I'm not convinced. I'm not saying that MongoDB is well designed -- I don't think it is -- but it seems to me that a process-wide write lock would be perfectly fine for a data store which is designed to cluster at a one-process-per-CPU-core level.
So one core is processing one request at a time, right? If it spends any time at all being queued up on disk IO or network traffic or anything else, that core is burning up XX watts for no good reason. A more efficient system, designed for concurrency, will have higher HW utilization and lower cost.
For a sign of things to come, I invite you to take a look at how relational database vendors are fighting to squeeze single-digit percentages from their engines to beat benchmarks such as TPC-C or TPC-E. NoSQL will get there too - fight for efficiency.
I'm sorry, but "being queued up on disk IO" and "burning up XX watts for no good reason" are not related at all. For decades we have systems which do not do busy loops on external store access. They go into low power state if they really have nothing else to do in that time. They also use crazy "new" things like interrupts, notifications and wait queues. This article actually explains how mongodb does not wait for IO, but handles other requests in the meantime. (within the current known limitations of writer/writer locks, etc. of course - i'm not saying it's all perfect)
since you refuse to actually learn about what's going on before arguing about it, i'll just leave this here, lifted directly from 10gen's official page on concurrency[1] (nevermind the fact that this is directly addressed in TFA)
mongod threads yield their lock (read or write) in two classes of situations:
* yield-on-page-fault – v2.0 implements a yield-on-page-fault feature which results in much more concurrency than one would achieve with a pure reader/writer lock. For common operational cases, file system page faults are detected in advanced and handled outside of any lock, then the lock is resumed. Not all fault situations yield, but many do. This results in v2.0 having much better concurrency in practice than v1.8.
* yield-on-long-operation – mongod also yields periodically on common operations that are extremely long running. The goal here is to allow interleaving so that other operations which are quick-running can execute soon.
And there's where your legacy understanding fails. You're really not supposed to be doing a lot of disk IO in modern datastores in the first place. Your working set should be in RAM.
> beat benchmarks
If my vendor is investing time in beating benchmarks instead of solving real problems, I'm finding a new vendor.
I am not at all addressing MongoDB here to be clear -- just your comment regarding worthwhile "problems" to "solve".
It is not too difficult to foresee a future where energy costs will trump all other considerations, including development time, sys ops, etc. Specially for <X>aaS efforts, energy efficiency can clearly end up being a competitive edge.
