I am in the middle of evaluating HyperDex. It's still early stages, but it's very promising when you want to slice and dice your data on multiple dimensions.
Having said that, I had trouble stopping and/or restarting the cluster in a clean way. To make me even consider using it on production, it should also offer me ways to backup data and as well as convince me that future upgrades will be somewhat smooth.
Not sure when you last looked at it, but we added the ability to cleanly shutdown and restart the cluster two releases ago. We are committed to providing a smooth upgrade path as well; that is not to say we will always be binary-compatible, as the next release involving the disk layer will change the on-disk representation, but we will always provide automatic upgrade scripts. So, if you last tested 0.29b or so, do check out the latest code in the repo.
I was wondering how long it would take for HyperDex to get some traction on HN. While I haven't had immediate need to use it myself, I have several friends who attest that it is a lovely system. The article doesn't mention it, but it's wicked fast, outperforming Redis in many metrics.
Clearly the consistency is the big kicker though. So many systems are saying eventual consistency is "good enough." Clearly that can be fine for a SoLoMo app, but probably not for a medical system.
Lack of consistency guarantees in most systems available in the open is the exact reason why we created Arakoon: our usage scenarios (safekeeping metadata for large-scale storage systems, among others) simply don't allow for 'eventual consistency'. It's also available as free software, check it out at http://arakoon.org.
Neat. From a very cursory glance at the HyperDex paper, it looks like a distributed kd-tree with the addition of a single-dimensional key subspace (to use their terminology). The real clever part, I believe, is the value-dependent chaining for the deterministic propagation of changes/deletion of objects.
Thanks! Unlike a kd-tree or b-tree variants, HyperDex does not build an auxiliary data structure. It turns out that keeping aux data structures in sync with the data is very difficult if you want to provide strong consistency guarantees. Hyperspace hashing is purely a mapping trick, not a distributed data-structure trick.
Agreed with you fully that value-dependent chains are neat. They allow the system to replicate and relocate data, without any need for background processes. VDCs are the key to HyperDex's strong consistency guarantees.
NoSQL/SQL trolling aside, doing data lookup in a hyperspace is such a great idea. Being able to slice my data on hyperplanes is such a useful and cool feature.
In fact, database theory was originally conceived as hyper-dimensional metric space in which both data and selection could be mapped to hyper-rectangles in the space. A hyper-plane or a point in the metric space is just a degenerate hyper-rectangle with the same dimensionality as the metric space. It has always been the most elegant way to represent a database.
One of the best written expositions of databases properly expressed as operations over hyper-dimensional spaces was produced by Rudolf Bayer, the guy that invented the B-Tree. In the late 1990s he invented a multidimensional indexing structure based on space-filling curves called a UB-Tree and they wrote at length about how various database operations are implemented using that representation. It is generally informative if you are unfamiliar with this aspect of database theory, not just in the context of UB-Trees about which it was written.
If it is such a great idea then why does no major database implement things this way? Despite several attempts by companies like Oracle, IBM, and Microsoft, no one has described a generalized data structure for databases with hyper-rectangle operands as your primitives. There are dozens of narrow algorithm solutions known, both published and unpublished, but none that you could legitimately use in a commercial database system because they all have limitations that will adversely affect real-world applications.
Hyperdex is a conventional algorithm from the standpoint of indexing hyper-dimensional spaces. They are not doing anything new there that has not been done before. However, the update value chaining element of it is actually pretty neat.
I need to add two factoids to this insightful discussion:
There is a big difference between space-filling curves and hyperspace hashing. Space-filling curves map a multidimensional space to a single path through that space that is then mapped to nodes. In the process, they do not retain locality. To our knowledge, hyperspace hashing is a direct intellectual descendant of consistent hashing and has not been done before. If you have pointers to work where data is mapped to nodes in a cluster using a multidimensional hash, please send them to us!
And one major reason why multidimensional databases failed to take off is a problem known as "the curse of dimensionality." If you implement a multi-dimensional representation naively, highly-dimensional data (say, an object with 10-20 attributes) will require a large number of nodes to be efficient. HyperDex solves this through something called space partitioning (I think the paper calls it "data partitioning," but we've changed the name to be a bit more descriptive). They're kind of analogous to materialized views, very loosely speaking.
Agreed completely that hyperspace hashing comes to its own when coupled with value-dependent chaining!
While there is a distinction between space-filling curves and what you are calling "hyperspace hashing", they are theoretical siblings and the conceptual gap between the two is very small, arguably differing interpretations of the same abstract construct. The "hash" interpretation was well-known when the space-filling curve indexing literature in the late 1980s was written though they were primarily interested in access methods for sequential storage.
With respect to "hyperspace hashing", it has been the standard way of interacting with large space decompositions for decades. The mathematics is from the 1960s (Morton et al) and the practical computer science was mature by the 1980s. When I was writing parallel codes for supercomputers, virtually all of our data structures were built this way. It is also the algorithm used for some large-scale geospatial systems; some of the Google Earth backends used it back in the day. While I've never personally worked on it, I've heard some graphics processing pipelines also use the algorithm. I first learned about it ten years ago because I ran into a couple systems using it.
I've never heard it called "hashing" until recently, largely because technically the algorithms are not a hash even if used as one in many implementations. This, and the fact that most of the literature on this idea significantly pre-dates electronic publishing, probably explains why you've seen so few references to hyper-dimensional hashing.
As you point out, the curse of dimensionality is a classic problem for these structures. The partitioning technique in the paper works for some use cases but is not used as much anymore. There have been some clever and fairly general attacks on the dimensionality problem over the last few years by a couple companies but I have not seen anything published.
All that said, this general approach is overdue for large-scale distributed systems. It has been proven out in niche markets like supercomputing for decades and is superior to distributed hash tables for many purposes. However, most of the recent research does not seem to be published.
I think it's weird when people believe there's a tool that will do their job for them, like a hammer that builds a roof by itself.
I'm sure HyperDex is totally useful for some cases, but it has clear disadvantages when you try to use it for what it wasn't intended (like global HP databases). All of a sudden you find yourself building glue to make it fit with your hybrid architecture. Instead you could take something simple and customize it, and build a huge successful business off of it, like the biggest sites in the world do currently with various tools that weren't engineered to solve simple problems like the number of round trips to look up an object.
http://news.ycombinator.com/item?id=3622059
https://groups.google.com/forum/?fromgroups#!topic/redis-db/...