[1] one pair in two locations, basically the minimum amount, unless you're really latency insensitive so you could have one in three locations.
Nowadays you easily have 32 cores on a machine, and each core is significantly faster than it was back then (probably at least 10x). That is a compute cluster by the definition of 1999.
So for certain "big data" tasks (really "medium data" but not everyone knows the difference), I just use a single machine and shell scripts / xargs -P instead of messing with cluster configs.
You can crunch a lot of data on those machines as long as you use all the cores. 32x is the difference between a day and less than an hour, so we should all make sure we are using it!
Common languages like node.js, Python, R, OCaml, etc. do NOT let you use all your cores "by default" (due to GILs / event loop concurrency). You have to do some extra work, and not everyone does.
If the code is written in a distributed fashion from the start then it can be designed so it's one python/node/R process per core. And that makes the transition to distributed servers simpler anyway.
That's a really glib dismissal of how hard the problem is. Python and node have pretty terrible support for building distributed systems. With Python, in practice most systems end up based on Celery, with huge long-running tasks. This configuration basically boils down to using Celery, and whatever queueing system it is running on, as a mainframe-style job control system.
The "shell scripts / xargs -P" mentioned by chubot is a better solution that is much easier to write, more efficient, requires no configuration, and has far fewer failure modes. That is because Unix shell scripting is really a job control language for running Unix processes in parallel and setting up I/O redirection between them.
Oh dear... Yeah, that's a terrible distributed system. Interestingly, all the distributed systems I've worked on with Python haven't had Celery as any kind of core component. It's just poorly suited for the job, as it is more of a task queue. A task queue is really not a good spine for a distributed system.
There are a lot of python distributed systems built around in memory stores, like pycos or dask, or built around existing distributed systems like Akka, Casandra, even Redis.
Cassandra and Redis just mean that you have a database-backed application. How do you schedule Python jobs? Either you build your own scheduler that pulls things out of the database, or you use an existing scheduler. I once worked on a Python system that scheduled tasks using Celery, used Redis for some synchronization flags, and Cassandra for the shared store (also for the main database). Building a custom scheduler for that system would have been a waste of time.
Oh there's a lot more to it than that. CRDT's... for example.
You can scale up web servers to handle more requests, which then uses Celery to offload jobs to different clusters.
I don't think any reasonable developer would dismiss concurrency/parallelism as easy problems.
[0] Well, my dad bought it, but y'know. He wasn't particularly interested, but I think he recognized an interest in computers in me :).
It predated the VIC-20 by just under 3 years. You might have had 3.5KB free for BASIC programs but the VIC-20 had a comparatively spacious 5KB of RAM and 20KB of ROM.
https://www.supermicro.com/products/system/7U/7088/SYS-7088B...
It depends what is meant by "commodity" I guess.
The largest server AWS offers is only 64 physical cores (128 logical) and less than 4 TB RAM.
Quad socket R1 (LGA 2011) supports Intel® Xeon® processor E7-8800 v4/v3, E7-4800 v4/v3 family (up to 24-Core)
Up to 12TB DDR4 (128GB 3DS LRDIMM); 96x DIMM slots (8x memory module boards: X10QBi-MEM2)
AFAIK the latest Skylake Xeons ("Scalable" - Platinum/Gold/Silver/Bronze) have regressed to 1.5TB support, see https://ark.intel.com/products/93794/Intel-Xeon-Processor-E7... http://www.colfax-intl.com/nd/downloads/Intel-Xeon-Scalable-... corroborated by https://ark.intel.com/products/120502/Intel-Xeon-Platinum-81...
Since a single 128GB stick costs about 2900 USD, 96 of them will run to ~280 000 USD plus the server so it's likely to be above 300 000K.
If you want to go with 6TB "only" then it's a lot, lot cheaper as 64GB RDIMM sticks can be had below 700 USD. The end result might cost closer to a third of the 12TB server than half of it.
RAM prices comparison: https://memory.net/memory-prices/
https://www.supermicro.com/products/system/7U/7089/SYS-7089P...
Server itself is might only be $25k but those 224 cores could add another $90k, so the total would be close to $400k.
The previous-gen version, SYS-7088B-TR4FT (link in my comment upthread), has 192 DIMM slots, so if you don't need CPU horsepower, you cn get the lower-density modules and still have 12TB (or the max of 24TB for the price premium!).
Even previous gen, if 12TiB main memory is your goal (NUMA concerns aside), it's probably worth going for the 8S system instead of the 4S one, since that's a savings of $144k, and 8 slower/fewer-core CPUs might even be cheaper than 4 that have twice the performance.
You can fit a lot of data in an in memory database of 2TB.