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Wow, yes, partition instead of index, that is interesting. Thanks
Corey and Justin. The index isn't required at all if all my pending jobs are in a partition of only pending jobs. In that case the plan can just be a sequential scan. And Jeff James, sorry, I failed to show the LIMIT 1 clause on my dequeue query. That was an omission. My query is actually somewhat more complex and I just translated it down to the essentials but forgot the LIMIT 1 clause. SELECT seqNo, action FROM Queue WHERE pending AND ... other criteria ... LIMIT 1 FOR UPDATE SKIP LOCKED; And sorry I didn't capture the stats for vacuum verbose. And they
would be confusing because there are other things involved. Anyway, I think the partitioned table is the right and brilliant
solution, because an index really isn't required. The actual
pending partition will always remain quite small, and being a
queue, it doesn't even matter how big it might grow, as long as
new rows are inserted at the end and not in the middle of the data
file and still there be some way of fast skip over the part of the
dead rows at the beginning that have already been processed and
moved away. Good thing is, I don't worry about maintenance window. I have the leisure to simply tear down my design now and make a better design. What's 2 million transactions if I can re-process them at a rate of 80/s? 7 hours max. I am still in development. So, no need to worry about migration / transition acrobatics. So if I take Corey's steps and envision the final result, not worrying about the transition steps, then I understand this: 1. Create the Queue table partitioned on the pending column, this
creates the partition with the pending jobs (on which I set the
fillfactor kind of low, maybe 50) and the default partition with
all the rest. Of course that allows people with a constant
transaction volume to also partition on jobId or completionTime
and move chunks out to cold archive storage. But that's beside the
current point. 2. Add all needed indexes on the partitioned table, except the
main partial index that I used before and that required all that
reindexing maintenance. Actually I won't need any other indexes
really, why invite another similar problem again.
That's really simple.
One question I have though: I imagine our pending partition
heap file to now be essentially sequentially organized as a queue.
New jobs are appended at the end, old jobs are at the beginning.
As pending jobs become completed (pending = false) these initial
rows will be marked as dead. So, while the number of live rows
will remain small in that pending partition, sequential scans will
have to skip over the dead rows in the beginning.
Does PostgreSQL structure its files such that skipping over
dead rows is fast? Or do the dead rows have to be read and
discarded during a table scan?
Of course vacuum eliminates dead rows, but unless I do vacuum
full, it will not re-pack the live rows, and that requires an
exclusive table lock. So, what is the benefit of vacuuming that
pending partition? What I don't want is insertion
of new jobs to go into open slots at the beginning of the file. I
want them to be appended (in Oracle there is an INSERT /*+APPEND*/
hint for that. How does that work in PostgreSQL?
Ultimately that partition will amass too many dead rows, then
what do I do? I don't think that the OS has a way to truncate
files physically from the head, does it? I guess it could set the
file pointer from the first block to a later block. But I don't
know of an IOCTL/FCNTL command for that. On some OS there is a way
of making blocks sparse again, is that how PostgreSQL might do it?
Just knock out blocks as sparse from the front of the file?
If not, the next thing I can think of is to partition the table
further by time, may be alternating even and odd days, such that
on any given day one of the two pending partitions are quiet? Is
that how it's done?
regards,
-Gunther
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