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Author SHA1 Message Date
Andrew Kane
bb72a6c063 Test BufferAccessStrategy [skip ci] 2023-04-10 22:07:58 -07:00
5 changed files with 74 additions and 106 deletions

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@@ -1,7 +1,6 @@
## 0.4.2 (unreleased)
- Added notice when index created with little data
- Fixed installation error with Postgres 12.0-12.2
## 0.4.1 (2023-03-21)

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@@ -2,11 +2,7 @@
Open-source vector similarity search for Postgres
Supports
- exact and approximate nearest neighbor search
- L2 distance, inner product, and cosine distance
- any [language](#languages) with a Postgres client
Supports exact and approximate nearest neighbor search for L2 distance, inner product, and cosine distance
[![Build Status](https://github.com/pgvector/pgvector/workflows/build/badge.svg?branch=master)](https://github.com/pgvector/pgvector/actions)
@@ -40,7 +36,7 @@ Create a vector column with 3 dimensions
CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3));
```
Insert vectors
Insert values
```sql
INSERT INTO items (embedding) VALUES ('[1,2,3]'), ('[4,5,6]');
@@ -128,7 +124,7 @@ SELECT embedding <-> '[3,1,2]' AS distance FROM items;
For inner product, multiply by -1 (since `<#>` returns the negative inner product)
```sql
SELECT (embedding <#> '[3,1,2]') * -1 AS inner_product FROM items;
SELECT -1 * (embedding <#> '[3,1,2]') AS inner_product FROM items;
```
For cosine similarity, use 1 - cosine distance
@@ -137,7 +133,7 @@ For cosine similarity, use 1 - cosine distance
SELECT 1 - (embedding <=> '[3,1,2]') AS cosine_similarity FROM items;
```
#### Aggregates
#### Averaging
Average vectors
@@ -160,8 +156,8 @@ You can add an index to use approximate nearest neighbor search, which trades so
Three keys to achieving good recall are:
1. Create the index *after* the table has some data
2. Choose an appropriate number of lists - a good place to start is `rows / 1000` for up to 1M rows and `sqrt(rows)` for over 1M rows
3. When querying, specify an appropriate number of [probes](#query-options) (higher is better for recall, lower is better for speed) - a good place to start is `lists / 10` for up to 1M rows and `sqrt(lists)` for over 1M rows
2. Choose an appropriate number of lists (a good place to start is `rows / 1000` for up to 1M rows and `sqrt(rows)` for over 1M rows)
3. When querying, specify an appropriate number of [probes](#query-options) (higher is better for recall, lower is better for speed)
Add an index for each distance function you want to use.
@@ -279,10 +275,8 @@ Language | Libraries / Examples
--- | ---
C++ | [pgvector-cpp](https://github.com/pgvector/pgvector-cpp)
C# | [pgvector-dotnet](https://github.com/pgvector/pgvector-dotnet)
Crystal | [pgvector-crystal](https://github.com/pgvector/pgvector-crystal)
Elixir | [pgvector-elixir](https://github.com/pgvector/pgvector-elixir)
Go | [pgvector-go](https://github.com/pgvector/pgvector-go)
Haskell | [pgvector-haskell](https://github.com/pgvector/pgvector-haskell)
Java, Scala | [pgvector-java](https://github.com/pgvector/pgvector-java)
Julia | [pgvector-julia](https://github.com/pgvector/pgvector-julia)
Lua | [pgvector-lua](https://github.com/pgvector/pgvector-lua)
@@ -293,7 +287,6 @@ Python | [pgvector-python](https://github.com/pgvector/pgvector-python)
R | [pgvector-r](https://github.com/pgvector/pgvector-r)
Ruby | [pgvector-ruby](https://github.com/pgvector/pgvector-ruby), [Neighbor](https://github.com/ankane/neighbor)
Rust | [pgvector-rust](https://github.com/pgvector/pgvector-rust)
Swift | [pgvector-swift](https://github.com/pgvector/pgvector-swift)
## Frequently Asked Questions
@@ -448,7 +441,7 @@ To request a new extension on other providers:
- Google Cloud SQL - vote or comment on [this page](https://issuetracker.google.com/issues/265172065)
- Azure Database - vote or comment on [this page](https://feedback.azure.com/d365community/idea/7b423322-6189-ed11-a81b-000d3ae49307)
- DigitalOcean Managed Databases - vote or comment on [this page](https://ideas.digitalocean.com/app-framework-services/p/pgvector-extension-for-postgresql)
- Heroku Postgres - vote or comment on [this page](https://github.com/heroku/roadmap/issues/156)
- Render - vote or comment on [this page](https://feedback.render.com/features/p/add-pgvector-extension-to-postgresql)
## Upgrading

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@@ -187,14 +187,6 @@ typedef struct IvfflatScanList
double distance;
} IvfflatScanList;
typedef struct IvfflatScanItem
{
pairingheap_node ph_node;
BlockNumber searchPage;
double distance;
ItemPointerData tid;
} IvfflatScanItem;
typedef struct IvfflatScanOpaqueData
{
int probes;
@@ -212,13 +204,6 @@ typedef struct IvfflatScanOpaqueData
FmgrInfo *normprocinfo;
Oid collation;
/* Items */
int maxItems;
int itemCount;
pairingheap *itemQueue;
IvfflatScanItem *items;
IvfflatScanItem **sortedItems;
/* Lists */
pairingheap *listQueue;
IvfflatScanList lists[FLEXIBLE_ARRAY_MEMBER]; /* must come last */

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@@ -26,21 +26,6 @@ CompareLists(const pairingheap_node *a, const pairingheap_node *b, void *arg)
return 0;
}
/*
* Compare item distances
*/
static int
CompareItems(const pairingheap_node *a, const pairingheap_node *b, void *arg)
{
if (((const IvfflatScanItem *) a)->distance > ((const IvfflatScanItem *) b)->distance)
return 1;
if (((const IvfflatScanItem *) a)->distance < ((const IvfflatScanItem *) b)->distance)
return -1;
return 0;
}
/*
* Get lists and sort by distance
*/
@@ -126,17 +111,20 @@ GetScanItems(IndexScanDesc scan, Datum value)
Datum datum;
bool isnull;
TupleDesc tupdesc = RelationGetDescr(scan->indexRelation);
int i;
double distance;
IvfflatScanItem *scanitem;
double maxDistance = DBL_MAX;
double tuples = 0;
#if PG_VERSION_NUM >= 120000
TupleTableSlot *slot = MakeSingleTupleTableSlot(so->tupdesc, &TTSOpsVirtual);
#else
TupleTableSlot *slot = MakeSingleTupleTableSlot(so->tupdesc);
#endif
/*
* Reuse same set of shared buffers for scan
*
* See postgres/src/backend/storage/buffer/README for description
*/
BufferAccessStrategy bas = GetAccessStrategy(BAS_BULKREAD);
BufferAccessStrategy bas = GetAccessStrategy(BAS_NORMAL);
/* Search closest probes lists */
while (!pairingheap_is_empty(so->listQueue))
@@ -155,40 +143,25 @@ GetScanItems(IndexScanDesc scan, Datum value)
{
itup = (IndexTuple) PageGetItem(page, PageGetItemId(page, offno));
datum = index_getattr(itup, 1, tupdesc, &isnull);
distance = DatumGetFloat8(FunctionCall2Coll(so->procinfo, so->collation, datum, value));
if (so->itemCount < so->maxItems)
{
scanitem = &so->items[so->itemCount];
scanitem->searchPage = searchPage;
scanitem->tid = itup->t_tid;
scanitem->distance = distance;
so->itemCount++;
/*
* Add virtual tuple
*
* Use procinfo from the index instead of scan key for
* performance
*/
ExecClearTuple(slot);
slot->tts_values[0] = FunctionCall2Coll(so->procinfo, so->collation, 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);
/* Add to heap */
pairingheap_add(so->itemQueue, &scanitem->ph_node);
tuplesort_puttupleslot(so->sortstate, slot);
/* Calculate max distance */
if (so->itemCount == so->maxItems)
{
maxDistance = ((IvfflatScanItem *) pairingheap_first(so->itemQueue))->distance;
scanitem = &so->items[so->itemCount];
}
}
else if (distance < maxDistance)
{
/* Reuse */
scanitem->searchPage = searchPage;
scanitem->tid = itup->t_tid;
scanitem->distance = distance;
pairingheap_add(so->itemQueue, &scanitem->ph_node);
/* Remove */
scanitem = (IvfflatScanItem *) pairingheap_remove_first(so->itemQueue);
/* Update max distance */
maxDistance = ((IvfflatScanItem *) pairingheap_first(so->itemQueue))->distance;
}
tuples++;
}
searchPage = IvfflatPageGetOpaque(page)->nextblkno;
@@ -197,10 +170,14 @@ GetScanItems(IndexScanDesc scan, Datum value)
}
}
for (i = 0; i < so->itemCount; i++)
so->sortedItems[i] = (IvfflatScanItem *) pairingheap_remove_first(so->itemQueue);
/* TODO Scan more lists */
if (tuples < 100)
ereport(DEBUG1,
(errmsg("index scan found few tuples"),
errdetail("index may have been created without data or lists is too high"),
errhint("recreate the index and possibly decrease lists")));
Assert(pairingheap_is_empty(so->itemQueue));
tuplesort_performsort(so->sortstate);
}
/*
@@ -212,6 +189,10 @@ ivfflatbeginscan(Relation index, int nkeys, int norderbys)
IndexScanDesc scan;
IvfflatScanOpaque so;
int lists;
AttrNumber attNums[] = {1};
Oid sortOperators[] = {Float8LessOperator};
Oid sortCollations[] = {InvalidOid};
bool nullsFirstFlags[] = {false};
int probes = ivfflat_probes;
scan = RelationGetIndexScan(index, nkeys, norderbys);
@@ -230,13 +211,26 @@ ivfflatbeginscan(Relation index, int nkeys, int norderbys)
so->normprocinfo = IvfflatOptionalProcInfo(index, IVFFLAT_NORM_PROC);
so->collation = index->rd_indcollation[0];
so->listQueue = pairingheap_allocate(CompareLists, scan);
/* Create tuple description for sorting */
#if PG_VERSION_NUM >= 120000
so->tupdesc = CreateTemplateTupleDesc(3);
#else
so->tupdesc = CreateTemplateTupleDesc(3, false);
#endif
TupleDescInitEntry(so->tupdesc, (AttrNumber) 1, "distance", FLOAT8OID, -1, 0);
TupleDescInitEntry(so->tupdesc, (AttrNumber) 2, "tid", TIDOID, -1, 0);
TupleDescInitEntry(so->tupdesc, (AttrNumber) 3, "indexblkno", INT4OID, -1, 0);
so->maxItems = 10;
so->itemCount = 0;
so->itemQueue = pairingheap_allocate(CompareItems, scan);
so->items = palloc(sizeof(IvfflatScanItem) * (so->maxItems + 1));
so->sortedItems = palloc(sizeof(IvfflatScanItem *) * so->maxItems);
/* Prep sort */
so->sortstate = tuplesort_begin_heap(so->tupdesc, 1, attNums, sortOperators, sortCollations, nullsFirstFlags, work_mem, NULL, false);
#if PG_VERSION_NUM >= 120000
so->slot = MakeSingleTupleTableSlot(so->tupdesc, &TTSOpsMinimalTuple);
#else
so->slot = MakeSingleTupleTableSlot(so->tupdesc);
#endif
so->listQueue = pairingheap_allocate(CompareLists, scan);
scan->opaque = so;
@@ -251,10 +245,13 @@ ivfflatrescan(IndexScanDesc scan, ScanKey keys, int nkeys, ScanKey orderbys, int
{
IvfflatScanOpaque so = (IvfflatScanOpaque) scan->opaque;
#if PG_VERSION_NUM >= 130000
if (!so->first)
tuplesort_reset(so->sortstate);
#endif
so->first = true;
pairingheap_reset(so->listQueue);
pairingheap_reset(so->itemQueue);
so->itemCount = 0;
if (keys && scan->numberOfKeys > 0)
memmove(scan->keyData, keys, scan->numberOfKeys * sizeof(ScanKeyData));
@@ -314,18 +311,15 @@ ivfflatgettuple(IndexScanDesc scan, ScanDirection dir)
pfree(DatumGetPointer(value));
}
if (so->itemCount > 0)
if (tuplesort_gettupleslot(so->sortstate, true, false, so->slot, NULL))
{
IvfflatScanItem *scanitem;
so->itemCount--;
scanitem = so->sortedItems[so->itemCount];
ItemPointer tid = (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 = scanitem->tid;
scan->xs_heaptid = *tid;
#else
scan->xs_ctup.t_self = scanitem->tid;
scan->xs_ctup.t_self = *tid;
#endif
if (BufferIsValid(so->buf))
@@ -337,7 +331,7 @@ ivfflatgettuple(IndexScanDesc scan, ScanDirection dir)
*
* https://www.postgresql.org/docs/current/index-locking.html
*/
so->buf = ReadBuffer(scan->indexRelation, scanitem->searchPage);
so->buf = ReadBuffer(scan->indexRelation, indexblkno);
scan->xs_recheckorderby = false;
return true;
@@ -359,10 +353,7 @@ ivfflatendscan(IndexScanDesc scan)
ReleaseBuffer(so->buf);
pairingheap_free(so->listQueue);
pairingheap_free(so->itemQueue);
pfree(so->items);
pfree(so->sortedItems);
tuplesort_end(so->sortstate);
pfree(so);
scan->opaque = NULL;

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@@ -100,7 +100,7 @@ CheckStateArray(ArrayType *statearray, const char *caller)
return (float8 *) ARR_DATA_PTR(statearray);
}
#if PG_VERSION_NUM < 120003
#if PG_VERSION_NUM < 120000
static pg_noinline void
float_overflow_error(void)
{