ALLUXIO COMMUNITY OFFICE HOUR
While adoption of the Cloud & Kubernetes has made it exceptionally easy to scale compute, the increasing spread of data across different systems and clouds has created new challenges for data engineers. Effectively accessing data from AWS S3 or on-premises HDFS becomes harder and data locality is also lost – how do you move data to compute workers efficiently, how do you unify data across multiple or remote clouds, and many more. Open source project Alluxio approaches this problem in a new way. It helps elastic compute workloads, such as Apache Spark, realize the true benefits of the cloud while bringing data locality and data accessibility to workloads orchestrated by Kubernetes.
One important performance optimization in Apache Spark is to schedule tasks on nodes with HDFS data nodes locally serving the task input data. However, more users are running Apache Spark natively on Kubernetes where HDFS is not an option. This office hour describes the concept and dataflow with respect to using the stack of Spark/Alluxio in Kubernetes with enhanced data locality even if the storage service is outside or remote.
In this Office Hour we’ll go over:
- Why Spark is able to make a locality-aware schedule when working with Alluxio in K8s environment using the host network
- Why a pod running Alluxio can share data efficiently with a pod running Spark on the same host using domain socket and host path volume
- The roadmap to improve this Spark / Alluxio stack in the context of K8s
ALLUXIO COMMUNITY OFFICE HOUR
While adoption of the Cloud & Kubernetes has made it exceptionally easy to scale compute, the increasing spread of data across different systems and clouds has created new challenges for data engineers. Effectively accessing data from AWS S3 or on-premises HDFS becomes harder and data locality is also lost – how do you move data to compute workers efficiently, how do you unify data across multiple or remote clouds, and many more. Open source project Alluxio approaches this problem in a new way. It helps elastic compute workloads, such as Apache Spark, realize the true benefits of the cloud while bringing data locality and data accessibility to workloads orchestrated by Kubernetes.
One important performance optimization in Apache Spark is to schedule tasks on nodes with HDFS data nodes locally serving the task input data. However, more users are running Apache Spark natively on Kubernetes where HDFS is not an option. This office hour describes the concept and dataflow with respect to using the stack of Spark/Alluxio in Kubernetes with enhanced data locality even if the storage service is outside or remote.
In this Office Hour we’ll go over:
- Why Spark is able to make a locality-aware schedule when working with Alluxio in K8s environment using the host network
- Why a pod running Alluxio can share data efficiently with a pod running Spark on the same host using domain socket and host path volume
- The roadmap to improve this Spark / Alluxio stack in the context of K8s
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Fireworks AI is a leading inference cloud provider for Generative AI, powering real-time inference and fine-tuning services for customers' applications that require minimal latency, high throughput, and high concurrency. Their GPU infrastructure spans 10+ clouds and 15+ regions, serving enterprises and developers deploying production AI workloads at scale.
With model sizes reaching 70GB+, Fireworks AI faced critical challenges: eliminating cold start delays, managing highly concurrent model downloads across GPU clusters, reducing tens of thousands in annual cloud egress costs, and automating manual pipeline management that consumed 4+ hours weekly. They chose Alluxio as their solution to scale with their hyper-growth without requiring dedicated infrastructure resources.
In this tech talk, Akram Bawayah, Software Engineer at Fireworks AI, and Bin Fan, VP of Technology at Alluxio, share how Fireworks AI uses Alluxio to power their multi-cloud inference infrastructure.
They discuss:
- How Fireworks AI uses Alluxio in its high-performance model distribution system to deliver fast, reliable inference across multiple clouds
- How implementing Alluxio distributed caching achieved 1TB/s+ model deployment throughput, reducing model loading from hours to minutes while significantly cutting cloud egress costs
- How to simplify infrastructure operations and seamlessly scale model distribution across multi-cloud GPU environments
