# ScyllaDB

ScyllaDB

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## 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.

## Full-Text Search, Object Storage Backend, and More in ScyllaDB 2026.3

DevFeed: [Full-Text Search, Object Storage Backend, and More in ScyllaDB 2026.3](<https://devfeed.tech/articles/full-text-search-object-storage-backend-and-more-in-scylladb-2026-3-4883.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/09/08/scylladb-2026-3/>)

Author: Tzach Livyatan

Published: 2026-09-08T21:32:47Z

Content type: release

Language: en

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

Topics: [releases](<https://devfeed.tech/topics/releases.md>), [Amazon S3](<https://devfeed.tech/topics/amazon-s3.md>), [AI search](<https://devfeed.tech/topics/ai-search.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [product](<https://devfeed.tech/tags/product.md>), [rag](<https://devfeed.tech/tags/rag.md>), [release](<https://devfeed.tech/tags/release.md>), [s3](<https://devfeed.tech/tags/s3.md>), [search](<https://devfeed.tech/tags/search.md>), [storage](<https://devfeed.tech/tags/storage.md>)

### AI overview

ScyllaDB 2026.3 adds full-text search, a preview object-storage backend, OCI integration, and experimental table and migration capabilities. It also introduces large-data guardrails and cluster-wide restoration from object-storage backups.

### Source excerpt

new updates should help you move even more workloads to ScyllaDB, at a fraction of the cost.

## 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

## Building a New Rust Driver for ScyllaDB's DynamoDB API - with 58% More Throughput

DevFeed: [Building a New Rust Driver for ScyllaDB's DynamoDB API - with 58% More Throughput](<https://devfeed.tech/articles/building-a-new-rust-driver-for-scylladb-s-dynamodb-api-with-58-more-throughput-4881.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/08/27/new-rust-driver-for-scylladbs-dynamodb-api/>)

Author: Cynthia Dunlop

Published: 2026-08-27T13:10:38Z

Content type: article

Language: en

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

Topics: [SDKs](<https://devfeed.tech/topics/sdks.md>), [networking](<https://devfeed.tech/topics/networking.md>)

Tags: [alternator-for-dynamodb](<https://devfeed.tech/tags/alternator-for-dynamodb.md>), [api](<https://devfeed.tech/tags/api.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [drivers](<https://devfeed.tech/tags/drivers.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [performance](<https://devfeed.tech/tags/performance.md>), [routing](<https://devfeed.tech/tags/routing.md>), [rust](<https://devfeed.tech/tags/rust.md>)

### AI overview

The article describes a dedicated Rust driver for ScyllaDB Alternator that load-balances DynamoDB-style requests across cluster nodes. It also covers extending the Latte benchmarking tool for the DynamoDB API and reports approximately 58% higher throughput than the AWS SDK driver on a three-node cluster.

### Source excerpt

How our new Rust driver load-balances DynamoDB-style requests across a ScyllaDB cluster, and how we extended Latte to measure its performance

## How Sprig Replaced Postgres, ClickHouse & Redis...with 4-8x Better Latency

DevFeed: [How Sprig Replaced Postgres, ClickHouse & Redis...with 4-8x Better Latency](<https://devfeed.tech/articles/how-sprig-replaced-postgres-clickhouse-redis-with-4-8x-better-latency-4880.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/08/24/sprig-replaced-postgres-clickhouse-redis-4-8x-better-latency/>)

Author: Cynthia Dunlop

Published: 2026-08-24T13:30:56Z

Content type: article

Language: en

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

Topics: [Latency](<https://devfeed.tech/topics/latency.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [PostgreSQL](<https://devfeed.tech/topics/postgresql.md>), [clickhouse](<https://devfeed.tech/topics/clickhouse.md>), [Redis](<https://devfeed.tech/topics/redis.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [Back end](<https://devfeed.tech/topics/backend.md>), [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [backend](<https://devfeed.tech/tags/backend.md>), [clickhouse](<https://devfeed.tech/tags/clickhouse.md>), [data](<https://devfeed.tech/tags/data.md>), [databases](<https://devfeed.tech/tags/databases.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [postgres](<https://devfeed.tech/tags/postgres.md>), [postgresql](<https://devfeed.tech/tags/postgresql.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [recommendation-systems](<https://devfeed.tech/tags/recommendation-systems.md>), [redis](<https://devfeed.tech/tags/redis.md>), [user-stories](<https://devfeed.tech/tags/user-stories.md>)

### AI overview

Sprig outgrew PostgreSQL as its AI-powered product research platform scaled to more than 1.3 trillion events, 75 billion attributes, and high-volume real-time processing. The article describes its database challenges and the path toward lower-latency data infrastructure involving PostgreSQL, ClickHouse, and Redis.

### Source excerpt

With ScyllaDB, a small engineering team could focus on building their product instead of battling their databases.

## 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 Customer Experience Spotlight: Susie Solis

DevFeed: [ScyllaDB Customer Experience Spotlight: Susie Solis](<https://devfeed.tech/articles/scylladb-customer-experience-spotlight-susie-solis-4878.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/08/10/cx-spotlight-susie-solis/>)

Author: Cynthia Dunlop

Published: 2026-08-10T14:28:35Z

Content type: article

Language: en

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

Topics: [Databases](<https://devfeed.tech/topics/databases.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [cloud-infrastructure](<https://devfeed.tech/topics/cloud-infrastructure.md>), [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [Terraform](<https://devfeed.tech/topics/terraform.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Linux](<https://devfeed.tech/topics/linux.md>)

Tags: [blog](<https://devfeed.tech/tags/blog.md>), [cloud-infrastructure](<https://devfeed.tech/tags/cloud-infrastructure.md>), [community](<https://devfeed.tech/tags/community.md>), [customer](<https://devfeed.tech/tags/customer.md>), [cx-profiles](<https://devfeed.tech/tags/cx-profiles.md>), [database](<https://devfeed.tech/tags/database.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [latency](<https://devfeed.tech/tags/latency.md>), [linux](<https://devfeed.tech/tags/linux.md>), [on-call](<https://devfeed.tech/tags/on-call.md>), [production](<https://devfeed.tech/tags/production.md>), [streaming](<https://devfeed.tech/tags/streaming.md>), [support](<https://devfeed.tech/tags/support.md>), [terraform](<https://devfeed.tech/tags/terraform.md>)

### AI overview

Susie Solis, a ScyllaDB Technical Support Engineer, discusses her background in technical support, cloud infrastructure, and distributed systems, along with a Kubernetes and Terraform project built to reproduce environments and troubleshoot customer issues. She also describes ScyllaDB's ability to sustain high-throughput production workloads and low latency during major streaming events.

### Source excerpt

Meet Susie Solis, a Technical Support Engineer on the Customer Experience team here at ScyllaDB.

## DDIA 2nd Edition Excerpt: On Scalability

DevFeed: [DDIA 2nd Edition Excerpt: On Scalability](<https://devfeed.tech/articles/ddia-2nd-edition-excerpt-on-scalability-4877.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/08/04/ddia-2nd-edition-excerpt-on-scalability/>)

Author: ScyllaDB Team

Published: 2026-08-04T12:59:50Z

Content type: article

Language: en

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

Topics: [Scalability](<https://devfeed.tech/topics/scalability.md>), [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>), [App](<https://devfeed.tech/topics/app.md>), [data](<https://devfeed.tech/topics/data.md>), [Database](<https://devfeed.tech/topics/database.md>)

Tags: [applications](<https://devfeed.tech/tags/applications.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [community](<https://devfeed.tech/tags/community.md>), [data](<https://devfeed.tech/tags/data.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [scalability](<https://devfeed.tech/tags/scalability.md>), [scale](<https://devfeed.tech/tags/scale.md>), [systems](<https://devfeed.tech/tags/systems.md>)

### AI overview

An excerpt from the second edition of Designing Data-Intensive Applications explains scalability as a system's ability to cope with increased load. It discusses growth in users and data, trade-offs for early-stage products, architectural limits, resource expansion, and performance bottlenecks.

### Source excerpt

Martin Kleppmann and Chris Riccomini's scalability considerations for designing data-intensive applications -- from the second edition of the Designing Data-Intensive Applications book

## Lessons Learned from Real-World NoSQL Database Migrations

DevFeed: [Lessons Learned from Real-World NoSQL Database Migrations](<https://devfeed.tech/articles/lessons-learned-from-real-world-nosql-database-migrations-4876.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/07/28/lessons-learned-from-real-world-nosql-database-migrations/>)

Author: Cynthia Dunlop

Published: 2026-07-28T13:49:40Z

Content type: article

Language: en

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

Topics: [migration](<https://devfeed.tech/topics/migration.md>)

Tags: [community](<https://devfeed.tech/tags/community.md>), [compression](<https://devfeed.tech/tags/compression.md>), [data](<https://devfeed.tech/tags/data.md>), [database](<https://devfeed.tech/tags/database.md>), [database-migration](<https://devfeed.tech/tags/database-migration.md>), [validation](<https://devfeed.tech/tags/validation.md>)

### AI overview

An analysis of real-world NoSQL database migrations to ScyllaDB, covering migration strategies, trade-offs, and differences between source and target databases.

### Source excerpt

Discover the strategies, challenges, and trade-offs teams faced in a few real-world migrations to ScyllaDB

## Offloading I/O to Dedicated Cores: An Asymmetric io\_uring Backend for Seastar and ScyllaDB

DevFeed: [Offloading I/O to Dedicated Cores: An Asymmetric io\_uring Backend for Seastar and ScyllaDB](<https://devfeed.tech/articles/offloading-i-o-to-dedicated-cores-an-asymmetric-io-uring-backend-for-seastar-and-scylladb-4875.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/07/22/asymmetric-io_uring-backend-seastar/>)

Author: Cynthia Dunlop

Published: 2026-07-22T18:03:10Z

Content type: article

Language: en

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

Topics: [IO](<https://devfeed.tech/topics/io.md>), [io\_uring](<https://devfeed.tech/topics/io-uring.md>), [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>), [C++](<https://devfeed.tech/topics/c-plus-plus.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [Linux](<https://devfeed.tech/topics/linux.md>), [networking](<https://devfeed.tech/topics/networking.md>), [Latency](<https://devfeed.tech/topics/latency.md>)

Tags: [aio](<https://devfeed.tech/tags/aio.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [c-plus-plus](<https://devfeed.tech/tags/c-plus-plus.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [design](<https://devfeed.tech/tags/design.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [i](<https://devfeed.tech/tags/i.md>), [io-uring](<https://devfeed.tech/tags/io-uring.md>), [latency](<https://devfeed.tech/tags/latency.md>), [linux](<https://devfeed.tech/tags/linux.md>), [networking](<https://devfeed.tech/tags/networking.md>), [performance](<https://devfeed.tech/tags/performance.md>), [seastar](<https://devfeed.tech/tags/seastar.md>), [systems](<https://devfeed.tech/tags/systems.md>)

### AI overview

This article presents Seastar's asymmetric io_uring backend, which moves low-level I/O work from application cores to dedicated cores. It explains the shared-nothing architecture, the motivations for offloading I/O, the design trade-offs, and the comparison with Seastar's existing linux-aio backend.

### Source excerpt

We moved low-level I/O execution off application cores to dedicated networking cores using Seastar's new asymmetric_io_uring backend. Explore the architecture design, trade-offs, and benchmark results.

## What Matters Most for NoSQL Migrations

DevFeed: [What Matters Most for NoSQL Migrations](<https://devfeed.tech/articles/what-matters-most-for-nosql-migrations-4874.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/07/21/what-matters-most-for-nosql-migrations/>)

Author: Cynthia Dunlop

Published: 2026-07-21T13:44:46Z

Content type: tutorial

Language: en

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

Topics: [NoSQL](<https://devfeed.tech/topics/nosql.md>), [Database Migration](<https://devfeed.tech/topics/database-migration.md>), [migration](<https://devfeed.tech/topics/migration.md>), [data-architecture](<https://devfeed.tech/topics/data-architecture.md>), [data](<https://devfeed.tech/topics/data.md>), [App](<https://devfeed.tech/topics/app.md>)

Tags: [apps](<https://devfeed.tech/tags/apps.md>), [community](<https://devfeed.tech/tags/community.md>), [complexity](<https://devfeed.tech/tags/complexity.md>), [data](<https://devfeed.tech/tags/data.md>), [data-modeling](<https://devfeed.tech/tags/data-modeling.md>), [database](<https://devfeed.tech/tags/database.md>), [database-migration](<https://devfeed.tech/tags/database-migration.md>), [databases](<https://devfeed.tech/tags/databases.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [migration](<https://devfeed.tech/tags/migration.md>), [offline](<https://devfeed.tech/tags/offline.md>), [performance](<https://devfeed.tech/tags/performance.md>), [scale](<https://devfeed.tech/tags/scale.md>), [validation](<https://devfeed.tech/tags/validation.md>), [what-matters](<https://devfeed.tech/tags/what-matters.md>)

### AI overview

This article presents practical guidance for planning, executing, and de-risking NoSQL database migrations. It compares online and offline approaches and emphasizes schema migration, data movement, and validation, with attention to performance, scale, complexity, and disruption.

### Source excerpt

How to prioritize the things that matter most for planning, executing and de-risking your NoSQL database migration

## Build Durable Chat Memory for RAG Using ScyllaDB and LangChain

DevFeed: [Build Durable Chat Memory for RAG Using ScyllaDB and LangChain](<https://devfeed.tech/articles/build-durable-chat-memory-for-rag-using-scylladb-and-langchain-4873.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/07/14/durable-chat-memory-for-rag-scylladb-and-langchain/>)

Author: Cynthia Dunlop

Published: 2026-07-14T12:54:49Z

Content type: tutorial

Language: en

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

Topics: [AI Chat](<https://devfeed.tech/topics/ai-chat.md>), [Language models](<https://devfeed.tech/topics/language-models.md>)

Tags: [2026-2](<https://devfeed.tech/tags/2026-2.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-ml](<https://devfeed.tech/tags/ai-ml.md>), [ann](<https://devfeed.tech/tags/ann.md>), [cassandra](<https://devfeed.tech/tags/cassandra.md>), [database](<https://devfeed.tech/tags/database.md>), [ecosystem](<https://devfeed.tech/tags/ecosystem.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [history](<https://devfeed.tech/tags/history.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [langchain](<https://devfeed.tech/tags/langchain.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [latency](<https://devfeed.tech/tags/latency.md>), [memory](<https://devfeed.tech/tags/memory.md>), [rag](<https://devfeed.tech/tags/rag.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [scalability](<https://devfeed.tech/tags/scalability.md>), [sentence-transformers](<https://devfeed.tech/tags/sentence-transformers.md>), [vector](<https://devfeed.tech/tags/vector.md>)

### AI overview

A tutorial on replacing LangChain's in-memory chat history with ScyllaDB for durable RAG chatbot memory across restarts and replicas.

### Source excerpt

How to replace LangChain's in-memory chat history with ScyllaDB -- so your RAG chatbot retains context across restarts and scales across replicas

## 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

## Agent Memory at Monster Scale with Mem0 and ScyllaDB Cloud

DevFeed: [Agent Memory at Monster Scale with Mem0 and ScyllaDB Cloud](<https://devfeed.tech/articles/agent-memory-at-monster-scale-with-mem0-and-scylladb-cloud-4871.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/07/07/agent-memory-at-monster-scale-with-mem0-and-scylladb-cloud/>)

Author: Cynthia Dunlop

Published: 2026-07-07T13:08:54Z

Content type: tutorial

Language: en

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

Topics: [AI Bots](<https://devfeed.tech/topics/ai-bots.md>), [AI search](<https://devfeed.tech/topics/ai-search.md>), [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>)

Tags: [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [ai-ml](<https://devfeed.tech/tags/ai-ml.md>), [ecosystem](<https://devfeed.tech/tags/ecosystem.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [llm](<https://devfeed.tech/tags/llm.md>), [memory](<https://devfeed.tech/tags/memory.md>), [persistence](<https://devfeed.tech/tags/persistence.md>), [scale](<https://devfeed.tech/tags/scale.md>), [scylladb-cloud](<https://devfeed.tech/tags/scylladb-cloud.md>), [search](<https://devfeed.tech/tags/search.md>), [vector](<https://devfeed.tech/tags/vector.md>)

### AI overview

The article explains how Mem0 and ScyllaDB Cloud can support persistent memory for large-scale AI agents. Mem0 extracts concise, entity-based facts from interactions, while ScyllaDB Cloud provides distributed, low-latency vector-search storage.

### Source excerpt

Combine Mem0's memory management with ScyllaDB's persistence features to deploy large-scale AI agents

## ScyllaDB vs Aerospike, Wide-Column vs. Key/Value

DevFeed: [ScyllaDB vs Aerospike, Wide-Column vs. Key/Value](<https://devfeed.tech/articles/scylladb-vs-aerospike-wide-column-vs-key-value-4870.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/07/06/scylladb-vs-aerospike-wide-column-vs-key-value/>)

Author: Cynthia Dunlop

Published: 2026-07-06T13:13:23Z

Content type: comparison

Language: en

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

Topics: [NoSQL](<https://devfeed.tech/topics/nosql.md>), [Database](<https://devfeed.tech/topics/database.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Time Series](<https://devfeed.tech/topics/time-series.md>), [Availability](<https://devfeed.tech/topics/availability.md>), [Replication](<https://devfeed.tech/topics/replication.md>), [Encryption](<https://devfeed.tech/topics/encryption.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [Latency](<https://devfeed.tech/topics/latency.md>)

Tags: [article](<https://devfeed.tech/tags/article.md>), [availability](<https://devfeed.tech/tags/availability.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [database](<https://devfeed.tech/tags/database.md>), [encryption](<https://devfeed.tech/tags/encryption.md>), [latency](<https://devfeed.tech/tags/latency.md>), [leadership-team](<https://devfeed.tech/tags/leadership-team.md>), [performance](<https://devfeed.tech/tags/performance.md>), [product](<https://devfeed.tech/tags/product.md>), [replication](<https://devfeed.tech/tags/replication.md>), [security](<https://devfeed.tech/tags/security.md>), [time-series](<https://devfeed.tech/tags/time-series.md>)

### AI overview

This comparison explains how wide-column databases differ from key-value stores, using ScyllaDB and Aerospike as examples. It argues that ScyllaDB's broader data model, clustering keys, replication, availability, and operational features can support more complex access patterns while maintaining strong performance and low latency.

### Source excerpt

Wide-column flexibility doesn't have to come at the expense of performance -- see where the two models differ, where each one wins, and why you no longer have to choose

## Cutting P99 Latency 1000X During Connection Storms by Hardening ScyllaDB Admission Control

DevFeed: [Cutting P99 Latency 1000X During Connection Storms by Hardening ScyllaDB Admission Control](<https://devfeed.tech/articles/cutting-p99-latency-1000x-during-connection-storms-by-hardening-scylladb-admission-control-4869.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/07/01/cutting-p99-during-connection-storms/>)

Author: Cynthia Dunlop

Published: 2026-07-01T13:35:06Z

Content type: article

Language: en

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

Topics: [Latency](<https://devfeed.tech/topics/latency.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [Caching](<https://devfeed.tech/topics/caching.md>), [client](<https://devfeed.tech/topics/client.md>), [Processes](<https://devfeed.tech/topics/processes.md>), [Code](<https://devfeed.tech/topics/code.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [passwords](<https://devfeed.tech/topics/passwords.md>)

Tags: [2026-2](<https://devfeed.tech/tags/2026-2.md>), [caching](<https://devfeed.tech/tags/caching.md>), [code](<https://devfeed.tech/tags/code.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [customer](<https://devfeed.tech/tags/customer.md>), [database](<https://devfeed.tech/tags/database.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [latency](<https://devfeed.tech/tags/latency.md>), [performance](<https://devfeed.tech/tags/performance.md>), [process](<https://devfeed.tech/tags/process.md>), [processes](<https://devfeed.tech/tags/processes.md>), [scale](<https://devfeed.tech/tags/scale.md>), [systems](<https://devfeed.tech/tags/systems.md>)

### AI overview

ScyllaDB reduced P99 latency during connection storms by optimizing connection establishment through throttling, caching, password-hashing improvements, and service-level isolation. The article explains how connection surges, retries, task-queue pressure, and per-core connection handling can amplify load at scale.

### Source excerpt

ScyllaDB successfully mitigated performance-degrading connection storms by optimizing caching, throttling, and password hashing to achieve a 1000x reduction in tail latency

## How ScyllaDB's Trie-Based Index Delivers Up to 3X More Throughput

DevFeed: [How ScyllaDB's Trie-Based Index Delivers Up to 3X More Throughput](<https://devfeed.tech/articles/how-scylladb-s-trie-based-index-delivers-up-to-3x-more-throughput-4868.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/06/30/trie-index-3x-more-throughput/>)

Author: Tzach Livyatan

Published: 2026-06-30T13:03:08Z

Content type: article

Language: en

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

Topics: [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Caching](<https://devfeed.tech/topics/caching.md>), [IO](<https://devfeed.tech/topics/io.md>), [Seastar](<https://devfeed.tech/topics/seastar.md>), [2026.2](<https://devfeed.tech/topics/2026-2.md>), [Apache Cassandra](<https://devfeed.tech/topics/cassandra.md>)

Tags: [2026-2](<https://devfeed.tech/tags/2026-2.md>), [apache](<https://devfeed.tech/tags/apache.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [cache](<https://devfeed.tech/tags/cache.md>), [cassandra](<https://devfeed.tech/tags/cassandra.md>), [deep-dive](<https://devfeed.tech/tags/deep-dive.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [latency](<https://devfeed.tech/tags/latency.md>), [memory](<https://devfeed.tech/tags/memory.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [performance](<https://devfeed.tech/tags/performance.md>), [product](<https://devfeed.tech/tags/product.md>), [seastar](<https://devfeed.tech/tags/seastar.md>), [storage](<https://devfeed.tech/tags/storage.md>), [time](<https://devfeed.tech/tags/time.md>)

### AI overview

ScyllaDB's Trie-based SSTable index replaces separate summary and index files with a prefix tree. The article explains the format change, its storage layout and lookup behavior, and reports benchmark results showing 30% to 230% higher throughput and 31% to 63% lower latency than legacy indexes across four read workloads.

### Source excerpt

By transitioning from separate summary and index files to a prefix tree, we optimized cache efficiency, reduced disk I/O, and reduced memory overhead

## 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.

## Riding the Raft to Strong Consistency in ScyllaDB

DevFeed: [Riding the Raft to Strong Consistency in ScyllaDB](<https://devfeed.tech/articles/riding-the-raft-to-strong-consistency-in-scylladb-4866.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/06/24/raft-strong-consistency/>)

Author: Cynthia Dunlop

Published: 2026-06-24T16:51:27Z

Content type: article

Language: en

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

Topics: [data](<https://devfeed.tech/topics/data.md>)

Tags: [2026-2](<https://devfeed.tech/tags/2026-2.md>), [availability](<https://devfeed.tech/tags/availability.md>), [cassandra](<https://devfeed.tech/tags/cassandra.md>), [database](<https://devfeed.tech/tags/database.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [performance](<https://devfeed.tech/tags/performance.md>), [raft](<https://devfeed.tech/tags/raft.md>)

### AI overview

The article explains ScyllaDB's use of per-tablet Raft groups to provide strong consistency while retaining parallelism. It contrasts this with ScyllaDB's Cassandra-compatible, eventually consistent model and discusses the tradeoffs among latency, availability, and consistency.

### Source excerpt

How ScyllaDB is using per-tablet Raft groups to bring strong consistency to data, without sacrificing the parallelism that makes it fast

## 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.

## 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.