I like CockroachDB serverless smooth scaling for microservices

that maintenance timing though

@NelsonXLee pls add @CockroachDB on 33 Yonge st!

Bhai CockroachDB use kare ki nhi inn cockroaches ki wajh se usko use karne se phle bhi sochna padrha hai

ChartDB is the database diagramming tool you didn't know you needed — and it's fully open-source. One query and your entire schema is visualized. PostgreSQL, MySQL, SQL Server, SQLite, MariaDB — it imports from all of them instantly. No account needed. The AI export is where it gets interesting: you can migrate from MySQL to PostgreSQL or SQLite to CockroachDB with a single prompt. It generates the DDL script in your target dialect. Self-host options: - `npm run dev` for local - Docker with OpenAI key for AI features - Supports custom LLM endpoints (vLLM, local models) Privacy note: analytics via Fathom (GDPR-friendly), fully disable-able. This is what good developer tooling looks like — zero friction to start, powerful when you need it. github.com/chartdb/chartdb

Excited to have Form3 at RoachFest London, where they'll walk through how they evolved from AWS into a cloud-agnostic, active/active/active architecture across AWS, GCP & Azure using CockroachDB, Kubernetes, NATS, and Go. cockroachlabs.com/roachfest/…

cockroachdb cluster 😂

The RUM Conjecture explains why there is no perfect storage engine. Storage engine designers optimize for tradeoffs. You can optimize: Reads Writes Space But you cannot simultaneously optimize all three. That's why: B Trees favor reads Logs favor writes Hash tables favor reads writes (at the cost of memory) LSM Trees sit in the middle This is one of the key ideas behind databases like RocksDB, Cassandra, TiKV, ScyllaDB, CockroachDB, YugabyteDB, and RisingWave. Every storage engine is choosing where to sit in the RUM triangle.

At RoachFest, Igor Stanko is sharing what's next for CockroachDB: BYOC, AI-powered migration tooling, agent-ready architecture, and the path to price-performance at scale. Learn more: cockroachlabs.com/roachfest/…

Introducing The Crawl Space RoachFest London's intimate interview series with silent disco headphones, unfiltered convos & maybe some spilled tea 🫖 Hot takes on AI. Real talk. Less keynote, more behind-the-scenes: cockroachlabs.com/roachfest/…

the honest answer is that db landscapes have changed drastically since 2026 started and even the big ones are experimenting with novel solutions like google's bigtable or cockroachdb, so your next bet might not be on a traditional one

cockroachDB以外で初めてcockroachという単語を見たかもしれぬ

Looking at the product CockroachDB (Published on September 26, 2024) do I see it as a sign that Cockroach Janata Party is going to morph into Bharatiya Janata Party? Or was this sign for suspended City Magistrate Alankar Agnihotri meant when he said CJP as C-team of BJP?

LSM Trees power a huge portion of today's modern data infrastructure. Data systems like: RocksDB Cassandra ScyllaDB TiKV CockroachDB YugabyteDB Google Bigtable Apache HBase LevelDB SlateDB turbopuffer RisingWave all build on the same fundamental idea: Optimize for writes first. Organize data later. But in the era of object-storage-native systems, the story has become even more interesting. Most databases face a fundamental challenge: How do you support massive write throughput without constantly rewriting data? Traditional B-Tree systems update data in place: Find page → Modify page → Write page back LSM Trees take a different approach: Write to MemTable → Flush to SSTables → Compact later In other words: Write fast now. Organize later. That simple idea unlocked a generation of systems optimized for: high-ingestion workloads stream processing object-storage-native systems The reason LSM Trees fit so naturally with object storage is that both favor: immutable files sequential writes append-heavy workloads This makes them a natural foundation for systems built on S3, GCS, and other object stores. But every design comes with tradeoffs. LSM-based systems gain: Fast writes Sequential I/O Efficient object storage utilization Excellent scalability while paying for: Compaction Write amplification Read amplification This is why concepts such as: SSTables Bloom Filters Compaction MVCC Snapshots Caching are so important. They are not isolated database features. They are the mechanisms that make LSM architectures practical at scale. Modern systems extend traditional LSM ideas even further. RisingWave's Hummock storage engine combines: LSM Trees Object Storage Distributed State Management MVCC Snapshots Barrier-Based Checkpointing Schema-Aware Bloom Filters to support real-time stream processing at scale. Similarly, modern object-storage-native systems increasingly combine: LSM-inspired storage layouts Object storage as the durable layer Intelligent caching as the performance layer Separation of storage and compute The result is a new generation of cloud-native data systems designed around the economics and scalability of object storage. Understanding LSM Trees today is no longer just about storage engines. It's about understanding one of the foundational building blocks behind modern data infra architectures.

Databases and Their Programming Languages • 🐘 PostgreSQL - C, PL/pgSQL, SQL • 🐬 MySQL - C, C , SQL • 🍃 MongoDB - C , JavaScript • ⚡ Redis - C, Lua • 🔍 Elasticsearch - Java • 🏛️ SQLite - C • 🪳 CockroachDB - Go • 🌊 Cassandra - Java • 🔥 Firebase - C , Java, TypeScript • 🐍 Neo4j - Java, Cypher

Now Available Learn how to build resilient, scalable and distributed application with CockroachDB, one of the world's leading cloud-native SQL databases. Publisher - @shroffpub & @OReillyMedia Link to buy - short-link.co.in/5g #cloudcomputing #guide #application #datascienc

Scalable Leader Leases For Multi Consensus Groups in CockroachDB: dl.acm.org/doi/abs/10.1145/3…. A new lease mechanism to enable quorum-less serving of read-only requests in Raft which scales to millions of Raft groups. If this excites you then you're a database nerd like me.

Tensorlake’s sandbox scheduler uses a replicated state machine (RSM) to replicate cluster and sandbox metadata across multiple machines for high availability. Recently, we increased our throughput for scheduling sandboxes by 12x. RSMs linearize writes across nodes to provide strong consistency. As we scaled our sandbox infrastructure, we found that creating a burst of 100k sandboxes concurrently competed for the replication pipeline with scheduler updates that place sandboxes on dataplanes. Worse, dataplane updates which inform users that their sandboxes have started were jammed behind create and placement messages across nodes. To solve this problem, we borrowed ideas from massively distributed databases such as TiKV and CockroachDB and implemented Multi-Raft. Among other use cases, these databases use Multi-Raft to replicate databases independently of one another. We now have three replication lanes across scheduler nodes: 1. Intent to create/delete/update sandboxes, driven by users 2. Scheduler placement updates 3. Dataplane updates Multi-Raft concurrently transfers messages across these three lanes. Both (1) and (3) drive the scheduler, so the scheduler is never blocked waiting for more messages.

CockroachDB is called cockroach because cockroaches are partition tolerant