Re: Optimize SnapBuildPurgeOlderTxn: use in-place compaction instead of temporary array

Hi,

On Tue, Dec 16, 2025 at 6:29 PM Xuneng Zhou <xunengzhou@gmail.com> wrote:
>
> On Mon, Nov 10, 2025 at 11:22 AM Xuneng Zhou <xunengzhou@gmail.com> wrote:
> >
> > Hi,
> >
> > With a sorted commited.xip array, we could replace the iteration with
> > two binary searches to find the interval to keep.
> >
> > Proposed Optimization
> > ---------------------
> >
> > Use binary search to locate the boundaries of XIDs to remove, then
> > compact with a single memmove. The key insight requires understanding
> > how XID precedence relates to numeric ordering.
> >
> > XID Precedence Definition
> > ~~~~~~~~~~~~~~~~~~~~~~~~~~
> >
> > PostgreSQL defines XID precedence as:
> >
> > /* compare two XIDs already known to be normal; this is a macro for speed */
> > #define NormalTransactionIdPrecedes(id1, id2) \
> > (AssertMacro(TransactionIdIsNormal(id1) && TransactionIdIsNormal(id2)), \
> > (int32) ((id1) - (id2)) < 0)
> >
> > This means: id1 precedes id2 if (int32)(id1 - id2) < 0.
> >
> > Equivalently, this identifies all XIDs in the modular interval
> > [id2 - 2^31, id2) on the 32-bit ring as "preceding id2". So XIDs
> > preceding xmin are exactly those in [xmin - 2^31, xmin).
> >
> > From Modular Interval to Array Positions
> > ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
> >
> > The arrays are sorted in numeric uint32 order (xip[i] <= xip[i+1] in
> > unsigned sense), which is a total order—not wraparound-aware. Therefore,
> > the modular interval we want to remove may appear as one or two numeric
> > blocks in the sorted array.
> >
> > Let boundary = xmin - 2^31 (mod 2^32). The modular interval [boundary, xmin)
> > contains all XIDs to remove (half-open: xmin itself is kept, matching
> > NormalTransactionIdPrecedes). Where does it appear in the numerically sorted
> > array?
> >
> > Case A: (uint32)boundary <= (uint32)xmin (numeric no wrap)
> >   Example: xmin = 3,000,000,000
> >            boundary = 3,000,000,000 - 2,147,483,648 = 852,516,352
> >
> >   Here, (uint32)boundary < (uint32)xmin, so the interval does not cross
> >   0 numerically. In the sorted array, XIDs to remove form one contiguous
> >   block: [idx_boundary, idx_xmin).
> >
> >   Array layout:
> >     [... keep ...][=== remove ===][... keep ...]
> >     0 ............ idx_boundary ... idx_xmin ...... n
> >
> >   Action: Keep prefix [0, idx_boundary) and suffix [idx_xmin, n).
> >
> > Case B: (uint32)boundary > (uint32)xmin (numeric wrap)
> >   Example: xmin = 100
> >            boundary = 100 - 2^31 (mod 2^32) = 2,147,483,748
> >
> >   Since (uint32)boundary > (uint32)xmin, the interval wraps through 0
> >   numerically. In the sorted array, XIDs to remove form two blocks:
> >   [0, idx_xmin) and [idx_boundary, n).
> >
> >   Array layout:
> >     [= remove =][... keep ...][= remove =]
> >     0 ......... idx_xmin .... idx_boundary ......... n
> >
> >   Action: Keep only the middle [idx_xmin, idx_boundary).
> >
> > Note: Case B often occurs when xmin is "small" (e.g., right after
> > startup), making xmin - 2^31 wrap numerically. This is purely about
> > positions in the numeric order; it does not imply the cluster has
> > "wrapped" XIDs operationally.
> >
> > In both cases, we locate idx_boundary and idx_xmin using binary search
> > in O(log n) time, then use one memmove to compact
> >
> > The algorithm:
> > 1. Compute boundary = xmin - 2^31
> > 2. Binary search for idx_boundary (first index with xip[i] >= boundary)
> > 3. Binary search for idx_xmin (first index with xip[i] >= xmin)
> > 4. Use memmove to compact based on case A or B above
> >
> > Benefits
> > --------
> >
> > 1. Performance: O(log n) binary search vs O(n) linear scan
> > 2. Memory: No workspace allocation needed
> > 3. Simplicity: One memmove instead of allocate + two copies + free
> >
> > The same logic is applied to both committed.xip and catchange.xip arrays.
> >
> > Faster binary search
> > --------
> >
> > While faster binary search variants exist, the current code already
> > introduces more complexity than the original. It’s uncertain that
> > further optimization would deliver a meaningful performance gain.
> >
>
> Adapt the patch with two-phase optimization:
>
> - Pre-CONSISTENT: Use in-place compaction O(n) since committed.xip is
> unsorted during this phase.
>
> - Post-CONSISTENT: Use binary search O(log n) since committed.xip is
> maintained in sorted order after reaching consistency.
>

v3-0001 fixes a critical issue where the snapshot->xip array in
SnapBuildBuildSnapshot might not be sorted before reaching the
consistent state. Sorry for the noise here.

--
Best,
Xuneng