Re: Batch insert heavily affecting query performance.

Thanks Rick,

We are now partitioning the DB (one table) into 100 sets of data.

As soon as we finish this new experiment we will provide a better EXPLAIN
as you suggested. :)

Em 27 de dez de 2017 13:38, "Rick Otten" <rottenwindfish(at)gmail(dot)com>
escreveu:

On Wed, Dec 27, 2017 at 10:13 AM, Jean Baro <jfbaro(at)gmail(dot)com> wrote:

> Hello,
>
> We are still seeing queries (by UserID + UserCountry) taking over 2
> seconds, even when there is no batch insert going on at the same time.
>
> Each query returns from 100 to 200 messagens, which would be a 400kb pay
> load, which is super tiny.
>
> I don't know what else I can do with the limitations (m4.large), 167MM
> rows, almost 500GB database and 29GB of indexes (all indexes).
>
> I am probably to optimistic, but I was expecting queries (up to 50 queries
> per second) to return (99th) under 500ms or even less, as the index is
> simple, there is no aggregation or join involves.
>

> Any suggestion?
>

Although you aren't querying by it, if your id column is actually a UUID,
as a best practice I strongly recommend switching the column type to uuid.
If you do query by the primary key, a uuid query will be much faster than a
char or varchar column query.

You'll need to submit a more complete explain plan than what you have below.
Try using:
explain (analyze, costs, verbose, buffers) select ...

> The table structure:
> CREATE TABLE public.card
> (
> id character(36) NOT NULL,
> user_id character varying(40) NOT NULL,
> user_country character(2) NOT NULL,
> user_channel character varying(40),
> user_role character varying(40),
> created_by_system_key character(36) NOT NULL,
> created_by_username character varying(40),
> created_at timestamp with time zone NOT NULL,
> last_modified_at timestamp with time zone NOT NULL,
> date_start timestamp with time zone NOT NULL,
> date_end timestamp with time zone NOT NULL,
> payload json NOT NULL,
> tags character varying(500),
> menu character varying(50),
> deleted boolean NOT NULL,
> campaign character varying(500) NOT NULL,
> correlation_id character varying(50),
> PRIMARY KEY (id)
> );
>
> CREATE INDEX idx_user_country
> ON public.card USING btree
> (user_id COLLATE pg_catalog."default", user_country COLLATE
> pg_catalog."default");
>
> CREATE INDEX idx_last_modified_at
> ON public.card USING btree
> (last_modified_at ASC NULLS LAST);
>
> CREATE INDEX idx_campaign
> ON public.card USING btree
> (campaign ASC NULLS LAST)
>
> The EXPLAIN
>
> 'Index Scan using idx_user_country on card (cost=0.57..1854.66 rows=460
> width=922)'
> ' Index Cond: (((user_id)::text = '4684'::text) AND (user_country =
> 'BR'::bpchar))'
>
>
>
> Em 25 de dez de 2017 01:10, "Jean Baro" <jfbaro(at)gmail(dot)com> escreveu:
>
>> Thanks for the clarification guys.
>>
>> It will be super useful. After trying this I'll post the results!
>>
>> Merry Christmas!
>>
>> Em 25 de dez de 2017 00:59, "Danylo Hlynskyi" <abcz2(dot)uprola(at)gmail(dot)com>
>> escreveu:
>>
>>> I had an opportunity to perform insertion of 700MM rows into Aurora
>>> Postgresql, for which performance insights are available. Turns out, that
>>> there are two stages of insert slowdown - first happens when max WAL
>>> buffers limit reached, second happens around 1 hour after.
>>>
>>> The first stage cuts insert performance twice, and WALWrite lock is main
>>> bottleneck. I think WAL just can't sync changes log that fast, so it waits
>>> while older log entries are flushed. This creates both read and write IO.
>>>
>>> The second stage is unique to Aurora/RDS and is characterized by
>>> excessive read data locks and total read IO. I couldn't figure out why does
>>> it read so much in a write only process, and AWS support didn't answer yet.
>>>
>>> So, for you, try to throttle inserts so WAL is never overfilled and you
>>> don't experience WALWrite locks, and then increase wal buffers to max.
>>>
>>> 24 груд. 2017 р. 21:51 "Jean Baro" <jfbaro(at)gmail(dot)com> пише:
>>>
>>> Hi there,
>>>
>>> We are testing a new application to try to find performance issues.
>>>
>>> AWS RDS m4.large 500GB storage (SSD)
>>>
>>> One table only, called Messages:
>>>
>>> Uuid
>>> Country (ISO)
>>> Role (Text)
>>> User id (Text)
>>> GroupId (integer)
>>> Channel (text)
>>> Title (Text)
>>> Payload (JSON, up to 20kb)
>>> Starts_in (UTC)
>>> Expires_in (UTC)
>>> Seen (boolean)
>>> Deleted (boolean)
>>> LastUpdate (UTC)
>>> Created_by (UTC)
>>> Created_in (UTC)
>>>
>>> Indexes:
>>>
>>> UUID (PK)
>>> UserID + Country (main index)
>>> LastUpdate
>>> GroupID
>>>
>>>
>>> We inserted 160MM rows, around 2KB each. No partitioning.
>>>
>>> Insert started at around 3.000 inserts per second, but (as expected)
>>> started to slow down as the number of rows increased. In the end we got
>>> around 500 inserts per second.
>>>
>>> Queries by Userd_ID + Country took less than 2 seconds, but while the
>>> batch insert was running the queries took over 20 seconds!!!
>>>
>>> We had 20 Lambda getting messages from SQS and bulk inserting them into
>>> Postgresql.
>>>
>>> The insert performance is important, but we would slow it down if needed
>>> in order to ensure a more flat query performance. (Below 2 seconds). Each
>>> query (userId + country) returns around 100 diferent messages, which are
>>> filtered and order by the synchronous Lambda function. So we don't do any
>>> special filtering, sorting, ordering or full text search in Postgres. In
>>> some ways we use it more like a glorified file system. :)
>>>
>>> We are going to limit the number of lambda workers to 1 or 2, and then
>>> run some queries concurrently to see if the query performance is not affect
>>> too much. We aim to get at least 50 queries per second (returning 100
>>> messages each) under 2 seconds, even when there is millions of messages on
>>> SQS being inserted into PG.
>>>
>>> We haven't done any performance tuning in the DB.
>>>
>>> With all that said, the question is:
>>>
>>> What can be done to ensure good query performance (UserID+ country) even
>>> when the bulk insert is running (low priority).
>>>
>>> We are limited to use AWS RDS at the moment.
>>>
>>> Cheers
>>>
>>>
>>>
>>>