It is equivalent in ANSI C to write (*funcptr) () and funcptr(). These
two styles have been applied inconsistently. After discussion, we'll
use the more verbose style for plain function pointer variables, to make
it clear that it's a variable, and the shorter style when the function
pointer is in a struct (s.func() or s->func()), because then it's clear
that it's not a plain function name, and otherwise the excessive
punctuation makes some of those invocations hard to read.
Discussion: https://www.postgresql.org/message-id/f52c16db-14ed-757d-4b48-7ef360b1631d@2ndquadrant.com
This attempts to buy back some of whatever performance we lost from fixing
bug #14174 by inlining the initial checks in MakeExpandedObjectReadOnly()
into the callers. We can do that in a macro without creating multiple-
evaluation hazards, so it's pretty much free notationally; and the amount
of code added to callers should be minimal as well. (Testing a value can't
take many more instructions than passing it to a subroutine.)
Might as well inline DatumIsReadWriteExpandedObject() while we're at it.
This is an ABI break for callers, so it doesn't seem safe to put into 9.5,
but I see no reason not to do it in HEAD.
This patch introduces the ability for complex datatypes to have an
in-memory representation that is different from their on-disk format.
On-disk formats are typically optimized for minimal size, and in any case
they can't contain pointers, so they are often not well-suited for
computation. Now a datatype can invent an "expanded" in-memory format
that is better suited for its operations, and then pass that around among
the C functions that operate on the datatype. There are also provisions
(rudimentary as yet) to allow an expanded object to be modified in-place
under suitable conditions, so that operations like assignment to an element
of an array need not involve copying the entire array.
The initial application for this feature is arrays, but it is not hard
to foresee using it for other container types like JSON, XML and hstore.
I have hopes that it will be useful to PostGIS as well.
In this initial implementation, a few heuristics have been hard-wired
into plpgsql to improve performance for arrays that are stored in
plpgsql variables. We would like to generalize those hacks so that
other datatypes can obtain similar improvements, but figuring out some
appropriate APIs is left as a task for future work. (The heuristics
themselves are probably not optimal yet, either, as they sometimes
force expansion of arrays that would be better left alone.)
Preliminary performance testing shows impressive speed gains for plpgsql
functions that do element-by-element access or update of large arrays.
There are other cases that get a little slower, as a result of added array
format conversions; but we can hope to improve anything that's annoyingly
bad. In any case most applications should see a net win.
Tom Lane, reviewed by Andres Freund
Bruce Momjian
Peter Eisentraut
Tom Lane