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15 Commits

Author SHA1 Message Date
Andrew Kane
8714dd1410 Init leader 2023-09-01 00:09:18 -07:00
Andrew Kane
65e04acab6 Added missing headers for Postgres < 14 2023-09-01 00:07:38 -07:00
Andrew Kane
86d72d15c2 Added support for on-disk parallel index builds for HNSW 2023-08-31 23:58:10 -07:00
Andrew Kane
da3b2fab46 Updated readme [skip ci] 2023-08-31 22:20:13 -07:00
Andrew Kane
884026a23c Updated changelog [skip ci] 2023-08-29 10:13:05 -07:00
Andrew Kane
4d352e6c30 Updated changelog [skip ci] 2023-08-29 10:11:53 -07:00
Andrew Kane
a8e257e1f1 Added comments [skip ci] 2023-08-28 22:02:48 -07:00
Andrew Kane
3913432303 Version bump to 0.5.0 [skip ci] 2023-08-28 17:01:16 -07:00
Andrew Kane
baeebec244 Updated readme [skip ci] 2023-08-28 15:34:07 -07:00
Andrew Kane
d578a4cccc Updated readme [skip ci] 2023-08-28 15:30:28 -07:00
Andrew Kane
1e18e19496 Updated readme [skip ci] 2023-08-28 15:25:11 -07:00
Andrew Kane
39f973dab2 Updated readme [skip ci] 2023-08-28 15:00:58 -07:00
Andrew Kane
453fa15f28 Updated readme [skip ci] 2023-08-28 09:57:46 -07:00
Jonathan S. Katz
e50a79108f Set default HNSW "ef_construction" to 64 (#230) 2023-08-26 16:05:08 -07:00
Andrew Kane
cfaa2ecd7f Improved HNSW graph repair - #239 2023-08-26 14:06:03 -07:00
13 changed files with 574 additions and 119 deletions

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@@ -1,13 +1,13 @@
## 0.5.0 (unreleased)
## 0.5.0 (2023-08-28)
- Added HNSW index type
- Added support for parallel index builds
- Added support for parallel index builds for IVFFlat
- Added `l1_distance` function
- Added element-wise multiplication for vectors
- Added `sum` aggregate
- Improved performance of distance functions
- Fixed out of range results for cosine distance
- Fixed results for NULL and NaN distances
- Fixed results for NULL and NaN distances for IVFFlat
## 0.4.4 (2023-06-12)

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@@ -2,7 +2,7 @@
"name": "vector",
"abstract": "Open-source vector similarity search for Postgres",
"description": "Supports L2 distance, inner product, and cosine distance",
"version": "0.4.4",
"version": "0.5.0",
"maintainer": [
"Andrew Kane <andrew@ankane.org>"
],
@@ -20,7 +20,7 @@
"vector": {
"file": "sql/vector.sql",
"docfile": "README.md",
"version": "0.4.4",
"version": "0.5.0",
"abstract": "Open-source vector similarity search for Postgres"
}
},

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@@ -1,5 +1,5 @@
EXTENSION = vector
EXTVERSION = 0.4.4
EXTVERSION = 0.5.0
MODULE_big = vector
DATA = $(wildcard sql/*--*.sql)

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@@ -1,5 +1,5 @@
EXTENSION = vector
EXTVERSION = 0.4.4
EXTVERSION = 0.5.0
OBJS = src\hnsw.obj src\hnswbuild.obj src\hnswinsert.obj src\hnswscan.obj src\hnswutils.obj src\hnswvacuum.obj src\ivfbuild.obj src\ivfflat.obj src\ivfinsert.obj src\ivfkmeans.obj src\ivfscan.obj src\ivfutils.obj src\ivfvacuum.obj src\vector.obj
HEADERS = src\vector.h

124
README.md
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@@ -18,7 +18,7 @@ Compile and install the extension (supports Postgres 11+)
```sh
cd /tmp
git clone --branch v0.4.4 https://github.com/pgvector/pgvector.git
git clone --branch v0.5.0 https://github.com/pgvector/pgvector.git
cd pgvector
make
make install # may need sudo
@@ -157,7 +157,16 @@ SELECT category_id, AVG(embedding) FROM items GROUP BY category_id;
By default, pgvector performs exact nearest neighbor search, which provides perfect recall.
You can add an index to use approximate nearest neighbor search, which trades some recall for performance. Unlike typical indexes, you will see different results for queries after adding an approximate index.
You can add an index to use approximate nearest neighbor search, which trades some recall for speed. Unlike typical indexes, you will see different results for queries after adding an approximate index.
Supported index types are:
- [IVFFlat](#ivfflat)
- [HNSW](#hnsw) - added in 0.5.0
## IVFFlat
An IVFFlat index divides vectors into lists, and then searches a subset of those lists that are closest to the query vector. It has faster build times and uses less memory than HNSW, but has lower query performance (in terms of speed-recall tradeoff).
Three keys to achieving good recall are:
@@ -206,7 +215,63 @@ SELECT ...
COMMIT;
```
### Indexing Progress
## 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.
Add an index for each distance function you want to use.
L2 distance
```sql
CREATE INDEX ON items USING hnsw (embedding vector_l2_ops);
```
Inner product
```sql
CREATE INDEX ON items USING hnsw (embedding vector_ip_ops);
```
Cosine distance
```sql
CREATE INDEX ON items USING hnsw (embedding vector_cosine_ops);
```
Vectors with up to 2,000 dimensions can be indexed.
### Index Options
Specify HNSW parameters
- `m` - the max number of connections per layer (16 by default)
- `ef_construction` - the size of the dynamic candidate list for constructing the graph (64 by default)
```sql
CREATE INDEX ON items USING hnsw (embedding vector_l2_ops) WITH (m = 16, ef_construction = 64);
```
### Query Options
Specify the size of the dynamic candidate list for search (40 by default)
```sql
SET hnsw.ef_search = 100;
```
A higher value provides better recall at the cost of speed.
Use `SET LOCAL` inside a transaction to set it for a single query
```sql
BEGIN;
SET LOCAL hnsw.ef_search = 100;
SELECT ...
COMMIT;
```
## Indexing Progress
Check [indexing progress](https://www.postgresql.org/docs/current/progress-reporting.html#CREATE-INDEX-PROGRESS-REPORTING) with Postgres 12+
@@ -217,8 +282,8 @@ SELECT phase, tuples_done, tuples_total FROM pg_stat_progress_create_index;
The phases are:
1. `initializing`
2. `performing k-means`
3. `sorting 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
@@ -283,7 +348,7 @@ SELECT * FROM items ORDER BY embedding <#> '[3,1,2]' LIMIT 5;
### Approximate Search
To speed up queries with an 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
CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 1000);
@@ -359,7 +424,7 @@ or choose to store vectors inline:
ALTER TABLE items ALTER COLUMN embedding SET STORAGE PLAIN;
```
#### Why are there less results for a query after adding an index?
#### Why are there less results for a query after adding an IVFFlat index?
The index was likely created with too little data for the number of lists. Drop the index until the table has more data.
@@ -375,32 +440,32 @@ Each vector takes `4 * dimensions + 8` bytes of storage. Each element is a singl
### Vector Operators
Operator | Description
--- | ---
\+ | element-wise addition
\- | element-wise subtraction
\* | element-wise multiplication [unreleased]
<-> | Euclidean distance
<#> | negative inner product
<=> | cosine distance
Operator | Description | Added
--- | --- | ---
\+ | element-wise addition |
\- | element-wise subtraction |
\* | element-wise multiplication | 0.5.0
<-> | Euclidean distance |
<#> | negative inner product |
<=> | cosine distance |
### Vector Functions
Function | Description
--- | ---
cosine_distance(vector, vector) → double precision | cosine distance
inner_product(vector, vector) → double precision | inner product
l2_distance(vector, vector) → double precision | Euclidean distance
l1_distance(vector, vector) → double precision | taxicab distance [unreleased]
vector_dims(vector) → integer | number of dimensions
vector_norm(vector) → double precision | Euclidean norm
Function | Description | Added
--- | --- | ---
cosine_distance(vector, vector) → double precision | cosine distance |
inner_product(vector, vector) → double precision | inner product |
l2_distance(vector, vector) → double precision | Euclidean distance |
l1_distance(vector, vector) → double precision | taxicab distance | 0.5.0
vector_dims(vector) → integer | number of dimensions |
vector_norm(vector) → double precision | Euclidean norm |
### Aggregate Functions
Function | Description
--- | ---
avg(vector) → vector | arithmetic mean
sum(vector) → vector | sum [unreleased]
Function | Description | Added
--- | --- | ---
avg(vector) → vector | average |
sum(vector) → vector | sum | 0.5.0
## Installation Notes
@@ -444,7 +509,7 @@ Then use `nmake` to build:
```cmd
set "PGROOT=C:\Program Files\PostgreSQL\15"
git clone --branch v0.4.4 https://github.com/pgvector/pgvector.git
git clone --branch v0.5.0 https://github.com/pgvector/pgvector.git
cd pgvector
nmake /F Makefile.win
nmake /F Makefile.win install
@@ -465,9 +530,8 @@ This adds pgvector to the [Postgres image](https://hub.docker.com/_/postgres) (r
You can also build the image manually:
```sh
git clone --branch v0.4.4 https://github.com/pgvector/pgvector.git
git clone --branch v0.5.0 https://github.com/pgvector/pgvector.git
cd pgvector
git cherry-pick 237a6df
docker build --build-arg PG_MAJOR=15 -t myuser/pgvector .
```

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@@ -4,6 +4,7 @@
#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() */
@@ -14,6 +15,10 @@
#error "Requires PostgreSQL 11+"
#endif
#if PG_VERSION_NUM < 120000
#include "access/relscan.h"
#endif
#define HNSW_MAX_DIM 2000
/* Support functions */
@@ -36,7 +41,7 @@
#define HNSW_DEFAULT_M 16
#define HNSW_MIN_M 2
#define HNSW_MAX_M 100
#define HNSW_DEFAULT_EF_CONSTRUCTION 40
#define HNSW_DEFAULT_EF_CONSTRUCTION 64
#define HNSW_MIN_EF_CONSTRUCTION 4
#define HNSW_MAX_EF_CONSTRUCTION 1000
#define HNSW_DEFAULT_EF_SEARCH 40
@@ -132,6 +137,49 @@ 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 */
@@ -165,6 +213,9 @@ typedef struct HnswBuildState
/* Memory */
MemoryContext tmpCtx;
/* Parallel builds */
HnswLeader *hnswleader;
} HnswBuildState;
typedef struct HnswMetaPageData
@@ -285,6 +336,7 @@ 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);
PGDLLEXPORT void HnswParallelBuildMain(dsm_segment *seg, shm_toc *toc);
/* Index access methods */
IndexBuildResult *hnswbuild(Relation heap, Relation index, IndexInfo *indexInfo);

View File

@@ -2,12 +2,15 @@
#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/memutils.h"
#if PG_VERSION_NUM >= 140000
@@ -35,6 +38,23 @@
#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
*/
@@ -351,6 +371,7 @@ BuildCallback(Relation index, CALLBACK_ITEM_POINTER, Datum *values,
oldCtx = MemoryContextSwitchTo(buildstate->tmpCtx);
/* TODO Fix progress for parallel builds */
if (HnswInsertTuple(buildstate->index, values, isnull, tid, buildstate->heap))
UpdateProgress(PROGRESS_CREATEIDX_TUPLES_DONE, ++buildstate->indtuples);
@@ -448,6 +469,8 @@ InitBuildState(HnswBuildState * buildstate, Relation heap, Relation index, Index
buildstate->tmpCtx = AllocSetContextCreate(CurrentMemoryContext,
"Hnsw build temporary context",
ALLOCSET_DEFAULT_SIZES);
buildstate->hnswleader = NULL;
}
/*
@@ -460,21 +483,373 @@ 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.maxInMemoryElements = 0;
buildstate.flushed = true;
#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;
hnswshared->indtuples += buildstate.indtuples;
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 */
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

@@ -185,6 +185,7 @@ hnswgettuple(IndexScanDesc scan, ScanDirection dir)
scan->xs_ctup.t_self = *tid;
#endif
/* Unpin buffer */
if (BufferIsValid(so->buf))
ReleaseBuffer(so->buf);

View File

@@ -623,10 +623,6 @@ HnswSearchLayer(Datum q, List *ep, int ef, int lc, Relation index, FmgrInfo *pro
Assert(!e->element->deleted);
/* Skip self for vacuuming update */
if (skipElement != NULL && e->element->blkno == skipElement->blkno && e->element->offno == skipElement->offno)
continue;
/* Make robust to issues */
if (e->element->level < lc)
continue;
@@ -913,10 +909,10 @@ HnswUpdateConnection(HnswElement element, HnswCandidate * hc, int m, int lc, int
}
/*
* Remove elements being deleted
* Remove elements being deleted or skipped
*/
static List *
RemoveElementsBeingDeleted(List *w)
RemoveElements(List *w, HnswElement skipElement)
{
ListCell *lc2;
List *w2 = NIL;
@@ -925,6 +921,10 @@ RemoveElementsBeingDeleted(List *w)
{
HnswCandidate *hc = (HnswCandidate *) lfirst(lc2);
/* Skip self for vacuuming update */
if (skipElement != NULL && hc->element->blkno == skipElement->blkno && hc->element->offno == skipElement->offno)
continue;
if (list_length(hc->element->heaptids) != 0)
w2 = lappend(w2, hc);
}
@@ -963,20 +963,27 @@ HnswInsertElement(HnswElement element, HnswElement entryPoint, Relation index, F
if (level > entryLevel)
level = entryLevel;
/* Add one for existing element */
if (existing)
efConstruction++;
/* 2nd phase */
for (int lc = level; lc >= 0; lc--)
{
int lm = HnswGetLayerM(m, lc);
List *neighbors;
List *lw;
w = HnswSearchLayer(q, ep, efConstruction, lc, index, procinfo, collation, true, skipElement);
/* Elements being deleted can help with search */
/* Elements being deleted or skipped can help with search */
/* but should be removed before selecting neighbors */
if (index != NULL)
w = RemoveElementsBeingDeleted(w);
lw = RemoveElements(w, skipElement);
else
lw = w;
neighbors = SelectNeighbors(w, lm, lc, procinfo, collation, NULL);
neighbors = SelectNeighbors(lw, lm, lc, procinfo, collation, NULL);
AddConnections(element, neighbors, lm, lc);

View File

@@ -611,67 +611,6 @@ FreeVacuumState(HnswVacuumState * vacuumstate)
MemoryContextDelete(vacuumstate->tmpCtx);
}
/*
* Print graph
*/
#ifdef HNSW_DEBUG
static void
PrintGraph(HnswVacuumState * vacuumstate)
{
BlockNumber blkno = HNSW_HEAD_BLKNO;
Relation index = vacuumstate->index;
while (BlockNumberIsValid(blkno))
{
Buffer buf;
Page page;
OffsetNumber offno;
OffsetNumber maxoffno;
buf = ReadBuffer(index, blkno);
LockBuffer(buf, BUFFER_LOCK_SHARE);
page = BufferGetPage(buf);
maxoffno = PageGetMaxOffsetNumber(page);
for (offno = FirstOffsetNumber; offno <= maxoffno; offno = OffsetNumberNext(offno))
{
HnswElementTuple etup = (HnswElementTuple) PageGetItem(page, PageGetItemId(page, offno));
HnswElement element;
/* Skip neighbor tuples */
if (!HnswIsElementTuple(etup))
continue;
/* Skip deleted tuples */
if (etup->deleted)
continue;
element = HnswInitElementFromBlock(blkno, offno);
HnswLoadElementFromTuple(element, etup, false, true);
HnswLoadNeighbors(element, index);
elog(INFO, "element (%d,%d)", element->blkno, element->offno);
for (int lc = element->level; lc >= 0; lc--)
{
HnswNeighborArray *neighbors = &element->neighbors[lc];
for (int i = 0; i < neighbors->length; i++)
{
HnswElement e = neighbors->items[i].element;
elog(INFO, "%d: (%d,%d)", lc, e->blkno, e->offno);
}
}
}
blkno = HnswPageGetOpaque(page)->nextblkno;
UnlockReleaseBuffer(buf);
}
}
#endif
/*
* Bulk delete tuples from the index
*/
@@ -692,10 +631,6 @@ hnswbulkdelete(IndexVacuumInfo *info, IndexBulkDeleteResult *stats,
/* Pass 3: Mark as deleted */
MarkDeleted(&vacuumstate);
#ifdef HNSW_DEBUG
PrintGraph(&vacuumstate);
#endif
FreeVacuumState(&vacuumstate);
return vacuumstate.stats;

View File

@@ -343,6 +343,7 @@ ivfflatgettuple(IndexScanDesc scan, ScanDirection dir)
scan->xs_ctup.t_self = *tid;
#endif
/* Unpin buffer */
if (BufferIsValid(so->buf))
ReleaseBuffer(so->buf);

View File

@@ -83,11 +83,31 @@ for my $i (0 .. $#operators)
push(@expected, $res);
}
# Add index
$node->safe_psql("postgres", "CREATE INDEX ON tst USING hnsw (v $opclass);");
# Build index serially
$node->safe_psql("postgres", qq(
SET max_parallel_maintenance_workers = 0;
CREATE INDEX idx 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;
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();

View File

@@ -1,4 +1,4 @@
comment = 'vector data type and ivfflat access method'
default_version = '0.4.4'
default_version = '0.5.0'
module_pathname = '$libdir/vector'
relocatable = true