# in-memory database

An in-memory database is a database designed to store data primarily in computer memory (RAM), enabling low-latency access while potentially persisting data to disk.

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## Dragonfly 2.0 adds Redis and Valkey compatibility and reduces processing overhead

DevFeed: [Dragonfly 2.0 adds Redis and Valkey compatibility and reduces processing overhead](<https://devfeed.tech/articles/dragonfly-2-0-delivers-more-performance-for-this-modern-replacement-to-redis-memcached-41399.md>)

Original publisher: [Read original article](<https://www.phoronix.com/news/Dragonfly-2.0-Released>)

Author: Michael Larabel

Published: 2026-09-17T10:02:19Z

Content type: release

Language: en

Sources: [Phoronix](<https://devfeed.tech/sources/phoronix.md>)

Topics: [in-memory database](<https://devfeed.tech/topics/in-memory-database.md>), [Memcached](<https://devfeed.tech/topics/memcached.md>), [Redis](<https://devfeed.tech/topics/redis.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [compatibility](<https://devfeed.tech/tags/compatibility.md>), [desktop-linux](<https://devfeed.tech/tags/desktop-linux.md>), [in-memory-database](<https://devfeed.tech/tags/in-memory-database.md>), [linux-benchmarking](<https://devfeed.tech/tags/linux-benchmarking.md>), [linux-hardware-benchmarks](<https://devfeed.tech/tags/linux-hardware-benchmarks.md>), [linux-hardware-reviews](<https://devfeed.tech/tags/linux-hardware-reviews.md>), [linux-how-to](<https://devfeed.tech/tags/linux-how-to.md>), [linux-performance](<https://devfeed.tech/tags/linux-performance.md>), [linux-server-benchmarks](<https://devfeed.tech/tags/linux-server-benchmarks.md>), [memcached](<https://devfeed.tech/tags/memcached.md>), [open-source-graphics](<https://devfeed.tech/tags/open-source-graphics.md>), [overhead](<https://devfeed.tech/tags/overhead.md>), [phoronix](<https://devfeed.tech/tags/phoronix.md>), [phoronix-test-suite](<https://devfeed.tech/tags/phoronix-test-suite.md>), [redis](<https://devfeed.tech/tags/redis.md>), [release](<https://devfeed.tech/tags/release.md>), [serialization](<https://devfeed.tech/tags/serialization.md>), [ubuntu-benchmarks](<https://devfeed.tech/tags/ubuntu-benchmarks.md>), [ubuntu-hardware](<https://devfeed.tech/tags/ubuntu-hardware.md>), [valkey](<https://devfeed.tech/tags/valkey.md>), [version](<https://devfeed.tech/tags/version.md>)

### AI overview

Dragonfly 2.0 is a new release of the in-memory database that expands Redis and Valkey compatibility, adds Valkey 9 RDB loading support, and reduces connection processing and memory accounting overhead.

### Source excerpt

Released on Wednesday was a new version of the Dragonfly database that continues to promote itself as "the world's most efficient in-memory data store" and a modern replacement to the likes of Redis and Memcached while being fully compatible with their APIs...

## Snapshotting in a high throughput shared nothing database

DevFeed: [Snapshotting in a high throughput shared nothing database](<https://devfeed.tech/articles/snapshotting-in-a-high-throughput-shared-nothing-database-39632.md>)

Original publisher: [Read original article](<https://www.gauravsarma.com/posts/2025-01-27_Snapshotting-in-a-high-throughput-shared-nothing-database>)

Published: 2025-01-27T00:00:00Z

Content type: tutorial

Language: en

Sources: [Gaurav Sarma's Blog](<https://devfeed.tech/sources/gaurav-sarma-s-blog.md>)

Topics: [Database](<https://devfeed.tech/topics/database.md>), [Go Language](<https://devfeed.tech/topics/go-language.md>), [in-memory database](<https://devfeed.tech/topics/in-memory-database.md>), [Concurrent Programming](<https://devfeed.tech/topics/concurrent-programming.md>), [Redis](<https://devfeed.tech/topics/redis.md>)

Tags: [database](<https://devfeed.tech/tags/database.md>), [go](<https://devfeed.tech/tags/go.md>), [golang](<https://devfeed.tech/tags/golang.md>), [goroutines](<https://devfeed.tech/tags/goroutines.md>), [in-memory-database](<https://devfeed.tech/tags/in-memory-database.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [redis](<https://devfeed.tech/tags/redis.md>), [single-threaded](<https://devfeed.tech/tags/single-threaded.md>)

### AI overview

This article explains the requirements and design considerations for adding point-in-time snapshots to a high-throughput, shared-nothing in-memory database written in Go. It examines copying data during writes, compares the approach with Redis's copy-on-write snapshotting, and begins testing copy-on-write behavior in a Go process.

### Source excerpt

. [Snapshotting in a High-Throughput Shared-Nothing Database](snapshotting-in-a-high-throughput-shared-nothing-database-cover...

## Learning full stack - Implementation details

DevFeed: [Learning full stack - Implementation details](<https://devfeed.tech/articles/learning-full-stack-implementation-details-28713.md>)

Original publisher: [Read original article](<https://jeroenmols.com/blog/2024/11/14/full-stack-implementation/>)

Author: info@jeroenmols.com (Jeroen Mols)

Published: 2024-11-14T00:00:00Z

Content type: tutorial

Language: en

Sources: [Jeroen Mols](<https://devfeed.tech/sources/jeroen-mols.md>)

Topics: [Full Stack Development](<https://devfeed.tech/topics/full-stack-development.md>), [implementation](<https://devfeed.tech/topics/implementation.md>), [API](<https://devfeed.tech/topics/api.md>), [Development](<https://devfeed.tech/topics/development.md>), [Go Language](<https://devfeed.tech/topics/go-language.md>), [Authentication](<https://devfeed.tech/topics/authentication.md>), [Cross-origin resource sharing (CORS)](<https://devfeed.tech/topics/cors.md>), [in-memory database](<https://devfeed.tech/topics/in-memory-database.md>), [Authorization](<https://devfeed.tech/topics/authorization.md>), [HTTP](<https://devfeed.tech/topics/http.md>), [JSON](<https://devfeed.tech/topics/json.md>)

Tags: [api](<https://devfeed.tech/tags/api.md>), [authentication](<https://devfeed.tech/tags/authentication.md>), [authorization](<https://devfeed.tech/tags/authorization.md>), [backend](<https://devfeed.tech/tags/backend.md>), [blogs](<https://devfeed.tech/tags/blogs.md>), [cors](<https://devfeed.tech/tags/cors.md>), [development](<https://devfeed.tech/tags/development.md>), [frontend](<https://devfeed.tech/tags/frontend.md>), [full-stack](<https://devfeed.tech/tags/full-stack.md>), [full-stack-development](<https://devfeed.tech/tags/full-stack-development.md>), [go](<https://devfeed.tech/tags/go.md>), [http](<https://devfeed.tech/tags/http.md>), [implementation](<https://devfeed.tech/tags/implementation.md>), [in-memory-database](<https://devfeed.tech/tags/in-memory-database.md>), [json](<https://devfeed.tech/tags/json.md>), [side-project](<https://devfeed.tech/tags/side-project.md>)

### AI overview

The final part of a full-stack development series explains the implementation of a to-do list project. It covers REST API design, authentication and CORS middleware, Go backend structure and routing, and an in-memory database serialized as JSON.

### Source excerpt

To conclude my series on full-stack development, this part will look at the implementation, design choices, what worked well and what didn't. This builds on the to-do list project proposed in part 1 and the tech stack described in part 2.

## Using H2 as a Test Database Product with jOOQ

DevFeed: [Using H2 as a Test Database Product with jOOQ](<https://devfeed.tech/articles/using-h2-as-a-test-database-product-with-jooq-28969.md>)

Original publisher: [Read original article](<https://blog.jooq.org/using-h2-as-a-test-database-product/>)

Author: lukaseder

Published: 2022-08-19T07:25:43Z

Content type: tutorial

Language: en

Sources: [jOOQ](<https://devfeed.tech/sources/jooq.md>)

Topics: [in-memory database](<https://devfeed.tech/topics/in-memory-database.md>), [Testing](<https://devfeed.tech/topics/testing.md>), [Java](<https://devfeed.tech/topics/java.md>), [SQL](<https://devfeed.tech/topics/sql.md>), [sql-server](<https://devfeed.tech/topics/sql-server.md>)

Tags: [compatibility-mode](<https://devfeed.tech/tags/compatibility-mode.md>), [database](<https://devfeed.tech/tags/database.md>), [h2](<https://devfeed.tech/tags/h2.md>), [in-memory-database](<https://devfeed.tech/tags/in-memory-database.md>), [integration-testing](<https://devfeed.tech/tags/integration-testing.md>), [java](<https://devfeed.tech/tags/java.md>), [jooq](<https://devfeed.tech/tags/jooq.md>), [jooq-in-use](<https://devfeed.tech/tags/jooq-in-use.md>), [sql](<https://devfeed.tech/tags/sql.md>), [sql-server](<https://devfeed.tech/tags/sql-server.md>), [testcontainers](<https://devfeed.tech/tags/testcontainers.md>), [testing](<https://devfeed.tech/tags/testing.md>), [vendor-agnosticity](<https://devfeed.tech/tags/vendor-agnosticity.md>)

### AI overview

This article explains the trade-offs of using H2 as a test database for Java applications with jOOQ. It shows why SQL that works on H2 may require syntax changes on SQL Server, describes H2 compatibility modes, and notes that jOOQ generates SQL for H2's native dialect rather than its compatibility modes.

### Source excerpt

The H2 database is an immensely popular in-memory database product mostly used by Java developers for testing. If you check out the DB-Engines ranking, it ranks 50th, which is quite impressive, as this rank outperforms products like: CockroachDB Ignite Single Store (previously MemSQL) Interbase (which was forked as Firebird) Ingres (which is a predecessor to ... Continue reading Using H2 as a Test Database Product with jOOQ ->

## Lessons Learned: Managing web server capacity with Auto Scaling

DevFeed: [Lessons Learned: Managing web server capacity with Auto Scaling](<https://devfeed.tech/articles/lessons-learned-managing-web-server-capacity-with-auto-scaling-27894.md>)

Original publisher: [Read original article](<https://clevertap.com/blog/lessons-learnt-managing-web-server-capacity-with-auto-scaling/>)

Author: Francis Pereira

Published: 2017-06-13T07:14:43Z

Content type: article

Language: en

Sources: [CleverTap](<https://devfeed.tech/sources/clevertap.md>)

Topics: [Server](<https://devfeed.tech/topics/server.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [Docker](<https://devfeed.tech/topics/docker.md>), [in-memory database](<https://devfeed.tech/topics/in-memory-database.md>), [Latency](<https://devfeed.tech/topics/latency.md>)

Tags: [agile](<https://devfeed.tech/tags/agile.md>), [cache](<https://devfeed.tech/tags/cache.md>), [clevertap](<https://devfeed.tech/tags/clevertap.md>), [clevertap-technology-servers-engineering](<https://devfeed.tech/tags/clevertap-technology-servers-engineering.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [docker](<https://devfeed.tech/tags/docker.md>), [docker-container](<https://devfeed.tech/tags/docker-container.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [latency](<https://devfeed.tech/tags/latency.md>), [scale](<https://devfeed.tech/tags/scale.md>), [server](<https://devfeed.tech/tags/server.md>), [servers](<https://devfeed.tech/tags/servers.md>), [technology](<https://devfeed.tech/tags/technology.md>), [web](<https://devfeed.tech/tags/web.md>)

### AI overview

This article shares lessons from using Auto Scaling to handle daily peaks of approximately 116K requests per second. It recommends fast responses using in-memory systems, measuring latency to guide capacity planning, ensuring consistent instance bootstrapping, avoiding external dependencies during deployment, and packaging applications in Docker containers.

### Source excerpt

We have been experimenting with agile, faster and reliable ways to work with Auto Scaling to minimise last minute surprises, The post Lessons Learned: Managing web server capacity with Auto Scaling first appeared on CleverTap.