Re: Dreaming About Redesigning SQL

In article <3F94BCBB(dot)7030001(at)atbusiness(dot)com>, Lauri Pietarinen
<lauri(dot)pietarinen(at)atbusiness(dot)com> writes
>>Okay. Give me a FORMULA that returns a time in seconds for your query.
>>
>>Let's assume I want to print a statement of how many invoices were sent
>>to a customer, along with various details of those invoices. My invoice
>>file is indexed by company/month, and we can reasonably assume that the
>>time taken to produce the statement is infinitesimal compared to the
>>time taken to retrieve the invoice data from disk. For MV
>>
>>T = (2 + N) * ST * 1.05
>>
>>Where T is the time taken to produce the report, N is the number of
>>invoices, and ST is the hard disk seek time.
>>
>First of all it is important to note that an important component of all modern
>SQL-DBMS's is
>the buffer pool (or cache) meaning that in a reasonably well tuned database you
>get very few
>disk I/O's, even when *writing* data into tables.

Fine. But MV *doesn't* *need* much of a cache. Let's assume both SQL and
MV have the same amount of RAM to cache in - i.e. *not* *much*. I did
say the spec said "extract maximum performance from the hardware
available".

You're assuming that you can throw hardware at the problem - fine, but
that's not always possible. You might have already maxed out the ram,
you might have a "huge" database, you might be sharing your db server
with other programs (BIND really likes to chew up every available drop
of ram, doesn't it :-).

I'm not saying that you shouldn't throw hardware at it, but what if you
can't?
>
>SQL-DBMS's also are very clever at using indexes, i.e. if they can find all
>necessary data
>from an index it will not even look at the table, so to speak.

Same with MV
>
>And, even when presuming conservatively that there is no data in cache,
>depending on how
>the data is clustered, you will get more than one row/disk read (= 8K in most(?)
>systems).

Same with MV
>
>So, assuming the (simplified) example
>
>Customer(cust_id, .....)
>Order(order_id, cust_id,...)
>OrderDetail(order_id, prod_id, ...
>Product(prod_id,....)
>
>If you created a clustering index on
>Customer(cust_id)
>Order(cust_id)
>OrderDetail(order_id)
>
>And presumed that the average length of
>customer = 1K
>order=500
>orderDetail=300
>
>You would get, with 3 I/O's
>- 8 customer rows
>- 16 order rows
>- 24 order detail rows (which would only apply to one order)
>
>so, granted, that would result in one I/O per order which is more than
>in your example.
>
>I could now denormalise OrderDetail so that it contains cust_id also
>and cluster by cust_id
>(might cause you trouble down the road, if you can change the customer
>of an order), in which case, with 3 I/O's I would get
>- 8 customer rows
>- 16 order rows
>- 24 order detail rows (which would all apply to one customer)
>
>Now the amout of I/O's would depend on how many detail rows
>we have per customer.
>
>And, of course, because we are using sequential prefetch, we would be
>getting more than one I/O block (8?, 16?) per disk seek, so it's a hard
>comparison to
>make but I suspect that it would about equal your example.

Except my example was an *average* case, and yours is a *best* case. Oh,
and my data is still normalised - I haven't had to denormalise it! AND I
haven't run an optimiser over it :-)
>
>Now, that was a *conservative* estimate, and we assumed that we did not have
>any rows lying around in the (global!) cache. As the size of the cache grows in
>proportion to the size of the total database we can assume less and less disk
>I/O.

You're relying on the hardware to bale you out :-) We can do the same!
>
>Note also that the cache can be configured many ways, you can put different
>tables (or indexes) in different caches, and even change the size of the cache
>on the fly (you might want a bigger cache during evening and night when your
>batch programs are running) so you can rig your system to favour certain
>types of queries.
>
>I havn't even gone into the topic of using thick indexes so table access can
>be totally avoided (=we are reading into memory only interesting columns).
>
>Now, in your example, what if the product department comes along and
>wants to make a report with sales / product? What would be your formula
>in that case?

I'm not quite sure what you're trying to do. I'll assume you want a
report of all invoices which refer to a given product. Assuming I've got
the relevant indices defined, I can simply read a list of invoices from
the product code index, a second list of invoices from the month index,
and do an intersect of the two lists.

So again, T = (2+N) * ST * 1.05 where N is the number of invoices that
reference that product. And now ALL the invoice data has been retrieved
from disk to ram ...
>
>And: what if I was just reading customer-data. Would the same formula
>apply (= (2+N)*ST*1.05)?

Nope. If I understand you correctly, you want attributes that belong to
the entity "customer", not the entity "invoice". T = ST * 1.05. (By the
way, billing and/or invoice address (for example) are invoice
attributes, not company attributes.)
>
>>But as I understand relational theory, such a question is completely
>>outside the scope of the theory. Seeing as it tries to divorce the
>>database logic from the practical implementation ...
>>
>The theory, indeed, does not say anything about buffer pools, but by decoupling
>logic
>from implementation we leave the implementor (DBMS) to do as it feels fit to do.
>As DBMS technology advances, we get faster systems without having to change our
>programs.

But with MV, if our database is too large for current technology, we
kick the shit out of relational for speed ...

Don't forget. You've already said that, if nothing is cached, my average
case exceeds your best. And my case is *already* assuming that the
system is seriously stressed and struggling ...
>
>When we design databases we can decouple logical planning from performance
>considerations, which, you must agree, are two separate issues.
>
>>And you know it's been proven that Huffman coding is the most efficient
>>compression algorithm? (Actually, it isn't - it's been proven it can't
>>be improved upon, which isn't the same thing...). Can you improve on the
>>formula I've just given you? Given that if we could change the 1.05 to 1
>>then we can prove it can't be improved upon ... again - I've taken the
>>liberty of assuming that a MV FILE is equivalent to an entity if we
>>assume the relational designer has been thinking in an entity-attribute-
>>relation sort of way. My maths isn't good enough to prove it, but I
>>think it would be pretty easy to prove that accessing data as "one and
>>only one complete entity" at a time is the most efficient way.
>>
>I think that in a typical system your cache hit ratio would approach 90%
>so that could mean 0.1 disk seeks.

That improves our performance just as much as improves yours. What
happens to your response time if you just DON'T HAVE the cache
available, for whatever reason?

I can't find the post now :-( but is Christopher reading this? You know
I compared that relational system on a twin Xeon 800, to an MV system
running on a P90? Christopher made the (reasonable in the circumstances)
assumption that the relational consultants must be crap, and the MV guy
a guru. Actually, I'd come to exactly the OPPOSITE conclusion. My MV
experience tells me that MV query was probably thrown together, by an
average programmer, in 30 seconds. On the other hand, those SQL
consultants had an axe to grind and a point to prove. They couldn't
afford to let this "old fashioned" system beat them. That SQL query
would have been optimised to within an inch of its life over weeks.
Don't forget how proud they were to beat this MV system! Yet with
hardware that was so much more powerful and a query that was heavily
optimised, they had great difficulty beating a query that was thrown
together in seconds by an average MV guy (or even just a luser!).

Don't forget. I said I am a database *engineer*. Engineers believe in
elegance, they believe in beauty. And when I look at relational, all I
see is the theorists pleading "power", "hardware", "brute force", to get
them out of trouble. And then all these people, who believe in maths
over reality, are surprised when I turn round and say I despise their
beliefs.

Note, I did NOT say I despise relational theory. I despise the belief
that it is the answer to life, the database universe, and everything
data related. (By the way, 6 times 9 DOES equal 42 :-)

>best regards,
>Lauri Pietarinen
>
Cheers,
Wol
--
Anthony W. Youngman - wol at thewolery dot demon dot co dot uk
Witches are curious by definition and inquisitive by nature. She moved in. "Let
me through. I'm a nosey person.", she said, employing both elbows.
Maskerade : (c) 1995 Terry Pratchett