my questions are What indexes are used? In what order? Why? in following sample
Query:
SELECT House
FROM myTable
WHERE 1=1
and City='myCity'
and Street='myStreet'
and Color='myColor'
Indexes:
Ind1: City
Ind2: Street
Ind3: Color
Ind4: Street,Color
It depends on... The server might have statistics, so it will choose the index which has the most effective filtering like:
if City='myCity' returns 100
if Street='myStreet' returns 1000
if Color='myColor' returns 10000
element, then City index will be used. This logic is valid for composite indexes as well.
The optimizer will try to get the smallest set first then the other filters will be applied on this.
This requires uptodate statistic, otherwise the wrong index might be used.
Related
I need to build one MSSQL query that selects one row that is the best match.
Ideally, we have a match on street, zip code and house number.
Only if that does not deliver any results, a match on just street and zip code is sufficient
I have this query so far:
SELECT TOP 1 * FROM realestates
WHERE
(Address_Street = '[Street]'
AND Address_ZipCode = '1200'
AND Address_Number = '160')
OR
(Address_Street = '[Street]'
AND Address_ZipCode = '1200')
MSSQL currently gives me the result where the Address_Number is NOT 160, so it seems like the 2nd clause (where only street and zipcode have to match) is taking precedence over the 1st. If I switch around the two OR clauses, same result :)
How could I prioritize the first OR clause, so that MSSQL stops looking for other results if we found a match where the three fields are present?
The problem here isn't the WHERE (though it is a "problem"), it's the lack of an ORDER BY. You have a TOP (1), but you have nothing that tells the data engine which row is the "top" row, so an arbitrary row is returned. You need to provide logic, in the ORDER BY to tell the data engine which is the "first" row. With the rudimentary logic you have in your question, this would like be:
SELECT TOP (1)
{Explicit Column List}
realestates
WHERE Address_Street = '[Street]'
AND Address_ZipCode = '1200'
ORDER BY CASE Address_Number WHEN '160' THEN 1 ELSE 2 END;
You can't prioritize anything in the WHERE clause. It always results in ALL the matching rows. What you can do is use TOP or FETCH to limit how many results you will see.
However, in order for this to be effective, you MUST have an ORDER BY clause. SQL tables are unordered sets by definition. This means without an ORDER BY clause the database is free to return rows in any order it finds convenient. Mostly this will be the order of the primary key, but there are plenty of things that can change this.
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").
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'm using the lsqlite3 lua wrapper and I'm making queries into a database. My DB has ~5million rows and the code I'm using to retrieve rows is akin to:
db = lsqlite3.open('mydb')
local temp = {}
local sql = "SELECT A,B FROM tab where FOO=BAR ORDER BY A DESC LIMIT N"
for row in db:nrows(sql) do temp[row['key']] = row['col1'] end
As you can see I'm trying to get the top N rows sorted in descending order by FOO (I want to get the top rows and then apply the LIMIT not the other way around). I indexed the column A but it doesn't seem to make much of a difference. How can I make this faster?
You need to index the column on which you filter (i.e. with the WHERE clause). THe reason is that ORDER BY comes into play after filtering, not the other way around.
So you probably should create an index on FOO.
Can you post your table schema?
UPDATE
Also you can increase the sqlite cache, e.g.:
PRAGMA cache_size=100000
You can adjust this depending on the memory available and the size of your database.
UPDATE 2
I you want to have a better understanding of how your query is handled by sqlite, you can ask it to provide you with the query plan:
http://www.sqlite.org/eqp.html
UPDATE 3
I did not understand your context properly with my initial answer. If you are to ORDER BY on some large data set, you probably want to use that index, not the previous one, so you can tell sqlite to not use the index on FOO this way:
SELECT a, b FROM foo WHERE +a > 30 ORDER BY b
I am going to express the idea in SQL:
SELECT key,value
FROM table1
WHERE value > 10
Or do we always need to know the key?
I suppose you can use secondary indexes which are available since version 0.7 of casssandra.
You might also checkout the following answer: Cassandra and Secondary-Indexes, how do they work internally?
it is recommended to use secondary indexes only for low-cardinality columns, which means for columns which do not have many different values (e.g. columns like 'status' or 'priority' which have usually only a handful different values like 'high', 'medium', 'low').
In case you are using Hector as your cassandra client you can find information here how to use them:
https://github.com/rantav/hector/wiki/User-Guide
Yes, of course, for example, you can use *
select * from CF where value = 10
If you use the Hector API (e.g. CqlQuery), you can get a list of rows back from this query.
Note, currently for secondary indexes, you must have at least one equality conditional, so your query with just value > 10 would not work. See this question