I have the following query:
UPDATE DestinTable
SET destin = geomVal
FROM GeomTable
WHERE st_contains(st_transform(geom, 4326), st_setsrid(
st_makepoint(d_lon::double precision, d_lat::double precision), 4326));
This query works, but it is very slow. I have to run an update on a very large table, and it is taking a 8+ hours to complete (I run this on 5 different columns). I wanted to know if there was a way to optimize this query to make it run faster. I am unaware of the behind the scenes work associated with an st_contains() method, so there may be some obvious solutions that I am missing.
The easiest way is to create an index on ST_TRANSFORM
CREATE INDEX idx_geom_4326_geomtable
ON GeomTable
USING gist
(ST_Transform(geom, 26986))
WHERE geom IS NOT NULL;
If you have all the fields in one SRID in the table it will be even easier to create a normal GIST index on that table and transform the point you're supplying to the local SRID
Related
I'm working on a query using the PostGIS extension that implements a 'spatial join' work. Running the query took an incredibly long time and failed in the end. The query is as follows:
CREATE INDEX buffer_table_geom_idx ON buffer_table USING GIST (geom);
CREATE INDEX point_table_geom_idx ON point_table USING GIST (geom);
SELECT
point_table.*,
buffer_table.something
FROM
point_table
LEFT JOIN buffer_table ON ST_Intersects (buffer_table.geom, point_table.geom);
where the point_table stands for a table that contains over 10 million rows of point records; the buffer_table stands for a table that contains only one multi-polygon geometry.
I would want to know if there is anything wrong with my code and ways to adjust. Thanks in advance.
With a LEFT JOIN you're going through every single record of point_table and therefore ignoring the index. Try this and see the difference:
SELECT point_table.*
FROM point_table
JOIN buffer_table ON ST_Contains(buffer_table.geom, point_table.geom);
Divide and conquer with ST_SubDivide
Considering the size of your multipolygon (see comments), it might be interesting to divide it into smaller pieces, so that the number of vertices for each containment/intersection calculation also gets reduced, consequently making the query less expensive.
First divide the large geometry into smaller pieces and store in another table (you can also use a CTE/Subquery)
CREATE TABLE buffer_table_divided AS
SELECT ST_SubDivide(geom) AS geom FROM buffer_table
CREATE INDEX buffer_table_geom_divided_idx ON buffer_table_divided USING GIST (geom);
.. and perform your query once again against this new table:
SELECT point_table.*
FROM point_table
JOIN buffer_table_divided d ON ST_Contains (d.geom, point_table.geom);
Demo: db<>fiddle
I have a table with geometry column.
I have 2 indexes on this column:
create index idg1 on tbl using gist(geom)
create index idg2 on tbl using gist(st_geomfromewkb((geom)::bytea))
I have a lot of queries using the geom (geometry) field.
Which index is used ? (when and why)
If there are two indexes on same column (as I show here), can the select queries run slower than define just one index on column ?
The use of an index depends on how the index was defined, and how the query is invoked. If you SELECT <cols> FROM tbl WHERE geom = <some_value>, then you will use the idg1 index. If you SELECT <cols> FROM tabl WHERE st_geomfromewkb(geom) = <some_value>, then you will use the idg2 index.
A good way to know which index will be used for a particular query is to call the query with EXPLAIN (i.e., EXPLAIN SELECT <cols> FROM tbl WHERE geom = <some_value>) -- this will print out the query plan, which access methods, which indexes, which joins, etc. will be used.
For your question regarding performance, the SELECT queries could run slower because there are more indexes to consider in the query planning phase. In terms of executing a given query plan, a SELECT query will not run slower because by then the query plan has been established and the decision of which index to use has been made.
You will certainly experience performance impact upon INSERT/UPDATE/DELETE of the table, as all indexes will need to be updated with respect to the changes in the table. As such, there will be extra I/O activity on disk to propagate the changes, slowing down the database, especially at scale.
Which index is used depends on the query.
Any query that has
WHERE geom && '...'::geometry
or
WHERE st_intersects(geom, '...'::geometry)
or similar will use the first index.
The second index will only be used for queries that have the expression st_geomfromewkb((geom)::bytea) in them.
This is completely useless: it converts the geometry to EWKB format and back. You should find and rewrite all queries that have this weird construct, then you should drop that index.
Having two indexes on a single column does not slow down your queries significantly (planning will take a bit longer, but I doubt if you can measure that). You will have a performance penalty for every data modification though, which will take almost twice as long as with a single index.
I wanted to load the table which is having a table size of more than 1 TB size from S3 to Redshift.
I cannot use DISTSTYLE as ALL because it is a big table.
I cannot use DISTSTYLE as EVEN because I want to use this table in joins which are making performance issue.
Columns on my table are
id INTEGER, name VARCHAR(10), another_id INTEGER, workday INTEGER, workhour INTEGER, worktime_number INTEGER
Our redshift cluster has 20 nodes.
So, I tried distribution key on a workday but the table is badly skewed.
There are 7 unique work days and 24 unique work hours.
How to avoid the skew in such cases?
How we avoid skewing of the table in case of an uneven number of row counts for the unique key (let's say hour1 have 1million rows, hour2 have 1.5million rows, hour3 have 2million rows, and so on)?
Distribute your table using DISTSTYLE EVEN and use either SORTKEY or COMPOUND SORTKEY. Sort Key will help your query performance. Try this first.
DISTSTYLE/DISTKEY determines how your data is distributed. From the columns used in your queries, it is advised choose a column that causes the least amount of skew as the DISTKEY. A column which has many distinct values, such as timestamp, would be a good first choice. Avoid columns with few distinct values, such as credit card types, or days of week.
You might need to recreate your table with different DISTKEY / SORTKEY combinations and try out which one will work best based on your typical queries.
For more info https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-sort-key.html
Here is the architecture that I recommend
1) load to a staging table with dist even and sort by something that is sorted on your loaded s3 data - this means you will not have to vacuum the staging table
2) set up a production table with the sort / dist you need for your queries. after each copy from s3, load that new data into the production table and vacuum.
3) you may wish to have 2 mirror production tables and flip flop between them using a late binding view.
its a bit complex to do this you need may need some professional help. There may be specifics to your use case.
As of writing this(Just after Re-invent 2018), Redshift has Automatic Distribution available, which is a good starter.
The following utilities will come in handy:
https://github.com/awslabs/amazon-redshift-utils/tree/master/src/AdminScripts
As indicated in Answers POSTED earlier try a few combinations by replicating the same table with different DIST keys ,if you don't like what Automatic DIST is doing. After the tables are created run the admin utility from the git repos (preferably create a view on the SQL script in the Redshift DB).
Also, if you have good clarity on query usage pattern then you can use the following queries to check how well the sort key are performing using the below SQLs.
/**Queries on tables that are not utilizing SORT KEYs**/
SELECT t.database, t.table_id,t.schema, t.schema || '.' || t.table AS "table", t.size, nvl(s.num_qs,0) num_qs
FROM svv_table_info t
LEFT JOIN (
SELECT tbl, COUNT(distinct query) num_qs
FROM stl_scan s
WHERE s.userid > 1
AND s.perm_table_name NOT IN ('Internal Worktable','S3')
GROUP BY tbl) s ON s.tbl = t.table_id
WHERE t.sortkey1 IS NULL
ORDER BY 5 desc;
/**INTERLEAVED SORT KEY**/
--check skew
select tbl as tbl_id, stv_tbl_perm.name as table_name,
col, interleaved_skew, last_reindex
from svv_interleaved_columns, stv_tbl_perm
where svv_interleaved_columns.tbl = stv_tbl_perm.id
and interleaved_skew is not null;
of course , there is always room for improvement in the SQLs above, depending on specific stats that you may want to look at or drill down to.
Hope this helps.
I have a query which creates an input to pgRouting pgr_drivingDistance function:
CREATE TEMP TABLE tmp_edge AS
SELECT
e."Id" as id,
e."Source" as source,
e."Target" as target,
e."Length" / (1000*LEAST("Speed", "SpeedMin")/60) as cost
FROM "Edge" e,
"SpeedLimit" sl
WHERE sl."VehicleKindId" = 1
AND e.the_geom &&
ST_MakeEnvelope(
x1-(1000*GREATEST("Speed", "SpeedMax")/60)*13,
y1-(1000*GREATEST("Speed", "SpeedMax")/60)*13,
x1+(1000*GREATEST("Speed", "SpeedMax")/60)*13,
y1+(1000*GREATEST("Speed", "SpeedMax")/60)*13, 3857)
AND sl."RoadCategoryId" = e."CategoryId";
In the WHERE clause I calculate the same thing several times to get bounding box coordinates.
I tried to put calculations into FROM part and use alias for calculated column, but then whole execution time increases twice.
Edge table is quite large (1 milion) and SpeedLimit is several dozen record.
Is there any way to enhance this query?
It is recommended way to join tables using JOIN syntax. And then later restrict given set wit WHERE. What is ST_MakeEnvelope? You can use Index on expression in PostgreSQL ;)
Expression indexes in PostgreSQL
Since you are using expressions you might benefit from them.
And you might use Explain analyize to notice your bottlenecks in the query
I have a table in postgresql that contains an array which is updated constantly.
In my application i need to get the number of rows for which a specific parameter is not present in that array column. My query looks like this:
select count(id)
from table
where not (ARRAY['parameter value'] <# table.array_column)
But when increasing the amount of rows and the amount of executions of that query (several times per second, possibly hundreds or thousands) the performance decreses a lot, it seems to me that the counting in postgresql might have a linear order of execution (I’m not completely sure of this).
Basically my question is:
Is there an existing pattern I’m not aware of that applies to this situation? what would be the best approach for this?
Any suggestion you could give me would be really appreciated.
PostgreSQL actually supports GIN indexes on array columns. Unfortunately, it doesn't seem to be usable for NOT ARRAY[...] <# indexed_col, and GIN indexes are unsuitable for frequently-updated tables anyway.
Demo:
CREATE TABLE arrtable (id integer primary key, array_column integer[]);
INSERT INTO arrtable(1, ARRAY[1,2,3,4]);
CREATE INDEX arrtable_arraycolumn_gin_arr_idx
ON arrtable USING GIN(array_column);
-- Use the following *only* for testing whether Pg can use an index
-- Do not use it in production.
SET enable_seqscan = off;
explain (buffers, analyze) select count(id)
from arrtable
where not (ARRAY[1] <# arrtable.array_column);
Unfortunately, this shows that as written we can't use the index. If you don't negate the condition it can be used, so you can search for and count rows that do contain the search element (by removing NOT).
You could use the index to count entries that do contain the target value, then subtract that result from a count of all entries. Since counting all rows in a table is quite slow in PostgreSQL (9.1 and older) and requires a sequential scan this will actually be slower than your current query. It's possible that on 9.2 an index-only scan can be used to count the rows if you have a b-tree index on id, in which case this might actually be OK:
SELECT (
SELECT count(id) FROM arrtable
) - (
SELECT count(id) FROM arrtable
WHERE (ARRAY[1] <# arrtable.array_column)
);
It's guaranteed to perform worse than your original version for Pg 9.1 and below, because in addition to the seqscan your original requires it also needs an GIN index scan. I've now tested this on 9.2 and it does appear to use an index for the count, so it's worth exploring for 9.2. With some less trivial dummy data:
drop index arrtable_arraycolumn_gin_arr_idx ;
truncate table arrtable;
insert into arrtable (id, array_column)
select s, ARRAY[1,2,s,s*2,s*3,s/2,s/4] FROM generate_series(1,1000000) s;
CREATE INDEX arrtable_arraycolumn_gin_arr_idx
ON arrtable USING GIN(array_column);
Note that a GIN index like this will slow updates down a LOT, and is quite slow to create in the first place. It is not suitable for tables that get updated much at all - like your table.
Worse, the query using this index takes up to twice times as long as your original query and at best half as long on the same data set. It's worst for cases where the index is not very selective like ARRAY[1] - 4s vs 2s for the original query. Where the index is highly selective (ie: not many matches, like ARRAY[199]) it runs in about 1.2 seconds vs the original's 3s. This index simply isn't worth having for this query.
The lesson here? Sometimes, the right answer is just to do a sequential scan.
Since that won't do for your hit rates, either maintain a materialized view with a trigger as #debenhur suggests, or try to invert the array to be a list of parameters that the entry does not have so you can use a GiST index as #maniek suggests.
Is there an existing pattern I’m not aware of that applies to this
situation? what would be the best approach for this?
Your best bet in this situation might be to normalize your schema. Split the array out into a table. Add a b-tree index on the table of properties, or order the primary key so it's efficiently searchable by property_id.
CREATE TABLE demo( id integer primary key );
INSERT INTO demo (id) SELECT id FROM arrtable;
CREATE TABLE properties (
demo_id integer not null references demo(id),
property integer not null,
primary key (demo_id, property)
);
CREATE INDEX properties_property_idx ON properties(property);
You can then query the properties:
SELECT count(id)
FROM demo
WHERE NOT EXISTS (
SELECT 1 FROM properties WHERE demo.id = properties.demo_id AND property = 1
)
I expected this to be a lot faster than the original query, but it's actually much the same with the same sample data; it runs in the same 2s to 3s range as your original query. It's the same issue where searching for what is not there is much slower than searching for what is there; if we're looking for rows containing a property we can avoid the seqscan of demo and just scan properties for matching IDs directly.
Again, a seq scan on the array-containing table does the job just as well.
I think with Your current data model You are out of luck. Try to think of an algorithm that the database has to execute for Your query. There is no way it could work without sequential scanning of data.
Can You arrange the column so that it stores the inverse of data (so that the the query would be select count(id) from table where ARRAY[‘parameter value’] <# table.array_column) ? This query would use a gin/gist index.