Rober,
Thank you once again for your input.
On Dec 5, 2007, at 3:23 PM, Robert Treat wrote:
On Monday 03 December 2007 17:32, Erik Jones wrote:
<snip> Too much to keep quoted here. Check the archives if you want
to read more about the setup for this conversation.
-[ RECORD 1 ]----+-----------------------
ttype | messages_history_opens
total_tables | 14027
total_tuples | 139284528
max_tuples | 2760599
avg_num_tuples | 9929.84
std_dev_from_avg | 59945.51
Now, for this discussion, let me also point out that we've had a
pretty steady growth rate of about 150% per year since the company
opened 5 years ago and, for the sake of argument, we'll use that here
although we really don't have any guarantees past personal faith that
we can maintaing that :)
So, I'm looking at both a bin partiioning system or range based on
the date timestamps and both seem to have their pros and cons. For
the bin example, for this table type if I set it at 300 partitions it
will take approximately 5 years before any of the partitions reaches
the size of our current largest opens table. This is obviously very
attractive from a management perspective and has the added advantage
that I could probably expect the spread of data (volume per table) to
be pretty even over time.
What would you base these bins on? If you are doing it based on
the surrogate
key, then your going to spread data across both dates and accounts
into the
bins, which seems like it would make the majority of your queries
not use
partitions smartly.
Given that these are the userdata tables, we don't run any queries
without there being an account context. We run queries that answer
questions like: "How many opens did this mailing have?" or "Show me
all members in this account who have opened in the last six
month...". We also keep system-wide a summary totals table that
allows us to run global system stats queries.
However, it's those times somebody wants
to ask, "Show me all my members who have opened in the last year"
that it becomes a problem as that data would most likely be spread
over a radically varying number of partitions. I could probably
solve that by making the partition based on modulo account_id.
if your bins are based on account_id that might run you the risk of
filling up
the bins disproprotionally.
While this is a possibility, there's ways to manage this. The best
way is to use a check constraint along the lines of:
CHECK account_id % 100 = 1 and account_id NOT IN some_func()
where some_func returns an array of account's that are large enough
to have their own partitions. For example, with this opens example
we have only 32 accounts out of over 14,000 with a tuple count over
500,000. Filtering those out of the same query I used before gives:
-[ RECORD 1 ]----+-----------------------
ttype | messages_history_opens
total_tables | 14072
total_tuples | 110349736
max_tuples | 495600
avg_num_tuples | 7841.83
std_dev_from_avg | 30860.06
So, after spreading about 14K accounts over 100 bins with those
statistics, their sizes will be fairly even. Whenever we have an
account grow disproportionately large we can separate them into their
own partition with a simple check constraint of account_id = dddd and
make sure that dddd is now returned in the array from some_func().
Actually, I'd be splitting accounts out based on their audience and
mailing sizes as that's what will determine their growth rates.
The other option, of course, is to go with the "standard" (my quotes
based on what I've seen on this and the performance list) range based
partitioning. However, it really wouldn't make a lot of sense to
make those partitions based on the timestamp fields as the data for
one mailing could then span many of the partitions and the same
question I noted in the last paragraph would again result in hitting
potentially many (although not as many as before) partitions.
the thing to think about is going one step past the CE. What is
better, an
index lookup based on time across account_id based partitions, or
and index
lookup on account_ids in time based partitions.
See my answer above about our summary table.
So,
what I'm left with here is to partition on id ranges but, in order to
maintain a somewhat stable maximum partition size, I'd have to play a
statistical guessing game wrt the size previous month's (or week's)
partitions grew to v. their defined id ranges. That's doable, but
not very attractive. To be honest, I'm not even convinced that
that's something I'd really need to worry about on a month-to-month
basis.
I dont think it is. And remember you don't have to keep your date
partitions
as equal intervals.
Seen from one perspective, partitioning on id ranges v. using the bin
partitioning method are kind of similar. The biggest differences
being that with the range setup the majority of the working set of
data will be in the last couple of month's partitions whereas with
the bin method it will be spread pretty evenly across all of them,
i.e. lots of pages for a couple pages constantly being worked with v.
a couple pages from a lot of tables constantly being worked with.
Any thoughts or advice?
i dont think you'll have much luck taking the "spread data evenly
throught the
partitions" approach; figure out how best to segment your data into
manageable chunks. HTH.
I agree. That's also why I'm not too worried about taking a bin
based approach to the partitioning. In addition, once this is done,
however I end up doing it, I'm going to be working on a horizontal
systems scaling approach wherein I set up another database, replicate
the global tables (all 132 of them) to it, and split the accounts'
userdata between the two databases -- fun, fun, fun!
Erik Jones
Software Developer | Emma®
erik@xxxxxxxxxx
800.595.4401 or 615.292.5888
615.292.0777 (fax)
Emma helps organizations everywhere communicate & market in style.
Visit us online at http://www.myemma.com
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