags/automation.md>), [blog](<https://devfeed.tech/tags/blog.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [community](<https://devfeed.tech/tags/community.md>), [customer](<https://devfeed.tech/tags/customer.md>), [cx-profiles](<https://devfeed.tech/tags/cx-profiles.md>), [data-modeling](<https://devfeed.tech/tags/data-modeling.md>), [database](<https://devfeed.tech/tags/database.md>), [demo](<https://devfeed.tech/tags/demo.md>), [india](<https://devfeed.tech/tags/india.md>), [migration](<https://devfeed.tech/tags/migration.md>), [on-prem](<https://devfeed.tech/tags/on-prem.md>), [scale](<https://devfeed.tech/tags/scale.md>), [scylladb](<https://devfeed.tech/tags/scylladb.md>), [scylladb-cloud](<https://devfeed.tech/tags/scylladb-cloud.md>), [testing](<https://devfeed.tech/tags/testing.md>), [tool](<https://devfeed.tech/tags/tool.md>)

### AI overview

Faisal Saeed, Principal Customer Engineer at ScyllaDB, describes his work supporting existing customers, evaluating deployments, data modeling, and migrations to ScyllaDB Enterprise or ScyllaDB Cloud. He also discusses the ScyllaDB Automation Framework, which automates cluster operations, workload execution, and stress testing, and highlights ScyllaDB Cloud and a large-scale customer use case in India.

### Source excerpt

Meet Faisal Saeed, Principal Customer Engineer on the Customer Experience team here at ScyllaDB.

## How Discord Automates ScyllaDB Clusters at Scale

DevFeed: [How Discord Automates ScyllaDB Clusters at Scale](<https://devfeed.tech/articles/how-discord-automates-scylladb-clusters-at-scale-246.md>)

Original publisher: [Read original article](<https://discord.com/blog/how-discord-automates-scylladb-clusters-at-scale>)

Author: Peter French

Published: 2026-05-08T00:00:00Z

Content type: article

Language: en

Sources: [Discord Blog](<https://devfeed.tech/sources/discord-blog.md>)

Topics: [Database](<https://devfeed.tech/topics/database.md>), [postgresql clusters](<https://devfeed.tech/topics/postgresql-clusters.md>), [Replication](<https://devfeed.tech/topics/replication.md>), [Concurrency](<https://devfeed.tech/topics/concurrency.md>), [configuration](<https://devfeed.tech/topics/configuration.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>), [Rust](<https://devfeed.tech/topics/rust.md>), [YAML](<https://devfeed.tech/topics/yaml.md>), [Prometheus](<https://devfeed.tech/topics/prometheus.md>), [API](<https://devfeed.tech/topics/api.md>)

Tags: [concurrency](<https://devfeed.tech/tags/concurrency.md>), [database](<https://devfeed.tech/tags/database.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [prometheus](<https://devfeed.tech/tags/prometheus.md>), [replication](<https://devfeed.tech/tags/replication.md>), [rust](<https://devfeed.tech/tags/rust.md>), [scylladb](<https://devfeed.tech/tags/scylladb.md>), [workflow](<https://devfeed.tech/tags/workflow.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

Discord describes how it automated ScyllaDB cluster operations with a Scylla Control Plane built around tasks, workflows, and jobs. The system uses explicit condition checks against Scylla's API and Prometheus metrics, idempotent Rust tasks, and YAML-defined workflows with retries, parallelism, configuration, and runtime templating.

### Source excerpt

You've been asked to stand up a brand-new database cluster, meaning a whole day of configuring dozens of nodes, validating replication, wiring up dual-write pipelines... what if this whole ordeal took less than two hours? This is how we got ourselves into this exact mess, and how we made our way out of it.

## How Discord Processes 30+ Petabytes of Data

DevFeed: [How Discord Processes 30+ Petabytes of Data](<https://devfeed.tech/articles/how-discord-processes-30-petabytes-of-data-17969.md>)

Original publisher: [Read original article](<https://newsletter.betterstack.com/p/how-discord-processes-30-petabytes>)

Author: Richard Oliver Bray

Published: 2024-09-25T13:02:10Z

Content type: article

Language: en

Sources: [Hacking Scale by Better Stack](<https://devfeed.tech/sources/hacking-scale-by-better-stack.md>)

Topics: [Discord](<https://devfeed.tech/topics/discord.md>), [data](<https://devfeed.tech/topics/data.md>), [scylladb](<https://devfeed.tech/topics/scylladb.md>), [NoSQL](<https://devfeed.tech/topics/nosql.md>), [C++](<https://devfeed.tech/topics/c-plus-plus.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>)

Tags: [c-plus-plus](<https://devfeed.tech/tags/c-plus-plus.md>), [data](<https://devfeed.tech/tags/data.md>), [discord](<https://devfeed.tech/tags/discord.md>), [nosql](<https://devfeed.tech/tags/nosql.md>), [scylladb](<https://devfeed.tech/tags/scylladb.md>)

### AI overview

This article explains how Discord analyzes billions of daily messages and manages large-scale data processing. It describes message storage in ScyllaDB, data cleaning, machine-learning use cases, and Discord's development of Derived to automate data transformation for analysis.

### Source excerpt

Discord's genius approach to automating insights from billions of messages