I would expect that R2 vs R3 would be negligible but perhaps gist works
much better and would be an improvement. When you are down to 7ms already,
I wouldn't hope for any big change. I assume you used btree for the
multi-column index on R2 range_first, range_last but am not familiar with
gist on range vs btree on two int columns.
It seems a little odd to have a jsonb value to hold multiple range values.
A range is already a complex type so separating out into the association
table like R3 would make sense to me.
*Michael Lewis*
On Tue, Feb 19, 2019 at 9:34 AM Mariel Cherkassky <
mariel.cherkas...@gmail.com> wrote:
> I dont have any indexes on R (the table with the jsonb column). I was
> asking if I can create any that can increase this query`s performance.
> If I understood you correctly I have 3 options right now :
> 1)R, without indexes
> 2)R2 with an index on first and last
> 3)R3 that should contain a single range column (type int4range) with gist
> index on it.
>
> In aspect of performance, R<R2<? R3
>
> בתאריך יום ג׳, 19 בפבר׳ 2019 ב-18:28 מאת Michael Lewis <
> mle...@entrata.com>:
>
>> Is your JSON data getting toasted? I wouldn't assume so if it is
>> remaining small but something to check. Regardless, if an index exists and
>> isn't being used, then that would be the primary concern. You didn't share
>> what the definition of the index on R.data is... what do you already have?
>>
>> You have an array of ranges stored as the value of key "ranges" in jsonb
>> field data. If you created a table like R2, but with a single "range"
>> column that is int4range type, then I would expect that you could add a
>> GiST and then use overlaps &&, or another operator. I would not expect that
>> you could index (unnest data->>'ranges' for instance) to get the separated
>> out range values.
>>
>>
>>
>> *Michael Lewis *
>>
>>
>> On Tue, Feb 19, 2019 at 8:59 AM Mariel Cherkassky <
>> mariel.cherkas...@gmail.com> wrote:
>>
>>> Hi,
>>> I have a table with json col : R(object int, data jsonb).
>>> Example for content :
>>> object | data
>>> ----------------+---------------------------------------
>>> 50 | {"ranges": [[1, 1]]}
>>> 51 | {"ranges": [[5, 700],[1,5],[9,10}
>>> 52 | {"ranges": [[4, 200],[2,4],[3,4]]}
>>> 53 | {"ranges": [[2, 2]]}
>>> 54 | {"ranges": [[5, 10]]}
>>>
>>> Now I tried to query for all the objects that contains a specific range,
>>> for example [2,2] :
>>> explain analyze SELECT *
>>> FROM R d
>>> WHERE EXISTS (
>>> SELECT FROM jsonb_array_elements(R.data -> 'ranges') rng
>>> WHERE (rng->>0)::bigint <= 2 and (rng->>1)::bigint >= 2
>>> );
>>>
>>> I saw that the gin index isnt suitable for this type of comparison.
>>> However, I saw that the gist index is suitable to handle ranges. Any idea
>>> of I can implement a gist index here ?
>>>
>>> In addition, I saved the same data in relational table
>>> R2(object,range_first,range_last).
>>> The previous data in this format :
>>> object range_first range_last
>>> 50 1 1
>>> 51 5 700
>>> 51 1 5
>>> 51 9 10
>>>
>>> i compared the first query with :
>>> explain analyze select * from R2 where range_first <=2 and
>>> range_last >= 2; (I have an index on range_first,range_last that is used)
>>>
>>> The query on the jsonb column was 100x slower (700 m/s vs 7m/s). The
>>> question is, Am I missing an index or the jsonb datatype isnt suitable for
>>> this structure of data. The R2 table contains 500K records while the R
>>> table contains about 200K records.
>>>
>>>
>>>
>>>
