I am new to PostgreSQL and PostGIS but the question is not trivial. I am using PostgreSQL 9.5 with PostGIS 2.2.
I need to run some queries that take a horrible amount of time.
First, let me explain the problem in non-GIS terms :
Basically, I have a set of several hundreds of thousands of points spread over a territory of about half a million square kilometres a (country).
Over this territory, I have about a dozen sets of areas coming from various databases. In each set, I have between a few hundreds and a few thousands of areas. I want to find which points are in any of these areas.
Now, how I am currently working out the problem in GIS terms :
Each set of areas is a Postgresql table with a geometry column of the type multipolygon and with, as explained before a few hundreds to a few thousand records.
All these tables are contained in a schema donnees but I am using a different schema for these operations, called traitements.
So the process is a/ merging all the geometries into a single geometry, and then b/ finding which points are contained in this geometry.
The problem is that, if step a/ took a reasonable amount of time (several minutes), step b/ takes forever.
I am currently working with only a sample of the points I must process (about 1% of them, i.e. about 7000) and it is not finished after several hours (the database connection eventually times out).
I am making tests running the query by limiting the number of return rows to 10 or 50 and it still takes about half an hour for that.
I am using a Linux Mint 18 machine with 4 CPU and 8 Gb of RAM if you wonder.
I have created indexes on the geometry columns. All geometry columns use the same SRID.
Creating the tables :
CREATE TABLE traitements.sites_candidats (
pkid serial PRIMARY KEY,
statut varchar(255) NOT NULL,
geom geometry(Point, 2154)
);
CREATE UNIQUE INDEX ON traitements.sites_candidats (origine, origine_id ) ;
CREATE INDEX ON traitements.sites_candidats (statut);
CREATE INDEX sites_candidats_geométrie ON traitements.sites_candidats USING GIST ( geom );
CREATE TABLE traitements.zones_traitements (
pkid serial PRIMARY KEY,
définition varchar(255) NOT NULL,
geom geometry (MultiPolygon, 2154)
);
CREATE UNIQUE INDEX ON traitements.zones_traitements (définition) ;
CREATE INDEX zones_traitements_geométrie ON traitements.zones_traitements USING GIST ( geom );
Please note that I specified the geometry type of the geom column in table traitements only because I wanted to specify the SRID but I was not sure what is the correct syntax for any type of Geometry. Maybe "geom geometry (Geometry, 2154)" ?
Merging all the geometries of the various sets of areas :
As said before, all the tables hold geometries of the type multipolygon.
This is the code I am using to merge all the geometries from one of the tables :
INSERT INTO traitements.zones_traitements
( définition, , geom )
VALUES
(
'first-level merge',
(
SELECT ST_Multi(ST_Collect(dumpedGeometries)) AS singleMultiGeometry
FROM
(
SELECT ST_Force2D((ST_Dump(geom)).geom) AS dumpedGeometries
FROM donnees.one_table
) AS dumpingGeometries
)
) ;
I found that some of the geometries in some of the records are in 3D, so that's why I am using _ST_Force2D_.
I do this for all the tables and then merge the geometries again using :
INSERT INTO traitements.zones_traitements
( définition, geom )
VALUES
(
'second-level merge',
(
SELECT ST_Multi(ST_Collect(dumpedGeometries)) AS singleMultiGeometry
FROM
(
SELECT (ST_Dump(geom)).geom AS dumpedGeometries
FROM traitements.zones_traitements
WHERE définition != 'second-level merge'
) AS dumpingGeometries
)
) ;
As said before, these queries take several minutes but that's fine.
Not the query that takes forever :
SELECT pkid
FROM traitements.sites_candidats AS sites
JOIN (
SELECT geom FROM traitements.zones_traitements
WHERE définition = 'zones_rédhibitoires' ) AS zones
ON ST_Contains(zones.geom , sites.geom)
LIMIT 50;
Analysing the problem :
Obviously, it is the subquery selecting the points that takes a lot of time, not the update.
So I have run an EXPLAIN (ANALYZE, BUFFERS) on the query :
EXPLAIN (ANALYZE, BUFFERS)
SELECT pkid
FROM traitements.sites_candidats AS sites
JOIN (
SELECT geom FROM traitements.zones_traitements
WHERE définition = 'second_level_merge' ) AS zones
ON ST_Contains(zones.geom , sites.geom)
LIMIT 10;
---------------------------------
"Limit (cost=4.18..20.23 rows=1 width=22) (actual time=6052.069..4393634.244 rows=10 loops=1)"
" Buffers: shared hit=1 read=688784"
" -> Nested Loop (cost=4.18..20.23 rows=1 width=22) (actual time=6052.068..4391938.803 rows=10 loops=1)"
" Buffers: shared hit=1 read=688784"
" -> Seq Scan on zones_traitements (cost=0.00..1.23 rows=1 width=54939392) (actual time=0.016..0.016 rows=1 loops=1)"
" Filter: (("définition")::text = 'zones_rédhibitoires'::text)"
" Rows Removed by Filter: 17"
" Buffers: shared hit=1"
" -> Bitmap Heap Scan on sites_candidats sites (cost=4.18..19.00 rows=1 width=54) (actual time=6052.044..4391260.053 rows=10 loops=1)"
" Recheck Cond: (zones_traitements.geom ~ geom)"
" Filter: _st_contains(zones_traitements.geom, geom)"
" Heap Blocks: exact=1"
" Buffers: shared read=688784"
" -> Bitmap Index Scan on "sites_candidats_geométrie" (cost=0.00..4.18 rows=4 width=0) (actual time=23.284..23.284 rows=3720 loops=1)"
" Index Cond: (zones_traitements.geom ~ geom)"
" Buffers: shared read=51"
"Planning time: 91.967 ms"
"Execution time: 4399271.394 ms"
I am not sure how to read this output.
Nevertheless, I suspect that the query is so slow because of the geometry obtained by merging all these multipolygons into a single one.
Questions :
Would that work better using a different type of geometry to merge the others, like a GeometryCollection ?
How does the indexes work in this case ?
Is there more efficient than ST_Contains() ?
Let´s see. First off, you should ask GIS specific questions over at GIS Stackexchange. But I´ll try to help here:
Technically, your geometry column definition is correct, and using
'primitives' (e.g. POINT, LINE, POLYGON and their MULTIs) is favorable
over GEOMETRYCOLLECTIONs.However, it is almost always the better
choice to run spatial relation functions on as small a geometry as
possible; for most of those functions, PostGIS has to check each and
every vertice of the input geometries against each other (so in this
case, it has to traverse the polygon's millions of vertices once for each point
to be checked in ST_Contains).PostGIS will in fact fire up a bbox
comparison prior to the relation checks (if an index is present on
both geometries) to limit the possible matches and effectively
speeding up the check by several magnitudes; this is rendered useless
here.(I would almost recommend to actually dump the MULTIs into simple POLYGONS, but not without knowing your data).
Why are you dumping the MULTI geometries just to collect them
back into MULTIs? If your source table's geometries are actually stored as MULTIPOLYGONS (and hopefully for good reason), simply copy them into the intermediate table, with ST_Force2D used on the MULTIs and ST_IsValid in the WHERE block (you can try ST_MakeValidon the geometries, but there's no guarantee it will work).If you have inserted all tables into the zones_traitements table, run VACUUM ANALYZE and REINDEX to actually make use of the index!
In your 'second merge' query...are you simply adding the 'merged' geometries to the existing ones in the table? Don´t, that´s just wrong. It messes up table statistics and the index and is quite the unnecessary overhead. You should do these things within your query, but it´s not necessary here.
Keep in mind that geometries of different types or extends created or derived by or within queries can neither have an index nor use the initial one. This applies to your 'merging' queries!
Then run
SELECT pkid
FROM traitements.sites_candidats AS sites
JOIN traitements.zones_traitements AS zones
ON ST_Intersects(zones.geom, sites.geom)
to return one pkid for every intersection with a zone so that if one point intersects two MULTIOLYGONs, you´ll get two rows for that point. Use SELECT DISTINCT pkid ... to only get one row per pkid that is intersecting any zone.(Note: I used ST_Intersection because that should imply on less check on the relation. If you absolutely need ST_Contains, just replace it)
Hope this helps. If not, say a word.
Again, thanks.
I had come to the same conclusion as your advice : that, instead of merging all the thousands of multipolygons into a single huge one, whose bbox is too huge, it would be more efficient to decompose all the multipolygons into simple polygons using ST_Dump and insert these into a dedicated table with an appropriate index.
Nevertheless, to do this, I first had to correct geometries : certain multipolygons had indeed unvalid geometries. St_MakeValid would make valid 90% of them as multipolygons but the rest was transformed into either GeometryCollections or MultilineStrings. To correct these, I used ST_Buffer, with a buffer of 0.01 meter, the result of which being a correct multipolygon.
Once this was done, all my multipolygons were valid and I could dump them into simple polygons.
Doing this, I reduced the search time by a factor of +/- 5000 !
:D
Related
I want to index an array column with either GIN or GiST. The fact that GIN is slower in insert/update operations, however, made me wonder if it would have any impact on performance - even though the indexed column itself will remain static.
So, assuming that, for instance, I have a table with columns (A, B, C) and that B is indexed, does the index get updated if I update only column C?
It depends :^)
Normally, PostgreSQL will have to modify the index, even if nothing changes in the indexed column, because an UPDATE in PostgreSQL creates a new row version, so you need a new index entry to point to the new location of the row in the table.
Since this is unfortunate, there is an optimization called “HOT update”: If none of the indexed columns are modified and there is enough free space in the block that contains the original row, PostgreSQL can create a “heap-only tuple” that is not referenced from the outside and therefore does not require a new index entry.
You can lower the fillfactor on the table to increase the likelihood for HOT updates.
For details, you may want to read my article on the topic.
Laurenz Albe answer is great. The following part is my interpretation.
Because the gin array_ops can not do index only scan. Which means that even if you only query the array column, you can only use bitmap index scan. for bitmap scan. with low fillfactor, you probably don't need to visit extract pages.
demo:
begin;
create table test_gin_update(cola int, colb int[]);
insert into test_gin_update values (1,array[1,2]);
insert into test_gin_update values (1,array[1,2,3]);
insert into test_gin_update(cola, colb) select g, array[g, g + 1] from generate_series(10, 10000) g;
commit;
for example, select colb from test_gin_update where colb = array[1,2]; see the following query plan.
because GIN cannot distinguish array[1,2] and array[1,2,3] then even if we created gin index. create index on test_gin_update using gin(colb array_ops ); We can only use bitmap index scan.
QUERY PLAN
-----------------------------------------------------------------------------
Bitmap Heap Scan on test_gin_update (actual rows=1 loops=1)
Recheck Cond: (colb = '{1,2}'::integer[])
Rows Removed by Index Recheck: 1
Heap Blocks: exact=1
-> Bitmap Index Scan on test_gin_update_colb_idx (actual rows=2 loops=1)
Index Cond: (colb = '{1,2}'::integer[])
(6 rows)
I have a Postgres 10 database in my Flask app. I'm trying to paginate the filtering results on table over milions of rows. The problem is, that paginate method do counting total number of query results totaly ineffective.
Heres the example with dummy filter:
paginate = Buildings.query.filter(height>10).paginate(1,10)
Under the hood if perform 2 queries:
SELECT * FROM buildings where height > 10
SELECT count(*) FROM (
SELECT * FROM buildings where height > 10
)
--------
count returns 200,000 rows
The problem is that count on raw select without subquery is quite fast ~30ms, but paginate method wraps that into subquery that takes ~30s.
The query plan on cold database:
Is there an option of using default paginate method from flask-sqlalchemy in performant way?
EDIT:
To get the better understanding of my problem here is the real filter operations used in my case, but with dummy field names:
paginate = Buildings.query.filter_by(owner_id=None).filter(Buildings.address.like('%A%')).paginate(1,10)
So the SQL the ORM produce is:
SELECT count(*) AS count_1
FROM (SELECT foo_column, [...]
FROM buildings
WHERE buildings.owner_id IS NULL AND buildings.address LIKE '%A%' ) AS anon_1
That query is already optimized by indices from:
CREATE INDEX ix_trgm_buildings_address ON public.buildings USING gin (address gin_trgm_ops);
CREATE INDEX ix_buildings_owner_id ON public.buildings USING btree (owner_id)
The problem is just this count function, that's very slow.
So it looks like a disk-reading problem. The solutions would be get faster disks, get more RAM is it all can be cached, or if you have enough RAM than to use pg_prewarm to get all the data into the cache ahead of need. Or try increasing effective_io_concurrency, so that the bitmap heap scan can have more than one IO request outstanding at a time.
Your actual query seems to be more complex than the one you show, based on the Filter: entry and based on the Row Removed by Index Recheck: entry in combination with the lack of Lossy blocks. There might be some other things to try, but we would need to see the real query and the index definition (which apparently is not just an ordinary btree index on "height").
We are changing DB(PostgreSQL 10.11) structure for one of our projects. And one of the changes is moving field of type uuid[] (called “areasoflawid”) into the jsonb field (called “data”).
So, we have a table which look like this:
CREATE TABLE public.documents
(
id serial,
areasoflawid uuid[], --the field to be moved into the ‘data’
data jsonb,
….
)
We are not changing the values of the array or its structure.
i.e. documents.data->'metadata'->'areaoflawids' contains the same items as documents.areasoflawid)
After data migration, the JSON stored in the “data” field has following structure:
{
...
"metadata": {
...
"areaoflawids": [
"e34e0ee5-78e0-4d92-9186-ac69c109408b",
"b3af9163-d910-4d19-8f40-0602b75c25b0",
"50dc7fd8-ebdf-4cd2-bcab-b8d755fe96e8",
"8955c062-363f-4a1a-ac3c-d1c2ffe96c9b",
"bdb79f9f-4539-45f5-ac82-92baaf915f6c"
],
....
},
...
}
So, after migrating data we started benchmarking jsonb field-related queries and figured out that searching over array field documents.data->’metadata’->’areaoflawids’ takes MUCH longer than searching over uuid[] field documents.areasoflawid.
Here are the queries:
--search over jsonb array field, takes 6.2 sec, returns 13615 rows
SELECT id FROM documents WHERE data->'metadata'->'areaoflawids' #> '"e34e0ee5-78e0-4d92-9186-ac69c109408b"'
--search over uuid[] field, takes 600ms, returns 13615 rows
SELECT id FROM documents WHERE areasoflawid #> ARRAY['e34e0ee5-78e0-4d92-9186-ac69c109408b']::uuid[]
Here is the index over jsonb field:
CREATE INDEX test_documents_aols_gin_idx
ON public.documents
USING gin
(((data -> 'metadata'::text) -> 'areaoflawids'::text) jsonb_path_ops);
And here is the execution plan:
EXPLAIN ANALYZE SELECT id FROM documents WHERE data->'metadata'->'areaoflawids' #> '"e34e0ee5-78e0-4d92-9186-ac69c109408b"'
"Bitmap Heap Scan on documents (cost=6.31..390.78 rows=201 width=4) (actual time=2.297..5859.886 rows=13614 loops=1)"
" Recheck Cond: (((data -> 'metadata'::text) -> 'areaoflawids'::text) #> '"e34e0ee5-78e0-4d92-9186-ac69c109408b"'::jsonb)"
" Heap Blocks: exact=4859"
" -> Bitmap Index Scan on test_documents_aols_gin_idx (cost=0.00..6.30 rows=201 width=0) (actual time=1.608..1.608 rows=13614 loops=1)"
" Index Cond: (((data -> 'metadata'::text) -> 'areaoflawids'::text) #> '"e34e0ee5-78e0-4d92-9186-ac69c109408b"'::jsonb)"
"Planning time: 0.133 ms"
"Execution time: 5862.807 ms"
Other queries over jsonb field work with acceptable speed, but this particular search is about 10 times slower than search over separated field. We were expecting it to be a bit slower but not that bad. We consider option of leaving this “areasoflawid” field as a separated field but we would definitely prefer to move it inside the json. I’ve been playing with different indexes and operations (also used ? and ?|) but the search is still slow. Any help is appreciated!
Finding the 13,614 candidate matches in the index is very fast (1.608 milliseconds). The slow part is reading all of those rows from the table itself. If you turn on track_io_timing, then do EXPLAIN (ANALYZE, BUFFERS), I'm sure you will find you are waiting on IO. If you run the query several times in a row, does it get faster?
I think you are doing an unequal benchmark here, where one table is already in cache and the alternative table is not. But it could also be that the new table is too large to actually fit in cache.
thank you for your response! We came up with another solution taken from this post: https://www.postgresql.org/message-id/CAONrwUFOtnR909gs+7UOdQQB12+pXsGUYu5YHPtbQk5vaE9Gaw#mail.gmail.com . The query now takes about 600-800ms to execute.
So, here is the solution:
CREATE OR REPLACE FUNCTION aol_uuids(data jsonb) RETURNS TEXT[] AS
$$
SELECT
array_agg(value::TEXT) as val
FROM
jsonb_array_elements(case jsonb_typeof(data) when 'array' then data else '[]' end)
$$ LANGUAGE SQL IMMUTABLE;
SELECT id FROM documents WHERE aol_uuids(data->'metadata'->'areaoflawids')#>ARRAY['"e34e0ee5-78e0-4d92-9186-ac69c109408b"']
I have the following query that checks whether is point (T.latitude, T.longitude) is inside a POLYGON
query = """
SELECT id
FROM T
WHERE ST_Intersects(ST_Point(T.latitude, T.longitude), 'POLYGON(({points}))')
"""
But it works slow, how can I speed up it if I have the following index:
(latitude, longitude)?
The query is slow because it must compute the formula for every possible pair of points. So it makes the postgress server do a lot of math, and it forces it to scan through your whole location table. How can we optimize this? Maybe we can eliminate the points that are too far north or too far south or too far east or west?
1) Add a geometry column of type Geometry(Point) and fill it:
ALTER TABLE T add COLUMN geom geometry(Point);
UPDATE T SET geom = ST_Point(T.latitude, T.longitude);
2) Create a spatial index:
CREATE INDEX t_gix ON t USING GIST (geom);
3) Use ST_DWithin instead of ST_Intersect:
WHERE ST_DWithin('POLYGON(({points}))', geom, 0)
You want actually find the points which are within a polygon, so ST_DWithin() is what you need. From the documentation:
This function call will automatically include a bounding box
comparison that will make use of any indexes that are available
PS:
If you for some reason cannot make the points 1 and 2, so at least use ST_Dwithin instead of ST_Intersect:
WHERE ST_DWithin('POLYGON(({points}))', ST_Point(T.latitude, T.longitude), 0)
The last parameter is the tolerance.
You can easly speed up your spatial queries that adding t1.geom&&t2.geom condition to your scripts
This condition;
required spatial indexies so your spatial columns must have spatial indexies
returns approximate result (but with st_ Operators gives exact result)
Here is a example at my database and query timings;
select p.id,k.id, p.poly&&k.poly as intersects
from parcel p , enterance k
where st_contains(p.poly,k.poly) and p.poly&&k.poly
--without && 10.4 sec
--with && 1.6 sec
select count(*) from parcel --34797
select count(*) from enterance --70715
https://postgis.net/docs/overlaps_geometry_box2df.html
I have the following Query/View:
CREATE OR REPLACE VIEW "SumAndSalesPerCountryYear" AS
SELECT date_part('year'::text, "Invoice"."InvoiceDate") AS year,
"Invoice"."BillingCountry" AS country,
sum("Invoice"."Total") AS total
FROM "Invoice"
GROUP BY date_part('year'::text, "Invoice"."InvoiceDate"), "Invoice"."BillingCountry"
ORDER BY date_part('year'::text, "Invoice"."InvoiceDate") DESC, sum("Invoice"."Total") DESC;
My table structure is as follows:
CREATE TABLE "Invoice"
(
"InvoiceId" integer NOT NULL,
"CustomerId" integer NOT NULL,
"InvoiceDate" timestamp without time zone NOT NULL,
"BillingAddress" character varying(70),
"BillingCity" character varying(40),
"BillingState" character varying(40),
"BillingCountry" character varying(40),
"BillingPostalCode" character varying(10),
"Total" numeric(10,2) NOT NULL,
CONSTRAINT "PK_Invoice" PRIMARY KEY ("InvoiceId"),
CONSTRAINT "FK_InvoiceCustomerId" FOREIGN KEY ("CustomerId")
REFERENCES "Customer" ("CustomerId") MATCH SIMPLE
ON UPDATE NO ACTION ON DELETE NO ACTION
)
WITH (
OIDS=FALSE
);
The current execution plan is
Sort (cost=33.65..34.54 rows=354 width=21) (actual time=0.691..0.698 rows=101 loops=1)"
Sort Key: (date_part('year'::text, "Invoice"."InvoiceDate")), (sum("Invoice"."Total"))
Sort Method: quicksort Memory: 32kB
-> HashAggregate (cost=14.24..18.67 rows=354 width=21) (actual time=0.540..0.567 rows=101 loops=1)
-> Seq Scan on "Invoice" (cost=0.00..11.15 rows=412 width=21) (actual time=0.015..0.216 rows=412 loops=1)
Total runtime: 0.753 ms
My task is to optimize the query by using indices, however i cannot think of a way to use indices for optimizing aggregate results.
You can try to penalize Hashagg by "SET enable_hashagg to OFF", but probably for small data, there will not be any benefit from index .. in this use case - hashagg is usually most fast method for aggregation and sort 32kB is pretty quick.
But .. you are trying do performance benchmark on table with 412 rows. It is nonsense. Any thinking about performance has sense on data with size related 2..3 years of production usage.
As noted by Pavel, Ramfjord, and horse, using an index is of little use with such a tiny amount of data. It's so small that it's faster for Postgres to read disk page or two and process everything in memory.
Further, you have the best possible plan for your query already. You're asking Postgres to compute an aggregate over an entire table and returning it in a certain order. Postgres proceeds by computing the aggregate in memory without bothering to sort the data first, by assigning intermediary results using a hash; it then sorts the small number of results according to your criteria.