"Given modern trends, if a given query isn’t scalable vertically, it also isn’t scalable horizontally, which makes SQL fundamentally unscalable, period."
That was the line that jumped out at me too. The justification: information grows exponentially, machines grow polynomially, therefore horizontal scaling doesn't help - the information growth is still exponential - and therefore vertical scaling is the same as horizontal scaling.
Seems rather unrealistic and academic to me.
The point about a query language that provides a complexity guarantee is more interesting.
I agree. That quote applies to all possible queries, especially the worst-case ones that touch every record in the database. Obviously such queries don't scale in any paradigm, and thus they aren't useful for comparing systems. Small queries are more interesting IMO, because they scale on some systems but not others.
You can't rule out queries to suit your implementation; if the data is needed and can't be computed within the constraints, then the user will just run multiple queries to get that data.
For example, a key-value store can run simple lookups fast - O(1) - but if you want to run a complex analytical query you'll be running lots of those queries - maybe O(N) because you have to fetch all the data. You'd be better off simply allowing one O(N) (or better) query, rather than dealing with the overhead of N O(1) queries.
Disallowing some fraction of queries doesn't magically make your database scalable, it's just an accountancy trick.
You don't allow N O(1) queries, you allow k O(1) queries, where k is a small constant independent from N. It is simply a physical reality that a massively scalable realtime system cannot run anything other than a small number of log(N) queries. You can't run N O(1) queries or one O(N) query - there is just no way to evaluate it in real time. If you need to present the result of such a query to users, you can run it as an analytical query and cache it, but you will not be able to run it on every request.
But the users are going to issue those N queries while you're still at the whiteboard talking about big-O notation.
Exposing a full query language to the database system means that you have the opportunity to optimize the query fully. Limiting the query language only limits your ability to optimize, so you won't even be able to explain to the user that there's an alternative way to get their answer without bringing down the database.
Are there limitations of databases such that it is possible to come up with better query execution strategies than the database can figure out automatically - of course there are! But in that case, fix the query optimizer or fix the database. Don't try to solve the problem by redefining it.
If the query language is designed with this in mind, the compiler would detect that a non-realtime query is passed to a database marked as realtime and would throw an error. Of course the user could issue N realtime queries from within a host language, but that would involve really trying to work against the database. A good analogy might be CORBA/DCOM vs. SOAP. In DCOM days it was easy to call many different functions while being oblivious that each call requires a network roundtrip. Most programmers made this mistake most of the time. With SOAP, you can still work this way, but it makes the likelihood of this happening in practice significantly lower because it exposes the issue to the programmer.
Regarding your argument of a fuller optimization, presumably a well designed query language would be flexible enough to allow the user to express every possible realtime query (assuming this is theoretically possible), so the user doesn't need to combine smaller queries via host-language trickery. People have been trying to fix the optimizer for decades, but most production optimizers still have plenty of edge cases. Perhaps redefining the problem is exactly what the doctor ordered?
Nice theory, but consider that the N+1 problem is so widespread that it actually has a name, and I think you'll realize that people are a much harder problem than you think. And this isn't even a place where SQL is at all problematic! Perhaps this supports your DCOM argument though, in that it's the abstraction layers that really cause the problem.
That said, it's great that you're re-examining the problem; that's how progress is made.
If you're willing to pay Oracle, you can get a SQL database that runs queries using the same execution plan every time. I don't remember exactly how to do it, but basically you run the query wrapped in a command that says "fix the execution plan of this query." The execution plan gets saved in some table keyed by the query text. Whenever Oracle sees that query, it commits itself to the same execution plan. This feature is like a decade old.
A lot of the NoSQL arguments are really "No-MySQL" or "NoMoney" arguments. If you've never used Oracle (or DB2, or even SQL Server), you really don't have a full understanding of the capabilities and limitations of SQL databases. AFAICT, there's almost nothing in MySQL that wasn't old and boring in Oracle over a decade ago.
Well, USE INDEX is just a hint, and MySQL is free to ignore it. You probably mean FORCE INDEX, but I've still encountered situations where the index was ignored because the optimizer (incorrectly) thought there was no way to use it. (That was probably a bug, though)
The Oracle fixed execution plan feature fixes the entire execution plan, not just which indexes are used. Think about a query where there's ten (indexed) tables involved. Saying "use this index for this table, this index for that table, etc." isn't the same as saying "First, filter the third table down to the fields that match the criteria given for that table using index Foo, then filter the 7th table given its criteria using index Bar, then join them together using nested loops, then join them to the result of ...".
Think about an Oracle execution plain as your declarative SQL query rewritten as a procedural program as you would write it in C/Java/Python/Ruby with hashtables, nested loops, if statements, etc.
No. What I'm saying is that P=NP is a problem, so don't put O(2^N) algorithms into your production software. People seem to be offended by the fact that you can't run certain queries in realtime the moment you start talking about scale. It isn't my arbitrary definition, it's a fundamental limit in the physical universe.
I get it. I just wanted to point out that's what we're talking about, because it's also still a P=NP problem to even know if something is a P=NP problem, so all we can do, it would seem, is put up guards against the likely suspects. And that's all great. I'm trying not to be a negative nancy, but its statements like these that just seem so sloppy:
"If this holds true, even if we can trivially parallelize each query, an exponential function (the amount of information) divided by a polynomial (number of machines) still dominates the logarithmic function we defined earlier as acceptable. This means that given modern trends, if a given query isn’t scalable vertically, it also isn’t scalable horizontally, which makes SQL fundamentally unscalable, period."
Cut out the fluff and all that's saying is "Given modern trends, SQL is fundamentally unscalable, period." And why? Because anyone can write a bad query. And what's the answer? Complexity guards? Well, it certainly sounds good, more guards in SQL, or let's not call it SQL if there are organizational issues (i.e. non-modular design) and syntactical paradigms to improve. And lets insert some other [undescribed] computational theory to improve it, but the paradigms don't change the algorithmic reality, they can only hope to strategize better.
Having said all that, I don't want to be misunderstood, it sounds like there may be some very interesting ideas behind all of this. It's hard to know in a case like this whether to comment on something, considering that much of the comment is negative. What motivates me is that in bullet points the article seems to imply something quite different than it sounds like when taken at face value. And I honestly appreciate push back if I'm just wrong about that. It's all about filters required to find the good stuff in all the the hyperbole that surrounds us.
20 comments
[ 3.3 ms ] story [ 58.4 ms ] threadSeems rather unrealistic and academic to me.
The point about a query language that provides a complexity guarantee is more interesting.
For example, a key-value store can run simple lookups fast - O(1) - but if you want to run a complex analytical query you'll be running lots of those queries - maybe O(N) because you have to fetch all the data. You'd be better off simply allowing one O(N) (or better) query, rather than dealing with the overhead of N O(1) queries.
Disallowing some fraction of queries doesn't magically make your database scalable, it's just an accountancy trick.
Exposing a full query language to the database system means that you have the opportunity to optimize the query fully. Limiting the query language only limits your ability to optimize, so you won't even be able to explain to the user that there's an alternative way to get their answer without bringing down the database.
Are there limitations of databases such that it is possible to come up with better query execution strategies than the database can figure out automatically - of course there are! But in that case, fix the query optimizer or fix the database. Don't try to solve the problem by redefining it.
Regarding your argument of a fuller optimization, presumably a well designed query language would be flexible enough to allow the user to express every possible realtime query (assuming this is theoretically possible), so the user doesn't need to combine smaller queries via host-language trickery. People have been trying to fix the optimizer for decades, but most production optimizers still have plenty of edge cases. Perhaps redefining the problem is exactly what the doctor ordered?
That said, it's great that you're re-examining the problem; that's how progress is made.
This seems backwards. The more general a language is, the harder it is to analyze and transform, and that is what optimizers do.
technology can support the players affect the soles of the feet and legs friction weight, these <a
href="http://www.shoesgroups.com/reebokanswer-c-70.html>wholes... reebok answer shoes</a> are the perfect combination of speed
and passion. This wholesale reebok answer shoe has launched an all-star red, black and the world, these two limited edition
colors. Series, as fans should not miss the fine, We are free shippmtand a week to your door.004
A lot of the NoSQL arguments are really "No-MySQL" or "NoMoney" arguments. If you've never used Oracle (or DB2, or even SQL Server), you really don't have a full understanding of the capabilities and limitations of SQL databases. AFAICT, there's almost nothing in MySQL that wasn't old and boring in Oracle over a decade ago.
Think about an Oracle execution plain as your declarative SQL query rewritten as a procedural program as you would write it in C/Java/Python/Ruby with hashtables, nested loops, if statements, etc.
It's an interesting article. But isn't he just saying, P=NP is a problem, so let's ban P=NP? Is this programmer newspeak?
"If this holds true, even if we can trivially parallelize each query, an exponential function (the amount of information) divided by a polynomial (number of machines) still dominates the logarithmic function we defined earlier as acceptable. This means that given modern trends, if a given query isn’t scalable vertically, it also isn’t scalable horizontally, which makes SQL fundamentally unscalable, period."
Cut out the fluff and all that's saying is "Given modern trends, SQL is fundamentally unscalable, period." And why? Because anyone can write a bad query. And what's the answer? Complexity guards? Well, it certainly sounds good, more guards in SQL, or let's not call it SQL if there are organizational issues (i.e. non-modular design) and syntactical paradigms to improve. And lets insert some other [undescribed] computational theory to improve it, but the paradigms don't change the algorithmic reality, they can only hope to strategize better.
Having said all that, I don't want to be misunderstood, it sounds like there may be some very interesting ideas behind all of this. It's hard to know in a case like this whether to comment on something, considering that much of the comment is negative. What motivates me is that in bullet points the article seems to imply something quite different than it sounds like when taken at face value. And I honestly appreciate push back if I'm just wrong about that. It's all about filters required to find the good stuff in all the the hyperbole that surrounds us.