There are lots of scale-out, parallel file systems about, from those of the big six array makers such as NetApp’s clustered Ontap and EMC’s Isilon OneFS to the open source and distributions thereof of Linux Lustre, Red Hat’s GlusterFS etc.
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But we have a new entrant in Elastifile, a software-only startup of Israeli origin that has built a new parallel file system from the ground up that it says can form a single namespace across on-prem and cloud locations. It aims to take on object storage, and in fact uses object representation to allow customers to burst workloads in the cloud.
It aims at traditional secondary storage use cases such as backup and restore but also analytics workloads.
Elastifile says its file system can scale from a minimum three nodes to potentially thousands, although it has only deployed 100. “So far we have found no limitations,” said CEO Amir Aharoni. “But we are working with billions of files. There’s no limit. We assume we can go to 1,000s of nodes.”
Ordinarily, scale-out storage begins to slow up as it reaches very large numbers of files as the tree-like hierarchy of the file system becomes cumbersome. Elastofile execs claim that their file system design distributes metadata so that there are no bottlenecks.
Replication is anything from 2-way upwards. Aharoni says they may add erasure coding at a later date but that this isn’t high on the company agenda because the file system was developed to use data reduction that is suited to replication rather than erasure coding.
The interesting bit is that the Elastofile file system can extend to the cloud where files are represented as native scale-out file for active data or objects when inactive. When a customer wants to burst a workload to the cloud they can “check out” data from that state and run, for example, analytics on it in the cloud in file format. Then when finished they check the data back in to object representation mode.
Aharoni gave an example of a microchip designer that does “lift and shift” to the cloud in that way.
So, for some use cases the aim is access to data that’s not particularly rapid and possibly infrequent and where storage in the cloud would be cost effective.
Aharoni said the company is aiming at scientific analytics, financial services, oil & gas.