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
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 am working currently on telecom analytics project and newbie in query optimisation. To show result in browser it takes a full minute while just 45,000 records are to be accessed. Could you please suggest on ways to reduce time for showing results.
I wrote following query to find call-duration of a person of age-group:
sigma=0
popn=len(Demo.objects.filter(age_group=age))
card_list=[Demo.objects.filter(age_group=age)[i].card_no
for i in range(popn)]
for card in card_list:
dic=Fact_table.objects.filter(card_no=card.aggregate(Sum('duration'))
sigma+=dic['duration__sum']
avgDur=sigma/popn
Above code is within for loop to iterate over age-groups.
Model is as follows:
class Demo(models.Model):
card_no=models.CharField(max_length=20,primary_key=True)
gender=models.IntegerField()
age=models.IntegerField()
age_group=models.IntegerField()
class Fact_table(models.Model):
pri_key=models.BigIntegerField(primary_key=True)
card_no=models.CharField(max_length=20)
duration=models.IntegerField()
time_8bit=models.CharField(max_length=8)
time_of_day=models.IntegerField()
isBusinessHr=models.IntegerField()
Day_of_week=models.IntegerField()
Day=models.IntegerField()
Thanks
Try that:
sigma=0
demo_by_age = Demo.objects.filter(age_group=age);
popn=demo_by_age.count() #One
card_list = demo_by_age.values_list('card_no', flat=True) # Two
dic = Fact_table.objects.filter(card_no__in=card_list).aggregate(Sum('duration') #Three
sigma = dic['duration__sum']
avgDur=sigma/popn
A statement like card_list=[Demo.objects.filter(age_group=age)[i].card_no for i in range(popn)] will generate popn seperate queries and database hits. The query in the for-loop will also hit the database popn times. As a general rule, you should try to minimize the amount of queries you use, and you should only select the records you need.
With a few adjustments to your code this can be done in just one query.
There's generally no need to manually specify a primary_key, and in all but some very specific cases it's even better not to define any. Django automatically adds an indexed, auto-incremental primary key field. If you need the card_no field as a unique field, and you need to find rows based on this field, use this:
class Demo(models.Model):
card_no = models.SlugField(max_length=20, unique=True)
...
SlugField automatically adds a database index to the column, essentially making selections by this field as fast as when it is a primary key. This still allows other ways to access the table, e.g. foreign keys (as I'll explain in my next point), to use the (slightly) faster integer field specified by Django, and will ease the use of the model in Django.
If you need to relate an object to an object in another table, use models.ForeignKey. Django gives you a whole set of new functionality that not only makes it easier to use the models, it also makes a lot of queries faster by using JOIN clauses in the SQL query. So for you example:
class Fact_table(models.Model):
card = models.ForeignKey(Demo, related_name='facts')
...
The related_name fields allows you to access all Fact_table objects related to a Demo instance by using instance.facts in Django. (See https://docs.djangoproject.com/en/dev/ref/models/fields/#module-django.db.models.fields.related)
With these two changes, your query (including the loop over the different age_groups) can be changed into a blazing-fast one-hit query giving you the average duration of calls made by each age_group:
age_groups = Demo.objects.values('age_group').annotate(duration_avg=Avg('facts__duration'))
for group in age_groups:
print "Age group: %s - Average duration: %s" % group['age_group'], group['duration_avg']
.values('age_group') selects just the age_group field from the Demo's database table. .annotate(duration_avg=Avg('facts__duration')) takes every unique result from values (thus each unique age_group), and for each unique result will fetch all Fact_table objects related to any Demo object within that age_group, and calculate the average of all the duration fields - all in a single query.
How would one go about writing a query that selects items 2000-2010 out of a collection of 10000 objects in the data store.
I know that it can be done like this in GQL:
select * from MyObject limit 10 offset 2000
According to the documentation, when using an offset the engine will still fetch all the rows, only not return them, thus making the query perform in a way that corresponds linearly with the value of offset.
Is there any better way? Such as using a pseudo ROWNUM column like one could do in other types of data stores.
There's no way to efficiently page using offsets, except to cache the results. You can, however, use datastore cursors to implement paging using a 'bookmark' type approach.
Besides using cursors you can also use a sort order approach. For example:
SELECT * FROM MyObject ORDER BY field LIMIT 10;
for the first 10 objects and then for the next 10 objects, etc.
SELECT * FROM MyObject WHERE field > largestFieldValueFromPreviousResult ORDER BY field LIMIT 10;
Field could even be a key if you don't have another appropriate field. Here is a more complete example:
http://code.google.com/appengine/articles/paging.html
I have a table-valued, inline UDF. I want to filter the results of that UDF to get one particular value. When I specify the filter using a constant parameter, everything is great and performance is almost instantaneous. When I specify the filter using a variable parameter, it takes a significantly larger chunk of time, on the order of 500x more logical reads and 20x greater duration.
The execution plan shows that in the variable parameter case the filter is not applied until very late in the process, causing multiple index scans rather than the seeks that are performed in the constant case.
I guess my questions are: Why, since I'm specifying a single filter parameter that is going to be highly selective against an indexed field, does my performance go into the weeds when that parameter is in a variable? Is there anything I can do about this?
Does it have something to do with the analytic function in the query?
Here are my queries:
CREATE FUNCTION fn_test()
RETURNS TABLE
WITH SCHEMABINDING
AS
RETURN
SELECT DISTINCT GCN_SEQNO, Drug_package_version_ID
FROM
(
SELECT COALESCE(ndctbla.GCN_SEQNO, ndctblb.GCN_SEQNO) AS GCN_SEQNO,
dpv.Drug_package_version_ID, ROW_NUMBER() OVER (PARTITION BY dpv.Drug_package_version_id ORDER BY
ndctbla.GCN_SEQNO DESC) AS Predicate
FROM dbo.Drug_Package_Version dpv
LEFT JOIN dbo.NDC ndctbla ON ndctbla.NDC = dpv.Sp_package_code
LEFT JOIN dbo.NDC ndctblb ON ndctblb.SPC_NDC = dpv.Sp_package_code
) iq
WHERE Predicate = 1
GO
GRANT SELECT ON fn_test TO public
GO
-- very fast
SELECT GCN_SEQNO
FROM dbo.fn_test()
WHERE Drug_package_version_id = 10000
GO
-- comparatively slow
DECLARE #dpvid int
SET #dpvid = 10000
SELECT GCN_SEQNO
FROM dbo.fn_test()
WHERE Drug_package_version_id = #dpvid
Once you create a new projection through a UDF, it can't be expected that your indexes will still apply on the columns that are indexed on the original table and included in the projection. When you filter on the projection (and not in the UDF against the original table with the indexes) the indexes no longer apply.
What you want to do is parameterize the function to take in the parameter.
If you find that you have too many fields that you want to set parameters on, then you might want to take a look at indexed views, as you can create your projection and index it as well and then run queries against that.
Simply, the constant is easy to evaluate in the plan. The local variable is not. Especially with the ranking function and filter Predicate = 1
Paraphrasing casparOne, you need to push the filter as far inwards as possible so that you filter on dpv.Drug_package_version_id inside the iq derived table.
If you do that, then you also have no need for the PARTITION BY because you have only a single dpv.Drug_package_version_id. Then you can do a cleaner ...TOP 1 ... ORDER BY ndctbla.GCN_SEQNO DESC.
The responses I got were good, and I learned from them, but I think I've found an answer that satisfies me.
I do think it's the use of the PARTITION BY clause that is causing the problem here. I reformulated the UDF using a variant of the self-join idiom:
SELECT t1.A, t1.B, t1.C
FROM T t1
INNER JOIN
(
SELECT A, MAX(C) AS C
FROM T
GROUP BY A
) t2 ON t1.A = t2.A AND t1.C = t2.C
Ironically, this is more performant than using the SQL 2008-specific query, and also the optimizer doesn't have a problem with joining this version of the query using variables rather than constants. At this point, I'm concluding that the optimizer just doesn't handle the more recent SQL extensions as well as the older stuff. As a bonus, I can make use of the UDF now, in my pre-upgraded SQL 2000 platforms.
Thanks for your help, everyone!