I have created a materialized view for the purposes of feeding into a dashboard.
My goal is to make this table selectable in the fastest way possible and I'm not sure how to approach it. I was hoping that if I describe the table and how it will be used, someone could offer some direction.
The context is a website with funnel steps.Each row is an instance of a user triggering a funnel step such as add to cart, checkout, payment details and then finally transaction.
Since the table is for the purposes of analytics, it will be refreshed automatically with cron once a day only, in the morning, so I'm not worried about real time update speed, only select speed with various where clauses.
Suppose I have the fields described below:
(N = ~13M and expected to be ~20 by January, growing several million per month)
Table is unique with the combination of session id, user id and funnel step.
- Session Id (Id, so some duplication but generally very very granular - Varchar)
- User Id (Id, so some duplication but generally very very granular - Varchar)
- Date (Date)
- Funnel Step (10 distinct value - Varchar)
- Device Category (3 distinct values - Varchar)
- Country (~ 100 distinct values - varchar)
- City (~1000+ distinct values - varchar)
- Source (several thousand distinct values, nevertheless, stakeholder would like a filter - varchar)
Would I index each field individually? Or, should I index all fields in a oner? Per the documentation, I think I can index up to 32 fields at once. But would that be advisable here given my primary goal of select query speed over everything else?
The table will feed into dashboard that reads the table and dynamically translates filter inputs into where clauses. Each time the user adjusts a filter, the table will be read and grouped and aggregated based on the filter / where clause inputs.
Example query:
select
event_action,
count(distinct user_id) as users
from website_data.ecom_funnel
where date >= $input_start_date
and date <= $input_end_date
and device_category in ($mobile, $desktop, $tablet)
and country in ($list of all countries minus any not selected)
and source in ($list of all sources minus any not selected)
group by 1 order by users desc
This will result in a funnel shaped table of data.
I cannot aggregate before hand because the primary metric of concern is users, not sessions. These must be de-duped from the underlying table. Classic example... Suppose a person visits a website once a day for a week. Then the sum of unique visitors for that week is 1, however if I summed visitors by day I would get 7. Similar with my table, some users take multiple sessions to complete the funnel. So, this is why I cannot pre aggregate the table, since I need to apply filters to the underlying data and then count(distinct user id).
Here's explain on a subset of fields if it is useful:
QUERY PLAN
Sort (cost=862194.66..862194.68 rows=9 width=24)
Sort Key: (count(DISTINCT client_id)) DESC
-> GroupAggregate (cost=847955.01..862194.51 rows=9 width=24)
Group Key: event_action
-> Sort (cost=847955.01..852701.48 rows=1898589 width=37)
Sort Key: event_action
-> Seq Scan on ecom_funnel (cost=0.00..589150.14 rows=1898589 width=37)
Filter: ((device_category = ANY ('{mobile,desktop}'::text[])) AND (source = 'google'::text))
My overarching, specific question is, given my use case, should I index each field individually or should I create one single index? Does it matter?
On top of that, any tips for optimising this materialized view to run a select query faster would be appreciated.
Looking at your filter conditions, you should check the cardinality of device_category field by posting
select device_category, count(*) from website_data.ecom_funnel group by device_category
and looking at the values to determine if an index should firstly include this column. Possible index here (without knowing the cardinality) would be multicolumn and include:
(device_category, date)
Saying that, there's no benefit from creating indexes on each separate column as your query wouldn't use them all, so it does matter. You would slow down other CRUD operations that aren't Read operation.
Creating an index on all columns won't probably speed it up too much for you as well, but that's based on the data lying under the hood (in the table) and how your filters compare to the overall query without them (cardinality of values in columns being filtered). This would most likely create a huge overhead of going through the index tree and then obtaining rowids to return the data you need.
Summing up, I would try to narrow the index down to the columns that matter most in your filtering which means they cut most of the data being retrieved. If your query is meant to return majority of rows from the table then there's a need to aggregate, unfortunately, as this wouldn't speed things up.
Hope it helps.
Edit: I've just read that you already posted count of distinct values among your table. I'm not sure what Funnel Step is bound to in your table, but assuming it's a column named event_action, it might be beneficial to instead create an index that would help in grouping as well by doing:
(date, event_action)
It seems like you have omitted the GROUP BY clause at all, which should be included and it should be grouping by event_action, since that's what your select part is doing.
If you narrow the date down to several days/months every time you perform a select query, it might be a huge benefit to create index with first date column.
Remember, that position of column in an index matters.
If you look for values from several months let's say, you should preaggregate and store precalculated values from each month in another table and then UNION ALL that data to the current query which would only select data from current (still being updated) time.
Related
I had a hard time titling this question but I hope its appropriate.
I have a table of transactions, and each transaction has a Date column (of type Date).
I want to run a query that gets the latest 100 transactions by date (simple enough with an ORDERBY query).
My question is, in order to make this an extremely cheap operation, would it make sense to sort my entire table so that I just need to select the top 100 rows every time, or do i simply create an index on the date column? Not sure if first option is even possible and or/good sql db practice.
You would add an index on the column with the date and query:
SELECT * FROM tab
ORDER BY datecol DESC
LIMIT 100;
The problem with your other idea is that there is no well-defined order in a table. Every UPDATE changes this "order", and even if you don't modify anything, a sequential scan need not start at the beginning of the table.
I have the following table
create table log
(
id bigint default nextval('log_id_seq'::regclass) not null
constraint log_pkey
primary key,
level integer,
category varchar(255),
log_time timestamp,
prefix text,
message text
);
It contains like 3 million of rows.
I'm comparing the following queries:
EXPLAIN SELECT id
FROM log
WHERE log_time < now() - INTERVAL '3 month'
LIMIT 100000
which yields the following plan:
Limit (cost=0.00..19498.87 rows=100000 width=8)
-> Seq Scan on log (cost=0.00..422740.48 rows=2168025 width=8)
Filter: (log_time < (now() - '3 mons'::interval))
And the same query with ORDER BY id instruction added:
EXPLAIN SELECT id
FROM log
WHERE log_time < now() - INTERVAL '3 month'
ORDER BY id ASC
LIMIT 100000
which yields
Limit (cost=0.43..25694.15 rows=100000 width=8)
-> Index Scan using log_pkey on log (cost=0.43..557048.28 rows=2168031 width=8)
Filter: (log_time < (now() - '3 mons'::interval))
I have the following questions:
The absence of ORDER BY instruction allows Postgres not to care about the order of rows. They may be as well delivered sorted. Why it does not use index without ORDER BY?
How can Postgres use index in the first place in such a query? WHERE clause of the query contains a non-indexed column and to fetch that column, sequential database scan will be required, but the query with ORDER BY doesn't indicate that.
The Postgres manual page says:
For a query that requires scanning a large fraction of the table, an explicit sort is likely to be faster than using an index because it requires less disk I/O due to following a sequential access pattern
Can you please clarify this statement for me? Index is always ordered. And reading an ordered structure is always faster, it is always a sequential access (at least in terms of page scanning) than reading non-ordered data and then ordering it manually.
Can you please clarify this statement for me? Index is always ordered. And reading an ordered structure is always faster, it is always a sequential access (at least in terms of page scanning) than reading non-ordered data and then ordering it manually.
The index is read sequentially, yes, but postgres needs to follow up with a read of the rows from the table. That is, in most cases, if an index identifies 100 rows, then postgres will need to perform up to 100 random reads against the table.
Internally, the postgres planner weighs sequential and random reads differently, with random reads generally much more expensive. The settings seq_page_cost and random_page_cost determine those. There are other settings you can view and tinker with if you want, though I recommend being very conservative with modifications.
Let's go back to your earlier questions:
The absence of ORDER BY instruction allows Postgres not to care about the order of rows. They may be as well delivered sorted. Why it does not use index without ORDER BY?
The reason is the sort. As you note later, the index doesn't include the constraining column, so it doesn't make any sense to use the index. Instead, the planner is basically saying "read the whole table, figure out which rows conform to the constraint, and then return the first 100000 of them, in whatever order we find them".
The sort changes things. In that case, the planner is saying "we need to sort by this field, and we have an index which is already sorted, so read rows from the table in index order, checking against the constraint, until we have 100000 of them, and return that set".
You'll note that the cost estimates (e.g. '0.43..25694.15') are much higher for the second query -- the planner thinks that doing so many random reads from the index scan is going to cost significantly more than just reading the whole table at once with no sorting.
Hope that helps, and let me know if you have further questions.
I have table
create table big_table (
id serial primary key,
-- other columns here
vote int
);
This table is very big, approximately 70 million rows, I need to query:
SELECT * FROM big_table
ORDER BY vote [ASC|DESC], id [ASC|DESC]
OFFSET x LIMIT n -- I need this for pagination
As you may know, when x is a large number, queries like this are very slow.
For performance optimization I added indexes:
create index vote_order_asc on big_table (vote asc, id asc);
and
create index vote_order_desc on big_table (vote desc, id desc);
EXPLAIN shows that the above SELECT query uses these indexes, but it's very slow anyway with a large offset.
What can I do to optimize queries with OFFSET in big tables? Maybe PostgreSQL 9.5 or even newer versions have some features? I've searched but didn't find anything.
A large OFFSET is always going to be slow. Postgres has to order all rows and count the visible ones up to your offset. To skip all previous rows directly you could add an indexed row_number to the table (or create a MATERIALIZED VIEW including said row_number) and work with WHERE row_number > x instead of OFFSET x.
However, this approach is only sensible for read-only (or mostly) data. Implementing the same for table data that can change concurrently is more challenging. You need to start by defining desired behavior exactly.
I suggest a different approach for pagination:
SELECT *
FROM big_table
WHERE (vote, id) > (vote_x, id_x) -- ROW values
ORDER BY vote, id -- needs to be deterministic
LIMIT n;
Where vote_x and id_x are from the last row of the previous page (for both DESC and ASC). Or from the first if navigating backwards.
Comparing row values is supported by the index you already have - a feature that complies with the ISO SQL standard, but not every RDBMS supports it.
CREATE INDEX vote_order_asc ON big_table (vote, id);
Or for descending order:
SELECT *
FROM big_table
WHERE (vote, id) < (vote_x, id_x) -- ROW values
ORDER BY vote DESC, id DESC
LIMIT n;
Can use the same index.
I suggest you declare your columns NOT NULL or acquaint yourself with the NULLS FIRST|LAST construct:
PostgreSQL sort by datetime asc, null first?
Note two things in particular:
The ROW values in the WHERE clause cannot be replaced with separated member fields. WHERE (vote, id) > (vote_x, id_x) cannot be replaced with:
WHERE vote >= vote_x
AND id > id_x
That would rule out all rows with id <= id_x, while we only want to do that for the same vote and not for the next. The correct translation would be:
WHERE (vote = vote_x AND id > id_x) OR vote > vote_x
... which doesn't play along with indexes as nicely, and gets increasingly complicated for more columns.
Would be simple for a single column, obviously. That's the special case I mentioned at the outset.
The technique does not work for mixed directions in ORDER BY like:
ORDER BY vote ASC, id DESC
At least I can't think of a generic way to implement this as efficiently. If at least one of both columns is a numeric type, you could use a functional index with an inverted value on (vote, (id * -1)) - and use the same expression in ORDER BY:
ORDER BY vote ASC, (id * -1) ASC
Related:
SQL syntax term for 'WHERE (col1, col2) < (val1, val2)'
Improve performance for order by with columns from many tables
Note in particular the presentation by Markus Winand I linked to:
"Pagination done the PostgreSQL way"
Have you tried partioning the table ?
Ease of management, improved scalability and availability, and a
reduction in blocking are common reasons to partition tables.
Improving query performance is not a reason to employ partitioning,
though it can be a beneficial side-effect in some cases. In terms of
performance, it is important to ensure that your implementation plan
includes a review of query performance. Confirm that your indexes
continue to appropriately support your queries after the table is
partitioned, and verify that queries using the clustered and
nonclustered indexes benefit from partition elimination where
applicable.
http://sqlperformance.com/2013/09/sql-indexes/partitioning-benefits
I have read from internet resources that a query will be slow when the offset increases. But in my case I think its too much slow. I am using postgres 9.3
Here is the query (id is primary key):
select * from test_table offset 3900000 limit 100;
It returns me data in around 10 seconds. And I think its too much slow. I have around 4 million records in table. Overall size of the database is 23GB.
Machine configuration:
RAM: 12 GB
CPU: 2.30 GHz
Core: 10
Few values from postgresql.conf file which I have changed are as below. Others are default.
shared_buffers = 2048MB
temp_buffers = 512MB
work_mem = 1024MB
maintenance_work_mem = 256MB
dynamic_shared_memory_type = posix
default_statistics_target = 10000
autovacuum = on
enable_seqscan = off ## its not making any effect as I can see from Analyze doing seq-scan
Apart from these I have also tried by changing the values of random_page_cost = 2.0 and cpu_index_tuple_cost = 0.0005 and result is same.
Explain (analyze, buffers) result over the query is as below:
"Limit (cost=10000443876.02..10000443887.40 rows=100 width=1034) (actual time=12793.975..12794.292 rows=100 loops=1)"
" Buffers: shared hit=26820 read=378984"
" -> Seq Scan on test_table (cost=10000000000.00..10000467477.70 rows=4107370 width=1034) (actual time=0.008..9036.776 rows=3900100 loops=1)"
" Buffers: shared hit=26820 read=378984"
"Planning time: 0.136 ms"
"Execution time: 12794.461 ms"
How people around the world negotiates with this problem in postgres? Any alternate solution will be helpful for me as well.
UPDATE:: Adding order by id (tried with other indexed column as well) and here is the explain:
"Limit (cost=506165.06..506178.04 rows=100 width=1034) (actual time=15691.132..15691.494 rows=100 loops=1)"
" Buffers: shared hit=110813 read=415344"
" -> Index Scan using test_table_pkey on test_table (cost=0.43..533078.74 rows=4107370 width=1034) (actual time=38.264..11535.005 rows=3900100 loops=1)"
" Buffers: shared hit=110813 read=415344"
"Planning time: 0.219 ms"
"Execution time: 15691.660 ms"
It's slow because it needs to locate the top offset rows and scan the next 100. No amounts of optimization will change that when you're dealing with huge offsets.
This is because your query literally instruct the DB engine to visit lots of rows by using offset 3900000 -- that's 3.9M rows. Options to speed this up somewhat aren't many.
Super-fast RAM, SSDs, etc. will help. But you'll only gain by a constant factor in doing so, meaning it's merely kicking the can down the road until you reach a larger enough offset.
Ensuring the table fits in memory, with plenty more to spare will likewise help by a larger constant factor -- except the first time. But this may not be possible with a large enough table or index.
Ensuring you're doing index-only scans will work to an extent. (See velis' answer; it has a lot of merit.) The problem here is that, for all practical purposes, you can think of an index as a table storing a disk location and the indexed fields. (It's more optimized than that, but it's a reasonable first approximation.) With enough rows, you'll still be running into problems with a larger enough offset.
Trying to store and maintain the precise position of the rows is bound to be an expensive approach too.(This is suggested by e.g. benjist.) While technically feasible, it suffers from limitations similar to those that stem from using MPTT with a tree structure: you'll gain significantly on reads but will end up with excessive write times when a node is inserted, updated or removed in such a way that large chunks of the data needs to be updated alongside.
As is hopefully more clear, there isn't any real magic bullet when you're dealing with offsets this large. It's often better to look at alternative approaches.
If you're paginating based on the ID (or a date field, or any other indexable set of fields), a potential trick (used by blogspot, for instance) would be to make your query start at an arbitrary point in the index.
Put another way, instead of:
example.com?page_number=[huge]
Do something like:
example.com?page_following=[huge]
That way, you keep a trace of where you are in your index, and the query becomes very fast because it can head straight to the correct starting point without plowing through a gazillion rows:
select * from foo where ID > [huge] order by ID limit 100
Naturally, you lose the ability to jump to e.g. page 3000. But give this some honest thought: when was the last time you jumped to a huge page number on a site instead of going straight for its monthly archives or using its search box?
If you're paginating but want to keep the page offset by any means, yet another approach is to forbid the use of larger page number. It's not silly: it's what Google is doing with search results. When running a search query, Google gives you an estimate number of results (you can get a reasonable number using explain), and then will allow you to brows the top few thousand results -- nothing more. Among other things, they do so for performance reasons -- precisely the one you're running into.
I have upvoted Denis's answer, but will add a suggestion myself, perhaps it can be of some performance benefit for your specific use-case:
Assuming your actual table is not test_table, but some huge compound query, possibly with multiple joins. You could first determine the required starting id:
select id from test_table order by id offset 3900000 limit 1
This should be much faster than original query as it only requires to scan the index vs the entire table. Getting this id then opens up a fast index-search option for full fetch:
select * from test_table where id >= (what I got from previous query) order by id limit 100
You didn't say if your data is mainly read-only or updated often. If you can manage to create your table at one time, and only update it every now and then (say every few minutes) your problem will be easy to solve:
Add a new column "offset_id"
For your complete data set ordered by ID, create an offset_id simply by incrementing numbers: 1,2,3,4...
Instead of "offset ... limit 100" use "where offset_id >= 3900000 limit 100"
you can optimise in two steps
First get maximum id out of 3900000 records
select max(id) (select id from test_table order by id limit 3900000);
Then use this maximum id to get the next 100 records.
select * from test_table id > {max id from previous step) order by id limit 100 ;
It will be faster as both queries will do index scan by id.
This way you get the rows in semi-random order. You are not ordering the results in a query, so as a result, you get the data as it is stored in the files. The problem is that when you update the rows, the order of them can change.
To fix that you should add order by to the query. This way the query will return the rows in the same order. What's more then it will be able to use an index to speed the query up.
So two things: add an index, add order by to the query. Both to the same column. If you want to use the id column, then don't add index, just change the query to something like:
select * from test_table order by id offset 3900000 limit 100;
First, you have to define limit and offset with order by clause or you will get inconsistent result.
To speed up the query, you can have a computed index, but only for these condition :
Newly inserted data is strictly in id order
No delete nor update on column id
Here's how You can do it :
Create a row position function
create or replace function id_pos (id) returns bigint
as 'select count(id) from test_table where id <= $1;'
language sql immutable;
Create a computed index on id_pos function
create index table_by_pos on test_table using btree(id_pos(id));
Here's how You call it (offset 3900000 limit 100):
select * from test_table where id_pos(id) >= 3900000 and sales_pos(day) < 3900100;
This way, the query will not compute the 3900000 offset data, but only will compute the 100 data, making it much faster.
Please note the 2 conditions where this approach can take place, or the position will change.
I don't know all of the details of your data, but 4 million rows can be a little hefty. If there's a reasonable way to shard the table and essentially break it up into smaller tables it could be beneficial.
To explain this, let me use an example. let's say that I have a database where I have a table called survey_answer, and it's getting very large and very slow. Now let's say that these survey answers all come from a distinct group of clients (and I also have a client table keeping track of these clients). Then something I could do is I could make it so that I have a table called survey_answer that doesn't have any data in it, but is a parent table, and it has a bunch of child tables that actually contain the data the follow the naming format survey_answer_<clientid>, meaning that I'd have child tables survey_answer_1, survey_answer_2, etc., one for each client. Then when I needed to select data for that client, I'd use that table. If I needed to select data across all clients, I can select from the parent survey_answer table, but it will be slow. But for getting data for an individual client, which is what I mostly do, then it would be fast.
This is one example of how to break up data, and there are many others. Another example would be if my survey_answer table didn't break up easily by client, but instead I know that I'm typically only accessing data over a year period of time at once, then I could potentially make child tables based off of year, such as survey_answer_2014, survey_answer_2013, etc. Then if I know that I won't access more than a year at a time, I only really need to access maybe two of my child tables to get all the data I need.
In your case, all I've been given is perhaps the id. We can break it up by that as well (though perhaps not as ideal). Let's say that we break it up so that there's only about 1000000 rows per table. So our child tables would be test_table_0000001_1000000, test_table_1000001_2000000, test_table_2000001_3000000, test_table_3000001_4000000, etc. So instead of passing in an offset of 3900000, you'd do a little math first and determine that the table that you want is table test_table_3000001_4000000 with an offset of 900000 instead. So something like:
SELECT * FROM test_table_3000001_4000000 ORDER BY id OFFSET 900000 LIMIT 100;
Now if sharding the table is out of the question, you might be able to use partial indexes to do something similar, but again, I'd recommend sharding first. Learn more about partial indexes here.
I hope that helps. (Also, I agree with Szymon Guz that you want an ORDER BY).
Edit: Note that if you need to delete rows or selectively exclude rows before getting your result of 100, then sharding by id will become very hard to deal with (as pointed out by Denis; and sharding by id is not great to begin with). But if your 'just' paginating the data, and you only insert or edit (not a common thing, but it does happen; logs come to mind), then sharding by id can be done reasonably (though I'd still choose something else to shard on).
How about if paginate based on IDs instead of offset/limit?
The following query will give IDs which split all the records into chunks of size per_page. It doesn't depend on were records deleted or not
SELECT id AS from_id FROM (
SELECT id, (ROW_NUMBER() OVER(ORDER BY id DESC)) AS num FROM test_table
) AS rn
WHERE num % (per_page + 1) = 0;
With these from_IDs you can add links to the page. Iterate over :from_ids with index and add the following link to the page:
:from_id_index
When user visits the page retrieve records with ID which is greater than requested :from_id:
SELECT * FROM test_table WHERE ID >= :from_id ORDER BY id DESC LIMIT :per_page
For the first page link with from_id=0 will work
1
To avoid slow pagination with big tables always use auto-increment primary key then use the query below:
SELECT * FROM test_table WHERE id > (SELECT min(id) FROM test_table WHERE id > ((1 * 10) - 10)) ORDER BY id DESC LIMIT 10
1: is the page number
10: is the records per page
Tested and work well with 50 millions records.
There are two simple approaches to solve such a problem
Splitting the query into two subqueries that the first one do all the heavy job on index-only scan as described here
Create calculated index that holds the offset as described here, this can be enhanced using window functions.
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.