Month: June 2016

Docker, NFS, ZFS, and extended attributes

Alex Aizman

It may be difficult to develop an emotional connection to all of the features of filesystems and filers. Take deduplication for instance. Dedup is cool. Rabin-Karp rolling hash, sliding-window Content Defined Chunking (CDC) – those were cool 15 years ago and remain cool today. Improvements and products (and startups) keep pouring in.

But when it comes to extended file attributes (xattrs), emotions range from a blank stare to dismay. As in: wouldn’t touch with a ten-foot pole.

Come to think of it, part of the problem is – NFS. And part of the NFS problem is that both v3 and v4 do not support xattrs. There is no support whatsoever: none, nada, zilch. And how there can be with no interoperable standard?

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Storage Cluster in Thermal Equilibrium (part II)

Alex Aizman

In essence, Part I of this post stipulates that distribution of states in large clusters can be approximated without making any assumptions on what kind of distribution it is in the first place.

The claim, hypothetical at this point, is that storage clusters under certain conditions must be conforming to the laws of statistical mechanics (StMch). The narrow version of the same claim relates strictly to the mathematics used in StMch.

Since the remaining part II came out pretty lengthy, heavy on math, and densely populated with equations, I’m including it here as a separate PDF.

TL;DR.

The results, I’d say, are inconclusive-but-promising. Part of being “inconclusive” is simply – not enough data, too early to say. Testing the theory on larger, 10K nodes and beyond, clusters seems like a no-brainer. However. Out of all possible whats-next ideas and steps my first preference would be to check out this theory not directly on the clustered nodes but instead on the load-balancing groups and their group-wide aggregated states..

Docker Detour

Alex Aizman

Docker keeps fascinating me, purely as a use case. From the image hosting perspective, there are a couple things that are missing in its current stage of development. The biggest and the most obvious one is – a shared,  distributed, and deduplicated store for both image manifests (image metadata) and layer content (the data).

Due to the immutable sha256-protected nature of both the related complexity is about 3 orders of magnitude lower than (this complexity would look like for) anything less specialized.

Distributing the content-hashed and stacked stuff like this:

docker-det-1

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Storage Cluster in Thermal Equilibrium (part I)

Alex Aizman

Mathematics is the art of giving the same name to different things.

Henri Poincare

1. Life’s most persistent question

The question is: how to compute distribution of states of clustered nodes in a large distributed cluster running a steady workload? The cluster is fully distributed, and:boltz-txt1None of this is uncommon. For one thing, we live at a time of ever-exploding problem sizes. Bigger problems lead to bigger clusters with nodes that, when failed, get replaced wholesale.

And small stuff. For instance, eventually consistent storage is fairly common knowledge today.  It is spreading fast, with clumps of apps (including venerable big data and NoSQL apps) growing and stacking on top.

Those software stacks enjoy minimal centralized processing (and often none at all), which causes CAP-imposed scalability limits to get crushed. This further leads to even bigger clusters, where nodes run complex stateful software, etcetera.

Why complex and stateful, by the way? The most generic sentiment is that the software must constantly evolve, and as it evolves its complexity and stateful-ness increases. Version (n+1) is always more complex and more stateful than the (n) – and if there are any exceptions that I’m not aware of, they only confirm the rule. Which fully applies to storage, especially due to the fact that this particular software must keep up with the avalanche of hardware changes. There’s a revolution going on. The SSD revolution is still in full swing and raging, with SCM revolution right around the corner. The result is millions of lines of code, and counting. It’s a mess.

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