{"id":2016,"date":"2014-01-16T03:05:14","date_gmt":"2014-01-16T03:05:14","guid":{"rendered":"http:\/\/wiki.davelevy.info\/?p=2016"},"modified":"2023-04-27T08:19:50","modified_gmt":"2023-04-27T08:19:50","slug":"rdbms-theory","status":"publish","type":"post","link":"https:\/\/davelevy.info\/wiki\/rdbms-theory\/","title":{"rendered":"RDBMS theory"},"content":{"rendered":"

This was written 2014, I wonder how relevant it is today (2023). Much of it points are theory, which should not have changed. 😀<\/p>\n

Barry Morris, CEO of Nuodb, has written a series of articles about the “Holy Grail”<\/a>, which he published at the Cloud Computing Journal, and somewhere within the NuoDB site<\/a>.<\/p>\n

The most import ant contribution that Morris makes, in my mind is that there are four models of scale out RDBMS. (Shared Disk, Shared Nothing, Synchronous Commit and their own Durable Distributed Cache invented, (or maybe substantiated), by Jim Starkey<\/a>.)<\/p>\n

Unsurprisingly, Morris’ third article extolling the superiority of what he has to sell  does not, as far as I can see describe how the consistency property<\/acronym> is met. I need to re-read the MVCC<\/acronym> part of the article. MVCC is based on a file\/item append model. MVCC obviates locks (How?) and thus removes a massive part of the seriality of a DBMS which is good because not only do we have Brewers Theory to deal with, but also Amdahl’s Law. The un-answered question to me is how does the relevant cache partition ensure that the page copy it gets from a remote node is the most recent and not required to be locked for update? He states the relationships are asynchronous between nodes, so we are back to eventually consistent, it would seem.<\/p>\n

From Morris’ article we learn that NuoDB (like MarkLogic?) and in fact like MySQL where Starkey worked for a while consists of a Transaction Engine and a Storage Manager entity.<\/p>\n

Morris mentions Google F1<\/a>, which is used to support their ad keywords database<\/a>.It is based on Google’s Spanner<\/a> which seems pretty much their answer to the CAP theorum, we’ll have to see what the latency cost is like, but being Google it may not be publicly open source.<\/p>\n

Morris’ article does not reference Brewer’s CAP theory.<\/p>\n

I originally posted some links via delicious but they went away a lng time ago. I found this, https:\/\/dzone.com\/articles\/understanding-the-cap-theorem later. At some stage  I found the proof that the CAP theorem was a theory.<\/p>\n

Can we break Brewer’s theory?<\/p>\n

I need a, personally, accessible definition of Consistent, Available and Partition Aware. (The first two are easy). Although the wikipedia entry, CAP Theorum<\/a> has a pretty good set of definitions<\/p>\n

In theoretical computer science<\/a>, the CAP theorem<\/b>, also known as Brewer’s theorem<\/b>, states that it is impossible for a distributed computer system<\/a> to simultaneously provide all three of the following guarantees:[1]<\/a><\/sup>[2]<\/a><\/sup><\/p>\n