Re: Table performance with millions of rows (partitioning)
On Dec 27, 2017, at 8:20 PM, Justin Pryzbywrote: > > That's one of the major use cases for partitioning (DROP rather than DELETE > and > thus avoiding any following vacuum+analyze). > https://www.postgresql.org/docs/10/static/ddl-partitioning.html#DDL-PARTITIONING-OVERVIEW That’s the plan to partition and I can easily change the code to insert directly into the child tables. Right now, I was going to use date ranges (per month) based on a timestamp. But could I just create 12 child tables, one for each month instead of creating one for Year+month ? ie: instead of: (CHECK (ts >= DATE ‘2017-12-01' AND ts < DATE ‘2018-01-01’)) use: (CHECK (EXTRACT(MONTH FROM ts) = 12)) I’ll never need more than the least six months, so I’ll just truncate the older child tables. By the time the data wraps around, the child table will be empty. I’m not even sure if the above CHECK (w/ EXTRACT) instead of just looking for a date range is valid.
Re: Table performance with millions of rows (partitioning)
On Wed, Dec 27, 2017 at 07:54:23PM -0500, Robert Blayzor wrote: > Question on large tables… > > When should one consider table partitioning vs. just stuffing 10 million rows > into one table? IMO, whenever constraint exclusion, DROP vs DELETE, or seq scan on individual children justify the minor administrative overhead of partitioning. Note that partitioning may be implemented as direct insertion into child tables, or may involve triggers or rules. > I currently have CDR’s that are injected into a table at the rate of over > 100,000 a day, which is large. > > At some point I’ll want to prune these records out, so being able to just > drop or truncate the table in one shot makes child table partitions > attractive. That's one of the major use cases for partitioning (DROP rather than DELETE and thus avoiding any following vacuum+analyze). https://www.postgresql.org/docs/10/static/ddl-partitioning.html#DDL-PARTITIONING-OVERVIEW Justin
Table performance with millions of rows
Question on large tables… When should one consider table partitioning vs. just stuffing 10 million rows into one table? I currently have CDR’s that are injected into a table at the rate of over 100,000 a day, which is large. At some point I’ll want to prune these records out, so being able to just drop or truncate the table in one shot makes child table partitions attractive. From a pure data warehousing standpoint, what are the do’s/don’t of keeping such large tables? Other notes… - This table is never updated, only appended (CDR’s) - Right now daily SQL called to delete records older than X days. (costly, purging ~100k records at a time) -- inoc.net!rblayzor XMPP: rblayzor.AT.inoc.net PGP: https://inoc.net/~rblayzor/
Re: Batch insert heavily affecting query performance.
Jean, It is very likely you are running out of IOPS with that size of server. We have several Postgres databases running at AWS. We consistently run out of IOPS on our development servers due to the types queries and sizing of our development databases. I would check the AWS monitoring graphs to determine the cause. We typically see low CPU and high IOPS just prior to our degraded performance. Our production environment runs provisioned IOPS to avoid this very issue. Regards, David From: Jean BaroTo: Jeremy Finzel Cc: Danylo Hlynskyi ; pgsql-performa...@postgresql.org Sent: Wednesday, December 27, 2017 11:03 AM Subject: Re: Batch insert heavily affecting query performance. Sorry guys, The performance problem is not caused by PG. 'Index Scan using idx_user_country on public.old_card (cost=0.57..1854.66 rows=460 width=922) (actual time=3.442..76.606 rows=200 loops=1)'' Output: id, user_id, user_country, user_channel, user_role, created_by_system_key, created_by_username, created_at, last_modified_at, date_start, date_end, payload, tags, menu, deleted, campaign, correlation_id'' Index Cond: (((old_card.user_id)::text = '1234'::text) AND (old_card.user_country = 'BR'::bpchar))'' Buffers: shared hit=11 read=138 written=35''Planning time: 7.748 ms''Execution time: 76.755 ms' 77ms on an 8GB database with 167MM rows and almost 500GB in size is amazing!! Now we are investigating other bottlenecks, is it the creation of a new connection to PG (no connection poller at the moment, like PGBouncer), is it the Lambda start up time? Is it the network performance between PG and Lambda? I am sorry for wasting your time guys, it helped us to find the problem though, even if it wasn't a PG problem. BTW, what a performance! I am impressed. Thanks PG community! Em 27 de dez de 2017 14:34, "Jean Baro" escreveu: Thanks Jeremy, We will provide a more complete EXPLAIN as other people have suggested. I am glad we might end up with a much better performance (currently each query takes around 2 seconds!). Cheers Em 27 de dez de 2017 14:02, "Jeremy Finzel" escreveu: 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))' Show 3 runs of the full explain analyze plan on given condition so that we can also see cold vs warm cache performance. There is definitely something wrong as there is no way a query like that should take 500ms. Your instinct is correct there.
RE: Batch insert heavily affecting query performance.
In my experience, that 77ms will stay quite constant even if your db grew to > 1TB. Postgres IS amazing. BTW, for a db, you should always have provisioned IOPS or else your performance can vary wildly, since the SSDs are shared. Re Lambda: another team is working on a new web app using Lambda calls and they were also experiencing horrific performance, just like yours (2 seconds per call). They discovered it was the Lambda connection/spin-up time causing the problem. They solved it by keeping several Lambda’s “hot”, for an instant connection…solved the problem, the last I heard. Google for that topic, you’ll find solutions. Mike From: Jean Baro [mailto:jfb...@gmail.com] Sent: Wednesday, December 27, 2017 9:03 AM Sorry guys, The performance problem is not caused by PG. 'Index Scan using idx_user_country on public.old_card (cost=0.57..1854.66 rows=460 width=922) (actual time=3.442..76.606 rows=200 loops=1)' ' Output: id, user_id, user_country, user_channel, user_role, created_by_system_key, created_by_username, created_at, last_modified_at, date_start, date_end, payload, tags, menu, deleted, campaign, correlation_id' ' Index Cond: (((old_card.user_id)::text = '1234'::text) AND (old_card.user_country = 'BR'::bpchar))' ' Buffers: shared hit=11 read=138 written=35' 'Planning time: 7.748 ms' 'Execution time: 76.755 ms' 77ms on an 8GB database with 167MM rows and almost 500GB in size is amazing!! Now we are investigating other bottlenecks, is it the creation of a new connection to PG (no connection poller at the moment, like PGBouncer), is it the Lambda start up time? Is it the network performance between PG and Lambda? I am sorry for wasting your time guys, it helped us to find the problem though, even if it wasn't a PG problem. BTW, what a performance! I am impressed. Thanks PG community! Em 27 de dez de 2017 14:34, "Jean Baro"> escreveu: Thanks Jeremy, We will provide a more complete EXPLAIN as other people have suggested. I am glad we might end up with a much better performance (currently each query takes around 2 seconds!). Cheers Em 27 de dez de 2017 14:02, "Jeremy Finzel" > escreveu: 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))' Show 3 runs of the full explain analyze plan on given condition so that we can also see cold vs warm cache performance. There is definitely something wrong as there is no way a query like that should take 500ms. Your instinct is correct there.
Re: Batch insert heavily affecting query performance.
On 27/12/17 18:02, Jean Baro wrote: Sorry guys, The performance problem is not caused by PG. 'Index Scan using idx_user_country on public.old_card (cost=0.57..1854.66 rows=460 width=922) (actual time=3.442..76.606 rows=200 loops=1)' ' Output: id, user_id, user_country, user_channel, user_role, created_by_system_key, created_by_username, created_at, last_modified_at, date_start, date_end, payload, tags, menu, deleted, campaign, correlation_id' ' Index Cond: (((old_card.user_id)::text = '1234'::text) AND (old_card.user_country = 'BR'::bpchar))' ' Buffers: shared hit=11 read=138 written=35' 'Planning time: 7.748 ms' 'Execution time: 76.755 ms' 77ms on an 8GB database with 167MM rows and almost 500GB in size is amazing!! gp2 disks are of *variable* performance. Once you exhaust the I/O credits, you are capped to a baseline IOPS that are proportional to the size. I guess you would experience low performance in this scenario since your disk is not big. And actually performance numbers with gp2 disks are unreliable as you don't know in which credit status you are. Benchmark with provisioned iops to get a right picture of the desired performance. Cheers, Álvaro -- Alvaro Hernandez --- OnGres Now we are investigating other bottlenecks, is it the creation of a new connection to PG (no connection poller at the moment, like PGBouncer), is it the Lambda start up time? Is it the network performance between PG and Lambda? I am sorry for wasting your time guys, it helped us to find the problem though, even if it wasn't a PG problem. BTW, what a performance! I am impressed. Thanks PG community! Em 27 de dez de 2017 14:34, "Jean Baro"> escreveu: Thanks Jeremy, We will provide a more complete EXPLAIN as other people have suggested. I am glad we might end up with a much better performance (currently each query takes around 2 seconds!). Cheers Em 27 de dez de 2017 14:02, "Jeremy Finzel" > escreveu: 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))' Show 3 runs of the full explain analyze plan on given condition so that we can also see cold vs warm cache performance. There is definitely something wrong as there is no way a query like that should take 500ms. Your instinct is correct there.
Re: Batch insert heavily affecting query performance.
Sorry guys, The performance problem is not caused by PG. 'Index Scan using idx_user_country on public.old_card (cost=0.57..1854.66 rows=460 width=922) (actual time=3.442..76.606 rows=200 loops=1)' ' Output: id, user_id, user_country, user_channel, user_role, created_by_system_key, created_by_username, created_at, last_modified_at, date_start, date_end, payload, tags, menu, deleted, campaign, correlation_id' ' Index Cond: (((old_card.user_id)::text = '1234'::text) AND (old_card.user_country = 'BR'::bpchar))' ' Buffers: shared hit=11 read=138 written=35' 'Planning time: 7.748 ms' 'Execution time: 76.755 ms' 77ms on an 8GB database with 167MM rows and almost 500GB in size is amazing!! Now we are investigating other bottlenecks, is it the creation of a new connection to PG (no connection poller at the moment, like PGBouncer), is it the Lambda start up time? Is it the network performance between PG and Lambda? I am sorry for wasting your time guys, it helped us to find the problem though, even if it wasn't a PG problem. BTW, what a performance! I am impressed. Thanks PG community! Em 27 de dez de 2017 14:34, "Jean Baro"escreveu: > Thanks Jeremy, > > We will provide a more complete EXPLAIN as other people have suggested. > > I am glad we might end up with a much better performance (currently each > query takes around 2 seconds!). > > Cheers > > > Em 27 de dez de 2017 14:02, "Jeremy Finzel" escreveu: > > > >> 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))' >> > > Show 3 runs of the full explain analyze plan on given condition so that we > can also see cold vs warm cache performance. > > There is definitely something wrong as there is no way a query like that > should take 500ms. Your instinct is correct there. > > >
Re: Batch insert heavily affecting query performance.
General purpose, 500GB but we are planing to increase it to 1TB before going into production. 500GB 1.500 iops (some burst of 3.000 iops) 1TB 3.000 iops Em 27 de dez de 2017 14:23, "Jeff Janes"escreveu: > On Sun, Dec 24, 2017 at 11:51 AM, Jean Baro wrote: > >> Hi there, >> >> We are testing a new application to try to find performance issues. >> >> AWS RDS m4.large 500GB storage (SSD) >> > > Is that general purpose SSD, or provisioned IOPS SSD? If provisioned, > what is the level of provisioning? > > Cheers, > > Jeff >
RE: Batch insert heavily affecting query performance.
Thanks Mike, We are using the standard RDS instance m4.large, it's not Aurora, which is a much more powerful server (according to AWS). Yes, we could install it on EC2, but it would take some extra effort from our side, it can be an investment though in case it will help us finding the bottle neck, BUT after tuning the database it must run on RDS for production use. As the company I work for demands we run microseconds DB as a managed service (RDS in this case). Mike, what can we expect to see if we run PG on EC2? More logging? More tuning options? Let me know what your intention is so that I can convince other people on the team. But keep in mind in the end that payload should run on RDS m4.large (500gb to 1TB of general purpose SSD). Again, thanks a lot! Em 27 de dez de 2017 13:59, "Mike Sofen"escreveu: Hi Jean, I’ve used Postgres on a regular EC2 instance (an m4.xlarge), storing complex genomic data, hundreds of millions of rows in a table and “normal” queries that used an index returned in 50-100ms, depending on the query…so this isn’t a Postgres issue per se. Your table and index structures look ok, although in PG, use the “text” datatype instead of varchar, it is the optimized type for storing string data of any size (even a 2 char country code). Since you have 2 such columns that you’ve indexed and are querying for, there is a chance you’ll see an improvement. I have not yet used Aurora or RDS for any large data…it sure seems like the finger could be pointing there, but it isn’t clear what mechanism in Aurora could be creating the slowness. Is there a possibility of you creating the same db on a normal EC2 instance with PG installed and running the same test? There is nothing else obvious about your data/structure that could result in such terrible performance. Mike Sofen *From:* Jean Baro [mailto:jfb...@gmail.com] *Sent:* Wednesday, December 27, 2017 7:14 AM 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? 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" escreveu: Thanks for the clarification guys. It will be super useful. After trying this I'll post the results! Merry Christmas!
Re: Batch insert heavily affecting query performance.
Thanks Jeremy, We will provide a more complete EXPLAIN as other people have suggested. I am glad we might end up with a much better performance (currently each query takes around 2 seconds!). Cheers Em 27 de dez de 2017 14:02, "Jeremy Finzel"escreveu: > 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))' > Show 3 runs of the full explain analyze plan on given condition so that we can also see cold vs warm cache performance. There is definitely something wrong as there is no way a query like that should take 500ms. Your instinct is correct there.
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"escreveu: On Wed, Dec 27, 2017 at 10:13 AM, Jean Baro 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" 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" >> 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" 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
Re: Batch insert heavily affecting query performance.
On Sun, Dec 24, 2017 at 11:51 AM, Jean Barowrote: > Hi there, > > We are testing a new application to try to find performance issues. > > AWS RDS m4.large 500GB storage (SSD) > Is that general purpose SSD, or provisioned IOPS SSD? If provisioned, what is the level of provisioning? Cheers, Jeff
Re: Batch insert heavily affecting query performance.
> > 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))' > Show 3 runs of the full explain analyze plan on given condition so that we can also see cold vs warm cache performance. There is definitely something wrong as there is no way a query like that should take 500ms. Your instinct is correct there.
Re: Batch insert heavily affecting query performance.
On Wed, Dec 27, 2017 at 10:13 AM, Jean Barowrote: > 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" 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" >> 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" 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