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v0.8.0
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hnsw-strea
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6
.github/workflows/build.yml
vendored
6
.github/workflows/build.yml
vendored
@@ -8,8 +8,8 @@ jobs:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
include:
|
||||
- postgres: 18
|
||||
os: ubuntu-24.04
|
||||
# - postgres: 18
|
||||
# os: ubuntu-24.04
|
||||
- postgres: 17
|
||||
os: ubuntu-24.04
|
||||
- postgres: 16
|
||||
@@ -49,7 +49,7 @@ jobs:
|
||||
- postgres: 16
|
||||
os: macos-14
|
||||
- postgres: 14
|
||||
os: macos-13
|
||||
os: macos-12
|
||||
steps:
|
||||
- uses: actions/checkout@v4
|
||||
- uses: ankane/setup-postgres@v1
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
## 0.8.0 (2024-10-30)
|
||||
## 0.8.0 (unreleased)
|
||||
|
||||
- Added support for iterative index scans
|
||||
- Added casts for arrays to `sparsevec`
|
||||
- Improved cost estimation for better index selection when filtering
|
||||
- Improved performance of HNSW index scans
|
||||
- Improved cost estimation
|
||||
- Improved performance of HNSW inserts and on-disk index builds
|
||||
- Reduced memory usage for HNSW index scans
|
||||
- Dropped support for Postgres 12
|
||||
|
||||
## 0.7.4 (2024-08-05)
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
"name": "vector",
|
||||
"abstract": "Open-source vector similarity search for Postgres",
|
||||
"description": "Supports L2 distance, inner product, and cosine distance",
|
||||
"version": "0.8.0",
|
||||
"version": "0.7.4",
|
||||
"maintainer": [
|
||||
"Andrew Kane <andrew@ankane.org>"
|
||||
],
|
||||
@@ -12,7 +12,7 @@
|
||||
"prereqs": {
|
||||
"runtime": {
|
||||
"requires": {
|
||||
"PostgreSQL": "13.0.0"
|
||||
"PostgreSQL": "12.0.0"
|
||||
}
|
||||
}
|
||||
},
|
||||
@@ -20,7 +20,7 @@
|
||||
"vector": {
|
||||
"file": "sql/vector.sql",
|
||||
"docfile": "README.md",
|
||||
"version": "0.8.0",
|
||||
"version": "0.7.4",
|
||||
"abstract": "Open-source vector similarity search for Postgres"
|
||||
}
|
||||
},
|
||||
|
||||
2
Makefile
2
Makefile
@@ -1,5 +1,5 @@
|
||||
EXTENSION = vector
|
||||
EXTVERSION = 0.8.0
|
||||
EXTVERSION = 0.7.4
|
||||
|
||||
MODULE_big = vector
|
||||
DATA = $(wildcard sql/*--*--*.sql)
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
EXTENSION = vector
|
||||
EXTVERSION = 0.8.0
|
||||
EXTVERSION = 0.7.4
|
||||
|
||||
DATA_built = sql\$(EXTENSION)--$(EXTVERSION).sql
|
||||
OBJS = src\bitutils.obj src\bitvec.obj src\halfutils.obj src\halfvec.obj src\hnsw.obj src\hnswbuild.obj src\hnswinsert.obj src\hnswscan.obj src\hnswutils.obj src\hnswvacuum.obj src\ivfbuild.obj src\ivfflat.obj src\ivfinsert.obj src\ivfkmeans.obj src\ivfscan.obj src\ivfutils.obj src\ivfvacuum.obj src\sparsevec.obj src\vector.obj
|
||||
|
||||
115
README.md
115
README.md
@@ -17,11 +17,11 @@ Plus [ACID](https://en.wikipedia.org/wiki/ACID) compliance, point-in-time recove
|
||||
|
||||
### Linux and Mac
|
||||
|
||||
Compile and install the extension (supports Postgres 13+)
|
||||
Compile and install the extension (supports Postgres 12+)
|
||||
|
||||
```sh
|
||||
cd /tmp
|
||||
git clone --branch v0.8.0 https://github.com/pgvector/pgvector.git
|
||||
git clone --branch v0.7.4 https://github.com/pgvector/pgvector.git
|
||||
cd pgvector
|
||||
make
|
||||
make install # may need sudo
|
||||
@@ -46,7 +46,7 @@ Then use `nmake` to build:
|
||||
```cmd
|
||||
set "PGROOT=C:\Program Files\PostgreSQL\16"
|
||||
cd %TEMP%
|
||||
git clone --branch v0.8.0 https://github.com/pgvector/pgvector.git
|
||||
git clone --branch v0.7.4 https://github.com/pgvector/pgvector.git
|
||||
cd pgvector
|
||||
nmake /F Makefile.win
|
||||
nmake /F Makefile.win install
|
||||
@@ -324,7 +324,7 @@ For a large number of workers, you may also need to increase `max_parallel_worke
|
||||
|
||||
### 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) with Postgres 12+
|
||||
|
||||
```sql
|
||||
SELECT phase, round(100.0 * blocks_done / nullif(blocks_total, 0), 1) AS "%" FROM pg_stat_progress_create_index;
|
||||
@@ -410,7 +410,7 @@ For a large number of workers, you may also need to increase `max_parallel_worke
|
||||
|
||||
### 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) with Postgres 12+
|
||||
|
||||
```sql
|
||||
SELECT phase, round(100.0 * tuples_done / nullif(tuples_total, 0), 1) AS "%" FROM pg_stat_progress_create_index;
|
||||
@@ -427,115 +427,92 @@ Note: `%` is only populated during the `loading tuples` phase
|
||||
|
||||
## Filtering
|
||||
|
||||
There are a few ways to index nearest neighbor queries with a `WHERE` clause.
|
||||
There are a few ways to index nearest neighbor queries with a `WHERE` clause
|
||||
|
||||
```sql
|
||||
SELECT * FROM items WHERE category_id = 123 ORDER BY embedding <-> '[3,1,2]' LIMIT 5;
|
||||
```
|
||||
|
||||
A good place to start is creating an index on the filter column. This can provide fast, exact nearest neighbor search in many cases. Postgres has a number of [index types](https://www.postgresql.org/docs/current/indexes-types.html) for this: B-tree (default), hash, GiST, SP-GiST, GIN, and BRIN.
|
||||
Create an index on one [or more](https://www.postgresql.org/docs/current/indexes-multicolumn.html) of the `WHERE` columns for exact search
|
||||
|
||||
```sql
|
||||
CREATE INDEX ON items (category_id);
|
||||
```
|
||||
|
||||
For multiple columns, consider a [multicolumn index](https://www.postgresql.org/docs/current/indexes-multicolumn.html).
|
||||
|
||||
```sql
|
||||
CREATE INDEX ON items (location_id, category_id);
|
||||
```
|
||||
|
||||
Exact indexes work well for conditions that match a low percentage of rows. Otherwise, [approximate indexes](#indexing) can work better.
|
||||
|
||||
```sql
|
||||
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`.
|
||||
|
||||
```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
|
||||
SET hnsw.iterative_scan = strict_order;
|
||||
```
|
||||
|
||||
If filtering by only a few distinct values, consider [partial indexing](https://www.postgresql.org/docs/current/indexes-partial.html).
|
||||
Or a [partial index](https://www.postgresql.org/docs/current/indexes-partial.html) on the vector column for approximate search
|
||||
|
||||
```sql
|
||||
CREATE INDEX ON items USING hnsw (embedding vector_l2_ops) WHERE (category_id = 123);
|
||||
```
|
||||
|
||||
If filtering by many different values, consider [partitioning](https://www.postgresql.org/docs/current/ddl-partitioning.html).
|
||||
Use [partitioning](https://www.postgresql.org/docs/current/ddl-partitioning.html) for approximate search on many different values of the `WHERE` columns
|
||||
|
||||
```sql
|
||||
CREATE TABLE items (embedding vector(3), category_id int) PARTITION BY LIST(category_id);
|
||||
```
|
||||
|
||||
## Iterative Index Scans
|
||||
## Iterative Search [unreleased]
|
||||
|
||||
*Added in 0.8.0*
|
||||
|
||||
With approximate indexes, queries with filtering can return less results since filtering is applied *after* the index is scanned. Starting with 0.8.0, you can enable iterative index scans, which will automatically scan more of the index until enough results are found (or it reaches `hnsw.max_scan_tuples` or `ivfflat.max_probes`).
|
||||
With approximate indexes, you can end up with less results than expected due to filtering conditions in the query.
|
||||
|
||||
Iterative scans can use strict or relaxed ordering.
|
||||
Starting with 0.8.0, you can enable iterative search. If too few results from the initial index scan match the query filters, it will resume scanning until enough results are found. This can significantly improve recall (at the cost of speed).
|
||||
|
||||
Strict ensures results are in the exact order by distance
|
||||
|
||||
```sql
|
||||
SET hnsw.iterative_scan = strict_order;
|
||||
```tsql
|
||||
SET hnsw.streaming = on;
|
||||
-- or
|
||||
SET ivfflat.streaming = on;
|
||||
```
|
||||
|
||||
Relaxed allows results to be slightly out of order by distance, but provides better recall
|
||||
However, there are some important caveats.
|
||||
|
||||
### Iterative Caveats
|
||||
|
||||
With iterative search, it’s possible for rows to be slightly out of order by distance. For strict ordering, use:
|
||||
|
||||
```sql
|
||||
SET hnsw.iterative_scan = relaxed_order;
|
||||
# or
|
||||
SET ivfflat.iterative_scan = relaxed_order;
|
||||
WITH approx_order AS MATERIALIZED (
|
||||
SELECT *, embedding <-> '[1,2,3]' AS distance FROM items WHERE ... ORDER BY distance LIMIT 5
|
||||
) SELECT * FROM approx_order ORDER BY distance;
|
||||
```
|
||||
|
||||
With relaxed ordering, you can use a [materialized CTE](https://www.postgresql.org/docs/current/queries-with.html#QUERIES-WITH-CTE-MATERIALIZATION) to get strict ordering
|
||||
For distance filters, use a CTE and place the filter outside it.
|
||||
|
||||
```sql
|
||||
WITH relaxed_results AS MATERIALIZED (
|
||||
SELECT id, embedding <-> '[1,2,3]' AS distance FROM items WHERE category_id = 123 ORDER BY distance LIMIT 5
|
||||
) SELECT * FROM relaxed_results ORDER BY distance;
|
||||
WITH approx_order AS MATERIALIZED (
|
||||
SELECT *, embedding <-> '[1,2,3]' AS distance FROM items WHERE ... ORDER BY distance LIMIT 5
|
||||
) SELECT * FROM approx_order WHERE distance < 0.1 ORDER BY distance;
|
||||
```
|
||||
|
||||
For queries that filter by distance, use a materialized CTE and place the distance filter outside of it for best performance (due to the [current behavior](https://www.postgresql.org/message-id/flat/CAOdR5yGUoMQ6j7M5hNUXrySzaqZVGf_Ne%2B8fwZMRKTFxU1nbJg%40mail.gmail.com) of the Postgres executor)
|
||||
### Iterative Options
|
||||
|
||||
```sql
|
||||
WITH nearest_results AS MATERIALIZED (
|
||||
SELECT id, embedding <-> '[1,2,3]' AS distance FROM items ORDER BY distance LIMIT 5
|
||||
) SELECT * FROM nearest_results WHERE distance < 5 ORDER BY distance;
|
||||
```
|
||||
|
||||
Note: Place any other filters inside the CTE
|
||||
|
||||
### Iterative Scan Options
|
||||
|
||||
Since scanning a large portion of an approximate index is expensive, there are options to control when a scan ends.
|
||||
Since scanning a large portion of the index is expensive, there are options to control when the scan ends.
|
||||
|
||||
#### HNSW
|
||||
|
||||
Specify the max number of tuples to visit (20,000 by default)
|
||||
Specify the max number of additional tuples visited
|
||||
|
||||
```sql
|
||||
SET hnsw.max_scan_tuples = 20000;
|
||||
SET hnsw.ef_stream = 10000;
|
||||
```
|
||||
|
||||
Note: This is approximate and does not affect the initial scan
|
||||
|
||||
Specify the max amount of memory to use, as a multiple of `work_mem` (1 by default)
|
||||
The scan will also end if reaches `work_mem`. You can see when this happens by enabling debug messages.
|
||||
|
||||
```sql
|
||||
SET hnsw.scan_mem_multiplier = 2;
|
||||
SET client_min_messages = debug1;
|
||||
```
|
||||
|
||||
Note: Try increasing this if increasing `hnsw.max_scan_tuples` does not improve recall
|
||||
```text
|
||||
DEBUG: hnsw index scan exceeded work_mem after 10000 tuples
|
||||
HINT: Increase work_mem to scan more tuples.
|
||||
```
|
||||
|
||||
If the server has enough memory, you can adjust this with:
|
||||
|
||||
```sql
|
||||
SET work_mem = '8MB';
|
||||
```
|
||||
|
||||
#### IVFFlat
|
||||
|
||||
@@ -545,8 +522,6 @@ Specify the max number of probes
|
||||
SET ivfflat.max_probes = 100;
|
||||
```
|
||||
|
||||
Note: If this is lower than `ivfflat.probes`, `ivfflat.probes` will be used
|
||||
|
||||
## Half-Precision Vectors
|
||||
|
||||
*Added in 0.7.0*
|
||||
@@ -1153,7 +1128,7 @@ This adds pgvector to the [Postgres image](https://hub.docker.com/_/postgres) (r
|
||||
You can also build the image manually:
|
||||
|
||||
```sh
|
||||
git clone --branch v0.8.0 https://github.com/pgvector/pgvector.git
|
||||
git clone --branch v0.7.4 https://github.com/pgvector/pgvector.git
|
||||
cd pgvector
|
||||
docker build --pull --build-arg PG_MAJOR=17 -t myuser/pgvector .
|
||||
```
|
||||
|
||||
40
src/hnsw.c
40
src/hnsw.c
@@ -18,17 +18,18 @@
|
||||
#define MarkGUCPrefixReserved(x) EmitWarningsOnPlaceholders(x)
|
||||
#endif
|
||||
|
||||
static const struct config_enum_entry hnsw_iterative_scan_options[] = {
|
||||
{"off", HNSW_ITERATIVE_SCAN_OFF, false},
|
||||
{"relaxed_order", HNSW_ITERATIVE_SCAN_RELAXED, false},
|
||||
{"strict_order", HNSW_ITERATIVE_SCAN_STRICT, false},
|
||||
static const struct config_enum_entry hnsw_iterative_search_options[] = {
|
||||
{"off", HNSW_ITERATIVE_SEARCH_OFF, false},
|
||||
{"strict", HNSW_ITERATIVE_SEARCH_STRICT, false},
|
||||
{"relaxed", HNSW_ITERATIVE_SEARCH_RELAXED, false},
|
||||
/* TODO Change to strict before merging */
|
||||
{"on", HNSW_ITERATIVE_SEARCH_RELAXED, false},
|
||||
{NULL, 0, false}
|
||||
};
|
||||
|
||||
int hnsw_ef_search;
|
||||
int hnsw_iterative_scan;
|
||||
int hnsw_max_scan_tuples;
|
||||
double hnsw_scan_mem_multiplier;
|
||||
int hnsw_max_iterative_tuples;
|
||||
int hnsw_iterative_search;
|
||||
int hnsw_lock_tranche_id;
|
||||
static relopt_kind hnsw_relopt_kind;
|
||||
|
||||
@@ -79,19 +80,16 @@ HnswInit(void)
|
||||
"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);
|
||||
|
||||
DefineCustomEnumVariable("hnsw.iterative_scan", "Sets the mode for iterative scans",
|
||||
NULL, &hnsw_iterative_scan,
|
||||
HNSW_ITERATIVE_SCAN_OFF, hnsw_iterative_scan_options, PGC_USERSET, 0, NULL, NULL, NULL);
|
||||
/* TODO Change name */
|
||||
DefineCustomEnumVariable("hnsw.streaming", "Iterative search mode",
|
||||
NULL, &hnsw_iterative_search,
|
||||
HNSW_ITERATIVE_SEARCH_OFF, hnsw_iterative_search_options, PGC_USERSET, 0, NULL, NULL, NULL);
|
||||
|
||||
/* This is approximate and does not affect the initial scan */
|
||||
DefineCustomIntVariable("hnsw.max_scan_tuples", "Sets the max number of tuples to visit for iterative scans",
|
||||
NULL, &hnsw_max_scan_tuples,
|
||||
20000, 1, INT_MAX, PGC_USERSET, 0, NULL, NULL, NULL);
|
||||
|
||||
/* Same range as hash_mem_multiplier */
|
||||
DefineCustomRealVariable("hnsw.scan_mem_multiplier", "Sets the multiple of work_mem to use for iterative scans",
|
||||
NULL, &hnsw_scan_mem_multiplier,
|
||||
1, 1, 1000, PGC_USERSET, 0, NULL, NULL, NULL);
|
||||
/* TODO Change name */
|
||||
/* TODO Ensure ivfflat.max_probes uses same value for "all" */
|
||||
DefineCustomIntVariable("hnsw.ef_stream", "Sets the max number of additional candidates to visit for streaming search",
|
||||
"-1 means all", &hnsw_max_iterative_tuples,
|
||||
HNSW_DEFAULT_EF_STREAM, HNSW_MIN_EF_STREAM, HNSW_MAX_EF_STREAM, PGC_USERSET, 0, NULL, NULL, NULL);
|
||||
|
||||
MarkGUCPrefixReserved("hnsw");
|
||||
}
|
||||
@@ -137,10 +135,6 @@ hnswcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
|
||||
*indexSelectivity = 0;
|
||||
*indexCorrelation = 0;
|
||||
*indexPages = 0;
|
||||
#if PG_VERSION_NUM >= 180000
|
||||
/* See "On disable_cost" thread on pgsql-hackers */
|
||||
path->path.disabled_nodes = 2;
|
||||
#endif
|
||||
return;
|
||||
}
|
||||
|
||||
|
||||
19
src/hnsw.h
19
src/hnsw.h
@@ -42,6 +42,9 @@
|
||||
#define HNSW_DEFAULT_EF_SEARCH 40
|
||||
#define HNSW_MIN_EF_SEARCH 1
|
||||
#define HNSW_MAX_EF_SEARCH 1000
|
||||
#define HNSW_DEFAULT_EF_STREAM -1
|
||||
#define HNSW_MIN_EF_STREAM -1
|
||||
#define HNSW_MAX_EF_STREAM INT_MAX
|
||||
|
||||
/* Tuple types */
|
||||
#define HNSW_ELEMENT_TUPLE_TYPE 1
|
||||
@@ -109,17 +112,16 @@
|
||||
|
||||
/* Variables */
|
||||
extern int hnsw_ef_search;
|
||||
extern int hnsw_iterative_scan;
|
||||
extern int hnsw_max_scan_tuples;
|
||||
extern double hnsw_scan_mem_multiplier;
|
||||
extern int hnsw_max_iterative_tuples;
|
||||
extern int hnsw_iterative_search;
|
||||
extern int hnsw_lock_tranche_id;
|
||||
|
||||
typedef enum HnswIterativeScanMode
|
||||
typedef enum HnswIterativeSearchType
|
||||
{
|
||||
HNSW_ITERATIVE_SCAN_OFF,
|
||||
HNSW_ITERATIVE_SCAN_RELAXED,
|
||||
HNSW_ITERATIVE_SCAN_STRICT
|
||||
} HnswIterativeScanMode;
|
||||
HNSW_ITERATIVE_SEARCH_OFF,
|
||||
HNSW_ITERATIVE_SEARCH_STRICT,
|
||||
HNSW_ITERATIVE_SEARCH_RELAXED
|
||||
} HnswIterativeSearchType;
|
||||
|
||||
typedef struct HnswElementData HnswElementData;
|
||||
typedef struct HnswNeighborArray HnswNeighborArray;
|
||||
@@ -373,7 +375,6 @@ typedef struct HnswScanOpaqueData
|
||||
int m;
|
||||
int64 tuples;
|
||||
double previousDistance;
|
||||
Size maxMemory;
|
||||
MemoryContext tmpCtx;
|
||||
|
||||
/* Support functions */
|
||||
|
||||
@@ -41,7 +41,7 @@ GetScanItems(IndexScanDesc scan, Datum value)
|
||||
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);
|
||||
return HnswSearchLayer(base, q, ep, hnsw_ef_search, 0, index, support, m, false, NULL, &so->v, hnsw_iterative_search != HNSW_ITERATIVE_SEARCH_OFF ? &so->discarded : NULL, true, &so->tuples);
|
||||
}
|
||||
|
||||
/*
|
||||
@@ -102,17 +102,6 @@ GetScanValue(IndexScanDesc scan)
|
||||
return value;
|
||||
}
|
||||
|
||||
#if defined(HNSW_MEMORY)
|
||||
/*
|
||||
* Show memory usage
|
||||
*/
|
||||
static void
|
||||
ShowMemoryUsage(HnswScanOpaque so)
|
||||
{
|
||||
elog(INFO, "memory: %zu KB, tuples: " INT64_FORMAT, MemoryContextMemAllocated(so->tmpCtx, false) / 1024, so->tuples);
|
||||
}
|
||||
#endif
|
||||
|
||||
/*
|
||||
* Prepare for an index scan
|
||||
*/
|
||||
@@ -121,29 +110,21 @@ hnswbeginscan(Relation index, int nkeys, int norderbys)
|
||||
{
|
||||
IndexScanDesc scan;
|
||||
HnswScanOpaque so;
|
||||
double maxMemory;
|
||||
|
||||
scan = RelationGetIndexScan(index, nkeys, norderbys);
|
||||
|
||||
so = (HnswScanOpaque) palloc(sizeof(HnswScanOpaqueData));
|
||||
so->typeInfo = HnswGetTypeInfo(index);
|
||||
so->first = true;
|
||||
so->v.tids = NULL;
|
||||
so->discarded = NULL;
|
||||
so->tmpCtx = AllocSetContextCreate(CurrentMemoryContext,
|
||||
"Hnsw scan temporary context",
|
||||
ALLOCSET_DEFAULT_SIZES);
|
||||
|
||||
/* Set support functions */
|
||||
HnswInitSupport(&so->support, index);
|
||||
|
||||
/*
|
||||
* Use a lower max allocation size than default to allow scanning more
|
||||
* tuples for iterative search before exceeding work_mem
|
||||
*/
|
||||
so->tmpCtx = AllocSetContextCreate(CurrentMemoryContext,
|
||||
"Hnsw scan temporary context",
|
||||
0, 8 * 1024, 256 * 1024);
|
||||
|
||||
/* Calculate max memory */
|
||||
/* Add 256 extra bytes to fill last block when close */
|
||||
maxMemory = (double) work_mem * hnsw_scan_mem_multiplier * 1024.0 + 256;
|
||||
so->maxMemory = Min(maxMemory, (double) SIZE_MAX);
|
||||
|
||||
scan->opaque = so;
|
||||
|
||||
return scan;
|
||||
@@ -157,10 +138,13 @@ hnswrescan(IndexScanDesc scan, ScanKey keys, int nkeys, ScanKey orderbys, int no
|
||||
{
|
||||
HnswScanOpaque so = (HnswScanOpaque) scan->opaque;
|
||||
|
||||
if (so->v.tids != NULL)
|
||||
tidhash_reset(so->v.tids);
|
||||
|
||||
if (so->discarded != NULL)
|
||||
pairingheap_reset(so->discarded);
|
||||
|
||||
so->first = true;
|
||||
/* v and discarded are allocated in tmpCtx */
|
||||
so->v.tids = NULL;
|
||||
so->discarded = NULL;
|
||||
so->tuples = 0;
|
||||
so->previousDistance = -get_float8_infinity();
|
||||
MemoryContextReset(so->tmpCtx);
|
||||
@@ -220,7 +204,7 @@ hnswgettuple(IndexScanDesc scan, ScanDirection dir)
|
||||
so->first = false;
|
||||
|
||||
#if defined(HNSW_MEMORY)
|
||||
ShowMemoryUsage(so);
|
||||
elog(INFO, "memory: %zu KB", MemoryContextMemAllocated(so->tmpCtx, false) / 1024);
|
||||
#endif
|
||||
}
|
||||
|
||||
@@ -233,15 +217,15 @@ hnswgettuple(IndexScanDesc scan, ScanDirection dir)
|
||||
|
||||
if (list_length(so->w) == 0)
|
||||
{
|
||||
if (hnsw_iterative_scan == HNSW_ITERATIVE_SCAN_OFF)
|
||||
if (hnsw_iterative_search == HNSW_ITERATIVE_SEARCH_OFF)
|
||||
break;
|
||||
|
||||
/* Empty index */
|
||||
if (so->discarded == NULL)
|
||||
break;
|
||||
|
||||
/* Reached max number of tuples or memory limit */
|
||||
if (so->tuples >= hnsw_max_scan_tuples || MemoryContextMemAllocated(so->tmpCtx, false) > so->maxMemory)
|
||||
/* Reached max number of additional tuples */
|
||||
if (hnsw_max_iterative_tuples != -1 && so->tuples >= hnsw_ef_search + hnsw_max_iterative_tuples)
|
||||
{
|
||||
if (pairingheap_is_empty(so->discarded))
|
||||
break;
|
||||
@@ -249,6 +233,21 @@ hnswgettuple(IndexScanDesc scan, ScanDirection dir)
|
||||
/* Return remaining tuples */
|
||||
so->w = lappend(so->w, HnswGetSearchCandidate(w_node, pairingheap_remove_first(so->discarded)));
|
||||
}
|
||||
/* Prevent scans from consuming too much memory */
|
||||
else if (MemoryContextMemAllocated(so->tmpCtx, false) > (Size) work_mem * 1024L)
|
||||
{
|
||||
if (pairingheap_is_empty(so->discarded))
|
||||
{
|
||||
ereport(DEBUG1,
|
||||
(errmsg("hnsw index scan exceeded work_mem after " INT64_FORMAT " tuples", so->tuples),
|
||||
errhint("Increase work_mem to scan more tuples.")));
|
||||
|
||||
break;
|
||||
}
|
||||
|
||||
/* Return remaining tuples */
|
||||
so->w = lappend(so->w, HnswGetSearchCandidate(w_node, pairingheap_remove_first(so->discarded)));
|
||||
}
|
||||
else
|
||||
{
|
||||
/*
|
||||
@@ -267,7 +266,7 @@ hnswgettuple(IndexScanDesc scan, ScanDirection dir)
|
||||
UnlockPage(scan->indexRelation, HNSW_SCAN_LOCK, ShareLock);
|
||||
|
||||
#if defined(HNSW_MEMORY)
|
||||
ShowMemoryUsage(so);
|
||||
elog(INFO, "memory: %zu KB", MemoryContextMemAllocated(so->tmpCtx, false) / 1024);
|
||||
#endif
|
||||
}
|
||||
|
||||
@@ -284,7 +283,7 @@ hnswgettuple(IndexScanDesc scan, ScanDirection dir)
|
||||
so->w = list_delete_last(so->w);
|
||||
|
||||
/* Mark memory as free for next iteration */
|
||||
if (hnsw_iterative_scan != HNSW_ITERATIVE_SCAN_OFF)
|
||||
if (hnsw_iterative_search != HNSW_ITERATIVE_SEARCH_OFF)
|
||||
{
|
||||
pfree(element);
|
||||
pfree(sc);
|
||||
@@ -295,7 +294,7 @@ hnswgettuple(IndexScanDesc scan, ScanDirection dir)
|
||||
|
||||
heaptid = &element->heaptids[--element->heaptidsLength];
|
||||
|
||||
if (hnsw_iterative_scan == HNSW_ITERATIVE_SCAN_STRICT)
|
||||
if (hnsw_iterative_search == HNSW_ITERATIVE_SEARCH_STRICT)
|
||||
{
|
||||
if (sc->distance < so->previousDistance)
|
||||
continue;
|
||||
|
||||
@@ -581,34 +581,21 @@ GetElementDistance(char *base, HnswElement element, HnswQuery * q, HnswSupport *
|
||||
return HnswGetDistance(q->value, value, support);
|
||||
}
|
||||
|
||||
/*
|
||||
* Allocate a search candidate
|
||||
*/
|
||||
static HnswSearchCandidate *
|
||||
HnswInitSearchCandidate(char *base, HnswElement element, double distance)
|
||||
{
|
||||
HnswSearchCandidate *sc = palloc(sizeof(HnswSearchCandidate));
|
||||
|
||||
HnswPtrStore(base, sc->element, element);
|
||||
sc->distance = distance;
|
||||
return sc;
|
||||
}
|
||||
|
||||
/*
|
||||
* Create a candidate for the entry point
|
||||
*/
|
||||
HnswSearchCandidate *
|
||||
HnswEntryCandidate(char *base, HnswElement entryPoint, HnswQuery * q, Relation index, HnswSupport * support, bool loadVec)
|
||||
{
|
||||
HnswSearchCandidate *sc = palloc(sizeof(HnswSearchCandidate));
|
||||
bool inMemory = index == NULL;
|
||||
double distance;
|
||||
|
||||
HnswPtrStore(base, sc->element, entryPoint);
|
||||
if (inMemory)
|
||||
distance = GetElementDistance(base, entryPoint, q, support);
|
||||
sc->distance = GetElementDistance(base, entryPoint, q, support);
|
||||
else
|
||||
HnswLoadElement(entryPoint, &distance, q, index, support, loadVec, NULL);
|
||||
|
||||
return HnswInitSearchCandidate(base, entryPoint, distance);
|
||||
HnswLoadElement(entryPoint, &sc->distance, q, index, support, loadVec, NULL);
|
||||
return sc;
|
||||
}
|
||||
|
||||
/*
|
||||
@@ -857,7 +844,6 @@ HnswSearchLayer(char *base, HnswQuery * q, List *ep, int ef, int lc, Relation in
|
||||
{
|
||||
AddToVisited(base, v, sc->element, inMemory, &found);
|
||||
|
||||
/* OK to count elements instead of tuples */
|
||||
if (tuples != NULL)
|
||||
(*tuples)++;
|
||||
}
|
||||
@@ -890,7 +876,6 @@ HnswSearchLayer(char *base, HnswQuery * q, List *ep, int ef, int lc, Relation in
|
||||
else
|
||||
HnswLoadUnvisitedFromDisk(cElement, unvisited, &unvisitedLength, v, index, m, lm, lc);
|
||||
|
||||
/* OK to count elements instead of tuples */
|
||||
if (tuples != NULL)
|
||||
(*tuples) += unvisitedLength;
|
||||
|
||||
@@ -927,7 +912,9 @@ HnswSearchLayer(char *base, HnswQuery * q, List *ep, int ef, int lc, Relation in
|
||||
if (discarded != NULL)
|
||||
{
|
||||
/* Create a new candidate */
|
||||
e = HnswInitSearchCandidate(base, eElement, eDistance);
|
||||
e = palloc(sizeof(HnswSearchCandidate));
|
||||
HnswPtrStore(base, e->element, eElement);
|
||||
e->distance = eDistance;
|
||||
pairingheap_add(*discarded, &e->w_node);
|
||||
}
|
||||
|
||||
@@ -939,7 +926,9 @@ HnswSearchLayer(char *base, HnswQuery * q, List *ep, int ef, int lc, Relation in
|
||||
continue;
|
||||
|
||||
/* Create a new candidate */
|
||||
e = HnswInitSearchCandidate(base, eElement, eDistance);
|
||||
e = palloc(sizeof(HnswSearchCandidate));
|
||||
HnswPtrStore(base, e->element, eElement);
|
||||
e->distance = eDistance;
|
||||
pairingheap_add(C, &e->c_node);
|
||||
pairingheap_add(W, &e->w_node);
|
||||
|
||||
|
||||
@@ -228,11 +228,11 @@ BuildCallback(Relation index, ItemPointer tid, Datum *values,
|
||||
static inline void
|
||||
GetNextTuple(Tuplesortstate *sortstate, TupleDesc tupdesc, TupleTableSlot *slot, IndexTuple *itup, int *list)
|
||||
{
|
||||
Datum value;
|
||||
bool isnull;
|
||||
|
||||
if (tuplesort_gettupleslot(sortstate, true, false, slot, NULL))
|
||||
{
|
||||
Datum value;
|
||||
bool isnull;
|
||||
|
||||
*list = DatumGetInt32(slot_getattr(slot, 1, &isnull));
|
||||
value = slot_getattr(slot, 3, &isnull);
|
||||
|
||||
@@ -254,8 +254,8 @@ InsertTuples(Relation index, IvfflatBuildState * buildstate, ForkNumber forkNum)
|
||||
IndexTuple itup = NULL; /* silence compiler warning */
|
||||
int64 inserted = 0;
|
||||
|
||||
TupleTableSlot *slot = MakeSingleTupleTableSlot(buildstate->sortdesc, &TTSOpsMinimalTuple);
|
||||
TupleDesc tupdesc = buildstate->tupdesc;
|
||||
TupleTableSlot *slot = MakeSingleTupleTableSlot(buildstate->tupdesc, &TTSOpsMinimalTuple);
|
||||
TupleDesc tupdesc = RelationGetDescr(index);
|
||||
|
||||
pgstat_progress_update_param(PROGRESS_CREATEIDX_SUBPHASE, PROGRESS_IVFFLAT_PHASE_LOAD);
|
||||
|
||||
@@ -319,7 +319,6 @@ InitBuildState(IvfflatBuildState * buildstate, Relation heap, Relation index, In
|
||||
buildstate->index = index;
|
||||
buildstate->indexInfo = indexInfo;
|
||||
buildstate->typeInfo = IvfflatGetTypeInfo(index);
|
||||
buildstate->tupdesc = RelationGetDescr(index);
|
||||
|
||||
buildstate->lists = IvfflatGetLists(index);
|
||||
buildstate->dimensions = TupleDescAttr(index->rd_att, 0)->atttypmod;
|
||||
@@ -357,12 +356,12 @@ InitBuildState(IvfflatBuildState * buildstate, Relation heap, Relation index, In
|
||||
errmsg("dimensions must be greater than one for this opclass")));
|
||||
|
||||
/* Create tuple description for sorting */
|
||||
buildstate->sortdesc = CreateTemplateTupleDesc(3);
|
||||
TupleDescInitEntry(buildstate->sortdesc, (AttrNumber) 1, "list", INT4OID, -1, 0);
|
||||
TupleDescInitEntry(buildstate->sortdesc, (AttrNumber) 2, "tid", TIDOID, -1, 0);
|
||||
TupleDescInitEntry(buildstate->sortdesc, (AttrNumber) 3, "vector", buildstate->tupdesc->attrs[0].atttypid, -1, 0);
|
||||
buildstate->tupdesc = CreateTemplateTupleDesc(3);
|
||||
TupleDescInitEntry(buildstate->tupdesc, (AttrNumber) 1, "list", INT4OID, -1, 0);
|
||||
TupleDescInitEntry(buildstate->tupdesc, (AttrNumber) 2, "tid", TIDOID, -1, 0);
|
||||
TupleDescInitEntry(buildstate->tupdesc, (AttrNumber) 3, "vector", RelationGetDescr(index)->attrs[0].atttypid, -1, 0);
|
||||
|
||||
buildstate->slot = MakeSingleTupleTableSlot(buildstate->sortdesc, &TTSOpsVirtual);
|
||||
buildstate->slot = MakeSingleTupleTableSlot(buildstate->tupdesc, &TTSOpsVirtual);
|
||||
|
||||
buildstate->centers = VectorArrayInit(buildstate->lists, buildstate->dimensions, buildstate->typeInfo->itemSize(buildstate->dimensions));
|
||||
buildstate->listInfo = palloc(sizeof(ListInfo) * buildstate->lists);
|
||||
@@ -634,7 +633,7 @@ IvfflatParallelScanAndSort(IvfflatSpool * ivfspool, IvfflatShared * ivfshared, S
|
||||
InitBuildState(&buildstate, ivfspool->heap, ivfspool->index, indexInfo);
|
||||
memcpy(buildstate.centers->items, ivfcenters, buildstate.centers->itemsize * buildstate.centers->maxlen);
|
||||
buildstate.centers->length = buildstate.centers->maxlen;
|
||||
ivfspool->sortstate = InitBuildSortState(buildstate.sortdesc, sortmem, coordinate);
|
||||
ivfspool->sortstate = InitBuildSortState(buildstate.tupdesc, sortmem, coordinate);
|
||||
buildstate.sortstate = ivfspool->sortstate;
|
||||
scan = table_beginscan_parallel(ivfspool->heap,
|
||||
ParallelTableScanFromIvfflatShared(ivfshared));
|
||||
@@ -951,7 +950,7 @@ AssignTuples(IvfflatBuildState * buildstate)
|
||||
}
|
||||
|
||||
/* Begin serial/leader tuplesort */
|
||||
buildstate->sortstate = InitBuildSortState(buildstate->sortdesc, maintenance_work_mem, coordinate);
|
||||
buildstate->sortstate = InitBuildSortState(buildstate->tupdesc, maintenance_work_mem, coordinate);
|
||||
|
||||
/* Add tuples to sort */
|
||||
if (buildstate->heap != NULL)
|
||||
|
||||
@@ -17,16 +17,8 @@
|
||||
#endif
|
||||
|
||||
int ivfflat_probes;
|
||||
int ivfflat_iterative_scan;
|
||||
int ivfflat_max_probes;
|
||||
static relopt_kind ivfflat_relopt_kind;
|
||||
|
||||
static const struct config_enum_entry ivfflat_iterative_scan_options[] = {
|
||||
{"off", IVFFLAT_ITERATIVE_SCAN_OFF, false},
|
||||
{"relaxed_order", IVFFLAT_ITERATIVE_SCAN_RELAXED, false},
|
||||
{NULL, 0, false}
|
||||
};
|
||||
|
||||
/*
|
||||
* Initialize index options and variables
|
||||
*/
|
||||
@@ -41,15 +33,6 @@ IvfflatInit(void)
|
||||
"Valid range is 1..lists.", &ivfflat_probes,
|
||||
IVFFLAT_DEFAULT_PROBES, IVFFLAT_MIN_LISTS, IVFFLAT_MAX_LISTS, PGC_USERSET, 0, NULL, NULL, NULL);
|
||||
|
||||
DefineCustomEnumVariable("ivfflat.iterative_scan", "Sets the mode for iterative scans",
|
||||
NULL, &ivfflat_iterative_scan,
|
||||
IVFFLAT_ITERATIVE_SCAN_OFF, ivfflat_iterative_scan_options, PGC_USERSET, 0, NULL, NULL, NULL);
|
||||
|
||||
/* If this is less than probes, probes is used */
|
||||
DefineCustomIntVariable("ivfflat.max_probes", "Sets the max number of probes for iterative scans",
|
||||
NULL, &ivfflat_max_probes,
|
||||
IVFFLAT_MAX_LISTS, IVFFLAT_MIN_LISTS, IVFFLAT_MAX_LISTS, PGC_USERSET, 0, NULL, NULL, NULL);
|
||||
|
||||
MarkGUCPrefixReserved("ivfflat");
|
||||
}
|
||||
|
||||
@@ -99,10 +82,6 @@ ivfflatcostestimate(PlannerInfo *root, IndexPath *path, double loop_count,
|
||||
*indexSelectivity = 0;
|
||||
*indexCorrelation = 0;
|
||||
*indexPages = 0;
|
||||
#if PG_VERSION_NUM >= 180000
|
||||
/* See "On disable_cost" thread on pgsql-hackers */
|
||||
path->path.disabled_nodes = 2;
|
||||
#endif
|
||||
return;
|
||||
}
|
||||
|
||||
|
||||
@@ -80,14 +80,6 @@
|
||||
|
||||
/* Variables */
|
||||
extern int ivfflat_probes;
|
||||
extern int ivfflat_iterative_scan;
|
||||
extern int ivfflat_max_probes;
|
||||
|
||||
typedef enum IvfflatIterativeScanMode
|
||||
{
|
||||
IVFFLAT_ITERATIVE_SCAN_OFF,
|
||||
IVFFLAT_ITERATIVE_SCAN_RELAXED
|
||||
} IvfflatIterativeScanMode;
|
||||
|
||||
typedef struct VectorArrayData
|
||||
{
|
||||
@@ -173,7 +165,6 @@ typedef struct IvfflatBuildState
|
||||
Relation index;
|
||||
IndexInfo *indexInfo;
|
||||
const IvfflatTypeInfo *typeInfo;
|
||||
TupleDesc tupdesc;
|
||||
|
||||
/* Settings */
|
||||
int dimensions;
|
||||
@@ -207,7 +198,7 @@ typedef struct IvfflatBuildState
|
||||
|
||||
/* Sorting */
|
||||
Tuplesortstate *sortstate;
|
||||
TupleDesc sortdesc;
|
||||
TupleDesc tupdesc;
|
||||
TupleTableSlot *slot;
|
||||
|
||||
/* Memory */
|
||||
@@ -256,11 +247,8 @@ typedef struct IvfflatScanOpaqueData
|
||||
{
|
||||
const IvfflatTypeInfo *typeInfo;
|
||||
int probes;
|
||||
int maxProbes;
|
||||
int dimensions;
|
||||
bool first;
|
||||
Datum value;
|
||||
MemoryContext tmpCtx;
|
||||
|
||||
/* Sorting */
|
||||
Tuplesortstate *sortstate;
|
||||
@@ -277,9 +265,7 @@ typedef struct IvfflatScanOpaqueData
|
||||
|
||||
/* Lists */
|
||||
pairingheap *listQueue;
|
||||
BlockNumber *listPages;
|
||||
int listIndex;
|
||||
IvfflatScanList *lists;
|
||||
IvfflatScanList lists[FLEXIBLE_ARRAY_MEMBER]; /* must come last */
|
||||
} IvfflatScanOpaqueData;
|
||||
|
||||
typedef IvfflatScanOpaqueData * IvfflatScanOpaque;
|
||||
|
||||
@@ -98,7 +98,7 @@ InsertTuple(Relation index, Datum *values, bool *isnull, ItemPointer heap_tid, R
|
||||
IvfflatGetMetaPageInfo(index, NULL, NULL);
|
||||
|
||||
/* Find the insert page - sets the page and list info */
|
||||
FindInsertPage(index, &value, &insertPage, &listInfo);
|
||||
FindInsertPage(index, values, &insertPage, &listInfo);
|
||||
Assert(BlockNumberIsValid(insertPage));
|
||||
originalInsertPage = insertPage;
|
||||
|
||||
|
||||
101
src/ivfscan.c
101
src/ivfscan.c
@@ -10,7 +10,10 @@
|
||||
#include "miscadmin.h"
|
||||
#include "pgstat.h"
|
||||
#include "storage/bufmgr.h"
|
||||
|
||||
#ifdef IVFFLAT_MEMORY
|
||||
#include "utils/memutils.h"
|
||||
#endif
|
||||
|
||||
#define GetScanList(ptr) pairingheap_container(IvfflatScanList, ph_node, ptr)
|
||||
#define GetScanListConst(ptr) pairingheap_const_container(IvfflatScanList, ph_node, ptr)
|
||||
@@ -62,7 +65,7 @@ GetScanLists(IndexScanDesc scan, Datum value)
|
||||
/* Use procinfo from the index instead of scan key for performance */
|
||||
distance = DatumGetFloat8(so->distfunc(so->procinfo, so->collation, PointerGetDatum(&list->center), value));
|
||||
|
||||
if (listCount < so->maxProbes)
|
||||
if (listCount < so->probes)
|
||||
{
|
||||
IvfflatScanList *scanlist;
|
||||
|
||||
@@ -75,7 +78,7 @@ GetScanLists(IndexScanDesc scan, Datum value)
|
||||
pairingheap_add(so->listQueue, &scanlist->ph_node);
|
||||
|
||||
/* Calculate max distance */
|
||||
if (listCount == so->maxProbes)
|
||||
if (listCount == so->probes)
|
||||
maxDistance = GetScanList(pairingheap_first(so->listQueue))->distance;
|
||||
}
|
||||
else if (distance < maxDistance)
|
||||
@@ -99,11 +102,6 @@ GetScanLists(IndexScanDesc scan, Datum value)
|
||||
|
||||
UnlockReleaseBuffer(cbuf);
|
||||
}
|
||||
|
||||
for (int i = listCount - 1; i >= 0; i--)
|
||||
so->listPages[i] = GetScanList(pairingheap_remove_first(so->listQueue))->startPage;
|
||||
|
||||
Assert(pairingheap_is_empty(so->listQueue));
|
||||
}
|
||||
|
||||
/*
|
||||
@@ -114,15 +112,13 @@ GetScanItems(IndexScanDesc scan, Datum value)
|
||||
{
|
||||
IvfflatScanOpaque so = (IvfflatScanOpaque) scan->opaque;
|
||||
TupleDesc tupdesc = RelationGetDescr(scan->indexRelation);
|
||||
double tuples = 0;
|
||||
TupleTableSlot *slot = so->vslot;
|
||||
int batchProbes = 0;
|
||||
|
||||
tuplesort_reset(so->sortstate);
|
||||
|
||||
/* Search closest probes lists */
|
||||
while (so->listIndex < so->maxProbes && (++batchProbes) <= so->probes)
|
||||
while (!pairingheap_is_empty(so->listQueue))
|
||||
{
|
||||
BlockNumber searchPage = so->listPages[so->listIndex++];
|
||||
BlockNumber searchPage = GetScanList(pairingheap_remove_first(so->listQueue))->startPage;
|
||||
|
||||
/* Search all entry pages for list */
|
||||
while (BlockNumberIsValid(searchPage))
|
||||
@@ -160,6 +156,8 @@ GetScanItems(IndexScanDesc scan, Datum value)
|
||||
ExecStoreVirtualTuple(slot);
|
||||
|
||||
tuplesort_puttupleslot(so->sortstate, slot);
|
||||
|
||||
tuples++;
|
||||
}
|
||||
|
||||
searchPage = IvfflatPageGetOpaque(page)->nextblkno;
|
||||
@@ -168,11 +166,13 @@ GetScanItems(IndexScanDesc scan, Datum value)
|
||||
}
|
||||
}
|
||||
|
||||
tuplesort_performsort(so->sortstate);
|
||||
if (tuples < 100)
|
||||
ereport(DEBUG1,
|
||||
(errmsg("index scan found few tuples"),
|
||||
errdetail("Index may have been created with little data."),
|
||||
errhint("Recreate the index and possibly decrease lists.")));
|
||||
|
||||
#if defined(IVFFLAT_MEMORY)
|
||||
elog(INFO, "memory: %zu MB", MemoryContextMemAllocated(CurrentMemoryContext, true) / (1024 * 1024));
|
||||
#endif
|
||||
tuplesort_performsort(so->sortstate);
|
||||
}
|
||||
|
||||
/*
|
||||
@@ -209,13 +209,7 @@ GetScanValue(IndexScanDesc scan)
|
||||
|
||||
/* Normalize if needed */
|
||||
if (so->normprocinfo != NULL)
|
||||
{
|
||||
MemoryContext oldCtx = MemoryContextSwitchTo(so->tmpCtx);
|
||||
|
||||
value = IvfflatNormValue(so->typeInfo, so->collation, value);
|
||||
|
||||
MemoryContextSwitchTo(oldCtx);
|
||||
}
|
||||
}
|
||||
|
||||
return value;
|
||||
@@ -246,30 +240,19 @@ ivfflatbeginscan(Relation index, int nkeys, int norderbys)
|
||||
int lists;
|
||||
int dimensions;
|
||||
int probes = ivfflat_probes;
|
||||
int maxProbes;
|
||||
MemoryContext oldCtx;
|
||||
|
||||
scan = RelationGetIndexScan(index, nkeys, norderbys);
|
||||
|
||||
/* Get lists and dimensions from metapage */
|
||||
IvfflatGetMetaPageInfo(index, &lists, &dimensions);
|
||||
|
||||
if (ivfflat_iterative_scan != IVFFLAT_ITERATIVE_SCAN_OFF)
|
||||
maxProbes = Max(ivfflat_max_probes, probes);
|
||||
else
|
||||
maxProbes = probes;
|
||||
|
||||
if (probes > lists)
|
||||
probes = lists;
|
||||
|
||||
if (maxProbes > lists)
|
||||
maxProbes = lists;
|
||||
|
||||
so = (IvfflatScanOpaque) palloc(sizeof(IvfflatScanOpaqueData));
|
||||
so = (IvfflatScanOpaque) palloc(offsetof(IvfflatScanOpaqueData, lists) + probes * sizeof(IvfflatScanList));
|
||||
so->typeInfo = IvfflatGetTypeInfo(index);
|
||||
so->first = true;
|
||||
so->probes = probes;
|
||||
so->maxProbes = maxProbes;
|
||||
so->dimensions = dimensions;
|
||||
|
||||
/* Set support functions */
|
||||
@@ -277,12 +260,6 @@ ivfflatbeginscan(Relation index, int nkeys, int norderbys)
|
||||
so->normprocinfo = IvfflatOptionalProcInfo(index, IVFFLAT_NORM_PROC);
|
||||
so->collation = index->rd_indcollation[0];
|
||||
|
||||
so->tmpCtx = AllocSetContextCreate(CurrentMemoryContext,
|
||||
"Ivfflat scan temporary context",
|
||||
ALLOCSET_DEFAULT_SIZES);
|
||||
|
||||
oldCtx = MemoryContextSwitchTo(so->tmpCtx);
|
||||
|
||||
/* Create tuple description for sorting */
|
||||
so->tupdesc = CreateTemplateTupleDesc(2);
|
||||
TupleDescInitEntry(so->tupdesc, (AttrNumber) 1, "distance", FLOAT8OID, -1, 0);
|
||||
@@ -303,11 +280,6 @@ ivfflatbeginscan(Relation index, int nkeys, int norderbys)
|
||||
so->bas = GetAccessStrategy(BAS_BULKREAD);
|
||||
|
||||
so->listQueue = pairingheap_allocate(CompareLists, scan);
|
||||
so->listPages = palloc(maxProbes * sizeof(BlockNumber));
|
||||
so->listIndex = 0;
|
||||
so->lists = palloc(maxProbes * sizeof(IvfflatScanList));
|
||||
|
||||
MemoryContextSwitchTo(oldCtx);
|
||||
|
||||
scan->opaque = so;
|
||||
|
||||
@@ -322,9 +294,11 @@ ivfflatrescan(IndexScanDesc scan, ScanKey keys, int nkeys, ScanKey orderbys, int
|
||||
{
|
||||
IvfflatScanOpaque so = (IvfflatScanOpaque) scan->opaque;
|
||||
|
||||
if (!so->first)
|
||||
tuplesort_reset(so->sortstate);
|
||||
|
||||
so->first = true;
|
||||
pairingheap_reset(so->listQueue);
|
||||
so->listIndex = 0;
|
||||
|
||||
if (keys && scan->numberOfKeys > 0)
|
||||
memmove(scan->keyData, keys, scan->numberOfKeys * sizeof(ScanKeyData));
|
||||
@@ -340,8 +314,6 @@ bool
|
||||
ivfflatgettuple(IndexScanDesc scan, ScanDirection dir)
|
||||
{
|
||||
IvfflatScanOpaque so = (IvfflatScanOpaque) scan->opaque;
|
||||
ItemPointer heaptid;
|
||||
bool isnull;
|
||||
|
||||
/*
|
||||
* Index can be used to scan backward, but Postgres doesn't support
|
||||
@@ -369,23 +341,28 @@ ivfflatgettuple(IndexScanDesc scan, ScanDirection dir)
|
||||
IvfflatBench("GetScanLists", GetScanLists(scan, value));
|
||||
IvfflatBench("GetScanItems", GetScanItems(scan, value));
|
||||
so->first = false;
|
||||
so->value = value;
|
||||
|
||||
#if defined(IVFFLAT_MEMORY)
|
||||
elog(INFO, "memory: %zu MB", MemoryContextMemAllocated(CurrentMemoryContext, true) / (1024 * 1024));
|
||||
#endif
|
||||
|
||||
/* Clean up if we allocated a new value */
|
||||
if (value != scan->orderByData->sk_argument)
|
||||
pfree(DatumGetPointer(value));
|
||||
}
|
||||
|
||||
while (!tuplesort_gettupleslot(so->sortstate, true, false, so->mslot, NULL))
|
||||
if (tuplesort_gettupleslot(so->sortstate, true, false, so->mslot, NULL))
|
||||
{
|
||||
if (so->listIndex == so->maxProbes)
|
||||
return false;
|
||||
bool isnull;
|
||||
ItemPointer heaptid = (ItemPointer) DatumGetPointer(slot_getattr(so->mslot, 2, &isnull));
|
||||
|
||||
IvfflatBench("GetScanItems", GetScanItems(scan, so->value));
|
||||
scan->xs_heaptid = *heaptid;
|
||||
scan->xs_recheck = false;
|
||||
scan->xs_recheckorderby = false;
|
||||
return true;
|
||||
}
|
||||
|
||||
heaptid = (ItemPointer) DatumGetPointer(slot_getattr(so->mslot, 2, &isnull));
|
||||
|
||||
scan->xs_heaptid = *heaptid;
|
||||
scan->xs_recheck = false;
|
||||
scan->xs_recheckorderby = false;
|
||||
return true;
|
||||
return false;
|
||||
}
|
||||
|
||||
/*
|
||||
@@ -396,10 +373,12 @@ ivfflatendscan(IndexScanDesc scan)
|
||||
{
|
||||
IvfflatScanOpaque so = (IvfflatScanOpaque) scan->opaque;
|
||||
|
||||
/* Free any temporary files */
|
||||
pairingheap_free(so->listQueue);
|
||||
tuplesort_end(so->sortstate);
|
||||
FreeAccessStrategy(so->bas);
|
||||
FreeTupleDesc(so->tupdesc);
|
||||
|
||||
MemoryContextDelete(so->tmpCtx);
|
||||
/* TODO Free vslot and mslot without freeing TupleDesc */
|
||||
|
||||
pfree(so);
|
||||
scan->opaque = NULL;
|
||||
|
||||
@@ -26,7 +26,7 @@ ivfflatbulkdelete(IndexVacuumInfo *info, IndexBulkDeleteResult *stats,
|
||||
Page cpage;
|
||||
OffsetNumber coffno;
|
||||
OffsetNumber cmaxoffno;
|
||||
BlockNumber listPages[MaxOffsetNumber];
|
||||
BlockNumber startPages[MaxOffsetNumber];
|
||||
ListInfo listInfo;
|
||||
|
||||
cbuf = ReadBuffer(index, blkno);
|
||||
@@ -40,7 +40,7 @@ ivfflatbulkdelete(IndexVacuumInfo *info, IndexBulkDeleteResult *stats,
|
||||
{
|
||||
IvfflatList list = (IvfflatList) PageGetItem(cpage, PageGetItemId(cpage, coffno));
|
||||
|
||||
listPages[coffno - FirstOffsetNumber] = list->startPage;
|
||||
startPages[coffno - FirstOffsetNumber] = list->startPage;
|
||||
}
|
||||
|
||||
listInfo.blkno = blkno;
|
||||
@@ -50,7 +50,7 @@ ivfflatbulkdelete(IndexVacuumInfo *info, IndexBulkDeleteResult *stats,
|
||||
|
||||
for (coffno = FirstOffsetNumber; coffno <= cmaxoffno; coffno = OffsetNumberNext(coffno))
|
||||
{
|
||||
BlockNumber searchPage = listPages[coffno - FirstOffsetNumber];
|
||||
BlockNumber searchPage = startPages[coffno - FirstOffsetNumber];
|
||||
BlockNumber insertPage = InvalidBlockNumber;
|
||||
|
||||
/* Iterate over entry pages */
|
||||
|
||||
@@ -4,7 +4,6 @@
|
||||
#include <math.h>
|
||||
|
||||
#include "catalog/pg_type.h"
|
||||
#include "common/shortest_dec.h"
|
||||
#include "common/string.h"
|
||||
#include "fmgr.h"
|
||||
#include "halfutils.h"
|
||||
@@ -13,10 +12,17 @@
|
||||
#include "sparsevec.h"
|
||||
#include "utils/array.h"
|
||||
#include "utils/builtins.h"
|
||||
#include "utils/float.h"
|
||||
#include "utils/lsyscache.h"
|
||||
#include "vector.h"
|
||||
|
||||
#if PG_VERSION_NUM >= 120000
|
||||
#include "common/shortest_dec.h"
|
||||
#include "utils/float.h"
|
||||
#else
|
||||
#include <float.h>
|
||||
#include "utils/builtins.h"
|
||||
#endif
|
||||
|
||||
typedef struct SparseInputElement
|
||||
{
|
||||
int32 index;
|
||||
|
||||
@@ -99,32 +99,6 @@ SELECT COUNT(*) FROM (SELECT * FROM t ORDER BY val <+> (SELECT NULL::vector)) t2
|
||||
4
|
||||
(1 row)
|
||||
|
||||
DROP TABLE t;
|
||||
-- iterative
|
||||
CREATE TABLE t (val vector(3));
|
||||
INSERT INTO t (val) VALUES ('[0,0,0]'), ('[1,2,3]'), ('[1,1,1]'), (NULL);
|
||||
CREATE INDEX ON t USING hnsw (val vector_l2_ops);
|
||||
SET hnsw.iterative_scan = strict_order;
|
||||
SET hnsw.ef_search = 1;
|
||||
SELECT * FROM t ORDER BY val <-> '[3,3,3]';
|
||||
val
|
||||
---------
|
||||
[1,2,3]
|
||||
[1,1,1]
|
||||
[0,0,0]
|
||||
(3 rows)
|
||||
|
||||
SET hnsw.iterative_scan = relaxed_order;
|
||||
SELECT * FROM t ORDER BY val <-> '[3,3,3]';
|
||||
val
|
||||
---------
|
||||
[1,2,3]
|
||||
[1,1,1]
|
||||
[0,0,0]
|
||||
(3 rows)
|
||||
|
||||
RESET hnsw.iterative_scan;
|
||||
RESET hnsw.ef_search;
|
||||
DROP TABLE t;
|
||||
-- unlogged
|
||||
CREATE UNLOGGED TABLE t (val vector(3));
|
||||
@@ -165,31 +139,4 @@ SET hnsw.ef_search = 0;
|
||||
ERROR: 0 is outside the valid range for parameter "hnsw.ef_search" (1 .. 1000)
|
||||
SET hnsw.ef_search = 1001;
|
||||
ERROR: 1001 is outside the valid range for parameter "hnsw.ef_search" (1 .. 1000)
|
||||
SHOW hnsw.iterative_scan;
|
||||
hnsw.iterative_scan
|
||||
---------------------
|
||||
off
|
||||
(1 row)
|
||||
|
||||
SET hnsw.iterative_scan = on;
|
||||
ERROR: invalid value for parameter "hnsw.iterative_scan": "on"
|
||||
HINT: Available values: off, relaxed_order, strict_order.
|
||||
SHOW hnsw.max_scan_tuples;
|
||||
hnsw.max_scan_tuples
|
||||
----------------------
|
||||
20000
|
||||
(1 row)
|
||||
|
||||
SET hnsw.max_scan_tuples = 0;
|
||||
ERROR: 0 is outside the valid range for parameter "hnsw.max_scan_tuples" (1 .. 2147483647)
|
||||
SHOW hnsw.scan_mem_multiplier;
|
||||
hnsw.scan_mem_multiplier
|
||||
--------------------------
|
||||
1
|
||||
(1 row)
|
||||
|
||||
SET hnsw.scan_mem_multiplier = 0;
|
||||
ERROR: 0 is outside the valid range for parameter "hnsw.scan_mem_multiplier" (1 .. 1000)
|
||||
SET hnsw.scan_mem_multiplier = 1001;
|
||||
ERROR: 1001 is outside the valid range for parameter "hnsw.scan_mem_multiplier" (1 .. 1000)
|
||||
DROP TABLE t;
|
||||
|
||||
@@ -81,37 +81,6 @@ SELECT COUNT(*) FROM (SELECT * FROM t ORDER BY val <=> (SELECT NULL::vector)) t2
|
||||
3
|
||||
(1 row)
|
||||
|
||||
DROP TABLE t;
|
||||
-- iterative
|
||||
CREATE TABLE t (val vector(3));
|
||||
INSERT INTO t (val) VALUES ('[0,0,0]'), ('[1,2,3]'), ('[1,1,1]'), (NULL);
|
||||
CREATE INDEX ON t USING ivfflat (val vector_l2_ops) WITH (lists = 3);
|
||||
SET ivfflat.iterative_scan = relaxed_order;
|
||||
SELECT * FROM t ORDER BY val <-> '[3,3,3]';
|
||||
val
|
||||
---------
|
||||
[1,2,3]
|
||||
[1,1,1]
|
||||
[0,0,0]
|
||||
(3 rows)
|
||||
|
||||
SET ivfflat.max_probes = 1;
|
||||
SELECT * FROM t ORDER BY val <-> '[3,3,3]';
|
||||
val
|
||||
---------
|
||||
[1,2,3]
|
||||
(1 row)
|
||||
|
||||
SET ivfflat.max_probes = 2;
|
||||
SELECT * FROM t ORDER BY val <-> '[3,3,3]';
|
||||
val
|
||||
---------
|
||||
[1,2,3]
|
||||
[1,1,1]
|
||||
(2 rows)
|
||||
|
||||
RESET ivfflat.iterative_scan;
|
||||
RESET ivfflat.max_probes;
|
||||
DROP TABLE t;
|
||||
-- unlogged
|
||||
CREATE UNLOGGED TABLE t (val vector(3));
|
||||
@@ -140,27 +109,4 @@ SHOW ivfflat.probes;
|
||||
1
|
||||
(1 row)
|
||||
|
||||
SET ivfflat.probes = 0;
|
||||
ERROR: 0 is outside the valid range for parameter "ivfflat.probes" (1 .. 32768)
|
||||
SET ivfflat.probes = 32769;
|
||||
ERROR: 32769 is outside the valid range for parameter "ivfflat.probes" (1 .. 32768)
|
||||
SHOW ivfflat.iterative_scan;
|
||||
ivfflat.iterative_scan
|
||||
------------------------
|
||||
off
|
||||
(1 row)
|
||||
|
||||
SET ivfflat.iterative_scan = on;
|
||||
ERROR: invalid value for parameter "ivfflat.iterative_scan": "on"
|
||||
HINT: Available values: off, relaxed_order.
|
||||
SHOW ivfflat.max_probes;
|
||||
ivfflat.max_probes
|
||||
--------------------
|
||||
32768
|
||||
(1 row)
|
||||
|
||||
SET ivfflat.max_probes = 0;
|
||||
ERROR: 0 is outside the valid range for parameter "ivfflat.max_probes" (1 .. 32768)
|
||||
SET ivfflat.max_probes = 32769;
|
||||
ERROR: 32769 is outside the valid range for parameter "ivfflat.max_probes" (1 .. 32768)
|
||||
DROP TABLE t;
|
||||
|
||||
@@ -57,23 +57,6 @@ SELECT COUNT(*) FROM (SELECT * FROM t ORDER BY val <+> (SELECT NULL::vector)) t2
|
||||
|
||||
DROP TABLE t;
|
||||
|
||||
-- iterative
|
||||
|
||||
CREATE TABLE t (val vector(3));
|
||||
INSERT INTO t (val) VALUES ('[0,0,0]'), ('[1,2,3]'), ('[1,1,1]'), (NULL);
|
||||
CREATE INDEX ON t USING hnsw (val vector_l2_ops);
|
||||
|
||||
SET hnsw.iterative_scan = strict_order;
|
||||
SET hnsw.ef_search = 1;
|
||||
SELECT * FROM t ORDER BY val <-> '[3,3,3]';
|
||||
|
||||
SET hnsw.iterative_scan = relaxed_order;
|
||||
SELECT * FROM t ORDER BY val <-> '[3,3,3]';
|
||||
|
||||
RESET hnsw.iterative_scan;
|
||||
RESET hnsw.ef_search;
|
||||
DROP TABLE t;
|
||||
|
||||
-- unlogged
|
||||
|
||||
CREATE UNLOGGED TABLE t (val vector(3));
|
||||
@@ -98,17 +81,4 @@ SHOW hnsw.ef_search;
|
||||
SET hnsw.ef_search = 0;
|
||||
SET hnsw.ef_search = 1001;
|
||||
|
||||
SHOW hnsw.iterative_scan;
|
||||
|
||||
SET hnsw.iterative_scan = on;
|
||||
|
||||
SHOW hnsw.max_scan_tuples;
|
||||
|
||||
SET hnsw.max_scan_tuples = 0;
|
||||
|
||||
SHOW hnsw.scan_mem_multiplier;
|
||||
|
||||
SET hnsw.scan_mem_multiplier = 0;
|
||||
SET hnsw.scan_mem_multiplier = 1001;
|
||||
|
||||
DROP TABLE t;
|
||||
|
||||
@@ -44,25 +44,6 @@ SELECT COUNT(*) FROM (SELECT * FROM t ORDER BY val <=> (SELECT NULL::vector)) t2
|
||||
|
||||
DROP TABLE t;
|
||||
|
||||
-- iterative
|
||||
|
||||
CREATE TABLE t (val vector(3));
|
||||
INSERT INTO t (val) VALUES ('[0,0,0]'), ('[1,2,3]'), ('[1,1,1]'), (NULL);
|
||||
CREATE INDEX ON t USING ivfflat (val vector_l2_ops) WITH (lists = 3);
|
||||
|
||||
SET ivfflat.iterative_scan = relaxed_order;
|
||||
SELECT * FROM t ORDER BY val <-> '[3,3,3]';
|
||||
|
||||
SET ivfflat.max_probes = 1;
|
||||
SELECT * FROM t ORDER BY val <-> '[3,3,3]';
|
||||
|
||||
SET ivfflat.max_probes = 2;
|
||||
SELECT * FROM t ORDER BY val <-> '[3,3,3]';
|
||||
|
||||
RESET ivfflat.iterative_scan;
|
||||
RESET ivfflat.max_probes;
|
||||
DROP TABLE t;
|
||||
|
||||
-- unlogged
|
||||
|
||||
CREATE UNLOGGED TABLE t (val vector(3));
|
||||
@@ -81,16 +62,4 @@ CREATE INDEX ON t USING ivfflat (val vector_l2_ops) WITH (lists = 32769);
|
||||
|
||||
SHOW ivfflat.probes;
|
||||
|
||||
SET ivfflat.probes = 0;
|
||||
SET ivfflat.probes = 32769;
|
||||
|
||||
SHOW ivfflat.iterative_scan;
|
||||
|
||||
SET ivfflat.iterative_scan = on;
|
||||
|
||||
SHOW ivfflat.max_probes;
|
||||
|
||||
SET ivfflat.max_probes = 0;
|
||||
SET ivfflat.max_probes = 32769;
|
||||
|
||||
DROP TABLE t;
|
||||
|
||||
@@ -6,7 +6,13 @@ use Test::More;
|
||||
|
||||
my $dim = 3;
|
||||
|
||||
my $array_sql = join(",", ('random()') x $dim);
|
||||
my @r = ();
|
||||
for (1 .. $dim)
|
||||
{
|
||||
my $v = int(rand(1000)) + 1;
|
||||
push(@r, "i % $v");
|
||||
}
|
||||
my $array_sql = join(", ", @r);
|
||||
|
||||
# Initialize node
|
||||
my $node = PostgreSQL::Test::Cluster->new('node');
|
||||
@@ -17,20 +23,19 @@ $node->start;
|
||||
$node->safe_psql("postgres", "CREATE EXTENSION vector;");
|
||||
$node->safe_psql("postgres", "CREATE TABLE tst (i int4, v vector($dim));");
|
||||
$node->safe_psql("postgres",
|
||||
"INSERT INTO tst SELECT i, ARRAY[$array_sql] FROM generate_series(1, 100000) i;"
|
||||
"INSERT INTO tst SELECT i % 10, ARRAY[$array_sql] FROM generate_series(1, 100000) i;"
|
||||
);
|
||||
$node->safe_psql("postgres", "CREATE INDEX ON tst USING ivfflat (v vector_l2_ops);");
|
||||
|
||||
# Get size
|
||||
my $size = $node->safe_psql("postgres", "SELECT pg_total_relation_size('tst_v_idx');");
|
||||
|
||||
# Store values
|
||||
$node->safe_psql("postgres", "CREATE TABLE tmp AS SELECT * FROM tst;");
|
||||
|
||||
# Delete all, vacuum, and insert same data
|
||||
$node->safe_psql("postgres", "DELETE FROM tst;");
|
||||
$node->safe_psql("postgres", "VACUUM tst;");
|
||||
$node->safe_psql("postgres", "INSERT INTO tst SELECT * FROM tmp;");
|
||||
$node->safe_psql("postgres",
|
||||
"INSERT INTO tst SELECT i % 10, ARRAY[$array_sql] FROM generate_series(1, 100000) i;"
|
||||
);
|
||||
|
||||
# Check size
|
||||
my $new_size = $node->safe_psql("postgres", "SELECT pg_total_relation_size('tst_v_idx');");
|
||||
|
||||
@@ -26,26 +26,25 @@ $node->safe_psql("postgres", qq(
|
||||
|
||||
my $count = $node->safe_psql("postgres", qq(
|
||||
SET enable_seqscan = off;
|
||||
SET hnsw.iterative_scan = relaxed_order;
|
||||
SET hnsw.max_scan_tuples = 100000;
|
||||
SET hnsw.scan_mem_multiplier = 2;
|
||||
SET hnsw.streaming = on;
|
||||
SET work_mem = '8MB';
|
||||
SELECT COUNT(*) FROM (SELECT v FROM tst WHERE i % 10000 = 0 ORDER BY v <-> (SELECT v FROM tst LIMIT 1) LIMIT 11) t;
|
||||
));
|
||||
is($count, 10);
|
||||
|
||||
foreach ((30000, 50000, 70000))
|
||||
{
|
||||
my $max_tuples = $_;
|
||||
my $expected = $max_tuples / 10000;
|
||||
my $ef_stream = $_;
|
||||
my $expected = $ef_stream / 10000;
|
||||
my $sum = 0;
|
||||
|
||||
for my $i (1 .. 20)
|
||||
{
|
||||
$count = $node->safe_psql("postgres", qq(
|
||||
SET enable_seqscan = off;
|
||||
SET hnsw.iterative_scan = relaxed_order;
|
||||
SET hnsw.max_scan_tuples = $max_tuples;
|
||||
SET hnsw.scan_mem_multiplier = 2;
|
||||
SET hnsw.streaming = on;
|
||||
SET hnsw.ef_stream = $ef_stream;
|
||||
SET work_mem = '8MB';
|
||||
SELECT COUNT(*) FROM (SELECT v FROM tst WHERE i % 10000 = 0 ORDER BY v <-> (SELECT v FROM tst WHERE i = $i) LIMIT 11) t;
|
||||
));
|
||||
$sum += $count;
|
||||
@@ -56,4 +55,13 @@ foreach ((30000, 50000, 70000))
|
||||
cmp_ok($avg, '<', $expected + 2);
|
||||
}
|
||||
|
||||
my ($ret, $stdout, $stderr) = $node->psql("postgres", qq(
|
||||
SET enable_seqscan = off;
|
||||
SET hnsw.streaming = on;
|
||||
SET client_min_messages = debug1;
|
||||
SET work_mem = '2MB';
|
||||
SELECT COUNT(*) FROM (SELECT v FROM tst WHERE i % 10000 = 0 ORDER BY v <-> (SELECT v FROM tst LIMIT 1) LIMIT 11) t;
|
||||
));
|
||||
like($stderr, qr/hnsw index scan exceeded work_mem after \d+ tuples/);
|
||||
|
||||
done_testing();
|
||||
@@ -1,54 +0,0 @@
|
||||
use strict;
|
||||
use warnings FATAL => 'all';
|
||||
use PostgreSQL::Test::Cluster;
|
||||
use PostgreSQL::Test::Utils;
|
||||
use Test::More;
|
||||
|
||||
my $dim = 3;
|
||||
my $array_sql = join(",", ('random()') x $dim);
|
||||
|
||||
# Initialize node
|
||||
my $node = PostgreSQL::Test::Cluster->new('node');
|
||||
$node->init;
|
||||
$node->start;
|
||||
|
||||
# Create table
|
||||
$node->safe_psql("postgres", "CREATE EXTENSION vector;");
|
||||
$node->safe_psql("postgres", "CREATE TABLE tst (i int4 PRIMARY KEY, v vector($dim));");
|
||||
$node->safe_psql("postgres",
|
||||
"INSERT INTO tst SELECT i, ARRAY[$array_sql] FROM generate_series(1, 100000) i;"
|
||||
);
|
||||
$node->safe_psql("postgres", "CREATE INDEX ON tst USING ivfflat (v vector_l2_ops);");
|
||||
|
||||
my $count = $node->safe_psql("postgres", qq(
|
||||
SET enable_seqscan = off;
|
||||
SET ivfflat.probes = 10;
|
||||
SET ivfflat.iterative_scan = relaxed_order;
|
||||
SELECT COUNT(*) FROM (SELECT v FROM tst WHERE i % 10000 = 0 ORDER BY v <-> (SELECT v FROM tst LIMIT 1) LIMIT 11) t;
|
||||
));
|
||||
is($count, 10);
|
||||
|
||||
foreach ((30, 50, 70))
|
||||
{
|
||||
my $max_probes = $_;
|
||||
my $expected = $max_probes / 10;
|
||||
my $sum = 0;
|
||||
|
||||
for my $i (1 .. 20)
|
||||
{
|
||||
$count = $node->safe_psql("postgres", qq(
|
||||
SET enable_seqscan = off;
|
||||
SET ivfflat.probes = 10;
|
||||
SET ivfflat.iterative_scan = relaxed_order;
|
||||
SET ivfflat.max_probes = $max_probes;
|
||||
SELECT COUNT(*) FROM (SELECT v FROM tst WHERE i % 10000 = 0 ORDER BY v <-> (SELECT v FROM tst WHERE i = $i) LIMIT 11) t;
|
||||
));
|
||||
$sum += $count;
|
||||
}
|
||||
|
||||
my $avg = $sum / 20;
|
||||
cmp_ok($avg, '>', $expected - 2);
|
||||
cmp_ok($avg, '<', $expected + 2);
|
||||
}
|
||||
|
||||
done_testing();
|
||||
@@ -10,18 +10,18 @@ my @expected;
|
||||
my $limit = 20;
|
||||
my $dim = 3;
|
||||
my $array_sql = join(",", ('random()') x $dim);
|
||||
my @cs = (50, 500);
|
||||
my @cs = (100, 1000);
|
||||
|
||||
sub test_recall
|
||||
{
|
||||
my ($c, $ef_search, $min, $operator, $mode) = @_;
|
||||
my ($c, $ef_search, $min, $operator) = @_;
|
||||
my $correct = 0;
|
||||
my $total = 0;
|
||||
|
||||
my $explain = $node->safe_psql("postgres", qq(
|
||||
SET enable_seqscan = off;
|
||||
SET hnsw.ef_search = $ef_search;
|
||||
SET hnsw.iterative_scan = $mode;
|
||||
SET hnsw.streaming = on;
|
||||
EXPLAIN ANALYZE SELECT i FROM tst WHERE i % $c = 0 ORDER BY v $operator '$queries[0]' LIMIT $limit;
|
||||
));
|
||||
like($explain, qr/Index Scan using idx on tst/);
|
||||
@@ -31,7 +31,7 @@ sub test_recall
|
||||
my $actual = $node->safe_psql("postgres", qq(
|
||||
SET enable_seqscan = off;
|
||||
SET hnsw.ef_search = $ef_search;
|
||||
SET hnsw.iterative_scan = $mode;
|
||||
SET hnsw.streaming = on;
|
||||
SELECT i FROM tst WHERE i % $c = 0 ORDER BY v $operator '$queries[$i]' LIMIT $limit;
|
||||
));
|
||||
my @actual_ids = split("\n", $actual);
|
||||
@@ -50,7 +50,7 @@ sub test_recall
|
||||
$total += $limit;
|
||||
}
|
||||
|
||||
cmp_ok($correct / $total, ">=", $min, "$operator $mode $c");
|
||||
cmp_ok($correct / $total, ">=", $min, $operator);
|
||||
}
|
||||
|
||||
# Initialize node
|
||||
@@ -62,7 +62,7 @@ $node->start;
|
||||
$node->safe_psql("postgres", "CREATE EXTENSION vector;");
|
||||
$node->safe_psql("postgres", "CREATE TABLE tst (i int4, v vector($dim));");
|
||||
$node->safe_psql("postgres",
|
||||
"INSERT INTO tst SELECT i, ARRAY[$array_sql] FROM generate_series(1, 50000) i;"
|
||||
"INSERT INTO tst SELECT i, ARRAY[$array_sql] FROM generate_series(1, 100000) i;"
|
||||
);
|
||||
|
||||
# Generate queries
|
||||
@@ -108,8 +108,21 @@ for my $i (0 .. $#operators)
|
||||
push(@expected, $res);
|
||||
}
|
||||
|
||||
test_recall($c, 40, 0.99, $operator, "strict_order");
|
||||
test_recall($c, 40, 0.99, $operator, "relaxed_order");
|
||||
if ($c == 100)
|
||||
{
|
||||
test_recall($c, 40, 0.99, $operator);
|
||||
}
|
||||
else
|
||||
{
|
||||
if ($operator eq "<->")
|
||||
{
|
||||
test_recall($c, 40, 0.99, $operator);
|
||||
}
|
||||
else
|
||||
{
|
||||
test_recall($c, 40, 0.99, $operator);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
$node->safe_psql("postgres", "DROP INDEX idx;");
|
||||
@@ -1,125 +0,0 @@
|
||||
use strict;
|
||||
use warnings FATAL => 'all';
|
||||
use PostgreSQL::Test::Cluster;
|
||||
use PostgreSQL::Test::Utils;
|
||||
use Test::More;
|
||||
|
||||
my $node;
|
||||
my @queries = ();
|
||||
my @expected;
|
||||
my $limit = 20;
|
||||
my @cs = (100, 1000);
|
||||
|
||||
sub test_recall
|
||||
{
|
||||
my ($c, $probes, $min, $operator) = @_;
|
||||
my $correct = 0;
|
||||
my $total = 0;
|
||||
|
||||
my $explain = $node->safe_psql("postgres", qq(
|
||||
SET enable_seqscan = off;
|
||||
SET ivfflat.probes = $probes;
|
||||
SET ivfflat.iterative_scan = relaxed_order;
|
||||
EXPLAIN ANALYZE SELECT i FROM tst WHERE i % $c = 0 ORDER BY v $operator '$queries[0]' LIMIT $limit;
|
||||
));
|
||||
like($explain, qr/Index Scan using idx on tst/);
|
||||
|
||||
for my $i (0 .. $#queries)
|
||||
{
|
||||
my $actual = $node->safe_psql("postgres", qq(
|
||||
SET enable_seqscan = off;
|
||||
SET ivfflat.probes = $probes;
|
||||
SET ivfflat.iterative_scan = relaxed_order;
|
||||
SELECT i FROM tst WHERE i % $c = 0 ORDER BY v $operator '$queries[$i]' LIMIT $limit;
|
||||
));
|
||||
my @actual_ids = split("\n", $actual);
|
||||
|
||||
my @expected_ids = split("\n", $expected[$i]);
|
||||
my %expected_set = map { $_ => 1 } @expected_ids;
|
||||
|
||||
foreach (@actual_ids)
|
||||
{
|
||||
if (exists($expected_set{$_}))
|
||||
{
|
||||
$correct++;
|
||||
}
|
||||
}
|
||||
|
||||
$total += $limit;
|
||||
}
|
||||
|
||||
cmp_ok($correct / $total, ">=", $min, "$operator $c");
|
||||
}
|
||||
|
||||
# Initialize node
|
||||
$node = PostgreSQL::Test::Cluster->new('node');
|
||||
$node->init;
|
||||
$node->start;
|
||||
|
||||
# Create table
|
||||
$node->safe_psql("postgres", "CREATE EXTENSION vector;");
|
||||
$node->safe_psql("postgres", "CREATE TABLE tst (i int4, v vector(3));");
|
||||
$node->safe_psql("postgres",
|
||||
"INSERT INTO tst SELECT i, ARRAY[random(), random(), random()] FROM generate_series(1, 100000) i;"
|
||||
);
|
||||
|
||||
# Generate queries
|
||||
for (1 .. 20)
|
||||
{
|
||||
my $r1 = rand();
|
||||
my $r2 = rand();
|
||||
my $r3 = rand();
|
||||
push(@queries, "[$r1,$r2,$r3]");
|
||||
}
|
||||
|
||||
# Check each index type
|
||||
my @operators = ("<->", "<=>");
|
||||
my @opclasses = ("vector_l2_ops", "vector_cosine_ops");
|
||||
|
||||
for my $i (0 .. $#operators)
|
||||
{
|
||||
my $operator = $operators[$i];
|
||||
my $opclass = $opclasses[$i];
|
||||
|
||||
$node->safe_psql("postgres", "CREATE INDEX idx ON tst USING ivfflat (v $opclass);");
|
||||
|
||||
foreach (@cs)
|
||||
{
|
||||
my $c = $_;
|
||||
|
||||
# Get exact results
|
||||
@expected = ();
|
||||
foreach (@queries)
|
||||
{
|
||||
my $res = $node->safe_psql("postgres", qq(
|
||||
SET enable_indexscan = off;
|
||||
WITH top AS (
|
||||
SELECT v $operator '$_' AS distance FROM tst WHERE i % $c = 0 ORDER BY distance LIMIT $limit
|
||||
)
|
||||
SELECT i FROM tst WHERE (v $operator '$_') <= (SELECT MAX(distance) FROM top)
|
||||
));
|
||||
push(@expected, $res);
|
||||
}
|
||||
|
||||
if ($c == 100)
|
||||
{
|
||||
test_recall($c, 1, 0.57, $operator);
|
||||
test_recall($c, 10, 0.98, $operator);
|
||||
}
|
||||
else
|
||||
{
|
||||
if ($operator eq "<->")
|
||||
{
|
||||
test_recall($c, 1, 0.80, $operator);
|
||||
}
|
||||
else
|
||||
{
|
||||
test_recall($c, 1, 0.88, $operator);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
$node->safe_psql("postgres", "DROP INDEX idx;");
|
||||
}
|
||||
|
||||
done_testing();
|
||||
@@ -1,4 +1,4 @@
|
||||
comment = 'vector data type and ivfflat and hnsw access methods'
|
||||
default_version = '0.8.0'
|
||||
default_version = '0.7.4'
|
||||
module_pathname = '$libdir/vector'
|
||||
relocatable = true
|
||||
|
||||
Reference in New Issue
Block a user