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13 Commits

Author SHA1 Message Date
Andrew Kane
47c7fa715c Updated IndexAmRoutine [skip ci] 2026-06-10 11:50:45 -07:00
Andrew Kane
12368bd79c Updated readme [skip ci] 2026-05-30 11:58:10 -07:00
Andrew Kane
88a0085459 Improved casting [skip ci] 2026-05-26 14:09:19 -07:00
Andrew Kane
ea23884efd Ran latest pgindent [skip ci] 2026-05-26 13:39:55 -07:00
Andrew Kane
3351f3d43e Simplified comment [skip ci] 2026-05-26 13:39:20 -07:00
Andrew Kane
d238409bec Merge pull request #979 from xingtanzjr/fix_ivf_create_concurrently
Fix CREATE INDEX CONCURRENTLY crash on ivfflat with assertion-enabled PostgreSQL
2026-04-27 03:28:52 -07:00
Zhang Jinrui
529f37175b Fix CREATE INDEX CONCURRENTLY crash on ivfflat with assertion-enabled PostgreSQL by passing anyvisible=false in ivfflat SampleRows 2026-04-27 10:15:32 +00:00
Andrew Kane
bce3946392 Added link to PgHero [skip ci] 2026-04-27 02:11:17 -07:00
Andrew Kane
13cb253d30 Added link to PgDog [skip ci] 2026-04-27 00:09:20 -07:00
Andrew Kane
41b3cdc011 Updated readme [skip ci] 2026-04-26 14:47:17 -07:00
Andrew Kane
ec02a96239 Updated readme [skip ci] 2026-04-26 14:45:12 -07:00
Andrew Kane
610d95b8d2 Moved scaling section [skip ci] 2026-04-26 13:12:28 -07:00
Andrew Kane
609d01f4c6 Added more scaling advice to readme [skip ci] 2026-04-26 12:56:07 -07:00
12 changed files with 66 additions and 141 deletions

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@@ -11,6 +11,8 @@ Store your vectors with the rest of your data. Supports:
Plus [ACID](https://en.wikipedia.org/wiki/ACID) compliance, point-in-time recovery, JOINs, and all of the other [great features](https://www.postgresql.org/about/) of Postgres Plus [ACID](https://en.wikipedia.org/wiki/ACID) compliance, point-in-time recovery, JOINs, and all of the other [great features](https://www.postgresql.org/about/) of Postgres
Have a lot of vectors? Use [quantization](#scaling) to scale
[![Build Status](https://github.com/pgvector/pgvector/actions/workflows/build.yml/badge.svg)](https://github.com/pgvector/pgvector/actions) [![Build Status](https://github.com/pgvector/pgvector/actions/workflows/build.yml/badge.svg)](https://github.com/pgvector/pgvector/actions)
## Installation ## Installation
@@ -314,6 +316,8 @@ For a large number of workers, you may need to increase `max_parallel_workers` (
The [index options](#index-options) also have a significant impact on build time (use the defaults unless seeing low recall) The [index options](#index-options) also have a significant impact on build time (use the defaults unless seeing low recall)
Use [binary quantization](#binary-quantization) for faster build times at scale
### Indexing Progress ### Indexing Progress
Check [indexing progress](https://www.postgresql.org/docs/current/progress-reporting.html#CREATE-INDEX-PROGRESS-REPORTING) Check [indexing progress](https://www.postgresql.org/docs/current/progress-reporting.html#CREATE-INDEX-PROGRESS-REPORTING)
@@ -443,13 +447,7 @@ Exact indexes work well for conditions that match a low percentage of rows. Othe
CREATE INDEX ON items USING hnsw (embedding vector_l2_ops); CREATE INDEX ON items USING hnsw (embedding vector_l2_ops);
``` ```
With approximate indexes, filtering is applied *after* the index is scanned. If a condition matches 10% of rows, with HNSW and the default `hnsw.ef_search` of 40, only 4 rows will match on average. For more rows, increase `hnsw.ef_search`. With approximate indexes, filtering is applied *after* the index is scanned. If a condition matches 10% of rows, with HNSW and the default `hnsw.ef_search` of 40, only 4 rows will match on average. For more rows, enable [iterative index scans](#iterative-index-scans), which will automatically scan more of the index when needed.
```sql
SET hnsw.ef_search = 200;
```
Starting with 0.8.0, you can enable [iterative index scans](#iterative-index-scans), which will automatically scan more of the index when needed.
```sql ```sql
SET hnsw.iterative_scan = strict_order; SET hnsw.iterative_scan = strict_order;
@@ -673,6 +671,10 @@ SHOW shared_buffers;
Be sure to restart Postgres for changes to take effect. Be sure to restart Postgres for changes to take effect.
### Storing
Use the `halfvec` type instead of `vector` for a smaller working set.
### Loading ### Loading
Use `COPY` for bulk loading data ([example](https://github.com/pgvector/pgvector-python/blob/master/examples/loading/example.py)). Use `COPY` for bulk loading data ([example](https://github.com/pgvector/pgvector-python/blob/master/examples/loading/example.py)).
@@ -687,6 +689,8 @@ Add any indexes *after* loading the initial data for best performance.
See index build time for [HNSW](#index-build-time) and [IVFFlat](#index-build-time-1). See index build time for [HNSW](#index-build-time) and [IVFFlat](#index-build-time-1).
Use [binary quantization](#binary-quantization) for smaller indexes and faster build times at scale.
In production environments, create indexes concurrently to avoid blocking writes. In production environments, create indexes concurrently to avoid blocking writes.
```sql ```sql
@@ -717,6 +721,8 @@ SELECT * FROM items ORDER BY embedding <#> '[3,1,2]' LIMIT 5;
#### Approximate Search #### Approximate Search
Use [binary quantization](#binary-quantization) with re-ranking to keep indexes in-memory at scale.
To speed up queries with an IVFFlat index, increase the number of inverted lists (at the expense of recall). To speed up queries with an IVFFlat index, increase the number of inverted lists (at the expense of recall).
```sql ```sql
@@ -732,21 +738,20 @@ REINDEX INDEX CONCURRENTLY index_name;
VACUUM table_name; VACUUM table_name;
``` ```
## Scaling
For a smaller working set:
1. Use the `halfvec` type instead of `vector` for tables
2. Use [binary quantization](#binary-quantization) for indexes (with re-ranking for search)
Scale vertically by increasing memory, CPU, and storage on a single instance. Use existing tools to [tune parameters](#tuning) and [monitor performance](#monitoring).
Scale horizontally with [replicas](https://www.postgresql.org/docs/current/hot-standby.html), or use [Citus](https://github.com/citusdata/citus), [PgDog](https://github.com/pgdogdev/pgdog), or another approach for sharding ([example](https://github.com/pgvector/pgvector-python/blob/master/examples/citus/example.py)).
## Monitoring ## Monitoring
Monitor performance with [pg_stat_statements](https://www.postgresql.org/docs/current/pgstatstatements.html) (be sure to add it to `shared_preload_libraries`). Use existing tools like [pg_stat_statements](https://www.postgresql.org/docs/current/pgstatstatements.html) or [PgHero](https://github.com/ankane/pghero) to monitor performance.
```sql
CREATE EXTENSION pg_stat_statements;
```
Get the most time-consuming queries with:
```sql
SELECT query, calls, ROUND((total_plan_time + total_exec_time) / calls) AS avg_time_ms,
ROUND((total_plan_time + total_exec_time) / 60000) AS total_time_min
FROM pg_stat_statements ORDER BY total_plan_time + total_exec_time DESC LIMIT 20;
```
Monitor recall by comparing results from approximate search with exact search. Monitor recall by comparing results from approximate search with exact search.
@@ -757,14 +762,6 @@ SELECT ...
COMMIT; COMMIT;
``` ```
## Scaling
Scale pgvector the same way you scale Postgres.
Scale vertically by increasing memory, CPU, and storage on a single instance. Use existing tools to [tune parameters](#tuning) and [monitor performance](#monitoring).
Scale horizontally with [replicas](https://www.postgresql.org/docs/current/hot-standby.html), or use [Citus](https://github.com/citusdata/citus) or another approach for sharding ([example](https://github.com/pgvector/pgvector-python/blob/master/examples/citus/example.py)).
## Languages ## Languages
Use pgvector from any language with a Postgres client. You can even generate and store vectors in one language and query them in another. Use pgvector from any language with a Postgres client. You can even generate and store vectors in one language and query them in another.
@@ -878,6 +875,8 @@ No, but like other index types, youll likely see better performance if they d
SELECT pg_size_pretty(pg_relation_size('index_name')); SELECT pg_size_pretty(pg_relation_size('index_name'));
``` ```
Use [half-precision indexing](#half-precision-indexing) or [binary quantization](#binary-quantization) for smaller indexes.
## Troubleshooting ## Troubleshooting
#### Why isnt a query using an index? #### Why isnt a query using an index?

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@@ -279,6 +279,7 @@ hnswhandler(PG_FUNCTION_ARGS)
.amconsistentequality = false, .amconsistentequality = false,
.amconsistentordering = false, .amconsistentordering = false,
.amcanbackward = false, .amcanbackward = false,
.amcanmarkpos = false,
.amcanunique = false, .amcanunique = false,
.amcanmulticol = false, .amcanmulticol = false,
.amoptionalkey = true, .amoptionalkey = true,
@@ -312,10 +313,13 @@ hnswhandler(PG_FUNCTION_ARGS)
.ambeginscan = hnswbeginscan, .ambeginscan = hnswbeginscan,
.amrescan = hnswrescan, .amrescan = hnswrescan,
.amgettuple = hnswgettuple, .amgettuple = hnswgettuple,
.amgetbatch = NULL,
.amunguardbatch = NULL,
.amkillitemsbatch = NULL,
.amgettransform = NULL,
.amgetbitmap = NULL, .amgetbitmap = NULL,
.amendscan = hnswendscan, .amendscan = hnswendscan,
.ammarkpos = NULL, .amposreset = NULL,
.amrestrpos = NULL,
.amestimateparallelscan = NULL, .amestimateparallelscan = NULL,
.aminitparallelscan = NULL, .aminitparallelscan = NULL,
.amparallelrescan = NULL, .amparallelrescan = NULL,

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@@ -372,13 +372,6 @@ typedef union
ItemPointerData indextid; ItemPointerData indextid;
} HnswUnvisited; } HnswUnvisited;
typedef struct HnswReadStreamData
{
HnswUnvisited *unvisited;
int unvisitedLength;
int visited;
} HnswReadStreamData;
typedef struct HnswScanOpaqueData typedef struct HnswScanOpaqueData
{ {
const HnswTypeInfo *typeInfo; const HnswTypeInfo *typeInfo;
@@ -434,13 +427,13 @@ bool HnswCheckNorm(HnswSupport * support, Datum value);
Buffer HnswNewBuffer(Relation index, ForkNumber forkNum); Buffer HnswNewBuffer(Relation index, ForkNumber forkNum);
void HnswInitPage(Buffer buf, Page page); void HnswInitPage(Buffer buf, Page page);
void HnswInit(void); void HnswInit(void);
List *HnswSearchLayer(char *base, HnswQuery * q, List *ep, int ef, int lc, Relation index, HnswSupport * support, int m, bool inserting, HnswElement skipElement, visited_hash * v, pairingheap **discarded, bool initVisited, int64 *tuples, bool maintenance); List *HnswSearchLayer(char *base, HnswQuery * q, List *ep, int ef, int lc, Relation index, HnswSupport * support, int m, bool inserting, HnswElement skipElement, visited_hash * v, pairingheap **discarded, bool initVisited, int64 *tuples);
HnswElement HnswGetEntryPoint(Relation index); HnswElement HnswGetEntryPoint(Relation index);
void HnswGetMetaPageInfo(Relation index, int *m, HnswElement * entryPoint); void HnswGetMetaPageInfo(Relation index, int *m, HnswElement * entryPoint);
void *HnswAlloc(HnswAllocator * allocator, Size size); void *HnswAlloc(HnswAllocator * allocator, Size size);
HnswElement HnswInitElement(char *base, ItemPointer tid, int m, double ml, int maxLevel, HnswAllocator * alloc); HnswElement HnswInitElement(char *base, ItemPointer tid, int m, double ml, int maxLevel, HnswAllocator * alloc);
HnswElement HnswInitElementFromBlock(BlockNumber blkno, OffsetNumber offno); HnswElement HnswInitElementFromBlock(BlockNumber blkno, OffsetNumber offno);
void HnswFindElementNeighbors(char *base, HnswElement element, HnswElement entryPoint, Relation index, HnswSupport * support, int m, int efConstruction, bool existing, bool maintenance); void HnswFindElementNeighbors(char *base, HnswElement element, HnswElement entryPoint, Relation index, HnswSupport * support, int m, int efConstruction, bool existing);
HnswSearchCandidate *HnswEntryCandidate(char *base, HnswElement entryPoint, HnswQuery * q, Relation index, HnswSupport * support, bool loadVec); HnswSearchCandidate *HnswEntryCandidate(char *base, HnswElement entryPoint, HnswQuery * q, Relation index, HnswSupport * support, bool loadVec);
void HnswUpdateMetaPage(Relation index, int updateEntry, HnswElement entryPoint, BlockNumber insertPage, ForkNumber forkNum, bool building); void HnswUpdateMetaPage(Relation index, int updateEntry, HnswElement entryPoint, BlockNumber insertPage, ForkNumber forkNum, bool building);
void HnswSetNeighborTuple(char *base, HnswNeighborTuple ntup, HnswElement e, int m); void HnswSetNeighborTuple(char *base, HnswNeighborTuple ntup, HnswElement e, int m);

View File

@@ -470,7 +470,7 @@ InsertTupleInMemory(HnswBuildState * buildstate, HnswElement element)
} }
/* Find neighbors for element */ /* Find neighbors for element */
HnswFindElementNeighbors(base, element, entryPoint, NULL, support, m, efConstruction, false, true); HnswFindElementNeighbors(base, element, entryPoint, NULL, support, m, efConstruction, false);
/* Update graph in memory */ /* Update graph in memory */
UpdateGraphInMemory(support, element, m, entryPoint, buildstate); UpdateGraphInMemory(support, element, m, entryPoint, buildstate);

View File

@@ -731,7 +731,7 @@ HnswInsertTupleOnDisk(Relation index, HnswSupport * support, Datum value, ItemPo
} }
/* Find neighbors for element */ /* Find neighbors for element */
HnswFindElementNeighbors(base, element, entryPoint, index, support, m, efConstruction, false, building); HnswFindElementNeighbors(base, element, entryPoint, index, support, m, efConstruction, false);
/* Update graph on disk */ /* Update graph on disk */
UpdateGraphOnDisk(index, support, element, m, entryPoint, building); UpdateGraphOnDisk(index, support, element, m, entryPoint, building);

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@@ -48,11 +48,11 @@ GetScanItems(IndexScanDesc scan, Datum value)
for (int lc = entryPoint->level; lc >= 1; lc--) for (int lc = entryPoint->level; lc >= 1; lc--)
{ {
w = HnswSearchLayer(base, q, ep, 1, lc, index, support, m, false, NULL, NULL, NULL, true, NULL, false); w = HnswSearchLayer(base, q, ep, 1, lc, index, support, m, false, NULL, NULL, NULL, true, NULL);
ep = w; ep = w;
} }
return HnswSearchLayer(base, q, ep, hnsw_ef_search, 0, index, support, m, false, NULL, &so->v, hnsw_iterative_scan != HNSW_ITERATIVE_SCAN_OFF ? &so->discarded : NULL, true, &so->tuples, false); return HnswSearchLayer(base, q, ep, hnsw_ef_search, 0, index, support, m, false, NULL, &so->v, hnsw_iterative_scan != HNSW_ITERATIVE_SCAN_OFF ? &so->discarded : NULL, true, &so->tuples);
} }
/* /*
@@ -83,7 +83,7 @@ ResumeScanItems(IndexScanDesc scan)
ep = lappend(ep, sc); ep = lappend(ep, sc);
} }
return HnswSearchLayer(base, &so->q, ep, batch_size, 0, index, &so->support, so->m, false, NULL, &so->v, &so->discarded, false, &so->tuples, false); return HnswSearchLayer(base, &so->q, ep, batch_size, 0, index, &so->support, so->m, false, NULL, &so->v, &so->discarded, false, &so->tuples);
} }
/* /*

View File

@@ -21,10 +21,6 @@
#include "varatt.h" #include "varatt.h"
#endif #endif
#if PG_VERSION_NUM >= 190000
#include "storage/read_stream.h"
#endif
#if PG_VERSION_NUM < 170000 #if PG_VERSION_NUM < 170000
static inline uint64 static inline uint64
murmurhash64(uint64 data) murmurhash64(uint64 data)
@@ -535,12 +531,14 @@ HnswGetDistance(Datum a, Datum b, HnswSupport * support)
* Load an element and optionally get its distance from q * Load an element and optionally get its distance from q
*/ */
static void static void
HnswLoadElementImpl(Buffer buf, OffsetNumber offno, double *distance, HnswQuery * q, Relation index, HnswSupport * support, bool loadVec, double *maxDistance, HnswElement * element) HnswLoadElementImpl(BlockNumber blkno, OffsetNumber offno, double *distance, HnswQuery * q, Relation index, HnswSupport * support, bool loadVec, double *maxDistance, HnswElement * element)
{ {
Buffer buf;
Page page; Page page;
HnswElementTuple etup; HnswElementTuple etup;
/* Read vector */ /* Read vector */
buf = ReadBuffer(index, blkno);
LockBuffer(buf, BUFFER_LOCK_SHARE); LockBuffer(buf, BUFFER_LOCK_SHARE);
page = BufferGetPage(buf); page = BufferGetPage(buf);
@@ -561,7 +559,7 @@ HnswLoadElementImpl(Buffer buf, OffsetNumber offno, double *distance, HnswQuery
if (distance == NULL || maxDistance == NULL || *distance < *maxDistance) if (distance == NULL || maxDistance == NULL || *distance < *maxDistance)
{ {
if (*element == NULL) if (*element == NULL)
*element = HnswInitElementFromBlock(BufferGetBlockNumber(buf), offno); *element = HnswInitElementFromBlock(blkno, offno);
HnswLoadElementFromTuple(*element, etup, true, loadVec); HnswLoadElementFromTuple(*element, etup, true, loadVec);
} }
@@ -575,9 +573,7 @@ HnswLoadElementImpl(Buffer buf, OffsetNumber offno, double *distance, HnswQuery
void void
HnswLoadElement(HnswElement element, double *distance, HnswQuery * q, Relation index, HnswSupport * support, bool loadVec, double *maxDistance) HnswLoadElement(HnswElement element, double *distance, HnswQuery * q, Relation index, HnswSupport * support, bool loadVec, double *maxDistance)
{ {
Buffer buf = ReadBuffer(index, element->blkno); HnswLoadElementImpl(element->blkno, element->offno, distance, q, index, support, loadVec, maxDistance, &element);
HnswLoadElementImpl(buf, element->offno, distance, q, index, support, loadVec, maxDistance, &element);
} }
/* /*
@@ -817,31 +813,11 @@ HnswLoadUnvisitedFromDisk(HnswElement element, HnswUnvisited * unvisited, int *u
} }
} }
#if PG_VERSION_NUM >= 190000
/*
* Get next block number for read stream
*/
static BlockNumber
HnswReadStreamNextBlock(ReadStream *stream, void *callback_private_data, void *per_buffer_data)
{
HnswReadStreamData *streamData = callback_private_data;
OffsetNumber *offno = per_buffer_data;
HnswUnvisited *uv;
if (streamData->visited == streamData->unvisitedLength)
return InvalidBlockNumber;
uv = &streamData->unvisited[streamData->visited++];
*offno = ItemPointerGetOffsetNumber(&uv->indextid);
return ItemPointerGetBlockNumber(&uv->indextid);
}
#endif
/* /*
* Algorithm 2 from paper * Algorithm 2 from paper
*/ */
List * List *
HnswSearchLayer(char *base, HnswQuery * q, List *ep, int ef, int lc, Relation index, HnswSupport * support, int m, bool inserting, HnswElement skipElement, visited_hash * v, pairingheap **discarded, bool initVisited, int64 *tuples, bool maintenance) HnswSearchLayer(char *base, HnswQuery * q, List *ep, int ef, int lc, Relation index, HnswSupport * support, int m, bool inserting, HnswElement skipElement, visited_hash * v, pairingheap **discarded, bool initVisited, int64 *tuples)
{ {
List *w = NIL; List *w = NIL;
pairingheap *C = pairingheap_allocate(CompareNearestCandidates, NULL); pairingheap *C = pairingheap_allocate(CompareNearestCandidates, NULL);
@@ -856,21 +832,6 @@ HnswSearchLayer(char *base, HnswQuery * q, List *ep, int ef, int lc, Relation in
int unvisitedLength; int unvisitedLength;
bool inMemory = index == NULL; bool inMemory = index == NULL;
#if PG_VERSION_NUM >= 190000
HnswReadStreamData streamData;
ReadStream *stream = NULL;
if (!inMemory)
{
int flags = READ_STREAM_USE_BATCHING;
if (maintenance)
flags |= READ_STREAM_MAINTENANCE;
stream = read_stream_begin_relation(flags, NULL, index, MAIN_FORKNUM, HnswReadStreamNextBlock, &streamData, sizeof(OffsetNumber));
}
#endif
if (v == NULL) if (v == NULL)
{ {
v = &vh; v = &vh;
@@ -933,23 +894,13 @@ HnswSearchLayer(char *base, HnswQuery * q, List *ep, int ef, int lc, Relation in
if (inMemory) if (inMemory)
HnswLoadUnvisitedFromMemory(base, cElement, unvisited, &unvisitedLength, v, lc, localNeighborhood, neighborhoodSize); HnswLoadUnvisitedFromMemory(base, cElement, unvisited, &unvisitedLength, v, lc, localNeighborhood, neighborhoodSize);
else else
{
HnswLoadUnvisitedFromDisk(cElement, unvisited, &unvisitedLength, v, index, m, lm, lc); HnswLoadUnvisitedFromDisk(cElement, unvisited, &unvisitedLength, v, index, m, lm, lc);
#if PG_VERSION_NUM >= 190000
read_stream_resume(stream);
streamData.unvisited = unvisited;
streamData.unvisitedLength = unvisitedLength;
streamData.visited = 0;
#endif
}
/* OK to count elements instead of tuples */ /* OK to count elements instead of tuples */
if (tuples != NULL) if (tuples != NULL)
(*tuples) += unvisitedLength; (*tuples) += unvisitedLength;
for (int i = 0;; i++) for (int i = 0; i < unvisitedLength; i++)
{ {
HnswElement eElement; HnswElement eElement;
HnswSearchCandidate *e; HnswSearchCandidate *e;
@@ -960,40 +911,18 @@ HnswSearchLayer(char *base, HnswQuery * q, List *ep, int ef, int lc, Relation in
if (inMemory) if (inMemory)
{ {
if (i == unvisitedLength)
break;
eElement = unvisited[i].element; eElement = unvisited[i].element;
eDistance = GetElementDistance(base, eElement, q, support); eDistance = GetElementDistance(base, eElement, q, support);
} }
else else
{ {
Buffer buf; ItemPointer indextid = &unvisited[i].indextid;
OffsetNumber offno; BlockNumber blkno = ItemPointerGetBlockNumber(indextid);
OffsetNumber offno = ItemPointerGetOffsetNumber(indextid);
#if PG_VERSION_NUM >= 190000
void *offnoPtr;
buf = read_stream_next_buffer(stream, &offnoPtr);
if (!BufferIsValid(buf))
break;
offno = *((OffsetNumber *) offnoPtr);
#else
ItemPointer indextid;
if (i == unvisitedLength)
break;
indextid = &unvisited[i].indextid;
buf = ReadBuffer(index, ItemPointerGetBlockNumber(indextid));
offno = ItemPointerGetOffsetNumber(indextid);
#endif
/* Avoid any allocations if not adding */ /* Avoid any allocations if not adding */
eElement = NULL; eElement = NULL;
HnswLoadElementImpl(buf, offno, &eDistance, q, index, support, inserting, alwaysAdd || discarded != NULL ? NULL : &f->distance, &eElement); HnswLoadElementImpl(blkno, offno, &eDistance, q, index, support, inserting, alwaysAdd || discarded != NULL ? NULL : &f->distance, &eElement);
if (eElement == NULL) if (eElement == NULL)
continue; continue;
@@ -1049,11 +978,6 @@ HnswSearchLayer(char *base, HnswQuery * q, List *ep, int ef, int lc, Relation in
w = lappend(w, sc); w = lappend(w, sc);
} }
#if PG_VERSION_NUM >= 190000
if (!inMemory)
read_stream_end(stream);
#endif
return w; return w;
} }
@@ -1349,7 +1273,7 @@ PrecomputeHash(char *base, HnswElement element)
* Algorithm 1 from paper * Algorithm 1 from paper
*/ */
void void
HnswFindElementNeighbors(char *base, HnswElement element, HnswElement entryPoint, Relation index, HnswSupport * support, int m, int efConstruction, bool existing, bool maintenance) HnswFindElementNeighbors(char *base, HnswElement element, HnswElement entryPoint, Relation index, HnswSupport * support, int m, int efConstruction, bool existing)
{ {
List *ep; List *ep;
List *w; List *w;
@@ -1376,7 +1300,7 @@ HnswFindElementNeighbors(char *base, HnswElement element, HnswElement entryPoint
/* 1st phase: greedy search to insert level */ /* 1st phase: greedy search to insert level */
for (int lc = entryLevel; lc >= level + 1; lc--) for (int lc = entryLevel; lc >= level + 1; lc--)
{ {
w = HnswSearchLayer(base, &q, ep, 1, lc, index, support, m, true, skipElement, NULL, NULL, true, NULL, maintenance); w = HnswSearchLayer(base, &q, ep, 1, lc, index, support, m, true, skipElement, NULL, NULL, true, NULL);
ep = w; ep = w;
} }
@@ -1395,7 +1319,7 @@ HnswFindElementNeighbors(char *base, HnswElement element, HnswElement entryPoint
List *lw = NIL; List *lw = NIL;
ListCell *lc2; ListCell *lc2;
w = HnswSearchLayer(base, &q, ep, efConstruction, lc, index, support, m, true, skipElement, NULL, NULL, true, NULL, maintenance); w = HnswSearchLayer(base, &q, ep, efConstruction, lc, index, support, m, true, skipElement, NULL, NULL, true, NULL);
/* Convert search candidates to candidates */ /* Convert search candidates to candidates */
foreach(lc2, w) foreach(lc2, w)

View File

@@ -218,7 +218,7 @@ RepairGraphElement(HnswVacuumState * vacuumstate, HnswElement element, HnswEleme
element->heaptidsLength = 0; element->heaptidsLength = 0;
/* Find neighbors for element, skipping itself */ /* Find neighbors for element, skipping itself */
HnswFindElementNeighbors(base, element, entryPoint, index, support, m, efConstruction, true, true); HnswFindElementNeighbors(base, element, entryPoint, index, support, m, efConstruction, true);
/* Zero memory for each element */ /* Zero memory for each element */
MemSet(ntup, 0, HNSW_TUPLE_ALLOC_SIZE); MemSet(ntup, 0, HNSW_TUPLE_ALLOC_SIZE);

View File

@@ -145,8 +145,9 @@ SampleRows(IvfflatBuildState * buildstate)
{ {
BlockNumber targblock = BlockSampler_Next(&buildstate->bs); BlockNumber targblock = BlockSampler_Next(&buildstate->bs);
/* Set anyvisible to false like table_index_build_scan */
table_index_build_range_scan(buildstate->heap, buildstate->index, buildstate->indexInfo, table_index_build_range_scan(buildstate->heap, buildstate->index, buildstate->indexInfo,
false, true, false, targblock, 1, SampleCallback, (void *) buildstate, NULL); false, false, false, targblock, 1, SampleCallback, (void *) buildstate, NULL);
} }
/* Normalize if needed */ /* Normalize if needed */

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@@ -196,6 +196,7 @@ ivfflathandler(PG_FUNCTION_ARGS)
.amconsistentequality = false, .amconsistentequality = false,
.amconsistentordering = false, .amconsistentordering = false,
.amcanbackward = false, .amcanbackward = false,
.amcanmarkpos = false,
.amcanunique = false, .amcanunique = false,
.amcanmulticol = false, .amcanmulticol = false,
.amoptionalkey = true, .amoptionalkey = true,
@@ -229,10 +230,13 @@ ivfflathandler(PG_FUNCTION_ARGS)
.ambeginscan = ivfflatbeginscan, .ambeginscan = ivfflatbeginscan,
.amrescan = ivfflatrescan, .amrescan = ivfflatrescan,
.amgettuple = ivfflatgettuple, .amgettuple = ivfflatgettuple,
.amgetbatch = NULL,
.amunguardbatch = NULL,
.amkillitemsbatch = NULL,
.amgettransform = NULL,
.amgetbitmap = NULL, .amgetbitmap = NULL,
.amendscan = ivfflatendscan, .amendscan = ivfflatendscan,
.ammarkpos = NULL, .amposreset = NULL,
.amrestrpos = NULL,
.amestimateparallelscan = NULL, .amestimateparallelscan = NULL,
.aminitparallelscan = NULL, .aminitparallelscan = NULL,
.amparallelrescan = NULL, .amparallelrescan = NULL,

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@@ -182,10 +182,10 @@ sparsevec_isspace(char ch)
static int static int
CompareIndices(const void *a, const void *b) CompareIndices(const void *a, const void *b)
{ {
if (((SparseInputElement *) a)->index < ((SparseInputElement *) b)->index) if (((const SparseInputElement *) a)->index < ((const SparseInputElement *) b)->index)
return -1; return -1;
if (((SparseInputElement *) a)->index > ((SparseInputElement *) b)->index) if (((const SparseInputElement *) a)->index > ((const SparseInputElement *) b)->index)
return 1; return 1;
return 0; return 0;

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