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Author SHA1 Message Date
Andrew Kane
4a323f97d7 Mark extension as trusted 2023-10-03 11:43:08 -07:00
17 changed files with 187 additions and 190 deletions

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@@ -1,7 +1,6 @@
## 0.5.1 (2023-10-10)
## 0.5.1 (unreleased)
- Improved performance of HNSW index builds
- Added check for MVCC-compliant snapshot for index scans
- Improved performance of index scans for IVFFlat after updates and deletes
## 0.5.0 (2023-08-28)

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@@ -2,7 +2,7 @@
"name": "vector",
"abstract": "Open-source vector similarity search for Postgres",
"description": "Supports L2 distance, inner product, and cosine distance",
"version": "0.5.1",
"version": "0.5.0",
"maintainer": [
"Andrew Kane <andrew@ankane.org>"
],
@@ -20,7 +20,7 @@
"vector": {
"file": "sql/vector.sql",
"docfile": "README.md",
"version": "0.5.1",
"version": "0.5.0",
"abstract": "Open-source vector similarity search for Postgres"
}
},

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@@ -1,5 +1,5 @@
EXTENSION = vector
EXTVERSION = 0.5.1
EXTVERSION = 0.5.0
MODULE_big = vector
DATA = $(wildcard sql/*--*.sql)

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@@ -1,5 +1,5 @@
EXTENSION = vector
EXTVERSION = 0.5.1
EXTVERSION = 0.5.0
OBJS = src\hnsw.obj src\hnswbuild.obj src\hnswinsert.obj src\hnswscan.obj src\hnswutils.obj src\hnswvacuum.obj src\ivfbuild.obj src\ivfflat.obj src\ivfinsert.obj src\ivfkmeans.obj src\ivfscan.obj src\ivfutils.obj src\ivfvacuum.obj src\vector.obj
HEADERS = src\vector.h

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@@ -18,7 +18,7 @@ Compile and install the extension (supports Postgres 11+)
```sh
cd /tmp
git clone --branch v0.5.1 https://github.com/pgvector/pgvector.git
git clone --branch v0.5.0 https://github.com/pgvector/pgvector.git
cd pgvector
make
make install # may need sudo
@@ -509,7 +509,7 @@ Then use `nmake` to build:
```cmd
set "PGROOT=C:\Program Files\PostgreSQL\15"
git clone --branch v0.5.1 https://github.com/pgvector/pgvector.git
git clone --branch v0.5.0 https://github.com/pgvector/pgvector.git
cd pgvector
nmake /F Makefile.win
nmake /F Makefile.win install
@@ -530,7 +530,7 @@ This adds pgvector to the [Postgres image](https://hub.docker.com/_/postgres) (r
You can also build the image manually:
```sh
git clone --branch v0.5.1 https://github.com/pgvector/pgvector.git
git clone --branch v0.5.0 https://github.com/pgvector/pgvector.git
cd pgvector
docker build --build-arg PG_MAJOR=15 -t myuser/pgvector .
```

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@@ -1,2 +0,0 @@
-- complain if script is sourced in psql, rather than via CREATE EXTENSION
\echo Use "ALTER EXTENSION vector UPDATE TO '0.5.1'" to load this file. \quit

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@@ -57,8 +57,6 @@
/* PROGRESS_CREATEIDX_SUBPHASE_INITIALIZE is 1 */
#define PROGRESS_HNSW_PHASE_LOAD 2
#define HNSW_MAX_SIZE (BLCKSZ - MAXALIGN(SizeOfPageHeaderData) - MAXALIGN(sizeof(HnswPageOpaqueData)) - sizeof(ItemIdData))
#define HNSW_ELEMENT_TUPLE_SIZE(_dim) MAXALIGN(offsetof(HnswElementTupleData, vec) + VECTOR_SIZE(_dim))
#define HNSW_NEIGHBOR_TUPLE_SIZE(level, m) MAXALIGN(offsetof(HnswNeighborTupleData, indextids) + ((level) + 2) * (m) * sizeof(ItemPointerData))
@@ -112,13 +110,11 @@ typedef struct HnswCandidate
{
HnswElement element;
float distance;
bool closer;
} HnswCandidate;
typedef struct HnswNeighborArray
{
int length;
bool closerSet;
HnswCandidate *items;
} HnswNeighborArray;

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@@ -117,12 +117,12 @@ CreateElementPages(HnswBuildState * buildstate)
ListCell *lc;
/* Calculate sizes */
maxSize = HNSW_MAX_SIZE;
maxSize = BLCKSZ - MAXALIGN(SizeOfPageHeaderData) - MAXALIGN(sizeof(HnswPageOpaqueData));
etupSize = HNSW_ELEMENT_TUPLE_SIZE(dimensions);
/* Allocate once */
etup = palloc0(etupSize);
ntup = palloc0(BLCKSZ);
ntup = palloc0(maxSize);
/* Prepare first page */
buf = HnswNewBuffer(index, forkNum);

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@@ -135,7 +135,7 @@ WriteNewElementPages(Relation index, HnswElement e, int m, BlockNumber insertPag
etupSize = HNSW_ELEMENT_TUPLE_SIZE(dimensions);
ntupSize = HNSW_NEIGHBOR_TUPLE_SIZE(e->level, m);
combinedSize = etupSize + ntupSize + sizeof(ItemIdData);
maxSize = HNSW_MAX_SIZE;
maxSize = BLCKSZ - MAXALIGN(SizeOfPageHeaderData) - MAXALIGN(sizeof(HnswPageOpaqueData));
minCombinedSize = etupSize + HNSW_NEIGHBOR_TUPLE_SIZE(0, m) + sizeof(ItemIdData);
/* Prepare element tuple */

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@@ -160,11 +160,6 @@ hnswgettuple(IndexScanDesc scan, ScanDirection dir)
if (scan->orderByData == NULL)
elog(ERROR, "cannot scan hnsw index without order");
/* Requires MVCC-compliant snapshot as not able to maintain a pin */
/* https://www.postgresql.org/docs/current/index-locking.html */
if (!IsMVCCSnapshot(scan->xs_snapshot))
elog(ERROR, "non-MVCC snapshots are not supported with hnsw");
/* Get scan value */
value = GetScanValue(scan);
@@ -206,6 +201,15 @@ hnswgettuple(IndexScanDesc scan, ScanDirection dir)
scan->xs_ctup.t_self = *heaptid;
#endif
/*
* Typically, an index scan must maintain a pin on the index page
* holding the item last returned by amgettuple. However, this is not
* needed with the current vacuum strategy, which ensures scans do not
* visit tuples in danger of being marked as deleted.
*
* https://www.postgresql.org/docs/current/index-locking.html
*/
scan->xs_recheckorderby = false;
return true;
}

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@@ -139,7 +139,6 @@ HnswInitNeighbors(HnswElement element, int m)
a = &element->neighbors[lc];
a->length = 0;
a->items = palloc(sizeof(HnswCandidate) * lm);
a->closerSet = false;
}
}
@@ -693,34 +692,6 @@ HnswSearchLayer(Datum q, List *ep, int ef, int lc, Relation index, FmgrInfo *pro
return w;
}
/*
* Compare candidate distances
*/
static int
#if PG_VERSION_NUM >= 130000
CompareCandidateDistances(const ListCell *a, const ListCell *b)
#else
CompareCandidateDistances(const void *a, const void *b)
#endif
{
HnswCandidate *hca = lfirst((ListCell *) a);
HnswCandidate *hcb = lfirst((ListCell *) b);
if (hca->distance < hcb->distance)
return 1;
if (hca->distance > hcb->distance)
return -1;
if (hca->element < hcb->element)
return 1;
if (hca->element > hcb->element)
return -1;
return 0;
}
/*
* Calculate the distance between elements
*/
@@ -777,77 +748,33 @@ CheckElementCloser(HnswCandidate * e, List *r, int lc, FmgrInfo *procinfo, Oid c
* Algorithm 4 from paper
*/
static List *
SelectNeighbors(List *c, int m, int lc, FmgrInfo *procinfo, Oid collation, HnswElement e2, HnswCandidate * newCandidate, HnswCandidate * *pruned, bool sortCandidates)
SelectNeighbors(List *c, int m, int lc, FmgrInfo *procinfo, Oid collation, HnswCandidate * *pruned)
{
List *r = NIL;
List *w = list_copy(c);
pairingheap *wd;
bool mustCalculate = !e2->neighbors[lc].closerSet;
List *added = NIL;
bool removedAny = false;
if (list_length(w) <= m)
return w;
wd = pairingheap_allocate(CompareNearestCandidates, NULL);
/* Ensure order of candidates is deterministic for closer caching */
if (sortCandidates)
list_sort(w, CompareCandidateDistances);
while (list_length(w) > 0 && list_length(r) < m)
{
/* Assumes w is already ordered desc */
HnswCandidate *e = llast(w);
bool closer;
w = list_delete_last(w);
/* Use previous state of r and wd to skip work when possible */
if (mustCalculate)
e->closer = CheckElementCloser(e, r, lc, procinfo, collation);
else if (list_length(added) > 0)
{
/*
* If the current candidate was closer, we only need to compare it
* with the other candidates that we have added.
*/
if (e->closer)
{
e->closer = CheckElementCloser(e, added, lc, procinfo, collation);
closer = CheckElementCloser(e, r, lc, procinfo, collation);
if (!e->closer)
removedAny = true;
}
else
{
/*
* If we have removed any candidates from closer, a candidate
* that was not closer earlier might now be.
*/
if (removedAny)
{
e->closer = CheckElementCloser(e, r, lc, procinfo, collation);
if (e->closer)
added = lappend(added, e);
}
}
}
else if (e == newCandidate)
{
e->closer = CheckElementCloser(e, r, lc, procinfo, collation);
if (e->closer)
added = lappend(added, e);
}
if (e->closer)
if (closer)
r = lappend(r, e);
else
pairingheap_add(wd, &(CreatePairingHeapNode(e)->ph_node));
}
/* Cached value can only be used in future if sorted deterministically */
e2->neighbors[lc].closerSet = sortCandidates;
/* Keep pruned connections */
while (!pairingheap_is_empty(wd) && list_length(r) < m)
r = lappend(r, ((HnswPairingHeapNode *) pairingheap_remove_first(wd))->inner);
@@ -901,6 +828,28 @@ AddConnections(HnswElement element, List *neighbors, int m, int lc)
a->items[a->length++] = *((HnswCandidate *) lfirst(lc2));
}
/*
* Compare candidate distances
*/
static int
#if PG_VERSION_NUM >= 130000
CompareCandidateDistances(const ListCell *a, const ListCell *b)
#else
CompareCandidateDistances(const void *a, const void *b)
#endif
{
HnswCandidate *hca = lfirst((ListCell *) a);
HnswCandidate *hcb = lfirst((ListCell *) b);
if (hca->distance < hcb->distance)
return 1;
if (hca->distance > hcb->distance)
return -1;
return 0;
}
/*
* Update connections
*/
@@ -954,12 +903,13 @@ HnswUpdateConnection(HnswElement element, HnswCandidate * hc, int m, int lc, int
{
List *c = NIL;
/* Add candidates */
/* Add and sort candidates */
for (int i = 0; i < currentNeighbors->length; i++)
c = lappend(c, &currentNeighbors->items[i]);
c = lappend(c, &hc2);
list_sort(c, CompareCandidateDistances);
SelectNeighbors(c, m, lc, procinfo, collation, hc->element, &hc2, &pruned, true);
SelectNeighbors(c, m, lc, procinfo, collation, &pruned);
/* Should not happen */
if (pruned == NULL)
@@ -1058,12 +1008,7 @@ HnswInsertElement(HnswElement element, HnswElement entryPoint, Relation index, F
else
lw = w;
/*
* Candidates are sorted, but not deterministically. Could set
* sortCandidates to true for in-memory builds to enable closer
* caching, but there does not seem to be a difference in performance.
*/
neighbors = SelectNeighbors(lw, lm, lc, procinfo, collation, element, NULL, NULL, false);
neighbors = SelectNeighbors(lw, lm, lc, procinfo, collation, NULL);
AddConnections(element, neighbors, lm, lc);

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@@ -11,7 +11,6 @@
#include "miscadmin.h"
#include "storage/bufmgr.h"
#include "tcop/tcopprot.h"
#include "utils/datum.h"
#include "utils/memutils.h"
#if PG_VERSION_NUM >= 140000
@@ -66,18 +65,11 @@
static void
AddSample(Datum *values, IvfflatBuildState * buildstate)
{
MemoryContext oldCtx;
Datum value;
int targsamples = buildstate->targsamples;
/* Use memory context since detoast can allocate */
oldCtx = MemoryContextSwitchTo(buildstate->tmpCtx);
VectorArray samples = buildstate->samples;
int targsamples = samples->maxlen;
/* Detoast once for all calls */
value = PointerGetDatum(PG_DETOAST_DATUM(values[0]));
/* Restore memory context */
MemoryContextSwitchTo(oldCtx);
Datum value = PointerGetDatum(PG_DETOAST_DATUM(values[0]));
/*
* Normalize with KMEANS_NORM_PROC since spherical distance function
@@ -89,23 +81,18 @@ AddSample(Datum *values, IvfflatBuildState * buildstate)
return;
}
/* Copy datum */
value = datumCopy(value, false, -1);
/* Reset memory context */
MemoryContextReset(buildstate->tmpCtx);
if (list_length(buildstate->samples) < targsamples)
buildstate->samples = lappend(buildstate->samples, DatumGetVector(value));
if (samples->length < targsamples)
{
VectorArraySet(samples, samples->length, DatumGetVector(value));
samples->length++;
}
else
{
if (buildstate->rowstoskip < 0)
buildstate->rowstoskip = reservoir_get_next_S(&buildstate->rstate, list_length(buildstate->samples), targsamples);
buildstate->rowstoskip = reservoir_get_next_S(&buildstate->rstate, samples->length, targsamples);
if (buildstate->rowstoskip <= 0)
{
ListCell *lc;
#if PG_VERSION_NUM >= 150000
int k = (int) (targsamples * sampler_random_fract(&buildstate->rstate.randstate));
#else
@@ -113,8 +100,7 @@ AddSample(Datum *values, IvfflatBuildState * buildstate)
#endif
Assert(k >= 0 && k < targsamples);
lc = list_nth_cell(buildstate->samples, k);
lfirst(lc) = DatumGetVector(value);
VectorArraySet(samples, k, DatumGetVector(value));
}
buildstate->rowstoskip -= 1;
@@ -129,13 +115,21 @@ SampleCallback(Relation index, CALLBACK_ITEM_POINTER, Datum *values,
bool *isnull, bool tupleIsAlive, void *state)
{
IvfflatBuildState *buildstate = (IvfflatBuildState *) state;
MemoryContext oldCtx;
/* Skip nulls */
if (isnull[0])
return;
/* Use memory context since detoast can allocate */
oldCtx = MemoryContextSwitchTo(buildstate->tmpCtx);
/* Add sample */
AddSample(values, buildstate);
AddSample(values, state);
/* Reset memory context */
MemoryContextSwitchTo(oldCtx);
MemoryContextReset(buildstate->tmpCtx);
}
/*
@@ -144,7 +138,7 @@ SampleCallback(Relation index, CALLBACK_ITEM_POINTER, Datum *values,
static void
SampleRows(IvfflatBuildState * buildstate)
{
int targsamples = buildstate->targsamples;
int targsamples = buildstate->samples->maxlen;
BlockNumber totalblocks = RelationGetNumberOfBlocks(buildstate->heap);
buildstate->rowstoskip = -1;
@@ -455,13 +449,12 @@ ComputeCenters(IvfflatBuildState * buildstate)
/* Sample rows */
/* TODO Ensure within maintenance_work_mem */
buildstate->samples = NIL;
buildstate->targsamples = numSamples;
buildstate->samples = VectorArrayInit(numSamples, buildstate->dimensions);
if (buildstate->heap != NULL)
{
SampleRows(buildstate);
if (list_length(buildstate->samples) < buildstate->lists)
if (buildstate->samples->length < buildstate->lists)
{
ereport(NOTICE,
(errmsg("ivfflat index created with little data"),
@@ -474,7 +467,7 @@ ComputeCenters(IvfflatBuildState * buildstate)
IvfflatBench("k-means", IvfflatKmeans(buildstate->index, buildstate->samples, buildstate->centers));
/* Free samples before we allocate more memory */
list_free_deep(buildstate->samples);
VectorArrayFree(buildstate->samples);
}
/*

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@@ -80,10 +80,6 @@
#define RandomInt() random()
#endif
#if PG_VERSION_NUM < 130000
#define list_sort(list, cmp) list_qsort(list, cmp)
#endif
/* Variables */
extern int ivfflat_probes;
@@ -182,8 +178,7 @@ typedef struct IvfflatBuildState
Oid collation;
/* Variables */
List *samples;
int targsamples;
VectorArray samples;
VectorArray centers;
ListInfo *listInfo;
Vector *normvec;
@@ -251,6 +246,8 @@ typedef struct IvfflatScanOpaqueData
int probes;
int dimensions;
bool first;
Buffer buf;
ItemPointerData heaptid;
/* Sorting */
Tuplesortstate *sortstate;
@@ -279,7 +276,7 @@ typedef IvfflatScanOpaqueData * IvfflatScanOpaque;
VectorArray VectorArrayInit(int maxlen, int dimensions);
void VectorArrayFree(VectorArray arr);
void PrintVectorArray(char *msg, VectorArray arr);
void IvfflatKmeans(Relation index, List *samples, VectorArray centers);
void IvfflatKmeans(Relation index, VectorArray samples, VectorArray centers);
FmgrInfo *IvfflatOptionalProcInfo(Relation index, uint16 procnum);
bool IvfflatNormValue(FmgrInfo *procinfo, Oid collation, Datum *value, Vector * result);
int IvfflatGetLists(Relation index);

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@@ -99,7 +99,7 @@ InsertTuple(Relation index, Datum *values, bool *isnull, ItemPointer heap_tid, R
/* Get tuple size */
itemsz = MAXALIGN(IndexTupleSize(itup));
Assert(itemsz <= BLCKSZ - MAXALIGN(SizeOfPageHeaderData) - MAXALIGN(sizeof(IvfflatPageOpaqueData)) - sizeof(ItemIdData));
Assert(itemsz <= BLCKSZ - MAXALIGN(SizeOfPageHeaderData) - MAXALIGN(sizeof(IvfflatPageOpaqueData)));
/* Find a page to insert the item */
for (;;)

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@@ -12,20 +12,20 @@
* https://theory.stanford.edu/~sergei/papers/kMeansPP-soda.pdf
*/
static void
InitCenters(Relation index, List *samples, VectorArray centers, float *lowerBound)
InitCenters(Relation index, VectorArray samples, VectorArray centers, float *lowerBound)
{
FmgrInfo *procinfo;
Oid collation;
int64 j;
float *weight = palloc(list_length(samples) * sizeof(float));
float *weight = palloc(samples->length * sizeof(float));
int numCenters = centers->maxlen;
int numSamples = list_length(samples);
int numSamples = samples->length;
procinfo = index_getprocinfo(index, 1, IVFFLAT_KMEANS_DISTANCE_PROC);
collation = index->rd_indcollation[0];
/* Choose an initial center uniformly at random */
VectorArraySet(centers, 0, list_nth(samples, RandomInt() % list_length(samples)));
VectorArraySet(centers, 0, VectorArrayGet(samples, RandomInt() % samples->length));
centers->length++;
for (j = 0; j < numSamples; j++)
@@ -42,7 +42,7 @@ InitCenters(Relation index, List *samples, VectorArray centers, float *lowerBoun
for (j = 0; j < numSamples; j++)
{
Vector *vec = list_nth(samples, j);
Vector *vec = VectorArrayGet(samples, j);
double distance;
/* Only need to compute distance for new center */
@@ -74,7 +74,7 @@ InitCenters(Relation index, List *samples, VectorArray centers, float *lowerBoun
break;
}
VectorArraySet(centers, i + 1, list_nth(samples, j));
VectorArraySet(centers, i + 1, VectorArrayGet(samples, j));
centers->length++;
}
@@ -106,41 +106,25 @@ CompareVectors(const void *a, const void *b)
return vector_cmp_internal((Vector *) a, (Vector *) b);
}
/*
* Compare list vectors
*/
static int
#if PG_VERSION_NUM >= 130000
CompareListVectors(const ListCell *a, const ListCell *b)
#else
CompareListVectors(const void *a, const void *b)
#endif
{
Vector *va = lfirst((ListCell *) a);
Vector *vb = lfirst((ListCell *) b);
return CompareVectors(va, vb);
}
/*
* Quick approach if we have little data
*/
static void
QuickCenters(Relation index, List *samples, VectorArray centers)
QuickCenters(Relation index, VectorArray samples, VectorArray centers)
{
int dimensions = centers->dim;
Oid collation = index->rd_indcollation[0];
FmgrInfo *normprocinfo = IvfflatOptionalProcInfo(index, IVFFLAT_KMEANS_NORM_PROC);
/* Copy existing vectors while avoiding duplicates */
if (list_length(samples) > 0)
if (samples->length > 0)
{
list_sort(samples, CompareListVectors);
for (int i = 0; i < list_length(samples); i++)
qsort(samples->items, samples->length, VECTOR_SIZE(samples->dim), CompareVectors);
for (int i = 0; i < samples->length; i++)
{
Vector *vec = list_nth(samples, i);
Vector *vec = VectorArrayGet(samples, i);
if (i == 0 || CompareVectors(vec, list_nth(samples, i - 1)) != 0)
if (i == 0 || CompareVectors(vec, VectorArrayGet(samples, i - 1)) != 0)
{
VectorArraySet(centers, centers->length, vec);
centers->length++;
@@ -176,7 +160,7 @@ QuickCenters(Relation index, List *samples, VectorArray centers)
* https://www.aaai.org/Papers/ICML/2003/ICML03-022.pdf
*/
static void
ElkanKmeans(Relation index, List *samples, VectorArray centers)
ElkanKmeans(Relation index, VectorArray samples, VectorArray centers)
{
FmgrInfo *procinfo;
FmgrInfo *normprocinfo;
@@ -187,7 +171,7 @@ ElkanKmeans(Relation index, List *samples, VectorArray centers)
int64 k;
int dimensions = centers->dim;
int numCenters = centers->maxlen;
int numSamples = list_length(samples);
int numSamples = samples->length;
VectorArray newCenters;
int *centerCounts;
int *closestCenters;
@@ -198,7 +182,7 @@ ElkanKmeans(Relation index, List *samples, VectorArray centers)
float *newcdist;
/* Calculate allocation sizes */
Size samplesSize = 0;
Size samplesSize = VECTOR_ARRAY_SIZE(samples->maxlen, samples->dim);
Size centersSize = VECTOR_ARRAY_SIZE(centers->maxlen, centers->dim);
Size newCentersSize = VECTOR_ARRAY_SIZE(numCenters, dimensions);
Size centerCountsSize = sizeof(int) * numCenters;
@@ -342,7 +326,7 @@ ElkanKmeans(Relation index, List *samples, VectorArray centers)
if (upperBound[j] <= halfcdist[closestCenters[j] * numCenters + k])
continue;
vec = list_nth(samples, j);
vec = VectorArrayGet(samples, j);
/* Step 3a */
if (rj)
@@ -393,7 +377,7 @@ ElkanKmeans(Relation index, List *samples, VectorArray centers)
{
int closestCenter;
vec = list_nth(samples, j);
vec = VectorArrayGet(samples, j);
closestCenter = closestCenters[j];
/* Increment sum and count of closest center */
@@ -530,9 +514,9 @@ CheckCenters(Relation index, VectorArray centers)
* We use spherical k-means for inner product and cosine
*/
void
IvfflatKmeans(Relation index, List *samples, VectorArray centers)
IvfflatKmeans(Relation index, VectorArray samples, VectorArray centers)
{
if (list_length(samples) <= centers->maxlen)
if (samples->length <= centers->maxlen)
QuickCenters(index, samples, centers);
else
ElkanKmeans(index, samples, centers);

View File

@@ -143,6 +143,10 @@ GetScanItems(IndexScanDesc scan, Datum value)
bool isnull;
ItemId itemid = PageGetItemId(page, offno);
/* Skip dead tuples */
if (scan->ignore_killed_tuples && ItemIdIsDead(itemid))
continue;
itup = (IndexTuple) PageGetItem(page, itemid);
datum = index_getattr(itup, 1, tupdesc, &isnull);
@@ -157,6 +161,8 @@ GetScanItems(IndexScanDesc scan, Datum value)
slot->tts_isnull[0] = false;
slot->tts_values[1] = PointerGetDatum(&itup->t_tid);
slot->tts_isnull[1] = false;
slot->tts_values[2] = Int32GetDatum((int) searchPage);
slot->tts_isnull[2] = false;
ExecStoreVirtualTuple(slot);
tuplesort_puttupleslot(so->sortstate, slot);
@@ -181,6 +187,55 @@ GetScanItems(IndexScanDesc scan, Datum value)
tuplesort_performsort(so->sortstate);
}
/*
* Mark prior tuple as dead
*/
static void
MarkPriorTupleDead(IndexScanDesc scan)
{
IvfflatScanOpaque so = (IvfflatScanOpaque) scan->opaque;
Buffer buf = so->buf;
Page page;
OffsetNumber maxoffno;
/* Safety check */
if (!BufferIsValid(so->buf) || !ItemPointerIsValid(&so->heaptid))
return;
/* Only a shared locked is needed for ItemIdMarkDead */
LockBuffer(buf, BUFFER_LOCK_SHARE);
page = BufferGetPage(buf);
maxoffno = PageGetMaxOffsetNumber(page);
for (OffsetNumber offno = FirstOffsetNumber; offno <= maxoffno; offno = OffsetNumberNext(offno))
{
ItemId itemid = PageGetItemId(page, offno);
IndexTuple itup = (IndexTuple) PageGetItem(page, itemid);
/*
* Find tuple. Since buffer has been pinned, tuple cannot have been
* vacuumed (and heap TID reused).
*/
if (ItemPointerEquals(&itup->t_tid, &so->heaptid))
{
/*
* Make sure tuple has not already been marked dead to avoid extra
* WAL if wal_log_hints or data checksums enabled
*/
if (!ItemIdIsDead(itemid))
{
ItemIdMarkDead(itemid);
MarkBufferDirtyHint(buf, true);
}
break;
}
}
/* Unlock buffer */
LockBuffer(buf, BUFFER_LOCK_UNLOCK);
}
/*
* Prepare for an index scan
*/
@@ -206,7 +261,9 @@ ivfflatbeginscan(Relation index, int nkeys, int norderbys)
probes = lists;
so = (IvfflatScanOpaque) palloc(offsetof(IvfflatScanOpaqueData, lists) + probes * sizeof(IvfflatScanList));
so->buf = InvalidBuffer;
so->first = true;
ItemPointerSetInvalid(&so->heaptid);
so->probes = probes;
so->dimensions = dimensions;
@@ -217,12 +274,13 @@ ivfflatbeginscan(Relation index, int nkeys, int norderbys)
/* Create tuple description for sorting */
#if PG_VERSION_NUM >= 120000
so->tupdesc = CreateTemplateTupleDesc(2);
so->tupdesc = CreateTemplateTupleDesc(3);
#else
so->tupdesc = CreateTemplateTupleDesc(2, false);
so->tupdesc = CreateTemplateTupleDesc(3, false);
#endif
TupleDescInitEntry(so->tupdesc, (AttrNumber) 1, "distance", FLOAT8OID, -1, 0);
TupleDescInitEntry(so->tupdesc, (AttrNumber) 2, "heaptid", TIDOID, -1, 0);
TupleDescInitEntry(so->tupdesc, (AttrNumber) 3, "indexblkno", INT4OID, -1, 0);
/* Prep sort */
so->sortstate = tuplesort_begin_heap(so->tupdesc, 1, attNums, sortOperators, sortCollations, nullsFirstFlags, work_mem, NULL, false);
@@ -254,6 +312,7 @@ ivfflatrescan(IndexScanDesc scan, ScanKey keys, int nkeys, ScanKey orderbys, int
#endif
so->first = true;
ItemPointerSetInvalid(&so->heaptid);
pairingheap_reset(so->listQueue);
if (keys && scan->numberOfKeys > 0)
@@ -288,11 +347,6 @@ ivfflatgettuple(IndexScanDesc scan, ScanDirection dir)
if (scan->orderByData == NULL)
elog(ERROR, "cannot scan ivfflat index without order");
/* Requires MVCC-compliant snapshot as not able to pin during sorting */
/* https://www.postgresql.org/docs/current/index-locking.html */
if (!IsMVCCSnapshot(scan->xs_snapshot))
elog(ERROR, "non-MVCC snapshots are not supported with ivfflat");
if (scan->orderByData->sk_flags & SK_ISNULL)
value = PointerGetDatum(InitVector(so->dimensions));
else
@@ -316,10 +370,17 @@ ivfflatgettuple(IndexScanDesc scan, ScanDirection dir)
if (value != scan->orderByData->sk_argument)
pfree(DatumGetPointer(value));
}
else
{
/* Mark prior tuple as dead */
if (scan->kill_prior_tuple)
MarkPriorTupleDead(scan);
}
if (tuplesort_gettupleslot(so->sortstate, true, false, so->slot, NULL))
{
ItemPointer heaptid = (ItemPointer) DatumGetPointer(slot_getattr(so->slot, 2, &so->isnull));
BlockNumber indexblkno = DatumGetInt32(slot_getattr(so->slot, 3, &so->isnull));
#if PG_VERSION_NUM >= 120000
scan->xs_heaptid = *heaptid;
@@ -327,6 +388,21 @@ ivfflatgettuple(IndexScanDesc scan, ScanDirection dir)
scan->xs_ctup.t_self = *heaptid;
#endif
/* Keep track of info needed to mark tuple as dead */
so->heaptid = *heaptid;
/* Unpin buffer */
if (BufferIsValid(so->buf))
ReleaseBuffer(so->buf);
/*
* An index scan must maintain a pin on the index page holding the
* item last returned by amgettuple
*
* https://www.postgresql.org/docs/current/index-locking.html
*/
so->buf = ReadBuffer(scan->indexRelation, indexblkno);
scan->xs_recheckorderby = false;
return true;
}
@@ -342,6 +418,10 @@ ivfflatendscan(IndexScanDesc scan)
{
IvfflatScanOpaque so = (IvfflatScanOpaque) scan->opaque;
/* Release pin */
if (BufferIsValid(so->buf))
ReleaseBuffer(so->buf);
pairingheap_free(so->listQueue);
tuplesort_end(so->sortstate);

View File

@@ -1,4 +1,5 @@
comment = 'vector data type and ivfflat and hnsw access methods'
default_version = '0.5.1'
default_version = '0.5.0'
module_pathname = '$libdir/vector'
relocatable = true
trusted = true