In article <[EMAIL PROTECTED]>, Lauri PietarinenFirst of all it is important to note that an important component of all modern SQL-DBMS's is
<[EMAIL PROTECTED]> writes
So in your opinion, is the problem
1) SQL is so hard that the average programmer will not know how to use it
efficiently
Nope
Yes.or 2) Relational (or SQL-) DBMS'es are just too slow
If 2) then why don't we get a bit more concrete. Could you give
an example of a query that in your experience would be too slow using
a standard SQL database (e.g. Oracle, or MySQL). We could then
actually try it out on some machine and compare. I suggest using
the customer-order-order_detail-product database
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.
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.
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.
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).
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.
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.
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?
And: what if I was just reading customer-data. Would the same formula apply (= (2+N)*ST*1.05)?
The theory, indeed, does not say anything about buffer pools, but by decoupling logicBut 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 ...
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.
When we design databases we can decouple logical planning from performance considerations, which, you must agree, are two separate issues.
I think that in a typical system your cache hit ratio would approach 90%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.
so that could mean 0.1 disk seeks.
best regards, Lauri Pietarinen
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