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Author SHA1 Message Date
Andrew Kane
64223989cd Use List for samples 2023-10-16 15:32:51 -07:00
17 changed files with 158 additions and 713 deletions

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@@ -8,8 +8,6 @@ jobs:
fail-fast: false
matrix:
include:
- postgres: 17
os: ubuntu-22.04
- postgres: 16
os: ubuntu-22.04
- postgres: 15
@@ -23,7 +21,7 @@ jobs:
- postgres: 11
os: ubuntu-20.04
steps:
- uses: actions/checkout@v4
- uses: actions/checkout@v3
- uses: ankane/setup-postgres@v1
with:
postgres-version: ${{ matrix.postgres }}
@@ -45,7 +43,7 @@ jobs:
runs-on: macos-latest
if: ${{ !startsWith(github.ref_name, 'windows') }}
steps:
- uses: actions/checkout@v4
- uses: actions/checkout@v3
- uses: ankane/setup-postgres@v1
with:
postgres-version: 14
@@ -67,7 +65,7 @@ jobs:
runs-on: windows-latest
if: ${{ !startsWith(github.ref_name, 'mac') }}
steps:
- uses: actions/checkout@v4
- uses: actions/checkout@v3
- uses: ankane/setup-postgres@v1
with:
postgres-version: 14

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@@ -1,7 +1,3 @@
## 0.5.2 (unreleased)
- Added support for on-disk parallel index builds for HNSW
## 0.5.1 (2023-10-10)
- Improved performance of HNSW index builds

View File

@@ -56,7 +56,7 @@ install:
copy $(EXTENSION).control "$(SHAREDIR)\extension"
copy sql\$(EXTENSION)--*.sql "$(SHAREDIR)\extension"
mkdir "$(INCLUDEDIR_SERVER)\extension\$(EXTENSION)"
for %f in ($(HEADERS)) do copy %f "$(INCLUDEDIR_SERVER)\extension\$(EXTENSION)"
copy $(HEADERS) "$(INCLUDEDIR_SERVER)\extension\$(EXTENSION)"
installcheck:
"$(BINDIR)\pg_regress" --bindir="$(BINDIR)" $(REGRESS_OPTS) $(REGRESS)

105
README.md
View File

@@ -26,7 +26,7 @@ make install # may need sudo
See the [installation notes](#installation-notes) if you run into issues
You can also install it with [Docker](#docker), [Homebrew](#homebrew), [PGXN](#pgxn), [APT](#apt), [Yum](#yum), or [conda-forge](#conda-forge), and it comes preinstalled with [Postgres.app](#postgresapp) and many [hosted providers](#hosted-postgres). There are also instructions for [GitHub Actions](https://github.com/pgvector/setup-pgvector).
You can also install it with [Docker](#docker), [Homebrew](#homebrew), [PGXN](#pgxn), [APT](#apt), [Yum](#yum), or [conda-forge](#conda-forge), and it comes preinstalled with [Postgres.app](#postgresapp) and many [hosted providers](#hosted-postgres)
## Getting Started
@@ -215,23 +215,6 @@ SELECT ...
COMMIT;
```
### Indexing Progress
Check [indexing progress](https://www.postgresql.org/docs/current/progress-reporting.html#CREATE-INDEX-PROGRESS-REPORTING) with Postgres 12+
```sql
SELECT phase, round(100.0 * tuples_done / nullif(tuples_total, 0), 1) AS "%" FROM pg_stat_progress_create_index;
```
The phases for IVFFlat are:
1. `initializing`
2. `performing k-means`
3. `assigning tuples`
4. `loading tuples`
Note: `%` is only populated during the `loading tuples` phase
## HNSW
An HNSW index creates a multilayer graph. It has slower build times and uses more memory than IVFFlat, but has better query performance (in terms of speed-recall tradeoff). Theres no training step like IVFFlat, so the index can be created without any data in the table.
@@ -288,18 +271,22 @@ SELECT ...
COMMIT;
```
### Indexing Progress
## Indexing Progress
Check [indexing progress](https://www.postgresql.org/docs/current/progress-reporting.html#CREATE-INDEX-PROGRESS-REPORTING) with Postgres 12+
```sql
SELECT phase, round(100.0 * blocks_done / nullif(blocks_total, 0), 1) AS "%" FROM pg_stat_progress_create_index;
SELECT phase, tuples_done, tuples_total FROM pg_stat_progress_create_index;
```
The phases for HNSW are:
The phases are:
1. `initializing`
2. `loading tuples`
2. `performing k-means` - IVFFlat only
3. `assigning tuples` - IVFFlat only
4. `loading tuples`
Note: `tuples_done` and `tuples_total` are only populated during the `loading tuples` phase
## Filtering
@@ -330,15 +317,13 @@ CREATE TABLE items (embedding vector(3), category_id int) PARTITION BY LIST(cate
## Hybrid Search
Use together with Postgres [full-text search](https://www.postgresql.org/docs/current/textsearch-intro.html) for hybrid search.
Use together with Postgres [full-text search](https://www.postgresql.org/docs/current/textsearch-intro.html) for hybrid search ([Python example](https://github.com/pgvector/pgvector-python/blob/master/examples/hybrid_search.py)).
```sql
SELECT id, content FROM items, plainto_tsquery('hello search') query
WHERE textsearch @@ query ORDER BY ts_rank_cd(textsearch, query) DESC LIMIT 5;
```
You can use [Reciprocal Rank Fusion](https://github.com/pgvector/pgvector-python/blob/master/examples/hybrid_search_rrf.py) or a [cross-encoder](https://github.com/pgvector/pgvector-python/blob/master/examples/hybrid_search.py) to combine results.
## Performance
Use `EXPLAIN ANALYZE` to debug performance.
@@ -375,7 +360,6 @@ Use pgvector from any language with a Postgres client. You can even generate and
Language | Libraries / Examples
--- | ---
C | [pgvector-c](https://github.com/pgvector/pgvector-c)
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)
@@ -383,11 +367,10 @@ Dart | [pgvector-dart](https://github.com/pgvector/pgvector-dart)
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, Kotlin, Groovy, Scala | [pgvector-java](https://github.com/pgvector/pgvector-java)
JavaScript, TypeScript | [pgvector-node](https://github.com/pgvector/pgvector-node)
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)
Nim | [pgvector-nim](https://github.com/pgvector/pgvector-nim)
Node.js | [pgvector-node](https://github.com/pgvector/pgvector-node)
Perl | [pgvector-perl](https://github.com/pgvector/pgvector-perl)
PHP | [pgvector-php](https://github.com/pgvector/pgvector-php)
Python | [pgvector-python](https://github.com/pgvector/pgvector-python)
@@ -395,7 +378,6 @@ 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)
Zig | [pgvector-zig](https://github.com/pgvector/pgvector-zig)
## Frequently Asked Questions
@@ -411,55 +393,6 @@ Yes, pgvector uses the write-ahead log (WAL), which allows for replication and p
Youll need to use [dimensionality reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction) at the moment.
#### Can I store vectors with different dimensions in the same column?
You can use `vector` as the type (instead of `vector(3)`).
```sql
CREATE TABLE embeddings (model_id bigint, item_id bigint, embedding vector, PRIMARY KEY (model_id, item_id));
```
However, you can only create indexes on rows with the same number of dimensions (using [expression](https://www.postgresql.org/docs/current/indexes-expressional.html) and [partial](https://www.postgresql.org/docs/current/indexes-partial.html) indexing):
```sql
CREATE INDEX ON embeddings USING hnsw ((embedding::vector(3)) vector_l2_ops) WHERE (model_id = 123);
```
and query with:
```sql
SELECT * FROM embeddings WHERE model_id = 123 ORDER BY embedding::vector(3) <-> '[3,1,2]' LIMIT 5;
```
#### Can I store vectors with more precision?
You can use the `double precision[]` or `numeric[]` type to store vectors with more precision.
```sql
CREATE TABLE items (id bigserial PRIMARY KEY, embedding double precision[]);
-- use {} instead of [] for Postgres arrays
INSERT INTO items (embedding) VALUES ('{1,2,3}'), ('{4,5,6}');
```
Optionally, add a [check constraint](https://www.postgresql.org/docs/current/ddl-constraints.html) to ensure data can be converted to the `vector` type and has the expected dimensions.
```sql
ALTER TABLE items ADD CHECK (vector_dims(embedding::vector) = 3);
```
Use [expression indexing](https://www.postgresql.org/docs/current/indexes-expressional.html) to index (at a lower precision):
```sql
CREATE INDEX ON items USING hnsw ((embedding::vector(3)) vector_l2_ops);
```
and query with:
```sql
SELECT * FROM items ORDER BY embedding::vector(3) <-> '[3,1,2]' LIMIT 5;
```
## Troubleshooting
#### Why isnt a query using an index?
@@ -473,8 +406,6 @@ SELECT ...
COMMIT;
```
Also, if the table is small, a table scan may be faster.
#### Why isnt a query using a parallel table scan?
The planner doesnt consider [out-of-line storage](https://www.postgresql.org/docs/current/storage-toast.html) in cost estimates, which can make a serial scan look cheaper. You can reduce the cost of a parallel scan for a query with:
@@ -664,7 +595,7 @@ pgvector is available on [these providers](https://github.com/pgvector/pgvector/
## Upgrading
[Install](#installation) the latest version (use the same method as the original installation). Then in each database you want to upgrade, run:
Install the latest version. Then in each database you want to upgrade, run:
```sql
ALTER EXTENSION vector UPDATE;
@@ -678,16 +609,6 @@ SELECT extversion FROM pg_extension WHERE extname = 'vector';
## Upgrade Notes
### 0.6.0
If upgrading with Postgres < 13, remove this line from `sql/vector--0.5.1--0.6.0.sql`:
```sql
ALTER TYPE vector SET (STORAGE = external);
```
Then run `make install` and `ALTER EXTENSION vector UPDATE;`.
### 0.4.0
If upgrading with Postgres < 13, remove this line from `sql/vector--0.3.2--0.4.0.sql`:

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@@ -1,5 +0,0 @@
-- complain if script is sourced in psql, rather than via CREATE EXTENSION
\echo Use "ALTER EXTENSION vector UPDATE TO '0.6.0'" to load this file. \quit
-- remove this single line for Postgres < 13
ALTER TYPE vector SET (STORAGE = external);

View File

@@ -26,7 +26,7 @@ CREATE TYPE vector (
TYPMOD_IN = vector_typmod_in,
RECEIVE = vector_recv,
SEND = vector_send,
STORAGE = external
STORAGE = extended
);
-- functions

View File

@@ -14,7 +14,6 @@
#endif
int hnsw_ef_search;
bool hnsw_enable_parallel_build;
static relopt_kind hnsw_relopt_kind;
/*
@@ -40,11 +39,6 @@ HnswInit(void)
DefineCustomIntVariable("hnsw.ef_search", "Sets the size of the dynamic candidate list for search",
"Valid range is 1..1000.", &hnsw_ef_search,
HNSW_DEFAULT_EF_SEARCH, HNSW_MIN_EF_SEARCH, HNSW_MAX_EF_SEARCH, PGC_USERSET, 0, NULL, NULL, NULL);
/* Behind a variable for now since can be slower than building in memory */
DefineCustomBoolVariable("hnsw.enable_parallel_build", "Enables or disables building indexes in parallel",
NULL, &hnsw_enable_parallel_build,
false, PGC_USERSET, 0, NULL, NULL, NULL);
}
/*

View File

@@ -4,7 +4,6 @@
#include "postgres.h"
#include "access/generic_xlog.h"
#include "access/parallel.h"
#include "access/reloptions.h"
#include "nodes/execnodes.h"
#include "port.h" /* for random() */
@@ -15,10 +14,6 @@
#error "Requires PostgreSQL 11+"
#endif
#if PG_VERSION_NUM < 120000
#include "access/relscan.h"
#endif
#define HNSW_MAX_DIM 2000
/* Support functions */
@@ -64,7 +59,7 @@
#define HNSW_MAX_SIZE (BLCKSZ - MAXALIGN(SizeOfPageHeaderData) - MAXALIGN(sizeof(HnswPageOpaqueData)) - sizeof(ItemIdData))
#define HNSW_ELEMENT_TUPLE_SIZE(size) MAXALIGN(offsetof(HnswElementTupleData, data) + (size))
#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))
#define HnswPageGetOpaque(page) ((HnswPageOpaque) PageGetSpecialPointer(page))
@@ -95,7 +90,6 @@
/* Variables */
extern int hnsw_ef_search;
extern bool hnsw_enable_parallel_build;
typedef struct HnswNeighborArray HnswNeighborArray;
@@ -109,7 +103,7 @@ typedef struct HnswElementData
OffsetNumber offno;
OffsetNumber neighborOffno;
BlockNumber neighborPage;
Datum value;
Vector *vec;
} HnswElementData;
typedef HnswElementData * HnswElement;
@@ -142,49 +136,6 @@ typedef struct HnswOptions
int efConstruction; /* size of dynamic candidate list */
} HnswOptions;
typedef struct HnswSpool
{
Relation heap;
Relation index;
} HnswSpool;
typedef struct HnswShared
{
/* Immutable state */
Oid heaprelid;
Oid indexrelid;
bool isconcurrent;
int scantuplesortstates;
/* Worker progress */
ConditionVariable workersdonecv;
/* Mutex for mutable state */
slock_t mutex;
/* Mutable state */
int nparticipantsdone;
double reltuples;
double indtuples;
#if PG_VERSION_NUM < 120000
ParallelHeapScanDescData heapdesc; /* must come last */
#endif
} HnswShared;
#if PG_VERSION_NUM >= 120000
#define ParallelTableScanFromHnswShared(shared) \
(ParallelTableScanDesc) ((char *) (shared) + BUFFERALIGN(sizeof(HnswShared)))
#endif
typedef struct HnswLeader
{
ParallelContext *pcxt;
int nparticipanttuplesorts;
HnswShared *hnswshared;
Snapshot snapshot;
} HnswLeader;
typedef struct HnswBuildState
{
/* Info */
@@ -212,16 +163,12 @@ typedef struct HnswBuildState
HnswElement entryPoint;
double ml;
int maxLevel;
long memoryLeft;
double maxInMemoryElements;
bool flushed;
Vector *normvec;
/* Memory */
MemoryContext tmpCtx;
/* Parallel builds */
HnswLeader *hnswleader;
HnswShared *hnswshared;
} HnswBuildState;
typedef struct HnswMetaPageData
@@ -257,7 +204,7 @@ typedef struct HnswElementTupleData
ItemPointerData heaptids[HNSW_HEAPTIDS];
ItemPointerData neighbortid;
uint16 unused2;
Vector data;
Vector vec;
} HnswElementTupleData;
typedef HnswElementTupleData * HnswElementTuple;
@@ -342,7 +289,6 @@ void HnswLoadElement(HnswElement element, float *distance, Datum *q, Relation i
void HnswSetElementTuple(HnswElementTuple etup, HnswElement element);
void HnswUpdateConnection(HnswElement element, HnswCandidate * hc, int m, int lc, int *updateIdx, Relation index, FmgrInfo *procinfo, Oid collation);
void HnswLoadNeighbors(HnswElement element, Relation index, int m);
PGDLLEXPORT void HnswParallelBuildMain(dsm_segment *seg, shm_toc *toc);
/* Index access methods */
IndexBuildResult *hnswbuild(Relation heap, Relation index, IndexInfo *indexInfo);

View File

@@ -2,16 +2,12 @@
#include <math.h>
#include "access/parallel.h"
#include "access/xact.h"
#include "catalog/index.h"
#include "hnsw.h"
#include "miscadmin.h"
#include "lib/pairingheap.h"
#include "nodes/pg_list.h"
#include "storage/bufmgr.h"
#include "tcop/tcopprot.h"
#include "utils/datum.h"
#include "utils/memutils.h"
#if PG_VERSION_NUM >= 140000
@@ -39,23 +35,6 @@
#define UpdateProgress(index, val) ((void)val)
#endif
#if PG_VERSION_NUM >= 140000
#include "utils/backend_status.h"
#include "utils/wait_event.h"
#endif
#if PG_VERSION_NUM >= 120000
#include "access/table.h"
#include "optimizer/optimizer.h"
#else
#include "access/heapam.h"
#include "optimizer/planner.h"
#include "pgstat.h"
#endif
#define PARALLEL_KEY_HNSW_SHARED UINT64CONST(0xA000000000000001)
#define PARALLEL_KEY_QUERY_TEXT UINT64CONST(0xA000000000000002)
/*
* Create the metapage
*/
@@ -126,7 +105,8 @@ CreateElementPages(HnswBuildState * buildstate)
{
Relation index = buildstate->index;
ForkNumber forkNum = buildstate->forkNum;
Size etupAllocSize;
int dimensions = buildstate->dimensions;
Size etupSize;
Size maxSize;
HnswElementTuple etup;
HnswNeighborTuple ntup;
@@ -137,11 +117,11 @@ CreateElementPages(HnswBuildState * buildstate)
ListCell *lc;
/* Calculate sizes */
etupAllocSize = BLCKSZ;
maxSize = HNSW_MAX_SIZE;
etupSize = HNSW_ELEMENT_TUPLE_SIZE(dimensions);
/* Allocate once */
etup = palloc0(etupAllocSize);
etup = palloc0(etupSize);
ntup = palloc0(BLCKSZ);
/* Prepare first page */
@@ -153,24 +133,15 @@ CreateElementPages(HnswBuildState * buildstate)
foreach(lc, buildstate->elements)
{
HnswElement element = lfirst(lc);
Size etupSize;
Size ntupSize;
Size combinedSize;
/* Zero memory for each element */
MemSet(etup, 0, etupAllocSize);
HnswSetElementTuple(etup, element);
/* Calculate sizes */
etupSize = HNSW_ELEMENT_TUPLE_SIZE(VARSIZE_ANY(DatumGetPointer(element->value)));
ntupSize = HNSW_NEIGHBOR_TUPLE_SIZE(element->level, buildstate->m);
combinedSize = etupSize + ntupSize + sizeof(ItemIdData);
/* Initial size check */
if (etupSize > etupAllocSize)
elog(ERROR, "index tuple too large");
HnswSetElementTuple(etup, element);
/* Keep element and neighbors on the same page if possible */
if (PageGetFreeSpace(page) < etupSize || (combinedSize <= maxSize && PageGetFreeSpace(page) < combinedSize))
HnswBuildAppendPage(index, &buf, &page, &state, forkNum);
@@ -293,14 +264,13 @@ FlushPages(HnswBuildState * buildstate)
* Insert tuple
*/
static bool
InsertTuple(Relation index, Datum *values, HnswElement element, HnswBuildState * buildstate, HnswElement * dup, MemoryContext outerCtx)
InsertTuple(Relation index, Datum *values, HnswElement element, HnswBuildState * buildstate, HnswElement * dup)
{
FmgrInfo *procinfo = buildstate->procinfo;
Oid collation = buildstate->collation;
HnswElement entryPoint = buildstate->entryPoint;
int efConstruction = buildstate->efConstruction;
int m = buildstate->m;
MemoryContext oldCtx;
/* Detoast once for all calls */
Datum value = PointerGetDatum(PG_DETOAST_DATUM(values[0]));
@@ -313,9 +283,7 @@ InsertTuple(Relation index, Datum *values, HnswElement element, HnswBuildState *
}
/* Copy value to element so accessible outside of memory context */
oldCtx = MemoryContextSwitchTo(outerCtx);
element->value = datumCopy(value, false, -1);
MemoryContextSwitchTo(oldCtx);
memcpy(element->vec, DatumGetVector(value), VECTOR_SIZE(buildstate->dimensions));
/* Insert element in graph */
HnswInsertElement(element, entryPoint, NULL, procinfo, collation, m, efConstruction, false);
@@ -345,21 +313,6 @@ InsertTuple(Relation index, Datum *values, HnswElement element, HnswBuildState *
return *dup == NULL;
}
/*
* Get the memory used by an element
*/
static long
HnswElementMemory(HnswElement e, int m)
{
long elementSize = sizeof(HnswElementData);
elementSize += sizeof(HnswNeighborArray) * (e->level + 1);
elementSize += sizeof(HnswCandidate) * (m * (e->level + 2));
elementSize += sizeof(ItemPointerData);
elementSize += VARSIZE_ANY(DatumGetPointer(e->value));
return elementSize;
}
/*
* Callback for table_index_build_scan
*/
@@ -381,7 +334,7 @@ BuildCallback(Relation index, CALLBACK_ITEM_POINTER, Datum *values,
if (isnull[0])
return;
if (buildstate->memoryLeft <= 0)
if (buildstate->indtuples >= buildstate->maxInMemoryElements)
{
if (!buildstate->flushed)
{
@@ -396,18 +349,7 @@ BuildCallback(Relation index, CALLBACK_ITEM_POINTER, Datum *values,
oldCtx = MemoryContextSwitchTo(buildstate->tmpCtx);
if (HnswInsertTuple(buildstate->index, values, isnull, tid, buildstate->heap))
{
if (buildstate->hnswshared)
{
HnswShared *hnswshared = buildstate->hnswshared;
SpinLockAcquire(&hnswshared->mutex);
UpdateProgress(PROGRESS_CREATEIDX_TUPLES_DONE, ++hnswshared->indtuples);
SpinLockRelease(&hnswshared->mutex);
}
else
UpdateProgress(PROGRESS_CREATEIDX_TUPLES_DONE, ++buildstate->indtuples);
}
UpdateProgress(PROGRESS_CREATEIDX_TUPLES_DONE, ++buildstate->indtuples);
/* Reset memory context */
MemoryContextSwitchTo(oldCtx);
@@ -418,12 +360,13 @@ BuildCallback(Relation index, CALLBACK_ITEM_POINTER, Datum *values,
/* Allocate necessary memory outside of memory context */
element = HnswInitElement(tid, buildstate->m, buildstate->ml, buildstate->maxLevel);
element->vec = palloc(VECTOR_SIZE(buildstate->dimensions));
/* Use memory context since detoast can allocate */
oldCtx = MemoryContextSwitchTo(buildstate->tmpCtx);
/* Insert tuple */
inserted = InsertTuple(index, values, element, buildstate, &dup, oldCtx);
inserted = InsertTuple(index, values, element, buildstate, &dup);
/* Reset memory context */
MemoryContextSwitchTo(oldCtx);
@@ -431,21 +374,31 @@ BuildCallback(Relation index, CALLBACK_ITEM_POINTER, Datum *values,
/* Add outside memory context */
if (dup != NULL)
{
HnswAddHeapTid(dup, tid);
buildstate->memoryLeft -= sizeof(ItemPointerData);
}
/* Add to buildstate or free */
if (inserted)
{
buildstate->elements = lappend(buildstate->elements, element);
buildstate->memoryLeft -= HnswElementMemory(element, buildstate->m);
}
else
HnswFreeElement(element);
}
/*
* Get the max number of elements that fit into maintenance_work_mem
*/
static double
HnswGetMaxInMemoryElements(int m, double ml, int dimensions)
{
Size elementSize = sizeof(HnswElementData);
double avgLevel = -log(0.5) * ml;
elementSize += sizeof(HnswNeighborArray) * (avgLevel + 1);
elementSize += sizeof(HnswCandidate) * (m * (avgLevel + 2));
elementSize += sizeof(ItemPointerData);
elementSize += VECTOR_SIZE(dimensions);
return (maintenance_work_mem * 1024L) / elementSize;
}
/*
* Initialize the build state
*/
@@ -483,7 +436,7 @@ InitBuildState(HnswBuildState * buildstate, Relation heap, Relation index, Index
buildstate->entryPoint = NULL;
buildstate->ml = HnswGetMl(buildstate->m);
buildstate->maxLevel = HnswGetMaxLevel(buildstate->m);
buildstate->memoryLeft = maintenance_work_mem * 1024L;
buildstate->maxInMemoryElements = HnswGetMaxInMemoryElements(buildstate->m, buildstate->ml, buildstate->dimensions);
buildstate->flushed = false;
/* Reuse for each tuple */
@@ -492,9 +445,6 @@ InitBuildState(HnswBuildState * buildstate, Relation heap, Relation index, Index
buildstate->tmpCtx = AllocSetContextCreate(CurrentMemoryContext,
"Hnsw build temporary context",
ALLOCSET_DEFAULT_SIZES);
buildstate->hnswleader = NULL;
buildstate->hnswshared = NULL;
}
/*
@@ -507,374 +457,21 @@ FreeBuildState(HnswBuildState * buildstate)
MemoryContextDelete(buildstate->tmpCtx);
}
/*
* Within leader, wait for end of heap scan
*/
static double
ParallelHeapScan(HnswBuildState * buildstate)
{
HnswShared *hnswshared = buildstate->hnswleader->hnswshared;
int nparticipanttuplesorts;
double reltuples;
nparticipanttuplesorts = buildstate->hnswleader->nparticipanttuplesorts;
for (;;)
{
SpinLockAcquire(&hnswshared->mutex);
if (hnswshared->nparticipantsdone == nparticipanttuplesorts)
{
buildstate->indtuples = hnswshared->indtuples;
reltuples = hnswshared->reltuples;
SpinLockRelease(&hnswshared->mutex);
break;
}
SpinLockRelease(&hnswshared->mutex);
ConditionVariableSleep(&hnswshared->workersdonecv,
WAIT_EVENT_PARALLEL_CREATE_INDEX_SCAN);
}
ConditionVariableCancelSleep();
return reltuples;
}
/*
* Perform a worker's portion of a parallel insert
*/
static void
HnswParallelScanAndInsert(HnswSpool * hnswspool, HnswShared * hnswshared, bool progress)
{
HnswBuildState buildstate;
#if PG_VERSION_NUM >= 120000
TableScanDesc scan;
#else
HeapScanDesc scan;
#endif
double reltuples;
IndexInfo *indexInfo;
/* Join parallel scan */
indexInfo = BuildIndexInfo(hnswspool->index);
indexInfo->ii_Concurrent = hnswshared->isconcurrent;
InitBuildState(&buildstate, hnswspool->heap, hnswspool->index, indexInfo, MAIN_FORKNUM);
/* TODO Support in-memory builds */
buildstate.memoryLeft = 0;
buildstate.flushed = true;
buildstate.hnswshared = hnswshared;
#if PG_VERSION_NUM >= 120000
scan = table_beginscan_parallel(hnswspool->heap,
ParallelTableScanFromHnswShared(hnswshared));
reltuples = table_index_build_scan(hnswspool->heap, hnswspool->index, indexInfo,
true, progress, BuildCallback,
(void *) &buildstate, scan);
#else
scan = heap_beginscan_parallel(hnswspool->heap, &hnswshared->heapdesc);
reltuples = IndexBuildHeapScan(hnswspool->heap, hnswspool->index, indexInfo,
true, BuildCallback,
(void *) &buildstate, scan);
#endif
/* Record statistics */
SpinLockAcquire(&hnswshared->mutex);
hnswshared->nparticipantsdone++;
hnswshared->reltuples += reltuples;
SpinLockRelease(&hnswshared->mutex);
/* Log statistics */
if (progress)
ereport(DEBUG1, (errmsg("leader processed " INT64_FORMAT " tuples", (int64) reltuples)));
else
ereport(DEBUG1, (errmsg("worker processed " INT64_FORMAT " tuples", (int64) reltuples)));
/* Notify leader */
ConditionVariableSignal(&hnswshared->workersdonecv);
FreeBuildState(&buildstate);
}
/*
* Perform work within a launched parallel process
*/
void
HnswParallelBuildMain(dsm_segment *seg, shm_toc *toc)
{
char *sharedquery;
HnswSpool *hnswspool;
HnswShared *hnswshared;
Relation heapRel;
Relation indexRel;
LOCKMODE heapLockmode;
LOCKMODE indexLockmode;
/* Set debug_query_string for individual workers first */
sharedquery = shm_toc_lookup(toc, PARALLEL_KEY_QUERY_TEXT, true);
debug_query_string = sharedquery;
/* Report the query string from leader */
pgstat_report_activity(STATE_RUNNING, debug_query_string);
/* Look up shared state */
hnswshared = shm_toc_lookup(toc, PARALLEL_KEY_HNSW_SHARED, false);
/* Open relations using lock modes known to be obtained by index.c */
if (!hnswshared->isconcurrent)
{
heapLockmode = ShareLock;
indexLockmode = AccessExclusiveLock;
}
else
{
heapLockmode = ShareUpdateExclusiveLock;
indexLockmode = RowExclusiveLock;
}
/* Open relations within worker */
#if PG_VERSION_NUM >= 120000
heapRel = table_open(hnswshared->heaprelid, heapLockmode);
#else
heapRel = heap_open(hnswshared->heaprelid, heapLockmode);
#endif
indexRel = index_open(hnswshared->indexrelid, indexLockmode);
/* Initialize worker's own spool */
hnswspool = (HnswSpool *) palloc0(sizeof(HnswSpool));
hnswspool->heap = heapRel;
hnswspool->index = indexRel;
/* Perform inserts */
HnswParallelScanAndInsert(hnswspool, hnswshared, false);
/* Close relations within worker */
index_close(indexRel, indexLockmode);
#if PG_VERSION_NUM >= 120000
table_close(heapRel, heapLockmode);
#else
heap_close(heapRel, heapLockmode);
#endif
}
/*
* End parallel build
*/
static void
HnswEndParallel(HnswLeader * hnswleader)
{
/* Shutdown worker processes */
WaitForParallelWorkersToFinish(hnswleader->pcxt);
/* Free last reference to MVCC snapshot, if one was used */
if (IsMVCCSnapshot(hnswleader->snapshot))
UnregisterSnapshot(hnswleader->snapshot);
DestroyParallelContext(hnswleader->pcxt);
ExitParallelMode();
}
/*
* Return size of shared memory required for parallel index build
*/
static Size
ParallelEstimateShared(Relation heap, Snapshot snapshot)
{
#if PG_VERSION_NUM >= 120000
return add_size(BUFFERALIGN(sizeof(HnswShared)), table_parallelscan_estimate(heap, snapshot));
#else
if (!IsMVCCSnapshot(snapshot))
{
Assert(snapshot == SnapshotAny);
return sizeof(HnswShared);
}
return add_size(offsetof(HnswShared, heapdesc) +
offsetof(ParallelHeapScanDescData, phs_snapshot_data),
EstimateSnapshotSpace(snapshot));
#endif
}
/*
* Within leader, participate as a parallel worker
*/
static void
HnswLeaderParticipateAsWorker(HnswBuildState * buildstate)
{
HnswLeader *hnswleader = buildstate->hnswleader;
HnswSpool *leaderworker;
/* Allocate memory and initialize private spool */
leaderworker = (HnswSpool *) palloc0(sizeof(HnswSpool));
leaderworker->heap = buildstate->heap;
leaderworker->index = buildstate->index;
/* Perform work common to all participants */
HnswParallelScanAndInsert(leaderworker, hnswleader->hnswshared, true);
}
/*
* Begin parallel build
*/
static void
HnswBeginParallel(HnswBuildState * buildstate, bool isconcurrent, int request)
{
ParallelContext *pcxt;
int scantuplesortstates;
Snapshot snapshot;
Size esthnswshared;
HnswShared *hnswshared;
HnswLeader *hnswleader = (HnswLeader *) palloc0(sizeof(HnswLeader));
bool leaderparticipates = true;
int querylen;
#ifdef DISABLE_LEADER_PARTICIPATION
leaderparticipates = false;
#endif
/* Enter parallel mode and create context */
EnterParallelMode();
Assert(request > 0);
#if PG_VERSION_NUM >= 120000
pcxt = CreateParallelContext("vector", "HnswParallelBuildMain", request);
#else
pcxt = CreateParallelContext("vector", "HnswParallelBuildMain", request, true);
#endif
scantuplesortstates = leaderparticipates ? request + 1 : request;
/* Get snapshot for table scan */
if (!isconcurrent)
snapshot = SnapshotAny;
else
snapshot = RegisterSnapshot(GetTransactionSnapshot());
/* Estimate size of workspaces */
esthnswshared = ParallelEstimateShared(buildstate->heap, snapshot);
shm_toc_estimate_chunk(&pcxt->estimator, esthnswshared);
shm_toc_estimate_keys(&pcxt->estimator, 1);
/* Finally, estimate PARALLEL_KEY_QUERY_TEXT space */
if (debug_query_string)
{
querylen = strlen(debug_query_string);
shm_toc_estimate_chunk(&pcxt->estimator, querylen + 1);
shm_toc_estimate_keys(&pcxt->estimator, 1);
}
else
querylen = 0; /* keep compiler quiet */
/* Everyone's had a chance to ask for space, so now create the DSM */
InitializeParallelDSM(pcxt);
/* If no DSM segment was available, back out (do serial build) */
if (pcxt->seg == NULL)
{
if (IsMVCCSnapshot(snapshot))
UnregisterSnapshot(snapshot);
DestroyParallelContext(pcxt);
ExitParallelMode();
return;
}
/* Store shared build state, for which we reserved space */
hnswshared = (HnswShared *) shm_toc_allocate(pcxt->toc, esthnswshared);
/* Initialize immutable state */
hnswshared->heaprelid = RelationGetRelid(buildstate->heap);
hnswshared->indexrelid = RelationGetRelid(buildstate->index);
hnswshared->isconcurrent = isconcurrent;
hnswshared->scantuplesortstates = scantuplesortstates;
ConditionVariableInit(&hnswshared->workersdonecv);
SpinLockInit(&hnswshared->mutex);
/* Initialize mutable state */
hnswshared->nparticipantsdone = 0;
hnswshared->reltuples = 0;
hnswshared->indtuples = 0;
#if PG_VERSION_NUM >= 120000
table_parallelscan_initialize(buildstate->heap,
ParallelTableScanFromHnswShared(hnswshared),
snapshot);
#else
heap_parallelscan_initialize(&hnswshared->heapdesc, buildstate->heap, snapshot);
#endif
shm_toc_insert(pcxt->toc, PARALLEL_KEY_HNSW_SHARED, hnswshared);
/* Store query string for workers */
if (debug_query_string)
{
char *sharedquery;
sharedquery = (char *) shm_toc_allocate(pcxt->toc, querylen + 1);
memcpy(sharedquery, debug_query_string, querylen + 1);
shm_toc_insert(pcxt->toc, PARALLEL_KEY_QUERY_TEXT, sharedquery);
}
/* Launch workers, saving status for leader/caller */
LaunchParallelWorkers(pcxt);
hnswleader->pcxt = pcxt;
hnswleader->nparticipanttuplesorts = pcxt->nworkers_launched;
if (leaderparticipates)
hnswleader->nparticipanttuplesorts++;
hnswleader->hnswshared = hnswshared;
hnswleader->snapshot = snapshot;
/* If no workers were successfully launched, back out (do serial build) */
if (pcxt->nworkers_launched == 0)
{
HnswEndParallel(hnswleader);
return;
}
/* Log participants */
ereport(DEBUG1, (errmsg("using %d parallel workers", pcxt->nworkers_launched)));
/* Save leader state now that it's clear build will be parallel */
buildstate->hnswleader = hnswleader;
/* Join heap scan ourselves */
if (leaderparticipates)
HnswLeaderParticipateAsWorker(buildstate);
/* Wait for all launched workers */
WaitForParallelWorkersToAttach(pcxt);
}
/*
* Build graph
*/
static void
BuildGraph(HnswBuildState * buildstate, ForkNumber forkNum)
{
int parallel_workers = 0;
UpdateProgress(PROGRESS_CREATEIDX_SUBPHASE, PROGRESS_HNSW_PHASE_LOAD);
/* Calculate parallel workers */
if (hnsw_enable_parallel_build)
parallel_workers = plan_create_index_workers(RelationGetRelid(buildstate->heap), RelationGetRelid(buildstate->index));
/* Attempt to launch parallel worker scan when required */
if (parallel_workers > 0)
{
/* TODO Support in-memory builds */
FlushPages(buildstate);
HnswBeginParallel(buildstate, buildstate->indexInfo->ii_Concurrent, parallel_workers);
}
/* Add tuples to sort */
if (buildstate->hnswleader)
buildstate->reltuples = ParallelHeapScan(buildstate);
else
{
#if PG_VERSION_NUM >= 120000
buildstate->reltuples = table_index_build_scan(buildstate->heap, buildstate->index, buildstate->indexInfo,
true, true, BuildCallback, (void *) buildstate, NULL);
buildstate->reltuples = table_index_build_scan(buildstate->heap, buildstate->index, buildstate->indexInfo,
true, true, BuildCallback, (void *) buildstate, NULL);
#else
buildstate->reltuples = IndexBuildHeapScan(buildstate->heap, buildstate->index, buildstate->indexInfo,
true, BuildCallback, (void *) buildstate, NULL);
buildstate->reltuples = IndexBuildHeapScan(buildstate->heap, buildstate->index, buildstate->indexInfo,
true, BuildCallback, (void *) buildstate, NULL);
#endif
}
/* End parallel build */
if (buildstate->hnswleader)
HnswEndParallel(buildstate->hnswleader);
}
/*

View File

@@ -123,6 +123,7 @@ WriteNewElementPages(Relation index, HnswElement e, int m, BlockNumber insertPag
Size minCombinedSize;
HnswElementTuple etup;
BlockNumber currentPage = insertPage;
int dimensions = e->vec->dim;
HnswNeighborTuple ntup;
Buffer nbuf;
Page npage;
@@ -131,7 +132,7 @@ WriteNewElementPages(Relation index, HnswElement e, int m, BlockNumber insertPag
BlockNumber newInsertPage = InvalidBlockNumber;
/* Calculate sizes */
etupSize = HNSW_ELEMENT_TUPLE_SIZE(VARSIZE_ANY(DatumGetPointer(e->value)));
etupSize = HNSW_ELEMENT_TUPLE_SIZE(dimensions);
ntupSize = HNSW_NEIGHBOR_TUPLE_SIZE(e->level, m);
combinedSize = etupSize + ntupSize + sizeof(ItemIdData);
maxSize = HNSW_MAX_SIZE;
@@ -404,9 +405,8 @@ HnswAddDuplicate(Relation index, HnswElement element, HnswElement dup)
Buffer buf;
Page page;
GenericXLogState *state;
ItemId itemid;
Size etupSize = HNSW_ELEMENT_TUPLE_SIZE(dup->vec->dim);
HnswElementTuple etup;
Size etupSize;
int i;
/* Read page */
@@ -416,9 +416,7 @@ HnswAddDuplicate(Relation index, HnswElement element, HnswElement dup)
page = GenericXLogRegisterBuffer(state, buf, 0);
/* Find space */
itemid = PageGetItemId(page, dup->offno);
etup = (HnswElementTuple) PageGetItem(page, itemid);
etupSize = ItemIdGetLength(itemid);
etup = (HnswElementTuple) PageGetItem(page, PageGetItemId(page, dup->offno));
for (i = 0; i < HNSW_HEAPTIDS; i++)
{
if (!ItemPointerIsValid(&etup->heaptids[i]))
@@ -517,7 +515,7 @@ HnswInsertTuple(Relation index, Datum *values, bool *isnull, ItemPointer heap_ti
/* Create an element */
element = HnswInitElement(heap_tid, m, HnswGetMl(m), HnswGetMaxLevel(m));
element->value = value;
element->vec = DatumGetVector(value);
/* Prevent concurrent inserts when likely updating entry point */
if (entryPoint == NULL || element->level > entryPoint->level)

View File

@@ -4,7 +4,6 @@
#include "hnsw.h"
#include "storage/bufmgr.h"
#include "utils/datum.h"
#include "vector.h"
/*
@@ -188,8 +187,7 @@ HnswFreeElement(HnswElement element)
{
HnswFreeNeighbors(element);
list_free_deep(element->heaptids);
if (DatumGetPointer(element->value))
pfree(DatumGetPointer(element->value));
pfree(element->vec);
pfree(element);
}
@@ -216,7 +214,7 @@ HnswInitElementFromBlock(BlockNumber blkno, OffsetNumber offno)
element->blkno = blkno;
element->offno = offno;
element->neighbors = NULL;
element->value = PointerGetDatum(NULL);
element->vec = NULL;
return element;
}
@@ -326,7 +324,7 @@ HnswSetElementTuple(HnswElementTuple etup, HnswElement element)
else
ItemPointerSetInvalid(&etup->heaptids[i]);
}
memcpy(&etup->data, DatumGetPointer(element->value), VARSIZE_ANY(DatumGetPointer(element->value)));
memcpy(&etup->vec, element->vec, VECTOR_SIZE(element->vec->dim));
}
/*
@@ -448,7 +446,10 @@ HnswLoadElementFromTuple(HnswElement element, HnswElementTuple etup, bool loadHe
}
if (loadVec)
element->value = datumCopy(PointerGetDatum(&etup->data), false, -1);
{
element->vec = palloc(VECTOR_SIZE(etup->vec.dim));
memcpy(element->vec, &etup->vec, VECTOR_SIZE(etup->vec.dim));
}
}
/*
@@ -475,7 +476,7 @@ HnswLoadElement(HnswElement element, float *distance, Datum *q, Relation index,
/* Calculate distance */
if (distance != NULL)
*distance = (float) DatumGetFloat8(FunctionCall2Coll(procinfo, collation, *q, PointerGetDatum(&etup->data)));
*distance = (float) DatumGetFloat8(FunctionCall2Coll(procinfo, collation, *q, PointerGetDatum(&etup->vec)));
UnlockReleaseBuffer(buf);
}
@@ -486,7 +487,7 @@ HnswLoadElement(HnswElement element, float *distance, Datum *q, Relation index,
static float
GetCandidateDistance(HnswCandidate * hc, Datum q, FmgrInfo *procinfo, Oid collation)
{
return DatumGetFloat8(FunctionCall2Coll(procinfo, collation, q, hc->element->value));
return DatumGetFloat8(FunctionCall2Coll(procinfo, collation, q, PointerGetDatum(hc->element->vec)));
}
/*
@@ -749,7 +750,7 @@ HnswGetDistance(HnswElement a, HnswElement b, int lc, FmgrInfo *procinfo, Oid co
}
}
return DatumGetFloat8(FunctionCall2Coll(procinfo, collation, a->value, b->value));
return DatumGetFloat8(FunctionCall2Coll(procinfo, collation, PointerGetDatum(a->vec), PointerGetDatum(b->vec)));
}
/*
@@ -876,7 +877,7 @@ HnswFindDuplicate(HnswElement e)
HnswCandidate *neighbor = &neighbors->items[i];
/* Exit early since ordered by distance */
if (!datumIsEqual(e->value, neighbor->element->value, false, -1))
if (vector_cmp_internal(e->vec, neighbor->element->vec) != 0)
break;
/* Check for space */
@@ -929,13 +930,13 @@ HnswUpdateConnection(HnswElement element, HnswCandidate * hc, int m, int lc, int
/* Load elements on insert */
if (index != NULL)
{
Datum q = hc->element->value;
Datum q = PointerGetDatum(hc->element->vec);
for (int i = 0; i < currentNeighbors->length; i++)
{
HnswCandidate *hc3 = &currentNeighbors->items[i];
if (DatumGetPointer(hc3->element->value) == NULL)
if (hc3->element->vec == NULL)
HnswLoadElement(hc3->element, &hc3->distance, &q, index, procinfo, collation, true);
else
hc3->distance = GetCandidateDistance(hc3, q, procinfo, collation);
@@ -1016,7 +1017,7 @@ HnswInsertElement(HnswElement element, HnswElement entryPoint, Relation index, F
List *w;
int level = element->level;
int entryLevel;
Datum q = element->value;
Datum q = PointerGetDatum(element->vec);
HnswElement skipElement = existing ? element : NULL;
/* No neighbors if no entry point */

View File

@@ -62,8 +62,7 @@ RemoveHeapTids(HnswVacuumState * vacuumstate)
/* Iterate over nodes */
for (offno = FirstOffsetNumber; offno <= maxoffno; offno = OffsetNumberNext(offno))
{
ItemId itemid = PageGetItemId(page, offno);
HnswElementTuple etup = (HnswElementTuple) PageGetItem(page, itemid);
HnswElementTuple etup = (HnswElementTuple) PageGetItem(page, PageGetItemId(page, offno));
int idx = 0;
bool itemUpdated = false;
@@ -94,7 +93,7 @@ RemoveHeapTids(HnswVacuumState * vacuumstate)
if (itemUpdated)
{
Size etupSize = ItemIdGetLength(itemid);
Size etupSize = HNSW_ELEMENT_TUPLE_SIZE(etup->vec.dim);
/* Mark rest as invalid */
for (int i = idx; i < HNSW_HEAPTIDS; i++)
@@ -478,8 +477,7 @@ MarkDeleted(HnswVacuumState * vacuumstate)
/* Update element and neighbors together */
for (offno = FirstOffsetNumber; offno <= maxoffno; offno = OffsetNumberNext(offno))
{
ItemId itemid = PageGetItemId(page, offno);
HnswElementTuple etup = (HnswElementTuple) PageGetItem(page, itemid);
HnswElementTuple etup = (HnswElementTuple) PageGetItem(page, PageGetItemId(page, offno));
HnswNeighborTuple ntup;
Size etupSize;
Size ntupSize;
@@ -507,7 +505,7 @@ MarkDeleted(HnswVacuumState * vacuumstate)
continue;
/* Calculate sizes */
etupSize = ItemIdGetLength(itemid);
etupSize = HNSW_ELEMENT_TUPLE_SIZE(etup->vec.dim);
ntupSize = HNSW_NEIGHBOR_TUPLE_SIZE(etup->level, vacuumstate->m);
/* Get neighbor page */
@@ -530,7 +528,7 @@ MarkDeleted(HnswVacuumState * vacuumstate)
/* Overwrite element */
etup->deleted = 1;
MemSet(&etup->data, 0, VARSIZE_ANY(&etup->data));
MemSet(&etup->vec.x, 0, etup->vec.dim * sizeof(float));
/* Overwrite neighbors */
for (int i = 0; i < ntup->count; i++)

View File

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

View File

@@ -80,6 +80,10 @@
#define RandomInt() random()
#endif
#if PG_VERSION_NUM < 130000
#define list_sort(list, cmp) list_qsort(list, cmp)
#endif
/* Variables */
extern int ivfflat_probes;
@@ -178,7 +182,8 @@ typedef struct IvfflatBuildState
Oid collation;
/* Variables */
VectorArray samples;
List *samples;
int targsamples;
VectorArray centers;
ListInfo *listInfo;
Vector *normvec;
@@ -274,7 +279,7 @@ typedef IvfflatScanOpaqueData * IvfflatScanOpaque;
VectorArray VectorArrayInit(int maxlen, int dimensions);
void VectorArrayFree(VectorArray arr);
void PrintVectorArray(char *msg, VectorArray arr);
void IvfflatKmeans(Relation index, VectorArray samples, VectorArray centers);
void IvfflatKmeans(Relation index, List *samples, VectorArray centers);
FmgrInfo *IvfflatOptionalProcInfo(Relation index, uint16 procnum);
bool IvfflatNormValue(FmgrInfo *procinfo, Oid collation, Datum *value, Vector * result);
int IvfflatGetLists(Relation index);

View File

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

View File

@@ -89,7 +89,7 @@ CheckDim(int dim)
}
/*
* Ensure finite element
* Ensure finite elements
*/
static inline void
CheckElement(float value)
@@ -437,18 +437,17 @@ vector_send(PG_FUNCTION_ARGS)
/*
* Convert vector to vector
* This is needed to check the type modifier
*/
PGDLLEXPORT PG_FUNCTION_INFO_V1(vector);
Datum
vector(PG_FUNCTION_ARGS)
{
Vector *vec = PG_GETARG_VECTOR_P(0);
Vector *arg = PG_GETARG_VECTOR_P(0);
int32 typmod = PG_GETARG_INT32(1);
CheckExpectedDim(typmod, vec->dim);
CheckExpectedDim(typmod, arg->dim);
PG_RETURN_POINTER(vec);
PG_RETURN_POINTER(arg);
}
/*
@@ -465,6 +464,7 @@ array_to_vector(PG_FUNCTION_ARGS)
bool typbyval;
char typalign;
Datum *elemsp;
bool *nullsp;
int nelemsp;
if (ARR_NDIM(array) > 1)
@@ -478,7 +478,7 @@ array_to_vector(PG_FUNCTION_ARGS)
errmsg("array must not contain nulls")));
get_typlenbyvalalign(ARR_ELEMTYPE(array), &typlen, &typbyval, &typalign);
deconstruct_array(array, ARR_ELEMTYPE(array), typlen, typbyval, typalign, &elemsp, NULL, &nelemsp);
deconstruct_array(array, ARR_ELEMTYPE(array), typlen, typbyval, typalign, &elemsp, &nullsp, &nelemsp);
CheckDim(nelemsp);
CheckExpectedDim(typmod, nelemsp);
@@ -512,12 +512,6 @@ array_to_vector(PG_FUNCTION_ARGS)
errmsg("unsupported array type")));
}
/*
* Free allocation from deconstruct_array. Do not free individual elements
* when pass-by-reference since they point to original array.
*/
pfree(elemsp);
/* Check elements */
for (int i = 0; i < result->dim; i++)
CheckElement(result->x[i]);

View File

@@ -83,32 +83,11 @@ for my $i (0 .. $#operators)
push(@expected, $res);
}
# Build index serially
$node->safe_psql("postgres", qq(
SET max_parallel_maintenance_workers = 0;
CREATE INDEX idx ON tst USING hnsw (v $opclass);
));
# Add index
$node->safe_psql("postgres", "CREATE INDEX ON tst USING hnsw (v $opclass);");
# Test approximate results
my $min = $operator eq "<#>" ? 0.80 : 0.99;
test_recall($min, $operator);
$node->safe_psql("postgres", "DROP INDEX idx;");
# Build index in parallel
my ($ret, $stdout, $stderr) = $node->psql("postgres", qq(
SET client_min_messages = DEBUG;
SET min_parallel_table_scan_size = 1;
SET hnsw.enable_parallel_build = on;
CREATE INDEX idx ON tst USING hnsw (v $opclass);
));
is($ret, 0, $stderr);
like($stderr, qr/using \d+ parallel workers/);
# Test approximate results
test_recall($min, $operator);
$node->safe_psql("postgres", "DROP INDEX idx;");
}
done_testing();