Michael Stonebreaker, a Turing awardee, called for complete destruction of old database order in 2008: https://www.dbms2.com/2008/02/18/mike-stonebraker-calls-for-... post which, I think, they created VoltDB (H-Store) and SciDB.
Interesting to note that Andy Pavlo worked on Stonebreaker's H-Store.
> The Peloton project is dead. We have abandoned this repository and moved on to build a new DBMS. There are a several engineering techniques and designs that we learned from this first system on how to support autonomous operations that we are doing a much better job at implementing in the second system.
Here's their alleged unannounced second-take: https://github.com/cmu-db/terrier
The archetypical differentiating features, relative to classic designs, of these future-looking (for lack of a better term) kernel designs is the prodigious exploitation of user space I/O, dearth of multithreaded synchronization, and the paucity of classic tree-like data structures (not necessarily hash tables, just not trees). The industry will get here eventually, some emerging tech sectors absolutely require it for their data models.
Database rearchitecture at the single node level can help optimize this, and maybe improve the megamachine SQL node to scale up to fatter sizes, but really big data is still a network IO and reliability challenge.
And it may be true we can pump a single fat node up to really huge throughput once some db rearchitecture fully leverages SSDs and I/O, but that just exposes more data to downtime if there is a network partition.
CockroachDB gives an SQL interface over the top of a distributed data store with better consistency and relations. Cassandra has no real relations over a BigTable/Column-Store solution. It really just depends.
I think with what's coming out of SSD/NVMe and even Optain DIMMS, that there will be databases directly tuned to control/set their own data storage in these environments.
I think this is already happening. I think it was Aerospike that used the FTL of of NANd/NVMe drives for a direct key value store and I think another vendor maybe Fusion had an SDK for this as well. The Optane stuff looks really interesting, are there any server vendors shipping with those?
[1] https://lenovopress.com/lp1066-intel-optane-dc-persistent-me... [2] https://www.storagereview.com/supermicro_superserver_with_in... [3] https://cloud.google.com/blog/topics/partners/available-firs... [4] https://docs.google.com/forms/d/e/1FAIpQLSeX1tN6Qt-aQUK2iVVi...
One could probably argue, quite convincingly, that the point of truly early NoSQL was that SQL hadn't been invented yet.
Moreover, ALL existing relational databases do denormalize data behind the scenes (think about caches and indexes) for the sake of IO optimization, which means that normalization does not achieve the IO optimization goal by itself (otherwise, why bother?)
Originally, schema normalization was just about logical consistency. But that was before query optimizers were invented, and long before they grew to be so very optimized for working with normalized schemata.
Nowadays, even someone who cares 100% about performance and 0% about data integrity (And is somehow still using a relational database, yeah. So what? It's MY hypothetical.) still has good reason to default to something like 3NF. Insert famous Knuth quote here.
Caches and indexes and all that stuff may technically count as denormalizations, but, insofar as the definition of 3NF doesn't mention them, that's a tangential issue.
That's just... wrong.
Best practices with database design are to start with a totally normalized schema because it reduces scope for errors/inconsistencies.
And then to intentionally denormalize where required to increase performance, in order to reduce lookups from joins or computing aggregate functions -- at the cost of having to manually ensure duplicated data remains consistent. Query optimizers can only do so much.
(If it weren't to increase performance, I can't really think of any reason why you ever would denormalize on a single database server, unless you have some extremely complicated calculations that SQL isn't capable of expressing?)
Hopefully under profiler guidance, like for any other optimization. For my part, the frequency that I find that trying a denormalization helps performance has been on a downward trend for a while now. Mostly down to just straight-up duplicating data at this point. Databases really are getting very good at what they do.
MongoDB's insight was that the overwhelming majority of data stores don't matter enough to justify worry about correctness. It doesn't go faster than other DBs if you turn on the "do it right" flags, but their customers mostly don't.
Would you be willing to wait an extra five seconds for page load to be sure the banner at the bottom of "people who looked at this also looked at these" list is fully up to date and correct? Or is filling the boxes with any old crap good enough?
This is a separate question — do you do a live query or pre-compute it? — and it’s not really significantly easier to do that with Mongo’s model.
The pitch I saw was usually based on it being easier than having to think about your data model in advance, which is relevant to your example: everyone I know who picked it did something like that, thought it was less work and that was great, and then had some problem which came down to copies of data getting out of sync and so e.g. the “also looked at” box had the wrong price or a typo which had been fixed elsewhere. Fixing that usually cancelled out what claimed performance advantages and then some.
Interestingly, the paper does say:
[eBPF] enables applications to perform up to L7 protocol (e.g., HTTP or Memcached) processing.
Furthermore, some programmable NICs are able to execute eBPF programs directly on the NIC hardware.