When working with large volumes of data that you need to access in parallel across multiple instances you need a distributed filesystem that will scale with your workload. Even better is when that same system provides multiple paradigms for interacting with the underlying storage. Ceph is a highly available, highly scalable, and performant system that has support for object storage, block storage, and native filesystem access. In this episode Sage Weil, the creator and lead maintainer of the project, discusses how it got started, how it works, and how you can start using it on your infrastructure today. He also explains where it fits in the current landscape of distributed storage and the plans for future improvements.
DataKitchen offers the first end-to-end DataOps Platform that empowers teams to reclaim control of their data pipelines and deliver business value instantly, without errors. The platform automates and coordinates all the people, tools, and environments in your entire data analytics organization – everything from orchestration, testing and monitoring to development and deployment. It’s DataOps Delivered.
Your data platform needs to be scalable, fault tolerant, and performant, which means that you need the same from your cloud provider. Linode has been powering production systems for over 17 years, and now they’ve launched a fully managed Kubernetes platform. With the combined power of the Kubernetes engine for flexible and scalable deployments, and features like dedicated CPU instances, GPU instances, and object storage you’ve got everything you need to build a bulletproof data pipeline. If you go to dataengineeringpodcast.com/linode today you’ll even get a $100 credit to use on building your own cluster, or object storage, or reliable backups, or… And while you’re there don’t forget to thank them for being a long-time supporter of the Data Engineering Podcast!
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
- Are you struggling to keep up with customer request and letting errors slip into production? Want to try some of the innovative ideas in this podcast but don’t have time? DataKitchen’s DataOps software allows your team to quickly iterate and deploy pipelines of code, models, and data sets while improving quality. Unlike a patchwork of manual operations, DataKitchen makes your team shine by providing an end to end DataOps solution with minimal programming that uses the tools you love. Join the DataOps movement and sign up for the newsletter at datakitchen.io/de today. After that learn more about why you should be doing DataOps by listening to the Head Chef in the Data Kitchen at dataengineeringpodcast.com/datakitchen
- Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
- Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
- Your host is Tobias Macey and today I’m interviewing Sage Weil about Ceph, an open source distributed file system that supports block storage, object storage, and a file system interface.
- How did you get involved in the area of data management?
- Can you start with an overview of what Ceph is?
- What was the motivation for starting the project?
- What are some of the most common use cases for Ceph?
- There are a large variety of distributed file systems. How would you characterize Ceph as it compares to other options (e.g. HDFS, GlusterFS, LionFS, SeaweedFS, etc.)?
- Given that there is no single point of failure, what mechanisms do you use to mitigate the impact of network partitions?
- What mechanisms are available to ensure data integrity across the cluster?
- How is Ceph implemented and how has the design evolved over time?
- What is required to deploy and manage a Ceph cluster?
- What are the scaling factors for a cluster?
- What are the limitations?
- How does Ceph handle mixed write workloads with either a high volume of small files or a smaller volume of larger files?
- In services such as S3 the data is segregated from block storage options like EBS or EFS. Since Ceph provides all of those interfaces in one project is it possible to use each of those interfaces to the same data objects in a Ceph cluster?
- In what situations would you advise someone against using Ceph?
- What are some of the most interested, unexpected, or challenging aspects of working with Ceph and the community?
- What are some of the plans that you have for the future of Ceph?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Red Hat
- UC Santa Cruz
- Los Alamos National Labs
- Dream Objects
- Ceph Architecture
- DNS SRV Record
- Erasure Coding