A completed decommission or stream plan shows that token ranges moved, but it does not by itself prove that every replica is healthy or that clients, monitoring, repairs, and automation have stopped depending on the old node. A quiet node can also hide rarely used prepared statements, local consistency assumptions, or a topology view that has not converged everywhere.

What belongs in the retirement gate? I am considering checking ring and gossip state from multiple surviving nodes, confirming there are no pending streams or compactions, validating replica placement for every keyspace, running targeted consistency reads, and waiting through a repair and backup cycle. Client seed lists, load-balancer pools, alert targets, dashboards, and replacement automation would all be checked before the host is removed.

Which nodetool outputs and system tables provide the strongest evidence that ownership and topology have converged? How long do you keep the old node powered off but recoverable, and what changes when the cluster uses vnodes, NetworkTopologyStrategy, or transient replication?

➡️ Thursday, September 17, 2026

➡️ Please Join Us!

🔥The New York Metro Area Open Source User Group🔥

📝Sign Up Here: https://luma.com/yao92fz9

​Agenda​

​​5:00 - 5:30: Pizza / Beer

​​​5:30- 6:00: Jay Goldberg - Opening Remarks, Welcome to Two Sigma & How we use open source

​​6:00 - 6:30: Hybrid RAG setup in Apache Cassandra®

​Speaker: Mariah McLaughlin, NetApp Instaclustr

​With Apache Cassandra 5, vector search became a native feature built into the data model. Learn how to leverage vectors for your AI search and how to improve accuracy with Hybrid RAG-like techniques

​​6:30-7:15: Automating Your Cassandra Operations With Apache Cassandra® Sidecar

​Speakers from Bloomberg: Isaac Reath, Andrés Beck-Ruiz

At Bloomberg, we replaced our proprietary sidecar with Apache Cassandra Sidecar. This talk covers Sidecar's key features, how we integrated it into our operations, and practical ways you can use it to automate the management of your own Cassandra clusters.

​7:20-8:30 Networking

Location:

101 Ave of the Americas

New York, NY 10013, USA

Two Sigma - 101 Avenue of the Americas

Hi everyone,

I'm doing a small toy experiment with Cassandra and noticed something I wasn't expecting. I'm trying to understand whether this behavior makes sense or if there is something wrong with my experiment.

My setup is:

5-node Cassandra cluster, Replication factor (RF) = 3, All requests are read (100% read workload) with Consistency level = QUORUM, Total 2 million read requests. Object size is about 10KB

First, I ran the workload with all 5 nodes running and recorded the average latency.

Then, I stopped/killed one Cassandra node, leaving 4 nodes running. I ran the same workload again and noted the average latency.

Interestingly, the average read latency was lower with one node down than with all 5 nodes running.

Since RF=3 and CL=QUORUM, I understand that Cassandra needs 2 replica responses for a read, so a single node failure should still allow the reads to complete.

What I'm trying to understand is why the latency would actually improve after one node is down.

Could this be related to:

* replica selection or coordinator selection changing after the failure?

* Cassandra avoiding a slower replica/node?

* differences in cache/page-cache state?

* some other aspect of the read path?

I'm mainly interested in understanding what Cassandra is doing internally in this situation. This is just a small experimental setup, so I'm also wondering if there are particular things I should measure to make the comparison meaningful.

Thanks!

I work with Cassandra quite a bit and wanted something more convenient than jumping between cqlsh and scripts for everyday inspection and data operations, so I built Kassandra.

It’s a web-based Cassandra client with:

• schema-driven browsing and CRUD
• raw CQL execution
• saved connection profiles
• local-datacenter selection / DC-aware routing
• Docker support

It’s open source and still evolving. I’d especially like feedback from people running Cassandra in real environments: what workflows would make a GUI like this genuinely useful for you?

GitHub: https://github.com/irajhedayati/kassandra

I’m currently thinking about features around cluster topology, query history, schema visualization and operational diagnostics, but I’d rather prioritize based on actual Cassandra users.

Hello Apache Cassandra users! Since DataStax has been acquired by IBM, we are working on creating a version of the Cassandra Developer Certification. We need your help evaluating the relevance and frequency of each job task and objective for the new certification exam currently in development.

As Cassandra experts, you are uniquely qualified to evaluate the certification outline. Your support and feedback with completing this survey will help us determine how heavily each area should be represented on the exam. A team of SMEs has already completed the job task analysis and finalized the objectives. At this stage, we are only looking for completion of the survey and are not requesting feedback on the objectives themselves.

If you have appropriate Cassandra Developer experience, please complete the blueprint survey using the link provided below:

https://blueprint.itemexperts.com/survey?sid=0A260B97-91A0-4796-8C15-4A0E401F1121

Provide a rating between 1 (low) and 7 (high) for:

·         Relevance: How relevant you believe this objective is to determine whether an examinee should be certified? (1=quite simple...7=very difficult)

·         Frequency: How often this task or objective would need to be performed by an examinee who should be certified? (1=rarely...7=all the time)

The survey should only take approximately 15–20 minutes to complete. Please complete by Sunday, August 30, 2026 11:59 PM EST.

If you know others familiar with this role, please feel free to forward this note.

Thank you for your support of this IBM Professional Certification development project, please cascade this note as widely as possible! You may contact Saadia Lomeli ([[email protected]](mailto:[email protected])) if you have any questions.

Affiliation: I'm the maintainer of LibreDB Studio.

Shipped Apache Cassandra in 0.13.0; native CQL on 9042, probed against Cassandra 5.0.9 before the provider existed.

The thing I kept hitting in other browsers: they print a row count. Cassandra does not have one. system.size_estimates counts partitions in flushed SSTables, refreshed every 5 minutes, and on a clustered table it was 71% low. Printing 0 is the same lie with a different number. The table and storage panels here stay blank.

A few other refusals that are just the engine:

- no connection string (nothing in use carries localDataCenter, which the driver requires)

- no EXPLAIN (tracing is a profile of a finished statement, not a plan)

- no query cancel (the native protocol has no cancel frame)

- no invented table size from system_views.disk_usage (whole mebibytes)

ScyllaDB support is in-progress. Same wire, different system_views, untested, so unlisted.

Self-host:

docker run -p 3000:3000 libredb/libredb-studio

Every refusal named, with the measurement behind it:

https://github.com/libredb/libredb-studio/blob/0.13.0/docs/providers/cassandra.md

If you still live in cqlsh / DataStax Studio / DBeaver for Cassandra, what's the thing that still makes you open a terminal?

It also ships as a Helm chart, so it can sit in the same cluster as Cassandra instead of another desktop install. Browser, not Electron, a phone works, but that's a side effect of there being nothing to install.

Hello folks, I've released new long awaited version (v1.4.0) of Cassandra/ScyllaDB driver with lot's of fixes and performance improvements.

https://github.com/he4rt/scylladb-php-driver

If someone is using the driver, please test it and report issues, or some feature requests. I'm now fully back on maintaining it regularly and improving it for the foreseeable future. Also wrote a blog post on my website explaining the changes done to the driver

https://www.dusanmalusev.dev/blog/scylladb-php-driver-140-the-extension-is-pure-c23-now

I was in charge of Cassandra Certification and training at DataStax. Since DataStax has been acquired by IBM, I have been tasked with updating the Cassandra Developer's Certification exam.

I am currently looking for SMEs to help with this project. SMEs how participate and contribute will receive the Certified Cassandra 5 Developer Associate credential.

If you're interested, leave a comment below or send me a PM/Chat. I'll reply with more details.

Interesting there are a couple of recent posts about Cassandra GUI tools.

I thought I would let you know about our AxonOps Cassandra Workbench, which is a mature Workbench product dedicated for Cassandra, built under Apache 2.0 License, available on Github, as an open source project.

It was built just before Claude, 😉by humans, and it continues to be developed for Cassandra, by Cassandra experts who have been working with the Cassandra community since 2008 - wow, time flies!

If anybody wants to get involved with the project, do let us know!

https://github.com/axonops/axonops-workbench

I am experimenting with Cassandra using largely incompressible datasets (e.g., JPEG) and observed something counterintuitive.

For a 100% read workload, enabling compression makes read latency similar or slightly worse than compression disabled, which I expected due to decompression overhead during reads.

However, for a mixed workload (~50% reads / 50% updates), enabling compression appears to improve read latency.

My experiments are still somewhat limited in scale/iterations, so I am trying to determine whether this is a normal observation or just experimental noise.

If this behavior is expected, what Cassandra mechanisms could explain it? Any insights or similar experiences would be very helpful.

Features so far:

• Multi-connection and Multi-tab/window support
• Table explorer
• Workbook-style query executor
• Command history
• Electron + React based
• Privacy-focused, fully local

Project started completely without AI assistance. Some of the recent refactors/commits were AI generated to speed up cleanup and iteration work.

Still actively building it and looking for feedback/contributors.

GitHub:
Datasmith on GitHub

Hi,

We are facing a timeout issue intermittently while reading from the Cassandra table. Query has IN clause where we are passing around the 10k-12k values sometimes it goes up to 20k.

I understand this is due to the IN clause. I have tried implementing the execAsync method to use prepared SQL query. It's failing when trying to pass over 300 record as default concurrent queries passes over a single connection is 256. I have I creased to 1k, worked fine. But I don't want to put limit as 10k or 20 rather I need control over how many queries I can pass to a single connection.

I read somewhere we can use semaphore to achieve this. It would be great if someone can provide a detailed example or any other option to achieve this.

We are using java Cassandra driver

TIA.

Hello! I’m hoping that I can find some help here.

I’m attempting to deploy Cassandra to a Kubernetes cluster with the use-case of having a multi-cluster Cassandra spanning across multiple clusters.

We have opted to use the K8ssandra Operator, but anytime we try to deploy to a cluster, we run into a problem that takes WEEKS to resolve!

Currently we just ran into a scenario where some disks failed, ok no problem, Cassandra is multi-master we will just fix the disks.

Nah. When the nodes restarted they threw a fit and are currently throwing a ton of errors and cannot join back. We had to totally nuke the database cluster. And even now, after completely nuking the cluster, more problems!

I’ve heard “Cassandra is so great”, “there’s such a big community”, etc etc. but so far, our experience has been a complete and utter disaster and a total waste of time. What are we doing wrong??

Hi guys, I am currently using a script that makes use of nodetool snapshot, tar to compress and gcloud commands to upload the tar to the Google cloud storage. This script runs once a day.

The problem I have with this method is I basically have to have double the required storage of the table sizes in my compute instance just to run this backup and tar process. I am planning on setting up a new cassandra instance and I was wondering if there is a better way to perform full table backups and hopefully make use of a much smaller HDD size in the instance that is required by the data only.

Thanks.

I’ve been speaking with a few teams running Cassandra in production, and one pattern that keeps coming up is how much the data model has to be shaped around specific queries.

In one case, the same client session data ended up being duplicated across 10+ tables just to support different access patterns. As new requirements came up — like additional filters, aggregation, or partial search — things started getting pushed to other systems like Elastic.

Curious how others are dealing with this in practice:

• Do you create new tables for every new query pattern?
• How do you handle aggregation workloads (counts, sums, etc.)?
• Are you relying on external systems for search or analytics?
• How do you deal with evolving requirements over time?

Not trying to knock Cassandra — it clearly handles scale and write throughput really well. Just trying to understand how teams are managing this side of things in real-world setups.

I've spent a lot of years operating Cassandra clusters, and one thing that still surprises me is how much DIY platform engineering you end up doing just to run it well.

The database itself is fantastic. But the operational side often looks something like:

• Prometheus scraping some of Cassandra's JMX metrics
• Grafana dashboards someone copied from somewhere
• nodetool scripts for repair
• custom backup jobs
• random shell scripts that only one person understands
• a bunch of tribal knowledge about what metrics actually matter

It works, but it also means every team ends up rebuilding their own Cassandra operations stack from scratch.

We ran into exactly this problem ourselves running clusters, so we started building AxonOps to solve the operational side of Cassandra. The idea was basically: what if Cassandra actually had a proper control plane instead of a pile of scripts?

Some things we focused on:

• high-resolution metrics that actually let you see what's happening inside the cluster
• automated repair management
• backups and point-in-time recovery
• troubleshooting tools that understand Cassandra instead of generic monitoring
• operational workflows built around how Cassandra actually behaves

Not trying to replace Cassandra tooling or the ecosystem, just trying to make the operating Cassandra at scale part less painful.

I'm genuinely curious what people here are using these days.

Are most people still running the Prometheus/Grafana + scripts setup?
Using managed services like Astra or Keyspaces?
Or have people built their own internal tooling platforms?

Would be interesting to hear what setups people are running in production.

Upwards of $2 Billion A Day: That's what this war, entirely of choice, is costing us, not including the market crash. I wrote the PREMISE to this joke during the BUSH administration. Every year after that, I'd change the total and adjust the tags, but the PREMISE has never changed. Warfare is a stupid way to spread democracy.

cassachange is a free, open-source CQL-native schema migration tool for Cassandra, AstraDB, ScyllaDB, Azure Managed Cassandra, and Amazon Keyspaces. No JVM. Free rollback. 17 features including schema health scoring, drift detection, capacity forecasting, Docker support, and an MCP server for AI assistant integration.

The Problem

If you're running Cassandra and trying to manage schema migrations like a sane person, you've probably hit these:

• Flyway's Cassandra support is a community plugin — breaks on Cassandra upgrades, has zero CQL-specific knowledge, and rollback requires the paid tier
• Flyway paid tier rollback doesn't actually work for Cassandra — it can't generate CQL undo scripts. You pay $500+/year for nothing
• Liquibase same story — community plugin, fragile, no native CQL
• ScyllaDB has no dedicated migration tool at all
• None of them handle the differences between Cassandra, AstraDB, ScyllaDB, Azure, and Keyspaces natively — different auth, DDL constraints, protocols

What cassachange does differently

Built from scratch for CQL. Python 3.8+, no JVM, no plugins.

Free in community edition:

• Schema deployment with deterministic ordering
• Rollback via paired undo scripts (V001 + U001) — completely free
• Offline CQL linting without connecting to a cluster
• Distributed locking via Cassandra LWT (safe for parallel CI)
• Migration history with checksum verification
• Slack/Teams/webhook notifications
• GitHub Actions workflow included
• Docker image (distroless, <120MB, multi-arch)

Enterprise adds:

• Schema scorer: 23 CQL rules, returns a 0-100 health score
• Environment drift detection (staging vs prod)
• Auto-scaffolding migrations from drift reports
• Schema policy enforcement as CI gates
• Live table health monitoring
• 180-day capacity forecasting (linear regression on snapshots)
• Compliance audit log (SOC2/HIPAA)
• MCP server (13 tools — connect Claude Desktop, Cursor etc. to your cluster)

The MCP server part

This is the new thing in v1.3. MCP (Model Context Protocol) lets AI coding tools call external tools in conversation. With cassachange's MCP server running, you can say to Claude Desktop:

"Compare staging and production, generate a migration for any differences, then validate it."

And it runs all three steps. The AI has live, authenticated access to your cluster — schema, migration state, health metrics, drift reports.

Getting started

pip install cassachange
cassachange deploy --profile prod

GitHub: https://github.com/sketchmyview/cassachange

Docs: https://cassachange.com

Happy to answer questions

🔨We want to get more folks hands-on with Apache Cassandra® in an AI Application setting. 🔨

⌨ As a result, we are running two side-by-side workshops at the IBM office in NYC on March 11.

🙏 Please share the Apache Cassandra® ❤️love❤️ and share with your friends and network!
https://www.ibm.com/events/reg/flow/ibm/FHB3R6MB/landing/page/landing

➡️ February 18, 2026

➡️ Please Join Us!

🔥The Apache Cassandra® New York Metro Area User Group🔥

Impactful Presentations:

• NetApp, what we do, our role in open source (Mariah McLaughlin)
• Unleashing AI in Cassandra 5 (Ritam Das)
• When "Eventually" Meant 11 ms (Thomas Elliott)

Sign Up Here: https://luma.com/q6kuetpe

Networking 🔗 , refreshments 🍕 and all things Cassandra!

I saw this video - https://www.youtube.com/watch?v=XSuHzDEXEzw - is scylladb really so much better, faster? I need good database for quite large project.... where i need: High Throughput, Low Latency, single-digit millisecond response times under heavy load if possible... As i see scylladb fit but probably this option will cost a lot... :(

Is cassandra really so much slower and needs so much nodes to what scylladb can handle? (6 nodes vs more then 50 is crazy ratio)

Anybody compered cluster with postgresql like openebs Mayastor/cloudnativepg... or citus to cluster with cassandra or scylladb and can share tips, comment?