Agreed, but which architectural features are you referring to?
At some point several years ago, a few people noticed that if you attack the problem of scalable distribution within a single server the same way you would in large distributed systems (e.g. shared nothing architectures) that you could realize huge performance increases on a single machine. The caveat is that the software architectures look unorthodox.
The general model looks like this:
- one process per core, each locked to a single core
- use locked local RAM only (effectively limiting NUMA)
- direct dedicated network queue (bypass kernel)
- direct storage I/O (bypass kernel)
If you do it right, you minimize the amount of silicon that is shared between processes which has surprisingly large performance benefits. Linux has facilities that make this relatively straightforward too.
As a consequence, adjacent cores on the same CPU have only marginally more interaction with each other than cores on different machines entirely. Treating a single server as a distributed cluster of 1-core machines, and writing the software in such a way that the operating system behavior reflects that model to the extent possible, is a great architecture for extreme performance but you rarely see it outside of closed source software.
As a corollary, garbage-collected languages do not work for this at all.
The architecture at the link looks unusual compared to open source databases but it is actually a common architecture pattern in closed source databases with significant benefits, particularly when it comes to performance. There is a lot of what I would call "guild knowledge" in advanced database engine design, much like with HPC, things the small number of experts all seem to know but no one ever writes down.
It is a path dependency problem. Most open source databases were someone's first serious attempt at designing a database, a project that turned into a product. This is an excellent way to learn but it would be unrealistic to expect a thoroughly expert design for a piece of software of such complexity on the first (or second, or third) go at it. The atypical quality of PostgreSQL is a testament to the importance of having an experienced designer involved in the architecture.
Some researchers made a big splash at OSDI by doing this securely:
http://people.inf.ethz.ch/troscoe/pubs/peter-arrakis-osdi14....
As a naive person about this...
Is possible to reap some of the benefits from this kind of architecture in a managed environment (like .NET)?
If a try to implement, for example a sqlite-like/kdb+, memory only database in .NET, how far is possible to go?
Or how avoid some common traps?
So for some abstractions such as column and other analytics databases, a sharded architecture works very well, but if you need isolated online transactions, strong consistency etc., then sharding no longer cuts it. Instead, you should rely on algorithms that minimize coherence traffic while maintaining a consistent shared-memory abstraction. In fact, there are algorithms that provide the exact same linear-scalability as sharding with an added small latency constant (not a constant factor but a constant addition) while providing much richer abstractions.
Similarly, your statement about "garbage-collected languages" is misplaced. First, there's that abstraction issue again -- if you need a consistent shared memory, then a good GC can be extremely effective. Second, while it is true that GCs don't contribute much to a sharded infrastructure (and may harm its performance), GCs and "GCed languages" are two different things. For example, in Java you don't have to use the GC. In fact, it is common practice for high-performance, sharded-architecture Java code to use manually-managed memory.
And yes, you can write high performance Java, but for whatever reasons the Cassandra codebase isn't an example of that. They just did a big storage engine rewrite and the result is slower.
And if that happens to be exactly all you need then that's great! :)
> And yes, you can write high performance Java, but for whatever reasons the Cassandra codebase isn't an example of that.
I don't know anything about the Cassandra codebase, but one thing I'm often asked is if you're not going to use the GC in Java, why write Java at all. The answer is that the very tight core code like a shard event loop turns out to be a rather small part of the total codebase. There's more code dedicated to support functions (such as monitoring and management) than the core, and relying on the GC for that makes writing all that code much more convenient and doesn't affect your performance.
And to be fair, some people are pretty productive in modern C++. It's a shame the JNI isn't better so you can have the best of both worlds.
(for the record, Quasar is #1 on my list of libraries to try if I go back to Java)
My point is that if you're providing a shared-memory abstraction to your user (like arbitrary transactions) -- even at a very high level -- then your architecture isn't "shared-nothing", period. Somewhere in your stack there's an implementation of a shared-memory abstraction. And if you decide to call anything that doesn't use shared memory at CPU/OS level "shared nothing", then that's an arbitrary and rather senseless distinction, because even at the CPU/OS level, shared memory is implemented on top of message-passing. So the cost of a shared abstraction is incurred when it's provided to the user, and is completely independent of how it's implemented. The only way to avoid it is to restrict the programming model and not provide the abstraction. If doing that is fine for the user -- great, but there's no way to have this abstraction without paying for it.
And JNI is better now[1] (I've used JNR to implement FUSE filesystems in pure Java). JNR will serve as the basis for "JNI 2.0" -- Project Panama[2]. And thanks!
I have no idea how to do any of these things. What are the system/api calls to lock a process to a kernel? How do you bypass kernel IO?
"The Scylla design, right, is based on a modern shared-nothing approach. Scylla runs multiple engines, one per core, each with its own memory, CPU and multi-queue NIC."