I think the Optimizer works something like this:
It can find the first row: MIN(name) WHERE foo=123
It can find the last row: MAX(name) WHERE foo=123
Those are done via a drill-down in the BTree, and assumed to be reasonably cheap. If there are a lot of rows for foo=123, then this is likely to skip over some blocks. Note: fetching blocks potentially ...
You don't need single column indexes on fields that are also the first column in multi column indexes.
However you shouldn't specify the column order just based on that. For best performance, you want the column that has the highest cardinality (ie "uniqueness") first in the column order.
I think that you should just get rid of the CREATED_AT single column ...
Loose index scan works because the first record in a group satisfies the query, so there is no need to read other records in the group.
If it helps - there are more details on files format and how it impacts queries in my slides - https://www.slideshare.net/akuzminsky/indexes-in-my-sql-aleksandr-kuzminsky-https-twindbcom
That is an index that was automatically generated for a constraint. Example:
SQL> select index_name from user_indexes;
no rows selected
SQL> create table t1(c1 number primary key);
SQL> select index_name from user_indexes;
The user_lookups column of sys.dm_db_index_usage_stats is a count of clustered index key lookups (a.k.a. bookmark lookups). This indicates a data row retrieved via a non-clustered index using the clustered index key row locator. The separate user_seeks and user_scans columns for a clustered index count the number of times the CI was used directly.
I should have tried a bit more first. Now that I run a few examples, I seems like yes, the above queries can all use an index-only scan. As soon as a column outside of the covering index is referred to, a regular access method is used (e.g. an index scan id a fitting index is in place).
It would be nice if the Postgres documentation would make this more ...
Take a look at this article on SQLShack which gives a great description of what to consider when selecting clustered index keys. To summarise:
Keep it short - the clustered index key is included in all non-clustered indexes, so a wide key means more data space required on disk, and in the buffer when reading pages.
Keep it static - If your clustering key ...
Have you ever tested something similar in production?
As a general rule, I don't test things in production if I can avoid it. I have certainly tested them in test, however.
Example 2: add the btree_gin extension and create a composite index on created_at and tags. The problem is the same as above: I think that PostgreSQL cannot use ordering since the ...
There are two approaches:
create an index on the array:
CREATE INDEX ON items USING gin (tags);
That allows the database to quickly find the matching rows, but then it has to perform a top-n sort.
create a B-tree index on created_at:
CREATE INDEX ON items (created_at);
That will allow the database to avoid the sort, but it has to scan and discard the ...
Generate the current explain plan using explain select * from table_name;
Create a gin index on tags column and btree index on created at column. Generate the new query/explain plan post index creation to notice the cost difference and execution times.
Order of your data returned is not guaranteed when you do not specify an order by.
Consider this example:
CREATE TABLE dbo.SmallTable([name] varchar(25) PRIMARY KEY NOT NULL,
INSERT INTO dbo.SmallTable ([name],[Number]) VALUES ('compi',15 ), ('jack',5 ), ('malik',20 ), ('nana',10 );
If you were to just select ...
here are a couple of ideas.
One does the constraint check in a function.
the second modifies the table, creates a trigger to add in the missing data and creates a new index on the three fields that have to be checked
CREATE TRIGGER check_jsonb
FOR EACH ROW
CREATE FUNCTION public._check_jsonb()...
What is order by 1? i think combining that with limit 10 is resulting in postgress not knowing which 10, so it first gets all of them and then returns 10. However this theoretically shouldn't slow down the query.
But, do try
explain select count(*)
where viewed_at between '2019-01-01' and '2019-01-31';
and see if that simplifies the ...
If the uniqueness constraint is the only issue (and I'm interested to learn more about why in the discussion above), here's an idea:
remove the uniqueness constraint
when you do reads (selects), do order by id asc limit 1 so that you ignore duplicates
have some sort of parallel process going through the table periodically and removing duplicates
It looks like the CBO wants to make a 90 M X 4 B matrix, THEN do the filtering.
You need to convince the CBO to "don't do that".
I suggest you research the following ides. From there, you can try (combinations) of the various suggestions
As I understand FAST REFRESH Materilaized Views with COUNT(*), SUM() need Materialized View Logs. ...
indexdef is still not exactly the same as the creation statement in the case of a partial index. For example if we create an index with the following statement:
CREATE INDEX item_orgunit_idx ON items (orgunit_id) WHERE type IN ('invoice', 'purchaseorder', 'beanpayment');
postgres will generate the following indexdef:
CREATE INDEX item_orgunit_idx ON public....
PostgreSQL thinks that it can avoid the expensive sort by using an index that returns the rows in sorted order and filter out the rows that do not match the conditions. This strategy turns out to be bad, either because of mis-estimates or an adversarial distribution.
Sorting by primary key is in no way faster than sorting by other indexed criteria. And in ...
Start by cleaning up the existing indexes:
id isn't useful as id the primary key already and should be dropped.
Both id_empresa and id_empresa_campana are superseded by idx_mejora_perf_clientes because idx_mejora_perf_clientes begins with the entire index columns of id_empresa and id_empresa_campana and these two indexes can be dropped.
With those ...
This index will take care of 3 of your queries:
INDEX(title, date, -- in either order
category) -- last
Then you will need two more indexes to handle the other two queries. Assuming you use the order above, then these could be the other two.
If you have a dozen columns and users can filter on virtually any ...
Can I do this against a live production table, or do I need to bring down the database?
Yes. You can do it against a live table. Will even be very fast since citext and varchar are binary coercible, so no table rewrite is required (since Postgres 9.1).
But this still acquires an ACCESS EXCLUSIVE lock on the table, which makes any concurrent access on ...
If you want a simple test, I would identity the top 5 most frequently executed insert queries on the table on your live system. Then on Dev, switch on the client statistics in ssms (https://www.brentozar.com/archive/2012/12/sql-server-management-studio-include-client-statistics-button/) and note the total execution times taken when you run the queries before ...
I want to know the methodology of testing the writing speed.
I am assuming you want to know if queries are running faster. It can be read, write, or a combination of both. There are many approaches you can take. It depends on the usage pattern of the table. I am sure I won't be able to list everything, but the following points will give you enough tools to ...
the other possible solution requires modifying the table. This more simple approach using simple equality method and uses composite index
create table plrange ( id serial primary key,
notify_start date default now()::date,
notify_end date default now()::date + 30);
Asked to give a sample of how to use RANGE type. I do not think it possible to implement this solution without adding/modifying column to the table, during writing this i thought of another solution that would not use Range Type but still requires modifying the table will add it as another answer
There are a few gotchas with range type with indexes and ...