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Author SHA1 Message Date
Andrew Kane
02fba79a28 Added benchmarking for HNSW vacuuming [skip ci] 2026-06-16 00:22:29 -07:00
35 changed files with 126 additions and 656 deletions

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@@ -8,12 +8,10 @@ jobs:
fail-fast: false
matrix:
include:
- postgres: 20
os: ubuntu-26.04
- postgres: 19
os: ubuntu-26.04
os: ubuntu-24.04
- postgres: 18
os: ubuntu-26.04-arm
os: ubuntu-24.04
- postgres: 17
os: ubuntu-24.04
- postgres: 16
@@ -25,7 +23,7 @@ jobs:
- postgres: 13
os: ubuntu-22.04
steps:
- uses: actions/checkout@v7
- uses: actions/checkout@v6
- uses: ankane/setup-postgres@v1
with:
postgres-version: ${{ matrix.postgres }}
@@ -55,7 +53,7 @@ jobs:
- postgres: 14
os: macos-15-intel
steps:
- uses: actions/checkout@v7
- uses: actions/checkout@v6
- uses: ankane/setup-postgres@v1
with:
postgres-version: ${{ matrix.postgres }}
@@ -94,7 +92,7 @@ jobs:
- postgres: 14
os: windows-2022
steps:
- uses: actions/checkout@v7
- uses: actions/checkout@v6
- uses: ankane/setup-postgres@v1
with:
postgres-version: ${{ matrix.postgres }}
@@ -135,7 +133,7 @@ jobs:
if: ${{ !startsWith(github.ref_name, 'mac') && !startsWith(github.ref_name, 'windows') }}
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v7
- uses: actions/checkout@v6
- uses: ankane/setup-postgres-valgrind@v1
with:
postgres-version: 18

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@@ -1,16 +1,5 @@
## 0.8.5 (2026-07-08)
## 0.8.3 (unreleased)
- Reduced memory usage for small tables for IVFFlat index builds
## 0.8.4 (2026-06-30)
- Fixed `hnsw graph not repaired` error with HNSW vacuuming
- Fixed possible error with inserts during HNSW vacuuming
- Fixed memory exceeding `maintenance_work_mem` with IVFFlat index builds
## 0.8.3 (2026-06-17)
- Fixed possible index corruption with HNSW vacuuming
- Fixed performance regression with Hamming distance and Jaccard distance with Postgres 18
## 0.8.2 (2026-02-25)

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@@ -5,7 +5,7 @@ ARG DEBIAN_CODENAME=bookworm
FROM postgres:$PG_MAJOR-$DEBIAN_CODENAME
ARG PG_MAJOR
ADD https://github.com/pgvector/pgvector.git#v0.8.5 /tmp/pgvector
ADD https://github.com/pgvector/pgvector.git#v0.8.2 /tmp/pgvector
RUN apt-get update && \
apt-mark hold locales && \

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@@ -2,12 +2,12 @@
"name": "vector",
"abstract": "Open-source vector similarity search for Postgres",
"description": "Supports L2 distance, inner product, and cosine distance",
"version": "0.8.5",
"version": "0.8.2",
"maintainer": [
"Andrew Kane <andrew@ankane.org>"
],
"license": {
"PostgreSQL": "https://www.postgresql.org/about/licence"
"PostgreSQL": "http://www.postgresql.org/about/licence"
},
"prereqs": {
"runtime": {
@@ -20,7 +20,7 @@
"vector": {
"file": "sql/vector.sql",
"docfile": "README.md",
"version": "0.8.5",
"version": "0.8.2",
"abstract": "Open-source vector similarity search for Postgres"
}
},
@@ -38,7 +38,7 @@
"generated_by": "Andrew Kane",
"meta-spec": {
"version": "1.0.0",
"url": "https://pgxn.org/meta/spec.txt"
"url": "http://pgxn.org/meta/spec.txt"
},
"tags": [
"vectors",

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@@ -1,5 +1,5 @@
EXTENSION = vector
EXTVERSION = 0.8.5
EXTVERSION = 0.8.2
MODULE_big = vector
DATA = $(wildcard sql/*--*--*.sql)

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@@ -1,5 +1,5 @@
EXTENSION = vector
EXTVERSION = 0.8.5
EXTVERSION = 0.8.2
DATA_built = sql\$(EXTENSION)--$(EXTVERSION).sql
OBJS = src\bitutils.obj src\bitvec.obj src\halfutils.obj src\halfvec.obj 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\sparsevec.obj src\vector.obj

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@@ -23,7 +23,7 @@ Compile and install the extension (supports Postgres 13+)
```sh
cd /tmp
git clone --branch v0.8.5 https://github.com/pgvector/pgvector.git
git clone --branch v0.8.2 https://github.com/pgvector/pgvector.git
cd pgvector
make
make install # may need sudo
@@ -40,7 +40,7 @@ Ensure [C++ support in Visual Studio](https://learn.microsoft.com/en-us/cpp/buil
```cmd
set "PGROOT=C:\Program Files\PostgreSQL\18"
cd %TEMP%
git clone --branch v0.8.5 https://github.com/pgvector/pgvector.git
git clone --branch v0.8.2 https://github.com/pgvector/pgvector.git
cd pgvector
nmake /F Makefile.win
nmake /F Makefile.win install
@@ -1151,23 +1151,23 @@ This adds pgvector to the [Postgres image](https://hub.docker.com/_/postgres) (r
Supported tags are:
- `pg18-trixie`, `0.8.5-pg18-trixie`
- `pg18-bookworm`, `0.8.5-pg18-bookworm`, `pg18`, `0.8.5-pg18`
- `pg17-trixie`, `0.8.5-pg17-trixie`
- `pg17-bookworm`, `0.8.5-pg17-bookworm`, `pg17`, `0.8.5-pg17`
- `pg16-trixie`, `0.8.5-pg16-trixie`
- `pg16-bookworm`, `0.8.5-pg16-bookworm`, `pg16`, `0.8.5-pg16`
- `pg15-trixie`, `0.8.5-pg15-trixie`
- `pg15-bookworm`, `0.8.5-pg15-bookworm`, `pg15`, `0.8.5-pg15`
- `pg14-trixie`, `0.8.5-pg14-trixie`
- `pg14-bookworm`, `0.8.5-pg14-bookworm`, `pg14`, `0.8.5-pg14`
- `pg13-trixie`, `0.8.5-pg13-trixie`
- `pg13-bookworm`, `0.8.5-pg13-bookworm`, `pg13`, `0.8.5-pg13`
- `pg18-trixie`, `0.8.2-pg18-trixie`
- `pg18-bookworm`, `0.8.2-pg18-bookworm`, `pg18`, `0.8.2-pg18`
- `pg17-trixie`, `0.8.2-pg17-trixie`
- `pg17-bookworm`, `0.8.2-pg17-bookworm`, `pg17`, `0.8.2-pg17`
- `pg16-trixie`, `0.8.2-pg16-trixie`
- `pg16-bookworm`, `0.8.2-pg16-bookworm`, `pg16`, `0.8.2-pg16`
- `pg15-trixie`, `0.8.2-pg15-trixie`
- `pg15-bookworm`, `0.8.2-pg15-bookworm`, `pg15`, `0.8.2-pg15`
- `pg14-trixie`, `0.8.2-pg14-trixie`
- `pg14-bookworm`, `0.8.2-pg14-bookworm`, `pg14`, `0.8.2-pg14`
- `pg13-trixie`, `0.8.2-pg13-trixie`
- `pg13-bookworm`, `0.8.2-pg13-bookworm`, `pg13`, `0.8.2-pg13`
You can also build the image manually:
```sh
git clone --branch v0.8.5 https://github.com/pgvector/pgvector.git
git clone --branch v0.8.2 https://github.com/pgvector/pgvector.git
cd pgvector
docker build --pull --build-arg PG_MAJOR=18 -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 '0.8.3'" to load this file. \quit

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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 '0.8.4'" to load this file. \quit

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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 '0.8.5'" to load this file. \quit

View File

@@ -427,11 +427,10 @@ typedef struct HnswVacuumState
HnswSupport support;
/* Variables */
struct tidhash_hash *deleting;
struct tidhash_hash *deleted;
BufferAccessStrategy bas;
HnswNeighborTuple ntup;
HnswElementData highestPoint;
HnswElementData fallbackPoint;
/* Memory */
MemoryContext tmpCtx;

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@@ -719,7 +719,7 @@ InitBuildState(HnswBuildState * buildstate, Relation heap, Relation index, Index
/* Get support functions */
HnswInitSupport(&buildstate->support, index);
InitGraph(&buildstate->graphData, NULL, maintenance_work_mem * (Size) 1024);
InitGraph(&buildstate->graphData, NULL, (Size) maintenance_work_mem * 1024L);
buildstate->graph = &buildstate->graphData;
buildstate->ml = HnswGetMl(buildstate->m);
buildstate->maxLevel = HnswGetMaxLevel(buildstate->m);
@@ -956,7 +956,7 @@ HnswBeginParallel(HnswBuildState * buildstate, bool isconcurrent, int request)
/* Leave space for other objects in shared memory */
/* Docker has a default limit of 64 MB for shm_size */
/* which happens to be the default value of maintenance_work_mem */
esthnswarea = maintenance_work_mem * (Size) 1024;
esthnswarea = maintenance_work_mem * 1024L;
estother = 3 * 1024 * 1024;
if (esthnswarea > estother)
esthnswarea -= estother;

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@@ -546,9 +546,6 @@ HnswLoadElementImpl(BlockNumber blkno, OffsetNumber offno, double *distance, Hns
Assert(HnswIsElementTuple(etup));
if (unlikely(etup->deleted))
elog(ERROR, "cannot load deleted element");
/* Calculate distance */
if (distance != NULL)
{

View File

@@ -19,12 +19,12 @@
#endif
/*
* Check if deletion list contains an element
* Check if deleted list contains an index TID
*/
static bool
DeletingElement(tidhash_hash * deleting, ItemPointer indextid)
DeletedContains(tidhash_hash * deleted, ItemPointer indextid)
{
return tidhash_lookup(deleting, *indextid) != NULL;
return tidhash_lookup(deleted, *indextid) != NULL;
}
/*
@@ -37,20 +37,17 @@ RemoveHeapTids(HnswVacuumState * vacuumstate)
{
BlockNumber blkno = HNSW_HEAD_BLKNO;
HnswElement highestPoint = &vacuumstate->highestPoint;
HnswElement fallbackPoint = &vacuumstate->fallbackPoint;
Relation index = vacuumstate->index;
BufferAccessStrategy bas = vacuumstate->bas;
HnswElement entryPoint = HnswGetEntryPoint(vacuumstate->index);
IndexBulkDeleteResult *stats = vacuumstate->stats;
/* Store separately since HnswElement level is uint8 */
/* Store separately since highestPoint.level is uint8 */
int highestLevel = -1;
int fallbackLevel = -1;
/* Initialize highest point and fallback point */
/* Initialize highest point */
highestPoint->blkno = InvalidBlockNumber;
highestPoint->offno = InvalidOffsetNumber;
fallbackPoint->blkno = InvalidBlockNumber;
fallbackPoint->offno = InvalidOffsetNumber;
while (BlockNumberIsValid(blkno))
{
@@ -80,14 +77,6 @@ RemoveHeapTids(HnswVacuumState * vacuumstate)
if (!HnswIsElementTuple(etup))
continue;
/*
* Skip deleted tuples. It is important they are not added to the
* deletion list to avoid false positives in NeedsUpdated and
* ConfirmRepaired.
*/
if (etup->deleted)
continue;
if (ItemPointerIsValid(&etup->heaptids[0]))
{
for (int i = 0; i < HNSW_HEAPTIDS; i++)
@@ -121,40 +110,23 @@ RemoveHeapTids(HnswVacuumState * vacuumstate)
if (!ItemPointerIsValid(&etup->heaptids[0]))
{
ItemPointerData indextid;
ItemPointerData ip;
bool found;
/* Add to deletion list */
ItemPointerSet(&indextid, blkno, offno);
/* Add to deleted list */
ItemPointerSet(&ip, blkno, offno);
tidhash_insert(vacuumstate->deleting, indextid, &found);
tidhash_insert(vacuumstate->deleted, ip, &found);
Assert(!found);
}
else if (etup->level > highestLevel)
else if (etup->level > highestLevel && !(entryPoint != NULL && blkno == entryPoint->blkno && offno == entryPoint->offno))
{
if (BlockNumberIsValid(highestPoint->blkno))
{
/* Current highest point becomes fallback */
fallbackPoint->blkno = highestPoint->blkno;
fallbackPoint->offno = highestPoint->offno;
fallbackPoint->level = highestPoint->level;
fallbackLevel = highestLevel;
}
/* Keep track of highest point */
/* Keep track of highest non-entry point */
highestPoint->blkno = blkno;
highestPoint->offno = offno;
highestPoint->level = etup->level;
highestLevel = etup->level;
}
else if (etup->level > fallbackLevel)
{
/* Keep track of second highest point */
fallbackPoint->blkno = blkno;
fallbackPoint->offno = offno;
fallbackPoint->level = etup->level;
fallbackLevel = etup->level;
}
}
blkno = HnswPageGetOpaque(page)->nextblkno;
@@ -166,10 +138,6 @@ RemoveHeapTids(HnswVacuumState * vacuumstate)
UnlockReleaseBuffer(buf);
}
#ifdef HNSW_MEMORY
elog(INFO, "memory: %zu KB", MemoryContextMemAllocated(CurrentMemoryContext, true) / 1024);
#endif
}
/*
@@ -200,8 +168,8 @@ NeedsUpdated(HnswVacuumState * vacuumstate, HnswElement element)
if (!ItemPointerIsValid(indextid))
continue;
/* Check if in deletion list */
if (DeletingElement(vacuumstate->deleting, indextid))
/* Check if in deleted list */
if (DeletedContains(vacuumstate->deleted, indextid))
{
needsUpdated = true;
break;
@@ -210,9 +178,13 @@ NeedsUpdated(HnswVacuumState * vacuumstate, HnswElement element)
/* Also update if layer 0 is not full */
/* This could indicate too many candidates being deleted during insert */
/* There should always be more than zero indextids, but check for safety */
if (!needsUpdated && ntup->count > 0)
if (!needsUpdated)
{
/* Keep clang-tidy happy */
Assert(ntup->count > 0);
needsUpdated = !ItemPointerIsValid(&ntup->indextids[ntup->count - 1]);
}
UnlockReleaseBuffer(buf);
@@ -297,27 +269,12 @@ RepairGraphEntryPoint(HnswVacuumState * vacuumstate)
/* Get a shared lock */
LockPage(index, HNSW_UPDATE_LOCK, ShareLock);
/* Get latest entry point */
entryPoint = HnswGetEntryPoint(index);
/* Load element */
HnswLoadElement(highestPoint, NULL, NULL, index, support, true, NULL);
/* Use fallback point if highest point is entry point */
if (entryPoint != NULL && entryPoint->blkno == highestPoint->blkno && entryPoint->offno == highestPoint->offno)
{
highestPoint = &vacuumstate->fallbackPoint;
if (!BlockNumberIsValid(highestPoint->blkno))
highestPoint = NULL;
}
if (highestPoint != NULL)
{
/* Load element */
HnswLoadElement(highestPoint, NULL, NULL, index, support, true, NULL);
/* Repair if needed */
if (NeedsUpdated(vacuumstate, highestPoint))
RepairGraphElement(vacuumstate, highestPoint, entryPoint);
}
/* Repair if needed */
if (NeedsUpdated(vacuumstate, highestPoint))
RepairGraphElement(vacuumstate, highestPoint, HnswGetEntryPoint(index));
/* Release lock */
UnlockPage(index, HNSW_UPDATE_LOCK, ShareLock);
@@ -335,7 +292,7 @@ RepairGraphEntryPoint(HnswVacuumState * vacuumstate)
ItemPointerSet(&epData, entryPoint->blkno, entryPoint->offno);
if (DeletingElement(vacuumstate->deleting, &epData))
if (DeletedContains(vacuumstate->deleted, &epData))
{
/*
* Replace the entry point with the highest point. If highest
@@ -421,10 +378,6 @@ RepairGraph(HnswVacuumState * vacuumstate)
if (!HnswIsElementTuple(etup))
continue;
/* Skip deleted tuples */
if (etup->deleted)
continue;
/* Skip updating neighbors if being deleted */
if (!ItemPointerIsValid(&etup->heaptids[0]))
continue;
@@ -488,103 +441,6 @@ RepairGraph(HnswVacuumState * vacuumstate)
/* Reset memory context */
MemoryContextSwitchTo(oldCtx);
MemoryContextReset(vacuumstate->tmpCtx);
#ifdef HNSW_VACUUM_PROGRESS
if (!BlockNumberIsValid(blkno) || (blkno - HNSW_HEAD_BLKNO) % 1000 == 0)
{
BlockNumber totalBlocks = RelationGetNumberOfBlocks(index);
BlockNumber currentBlocks = BlockNumberIsValid(blkno) ? blkno : totalBlocks;
elog(INFO, "hnsw vacuum progress: %.1f%%", 100.0 * currentBlocks / totalBlocks);
}
#endif
}
}
/*
* Confirm graph was repaired
*/
static void
ConfirmRepaired(HnswVacuumState * vacuumstate)
{
BlockNumber blkno = HNSW_HEAD_BLKNO;
Relation index = vacuumstate->index;
BufferAccessStrategy bas = vacuumstate->bas;
while (BlockNumberIsValid(blkno))
{
Buffer buf;
Page page;
OffsetNumber offno;
OffsetNumber maxoffno;
vacuum_delay_point();
buf = ReadBufferExtended(index, MAIN_FORKNUM, blkno, RBM_NORMAL, bas);
LockBuffer(buf, BUFFER_LOCK_SHARE);
page = BufferGetPage(buf);
maxoffno = PageGetMaxOffsetNumber(page);
/* Iterate over nodes */
for (offno = FirstOffsetNumber; offno <= maxoffno; offno = OffsetNumberNext(offno))
{
HnswElementTuple etup = (HnswElementTuple) PageGetItem(page, PageGetItemId(page, offno));
HnswNeighborTuple ntup;
Buffer nbuf;
Page npage;
BlockNumber neighborPage;
OffsetNumber neighborOffno;
/* Skip neighbor tuples */
if (!HnswIsElementTuple(etup))
continue;
/* Skip deleted tuples */
if (etup->deleted)
continue;
/* Skip if being deleted */
if (!ItemPointerIsValid(&etup->heaptids[0]))
continue;
/* Get neighbor page */
neighborPage = ItemPointerGetBlockNumber(&etup->neighbortid);
neighborOffno = ItemPointerGetOffsetNumber(&etup->neighbortid);
if (neighborPage == blkno)
{
nbuf = buf;
npage = page;
}
else
{
nbuf = ReadBufferExtended(index, MAIN_FORKNUM, neighborPage, RBM_NORMAL, bas);
LockBuffer(nbuf, BUFFER_LOCK_SHARE);
npage = BufferGetPage(nbuf);
}
ntup = (HnswNeighborTuple) PageGetItem(npage, PageGetItemId(npage, neighborOffno));
/* Check neighbors */
for (int i = 0; i < ntup->count; i++)
{
ItemPointer indextid = &ntup->indextids[i];
if (!ItemPointerIsValid(indextid))
continue;
/* Check if in deletion list */
if (DeletingElement(vacuumstate->deleting, indextid))
elog(ERROR, "hnsw graph not repaired");
}
if (nbuf != buf)
UnlockReleaseBuffer(nbuf);
}
blkno = HnswPageGetOpaque(page)->nextblkno;
UnlockReleaseBuffer(buf);
}
}
@@ -600,15 +456,10 @@ MarkDeleted(HnswVacuumState * vacuumstate)
BufferAccessStrategy bas = vacuumstate->bas;
/*
* Wait for inserts and index scans to complete. Inserts and scans before
* this point may visit tuples about to be deleted. Inserts and scans
* after this point will not, since the graph has been repaired.
* Wait for index scans to complete. Scans before this point may contain
* tuples about to be deleted. Scans after this point will not, since the
* graph has been repaired.
*/
LockPage(index, HNSW_UPDATE_LOCK, ExclusiveLock);
UnlockPage(index, HNSW_UPDATE_LOCK, ExclusiveLock);
ConfirmRepaired(vacuumstate);
LockPage(index, HNSW_SCAN_LOCK, ExclusiveLock);
UnlockPage(index, HNSW_SCAN_LOCK, ExclusiveLock);
@@ -756,7 +607,7 @@ InitVacuumState(HnswVacuumState * vacuumstate, IndexVacuumInfo *info, IndexBulkD
HnswGetMetaPageInfo(index, &vacuumstate->m, NULL);
/* Create hash table */
vacuumstate->deleting = tidhash_create(CurrentMemoryContext, 256, NULL);
vacuumstate->deleted = tidhash_create(CurrentMemoryContext, 256, NULL);
}
/*
@@ -765,7 +616,7 @@ InitVacuumState(HnswVacuumState * vacuumstate, IndexVacuumInfo *info, IndexBulkD
static void
FreeVacuumState(HnswVacuumState * vacuumstate)
{
tidhash_destroy(vacuumstate->deleting);
tidhash_destroy(vacuumstate->deleted);
FreeAccessStrategy(vacuumstate->bas);
pfree(vacuumstate->ntup);
MemoryContextDelete(vacuumstate->tmpCtx);
@@ -788,7 +639,7 @@ hnswbulkdelete(IndexVacuumInfo *info, IndexBulkDeleteResult *stats,
/* Pass 2: Repair graph */
HnswBench("RepairGraph", RepairGraph(&vacuumstate));
/* Passes 3 and 4: Confirm repaired and mark as deleted */
/* Pass 3: Mark as deleted */
HnswBench("MarkDeleted", MarkDeleted(&vacuumstate));
FreeVacuumState(&vacuumstate);

View File

@@ -152,7 +152,18 @@ SampleRows(IvfflatBuildState * buildstate)
/* Normalize if needed */
if (buildstate->kmeansnormprocinfo != NULL)
IvfflatNormVectors(buildstate->typeInfo, buildstate->collation, buildstate->samples, buildstate->tmpCtx);
{
VectorArray samples = buildstate->samples;
for (int i = 0; i < samples->length; i++)
{
Datum value = PointerGetDatum(VectorArrayGet(samples, i));
Datum normValue = IvfflatNormValue(buildstate->typeInfo, buildstate->collation, value);
VectorArraySet(samples, i, DatumGetPointer(normValue));
pfree(DatumGetPointer(normValue));
}
}
}
/*
@@ -388,14 +399,7 @@ InitBuildState(IvfflatBuildState * buildstate, Relation heap, Relation index, In
buildstate->slot = MakeSingleTupleTableSlot(buildstate->sortdesc, &TTSOpsVirtual);
buildstate->memoryUsed = 0;
buildstate->itemsize = buildstate->typeInfo->itemSize(buildstate->dimensions);
buildstate->memoryUsed += VECTOR_ARRAY_SIZE(buildstate->lists, buildstate->itemsize);
IvfflatCheckMemoryUsage(buildstate->memoryUsed);
buildstate->centers = VectorArrayInit(buildstate->lists, buildstate->dimensions, buildstate->itemsize);
/* TODO Move allocation to page creation */
buildstate->centers = VectorArrayInit(buildstate->lists, buildstate->dimensions, buildstate->typeInfo->itemSize(buildstate->dimensions));
buildstate->listInfo = palloc(sizeof(ListInfo) * buildstate->lists);
buildstate->tmpCtx = AllocSetContextCreate(CurrentMemoryContext,
@@ -438,27 +442,19 @@ ComputeCenters(IvfflatBuildState * buildstate)
pgstat_progress_update_param(PROGRESS_CREATEIDX_SUBPHASE, PROGRESS_IVFFLAT_PHASE_KMEANS);
/* Target 50 samples per list, with at least 10000 samples */
/* The number of samples has a large effect on index build time */
numSamples = buildstate->lists * 50;
if (numSamples < 10000)
numSamples = 10000;
/* Skip samples for unlogged table */
if (buildstate->heap == NULL)
numSamples = 1;
else
{
int64 maxTuples = (int64) RelationGetNumberOfBlocks(buildstate->heap) * MaxHeapTuplesPerPage;
/* Target 50 samples per list, with at least 10000 samples */
/* The number of samples has a large effect on index build time */
numSamples = buildstate->lists * 50;
if (numSamples < 10000)
numSamples = 10000;
/* Save memory since will not have more than max tuples */
numSamples = Max(Min(numSamples, maxTuples), 1);
}
/* Sample rows */
buildstate->memoryUsed += VECTOR_ARRAY_SIZE(numSamples, buildstate->itemsize);
IvfflatCheckMemoryUsage(buildstate->memoryUsed);
buildstate->samples = VectorArrayInit(numSamples, buildstate->dimensions, buildstate->itemsize);
/* TODO Ensure within maintenance_work_mem */
buildstate->samples = VectorArrayInit(numSamples, buildstate->dimensions, buildstate->centers->itemsize);
if (buildstate->heap != NULL)
{
IvfflatBench("sample rows", SampleRows(buildstate));
@@ -473,7 +469,7 @@ ComputeCenters(IvfflatBuildState * buildstate)
}
/* Calculate centers */
IvfflatBench("k-means", IvfflatKmeans(buildstate->index, buildstate->samples, buildstate->centers, buildstate->typeInfo, buildstate->memoryUsed));
IvfflatBench("k-means", IvfflatKmeans(buildstate->index, buildstate->samples, buildstate->centers, buildstate->typeInfo));
/* Free samples before we allocate more memory */
VectorArrayFree(buildstate->samples);

View File

@@ -204,7 +204,6 @@ typedef struct IvfflatBuildState
VectorArray samples;
VectorArray centers;
ListInfo *listInfo;
Size itemsize;
#ifdef IVFFLAT_KMEANS_DEBUG
double inertia;
@@ -224,7 +223,6 @@ typedef struct IvfflatBuildState
TupleTableSlot *slot;
/* Memory */
Size memoryUsed;
MemoryContext tmpCtx;
/* Parallel builds */
@@ -305,32 +303,22 @@ typedef IvfflatScanOpaqueData * IvfflatScanOpaque;
static inline Pointer
VectorArrayGet(VectorArray arr, int offset)
{
if (offset >= arr->maxlen)
elog(ERROR, "safety check failed");
return ((char *) arr->items) + (offset * arr->itemsize);
}
static inline void
VectorArraySet(VectorArray arr, int offset, Pointer val)
{
Size size = VARSIZE_ANY(val);
if (size > arr->itemsize)
elog(ERROR, "safety check failed");
memcpy(VectorArrayGet(arr, offset), val, size);
memcpy(VectorArrayGet(arr, offset), val, VARSIZE_ANY(val));
}
/* Methods */
VectorArray VectorArrayInit(int maxlen, int dimensions, Size itemsize);
void VectorArrayFree(VectorArray arr);
void IvfflatKmeans(Relation index, VectorArray samples, VectorArray centers, const IvfflatTypeInfo * typeInfo, Size memoryUsed);
void IvfflatKmeans(Relation index, VectorArray samples, VectorArray centers, const IvfflatTypeInfo * typeInfo);
FmgrInfo *IvfflatOptionalProcInfo(Relation index, uint16 procnum);
Datum IvfflatNormValue(const IvfflatTypeInfo * typeInfo, Oid collation, Datum value);
bool IvfflatCheckNorm(FmgrInfo *procinfo, Oid collation, Datum value);
void IvfflatNormVectors(const IvfflatTypeInfo * typeInfo, Oid collation, VectorArray arr, MemoryContext tmpCtx);
void IvfflatCheckMemoryUsage(Size totalSize);
int IvfflatGetLists(Relation index);
void IvfflatGetMetaPageInfo(Relation index, int *lists, int *dimensions);
void IvfflatUpdateList(Relation index, ListInfo listInfo, BlockNumber insertPage, BlockNumber originalInsertPage, BlockNumber startPage, ForkNumber forkNum);

View File

@@ -99,8 +99,22 @@ NormCenters(const IvfflatTypeInfo * typeInfo, Oid collation, VectorArray centers
MemoryContext normCtx = AllocSetContextCreate(CurrentMemoryContext,
"Ivfflat norm temporary context",
ALLOCSET_DEFAULT_SIZES);
MemoryContext oldCtx = MemoryContextSwitchTo(normCtx);
IvfflatNormVectors(typeInfo, collation, centers, normCtx);
for (int j = 0; j < centers->length; j++)
{
Datum center = PointerGetDatum(VectorArrayGet(centers, j));
Datum newCenter = IvfflatNormValue(typeInfo, collation, center);
Size size = VARSIZE_ANY(DatumGetPointer(newCenter));
if (size > centers->itemsize)
elog(ERROR, "safety check failed");
memcpy(DatumGetPointer(center), DatumGetPointer(newCenter), size);
MemoryContextReset(normCtx);
}
MemoryContextSwitchTo(oldCtx);
MemoryContextDelete(normCtx);
}
@@ -244,7 +258,7 @@ ComputeNewCenters(VectorArray samples, float *agg, VectorArray newCenters, int *
* https://www.aaai.org/Papers/ICML/2003/ICML03-022.pdf
*/
static void
ElkanKmeans(Relation index, VectorArray samples, VectorArray centers, const IvfflatTypeInfo * typeInfo, Size memoryUsed)
ElkanKmeans(Relation index, VectorArray samples, VectorArray centers, const IvfflatTypeInfo * typeInfo)
{
FmgrInfo *procinfo;
FmgrInfo *normprocinfo;
@@ -263,6 +277,8 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers, const Ivff
float *newcdist;
/* Calculate allocation sizes */
Size samplesSize = VECTOR_ARRAY_SIZE(samples->maxlen, samples->itemsize);
Size centersSize = VECTOR_ARRAY_SIZE(centers->maxlen, centers->itemsize);
Size newCentersSize = VECTOR_ARRAY_SIZE(numCenters, centers->itemsize);
Size aggSize = sizeof(float) * (int64) numCenters * dimensions;
Size centerCountsSize = sizeof(int) * numCenters;
@@ -274,13 +290,18 @@ ElkanKmeans(Relation index, VectorArray samples, VectorArray centers, const Ivff
Size newcdistSize = sizeof(float) * numCenters;
/* Calculate total size */
Size totalSize = memoryUsed + newCentersSize + aggSize + centerCountsSize + closestCentersSize + lowerBoundSize + upperBoundSize + sSize + halfcdistSize + newcdistSize;
Size totalSize = samplesSize + centersSize + newCentersSize + aggSize + centerCountsSize + closestCentersSize + lowerBoundSize + upperBoundSize + sSize + halfcdistSize + newcdistSize;
/* Check memory requirements */
IvfflatCheckMemoryUsage(totalSize);
/* Add one to error message to ceil */
if (totalSize > (Size) maintenance_work_mem * 1024L)
ereport(ERROR,
(errcode(ERRCODE_PROGRAM_LIMIT_EXCEEDED),
errmsg("memory required is %zu MB, maintenance_work_mem is %d MB",
totalSize / (1024 * 1024) + 1, maintenance_work_mem / 1024)));
/* Ensure indexing does not overflow */
if (numCenters > INT_MAX / numCenters)
if (numCenters * numCenters > INT_MAX)
elog(ERROR, "Indexing overflow detected. Please report a bug.");
/* Set support functions */
@@ -541,7 +562,7 @@ CheckCenters(Relation index, VectorArray centers, const IvfflatTypeInfo * typeIn
* We use spherical k-means for inner product and cosine
*/
void
IvfflatKmeans(Relation index, VectorArray samples, VectorArray centers, const IvfflatTypeInfo * typeInfo, Size memoryUsed)
IvfflatKmeans(Relation index, VectorArray samples, VectorArray centers, const IvfflatTypeInfo * typeInfo)
{
MemoryContext kmeansCtx = AllocSetContextCreate(CurrentMemoryContext,
"Ivfflat kmeans temporary context",
@@ -551,7 +572,7 @@ IvfflatKmeans(Relation index, VectorArray samples, VectorArray centers, const Iv
if (samples->length == 0)
RandomCenters(index, centers, typeInfo);
else
ElkanKmeans(index, samples, centers, typeInfo, memoryUsed);
ElkanKmeans(index, samples, centers, typeInfo);
CheckCenters(index, centers, typeInfo);

View File

@@ -6,9 +6,7 @@
#include "halfutils.h"
#include "halfvec.h"
#include "ivfflat.h"
#include "miscadmin.h"
#include "storage/bufmgr.h"
#include "utils/memutils.h"
#include "utils/relcache.h"
#include "utils/varbit.h"
#include "vector.h"
@@ -23,15 +21,11 @@
VectorArray
VectorArrayInit(int maxlen, int dimensions, Size itemsize)
{
VectorArray res;
if (maxlen < 1 || dimensions < 1)
elog(ERROR, "safety check failed");
VectorArray res = palloc(sizeof(VectorArrayData));
/* Ensure items are aligned to prevent UB */
itemsize = MAXALIGN(itemsize);
res = palloc(sizeof(VectorArrayData));
res->length = 0;
res->maxlen = maxlen;
res->dim = dimensions;
@@ -94,40 +88,6 @@ IvfflatCheckNorm(FmgrInfo *procinfo, Oid collation, Datum value)
return DatumGetFloat8(FunctionCall1Coll(procinfo, collation, value)) > 0;
}
/*
* Normalize vectors
*/
void
IvfflatNormVectors(const IvfflatTypeInfo * typeInfo, Oid collation, VectorArray arr, MemoryContext tmpCtx)
{
MemoryContext oldCtx = MemoryContextSwitchTo(tmpCtx);
for (int i = 0; i < arr->length; i++)
{
Datum value = PointerGetDatum(VectorArrayGet(arr, i));
Datum newValue = IvfflatNormValue(typeInfo, collation, value);
VectorArraySet(arr, i, DatumGetPointer(newValue));
MemoryContextReset(tmpCtx);
}
MemoryContextSwitchTo(oldCtx);
}
/*
* Check memory usage
*/
void
IvfflatCheckMemoryUsage(Size totalSize)
{
/* Add one to error message to ceil */
if (totalSize > maintenance_work_mem * (Size) 1024)
ereport(ERROR,
(errcode(ERRCODE_PROGRAM_LIMIT_EXCEEDED),
errmsg("memory required is %zu MB, maintenance_work_mem is %d MB",
totalSize / (1024 * 1024) + 1, maintenance_work_mem / 1024)));
}
/*
* New buffer
*/

View File

@@ -40,7 +40,7 @@
#endif
#if PG_VERSION_NUM >= 180000
PG_MODULE_MAGIC_EXT(.name = "vector", .version = "0.8.5");
PG_MODULE_MAGIC_EXT(.name = "vector", .version = "0.8.2");
#else
PG_MODULE_MAGIC;
#endif

View File

@@ -49,11 +49,3 @@ CREATE INDEX ON t USING hnsw ((val::bit(3)) bit_hamming_ops);
CREATE INDEX ON t USING hnsw ((val::bit(64001)) bit_hamming_ops);
ERROR: column cannot have more than 64000 dimensions for hnsw index
DROP TABLE t;
-- dimensions
CREATE TABLE t (val bit(64000));
CREATE INDEX ON t USING hnsw (val bit_hamming_ops);
DROP TABLE t;
CREATE TABLE t (val bit(64001));
CREATE INDEX ON t USING hnsw (val bit_hamming_ops);
ERROR: column cannot have more than 64000 dimensions for hnsw index
DROP TABLE t;

View File

@@ -100,11 +100,3 @@ SELECT COUNT(*) FROM (SELECT * FROM t ORDER BY val <+> (SELECT NULL::halfvec)) t
(1 row)
DROP TABLE t;
-- dimensions
CREATE TABLE t (val halfvec(4000));
CREATE INDEX ON t USING hnsw (val halfvec_l2_ops);
DROP TABLE t;
CREATE TABLE t (val halfvec(4001));
CREATE INDEX ON t USING hnsw (val halfvec_l2_ops);
ERROR: column cannot have more than 4000 dimensions for hnsw index
DROP TABLE t;

View File

@@ -161,7 +161,6 @@ ERROR: value 1001 out of bounds for option "ef_construction"
DETAIL: Valid values are between "4" and "1000".
CREATE INDEX ON t USING hnsw (val vector_l2_ops) WITH (m = 16, ef_construction = 31);
ERROR: ef_construction must be greater than or equal to 2 * m
DROP TABLE t;
SHOW hnsw.ef_search;
hnsw.ef_search
----------------
@@ -199,11 +198,4 @@ SET hnsw.scan_mem_multiplier = 0;
ERROR: 0 is outside the valid range for parameter "hnsw.scan_mem_multiplier" (1 .. 1000)
SET hnsw.scan_mem_multiplier = 1001;
ERROR: 1001 is outside the valid range for parameter "hnsw.scan_mem_multiplier" (1 .. 1000)
-- dimensions
CREATE TABLE t (val vector(2000));
CREATE INDEX ON t USING hnsw (val vector_l2_ops);
DROP TABLE t;
CREATE TABLE t (val vector(2001));
CREATE INDEX ON t USING hnsw (val vector_l2_ops);
ERROR: column cannot have more than 2000 dimensions for hnsw index
DROP TABLE t;

View File

@@ -35,32 +35,3 @@ NOTICE: ivfflat index created with little data
DETAIL: This will cause low recall.
HINT: Drop the index until the table has more data.
DROP TABLE t;
-- dimensions
CREATE TABLE t (val bit(64000));
CREATE INDEX ON t USING ivfflat (val bit_hamming_ops);
NOTICE: ivfflat index created with little data
DETAIL: This will cause low recall.
HINT: Drop the index until the table has more data.
DROP TABLE t;
CREATE TABLE t (val bit(64001));
CREATE INDEX ON t USING ivfflat (val bit_hamming_ops);
ERROR: column cannot have more than 64000 dimensions for ivfflat index
DROP TABLE t;
-- memory
SET maintenance_work_mem = '1MB';
CREATE TABLE t (val bit(64000));
CREATE INDEX ON t USING ivfflat (val bit_hamming_ops);
NOTICE: ivfflat index created with little data
DETAIL: This will cause low recall.
HINT: Drop the index until the table has more data.
DROP TABLE t;
RESET maintenance_work_mem;
SET maintenance_work_mem = '29MB';
CREATE TABLE t (val bit(64000));
INSERT INTO t (val) VALUES (B'0'::bit(64000));
CREATE INDEX ON t USING ivfflat (val bit_hamming_ops);
NOTICE: ivfflat index created with little data
DETAIL: This will cause low recall.
HINT: Drop the index until the table has more data.
DROP TABLE t;
RESET maintenance_work_mem;

View File

@@ -82,32 +82,3 @@ SELECT COUNT(*) FROM (SELECT * FROM t ORDER BY val <=> (SELECT NULL::halfvec)) t
(1 row)
DROP TABLE t;
-- dimensions
CREATE TABLE t (val halfvec(4000));
CREATE INDEX ON t USING ivfflat (val halfvec_l2_ops);
NOTICE: ivfflat index created with little data
DETAIL: This will cause low recall.
HINT: Drop the index until the table has more data.
DROP TABLE t;
CREATE TABLE t (val halfvec(4001));
CREATE INDEX ON t USING ivfflat (val halfvec_l2_ops);
ERROR: column cannot have more than 4000 dimensions for ivfflat index
DROP TABLE t;
-- memory
SET maintenance_work_mem = '1MB';
CREATE TABLE t (val halfvec(4000));
CREATE INDEX ON t USING ivfflat (val halfvec_l2_ops);
NOTICE: ivfflat index created with little data
DETAIL: This will cause low recall.
HINT: Drop the index until the table has more data.
DROP TABLE t;
RESET maintenance_work_mem;
SET maintenance_work_mem = '6MB';
CREATE TABLE t (val halfvec(4000));
INSERT INTO t (val) VALUES (array_fill(0, ARRAY[4000]));
CREATE INDEX ON t USING ivfflat (val halfvec_l2_ops);
NOTICE: ivfflat index created with little data
DETAIL: This will cause low recall.
HINT: Drop the index until the table has more data.
DROP TABLE t;
RESET maintenance_work_mem;

View File

@@ -143,7 +143,6 @@ DETAIL: Valid values are between "1" and "32768".
CREATE INDEX ON t USING ivfflat (val vector_l2_ops) WITH (lists = 32769);
ERROR: value 32769 out of bounds for option "lists"
DETAIL: Valid values are between "1" and "32768".
DROP TABLE t;
SHOW ivfflat.probes;
ivfflat.probes
----------------
@@ -173,32 +172,4 @@ SET ivfflat.max_probes = 0;
ERROR: 0 is outside the valid range for parameter "ivfflat.max_probes" (1 .. 32768)
SET ivfflat.max_probes = 32769;
ERROR: 32769 is outside the valid range for parameter "ivfflat.max_probes" (1 .. 32768)
-- dimensions
CREATE TABLE t (val vector(2000));
CREATE INDEX ON t USING ivfflat (val vector_l2_ops);
NOTICE: ivfflat index created with little data
DETAIL: This will cause low recall.
HINT: Drop the index until the table has more data.
DROP TABLE t;
CREATE TABLE t (val vector(2001));
CREATE INDEX ON t USING ivfflat (val vector_l2_ops);
ERROR: column cannot have more than 2000 dimensions for ivfflat index
DROP TABLE t;
-- memory
SET maintenance_work_mem = '1MB';
CREATE TABLE t (val vector(2000));
CREATE INDEX ON t USING ivfflat (val vector_l2_ops);
NOTICE: ivfflat index created with little data
DETAIL: This will cause low recall.
HINT: Drop the index until the table has more data.
DROP TABLE t;
RESET maintenance_work_mem;
SET maintenance_work_mem = '5MB';
CREATE TABLE t (val vector(2000));
INSERT INTO t (val) VALUES (array_fill(0, ARRAY[2000]));
CREATE INDEX ON t USING ivfflat (val vector_l2_ops);
NOTICE: ivfflat index created with little data
DETAIL: This will cause low recall.
HINT: Drop the index until the table has more data.
DROP TABLE t;
RESET maintenance_work_mem;

View File

@@ -33,13 +33,3 @@ CREATE INDEX ON t USING hnsw (val bit_hamming_ops);
CREATE INDEX ON t USING hnsw ((val::bit(3)) bit_hamming_ops);
CREATE INDEX ON t USING hnsw ((val::bit(64001)) bit_hamming_ops);
DROP TABLE t;
-- dimensions
CREATE TABLE t (val bit(64000));
CREATE INDEX ON t USING hnsw (val bit_hamming_ops);
DROP TABLE t;
CREATE TABLE t (val bit(64001));
CREATE INDEX ON t USING hnsw (val bit_hamming_ops);
DROP TABLE t;

View File

@@ -56,13 +56,3 @@ SELECT * FROM t ORDER BY val <+> '[3,3,3]';
SELECT COUNT(*) FROM (SELECT * FROM t ORDER BY val <+> (SELECT NULL::halfvec)) t2;
DROP TABLE t;
-- dimensions
CREATE TABLE t (val halfvec(4000));
CREATE INDEX ON t USING hnsw (val halfvec_l2_ops);
DROP TABLE t;
CREATE TABLE t (val halfvec(4001));
CREATE INDEX ON t USING hnsw (val halfvec_l2_ops);
DROP TABLE t;

View File

@@ -95,28 +95,23 @@ CREATE INDEX ON t USING hnsw (val vector_l2_ops) WITH (m = 101);
CREATE INDEX ON t USING hnsw (val vector_l2_ops) WITH (ef_construction = 3);
CREATE INDEX ON t USING hnsw (val vector_l2_ops) WITH (ef_construction = 1001);
CREATE INDEX ON t USING hnsw (val vector_l2_ops) WITH (m = 16, ef_construction = 31);
DROP TABLE t;
SHOW hnsw.ef_search;
SET hnsw.ef_search = 0;
SET hnsw.ef_search = 1001;
SHOW hnsw.iterative_scan;
SET hnsw.iterative_scan = on;
SHOW hnsw.max_scan_tuples;
SET hnsw.max_scan_tuples = 0;
SHOW hnsw.scan_mem_multiplier;
SET hnsw.scan_mem_multiplier = 0;
SET hnsw.scan_mem_multiplier = 1001;
-- dimensions
CREATE TABLE t (val vector(2000));
CREATE INDEX ON t USING hnsw (val vector_l2_ops);
DROP TABLE t;
CREATE TABLE t (val vector(2001));
CREATE INDEX ON t USING hnsw (val vector_l2_ops);
DROP TABLE t;

View File

@@ -21,28 +21,3 @@ CREATE INDEX ON t USING ivfflat ((val::bit(3)) bit_hamming_ops) WITH (lists = 1)
CREATE INDEX ON t USING ivfflat ((val::bit(64001)) bit_hamming_ops) WITH (lists = 1);
CREATE INDEX ON t USING ivfflat ((val::bit(2)) bit_hamming_ops) WITH (lists = 5);
DROP TABLE t;
-- dimensions
CREATE TABLE t (val bit(64000));
CREATE INDEX ON t USING ivfflat (val bit_hamming_ops);
DROP TABLE t;
CREATE TABLE t (val bit(64001));
CREATE INDEX ON t USING ivfflat (val bit_hamming_ops);
DROP TABLE t;
-- memory
SET maintenance_work_mem = '1MB';
CREATE TABLE t (val bit(64000));
CREATE INDEX ON t USING ivfflat (val bit_hamming_ops);
DROP TABLE t;
RESET maintenance_work_mem;
SET maintenance_work_mem = '29MB';
CREATE TABLE t (val bit(64000));
INSERT INTO t (val) VALUES (B'0'::bit(64000));
CREATE INDEX ON t USING ivfflat (val bit_hamming_ops);
DROP TABLE t;
RESET maintenance_work_mem;

View File

@@ -43,28 +43,3 @@ SELECT COUNT(*) FROM (SELECT * FROM t ORDER BY val <=> '[0,0,0]') t2;
SELECT COUNT(*) FROM (SELECT * FROM t ORDER BY val <=> (SELECT NULL::halfvec)) t2;
DROP TABLE t;
-- dimensions
CREATE TABLE t (val halfvec(4000));
CREATE INDEX ON t USING ivfflat (val halfvec_l2_ops);
DROP TABLE t;
CREATE TABLE t (val halfvec(4001));
CREATE INDEX ON t USING ivfflat (val halfvec_l2_ops);
DROP TABLE t;
-- memory
SET maintenance_work_mem = '1MB';
CREATE TABLE t (val halfvec(4000));
CREATE INDEX ON t USING ivfflat (val halfvec_l2_ops);
DROP TABLE t;
RESET maintenance_work_mem;
SET maintenance_work_mem = '6MB';
CREATE TABLE t (val halfvec(4000));
INSERT INTO t (val) VALUES (array_fill(0, ARRAY[4000]));
CREATE INDEX ON t USING ivfflat (val halfvec_l2_ops);
DROP TABLE t;
RESET maintenance_work_mem;

View File

@@ -81,40 +81,19 @@ DROP TABLE t;
CREATE TABLE t (val vector(3));
CREATE INDEX ON t USING ivfflat (val vector_l2_ops) WITH (lists = 0);
CREATE INDEX ON t USING ivfflat (val vector_l2_ops) WITH (lists = 32769);
DROP TABLE t;
SHOW ivfflat.probes;
SET ivfflat.probes = 0;
SET ivfflat.probes = 32769;
SHOW ivfflat.iterative_scan;
SET ivfflat.iterative_scan = on;
SHOW ivfflat.max_probes;
SET ivfflat.max_probes = 0;
SET ivfflat.max_probes = 32769;
-- dimensions
CREATE TABLE t (val vector(2000));
CREATE INDEX ON t USING ivfflat (val vector_l2_ops);
DROP TABLE t;
CREATE TABLE t (val vector(2001));
CREATE INDEX ON t USING ivfflat (val vector_l2_ops);
DROP TABLE t;
-- memory
SET maintenance_work_mem = '1MB';
CREATE TABLE t (val vector(2000));
CREATE INDEX ON t USING ivfflat (val vector_l2_ops);
DROP TABLE t;
RESET maintenance_work_mem;
SET maintenance_work_mem = '5MB';
CREATE TABLE t (val vector(2000));
INSERT INTO t (val) VALUES (array_fill(0, ARRAY[2000]));
CREATE INDEX ON t USING ivfflat (val vector_l2_ops);
DROP TABLE t;
RESET maintenance_work_mem;

View File

@@ -1,38 +0,0 @@
use strict;
use warnings FATAL => 'all';
use PostgreSQL::Test::Cluster;
use PostgreSQL::Test::Utils;
use Test::More;
my $dim = 3;
my $array_sql = join(",", ('random()') x $dim);
# Initialize node
my $node = PostgreSQL::Test::Cluster->new('node');
$node->init;
$node->start;
# Create table and index
$node->safe_psql("postgres", "CREATE EXTENSION vector;");
$node->safe_psql("postgres", "CREATE TABLE tst (i serial, v vector($dim));");
$node->safe_psql("postgres", "ALTER TABLE tst SET (autovacuum_enabled = false);");
$node->safe_psql("postgres",
"INSERT INTO tst (v) SELECT ARRAY[$array_sql] FROM generate_series(1, 1000) i;"
);
$node->safe_psql("postgres", "CREATE INDEX ON tst USING hnsw (v vector_l2_ops);");
$node->safe_psql("postgres", "DELETE FROM tst");
# Test HNSW_SCAN_LOCK at the beginning of MarkDeleted is effective
$node->pgbench(
"--no-vacuum --client=5 --transactions=1000",
0,
[qr{actually processed}],
[qr{^$}],
"concurrent SELECTs and VACUUM",
{
"046_hnsw_vacuum_scan_select\@1000" => "SELECT i FROM tst ORDER BY v <-> '[0,0,0]' LIMIT 10;",
"046_hnsw_vacuum_scan_vacuum\@1" => "VACUUM tst;"
}
);
done_testing();

View File

@@ -1,39 +0,0 @@
use strict;
use warnings FATAL => 'all';
use PostgreSQL::Test::Cluster;
use PostgreSQL::Test::Utils;
use Test::More;
my $dim = 3;
my $array_sql = join(",", ('random()') x $dim);
# Initialize node
my $node = PostgreSQL::Test::Cluster->new('node');
$node->init;
$node->start;
# Create table and index
$node->safe_psql("postgres", "CREATE EXTENSION vector;");
$node->safe_psql("postgres", "CREATE TABLE tst (i serial, v vector($dim));");
$node->safe_psql("postgres", "ALTER TABLE tst SET (autovacuum_enabled = false);");
$node->safe_psql("postgres",
"INSERT INTO tst (v) SELECT ARRAY[$array_sql] FROM generate_series(1, 1000) i;"
);
$node->safe_psql("postgres", "CREATE INDEX ON tst USING hnsw (v vector_l2_ops);");
# Test no "hnsw graph not repaired" errors
$node->pgbench(
"--no-vacuum --client=5 --transactions=1000",
0,
[qr{actually processed}],
[qr{^$}],
"concurrent INSERTs, DELETEs, SELECTs, and VACUUM",
{
"047_hnsw_vacuum_insert_insert\@500" => "INSERT INTO tst (v) VALUES (ARRAY[$array_sql]);",
"047_hnsw_vacuum_insert_delete\@500" => "DELETE FROM tst WHERE i = (SELECT i FROM tst LIMIT 1);",
"047_hnsw_vacuum_insert_select\@20" => "SELECT i FROM tst ORDER BY v <-> (SELECT ARRAY[$array_sql]::vector) LIMIT 10;",
"047_hnsw_vacuum_insert_vacuum\@1" => "VACUUM tst;"
}
);
done_testing();

View File

@@ -1,39 +0,0 @@
use strict;
use warnings FATAL => 'all';
use PostgreSQL::Test::Cluster;
use PostgreSQL::Test::Utils;
use Test::More;
my $dim = 3;
my $array_sql = join(",", ('random()') x $dim);
# Initialize node
my $node = PostgreSQL::Test::Cluster->new('node');
$node->init;
$node->start;
# Create table and index
$node->safe_psql("postgres", "CREATE EXTENSION vector;");
$node->safe_psql("postgres", "CREATE TABLE tst (i serial, v vector($dim));");
$node->safe_psql("postgres", "ALTER TABLE tst SET (autovacuum_enabled = false);");
$node->safe_psql("postgres",
"INSERT INTO tst (v) SELECT ARRAY[$array_sql] FROM generate_series(1, 1000) i;"
);
$node->safe_psql("postgres", "CREATE INDEX ON tst USING ivfflat (v vector_l2_ops) WITH (lists = 10);");
# Test no errors
$node->pgbench(
"--no-vacuum --client=5 --transactions=1500",
0,
[qr{actually processed}],
[qr{^$}],
"concurrent INSERTs, DELETEs, SELECTs, and VACUUM",
{
"048_ivfflat_vacuum_insert_insert\@500" => "INSERT INTO tst (v) VALUES (ARRAY[$array_sql]);",
"048_ivfflat_vacuum_insert_delete\@500" => "DELETE FROM tst WHERE i = (SELECT i FROM tst LIMIT 1);",
"048_ivfflat_vacuum_insert_select\@500" => "SELECT i FROM tst ORDER BY v <-> (SELECT ARRAY[$array_sql]::vector) LIMIT 10;",
"048_ivfflat_vacuum_insert_vacuum\@1" => "VACUUM tst;"
}
);
done_testing();

View File

@@ -1,4 +1,4 @@
comment = 'vector data type and ivfflat and hnsw access methods'
default_version = '0.8.5'
default_version = '0.8.2'
module_pathname = '$libdir/vector'
relocatable = true