Re: 3000x Slower query when using Foreign Data Wrapper vs. local

2015-10-11 12:10 GMT+02:00 Mohammad Habbab <moh(dot)habbab(at)gmail(dot)com>:

> Awesome ! Thank you very much, that solved it :) . But, do you have any
> idea why this isn't enabled by default ?
> As a first time user for FDW I would assume that usage of remote estimates
> would be enabled by default because they would be more authoritative and
> more representative of access patterns. Correct ?
>
> Best Regards,
> Mohammad
>
> On Sun, Oct 11, 2015 at 5:42 PM, desmodemone <desmodemone(at)gmail(dot)com>
> wrote:
>
>> Hi Mohammad,
>> I think it's not enable
>> "use_remote_estimate" during the creation of the foreign table
>>
>> http://www.postgresql.org/docs/9.4/static/postgres-fdw.html
>>
>> use_remote_estimate
>>
>> This option, which can be specified for a foreign table or a foreign
>> server, controls whether postgres_fdw issues remote EXPLAIN commands to
>> obtain cost estimates. A setting for a foreign table overrides any setting
>> for its server, but only for that table. The default is false.
>>
>>
>> try it
>>
>>
>> Bye
>>
>>
>> 2015-10-11 10:05 GMT+02:00 Mohammad Habbab <moh(dot)habbab(at)gmail(dot)com>:
>>
>>> Hi there,
>>>
>>> If it's possible, I would really appreciate any hints or help on an
>>> issue I've been facing lately.
>>> I'm running two instance of Postgres locally: 9.4.4 (operational db) and
>>> 9.5beta1 (analytical db). I've already imported schema to analytical db and
>>> while doing the following query I find very different query plans being
>>> executed:
>>>
>>> Query:
>>>
>>> EXPLAIN ANALYZE VERBOSE SELECT
>>> o.id AS id,
>>> o.company_id AS company_id,
>>> o.created_at::date AS created_at,
>>> COALESCE(o.assignee_id, 0) AS assignee_id,
>>> (o.tax_treatment)::text AS tax_treatment,
>>> COALESCE(o.tax_override, 0) AS tax_override,
>>> COALESCE(o.stock_location_id, 0) AS stock_location_id,
>>> COALESCE(l.label, 'N/A')::text AS stock_location_name,
>>> COALESCE(sa.country, 'N/A')::text AS shipping_address_country,
>>> COALESCE(o.tags, ARRAY[]::text[]) AS tags
>>> FROM orders AS o
>>> INNER JOIN locations AS l ON l.id = o.stock_location_id
>>> INNER JOIN addresses AS sa ON sa.id = o.shipping_address_id
>>> WHERE o.account_id = <some_value> AND l.account_id = <another_value>
>>> LIMIT 10;
>>>
>>>
>>> Plan when I run it locally on operational db:
>>>
>>> Limit (cost=747.62..811.46 rows=1 width=76) (actual time=28.208..28.397
>>> rows=10 loops=1)
>>> Output: o.id, o.company_id, ((o.created_at)::date),
>>> (COALESCE(o.assignee_id, 0)), ((o.tax_treatment)::text),
>>> (COALESCE(o.tax_override, 0::numeric)), (COALESCE(o.stock_location_id, 0)),
>>> ((COALESCE(l.label, 'N/A'::character varying))::text),
>>> ((COALESCE(sa.country, 'N/A'::character varying))::text), (COALESCE(o.tags,
>>> '{}'::character varying[]))
>>> -> Nested Loop (cost=747.62..811.46 rows=1 width=76) (actual
>>> time=28.208..28.395 rows=10 loops=1)
>>> Output: o.id, o.company_id, (o.created_at)::date,
>>> COALESCE(o.assignee_id, 0), (o.tax_treatment)::text,
>>> COALESCE(o.tax_override, 0::numeric), COALESCE(o.stock_location_id, 0),
>>> (COALESCE(l.label, 'N/A'::character varying))::text, (COALESCE(sa.country,
>>> 'N/A'::character varying))::text, COALESCE(o.tags, '{}'::character
>>> varying[])
>>> -> Nested Loop (cost=747.19..807.15 rows=1 width=73) (actual
>>> time=28.164..28.211 rows=10 loops=1)
>>> Output: o.id, o.company_id, o.created_at, o.assignee_id,
>>> o.tax_treatment, o.tax_override, o.stock_location_id, o.tags,
>>> o.shipping_address_id, l.label
>>> -> Index Scan using index_locations_on_account_id on
>>> public.locations l (cost=0.29..8.31 rows=1 width=20) (actual
>>> time=0.025..0.025 rows=1 loops=1)
>>> Output: l.id, l.address1, l.address2, l.city,
>>> l.country, l.zip_code, l.suburb, l.state, l.label, l.status, l.latitude,
>>> l.longitude, l.created_at, l.updated_at, l.account_id, l.holds_stock
>>> Index Cond: (l.account_id = 18799)
>>> -> Bitmap Heap Scan on public.orders o
>>> (cost=746.90..798.71 rows=13 width=57) (actual time=28.133..28.176 rows=10
>>> loops=1)
>>> Output: o.id, o.account_id, o.company_id, o.status,
>>> o.invoice_number, o.reference_number, o.due_at, o.issued_at, o.user_id,
>>> o.notes, o.created_at, o.updated_at, o.order_number, o.billing_address_id,
>>> o.shipping_address_id, o.payment_status, o.email, o.fulfillment_status,
>>> o.phone_number, o.assignee_id, o.tax_treatment, o.tax_override,
>>> o.tax_label_override, o.stock_location_id, o.currency_id, o.source,
>>> o.source_url, o.demo, o.invoice_status, o.ship_at, o.source_id, o.search,
>>> o.default_price_list_id, o.contact_id, o.return_status, o.tags,
>>> o.packed_status, o.returning_status, o.shippability_status,
>>> o.backordering_status
>>> Recheck Cond: ((o.stock_location_id = l.id) AND
>>> (o.account_id = 18799))
>>> Heap Blocks: exact=7
>>> -> BitmapAnd (cost=746.90..746.90 rows=13 width=0)
>>> (actual time=23.134..23.134 rows=0 loops=1)
>>> -> Bitmap Index Scan on
>>> index_orders_on_stock_location_id_manual (cost=0.00..18.02 rows=745
>>> width=0) (actual time=9.282..9.282 rows=40317 loops=1)
>>> Index Cond: (o.stock_location_id = l.id)
>>> -> Bitmap Index Scan on
>>> index_orders_on_account_id (cost=0.00..718.94 rows=38735 width=0) (actual
>>> time=9.856..9.856 rows=40317 loops=1)
>>> Index Cond: (o.account_id = 18799)
>>> -> Index Scan using addresses_pkey on public.addresses sa
>>> (cost=0.43..4.30 rows=1 width=11) (actual time=0.015..0.016 rows=1
>>> loops=10)
>>> Output: sa.id, sa.company_id, sa.address1, sa.city,
>>> sa.country, sa.zip_code, sa.created_at, sa.updated_at, sa.suburb, sa.state,
>>> sa.label, sa.status, sa.address2, sa.phone_number, sa.email,
>>> sa.company_name, sa.latitude, sa.longitude, sa.first_name, sa.last_name
>>> Index Cond: (sa.id = o.shipping_address_id)
>>> Planning time: 1.136 ms
>>> Execution time: 28.621 ms
>>> (23 rows)
>>>
>>> Plan when I run it from analytical db via FDW:
>>>
>>> Limit (cost=300.00..339.95 rows=1 width=1620) (actual
>>> time=7630.240..82368.326 rows=10 loops=1)
>>> Output: o.id, o.company_id, ((o.created_at)::date),
>>> (COALESCE(o.assignee_id, 0)), ((o.tax_treatment)::text),
>>> (COALESCE(o.tax_override, '0'::numeric)), (COALESCE(o.stock_location_id,
>>> 0)), ((COALESCE(l.label, 'N/A'::character varying))::
>>> text), ((COALESCE(sa.country, 'N/A'::character varying))::text),
>>> (COALESCE(o.tags, '{}'::character varying[]))
>>> -> Nested Loop (cost=300.00..339.95 rows=1 width=1620) (actual
>>> time=7630.238..82368.314 rows=10 loops=1)
>>> Output: o.id, o.company_id, (o.created_at)::date,
>>> COALESCE(o.assignee_id, 0), (o.tax_treatment)::text,
>>> COALESCE(o.tax_override, '0'::numeric), COALESCE(o.stock_location_id, 0),
>>> (COALESCE(l.label, 'N/A'::character varying))::text,
>>> (COALESCE(sa.country, 'N/A'::character varying))::text,
>>> COALESCE(o.tags, '{}'::character varying[])
>>> Join Filter: (o.shipping_address_id = sa.id)
>>> Rows Removed by Join Filter: 19227526
>>> -> Nested Loop (cost=200.00..223.58 rows=1 width=1108)
>>> (actual time=69.758..69.812 rows=10 loops=1)
>>> Output: o.id, o.company_id, o.created_at, o.assignee_id,
>>> o.tax_treatment, o.tax_override, o.stock_location_id, o.tags,
>>> o.shipping_address_id, l.label
>>> Join Filter: (o.stock_location_id = l.id)
>>> Rows Removed by Join Filter: 18
>>> -> Foreign Scan on remote.orders o (cost=100.00..111.67
>>> rows=1 width=592) (actual time=68.009..68.014 rows=10 loops=1)
>>> Output: o.id, o.account_id, o.company_id,
>>> o.status, o.invoice_number, o.reference_number, o.due_at, o.issued_at,
>>> o.user_id, o.notes, o.created_at, o.updated_at, o.order_number,
>>> o.billing_address_id, o.shipping_address
>>> _id, o.payment_status, o.email, o.fulfillment_status, o.phone_number,
>>> o.assignee_id, o.tax_treatment, o.tax_override, o.tax_label_override,
>>> o.stock_location_id, o.currency_id, o.source, o.source_url, o.demo,
>>> o.invoice_status, o.ship_at, o
>>> .source_id, o.search, o.default_price_list_id, o.contact_id,
>>> o.return_status, o.tags, o.packed_status, o.returning_status,
>>> o.shippability_status, o.backordering_status
>>> Remote SQL: SELECT id, company_id, created_at,
>>> shipping_address_id, assignee_id, tax_treatment, tax_override,
>>> stock_location_id, tags FROM public.orders WHERE ((account_id = 18799))
>>> -> Foreign Scan on remote.locations l
>>> (cost=100.00..111.90 rows=1 width=520) (actual time=0.174..0.174 rows=3
>>> loops=10)
>>> Output: l.id, l.address1, l.address2, l.city,
>>> l.country, l.zip_code, l.suburb, l.state, l.label, l.status, l.latitude,
>>> l.longitude, l.created_at, l.updated_at, l.account_id, l.holds_stock
>>> Remote SQL: SELECT id, label FROM public.locations
>>> WHERE ((account_id = 18799))
>>> -> Foreign Scan on remote.addresses sa (cost=100.00..114.50
>>> rows=150 width=520) (actual time=0.634..8029.415 rows=1922754 loops=10)
>>> Output: sa.id, sa.company_id, sa.address1, sa.city,
>>> sa.country, sa.zip_code, sa.created_at, sa.updated_at, sa.suburb, sa.state,
>>> sa.label, sa.status, sa.address2, sa.phone_number, sa.email,
>>> sa.company_name, sa.latitude, sa.l
>>> ongitude, sa.first_name, sa.last_name
>>> Remote SQL: SELECT id, country FROM public.addresses
>>> Planning time: 0.209 ms
>>> Execution time: 82391.610 ms
>>> (21 rows)
>>>
>>> Time: 82393.211 ms
>>>
>>> What am I doing wrong ? really appreciate any guidance possible. Thank
>>> you very much for taking the time to helping me with this.
>>>
>>> Best Regards,
>>> Mohammad
>>>
>>
>>
>>
>> --
>> Matteo Durighetto
>>
>> - - - - - - - - - - - - - - - - - - - - - - -
>>
>> Italian PostgreSQL User Group <http://www.itpug.org/index.it.html>
>> Italian Community for Geographic Free/Open-Source Software
>> <http://www.gfoss.it>
>>
>
>
>
> --
> Mohammad Habbab
> Bangsar, KL, Malaysia
> Mobile No. +601111582144
> Email: moh(dot)habbab(at)gmail(dot)com
> LinkedIn: https://www.linkedin.com/in/mohammadhabbab
>

Hi,
I am not sure why, by the way I think because you could have the
local tables mixed with the foreign tables, so in that case, you have to use
the local cost base optimizer [if you not rewrite query with CTE with only
the fdw tables and use so the use_remote_estimate], and so you need local
statistics of local and remote table [ infact you could also analyze fdw
table and store the statistics in local dictionary ].

In your case I see you have all fdw tables, so it makes more sense to use
remote cost base optmizer.

Have a nice day
--
Matteo Durighetto

- - - - - - - - - - - - - - - - - - - - - - -

Italian PostgreSQL User Group <http://www.itpug.org/index.it.html>
Italian Community for Geographic Free/Open-Source Software
<http://www.gfoss.it>