We have never talked with Alluxio before but after coming back last week from Cloud Field Day 15 (CFD15) it seemed a good time to talk with other solution providers attempting to make hybrid cloud easier to use. Adit Madan (@madanadit) , Director of Product Management, Alluxio, which is a data orchestration solution that’s available in both a free to download/use, open source, community edition (apparently, Meta is a customer ) or a licensed, closed source, enterprise edition.
Alluxio data orchestration is all about suppling local like, IO access to data that resides elsewhere for BI, AI/ML/DL, and just about any other application needing to process data residing elsewhere. Listen to the podcast to learn more
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Alluxio started out at UC Berkeley’s AMPlab, which is focused on big data problems and was designed to provide local access to massive amounts of distributed data. Alluxio ends up constructing a locally accessible, federation of data sources for compute apps running elsewhere,
Alluxio software installs near where compute apps run that need access to remote data. We asked about a typical cloud bursting case where S3 object data needed by an app are sitting on prem, but the apps need to run in a cloud, e.g., AWS.
He said Alluxio software would be deployed in AWS, close to app compute and that’s all there is. There’s no Alluxio software running on prem, as Alluxio just uses normal (remote access) S3 APIs to supply data to the compute apps running in AWS.
Adit mentioned that BI was one of the main applications to take advantage of Alluxio, but AI/ML/DL learning is another that could use data orchestration. It turns out that AI/ ML/DL training’s consumption of data is repetitive and highly sequential, so caching, sequential pre-fetch and other Alluxio techniques can work well there to provide local-like access to remote data.
Adit said that enterprises are increasingly looking to avoid vendor lock-in and this applies equally well to the cloud. By supporting data access in one location, say GC,P and accessing that data from another, say Azure, data gravity need no longer limit where work is done.
Adit said what makes their solution so valuable is that instead of duplicating all data from one place to another all that Alluxio moves is just the data required/requested by the apps running there.
Keith asked whether Adit considered Alluxio a data mesh or data fabric. Keith had to explain the terms to me and said data fabrics are pipes and physical infrastructure/functionality that moves data around and data mesh is what gives clients/apps/users access to that data. From that perspective Alluxio is a data mesh.
Adit said that caching is one of the keys to making Alluxio work. Much of the success of their solution depends on applications having a well behaved working set. He also mentioned they use pre-fetching and other techniques to minimize access latency and maximize throughput. However, the first byte of data being accessed may take some time to get to where compute executes.
Adit said it’s not unusual for them to have a 1/2PB of cache (storage) for an application with multiPBs of source data.
Keith asked how Alluxio’s performance can be managed. Adit said they (we assume enterprise edition) have a solution called Cache Insights which uses Alluxio’s extensive access pattern history to predict application IO performance with larger cache (storage), higher speed networking, higher performing/more compute cores, etc. In this way, customers can see what can be done to improve application IO performance and what it would cost.
Keith asked if Alluxio were available as a SaaS solution. Adit said, although it could be deployed in that fashion, it’s not currently a SaaS solution. When asked how Alluxio (enterprise) was priced, Adit said it’s a function of the total resources consumed by their service, i.e, storage (cache), cores, networking that runs Alluxio software etc.
As for deployment options, it turns out for Spark, Alluxio is just another lib package installed inside Spark. For K8s, Alluxio is installed as a CSI drivers and a set of containers and can be deployed as containers within a cluster that needs access to data or in an external, standalone K8s cluster, servicing IO from other clusters. Alluxio HA is supplied by using multiple nodes to provide IO access.
Alluxio also supports access to multiple data locations. In this case, the applications would just access different mount points.
Data reads are easy, writes can be harder due to data integrity issues. As such, trying to supply IO performance becomes a trade off for data integrity when data updates are supported. Adit said Alluxio offers a couple of different configuration options for write concurrency (data integrity) that customers can select from. We assume this includes write through, write back and perhaps other write consistency options.
Alluxio supports AWS, Azure and GCP cloud compute accessing HDFS, S3 and Posix protocol access to data residing at remote sites. At remote sites, they currently support MinIO, Cloudian and any other S3 compatible storage solutions as well as NetApp (ONTAP) and Dell (ECS) storage as data sources.
Adit Madan, Director of Product, Alluxio
Adit Madan is the Director of Product Management at Alluxio. Adit has extensive experience in distributed systems, storage systems, and large-scale data analytics.
Adit holds an MS from Carnegie Mellon University and a BS from the Indian Institute of Technology – Delhi.
Adit is the Director of Product Management at Alluxio and is also a core maintainer and Project Management Committee (PMC) member of the Alluxio Open Source project.